{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=6>\n",
    "    <b>Sentiment_Analysis.ipynb:</b> Sentiment Analysis with Pandas and Watson Natural Language Understanding\n",
    " </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "With the significant growth in the volume of highly subjective user-generated text in the form of online products reviews, recommendations, blogs, discussion forums and etc., the sentiment analysis has gained a lot of attention in the last decade. The sentiment analysis goal is to automatically detect the underlying sentiment of the user towards the entity of interest. While the Sentiment analysis is  one of the most prominent and commonly used natural language processing (NLP) features, it is typically used in combination with other NLP features and text analytics to gain insight about the user experience for the sake of customer care and feedback analytics, product analytics and brand intelligence.\n",
    "This notebook shows how the open source library [Text Extensions for Pandas](https://github.com/CODAIT/text-extensions-for-pandas) lets you use use [Pandas](https://pandas.pydata.org/) DataFrames and the [Watson Natural Language Understanding](https://www.ibm.com/cloud/watson-natural-language-understanding) service to conduct exploratory sentiment analysis over the product reviews. \n",
    "\n",
    "We start out with a dataset from the [Edmunds-Consumer Car Ratings and Reviews](https://www.kaggle.com/ankkur13/edmundsconsumer-car-ratings-and-reviews) obtained from the Kaggle datasets. This is a dataset containing consumer's thought and the star rating of car manufacturer/model/type.\n",
    "We pass each review to the Watson Natural Language \n",
    "Understanding (NLU) service. Then we use Text Extensions for Pandas to convert the output of the \n",
    "Watson NLU service to Pandas DataFrames. Next, we perform an example exploratory data analysis and machine learning task with \n",
    "Pandas to show how Pandas makes analyzing the dataset and prediction task much easier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Environment Setup\n",
    "\n",
    "This notebook requires a Python 3.7 or later environment with the following packages:\n",
    "* The dependencies listed in the [\"requirements.txt\" file for Text Extensions for Pandas](https://github.com/CODAIT/text-extensions-for-pandas/blob/master/requirements.txt)\n",
    "* The \"[ibm-watson](https://pypi.org/project/ibm-watson/)\" package, available via `pip install ibm-watson`\n",
    "* `text_extensions_for_pandas`\n",
    "\n",
    "You can satisfy the dependency on `text_extensions_for_pandas` in either of two ways:\n",
    "\n",
    "* Run `pip install text_extensions_for_pandas` before running this notebook. This command adds the library to your Python environment.\n",
    "* Run this notebook out of your local copy of the Text Extensions for Pandas project's [source tree](https://github.com/CODAIT/text-extensions-for-pandas). In this case, the notebook will use the version of Text Extensions for Pandas in your local source tree **if the package is not installed in your Python environment**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Core Python libraries\n",
    "import json\n",
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import re\n",
    "import time\n",
    "import warnings\n",
    "from typing import *\n",
    "\n",
    "# IBM Watson libraries\n",
    "import ibm_watson\n",
    "import ibm_watson.natural_language_understanding_v1 as nlu\n",
    "import ibm_cloud_sdk_core\n",
    "\n",
    "# Machine Learning libraries\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# And of course we need the text_extensions_for_pandas library itself.\n",
    "try:\n",
    "    import text_extensions_for_pandas as tp\n",
    "except ModuleNotFoundError as e:\n",
    "    # If we're running from within the project source tree and the parent Python\n",
    "    # environment doesn't have the text_extensions_for_pandas package, use the\n",
    "    # version in the local source tree.\n",
    "    if not os.getcwd().endswith(\"notebooks\"):\n",
    "        raise e\n",
    "    if \"..\" not in sys.path:\n",
    "        sys.path.insert(0, \"..\")\n",
    "    import text_extensions_for_pandas as tp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set up the Watson Natural Language Understanding Service\n",
    "\n",
    "In this part of the notebook, we will use the Watson Natural Language Understanding (NLU) service to extract the keywords and their sentiment and emotion from each of the product reviews.\n",
    "\n",
    "You can create an instance of Watson NLU on the IBM Cloud for free by navigating to [this page](https://www.ibm.com/cloud/watson-natural-language-understanding) and clicking on the button marked \"Get started free\". You can also install your own instance of Watson NLU on [OpenShift](https://www.openshift.com/) by using [IBM Watson Natural Language Understanding for IBM Cloud Pak for Data](\n",
    "https://catalog.redhat.com/software/operators/detail/5e9873e13f398525a0ceafe5).\n",
    "\n",
    "You'll need two pieces of information to access your instance of Watson NLU: An **API key** and a **service URL**. If you're using Watson NLU on the IBM Cloud, you can find your API key and service URL in the IBM Cloud web UI. Navigate to the [resource list](https://cloud.ibm.com/resources) and click on your instance of Natural Language Understanding to open the management UI for your service. Then click on the \"Manage\" tab to show a page with your API key and service URL.\n",
    "\n",
    "The cell that follows assumes that you are using the environment variables `IBM_API_KEY` and `IBM_SERVICE_URL` to store your credentials. If you're running this notebook in Jupyter on your laptop, you can set these environment variables while starting up `jupyter notebook` or `jupyter lab`. For example:\n",
    "``` console\n",
    "IBM_API_KEY='<my API key>' \\\n",
    "IBM_SERVICE_URL='<my service URL>' \\\n",
    "  jupyter lab\n",
    "```\n",
    "\n",
    "Alternately, you can uncomment the first two lines of code below to set the `IBM_API_KEY` and `IBM_SERVICE_URL` environment variables directly.\n",
    "**Be careful not to store your API key in any publicly-accessible location!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you need to embed your credentials inline, uncomment the following two lines and\n",
    "# paste your credentials in the indicated locations.\n",
    "# os.environ[\"IBM_API_KEY\"] = \"<API key goes here>\"\n",
    "# os.environ[\"IBM_SERVICE_URL\"] = \"<Service URL goes here>\"\n",
    "\n",
    "# Retrieve the API key for your Watson NLU service instance\n",
    "if \"IBM_API_KEY\" not in os.environ:\n",
    "    raise ValueError(\"Expected Watson NLU api key in the environment variable 'IBM_API_KEY'\")\n",
    "api_key = os.environ.get(\"IBM_API_KEY\")\n",
    "    \n",
    "# Retrieve the service URL for your Watson NLU service instance\n",
    "if \"IBM_SERVICE_URL\" not in os.environ:\n",
    "    raise ValueError(\"Expected Watson NLU service URL in the environment variable 'IBM_SERVICE_URL'\")\n",
    "service_url = os.environ.get(\"IBM_SERVICE_URL\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connect to the Watson Natural Language Understanding Python API\n",
    "\n",
    "This notebook uses the IBM Watson Python SDK to perform authentication on the IBM Cloud via the \n",
    "`IAMAuthenticator` class. See [the IBM Watson Python SDK documentation](https://github.com/watson-developer-cloud/python-sdk#iam) for more information. \n",
    "\n",
    "We start by using the API key and service URL from the previous cell to create an instance of the\n",
    "Python API for Watson NLU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "natural_language_understanding = ibm_watson.NaturalLanguageUnderstandingV1(\n",
    "    version=\"2019-07-12\",\n",
    "    authenticator=ibm_cloud_sdk_core.authenticators.IAMAuthenticator(api_key)\n",
    ")\n",
    "natural_language_understanding.set_service_url(service_url)\n",
    "natural_language_understanding.set_disable_ssl_verification(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pass a Review through the Watson NLU Service\n",
    "\n",
    "Once you've opened a connection to the Watson NLU service, you can pass documents through \n",
    "the service by invoking the [`analyze()` method](https://cloud.ibm.com/apidocs/natural-language-understanding?code=python#analyze).\n",
    "\n",
    "To do so, you should download the [Edmunds-Consumer Car Ratings and Reviews](https://www.kaggle.com/ankkur13/edmundsconsumer-car-ratings-and-reviews/download/) from the Kaggle website and place the archive.zip folder to our notebooks/outputs directory. Note that the directory of the dataset contains 50 csv files of reviews of 50 major car brands which we read into one dataframe with the brand name is listed under the \"Car_Make\" column.\n",
    "\n",
    "Let's read the reviews and show what the reviews looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review_Date</th>\n",
       "      <th>Author_Name</th>\n",
       "      <th>Vehicle_Title</th>\n",
       "      <th>Review_Title</th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating\\r</th>\n",
       "      <th>Car_Make</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>on 09/18/11 00:19 AM (PDT)</td>\n",
       "      <td>wizbang_fl</td>\n",
       "      <td>2007 Volkswagen New Beetle Convertible 2.5 2dr...</td>\n",
       "      <td>New Beetle- Holds up well &amp; Fun to Drive, but ...</td>\n",
       "      <td>I've had my Beetle Convertible for over 4.5 y...</td>\n",
       "      <td>4.500</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>on 07/07/10 05:28 AM (PDT)</td>\n",
       "      <td>carlo frazzano</td>\n",
       "      <td>2007 Volkswagen New Beetle Convertible 2.5 PZE...</td>\n",
       "      <td>Quality Review</td>\n",
       "      <td>We bought the car new in 2007 and are general...</td>\n",
       "      <td>4.375</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>on 10/19/09 21:41 PM (PDT)</td>\n",
       "      <td>NewBeetleDriver</td>\n",
       "      <td>2007 Volkswagen New Beetle Convertible Triple ...</td>\n",
       "      <td>Adore it</td>\n",
       "      <td>I adore my New Beetle. Even though I'm a male...</td>\n",
       "      <td>4.375</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>on 01/01/09 19:13 PM (PST)</td>\n",
       "      <td>Kayemtee</td>\n",
       "      <td>2007 Volkswagen New Beetle Convertible 2.5 2dr...</td>\n",
       "      <td>Nice Ragtop</td>\n",
       "      <td>My wife chose this car to replace a Sebring c...</td>\n",
       "      <td>4.375</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>on 08/02/08 13:43 PM (PDT)</td>\n",
       "      <td>jik</td>\n",
       "      <td>2007 Volkswagen New Beetle Convertible 2.5 2dr...</td>\n",
       "      <td>Luv, luv, luv my dream car</td>\n",
       "      <td>4 of us carpool 1 way 30 min.  Backseat ok fo...</td>\n",
       "      <td>4.750</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>on 05/16/08 12:07 PM (PDT)</td>\n",
       "      <td>Ray Cavanagh</td>\n",
       "      <td>2007 Volkswagen New Beetle Convertible Triple ...</td>\n",
       "      <td>The Best One So Far....</td>\n",
       "      <td>I owned a 2002 SLK and 2003 BMW Z-4.  After s...</td>\n",
       "      <td>5.000</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>on 03/28/08 22:04 PM (PDT)</td>\n",
       "      <td>harvestmoon</td>\n",
       "      <td>2007 Volkswagen New Beetle Convertible 2.5 2dr...</td>\n",
       "      <td>Don't Fall Under The Cute Spell!</td>\n",
       "      <td>Fell in love with the car's look and would be...</td>\n",
       "      <td>2.750</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>on 01/03/08 17:53 PM (PST)</td>\n",
       "      <td>The Husband</td>\n",
       "      <td>2007 Volkswagen New Beetle Convertible Triple ...</td>\n",
       "      <td>Not for Cold Weather!!!</td>\n",
       "      <td>The car is beautiful and performs well in the...</td>\n",
       "      <td>3.750</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>on 09/27/07 08:42 AM (PDT)</td>\n",
       "      <td>Kristina</td>\n",
       "      <td>2007 Volkswagen New Beetle Convertible 2.5 2dr...</td>\n",
       "      <td>I love my Beetle</td>\n",
       "      <td>I love my car.  I previously owned an Explore...</td>\n",
       "      <td>5.000</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>on 08/01/07 22:24 PM (PDT)</td>\n",
       "      <td>bug lover</td>\n",
       "      <td>2007 Volkswagen New Beetle Convertible Triple ...</td>\n",
       "      <td>Bug lover review</td>\n",
       "      <td>My 2005 was so good, I had to have the Triple...</td>\n",
       "      <td>5.000</td>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Review_Date       Author_Name  \\\n",
       "0   on 09/18/11 00:19 AM (PDT)       wizbang_fl    \n",
       "1   on 07/07/10 05:28 AM (PDT)   carlo frazzano    \n",
       "2   on 10/19/09 21:41 PM (PDT)  NewBeetleDriver    \n",
       "3   on 01/01/09 19:13 PM (PST)         Kayemtee    \n",
       "4   on 08/02/08 13:43 PM (PDT)              jik    \n",
       "5   on 05/16/08 12:07 PM (PDT)     Ray Cavanagh    \n",
       "6   on 03/28/08 22:04 PM (PDT)      harvestmoon    \n",
       "7   on 01/03/08 17:53 PM (PST)      The Husband    \n",
       "8   on 09/27/07 08:42 AM (PDT)         Kristina    \n",
       "9   on 08/01/07 22:24 PM (PDT)        bug lover    \n",
       "\n",
       "                                       Vehicle_Title  \\\n",
       "0  2007 Volkswagen New Beetle Convertible 2.5 2dr...   \n",
       "1  2007 Volkswagen New Beetle Convertible 2.5 PZE...   \n",
       "2  2007 Volkswagen New Beetle Convertible Triple ...   \n",
       "3  2007 Volkswagen New Beetle Convertible 2.5 2dr...   \n",
       "4  2007 Volkswagen New Beetle Convertible 2.5 2dr...   \n",
       "5  2007 Volkswagen New Beetle Convertible Triple ...   \n",
       "6  2007 Volkswagen New Beetle Convertible 2.5 2dr...   \n",
       "7  2007 Volkswagen New Beetle Convertible Triple ...   \n",
       "8  2007 Volkswagen New Beetle Convertible 2.5 2dr...   \n",
       "9  2007 Volkswagen New Beetle Convertible Triple ...   \n",
       "\n",
       "                                        Review_Title  \\\n",
       "0  New Beetle- Holds up well & Fun to Drive, but ...   \n",
       "1                                     Quality Review   \n",
       "2                                           Adore it   \n",
       "3                                        Nice Ragtop   \n",
       "4                         Luv, luv, luv my dream car   \n",
       "5                            The Best One So Far....   \n",
       "6                   Don't Fall Under The Cute Spell!   \n",
       "7                            Not for Cold Weather!!!   \n",
       "8                                   I love my Beetle   \n",
       "9                                   Bug lover review   \n",
       "\n",
       "                                              Review  Rating\\r    Car_Make  \n",
       "0   I've had my Beetle Convertible for over 4.5 y...     4.500  Volkswagen  \n",
       "1   We bought the car new in 2007 and are general...     4.375  Volkswagen  \n",
       "2   I adore my New Beetle. Even though I'm a male...     4.375  Volkswagen  \n",
       "3   My wife chose this car to replace a Sebring c...     4.375  Volkswagen  \n",
       "4   4 of us carpool 1 way 30 min.  Backseat ok fo...     4.750  Volkswagen  \n",
       "5   I owned a 2002 SLK and 2003 BMW Z-4.  After s...     5.000  Volkswagen  \n",
       "6   Fell in love with the car's look and would be...     2.750  Volkswagen  \n",
       "7   The car is beautiful and performs well in the...     3.750  Volkswagen  \n",
       "8   I love my car.  I previously owned an Explore...     5.000  Volkswagen  \n",
       "9   My 2005 was so good, I had to have the Triple...     5.000  Volkswagen  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from zipfile import ZipFile\n",
    "path = r'./outputs/archive' # path to compressed  directory of data\n",
    "\n",
    "with ZipFile(path+'.zip', 'r') as zipObj:\n",
    "   # Extract all the contents of zip file in the notebooks/output/archive directory\n",
    "   zipObj.extractall(path)\n",
    "    \n",
    "all_files = glob.glob(path + \"/*.csv\")\n",
    "\n",
    "li = []\n",
    "\n",
    "for filename in all_files:\n",
    "    df = pd.read_csv(filename, index_col=0, header=0, lineterminator='\\n')\n",
    "    df['Car_Make'] = re.split('_|\\\\.',os.path.basename(filename))[-2] # Extracting the car brand from file name\n",
    "    li.append(df)\n",
    "\n",
    "frame = pd.concat(li, axis=0, ignore_index=True)\n",
    "frame.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many car models, reviews and reviewers and etc. we have per car make in our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review_Date</th>\n",
       "      <th>Author_Name</th>\n",
       "      <th>Vehicle_Title</th>\n",
       "      <th>Review_Title</th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating\\r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car_Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMGeneral</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acura</th>\n",
       "      <td>5632</td>\n",
       "      <td>5807</td>\n",
       "      <td>494</td>\n",
       "      <td>5681</td>\n",
       "      <td>6512</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AlfaRomeo</th>\n",
       "      <td>77</td>\n",
       "      <td>76</td>\n",
       "      <td>22</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AstonMartin</th>\n",
       "      <td>82</td>\n",
       "      <td>89</td>\n",
       "      <td>31</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Audi</th>\n",
       "      <td>5069</td>\n",
       "      <td>5389</td>\n",
       "      <td>753</td>\n",
       "      <td>5467</td>\n",
       "      <td>6006</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>6833</td>\n",
       "      <td>7106</td>\n",
       "      <td>829</td>\n",
       "      <td>7202</td>\n",
       "      <td>7984</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bentley</th>\n",
       "      <td>150</td>\n",
       "      <td>146</td>\n",
       "      <td>39</td>\n",
       "      <td>141</td>\n",
       "      <td>150</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bugatti</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buick</th>\n",
       "      <td>3406</td>\n",
       "      <td>3242</td>\n",
       "      <td>374</td>\n",
       "      <td>3334</td>\n",
       "      <td>3615</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cadillac</th>\n",
       "      <td>3539</td>\n",
       "      <td>3531</td>\n",
       "      <td>457</td>\n",
       "      <td>3593</td>\n",
       "      <td>3902</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chevrolet</th>\n",
       "      <td>15781</td>\n",
       "      <td>16254</td>\n",
       "      <td>2760</td>\n",
       "      <td>16500</td>\n",
       "      <td>19334</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC</th>\n",
       "      <td>4327</td>\n",
       "      <td>4425</td>\n",
       "      <td>1261</td>\n",
       "      <td>4415</td>\n",
       "      <td>4964</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honda</th>\n",
       "      <td>11611</td>\n",
       "      <td>10646</td>\n",
       "      <td>1704</td>\n",
       "      <td>11141</td>\n",
       "      <td>12559</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota</th>\n",
       "      <td>16145</td>\n",
       "      <td>15483</td>\n",
       "      <td>2328</td>\n",
       "      <td>15913</td>\n",
       "      <td>18553</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volkswagen</th>\n",
       "      <td>8260</td>\n",
       "      <td>8219</td>\n",
       "      <td>1577</td>\n",
       "      <td>8481</td>\n",
       "      <td>9334</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrysler</th>\n",
       "      <td>4958</td>\n",
       "      <td>4960</td>\n",
       "      <td>495</td>\n",
       "      <td>4996</td>\n",
       "      <td>5529</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dodge</th>\n",
       "      <td>6781</td>\n",
       "      <td>7373</td>\n",
       "      <td>1173</td>\n",
       "      <td>7462</td>\n",
       "      <td>8460</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ferrari</th>\n",
       "      <td>156</td>\n",
       "      <td>159</td>\n",
       "      <td>47</td>\n",
       "      <td>156</td>\n",
       "      <td>161</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiat</th>\n",
       "      <td>394</td>\n",
       "      <td>380</td>\n",
       "      <td>68</td>\n",
       "      <td>391</td>\n",
       "      <td>391</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>16908</td>\n",
       "      <td>17136</td>\n",
       "      <td>3261</td>\n",
       "      <td>17718</td>\n",
       "      <td>20576</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genesis</th>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>16</td>\n",
       "      <td>78</td>\n",
       "      <td>77</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hummer</th>\n",
       "      <td>537</td>\n",
       "      <td>541</td>\n",
       "      <td>35</td>\n",
       "      <td>531</td>\n",
       "      <td>559</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai</th>\n",
       "      <td>7679</td>\n",
       "      <td>7032</td>\n",
       "      <td>943</td>\n",
       "      <td>7250</td>\n",
       "      <td>8156</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infiniti</th>\n",
       "      <td>3914</td>\n",
       "      <td>3874</td>\n",
       "      <td>370</td>\n",
       "      <td>3846</td>\n",
       "      <td>4277</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isuzu</th>\n",
       "      <td>1002</td>\n",
       "      <td>1146</td>\n",
       "      <td>175</td>\n",
       "      <td>1093</td>\n",
       "      <td>1173</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jaguar</th>\n",
       "      <td>1730</td>\n",
       "      <td>1729</td>\n",
       "      <td>254</td>\n",
       "      <td>1770</td>\n",
       "      <td>1878</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jeep</th>\n",
       "      <td>4824</td>\n",
       "      <td>4311</td>\n",
       "      <td>643</td>\n",
       "      <td>4540</td>\n",
       "      <td>4932</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kia</th>\n",
       "      <td>5561</td>\n",
       "      <td>5225</td>\n",
       "      <td>705</td>\n",
       "      <td>5353</td>\n",
       "      <td>5926</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamborghini</th>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "      <td>24</td>\n",
       "      <td>82</td>\n",
       "      <td>86</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land-rover</th>\n",
       "      <td>1752</td>\n",
       "      <td>1711</td>\n",
       "      <td>186</td>\n",
       "      <td>1743</td>\n",
       "      <td>1831</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lexus</th>\n",
       "      <td>5371</td>\n",
       "      <td>5474</td>\n",
       "      <td>370</td>\n",
       "      <td>5424</td>\n",
       "      <td>6083</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lincoln</th>\n",
       "      <td>2801</td>\n",
       "      <td>2776</td>\n",
       "      <td>308</td>\n",
       "      <td>2792</td>\n",
       "      <td>3012</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lotus</th>\n",
       "      <td>136</td>\n",
       "      <td>133</td>\n",
       "      <td>16</td>\n",
       "      <td>133</td>\n",
       "      <td>137</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maserati</th>\n",
       "      <td>235</td>\n",
       "      <td>234</td>\n",
       "      <td>61</td>\n",
       "      <td>234</td>\n",
       "      <td>239</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maybach</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mazda</th>\n",
       "      <td>7165</td>\n",
       "      <td>6830</td>\n",
       "      <td>938</td>\n",
       "      <td>7036</td>\n",
       "      <td>7820</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mclaren</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes-benz</th>\n",
       "      <td>6063</td>\n",
       "      <td>6542</td>\n",
       "      <td>804</td>\n",
       "      <td>6638</td>\n",
       "      <td>7308</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercury</th>\n",
       "      <td>3002</td>\n",
       "      <td>3002</td>\n",
       "      <td>291</td>\n",
       "      <td>3037</td>\n",
       "      <td>3355</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mini</th>\n",
       "      <td>1033</td>\n",
       "      <td>977</td>\n",
       "      <td>127</td>\n",
       "      <td>997</td>\n",
       "      <td>1036</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mitsubishi</th>\n",
       "      <td>3982</td>\n",
       "      <td>4382</td>\n",
       "      <td>601</td>\n",
       "      <td>4222</td>\n",
       "      <td>4773</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nissan</th>\n",
       "      <td>10729</td>\n",
       "      <td>10025</td>\n",
       "      <td>1735</td>\n",
       "      <td>10401</td>\n",
       "      <td>11760</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pontiac</th>\n",
       "      <td>5066</td>\n",
       "      <td>5294</td>\n",
       "      <td>345</td>\n",
       "      <td>5239</td>\n",
       "      <td>5927</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>porsche</th>\n",
       "      <td>1636</td>\n",
       "      <td>1646</td>\n",
       "      <td>280</td>\n",
       "      <td>1657</td>\n",
       "      <td>1774</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram</th>\n",
       "      <td>564</td>\n",
       "      <td>505</td>\n",
       "      <td>281</td>\n",
       "      <td>551</td>\n",
       "      <td>553</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rolls-royce</th>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subaru</th>\n",
       "      <td>5994</td>\n",
       "      <td>5711</td>\n",
       "      <td>970</td>\n",
       "      <td>5958</td>\n",
       "      <td>6510</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suzuki</th>\n",
       "      <td>2142</td>\n",
       "      <td>2151</td>\n",
       "      <td>460</td>\n",
       "      <td>2124</td>\n",
       "      <td>2326</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tesla</th>\n",
       "      <td>140</td>\n",
       "      <td>136</td>\n",
       "      <td>37</td>\n",
       "      <td>139</td>\n",
       "      <td>140</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volvo</th>\n",
       "      <td>4269</td>\n",
       "      <td>4310</td>\n",
       "      <td>452</td>\n",
       "      <td>4405</td>\n",
       "      <td>4818</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Review_Date  Author_Name  Vehicle_Title  Review_Title  Review  \\\n",
       "Car_Make                                                                       \n",
       "AMGeneral                5            5              2             5       5   \n",
       "Acura                 5632         5807            494          5681    6512   \n",
       "AlfaRomeo               77           76             22            77      77   \n",
       "AstonMartin             82           89             31            89      89   \n",
       "Audi                  5069         5389            753          5467    6006   \n",
       "BMW                   6833         7106            829          7202    7984   \n",
       "Bentley                150          146             39           141     150   \n",
       "Bugatti                  9            9              4             9       9   \n",
       "Buick                 3406         3242            374          3334    3615   \n",
       "Cadillac              3539         3531            457          3593    3902   \n",
       "Chevrolet            15781        16254           2760         16500   19334   \n",
       "GMC                   4327         4425           1261          4415    4964   \n",
       "Honda                11611        10646           1704         11141   12559   \n",
       "Toyota               16145        15483           2328         15913   18553   \n",
       "Volkswagen            8260         8219           1577          8481    9334   \n",
       "chrysler              4958         4960            495          4996    5529   \n",
       "dodge                 6781         7373           1173          7462    8460   \n",
       "ferrari                156          159             47           156     161   \n",
       "fiat                   394          380             68           391     391   \n",
       "ford                 16908        17136           3261         17718   20576   \n",
       "genesis                 78           75             16            78      77   \n",
       "hummer                 537          541             35           531     559   \n",
       "hyundai               7679         7032            943          7250    8156   \n",
       "infiniti              3914         3874            370          3846    4277   \n",
       "isuzu                 1002         1146            175          1093    1173   \n",
       "jaguar                1730         1729            254          1770    1878   \n",
       "jeep                  4824         4311            643          4540    4932   \n",
       "kia                   5561         5225            705          5353    5926   \n",
       "lamborghini             83           85             24            82      86   \n",
       "land-rover            1752         1711            186          1743    1831   \n",
       "lexus                 5371         5474            370          5424    6083   \n",
       "lincoln               2801         2776            308          2792    3012   \n",
       "lotus                  136          133             16           133     137   \n",
       "maserati               235          234             61           234     239   \n",
       "maybach                 24           24              6            24      24   \n",
       "mazda                 7165         6830            938          7036    7820   \n",
       "mclaren                  1            1              1             1       1   \n",
       "mercedes-benz         6063         6542            804          6638    7308   \n",
       "mercury               3002         3002            291          3037    3355   \n",
       "mini                  1033          977            127           997    1036   \n",
       "mitsubishi            3982         4382            601          4222    4773   \n",
       "nissan               10729        10025           1735         10401   11760   \n",
       "pontiac               5066         5294            345          5239    5927   \n",
       "porsche               1636         1646            280          1657    1774   \n",
       "ram                    564          505            281           551     553   \n",
       "rolls-royce             33           34             15            33      33   \n",
       "subaru                5994         5711            970          5958    6510   \n",
       "suzuki                2142         2151            460          2124    2326   \n",
       "tesla                  140          136             37           139     140   \n",
       "volvo                 4269         4310            452          4405    4818   \n",
       "\n",
       "               Rating\\r  \n",
       "Car_Make                 \n",
       "AMGeneral             4  \n",
       "Acura                32  \n",
       "AlfaRomeo             5  \n",
       "AstonMartin          17  \n",
       "Audi                 33  \n",
       "BMW                  33  \n",
       "Bentley              21  \n",
       "Bugatti               7  \n",
       "Buick                33  \n",
       "Cadillac             33  \n",
       "Chevrolet            33  \n",
       "GMC                  33  \n",
       "Honda                33  \n",
       "Toyota               33  \n",
       "Volkswagen           33  \n",
       "chrysler             33  \n",
       "dodge                33  \n",
       "ferrari              17  \n",
       "fiat                 25  \n",
       "ford                 33  \n",
       "genesis               5  \n",
       "hummer               29  \n",
       "hyundai              33  \n",
       "infiniti             32  \n",
       "isuzu                33  \n",
       "jaguar               32  \n",
       "jeep                 33  \n",
       "kia                  33  \n",
       "lamborghini          15  \n",
       "land-rover           33  \n",
       "lexus                32  \n",
       "lincoln              32  \n",
       "lotus                16  \n",
       "maserati             25  \n",
       "maybach               8  \n",
       "mazda                33  \n",
       "mclaren               1  \n",
       "mercedes-benz        33  \n",
       "mercury              33  \n",
       "mini                 29  \n",
       "mitsubishi           33  \n",
       "nissan               33  \n",
       "pontiac              33  \n",
       "porsche              30  \n",
       "ram                  22  \n",
       "rolls-royce          11  \n",
       "subaru               33  \n",
       "suzuki               33  \n",
       "tesla                11  \n",
       "volvo                33  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.groupby('Car_Make').nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And number of the car makes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.groupby('Car_Make').nunique().shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's then sample randomly from the dataframe by keeping <=200 of the records per car make:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Review_Date      7321\n",
       "Author_Name      7434\n",
       "Vehicle_Title    5292\n",
       "Review_Title     7665\n",
       "Review           8338\n",
       "Rating\\r           33\n",
       "Car_Make           50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 200\n",
    "sampled_df = frame.groupby('Car_Make').apply(lambda x: x.sample(min(n,len(x)))).reset_index(drop=True)\n",
    "sampled_df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the number of reviews and columns in the imported corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8392, 7)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's combine the review titles and the review into the review_content for the later analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review_Date</th>\n",
       "      <th>Author_Name</th>\n",
       "      <th>Vehicle_Title</th>\n",
       "      <th>Review_Title</th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating\\r</th>\n",
       "      <th>Car_Make</th>\n",
       "      <th>Review_Content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>on 12/18/05 19:55 PM (PST)</td>\n",
       "      <td>Clayton</td>\n",
       "      <td>2000 AM General Hummer SUV 4dr SUV AWD</td>\n",
       "      <td>HUMMER NOT A bummer</td>\n",
       "      <td>Vehicle is a beast. I don't recommend HUMMER ...</td>\n",
       "      <td>5.000</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>on 01/19/06 19:46 PM (PST)</td>\n",
       "      <td>REUBEN</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>AWESOME HUMMER</td>\n",
       "      <td>Hummer is unstoppable. May only get 12 mpg bu...</td>\n",
       "      <td>5.000</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>AWESOME HUMMER: Hummer is unstoppable. May onl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>on 08/23/03 00:00 AM (PDT)</td>\n",
       "      <td>Bobby Keene</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>H1 Review</td>\n",
       "      <td>The truck is incredible.  I have a long histo...</td>\n",
       "      <td>4.500</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>H1 Review: The truck is incredible.  I have a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>on 08/30/02 00:00 AM (PDT)</td>\n",
       "      <td>bluice3309</td>\n",
       "      <td>2000 AM General Hummer SUV 4dr SUV AWD</td>\n",
       "      <td>a true ride</td>\n",
       "      <td>this beast can go through just about \\ranythi...</td>\n",
       "      <td>4.625</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>a true ride: this beast can go through just ab...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Review_Date   Author_Name  \\\n",
       "0   on 06/15/02 00:00 AM (PDT)     mike6382    \n",
       "1   on 12/18/05 19:55 PM (PST)     Clayton     \n",
       "2   on 01/19/06 19:46 PM (PST)       REUBEN    \n",
       "3   on 08/23/03 00:00 AM (PDT)  Bobby Keene    \n",
       "4   on 08/30/02 00:00 AM (PDT)   bluice3309    \n",
       "\n",
       "                                     Vehicle_Title          Review_Title  \\\n",
       "0  2000 AM General Hummer SUV Hard Top 4dr SUV AWD          What a waste   \n",
       "1           2000 AM General Hummer SUV 4dr SUV AWD  HUMMER NOT A bummer    \n",
       "2  2000 AM General Hummer SUV Hard Top 4dr SUV AWD        AWESOME HUMMER   \n",
       "3  2000 AM General Hummer SUV Hard Top 4dr SUV AWD             H1 Review   \n",
       "4           2000 AM General Hummer SUV 4dr SUV AWD           a true ride   \n",
       "\n",
       "                                              Review  Rating\\r   Car_Make  \\\n",
       "0   I have owned this car for a year and a \\rhalf...     1.000  AMGeneral   \n",
       "1   Vehicle is a beast. I don't recommend HUMMER ...     5.000  AMGeneral   \n",
       "2   Hummer is unstoppable. May only get 12 mpg bu...     5.000  AMGeneral   \n",
       "3   The truck is incredible.  I have a long histo...     4.500  AMGeneral   \n",
       "4   this beast can go through just about \\ranythi...     4.625  AMGeneral   \n",
       "\n",
       "                                      Review_Content  \n",
       "0  What a waste: I have owned this car for a year...  \n",
       "1  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "2  AWESOME HUMMER: Hummer is unstoppable. May onl...  \n",
       "3  H1 Review: The truck is incredible.  I have a ...  \n",
       "4  a true ride: this beast can go through just ab...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_df['Review_Content'] = sampled_df['Review_Title']+ ':' + sampled_df['Review']\n",
    "sampled_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the reiews look like in our dataset by showing one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'What a waste: I have owned this car for a year and a \\rhalf now and it is not reliabile at \\rall.  I have driven it through \\reverything and it stalls on me all the \\rtime.  I would never buy this car \\ragain.  and trying to sell it is like \\rtrying to sell fire in hell, just wont \\rhappen.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_df['Review_Content'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Watson Natural Language Understanding Analysis:\n",
    "Now it is time to check how Watson Natural Language Understanding can help us analyzing the reviews starting from the first review:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code below, we instruct Watson Natural Language Understanding to perform keywords (with sentiment and emotion) analysis on the first review:\n",
    "\n",
    "See [the Watson NLU documentation](https://cloud.ibm.com/apidocs/natural-language-understanding?code=python#text-analytics-features) for a full description of the types of analysis that NLU can perform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "# Using Watson Natural Language Understanding for analyzing the Review_Content\n",
    "# Make the request\n",
    "nlu_response_review = natural_language_understanding.analyze(\n",
    "    text=sampled_df['Review_Content'][0],\n",
    "    return_analyzed_text=True,\n",
    "    features=nlu.Features(\n",
    "        keywords=nlu.KeywordsOptions(sentiment=True, emotion=True)\n",
    "    )).get_result()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The response from the analyze() method is a Python dictionary. The dictionary contains an entry for each pass of analysis requested, plus some additional entries with metadata about the API request itself. Here's a list of the keys in response:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['usage', 'language', 'keywords', 'analyzed_text'])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlu_response_review.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's the whole output of Watson NLU's text analysis for the first review in the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'usage': {'text_units': 1, 'text_characters': 284, 'features': 1},\n",
       " 'language': 'en',\n",
       " 'keywords': [{'text': 'waste',\n",
       "   'sentiment': {'score': -0.875215, 'label': 'negative'},\n",
       "   'relevance': 0.685741,\n",
       "   'emotion': {'sadness': 0.192383,\n",
       "    'joy': 0.024961,\n",
       "    'fear': 0.313145,\n",
       "    'disgust': 0.08332,\n",
       "    'anger': 0.277825},\n",
       "   'count': 1},\n",
       "  {'text': 'fire',\n",
       "   'sentiment': {'score': -0.934513, 'label': 'negative'},\n",
       "   'relevance': 0.598326,\n",
       "   'emotion': {'sadness': 0.360925,\n",
       "    'joy': 0.002355,\n",
       "    'fear': 0.26649,\n",
       "    'disgust': 0.069938,\n",
       "    'anger': 0.442759},\n",
       "   'count': 1},\n",
       "  {'text': 'car',\n",
       "   'sentiment': {'score': -0.844774, 'label': 'negative'},\n",
       "   'relevance': 0.581432,\n",
       "   'emotion': {'sadness': 0.144346,\n",
       "    'joy': 0.150177,\n",
       "    'fear': 0.246102,\n",
       "    'disgust': 0.06176,\n",
       "    'anger': 0.203999},\n",
       "   'count': 2},\n",
       "  {'text': 'hell',\n",
       "   'sentiment': {'score': -0.934513, 'label': 'negative'},\n",
       "   'relevance': 0.577011,\n",
       "   'emotion': {'sadness': 0.360925,\n",
       "    'joy': 0.002355,\n",
       "    'fear': 0.26649,\n",
       "    'disgust': 0.069938,\n",
       "    'anger': 0.442759},\n",
       "   'count': 1},\n",
       "  {'text': 'year',\n",
       "   'sentiment': {'score': -0.875215, 'label': 'negative'},\n",
       "   'relevance': 0.563676,\n",
       "   'emotion': {'sadness': 0.192383,\n",
       "    'joy': 0.024961,\n",
       "    'fear': 0.313145,\n",
       "    'disgust': 0.08332,\n",
       "    'anger': 0.277825},\n",
       "   'count': 1},\n",
       "  {'text': 'time',\n",
       "   'sentiment': {'score': 0, 'label': 'neutral'},\n",
       "   'relevance': 0.466983,\n",
       "   'emotion': {'sadness': 0.266573,\n",
       "    'joy': 0.401314,\n",
       "    'fear': 0.08908,\n",
       "    'disgust': 0.024027,\n",
       "    'anger': 0.065767},\n",
       "   'count': 1}],\n",
       " 'analyzed_text': 'What a waste: I have owned this car for a year and a \\rhalf now and it is not reliabile at \\rall.  I have driven it through \\reverything and it stalls on me all the \\rtime.  I would never buy this car \\ragain.  and trying to sell it is like \\rtrying to sell fire in hell, just wont \\rhappen.'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlu_response_review"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore the output dictionary based on its keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'What a waste: I have owned this car for a year and a \\rhalf now and it is not reliabile at \\rall.  I have driven it through \\reverything and it stalls on me all the \\rtime.  I would never buy this car \\ragain.  and trying to sell it is like \\rtrying to sell fire in hell, just wont \\rhappen.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlu_response_review['analyzed_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': 'waste',\n",
       "  'sentiment': {'score': -0.875215, 'label': 'negative'},\n",
       "  'relevance': 0.685741,\n",
       "  'emotion': {'sadness': 0.192383,\n",
       "   'joy': 0.024961,\n",
       "   'fear': 0.313145,\n",
       "   'disgust': 0.08332,\n",
       "   'anger': 0.277825},\n",
       "  'count': 1},\n",
       " {'text': 'fire',\n",
       "  'sentiment': {'score': -0.934513, 'label': 'negative'},\n",
       "  'relevance': 0.598326,\n",
       "  'emotion': {'sadness': 0.360925,\n",
       "   'joy': 0.002355,\n",
       "   'fear': 0.26649,\n",
       "   'disgust': 0.069938,\n",
       "   'anger': 0.442759},\n",
       "  'count': 1},\n",
       " {'text': 'car',\n",
       "  'sentiment': {'score': -0.844774, 'label': 'negative'},\n",
       "  'relevance': 0.581432,\n",
       "  'emotion': {'sadness': 0.144346,\n",
       "   'joy': 0.150177,\n",
       "   'fear': 0.246102,\n",
       "   'disgust': 0.06176,\n",
       "   'anger': 0.203999},\n",
       "  'count': 2},\n",
       " {'text': 'hell',\n",
       "  'sentiment': {'score': -0.934513, 'label': 'negative'},\n",
       "  'relevance': 0.577011,\n",
       "  'emotion': {'sadness': 0.360925,\n",
       "   'joy': 0.002355,\n",
       "   'fear': 0.26649,\n",
       "   'disgust': 0.069938,\n",
       "   'anger': 0.442759},\n",
       "  'count': 1},\n",
       " {'text': 'year',\n",
       "  'sentiment': {'score': -0.875215, 'label': 'negative'},\n",
       "  'relevance': 0.563676,\n",
       "  'emotion': {'sadness': 0.192383,\n",
       "   'joy': 0.024961,\n",
       "   'fear': 0.313145,\n",
       "   'disgust': 0.08332,\n",
       "   'anger': 0.277825},\n",
       "  'count': 1},\n",
       " {'text': 'time',\n",
       "  'sentiment': {'score': 0, 'label': 'neutral'},\n",
       "  'relevance': 0.466983,\n",
       "  'emotion': {'sadness': 0.266573,\n",
       "   'joy': 0.401314,\n",
       "   'fear': 0.08908,\n",
       "   'disgust': 0.024027,\n",
       "   'anger': 0.065767},\n",
       "  'count': 1}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlu_response_review['keywords']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For many data scientists and machine learning engineers a common task workflow includes using Pandas to do exploratory data analysis followed by using scikit-learn for applying the machine learning techniques over the data. \n",
    "\n",
    "Text Extensions for Pandas includes a function parse_response() that turns the output of Watson NLU's analyze() function into a dictionary of Pandas DataFrames. Let's run our response object through that conversion. Let's first begin by parsing the Watson NLU response by text extensions for pandas, to see what information has been captured for each review:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'syntax': Empty DataFrame\n",
       " Columns: []\n",
       " Index: [],\n",
       " 'entities': Empty DataFrame\n",
       " Columns: []\n",
       " Index: [],\n",
       " 'entity_mentions': Empty DataFrame\n",
       " Columns: []\n",
       " Index: [],\n",
       " 'keywords':     text sentiment.label  sentiment.score  relevance  emotion.sadness  \\\n",
       " 0  waste        negative        -0.875215   0.685741         0.192383   \n",
       " 1   fire        negative        -0.934513   0.598326         0.360925   \n",
       " 2    car        negative        -0.844774   0.581432         0.144346   \n",
       " 3   hell        negative        -0.934513   0.577011         0.360925   \n",
       " 4   year        negative        -0.875215   0.563676         0.192383   \n",
       " 5   time         neutral         0.000000   0.466983         0.266573   \n",
       " \n",
       "    emotion.joy  emotion.fear  emotion.disgust  emotion.anger  count  \n",
       " 0     0.024961      0.313145         0.083320       0.277825      1  \n",
       " 1     0.002355      0.266490         0.069938       0.442759      1  \n",
       " 2     0.150177      0.246102         0.061760       0.203999      2  \n",
       " 3     0.002355      0.266490         0.069938       0.442759      1  \n",
       " 4     0.024961      0.313145         0.083320       0.277825      1  \n",
       " 5     0.401314      0.089080         0.024027       0.065767      1  ,\n",
       " 'relations': Empty DataFrame\n",
       " Columns: []\n",
       " Index: [],\n",
       " 'semantic_roles': Empty DataFrame\n",
       " Columns: []\n",
       " Index: []}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analyzed_review = tp.io.watson.nlu.parse_response(nlu_response_review)\n",
    "df_analyzed_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['syntax', 'entities', 'entity_mentions', 'keywords', 'relations', 'semantic_roles'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analyzed_review.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of each analysis pass that Watson NLU performed is now a DataFrame. Let's look at the DataFrame for the \"keywords\" pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment.label</th>\n",
       "      <th>sentiment.score</th>\n",
       "      <th>relevance</th>\n",
       "      <th>emotion.sadness</th>\n",
       "      <th>emotion.joy</th>\n",
       "      <th>emotion.fear</th>\n",
       "      <th>emotion.disgust</th>\n",
       "      <th>emotion.anger</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>waste</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.875215</td>\n",
       "      <td>0.685741</td>\n",
       "      <td>0.192383</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.083320</td>\n",
       "      <td>0.277825</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fire</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.934513</td>\n",
       "      <td>0.598326</td>\n",
       "      <td>0.360925</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.266490</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>car</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.844774</td>\n",
       "      <td>0.581432</td>\n",
       "      <td>0.144346</td>\n",
       "      <td>0.150177</td>\n",
       "      <td>0.246102</td>\n",
       "      <td>0.061760</td>\n",
       "      <td>0.203999</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hell</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.934513</td>\n",
       "      <td>0.577011</td>\n",
       "      <td>0.360925</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.266490</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>year</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.875215</td>\n",
       "      <td>0.563676</td>\n",
       "      <td>0.192383</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.083320</td>\n",
       "      <td>0.277825</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>time</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.466983</td>\n",
       "      <td>0.266573</td>\n",
       "      <td>0.401314</td>\n",
       "      <td>0.089080</td>\n",
       "      <td>0.024027</td>\n",
       "      <td>0.065767</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    text sentiment.label  sentiment.score  relevance  emotion.sadness  \\\n",
       "0  waste        negative        -0.875215   0.685741         0.192383   \n",
       "1   fire        negative        -0.934513   0.598326         0.360925   \n",
       "2    car        negative        -0.844774   0.581432         0.144346   \n",
       "3   hell        negative        -0.934513   0.577011         0.360925   \n",
       "4   year        negative        -0.875215   0.563676         0.192383   \n",
       "5   time         neutral         0.000000   0.466983         0.266573   \n",
       "\n",
       "   emotion.joy  emotion.fear  emotion.disgust  emotion.anger  count  \n",
       "0     0.024961      0.313145         0.083320       0.277825      1  \n",
       "1     0.002355      0.266490         0.069938       0.442759      1  \n",
       "2     0.150177      0.246102         0.061760       0.203999      2  \n",
       "3     0.002355      0.266490         0.069938       0.442759      1  \n",
       "4     0.024961      0.313145         0.083320       0.277825      1  \n",
       "5     0.401314      0.089080         0.024027       0.065767      1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analyzed_review['keywords']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buried in the above data structure is all the information we need to perform our sentence-level sentiment analysis task:\n",
    "\n",
    "\n",
    " - The sentiment label and score of every sentence in the review. The score ranges from -1 to 1, with -1 being negative, 0 being neutral and 1 being positive. It provides sentiment on each keyword based on its sentence's sentiment, which can come in useful since it calculates the sentiment in the context.\n",
    " - The emotion score of every sentence (i.e., sadness, joy, fear, disgust, and anger) in the review.\n",
    "\n",
    " - The list of the most important words/phrases in a review including both sentiment/emotion-bearing words/phrases as well as objective words/phrases in the review extracted under the keywords. Note that the sentiment assigned to each keyword has calculated based on its context and in the sentence level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's concat the watson nlu sentiment analysis dataframe above(output of text enstensions for pandas) with its corresponding review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment.label</th>\n",
       "      <th>sentiment.score</th>\n",
       "      <th>relevance</th>\n",
       "      <th>emotion.sadness</th>\n",
       "      <th>emotion.joy</th>\n",
       "      <th>emotion.fear</th>\n",
       "      <th>emotion.disgust</th>\n",
       "      <th>emotion.anger</th>\n",
       "      <th>count</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>waste</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.875215</td>\n",
       "      <td>0.685741</td>\n",
       "      <td>0.192383</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.083320</td>\n",
       "      <td>0.277825</td>\n",
       "      <td>1</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fire</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.934513</td>\n",
       "      <td>0.598326</td>\n",
       "      <td>0.360925</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.266490</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>1</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>car</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.844774</td>\n",
       "      <td>0.581432</td>\n",
       "      <td>0.144346</td>\n",
       "      <td>0.150177</td>\n",
       "      <td>0.246102</td>\n",
       "      <td>0.061760</td>\n",
       "      <td>0.203999</td>\n",
       "      <td>2</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hell</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.934513</td>\n",
       "      <td>0.577011</td>\n",
       "      <td>0.360925</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.266490</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>1</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>year</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.875215</td>\n",
       "      <td>0.563676</td>\n",
       "      <td>0.192383</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.083320</td>\n",
       "      <td>0.277825</td>\n",
       "      <td>1</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>time</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.466983</td>\n",
       "      <td>0.266573</td>\n",
       "      <td>0.401314</td>\n",
       "      <td>0.089080</td>\n",
       "      <td>0.024027</td>\n",
       "      <td>0.065767</td>\n",
       "      <td>1</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    text sentiment.label  sentiment.score  relevance  emotion.sadness  \\\n",
       "0  waste        negative        -0.875215   0.685741         0.192383   \n",
       "1   fire        negative        -0.934513   0.598326         0.360925   \n",
       "2    car        negative        -0.844774   0.581432         0.144346   \n",
       "3   hell        negative        -0.934513   0.577011         0.360925   \n",
       "4   year        negative        -0.875215   0.563676         0.192383   \n",
       "5   time         neutral         0.000000   0.466983         0.266573   \n",
       "\n",
       "   emotion.joy  emotion.fear  emotion.disgust  emotion.anger  count  \\\n",
       "0     0.024961      0.313145         0.083320       0.277825      1   \n",
       "1     0.002355      0.266490         0.069938       0.442759      1   \n",
       "2     0.150177      0.246102         0.061760       0.203999      2   \n",
       "3     0.002355      0.266490         0.069938       0.442759      1   \n",
       "4     0.024961      0.313145         0.083320       0.277825      1   \n",
       "5     0.401314      0.089080         0.024027       0.065767      1   \n",
       "\n",
       "                                                   0  \n",
       "0  What a waste: I have owned this car for a year...  \n",
       "1  What a waste: I have owned this car for a year...  \n",
       "2  What a waste: I have owned this car for a year...  \n",
       "3  What a waste: I have owned this car for a year...  \n",
       "4  What a waste: I have owned this car for a year...  \n",
       "5  What a waste: I have owned this car for a year...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords_review = pd.concat ([df_analyzed_review['keywords'] , pd.Series([nlu_response_review['analyzed_text']]*len(df_analyzed_review['keywords']))], axis = 1)\n",
    "keywords_review"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's merge the above dataframe with its corresponding review's information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment.label</th>\n",
       "      <th>sentiment.score</th>\n",
       "      <th>relevance</th>\n",
       "      <th>emotion.sadness</th>\n",
       "      <th>emotion.joy</th>\n",
       "      <th>emotion.fear</th>\n",
       "      <th>emotion.disgust</th>\n",
       "      <th>emotion.anger</th>\n",
       "      <th>count</th>\n",
       "      <th>Review_Date</th>\n",
       "      <th>Author_Name</th>\n",
       "      <th>Vehicle_Title</th>\n",
       "      <th>Review_Title</th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating\\r</th>\n",
       "      <th>Car_Make</th>\n",
       "      <th>Review_Content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>waste</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.875215</td>\n",
       "      <td>0.685741</td>\n",
       "      <td>0.192383</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.083320</td>\n",
       "      <td>0.277825</td>\n",
       "      <td>1</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fire</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.934513</td>\n",
       "      <td>0.598326</td>\n",
       "      <td>0.360925</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.266490</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>1</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>car</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.844774</td>\n",
       "      <td>0.581432</td>\n",
       "      <td>0.144346</td>\n",
       "      <td>0.150177</td>\n",
       "      <td>0.246102</td>\n",
       "      <td>0.061760</td>\n",
       "      <td>0.203999</td>\n",
       "      <td>2</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hell</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.934513</td>\n",
       "      <td>0.577011</td>\n",
       "      <td>0.360925</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.266490</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>1</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>year</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.875215</td>\n",
       "      <td>0.563676</td>\n",
       "      <td>0.192383</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.083320</td>\n",
       "      <td>0.277825</td>\n",
       "      <td>1</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>time</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.466983</td>\n",
       "      <td>0.266573</td>\n",
       "      <td>0.401314</td>\n",
       "      <td>0.089080</td>\n",
       "      <td>0.024027</td>\n",
       "      <td>0.065767</td>\n",
       "      <td>1</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    text sentiment.label  sentiment.score  relevance  emotion.sadness  \\\n",
       "0  waste        negative        -0.875215   0.685741         0.192383   \n",
       "1   fire        negative        -0.934513   0.598326         0.360925   \n",
       "2    car        negative        -0.844774   0.581432         0.144346   \n",
       "3   hell        negative        -0.934513   0.577011         0.360925   \n",
       "4   year        negative        -0.875215   0.563676         0.192383   \n",
       "5   time         neutral         0.000000   0.466983         0.266573   \n",
       "\n",
       "   emotion.joy  emotion.fear  emotion.disgust  emotion.anger  count  \\\n",
       "0     0.024961      0.313145         0.083320       0.277825      1   \n",
       "1     0.002355      0.266490         0.069938       0.442759      1   \n",
       "2     0.150177      0.246102         0.061760       0.203999      2   \n",
       "3     0.002355      0.266490         0.069938       0.442759      1   \n",
       "4     0.024961      0.313145         0.083320       0.277825      1   \n",
       "5     0.401314      0.089080         0.024027       0.065767      1   \n",
       "\n",
       "                   Review_Date Author_Name  \\\n",
       "0   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "1   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "2   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "3   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "4   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "5   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "\n",
       "                                     Vehicle_Title  Review_Title  \\\n",
       "0  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "1  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "2  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "3  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "4  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "5  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "\n",
       "                                              Review  Rating\\r   Car_Make  \\\n",
       "0   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "1   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "2   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "3   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "4   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "5   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "\n",
       "                                      Review_Content  \n",
       "0  What a waste: I have owned this car for a year...  \n",
       "1  What a waste: I have owned this car for a year...  \n",
       "2  What a waste: I have owned this car for a year...  \n",
       "3  What a waste: I have owned this car for a year...  \n",
       "4  What a waste: I have owned this car for a year...  \n",
       "5  What a waste: I have owned this car for a year...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(keywords_review.merge(sampled_df, left_on=0, right_on = sampled_df.Review_Content)).drop(columns=[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Repeat the Preprocessing over Multiple Reviews\n",
    "Let's see how we can apply same operations on multiple entries from our car reviews dataset and use the outcome for correlation and sentiment analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_with_retry(text: str) -> Any:\n",
    "    \"\"\"\n",
    "    Compensate for the occasional \"service unavailable due to rate-limiting\"\n",
    "    error message.\n",
    "    \"\"\"\n",
    "    num_retries_left = 5\n",
    "    last_exception = None\n",
    "    while num_retries_left > 0:\n",
    "        num_retries_left -= 1\n",
    "        try:\n",
    "            return natural_language_understanding.analyze(\n",
    "                text=text,\n",
    "                language=\"en\",\n",
    "                return_analyzed_text=True,\n",
    "                features=nlu.Features(\n",
    "                    keywords=nlu.KeywordsOptions(sentiment=True, emotion=True))\n",
    "            ).get_result()\n",
    "        except BaseException as e:\n",
    "            last_exception = e\n",
    "            # Backoff\n",
    "            time.sleep(0.2)\n",
    "    raise last_exception\n",
    "\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "nlu_response_reviews = sampled_df['Review_Content'].dropna().apply(lambda x: analyze_with_retry(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp_parsed_reviews = [tp.io.watson.nlu.parse_response(r) for r in nlu_response_reviews]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it. With the DataFrame version of this data, we can perform our exploratory and sentiment analysis task easily with few line of code.\n",
    "\n",
    "Specifically, we use Pandas  to concat the Watson NLU sentiments dataframe (output of text enstensions for pandas) with its corresponding review, and then we conduct some exploratory analysis on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment.label</th>\n",
       "      <th>sentiment.score</th>\n",
       "      <th>relevance</th>\n",
       "      <th>emotion.sadness</th>\n",
       "      <th>emotion.joy</th>\n",
       "      <th>emotion.fear</th>\n",
       "      <th>emotion.disgust</th>\n",
       "      <th>emotion.anger</th>\n",
       "      <th>count</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>waste</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.875215</td>\n",
       "      <td>0.685741</td>\n",
       "      <td>0.192383</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.083320</td>\n",
       "      <td>0.277825</td>\n",
       "      <td>1.0</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fire</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.934513</td>\n",
       "      <td>0.598326</td>\n",
       "      <td>0.360925</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.266490</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>1.0</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>car</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.844774</td>\n",
       "      <td>0.581432</td>\n",
       "      <td>0.144346</td>\n",
       "      <td>0.150177</td>\n",
       "      <td>0.246102</td>\n",
       "      <td>0.061760</td>\n",
       "      <td>0.203999</td>\n",
       "      <td>2.0</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hell</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.934513</td>\n",
       "      <td>0.577011</td>\n",
       "      <td>0.360925</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.266490</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>1.0</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>year</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.875215</td>\n",
       "      <td>0.563676</td>\n",
       "      <td>0.192383</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.083320</td>\n",
       "      <td>0.277825</td>\n",
       "      <td>1.0</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>time</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.466983</td>\n",
       "      <td>0.266573</td>\n",
       "      <td>0.401314</td>\n",
       "      <td>0.089080</td>\n",
       "      <td>0.024027</td>\n",
       "      <td>0.065767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Top speed</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.537564</td>\n",
       "      <td>0.881037</td>\n",
       "      <td>0.509224</td>\n",
       "      <td>0.199172</td>\n",
       "      <td>0.038777</td>\n",
       "      <td>0.065161</td>\n",
       "      <td>0.044472</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OK cause</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.647515</td>\n",
       "      <td>0.786985</td>\n",
       "      <td>0.063022</td>\n",
       "      <td>0.432975</td>\n",
       "      <td>0.107965</td>\n",
       "      <td>0.016918</td>\n",
       "      <td>0.090944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HUMMER H</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.639671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>seat cushion</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.537564</td>\n",
       "      <td>0.593566</td>\n",
       "      <td>0.509224</td>\n",
       "      <td>0.199172</td>\n",
       "      <td>0.038777</td>\n",
       "      <td>0.065161</td>\n",
       "      <td>0.044472</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HUMMER</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.913874</td>\n",
       "      <td>0.582162</td>\n",
       "      <td>0.172604</td>\n",
       "      <td>0.221188</td>\n",
       "      <td>0.146469</td>\n",
       "      <td>0.022002</td>\n",
       "      <td>0.029588</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speed</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.305110</td>\n",
       "      <td>0.548092</td>\n",
       "      <td>0.286123</td>\n",
       "      <td>0.316074</td>\n",
       "      <td>0.073371</td>\n",
       "      <td>0.041039</td>\n",
       "      <td>0.067708</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>thing</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.949193</td>\n",
       "      <td>0.534867</td>\n",
       "      <td>0.645165</td>\n",
       "      <td>0.010028</td>\n",
       "      <td>0.216581</td>\n",
       "      <td>0.022772</td>\n",
       "      <td>0.055427</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Vehicle</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.961235</td>\n",
       "      <td>0.531410</td>\n",
       "      <td>0.180207</td>\n",
       "      <td>0.061332</td>\n",
       "      <td>0.192902</td>\n",
       "      <td>0.008274</td>\n",
       "      <td>0.046232</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>beast</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.961235</td>\n",
       "      <td>0.524488</td>\n",
       "      <td>0.180207</td>\n",
       "      <td>0.061332</td>\n",
       "      <td>0.192902</td>\n",
       "      <td>0.008274</td>\n",
       "      <td>0.046232</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>thats</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.647515</td>\n",
       "      <td>0.462435</td>\n",
       "      <td>0.063022</td>\n",
       "      <td>0.432975</td>\n",
       "      <td>0.107965</td>\n",
       "      <td>0.016918</td>\n",
       "      <td>0.090944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>bummer</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.961235</td>\n",
       "      <td>0.360189</td>\n",
       "      <td>0.180207</td>\n",
       "      <td>0.061332</td>\n",
       "      <td>0.192902</td>\n",
       "      <td>0.008274</td>\n",
       "      <td>0.046232</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>average</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.857270</td>\n",
       "      <td>0.341687</td>\n",
       "      <td>0.165002</td>\n",
       "      <td>0.381043</td>\n",
       "      <td>0.100036</td>\n",
       "      <td>0.035730</td>\n",
       "      <td>0.012944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HUMMER NOT A bummer : Vehicle is a beast. I do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AWESOME HUMMER</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.734682</td>\n",
       "      <td>0.833177</td>\n",
       "      <td>0.032499</td>\n",
       "      <td>0.493942</td>\n",
       "      <td>0.116809</td>\n",
       "      <td>0.009257</td>\n",
       "      <td>0.024046</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AWESOME HUMMER: Hummer is unstoppable. May onl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mph</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.635404</td>\n",
       "      <td>0.499977</td>\n",
       "      <td>0.151388</td>\n",
       "      <td>0.039640</td>\n",
       "      <td>0.036049</td>\n",
       "      <td>0.064654</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AWESOME HUMMER: Hummer is unstoppable. May onl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              text sentiment.label  sentiment.score  relevance  \\\n",
       "0            waste        negative        -0.875215   0.685741   \n",
       "1             fire        negative        -0.934513   0.598326   \n",
       "2              car        negative        -0.844774   0.581432   \n",
       "3             hell        negative        -0.934513   0.577011   \n",
       "4             year        negative        -0.875215   0.563676   \n",
       "5             time         neutral         0.000000   0.466983   \n",
       "0        Top speed        negative        -0.537564   0.881037   \n",
       "1         OK cause        positive         0.647515   0.786985   \n",
       "2         HUMMER H         neutral         0.000000   0.639671   \n",
       "3     seat cushion        negative        -0.537564   0.593566   \n",
       "4           HUMMER        negative        -0.913874   0.582162   \n",
       "5            speed        positive         0.305110   0.548092   \n",
       "6            thing        negative        -0.949193   0.534867   \n",
       "7          Vehicle        negative        -0.961235   0.531410   \n",
       "8            beast        negative        -0.961235   0.524488   \n",
       "9            thats        positive         0.647515   0.462435   \n",
       "10          bummer        negative        -0.961235   0.360189   \n",
       "11         average        negative        -0.857270   0.341687   \n",
       "0   AWESOME HUMMER        positive         0.734682   0.833177   \n",
       "1              mph         neutral         0.000000   0.635404   \n",
       "\n",
       "    emotion.sadness  emotion.joy  emotion.fear  emotion.disgust  \\\n",
       "0          0.192383     0.024961      0.313145         0.083320   \n",
       "1          0.360925     0.002355      0.266490         0.069938   \n",
       "2          0.144346     0.150177      0.246102         0.061760   \n",
       "3          0.360925     0.002355      0.266490         0.069938   \n",
       "4          0.192383     0.024961      0.313145         0.083320   \n",
       "5          0.266573     0.401314      0.089080         0.024027   \n",
       "0          0.509224     0.199172      0.038777         0.065161   \n",
       "1          0.063022     0.432975      0.107965         0.016918   \n",
       "2               NaN          NaN           NaN              NaN   \n",
       "3          0.509224     0.199172      0.038777         0.065161   \n",
       "4          0.172604     0.221188      0.146469         0.022002   \n",
       "5          0.286123     0.316074      0.073371         0.041039   \n",
       "6          0.645165     0.010028      0.216581         0.022772   \n",
       "7          0.180207     0.061332      0.192902         0.008274   \n",
       "8          0.180207     0.061332      0.192902         0.008274   \n",
       "9          0.063022     0.432975      0.107965         0.016918   \n",
       "10         0.180207     0.061332      0.192902         0.008274   \n",
       "11         0.165002     0.381043      0.100036         0.035730   \n",
       "0          0.032499     0.493942      0.116809         0.009257   \n",
       "1          0.499977     0.151388      0.039640         0.036049   \n",
       "\n",
       "    emotion.anger  count                                                  0  \n",
       "0        0.277825    1.0  What a waste: I have owned this car for a year...  \n",
       "1        0.442759    1.0  What a waste: I have owned this car for a year...  \n",
       "2        0.203999    2.0  What a waste: I have owned this car for a year...  \n",
       "3        0.442759    1.0  What a waste: I have owned this car for a year...  \n",
       "4        0.277825    1.0  What a waste: I have owned this car for a year...  \n",
       "5        0.065767    1.0  What a waste: I have owned this car for a year...  \n",
       "0        0.044472    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "1        0.090944    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "2             NaN    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "3        0.044472    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "4        0.029588    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "5        0.067708    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "6        0.055427    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "7        0.046232    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "8        0.046232    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "9        0.090944    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "10       0.046232    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "11       0.012944    1.0  HUMMER NOT A bummer : Vehicle is a beast. I do...  \n",
       "0        0.024046    1.0  AWESOME HUMMER: Hummer is unstoppable. May onl...  \n",
       "1        0.064654    1.0  AWESOME HUMMER: Hummer is unstoppable. May onl...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenation\n",
    "keywords_review = [pd.concat ([parsed_review['keywords'] , pd.Series([r['analyzed_text']]*len(parsed_review['keywords']))], axis = 1) for (parsed_review,r) in zip(tp_parsed_reviews,pd.Series(nlu_response_reviews))]\n",
    "# Convert list of dataframes to the dataframe\n",
    "keywords_review_df = pd.concat(keywords_review, axis = 0)\n",
    "keywords_review_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging each review in the resulted dataframe with its Title, Author, Rating, and other info as below and then grouping based on the Review_Title:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
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       "<IPython.core.display.HTML object>"
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment.label</th>\n",
       "      <th>sentiment.score</th>\n",
       "      <th>relevance</th>\n",
       "      <th>emotion.sadness</th>\n",
       "      <th>emotion.joy</th>\n",
       "      <th>emotion.fear</th>\n",
       "      <th>emotion.disgust</th>\n",
       "      <th>emotion.anger</th>\n",
       "      <th>count</th>\n",
       "      <th>Review_Date</th>\n",
       "      <th>Author_Name</th>\n",
       "      <th>Vehicle_Title</th>\n",
       "      <th>Review_Title</th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating\\r</th>\n",
       "      <th>Car_Make</th>\n",
       "      <th>Review_Content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>waste</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.875215</td>\n",
       "      <td>0.685741</td>\n",
       "      <td>0.192383</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.083320</td>\n",
       "      <td>0.277825</td>\n",
       "      <td>1.0</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fire</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.934513</td>\n",
       "      <td>0.598326</td>\n",
       "      <td>0.360925</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.266490</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>1.0</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>car</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.844774</td>\n",
       "      <td>0.581432</td>\n",
       "      <td>0.144346</td>\n",
       "      <td>0.150177</td>\n",
       "      <td>0.246102</td>\n",
       "      <td>0.061760</td>\n",
       "      <td>0.203999</td>\n",
       "      <td>2.0</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hell</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.934513</td>\n",
       "      <td>0.577011</td>\n",
       "      <td>0.360925</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.266490</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.442759</td>\n",
       "      <td>1.0</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>year</td>\n",
       "      <td>negative</td>\n",
       "      <td>-0.875215</td>\n",
       "      <td>0.563676</td>\n",
       "      <td>0.192383</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.083320</td>\n",
       "      <td>0.277825</td>\n",
       "      <td>1.0</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>time</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.466983</td>\n",
       "      <td>0.266573</td>\n",
       "      <td>0.401314</td>\n",
       "      <td>0.089080</td>\n",
       "      <td>0.024027</td>\n",
       "      <td>0.065767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>on 06/15/02 00:00 AM (PDT)</td>\n",
       "      <td>mike6382</td>\n",
       "      <td>2000 AM General Hummer SUV Hard Top 4dr SUV AWD</td>\n",
       "      <td>What a waste</td>\n",
       "      <td>I have owned this car for a year and a \\rhalf...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMGeneral</td>\n",
       "      <td>What a waste: I have owned this car for a year...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    text sentiment.label  sentiment.score  relevance  emotion.sadness  \\\n",
       "0  waste        negative        -0.875215   0.685741         0.192383   \n",
       "1   fire        negative        -0.934513   0.598326         0.360925   \n",
       "2    car        negative        -0.844774   0.581432         0.144346   \n",
       "3   hell        negative        -0.934513   0.577011         0.360925   \n",
       "4   year        negative        -0.875215   0.563676         0.192383   \n",
       "5   time         neutral         0.000000   0.466983         0.266573   \n",
       "\n",
       "   emotion.joy  emotion.fear  emotion.disgust  emotion.anger  count  \\\n",
       "0     0.024961      0.313145         0.083320       0.277825    1.0   \n",
       "1     0.002355      0.266490         0.069938       0.442759    1.0   \n",
       "2     0.150177      0.246102         0.061760       0.203999    2.0   \n",
       "3     0.002355      0.266490         0.069938       0.442759    1.0   \n",
       "4     0.024961      0.313145         0.083320       0.277825    1.0   \n",
       "5     0.401314      0.089080         0.024027       0.065767    1.0   \n",
       "\n",
       "                   Review_Date Author_Name  \\\n",
       "0   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "1   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "2   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "3   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "4   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "5   on 06/15/02 00:00 AM (PDT)   mike6382    \n",
       "\n",
       "                                     Vehicle_Title  Review_Title  \\\n",
       "0  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "1  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "2  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "3  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "4  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "5  2000 AM General Hummer SUV Hard Top 4dr SUV AWD  What a waste   \n",
       "\n",
       "                                              Review  Rating\\r   Car_Make  \\\n",
       "0   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "1   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "2   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "3   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "4   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "5   I have owned this car for a year and a \\rhalf...       1.0  AMGeneral   \n",
       "\n",
       "                                      Review_Content  \n",
       "0  What a waste: I have owned this car for a year...  \n",
       "1  What a waste: I have owned this car for a year...  \n",
       "2  What a waste: I have owned this car for a year...  \n",
       "3  What a waste: I have owned this car for a year...  \n",
       "4  What a waste: I have owned this car for a year...  \n",
       "5  What a waste: I have owned this car for a year...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "merged_keywords_review_df = (keywords_review_df.merge(sampled_df, left_on=0, right_on = sampled_df.Review_Content)).drop(columns=[0])\n",
    "grouped_merged_keywords_review_df = merged_keywords_review_df.groupby('Review_Title')\n",
    "grouped_merged_keywords_review_df.get_group('What a waste').head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "merged_keywords_review_dfAs we mentioned above, Watson NLU assigns the sentiment to the keywords based on their context within the sentence. Hence, all keywords within one sentence get the same sentiment score. Thus, to get the aggregated sentiment of each review we calulate the mean sentiment score of its sentences by considering the sentiment assigned to one keyword in each sentence. More specifically, we first drop duplicate sentiment scores for each review and then we calculate the average sentiment and emotion score for each review:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emotion.sadness</th>\n",
       "      <th>emotion.joy</th>\n",
       "      <th>emotion.fear</th>\n",
       "      <th>emotion.disgust</th>\n",
       "      <th>emotion.anger</th>\n",
       "      <th>sentiment.score</th>\n",
       "      <th>Rating\\r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Review_Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 sweet R32</th>\n",
       "      <td>0.151543</td>\n",
       "      <td>0.532162</td>\n",
       "      <td>0.067859</td>\n",
       "      <td>0.018501</td>\n",
       "      <td>0.112994</td>\n",
       "      <td>0.649825</td>\n",
       "      <td>4.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002 Trans Am/Sunset Orange Metallic</th>\n",
       "      <td>0.176322</td>\n",
       "      <td>0.465210</td>\n",
       "      <td>0.257064</td>\n",
       "      <td>0.032842</td>\n",
       "      <td>0.038908</td>\n",
       "      <td>0.148035</td>\n",
       "      <td>4.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42 days of driving 8 days in the shop</th>\n",
       "      <td>0.206478</td>\n",
       "      <td>0.563466</td>\n",
       "      <td>0.114506</td>\n",
       "      <td>0.010082</td>\n",
       "      <td>0.082325</td>\n",
       "      <td>-0.054126</td>\n",
       "      <td>3.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A great little car</th>\n",
       "      <td>0.278575</td>\n",
       "      <td>0.470586</td>\n",
       "      <td>0.063823</td>\n",
       "      <td>0.015218</td>\n",
       "      <td>0.039688</td>\n",
       "      <td>0.503785</td>\n",
       "      <td>4.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AWESOME FUN MY LITTLE TIGER</th>\n",
       "      <td>0.007629</td>\n",
       "      <td>0.628312</td>\n",
       "      <td>0.013015</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.024782</td>\n",
       "      <td>0.986029</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I LOVE my Focus</th>\n",
       "      <td>0.074019</td>\n",
       "      <td>0.589196</td>\n",
       "      <td>0.111722</td>\n",
       "      <td>0.008124</td>\n",
       "      <td>0.066092</td>\n",
       "      <td>0.621983</td>\n",
       "      <td>4.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Looks Good But Hunk Of Junk</th>\n",
       "      <td>0.144671</td>\n",
       "      <td>0.061358</td>\n",
       "      <td>0.060613</td>\n",
       "      <td>0.050494</td>\n",
       "      <td>0.116835</td>\n",
       "      <td>-0.984622</td>\n",
       "      <td>2.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr  TACOMA</th>\n",
       "      <td>0.122766</td>\n",
       "      <td>0.825653</td>\n",
       "      <td>0.034777</td>\n",
       "      <td>0.023124</td>\n",
       "      <td>0.030344</td>\n",
       "      <td>0.633803</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veracruz</th>\n",
       "      <td>0.106981</td>\n",
       "      <td>0.524371</td>\n",
       "      <td>0.091482</td>\n",
       "      <td>0.012344</td>\n",
       "      <td>0.054493</td>\n",
       "      <td>0.591816</td>\n",
       "      <td>4.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>You will pay for that warranty</th>\n",
       "      <td>0.396306</td>\n",
       "      <td>0.110458</td>\n",
       "      <td>0.056980</td>\n",
       "      <td>0.021192</td>\n",
       "      <td>0.119030</td>\n",
       "      <td>-0.373583</td>\n",
       "      <td>2.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>everyday rSx</th>\n",
       "      <td>0.038486</td>\n",
       "      <td>0.515852</td>\n",
       "      <td>0.133419</td>\n",
       "      <td>0.008035</td>\n",
       "      <td>0.033998</td>\n",
       "      <td>0.677286</td>\n",
       "      <td>4.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>got new  weel</th>\n",
       "      <td>0.108507</td>\n",
       "      <td>0.348390</td>\n",
       "      <td>0.079194</td>\n",
       "      <td>0.034643</td>\n",
       "      <td>0.239177</td>\n",
       "      <td>0.654034</td>\n",
       "      <td>4.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i'm on my second one</th>\n",
       "      <td>0.063124</td>\n",
       "      <td>0.024840</td>\n",
       "      <td>0.053951</td>\n",
       "      <td>0.026402</td>\n",
       "      <td>0.165089</td>\n",
       "      <td>-0.973446</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>! un happy Camper</th>\n",
       "      <td>0.424926</td>\n",
       "      <td>0.219506</td>\n",
       "      <td>0.066627</td>\n",
       "      <td>0.036578</td>\n",
       "      <td>0.084982</td>\n",
       "      <td>-0.388182</td>\n",
       "      <td>2.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"\"\"I can't believe it \"\"</th>\n",
       "      <td>0.244671</td>\n",
       "      <td>0.025868</td>\n",
       "      <td>0.053133</td>\n",
       "      <td>0.067597</td>\n",
       "      <td>0.165597</td>\n",
       "      <td>-0.904022</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"06\" GTO</th>\n",
       "      <td>0.102005</td>\n",
       "      <td>0.632400</td>\n",
       "      <td>0.113796</td>\n",
       "      <td>0.055768</td>\n",
       "      <td>0.067348</td>\n",
       "      <td>0.759998</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Acceleration failure\" - Genesis phraseology</th>\n",
       "      <td>0.139895</td>\n",
       "      <td>0.143780</td>\n",
       "      <td>0.259497</td>\n",
       "      <td>0.049344</td>\n",
       "      <td>0.086211</td>\n",
       "      <td>-0.632639</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Cry wolf\" tire light and redundant warning screen</th>\n",
       "      <td>0.304694</td>\n",
       "      <td>0.165782</td>\n",
       "      <td>0.128762</td>\n",
       "      <td>0.043923</td>\n",
       "      <td>0.172683</td>\n",
       "      <td>-0.744237</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Downgraded\" to an LS 430 but best upgrade ever!</th>\n",
       "      <td>0.381772</td>\n",
       "      <td>0.401842</td>\n",
       "      <td>0.028101</td>\n",
       "      <td>0.018007</td>\n",
       "      <td>0.026035</td>\n",
       "      <td>0.480924</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"First Ride\" Impressions when I visited Tesla's Factory</th>\n",
       "      <td>0.231870</td>\n",
       "      <td>0.443138</td>\n",
       "      <td>0.036262</td>\n",
       "      <td>0.023640</td>\n",
       "      <td>0.051412</td>\n",
       "      <td>0.610619</td>\n",
       "      <td>4.875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    emotion.sadness  \\\n",
       "Review_Title                                                          \n",
       " 1 sweet R32                                               0.151543   \n",
       " 2002 Trans Am/Sunset Orange Metallic                      0.176322   \n",
       " 42 days of driving 8 days in the shop                     0.206478   \n",
       " A great little car                                        0.278575   \n",
       " AWESOME FUN MY LITTLE TIGER                               0.007629   \n",
       " I LOVE my Focus                                           0.074019   \n",
       " Looks Good But Hunk Of Junk                               0.144671   \n",
       " Mr  TACOMA                                                0.122766   \n",
       " Veracruz                                                  0.106981   \n",
       " You will pay for that warranty                            0.396306   \n",
       " everyday rSx                                              0.038486   \n",
       " got new  weel                                             0.108507   \n",
       " i'm on my second one                                      0.063124   \n",
       "! un happy Camper                                          0.424926   \n",
       "\"\"\"I can't believe it \"\"                                   0.244671   \n",
       "\"06\" GTO                                                   0.102005   \n",
       "\"Acceleration failure\" - Genesis phraseology               0.139895   \n",
       "\"Cry wolf\" tire light and redundant warning screen         0.304694   \n",
       "\"Downgraded\" to an LS 430 but best upgrade ever!           0.381772   \n",
       "\"First Ride\" Impressions when I visited Tesla's...         0.231870   \n",
       "\n",
       "                                                    emotion.joy  emotion.fear  \\\n",
       "Review_Title                                                                    \n",
       " 1 sweet R32                                           0.532162      0.067859   \n",
       " 2002 Trans Am/Sunset Orange Metallic                  0.465210      0.257064   \n",
       " 42 days of driving 8 days in the shop                 0.563466      0.114506   \n",
       " A great little car                                    0.470586      0.063823   \n",
       " AWESOME FUN MY LITTLE TIGER                           0.628312      0.013015   \n",
       " I LOVE my Focus                                       0.589196      0.111722   \n",
       " Looks Good But Hunk Of Junk                           0.061358      0.060613   \n",
       " Mr  TACOMA                                            0.825653      0.034777   \n",
       " Veracruz                                              0.524371      0.091482   \n",
       " You will pay for that warranty                        0.110458      0.056980   \n",
       " everyday rSx                                          0.515852      0.133419   \n",
       " got new  weel                                         0.348390      0.079194   \n",
       " i'm on my second one                                  0.024840      0.053951   \n",
       "! un happy Camper                                      0.219506      0.066627   \n",
       "\"\"\"I can't believe it \"\"                               0.025868      0.053133   \n",
       "\"06\" GTO                                               0.632400      0.113796   \n",
       "\"Acceleration failure\" - Genesis phraseology           0.143780      0.259497   \n",
       "\"Cry wolf\" tire light and redundant warning screen     0.165782      0.128762   \n",
       "\"Downgraded\" to an LS 430 but best upgrade ever!       0.401842      0.028101   \n",
       "\"First Ride\" Impressions when I visited Tesla's...     0.443138      0.036262   \n",
       "\n",
       "                                                    emotion.disgust  \\\n",
       "Review_Title                                                          \n",
       " 1 sweet R32                                               0.018501   \n",
       " 2002 Trans Am/Sunset Orange Metallic                      0.032842   \n",
       " 42 days of driving 8 days in the shop                     0.010082   \n",
       " A great little car                                        0.015218   \n",
       " AWESOME FUN MY LITTLE TIGER                               0.001452   \n",
       " I LOVE my Focus                                           0.008124   \n",
       " Looks Good But Hunk Of Junk                               0.050494   \n",
       " Mr  TACOMA                                                0.023124   \n",
       " Veracruz                                                  0.012344   \n",
       " You will pay for that warranty                            0.021192   \n",
       " everyday rSx                                              0.008035   \n",
       " got new  weel                                             0.034643   \n",
       " i'm on my second one                                      0.026402   \n",
       "! un happy Camper                                          0.036578   \n",
       "\"\"\"I can't believe it \"\"                                   0.067597   \n",
       "\"06\" GTO                                                   0.055768   \n",
       "\"Acceleration failure\" - Genesis phraseology               0.049344   \n",
       "\"Cry wolf\" tire light and redundant warning screen         0.043923   \n",
       "\"Downgraded\" to an LS 430 but best upgrade ever!           0.018007   \n",
       "\"First Ride\" Impressions when I visited Tesla's...         0.023640   \n",
       "\n",
       "                                                    emotion.anger  \\\n",
       "Review_Title                                                        \n",
       " 1 sweet R32                                             0.112994   \n",
       " 2002 Trans Am/Sunset Orange Metallic                    0.038908   \n",
       " 42 days of driving 8 days in the shop                   0.082325   \n",
       " A great little car                                      0.039688   \n",
       " AWESOME FUN MY LITTLE TIGER                             0.024782   \n",
       " I LOVE my Focus                                         0.066092   \n",
       " Looks Good But Hunk Of Junk                             0.116835   \n",
       " Mr  TACOMA                                              0.030344   \n",
       " Veracruz                                                0.054493   \n",
       " You will pay for that warranty                          0.119030   \n",
       " everyday rSx                                            0.033998   \n",
       " got new  weel                                           0.239177   \n",
       " i'm on my second one                                    0.165089   \n",
       "! un happy Camper                                        0.084982   \n",
       "\"\"\"I can't believe it \"\"                                 0.165597   \n",
       "\"06\" GTO                                                 0.067348   \n",
       "\"Acceleration failure\" - Genesis phraseology             0.086211   \n",
       "\"Cry wolf\" tire light and redundant warning screen       0.172683   \n",
       "\"Downgraded\" to an LS 430 but best upgrade ever!         0.026035   \n",
       "\"First Ride\" Impressions when I visited Tesla's...       0.051412   \n",
       "\n",
       "                                                    sentiment.score  Rating\\r  \n",
       "Review_Title                                                                   \n",
       " 1 sweet R32                                               0.649825     4.875  \n",
       " 2002 Trans Am/Sunset Orange Metallic                      0.148035     4.625  \n",
       " 42 days of driving 8 days in the shop                    -0.054126     3.375  \n",
       " A great little car                                        0.503785     4.875  \n",
       " AWESOME FUN MY LITTLE TIGER                               0.986029     5.000  \n",
       " I LOVE my Focus                                           0.621983     4.750  \n",
       " Looks Good But Hunk Of Junk                              -0.984622     2.875  \n",
       " Mr  TACOMA                                                0.633803     5.000  \n",
       " Veracruz                                                  0.591816     4.750  \n",
       " You will pay for that warranty                           -0.373583     2.750  \n",
       " everyday rSx                                              0.677286     4.000  \n",
       " got new  weel                                             0.654034     4.625  \n",
       " i'm on my second one                                     -0.973446     5.000  \n",
       "! un happy Camper                                         -0.388182     2.625  \n",
       "\"\"\"I can't believe it \"\"                                  -0.904022     1.000  \n",
       "\"06\" GTO                                                   0.759998     5.000  \n",
       "\"Acceleration failure\" - Genesis phraseology              -0.632639     3.000  \n",
       "\"Cry wolf\" tire light and redundant warning screen        -0.744237     3.000  \n",
       "\"Downgraded\" to an LS 430 but best upgrade ever!           0.480924     5.000  \n",
       "\"First Ride\" Impressions when I visited Tesla's...         0.610619     4.875  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_cols = [str(c) for c in merged_keywords_review_df.columns\n",
    "                  if c.startswith('emotion.')] + ['sentiment.score']\n",
    "agg_merged_keywords_review_df = (\n",
    "    merged_keywords_review_df[sentiment_cols + ['Review_Title', 'Rating\\r']]\n",
    "    .drop_duplicates(['Review_Title','sentiment.score'])\n",
    "    .groupby('Review_Title')\n",
    "    .mean())\n",
    "agg_merged_keywords_review_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can find the correlation among the variables using pearson method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0d8dd_level0_col0\" class=\"col_heading level0 col0\" >emotion.sadness</th>\n",
       "      <th id=\"T_0d8dd_level0_col1\" class=\"col_heading level0 col1\" >emotion.joy</th>\n",
       "      <th id=\"T_0d8dd_level0_col2\" class=\"col_heading level0 col2\" >emotion.fear</th>\n",
       "      <th id=\"T_0d8dd_level0_col3\" class=\"col_heading level0 col3\" >emotion.disgust</th>\n",
       "      <th id=\"T_0d8dd_level0_col4\" class=\"col_heading level0 col4\" >emotion.anger</th>\n",
       "      <th id=\"T_0d8dd_level0_col5\" class=\"col_heading level0 col5\" >sentiment.score</th>\n",
       "      <th id=\"T_0d8dd_level0_col6\" class=\"col_heading level0 col6\" >Rating\r",
       "</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0d8dd_level0_row0\" class=\"row_heading level0 row0\" >emotion.sadness</th>\n",
       "      <td id=\"T_0d8dd_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_0d8dd_row0_col1\" class=\"data row0 col1\" >-0.635496</td>\n",
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       "      <td id=\"T_0d8dd_row0_col5\" class=\"data row0 col5\" >-0.519823</td>\n",
       "      <td id=\"T_0d8dd_row0_col6\" class=\"data row0 col6\" >-0.353612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0d8dd_level0_row1\" class=\"row_heading level0 row1\" >emotion.joy</th>\n",
       "      <td id=\"T_0d8dd_row1_col0\" class=\"data row1 col0\" >-0.635496</td>\n",
       "      <td id=\"T_0d8dd_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_0d8dd_row1_col2\" class=\"data row1 col2\" >-0.391679</td>\n",
       "      <td id=\"T_0d8dd_row1_col3\" class=\"data row1 col3\" >-0.226616</td>\n",
       "      <td id=\"T_0d8dd_row1_col4\" class=\"data row1 col4\" >-0.484740</td>\n",
       "      <td id=\"T_0d8dd_row1_col5\" class=\"data row1 col5\" >0.761211</td>\n",
       "      <td id=\"T_0d8dd_row1_col6\" class=\"data row1 col6\" >0.518204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0d8dd_level0_row2\" class=\"row_heading level0 row2\" >emotion.fear</th>\n",
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       "      <td id=\"T_0d8dd_row2_col1\" class=\"data row2 col1\" >-0.391679</td>\n",
       "      <td id=\"T_0d8dd_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_0d8dd_row2_col3\" class=\"data row2 col3\" >0.077824</td>\n",
       "      <td id=\"T_0d8dd_row2_col4\" class=\"data row2 col4\" >0.149845</td>\n",
       "      <td id=\"T_0d8dd_row2_col5\" class=\"data row2 col5\" >-0.321152</td>\n",
       "      <td id=\"T_0d8dd_row2_col6\" class=\"data row2 col6\" >-0.187657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0d8dd_level0_row3\" class=\"row_heading level0 row3\" >emotion.disgust</th>\n",
       "      <td id=\"T_0d8dd_row3_col0\" class=\"data row3 col0\" >0.062068</td>\n",
       "      <td id=\"T_0d8dd_row3_col1\" class=\"data row3 col1\" >-0.226616</td>\n",
       "      <td id=\"T_0d8dd_row3_col2\" class=\"data row3 col2\" >0.077824</td>\n",
       "      <td id=\"T_0d8dd_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_0d8dd_row3_col4\" class=\"data row3 col4\" >0.136611</td>\n",
       "      <td id=\"T_0d8dd_row3_col5\" class=\"data row3 col5\" >-0.213541</td>\n",
       "      <td id=\"T_0d8dd_row3_col6\" class=\"data row3 col6\" >-0.155754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0d8dd_level0_row4\" class=\"row_heading level0 row4\" >emotion.anger</th>\n",
       "      <td id=\"T_0d8dd_row4_col0\" class=\"data row4 col0\" >0.158305</td>\n",
       "      <td id=\"T_0d8dd_row4_col1\" class=\"data row4 col1\" >-0.484740</td>\n",
       "      <td id=\"T_0d8dd_row4_col2\" class=\"data row4 col2\" >0.149845</td>\n",
       "      <td id=\"T_0d8dd_row4_col3\" class=\"data row4 col3\" >0.136611</td>\n",
       "      <td id=\"T_0d8dd_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_0d8dd_row4_col5\" class=\"data row4 col5\" >-0.440811</td>\n",
       "      <td id=\"T_0d8dd_row4_col6\" class=\"data row4 col6\" >-0.352083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0d8dd_level0_row5\" class=\"row_heading level0 row5\" >sentiment.score</th>\n",
       "      <td id=\"T_0d8dd_row5_col0\" class=\"data row5 col0\" >-0.519823</td>\n",
       "      <td id=\"T_0d8dd_row5_col1\" class=\"data row5 col1\" >0.761211</td>\n",
       "      <td id=\"T_0d8dd_row5_col2\" class=\"data row5 col2\" >-0.321152</td>\n",
       "      <td id=\"T_0d8dd_row5_col3\" class=\"data row5 col3\" >-0.213541</td>\n",
       "      <td id=\"T_0d8dd_row5_col4\" class=\"data row5 col4\" >-0.440811</td>\n",
       "      <td id=\"T_0d8dd_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_0d8dd_row5_col6\" class=\"data row5 col6\" >0.620320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0d8dd_level0_row6\" class=\"row_heading level0 row6\" >Rating\r",
       "</th>\n",
       "      <td id=\"T_0d8dd_row6_col0\" class=\"data row6 col0\" >-0.353612</td>\n",
       "      <td id=\"T_0d8dd_row6_col1\" class=\"data row6 col1\" >0.518204</td>\n",
       "      <td id=\"T_0d8dd_row6_col2\" class=\"data row6 col2\" >-0.187657</td>\n",
       "      <td id=\"T_0d8dd_row6_col3\" class=\"data row6 col3\" >-0.155754</td>\n",
       "      <td id=\"T_0d8dd_row6_col4\" class=\"data row6 col4\" >-0.352083</td>\n",
       "      <td id=\"T_0d8dd_row6_col5\" class=\"data row6 col5\" >0.620320</td>\n",
       "      <td id=\"T_0d8dd_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7faa46f619d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = agg_merged_keywords_review_df.corr(method ='pearson')\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the table above shows, there is an association between the review's Ratings and the Watson NLU sentiment score and joy emotion but repulsion between review's Ratings and sadness emotion. The results also demonstrate the strong positive correlation between Watson NLU sentiment score and Watson NLU joy emotion. In contrary, there is a strong negative correlation between sadness emotion and the sentiment score as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's perform the regression. To do that, we first need to determine the input features. Since the sentiment.score field shows a relatively high correlation with the rating, let's try a regression based on just that value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X = agg_merged_keywords_review_df.dropna()['sentiment.score'].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "Y = agg_merged_keywords_review_df.dropna()['Rating\\r'].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's split the dataframe into training and testing sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now need to create an instance of the LinearRegression model from Scikit-Learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X_train, Y_train)  # fit the model on the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the model has been fit we can make predictions by calling the predict command. We are making predictions on the testing set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = linear_regressor.predict(X_test) # make predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now check the predictions against the actual values by using the mean squared error (MSE) and R-2 metrics, two metrics commonly used to evaluate regression tasks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error = 0.6260140152618712\n",
      "R-Squared = 0.37732446794693686\n"
     ]
    }
   ],
   "source": [
    "test_set_mse = mean_squared_error(Y_test, Y_pred)\n",
    "print(f\"Mean Squared Error = {test_set_mse}\")\n",
    "test_set_r2 = r2_score(Y_test, Y_pred)\n",
    "print(f\"R-Squared = {test_set_r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try adding the fine-grained sentiment scores from Watson to our model and see if the coefficient of determination (r^2) goes up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's determine the input features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emotion.sadness</th>\n",
       "      <th>emotion.joy</th>\n",
       "      <th>emotion.fear</th>\n",
       "      <th>emotion.disgust</th>\n",
       "      <th>emotion.anger</th>\n",
       "      <th>sentiment.score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Review_Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 sweet R32</th>\n",
       "      <td>0.151543</td>\n",
       "      <td>0.532162</td>\n",
       "      <td>0.067859</td>\n",
       "      <td>0.018501</td>\n",
       "      <td>0.112994</td>\n",
       "      <td>0.649825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002 Trans Am/Sunset Orange Metallic</th>\n",
       "      <td>0.176322</td>\n",
       "      <td>0.465210</td>\n",
       "      <td>0.257064</td>\n",
       "      <td>0.032842</td>\n",
       "      <td>0.038908</td>\n",
       "      <td>0.148035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42 days of driving 8 days in the shop</th>\n",
       "      <td>0.206478</td>\n",
       "      <td>0.563466</td>\n",
       "      <td>0.114506</td>\n",
       "      <td>0.010082</td>\n",
       "      <td>0.082325</td>\n",
       "      <td>-0.054126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A great little car</th>\n",
       "      <td>0.278575</td>\n",
       "      <td>0.470586</td>\n",
       "      <td>0.063823</td>\n",
       "      <td>0.015218</td>\n",
       "      <td>0.039688</td>\n",
       "      <td>0.503785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AWESOME FUN MY LITTLE TIGER</th>\n",
       "      <td>0.007629</td>\n",
       "      <td>0.628312</td>\n",
       "      <td>0.013015</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.024782</td>\n",
       "      <td>0.986029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         emotion.sadness  emotion.joy  \\\n",
       "Review_Title                                                            \n",
       " 1 sweet R32                                    0.151543     0.532162   \n",
       " 2002 Trans Am/Sunset Orange Metallic           0.176322     0.465210   \n",
       " 42 days of driving 8 days in the shop          0.206478     0.563466   \n",
       " A great little car                             0.278575     0.470586   \n",
       " AWESOME FUN MY LITTLE TIGER                    0.007629     0.628312   \n",
       "\n",
       "                                         emotion.fear  emotion.disgust  \\\n",
       "Review_Title                                                             \n",
       " 1 sweet R32                                 0.067859         0.018501   \n",
       " 2002 Trans Am/Sunset Orange Metallic        0.257064         0.032842   \n",
       " 42 days of driving 8 days in the shop       0.114506         0.010082   \n",
       " A great little car                          0.063823         0.015218   \n",
       " AWESOME FUN MY LITTLE TIGER                 0.013015         0.001452   \n",
       "\n",
       "                                         emotion.anger  sentiment.score  \n",
       "Review_Title                                                             \n",
       " 1 sweet R32                                  0.112994         0.649825  \n",
       " 2002 Trans Am/Sunset Orange Metallic         0.038908         0.148035  \n",
       " 42 days of driving 8 days in the shop        0.082325        -0.054126  \n",
       " A great little car                           0.039688         0.503785  \n",
       " AWESOME FUN MY LITTLE TIGER                  0.024782         0.986029  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_df = agg_merged_keywords_review_df.drop(columns='Rating\\r').dropna().iloc[:, :7]\n",
    "X_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X = X_df.values.reshape(-1, 6)  # values converts it into a numpy array\n",
    "Y = agg_merged_keywords_review_df.dropna()['Rating\\r'].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X_train, Y_train)  # fit the model on the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = linear_regressor.predict(X_test) # make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error = 0.6149240275777909\n",
      "R-Squared = 0.3883553136042148\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(Y_test, Y_pred)\n",
    "print(f\"Mean Squared Error = {mse}\")\n",
    "test_set_r2 = r2_score(Y_test, Y_pred)\n",
    "print(f\"R-Squared = {test_set_r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our multivariate model shows better value of Coefficient of determination or R-squared and hence the better fit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For every feature we get the coefficient value. Since we have 7 features we get 7 coefficients. Magnitude and direction(+/-) of all these values affect the prediction results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Coefficients = [[-0.22073291  0.32887635  0.37191993 -0.32082964 -1.60607343  1.01721109]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([4.0674602])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef = linear_regressor.coef_\n",
    "print(f\"Feature Coefficients = {coef}\")\n",
    "linear_regressor.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicted Rating against actual Rating plot\n",
    "We have our predictions in Y_pred. Now lets first create a dataframe for the prediction and actual ratings and then visualize it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted Rating</th>\n",
       "      <th>Actual Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.300783</td>\n",
       "      <td>4.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.895731</td>\n",
       "      <td>4.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.342326</td>\n",
       "      <td>3.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.705346</td>\n",
       "      <td>4.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.256459</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>4.623339</td>\n",
       "      <td>3.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528</th>\n",
       "      <td>3.747142</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>3.874102</td>\n",
       "      <td>4.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>3.468226</td>\n",
       "      <td>3.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>4.406095</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1532 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Predicted Rating  Actual Rating\n",
       "0             4.300783          4.875\n",
       "1             3.895731          4.625\n",
       "2             4.342326          3.375\n",
       "3             4.705346          4.875\n",
       "4             3.256459          5.000\n",
       "...                ...            ...\n",
       "1527          4.623339          3.125\n",
       "1528          3.747142          5.000\n",
       "1529          3.874102          4.500\n",
       "1530          3.468226          3.875\n",
       "1531          4.406095          5.000\n",
       "\n",
       "[1532 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_actual = pd.DataFrame(zip(np.squeeze(Y_pred), np.squeeze(Y)), columns=['Predicted Rating', 'Actual Rating'])\n",
    "predicted_actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rating From Dataset Vs Rating Predicted By Model')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test, Y_pred, alpha=0.2)\n",
    "plt.xlabel('Rating From Dataset')\n",
    "plt.ylabel('Rating Predicted By Model')\n",
    "plt.rcParams[\"figure.figsize\"] = (10,6) # Custom figure size in inches\n",
    "plt.title(\"Rating From Dataset Vs Rating Predicted By Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit Random forest regressor to the dataset to see if can improve the R-squared value even more:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Random Forest Regression to the dataset\n",
    "# import the regressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "  \n",
    "# create regressor object\n",
    "regressor = RandomForestRegressor(n_estimators = 100, random_state = 0)\n",
    "  \n",
    "# fit the regressor with x and y data\n",
    "regressor.fit(X_train, Y_train)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting a new result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = regressor.predict(X_test)  # test the output by changing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reporting mean squared error and R-2 score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error = 0.572035684052662\n",
      "R-Squared = 0.43101493698694837\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(Y_test, Y_pred)\n",
    "print(f\"Mean Squared Error = {mse}\")\n",
    "\n",
    "test_set_r2 = r2_score(Y_test, Y_pred)\n",
    "print(f\"R-Squared = {test_set_r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicted against actual Y plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rating From Dataset Vs Rating Predicted By Model')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RdR3LsoAkVGZiYoIwDHnyk5/MbbfddtTf31whhcRDlc/neeSRRwCIoogbb7yRyy67jLVr17banXjiiVxyySVtf4/jOPzkJz+Z8ffhD394Rps/+ZM/mfH6Bz/4AcAsdba3v/3tQLLKP51TTz2V8847r/X6nHPOAeCZz3wmJ5xwwqztzWM8GrLZLOVyufU6lUq1/h8EAePj45x44on09PS0fa6m76NcLjM2NsYFF1xArVbjvvvuA2h5lG644QZqtdqc+/nOd75DHMe89KUvZWxsrPW3Zs0aHve4x80I9eyUP/iDP8A0zRnhST/72c/Yt2/fjDC9VCqFZVncfPPNTE5OLuq7Tj75ZAYHB9m0aROvfe1rOfHEE/nhD39IOp1utbFtu+U9jKKI8fFxstksJ5988lHfI3/wB38ww7vV9FQ1r5/9+/dz11138cpXvnKGp+7CCy/kjDPOaPt77rvvPgYHBxkcHOSUU07hE5/4BJdeeumsULYLL7yQU089tfVaSsm3v/1tnve85yGlnHGuL774YorFYmsMfvCDHzA0NDQjZy+dTre8Q0fi29/+NkII3vOe98x6b6GQ3WPVx+n84z/+Y+tZ89WvfpWLLrqI173udXznO99ptTnppJM455xz+NrXvtbaNjExwQ9/+EOuvPLKtkORdV3npS99KV//+teBRBRiw4YNrWtlOgcOHOCOO+7g1a9+9Qwv1+Mf/3j+z//5P63nXhRF3HDDDVx22WUznl+nnHLKrPC/bt7rCsWjBRWqp1A8CvjHf/xHTjrpJIrFIl/4whf47//+b2zbntXuS1/6Eh/+8Ie57777CIKgtX3z5s1H9f3Tf5Cb9Pb2tia5IyMj1Ot1TjzxxFnt5to2H7quzyvZ2+TwY9m1axeaps36njVr1tDT08OuXbtmbD/8WJrGxYYNG+bcvtiJ/HQqlQq5XK71ul6vc+211/LFL36Rffv2tUIOgVm5R/Nxzz338O53v5ubbrqJUqk0473mPjZv3szb3vY2PvKRj/C1r32NCy64gOc///m84hWvaB3fgw8+iJSSxz3ucXN+z9Ek//f393PxxRfz3e9+l09/+tM4jsP111+PYRgzRE1s2+Zv//Zvefvb387q1as599xzee5zn8srX/nKWQpk8/Htb3+bfD7P6OgoH//4x9mxY8cM4xIO5d780z/9Ezt27JiRD9YMOV0sh19XTSOqef00r8P57pF2DbdNmza15Ksdx+Fxj3scq1atmtXu8PtkdHSUqakpPvvZz/LZz352zn2PjIy0+nriiSfOMghOPvnkBfv38MMPs3bt2hmT/XY5Vn2czlOe8pQZ4hAve9nLeOITn8ib3vQmnvvc57YWo175ylfypje9iV27drFx40a++c1vEgQBf/iHf9jR97385S/n4x//OHfeeSfXX389V1xxxZyGV/N6met4TjnlFG644Qaq1Srlcpl6vT7n/XvyySe3DCzo7r2uUDxaUIaTQvEoYPqP+2WXXcbTnvY0Xv7yl3P//fe3Vq+/+tWv8upXv5rLLruMd7zjHaxatQpd17n22mtnJMgvhun5KdOZPuE/Vhw+GW7SyapvJ9uP9hiDIOCBBx7g9NNPb237sz/7M774xS/y1re+lfPOO49CoYAQgiuuuKItaeepqSkuvPBC8vk8733ve9m6dSuO43Dbbbfxl3/5lzP28eEPf5hXv/rV/Nu//Rs//vGPefOb38y1117Lr371K9avX08cxwgh+OEPfzjnGBwpj6kdXvGKV/D973+f73//+zz/+c/n29/+disfYzpvfetbed7znsf3vvc9brjhBv7qr/6Ka6+9lptuuoknPvGJC37P05/+9Jaq3vOe9zzOOOMMrrzySn7729+2vEwf/OAH+au/+ite+9rX8r73vY++vj40TeOtb33rUUuMH6t7JJPJLLi4ALPvk+bxveIVr+BVr3rVnJ9ZKNex2yyHPmqaxkUXXcQ//MM/8OCDD3LaaacBcMUVV/Dnf/7nfO1rX+Nd73oXX/3qV3nyk5/csaF2zjnnsHXrVt761reyY8eOGXl+3abb97pC8WhAGU4KxaOMpjF00UUX8clPfrJVd+lb3/oWW7Zs4Tvf+c4MI+LwkJmlULg7nFWrVuE4Dg899NCs9+batpRs3LiROI558MEHW8IJkCSZT01NsXHjxq5+/0J861vfol6vzwib+da3vsWrXvWqGWGIruvOKtg637m6+eabGR8f5zvf+Q5Pf/rTW9t37NgxZ/szzjiDM844g3e/+93ccsstPPWpT+XTn/4073//+9m6dStSSjZv3sxJJ510xGNZzLXz/Oc/n1wux/XXX49pmkxOTs4I05vO1q1befvb387b3/52HnzwQc4880w+/OEP89WvfrWj78xms7znPe/hNa95Dd/4xje44oorgGTcL7roIv75n/95RvupqamW0QXduUea1+HxuEcABgcHyeVyRFG0oOG1ceNG7r77bqSUM8bi/vvvX/B7tm7dyg033DBLSOFw5hrjY9XHhQjDEEg8xU36+vq49NJL+drXvsaVV17J//zP/7RVDHouXvayl/H+97+fU045ZVadsCbN62Wu47nvvvsYGBggk8ngOA6pVIoHH3xwVrvDP9vJva5QPFZROU4KxaOQZzzjGTzlKU/hYx/7WKtoYnMFcfoK969//Wt++ctfzvhsM+fj8En60dAMsfve977H/v37W9sfeughfvjDHy7Z98zF7//+7wPMmsR85CMfAeDSSy/t6vcfiTvvvJO3vvWt9Pb28sY3vrG1Xdf1WZ6IT3ziE7OkxJs1qg4/V3Oda9/3+ad/+qcZ7UqlUmsS2OSMM85A07SWbPOLXvQidF3nmmuumdUnKSXj4+Mz+tNuKGGTVCrFC1/4Qn7wgx/wqU99ikwmM0vauVarzSr+uXXrVnK53KLlpa+88krWr1/P3/7t37a2zTXu3/zmN9m3b9+MbfON+9Gwdu1aTj/9dL785S/PmJD/7Gc/46677lqy75kPXde5/PLL+fa3v83dd9896/2mlDkk99T+/fv51re+1dpWq9XmDZ+bzuWXX46UslVkezrTxz6Tycx5XR+LPh6JIAj48Y9/jGVZMxZiAP7wD/+Q7du38453vANd11sGeae87nWv4z3vec+s/M3pDA0NceaZZ/KlL31pxjjdfffd/PjHP24993Rd5+KLL+Z73/seu3fvbrW79957ueGGG2bss5N7XaF4rKI8TgrFo5R3vOMdvOQlL+G6667jT/7kT3juc5/Ld77zHV74whdy6aWXsmPHDj796U9z6qmnzpiopVIpTj31VP71X/+Vk046ib6+Pk4//fQZoWSL4eqrr+bHP/4xT33qU/nTP/1Toijik5/8JKeffjp33HHHUR7t/DzhCU/gVa96FZ/97GdbIWy/+c1v+NKXvsRll13GRRdd1LXvns7Pf/5zXNdtiQ78z//8D//+7/9OoVDgu9/97oxcnec+97l85StfoVAocOqpp/LLX/6SG2+8cVaezZlnnomu6/zt3/4txWIR27Z55jOfyfnnn09vby+vetWrePOb34wQgq985SuzJkM33XQTb3rTm3jJS17CSSedRBiGfOUrX2lNUCExUN7//vfzzne+k507d3LZZZeRy+XYsWMH3/3ud/njP/5j/uIv/gKAs846i3/913/lbW97G2effTbZbJbnPe95C47NK17xCr785S9zww03cOWVV84qWvzAAw/wrGc9i5e+9KWceuqpGIbBd7/7XQ4ePLjoyalpmrzlLW/hHe94Bz/60Y94znOew3Of+1ze+9738prXvIbzzz+fu+66i6997Wts2bJlxme3bt1KT08Pn/70p8nlcmQyGc4555yjzhX84Ac/yAte8AKe+tSn8prXvIbJycnWPTL9Hu0Wf/M3f8NPf/pTzjnnHP7oj/6IU089lYmJCW677TZuvPFGJiYmAPijP/ojPvnJT/LKV76S3/72twwNDfGVr3xlhtDGfFx00UX84R/+IR//+Md58MEHec5znkMcx/z85z/noosu4k1vehOQXEs33ngjH/nIR1i7di2bN2/mnHPOOSZ9nM4Pf/jDlpjKyMgI119/PQ8++CD/7//9P/L5/Iy2l156Kf39/Xzzm9/kkksumTO3rB02btzYqp13JD70oQ9xySWXcN5553HVVVe15MgLhcKMz19zzTX86Ec/4oILLuANb3gDYRjyiU98gtNOO43f/e53rXad3OsKxWOWY6bfp1Aolpy5JGqbRFEkt27dKrdu3SrDMJRxHMsPfvCDcuPGjdK2bfnEJz5Rfv/7359TVvaWW26RZ511lrQsa4Y0+Xxy5G984xtnff/GjRtnyQn/13/9l3ziE58oLcuSW7dulZ///Ofl29/+duk4zoLH+qpXvUpmMpl532/2bXR0dNZ7QRDIa665Rm7evFmapik3bNgg3/nOd7Zkuaf3+dJLL531+bmOsSkzvZBEdlOOvPlnmqYcHByUT3/60+UHPvABOTIyMuszk5OT8jWveY0cGBiQ2WxWXnzxxfK+++6bc0w/97nPyS1btrSkhZsyy//zP/8jzz33XJlKpeTatWvl//2//7clrd5s88gjj8jXvva1cuvWrdJxHNnX1ycvuugieeONN87q07e//W35tKc9TWYyGZnJZOS2bdvkG9/4Rnn//fe32lQqFfnyl79c9vT0tC2hLWUilT80NCQB+YMf/GDW+2NjY/KNb3yj3LZtm8xkMrJQKMhzzjlnhtT0fBzpuigWi7JQKLQk7V3XlW9/+9vl0NCQTKVS8qlPfar85S9/KS+88MJWmyb/9m//Jk899dSWnH5Tmnw+OfK5rpPp91aTf/mXf5Hbtm2Ttm3L008/Xf77v/+7vPzyy+W2bdsWPNYLL7xQnnbaaQu2m++elVLKgwcPyje+8Y1yw4YN0jRNuWbNGvmsZz1Lfvazn53RbteuXfL5z3++TKfTcmBgQL7lLW+RP/rRjxaUI5cyOd8f+tCH5LZt26RlWXJwcFBecskl8re//W2rzX333Sef/vSny1QqJYEZ1/1S93Eu5pIjdxxHnnnmmfJTn/rUvBLjb3jDGyQgr7/++iPufzrzPXfm6s/hz/obb7xRPvWpT5WpVErm83n5vOc9T27fvn3W53/2s5+1nulbtmyRn/70p+d8nkvZ3r2u5MgVj1WElMche1uhUCgaXHbZZdxzzz1zxuArFApaxal/8pOfHO+uKBbgz//8z/nnf/5nhoeHO/ZuKRSK5Y/KcVIoFMeMer0+4/WDDz7ID37wA57xjGccnw4pFMuIIAhm5ZzdfPPN3HnnneoeWQG4rstXv/pVLr/8cmU0KRSPUpTHSaFQHDOGhoZ49atfzZYtW9i1axef+tSn8DyP22+/fd7aIQrFY4WdO3fy7Gc/m1e84hWsXbuW++67j09/+tMUCgXuvvvuo64lpegOIyMj3HjjjXzrW9/ie9/7Hrfddtu8angKhWJlo8QhFArFMeM5z3kOX//61xkeHsa2bc477zw++MEPKqNJoSApinvWWWfx+c9/ntHRUTKZDJdeeil/8zd/o4ymZcz27du58sorWbVqFR//+MeV0aRQPIpRHieFQqFQKBQKhUKhWACV46RQKBQKhUKhUCgUC6AMJ4VCoVAoFAqFQqFYgMdcjlMcx+zfv59cLocQ4nh3R6FQKBQKhUKhUBwnpJSUy2XWrl2Lph3Zp/SYM5z279/Phg0bjnc3FAqFQqFQKBQKxTJhz549rF+//ohtHnOGUy6XA5LByefzx7k3CoVCoVAoFAqF4nhRKpXYsGFDy0Y4Eo85w6kZnpfP55XhpFAoFAqFQqFQKNpK4VHiEAqFQqFQKBQKhUKxAMpwUigUCoVCoVAoFIoFUIaTQqFQKBQKhUKhUCyAMpwUCoVCoVAoFAqFYgGU4aRQKBQKhUKhUCgUC6AMJ4VCoVAoFAqFQqFYAGU4KRQKhUKhUCgUCsUCKMNJoVAoFAqFQqFQKBZAGU4KhUKhUCgUCoVCsQDKcFIoFAqFQqFQKBSKBVCGk0KhUCgUCoVCoVAsgDKcFAqFQqFQKBQKhWIBjqvhdPXVVyOEmPG3bdu2I37mm9/8Jtu2bcNxHM444wx+8IMfHKPeKhQKhUKhUCgUiscqx93jdNppp3HgwIHW3y9+8Yt5295yyy287GUv46qrruL222/nsssu47LLLuPuu+8+hj1WKBQKhUKhUCgUi0FKScULmar5VLwQKeXx7lLbGMe9A4bBmjVr2mr7D//wDzznOc/hHe94BwDve9/7+MlPfsInP/lJPv3pT3ezmwqFQqFQKBQKheIoKNYDdo1Xmaj4hLHE0AR9WYuN/RkKKfN4d29BjrvH6cEHH2Tt2rVs2bKFK6+8kt27d8/b9pe//CXPfvazZ2y7+OKL+eUvfznvZzzPo1QqzfhTKBQKhUKhUCgUx45iPeDufUUOTNURGjimhtDgwFSdu/cVKdaD493FBTmuhtM555zDddddx49+9CM+9alPsWPHDi644ALK5fKc7YeHh1m9evWMbatXr2Z4eHje77j22mspFAqtvw0bNizpMSgUCoVCoVAoFIr5kVKya7zKaNmj4oXcu7/M7bunuHd/mYoXMlr22D1RXfZhe8c1VO+SSy5p/f/xj38855xzDhs3buQb3/gGV1111ZJ8xzvf+U7e9ra3tV6XSiVlPCkUCoVCoVAojilSSqp+RBjFGLpGxtIRQhzvbs3LUva36kfsnqixb7LGWNVHStAExBIOll0GMhaGLtg8kCVrH/dMonlZVj3r6enhpJNO4qGHHprz/TVr1nDw4MEZ2w4ePHjEHCnbtrFte0n7qVAoFAqFQqFQtMtKy+1Z6v4GYcSO0Qr7p+o4pkHWNjB0QRhJql7InskaEkmwtR+WseF03HOcplOpVHj44YcZGhqa8/3zzjuP//qv/5qx7Sc/+QnnnXfeseieQqFQKBQKhULREc3cnuGiS8Y2GMzZZGyD4aK7LHN7utFfL4w5MOUigd60haVraAgsXaM3bQFwYNLFC+MlPpql5bgaTn/xF3/Bz372M3bu3Mktt9zCC1/4QnRd52UvexkAr3zlK3nnO9/Zav+Wt7yFH/3oR3z4wx/mvvvu4+qrr+Z///d/edOb3nS8DkGhUCgUCoVCoZiTZm5P1QsZKqRwTB1NCBxTZ6iQouqFyyq3p1v9DaOYGImuzW166EIjQhJGy9twOq6+sL179/Kyl72M8fFxBgcHedrTnsavfvUrBgcHAdi9ezfatAE+//zzuf7663n3u9/Nu971Lh73uMfxve99j9NPP/14HYJCoVAoFAqFQjEnVT9iouLTm7aQSOpBRBRJdF2QMnV60xbjZZ+qHy2L3J7p/Z2LxfZXCEGPYxLEkomqi0AjRqIhkMQ4poapLe+cLzjOhtO//Mu/HPH9m2++eda2l7zkJbzkJS/pUo8UCoVCoVAoljcrTWTgsUwYxYSxxI9idk9UGS37BFGMqWsM5izW5FOE8fLxtDT7axnanIaeqWuL6m8+ZbK64LBzrMJwyWOs7BPGMYaWjMPqvM2Gviz5ZZjvNZ3jb9oqFAqFQqFQKNpipYkMPNYxdA03jLh/uDSnmtxw0WXjQAZDX3z2zFIa0oauYWiCiarPZM2nVAsIpcQQgnzapDdtYWii4/5mbYP+rM1/3TeC70dkHB0NjRjBRMWn4kectal/WXjdjsTy7p1CoVAoFAqFAjiUtF/1wiTB3tDww5jhokvZDTl9XUEZT8uMtKkxVfO5b7jEQNbBNgRCCKSU+KHkvuESvRmTtLk4w2mpDemMpWObOr/ZMU7K1LANHVvXiGPJWNllz0SNp2zuJ2PpHe1XSknZDYllzETdozQREMWga4k3apXpUPFCpJTL2nuqDCeFQqFQKBSKLrCUnoDDk/abNJP2DxTr7J6ocvrawrKeeK4Elrp+UbkeomsaYxWXIIoBAUhMXUPXNIq1kKofkXM6M56ahnTFDUiZBo4piGM4MFU/KkNaInGDiMmqRz2IWgZZytRJWQaIzoUsRis+24eLjJU8xio+QSRbRlIQ+WgI7jlQZLSymtV5p+P9HyuU4aRQKBQKhUKxxCy1J6BbSfsrmW7kehXrATvHKuyfSqSxbUNjbY/DpoHsos5bqR4wWfcxDY3xKRc3lIAEBClDY3VPism6T6kekHPa33/TkB4tu0gJB6bcVkhdIW1SDyJ2TxgdG9JVP2Ky4hNFMQ+NVijWQuI4RtM0CmmDU9fkmVjEdVZ1fe7YPcGBYiJJLqREShBIgggOFF3u2D1B1fVBGU4KhUKhUCgUjw2mewIcS8fRNSIpj8oT0K2k/ZVKN3K9ivWAXz8yzs7x6oxcpD2TNQ6WPM7Z0j/nvo9kwEkJYyWPYj2gkLYY1LWGvwn8KGai7BFFMZ2qkVf9iD3jNSaqPmEE+ZSBqWsEUcxo2cfQwRyrsXkg25GBE4QR24eLbB8u4/oxtqmReMgErh+zfbiMaWqcf2JnhWoPllz2Tbi4Ydw6ftnas0QC+yZcDpZctqzKdzQWxxJlOCkUCoVCoVAsEYc8AR6xlDM8Afm0ST2IFxVS162k/ZVIN3K9pJTcs7/IvQdKpEyDwjRDpFQPufdAiVzK4NzN/TPO20IGnK5BzQ/xo5g1TmrGd6ZMKNaq1LyITk9bEEYcKLpEUrIqd8hDYxs6gzmdkbLLgZJLEEYdGThuEHHfgRLjZQ/bTHKbpEii8zRNMF4Oue9ACTeIOurveNmjFkQEMRjTLvumARVKqAUR42Wvo/0eax79d5dCoVAoFArFMaLqR+yeSDwB4xUPx0pq9TiWznjFY6Lqs2u8RtXvbOLZTNr/3d6pOff7u71T2KbecdL+SqNbBVorXsj9B8oYmmAwZ2MbyX4TQ8TG0AT37y9T8cLWZ5oG3HDRJWMbDOZsMrbBcNHl7n1FivWAKJZkbAPL1Ci5AUEcE0tJEMeU3ADT0MjYOlHcWX/9SFLzQ1Lm3Oc7ZerUvBA/6my/xXrASNmj7gfUghg3lHihxA0ltSCm7geMlhMPWieU6gFh45IP5ew/gDBK2i1nlOGkUCgUCoVCsUQknoA6YRQ1VNQOTcAHsg5BFDFcrCeegA5JAppAStHcMPP1IpL2Vxqd5Hp1QqkeMFX36c3Mvd+etNXKRYL2DTiAwazNUN5BIKl7IVU/oO6FCCRrCw6DWbvj3CxLF6RsHTdIlOjcIKTqh63X9SAkbetYemf7rbgBdT8mlOAHEbGUICWxlPhBRCCh5sdU3A4NHK11uc6LbLRbzqhQPYVCoVAoFIolwo8kdS+iZ56Jfco0mKr5HXsCqn6EH8Q8YX0PY+U6O8Zr1IOQlGmwZSBNfzaL58ePenGI6blec3FUuV5HOCVCzHy/XQNuMGuzuuBgVn36UiZ7Juu4QYxjapzQlyJG0J+1Oi78ahqJgbZrrMpd+4sNoYVG3pCAgYzFut40ptGZB1LTBLGMAUkYx5TqATGJPZO2DQSSWMZoWmcGWd5qzyJqt93x4tF7ZykUCoVCoVAcYyxdkLYM6kE0p0paPYhI20bHnoCmwVDxAn67e4o9k3WCMMY0NCaqHk/Z3E/OMR/14hDNXC8/jHHmCFMLonhRuV75lElP2mSy6pPqmT09nqz69GbMloHTrgFnGxrbhgp8/3f72DlWZbIWEEUxuq4xUvHYNJDh/BMHOzZ2M5ZOf8bmnn0lojBkshoSxDGmptGbNah4yfudhm5mTB1TaIx7AcRxkoMUS2JNUK77oGkMWSaZeUIE5yNCtOVxiljeUvrKcFIoFAqFQqFYIhJPgMPBssto2UvUzjQtyWmph5i6YHXO6dgTYOga+ydr/PeDI1S9mMGcTcrSqPsxO8dqjJZdnv64VRhb+xfd927Iey81GUunL2sxXHRn1LNqMlnzGepxOjYYsrbBtqECv9kxzljFJeeYGJpGGMeU3YAwlpy8ptAycNo14ExDR9fh4ZEK+yZrWLqO0CD0I3bXfaJYoussapwlkvGKxwMHS0zWA6IwRjc0elMmJ63OLyp0M2UbGKaG74d40XT1O4kATC3G6HVIdWjoeX64cKMO2h0vlOGkUCgUCoVCsURkLJ0N/WmCOJGYLtYCKjLEEILBnIUQcMJAuuOJvaPDvQdLjFVCTlt7SK455+jkHJN79pe4f6SEs0htiG7Ie3cDIQQb+zOU3ZD9UzVSpoEQiex3PQjJOiYn9GU6NkSEEJy6Nk/ZDdg5VqVUC5L4tEac2ilDeU5dm2/tt10DztHhp/cepOpH9Gdt3DBOCr/qglzKoOpH/Oy+g5y1oQddb//kVf2Iu/ZMce9wkalagG3opGyNUMJ41efe4SJbd2c4Y11PR94sP0zyl/woOfQmTQPKi2Gs4jFR9TihP9v2fktuewZRu+2OF8pwUigUCoVCoVgipk/sK25Af8Zekon97kmXYj1kTcGi7AU4po4uBJGUuEHEmoLFZC1k96TLlsH2J7TQHXnv6cRxzGjFxw0iHFNnMGuhaYvPZSmkTE7oT/ObR8a4a2+xlTO0ZVWaU4+ir4WUyTlb+lmdt9lfdPGCpI7Ruh6Hjf0zC+BOP88HinV601ZLvnyy5pOxDU7oy7Bros7v9hTRNUHW1skhWgWMJJKKF3HH7iI7x2tsXZVru6+u5/OLh8Yo1yPyjkHFi3Bjia4J8o5BuR7xPw+PcfmT1nZWqLbuMVnzkSRGQpLtRCuALiZZDPjdnkk2D+YppMy2PJVTdb+t72+33fFCGU4KhUKhUCgUS0ghZXL6ugI7xyoLTsDbpeqFCAkb+zOU6gGT9YAglJiGoDdtsibvsG+yTtXrbMX+cHW4Jk11uAPF+qLqTjXZM1nj1h3j7J2o44cSyxCs70tx9uZ+NvSmO94fJIbevftLjJU9EGCaAgSMljzu3V8i75hHZTydsa7AmkJqQUOveZ6ne+p0LVHgW1Ow0TXBaKnORN0naxqEYUzZi1oevZytYwjBRN1nrOx2ZDg9Ml5j71QdP4rwqhGaJhBAGEsmqgFCwJ7JOo+M1xjItz/OD47W8RtFaqdfSU3jSQMiCY+MuOyeqLKhN53I7y/gqWxXvrxTmfNjjTKcFAqFQqF4jLISclpWPPLQvx2WFppBxjYwDY3Jqk+xHjBW8VtiADKWRJFs1ATqbGo3XR0uljGTtQA/jLEMjd5GYd2mvHenAgZ7Jmv84HcHmKz69GctCunEk/XAcIXRss/vP36oY+OpWaj2jj1T1IMQYhAayBiqfkjRjeYsVNsuxXowy+BdW3DYNDC3wds0tKp+xETVZ/9Ujaob8EA9wNCq7J2o4gZRS/EwjKKWSt2YntTd0oSgwzJOeH5E1Q3wwoiUZRLF0wPrBDUvEaHwGrLsh9/raVOjFsSz7n0/ighjmEvMvRmuF8dgmrBrvMbBYlLoeSFPZdymimS77Y4XynBSKBQKheIxSGuCOOXihTG2obG2Z/4JoqJ9poe+DWTt1oTyYMmj4kWLCn3b2JeiL23yswdGsU0dQ9fQ0AhiyZ7JOg+NVHjGtlVs7Judb3Mkmupwk3Wfe/ZNsXuijhdE2KbOCX0pTlvbgyZEx2p9cRxz645xDpZcCo7B3ok6URyjaxp9GZODJZf/3TnBuoLTUdhexQu5ffckYxWPrG2QdgwMXRA2CsKOVTxu3zXJ6WsLc6oaHoliPeDXj4yzY7SCH8YtL8vusSoHSx7nbOmf87wJIYhiyZ6J2qxwR4TE9WMmqx6ObeAY2qEQyzCiWPRZ35NibY/TUV8jKYliiZTJOdSEaBhgiVy4lIn3KZJyVv6aF0R4UYRl6DiGPsNLlLMN4gVOdSQhZxscKNYZyNicOM1TNp+ncj55/sNpt93xQhlOCoVCoXjM8Vj3tDQniDvHq0gJmoBYJh6CI00QFQvTrdA3IQRpS8ePYoIoImuamIYgDCWVIEAiSJudX8eGrjFZ8/mfB0fZN1lPjIWGB2fveI1dYzWe+rjBjuW9Rys+Dx2sUPcjRstecgwkHouJmk/WNnhwuMzoVp/V+faNhmLNZ/dEDUsXFFImQRjjhRKdxPvjhRG7J2oUa35HhpOUku37S9y+exIviIgRrdw0DclUPSDnmJy7pW/WGB/xnOdTyLhRujiWhJEkPFS5uJE7lIS4dUJf2sDQBF4oCWNJEEXEsUTTBKauEUuJoQssTczIX/OjmN0TVUbLPoM5m5PX5LB0reUlcrSIhUxkCZgipu5F9K+y52xzuKcyZ7d3/bTb7nihDCeFQqFQLFu6YeCsFPWwbtEMdbr3QImUaVBIGa2k9lI95N4DpaMKdXqsMz30TSKpBxFRJNF1QcrUFx36NlL2mKj5bO7PsHeyzkjVa12/+ZTJ+p4UE1WPkbLHmjlU3uYjZQjuO1Bk+/4ShbRFzjEwdUEQSSpuyPb9JQayFi964tqOxqHuh+yfcqn6ASnTIGXq6FrimakHEaNll7ofUe9QfroWxLh+RCplMVysM1r2CaIYU9cYzFlYuk7F96kFnXnIKl7IHbsnGK/4ZByDnJUYJmEsqfgh4xWfO/ZMcPq6/CyD7EjFcCdqPralk4506kFE4B0KgrMMKKQsTENnpOyxrq99UY8YnaxtUHID6n6I1lIATDxclgEZ02B/ySefMhkqpJBSsneyRhjBSatzjFVcRsouWwezLaP+f3eX2vr++0dqnL6hj3xq7mv48ELEY2W3rf222+54oQwnhUKhUCxLumHgdFs9rFsspQFZ8ULuP1DG0ASDuUOrxbahM5jT2T8Vcf/+8qJCnRSHQt9aK/sVv1WodjBrsSafmjGhbJfJms9w0SOIY3pSRuPcJdkyQRjhRzEHiokiWieG00jZY8doFdPUSJmJWIFAYAhImTo1U2PHWJWRssdQT/v5SFEsmaz5GLqYcR0ZuiCna1T9kMlGHaNOSJsauibYfqDEZM2jHsQtT8uucY3etM36vhRpszPPRbHms2u8hmlo5G2dYi3Aj2MsTaOQNvDDmF1jc3uyjlQMN4whlpIgSgQX7GmK41LS8hQFHeb29KZ0TENDxslV0Lqc4sSzF8dgGBpRHNGbzgBJ8eViLWgZOznHpFgNqPdEpE2D3rTFnsl6W99f9wOG8g5BlAhiHM7hhYgfOFhpa7/ttjteKMNJoVAoFMuObhg43VYP6xZLbUCW6gFT9fnDo3rSFiNll1IjNOmxwFIapoau4YYR9w+XGC17hJFEIhEIhqfqDOdcNg5kOg59E8Bk1UMCawqzDZjhYg0viOi01/un6pT9kE19KSIpcIMIGYEQkLZ1sk6K0YrP/ql6R4aTpYtDeT5zEEYS29Sw9M56nE+ZBGHEw2MVZCwxdQ2hQRRKal7IRC1gdcEi3+G9UfMj6kGEEUl+c7DSMOqSnKzelMXa3hRhHFPzZ8smHKkYbs7Rcf1ESS9l6i1DShMCUwcviqn5IT3zeG7mI4ih7Pn48SH9kSay8X7J9fEaQh+QGLOhTMYMSAr8ypAokmA2vERzqULMwaqcw4b+dNuFiHdPtGcQtdvueKEMJ4VCoVAsK7pl4BwpnAZmx+QvB7rmITvC4rYQR37/0cZSG6ZpU6NYC7hr3xSWrhPDtFwZGC7V6c1YHXtEDE0gRCKCALJhkCUGlaELohgsQ+s4V6a5D1PXyZs6rqkTxaBr4BgatYYx1ukloWka63rTHCjWOVCsk7WNVqhexQtxrOR+7rSeUxzHjJRcgihGA2p+2DJMdU0QR5KRkke8kMLBYaQtg7of8cjoVJIvZplkbB0/hJGKy2ilztbBHGlr9rNhoWK4URwTSxqGrUiU6ZB4kSSSiSHVKZW6x1QlaCn0TT/a5utyLcD3g5ZBp2sCQwiCKMY2dMI4xhACvWG8BlHMYLa9Z9+WgVRbdayaz+ip9hxZbbc7XiyPXwaFQqFQKBp0y8A5UjgNzI7JP950y4DMp0x60iaTVZ9Uz+zxm6z69GbMjlfsVyLdMEyrfsRIqU7dj/F1SW/KPDShrAdEkeRgqU7Vj8g57RsNhq6xppBi32SV3RNVdF1HJ5GNjqIIUxOsyTsde7LW9aToS1uJ+l3Kwg0jpEyMPcfQKdZ9+tI263o6U+vLp0w29qWIZczBosu+qTphlAgWDOZsVuVtNvWnOr7OdozXGKv6GA1BE9vUW4ZpGMUYAsYqHjvGazwx077oRNbWqHghFS8i55jU/ZCa36hdJARlN2w8c2aP75GK4e4YqyXGoYzwY9A12TJEoxgMAUJoTNQ6y/W6+0AFv/GoOvyJ1XzthnCg5LOh6rG2J03K1CmkzYYwhE7ZDRjI2aQaXrLJmk9fpj1VO8fUZ9Yrm6bOua53dr2yoM3yTO22O14sb+kKhUKxbJEyWTWcqvlUvBB5NAVKFIppdMvAmR5OMxeHx+QfbzoxIDshaxtsGyoQxpKxiosXRkSxxAsjxiouYSw5eU3hqLxuK+H5cLhh6phJPZ2mYVr1QnZPVDvue7Hmc7DksSpvsyZn4wUxxXqAF8QM5RxW5W0OFj2KNb+j/aYsg6Eeh3zKSoyEMMIPI8KGoZNPWazpcUjN4RE5EqvzDqetKzBZD9g3WcMPE4+WH0r2TdaYrAecviHfkfIdJNfZhr4MVTcia+k8bnWGU4ayPG51hoypUXUj1vdmOr7OpqoeZTcpTNuftUlZiaR2ykpeO6ZO2Y2Yqnod7Xe8GhDJJKyt7PqU/YCqF1L2A8quj2VoBHHEeHXumX3TiFhTcKh6iSx61QvpSRtIJLYhMDUII/Cj5F9TA9MQuEFIqcN7xfODhWXDgUdGKuyaqPHwaBkvjBnMORg6PHCwjK5prMo6eEHMgWKdjG207bEsuYcZeuLQv3MdQrt30fJ7UsxEeZwUCkXHPNZVyRTd5Uj5ArB4A2ehcJrDY/KPN90yIIUQnLo2T9kN2DlWpVQLDsX2aHDKUJ5T1+YXneOzUp4P3fJsNnNletImQZi4bqQk+ZdEcKFYD+bMlTkSAxmTvGNgGoIzT+hhshq0PDh9GZPhkktPymQg09kYCyHYNpTnjj1T7J+qM1LyaF4MlpF4pLatWVzeX8Y2KGRMRoo1RioeYQSGnhgZqwp2x8V6AWIpiJHomkbK0jFjjbghqZ88NyL8OCaWnfW36oWEocQQAh9BFMdEEeg6GELDEA3pdzeg4oVz5sNNL4bbfD9rATF4oSQ6rLCsH0EQSaSMeWikhGUabd8rlqHPWaT2cDQBk9UgCcfUNGxTZ3XOoTdjYuoNpb8oZqjH4YS+DP9x2662xuuBgxWKNZ+795eSemWZI9crMw+PJ5yHDiNYjznKcFIoFB2xUlXJFCuHbhk4RwqnmSsm/3jTLQMSkgneOVv6WZ232V908YIY29RY1zM7xKYTms+HihvgWDqOrhFJyYGp+pI8H5ZSxKFbhmnaMtCFYO9EnbRjkLH1VsHTsh9Sc0MKKXPOXJkjUQ8lawspRisedTdiIGuhaYI4ltS9iLU9KdbkHeqhJNvBrVH1I7ww5rS1eVKmYKIaEsQxpqbRlzHYMpjDDaKODciqHzFe9TCEYKoeUqyFRDJGF8l4r+1JM171Ot7v6rxFwTEou0Hi9ZCHcsgQ4IURBcdkdb6zQqpCJFLnfpQoInpBTIxECwW2mQgpVLyQh8eqTNbCeRcFhBBkLJ2qn1xjNT8mknErrA4O1bNqGT4ipieTGJLt/JYW6wFhmzFtm/sd+jImUkoGcjYnrc5iGjppU6MWxLPupXY9dQ+PVPiv+w6iCY2tg4dk1OcLJTYNoA0nq7nMLZNl3j2FQrGcWKmqZIqVRTcNnGY4zeEekeZq63Iy+rvtISukTB6/voetq5bGEGk+H0bLHrGUHJhyCWWygp9Pm9SD+KieD0vtyeqWYZpPGWQdg+GSS9o2SALfGiFIMpGEXt+Xmrf+zXyEUUxvxubJG/v45YOj3L23hBtGOIbOtjUZzjqhj5xjdmzoBWHEgWIdQxOcuDrPSNnDbyixrcrZCGC4WCcII+jAwAnCiHv3l3jwYBlL11jfl27lZHlBmGw3NJ66tb+j/Q7kUmwezHLn3iLleoAuRMsSiaTEMDQ2D2YZyHWYk2XreGHEeM1Nrg1d0Iw/C+I4CWOVFtW6z4bezLyLhodfpw8OT1HxZp6Tw8PRvACQcVu/pc3FiUfG26t3VHJjTnRMSrWAiZqPaegtQzVra63FiGI9wNA12n2c2KbGjrEavWmL1flwlvF7uMe2XbX1DlXZjznKcFIoFG2zElXJVirdKPy6kuimgTNXOM1yHN9j4SETQizZvVr1I3ZP1JioJjLOOeeQKMJ4xUPXNIxxweaBbMff2Q1Pdzc9m6sLDiU3IJJJ6F7Tw6AJWJW3WZ13Oj5vhq7hBRG/fmSc3+6eZNINiEKJbggqno/QNJ524mDHhp4fSSYrPlU/RAiBrevYmgZCMFULkFLiNTwxneAGEQ+PlKmHMT2pRGyhqeDnmDpT9YCHDlZwg85CFgezFo9bleXBkQrjnk/FT4wxHbAtyKdTnLw6x2C2M49TPYzxw5g4gkAmMuSJAp4kimOCGMIwpidttwztww2dE3rTrdC15nX6250RC5WqiiUMlzzOaLye77d0+uJl1GYuVNELMTQNNPAaHqbWe3MsRrhhe/tNNcQhwihiuFRn62AWMU0M/3CPrWUAbTjJOnTEHnOWefcUCsVyYqWpkq1UVkqOSLfppoGzlAZDN1lJHrKm50LGksFpq/22oWNndUbK9UV5Lrrl6e6WYRrFkg29aQxNY7hYww9pyWVbJqzJpRnqcRZV+PX2PRP85J5hvChOitUaSd7UwbLHT+4ZJufoXHLGmo72a2rgBjEHiz4ISbEWEEmJLgSFtAlSsK5Xa+WetLuoU6oHFN2QIIyo+RqWobXkyGt+RBBGFN2k3VBP+/2th4kMe8UN8ALQ9MRokiSem4qb9L/TkMXxiocfRo0xPST7DiRGvxYTxjDlzp7996YtxkoeNS+adZ0GbfweSqDmHTIg5/stnb54KdqUUZBIwjipkmubWsuwnm8xotRB7p0hBJppzCii2+Rwj23W0jhYX3gsstbyTnJa/r8aCoVi2dDNnAtFgsohm8lKMXC6yUrxkPlRkmvTM49HOmUaTNX8jj0X3fR0d8MwNXSNvGOSd0wGcxYjZZ8gjDANndU5u3UcnT4nizWPXz08Ti0IMXW9pX4HAk0IakHILQ9PUKx59GbbD1MLYvDiiJGKSxBF9KZtsobACyX7pmqYuk5/3iSIO1vUieLEyBJCIKUkiiVxnBg9M7d3NAy4ns9vd04ShhLHEkRRo0aWSDxOYSi5beckrud3dD34QUwsJTpgGhpSP1TjKkbi+hATE8zhITN1jaofUfVDVudnjn3a1BfURIiB9DTP5ny/pTMXL9u7j0QcU3YTAZh1DQ/qkRYjelM2UF5wv45lUEibjJS9xCBuFNFtcrjHNme3pw6Rm0PufTnx2P41UigUHbHSVMlWGiqHTLGSsXSRFBENkjo4h1MPItK2gaV3du1229O91Ibp9Ofk1oEsfemglTPUmzY5WPYW9Zy8bfcU+6ZcDE1DCIEQIKRACkAKDE1j32Sd23ZP8axT2zecDCEp1wM0CWsL6YYBEKMJkQg4lDyqbkjd83loxG97Ucc2NRxDJ4giwkgyVXNbxVqztomtC0xdx+5QRu2h0Sq7xqtIZCuvSQJCgi4FCMnO8SoPjVYZyKfb3q9laGhCgKYRRJIgig8V1tWT94QQjdynmTS9SrKxn+m0W1B2MHPoepjvt3T64mW53p6c/UjJpeZHnDKUZ2N/FiEEFS+cdzFida69xYI1eZs1hRSTNZ/JWkAQx0SxnNdjaxgGsHCtqqTd8mV5906hUCwrVpoq2UpD5ZAp5mKlhG6ahs5QweFg2WW07JFPGZiaRhDHlOohpi5YnXMwjc4MhumTRdvQqAdJ3SldE6RMfUk83Uvp2Ww+J4eLLr/eOZEUk6XhvRCwqT+zqOfkSMnFDUJsQydjJV6MZqFaDah4EjcIGSm1JxrQpOhGRBEUMhZVP0g8RUikFFS9mELGwg9i7h2uomui7UWd3rTJYN7mvgMlbFOjJ2NhaIIwltT9EC+IOWVtmt50Z9fwaNml6kUEYYwUyfFDMr5uKBENL9ZoubNx6M/a2IZOyQ2IJci4Ob6SKE68TBkNejP2rM9O1nwG8xY1L5oVkTFSOZTjNh8CGC5HuEF0xN/S6Ub5vqn21O9KbshTNvdz6tp863lxpMWIdu9P09DI2gZ9GYvejEkcS8Yq3rwe23bl4TuVkT/WqF9ehULRESsp52KloXLIFIfT7dDNpRQhyVg6G/rTBHGMlFCsBVRkiCEEgzkLIeCEgXTHnpbmZPGR0QqxlDNC31blLDQh2Loquzw93TLxhIhmlNJRzAlNQ0MKjVjGBJFMJveNkDdNgJQxUmiY8zw/5kMTYOqCWhCDhLgRVqdriQcnimM0Q6dY99k6mJtzH3Mt6piGztqCw/6pOl4Q4fkRgUiEEIQU5FMmQ4XODemmV8OXYAiY7gAKJQQSiOLOc8hsg5SlE4WJ2ITWENVr7A4NsAyDYj3ADaJZi4Ynr86ze6I2KyLDj2J0Deapuw2ArsFELfHmHem3dPriZb1NEYd1vQ7nbumbcV8fKezea3O/FS8pmjuYczhtbR5D1474HImi9gy9dtsdL5ThpFAoOmal5FysNFQOmWI63Q7dXGpP1vRJXcUN6M/Yrfo69SAk65iL8rQIIejNWOy6r8ae8RpOQxQhDCUPDZfZ0J/mrE19y+b50zxvAOdu7U88ZJFE1xMP2XDRXdR5O2lVloylUa6HaCLG0BsGExI/lLiBJJ/SOWlVduGdTcMxdQxNo94o3Fv3YmIZowmNlK0l6mm2ia6Jjhd1etI2Jw5mOFDyGCl5BFGMqWusztusyduNfJrOSBmipVKnNwyxJrpIgsHiGOIoouKFbf82eUFSz0pvKk1AK8lJb/xpIjESq14456KhEGJWREbeMdCOYIskhWnhnM19PHlzX6u/8y1qNBcvN/RYbD9YW/C41vemj+i5Ojzs3mr790V2tGBaba/sVNvtjhfKcFIoFItCJe0vPSqH7NixEuTeuxm62S1PVnNSt3Oswv4pF68RXreud/GFdaWU7J2o4wYx2ZSReBtCiRCQSxm4QczeiTrre1LL4hzOVD4TidLYtMNe7HkbzKfY2JfmvoNl3DDCkofqF/mRxDAEJ/SlGcx3Vr8obekYhsZY2SOKIRYy2a2MCGoxVS1kfW+atKV3tKgTxZKUpSE0jaxpsHqdjSYEsZRU3QiEhmNpHXuGIikwdYjDhicoSUsijhOPEySvHxip0bdjou3FgMmqjx9EWLqOrifqf7GUaEKga4nKXhhJBrMmp67vm/PZMVdExmDWwtDBD5kVstd8bRqCJ51QaF0Pcy1q9GZMBnNOcr50jSdu6OGG+6cWHK+NfZlZ244Udl/z28udetxAqiPjP4512hGHSNotX9SsR6FQKJYJKodsNt0wcFZKztD00E0p5azcnsWGbh4zERJx6N82S87MScULue9AEVMX2KbJVDVonbdC2iSO4f7hIqevy88pSnGsmXHekLM8Tos9bxnb4PR1vUy5IaMll1oYtwoYpQ2NgbzDGet7yXRoRIdRTN1LxlTXSOpvCUEgJVU3SOoXRSEFx0wWb9pc1NE1Qd1P6nn1pS0qbkiEREcwmHMIYonrx+haZ9dYxtZJWwYQESGRMYQNoTshwBLgmBqr8hYZ22h7MWDKTWojOZYgRqDrIKVo5FAlxlokJVPukQ3ewyMyHhnRyNo6tTBqGU3NyM3ma8fQk1BJ5l7UmKz6/HrHBEEkWdeboscx2V9sM/RtnntvvrB70abHqRzS0fNB19u73tttd7xQhpNCoVgUK2HFfiWicsgO0Q0DZyXJvTdDNyerPpM1n2ItIJQSo1FfpzdtLSp081h5sgYydmt8D5Y8Kl60qPEt1QMOlFyiKFFPyzomhp54AEr1RKXLDSNK9WBZGE7N8zbROG+laectfxTnLZawpscmbWqkTS1Ro2vI1OkkdZ5WF+wFi60ezmQtqbe0rpAmRlLxQlwZoQmNVfkUmhRM1SNyKQOhxR0u6kgcQ2OokMILIyKZhLzZhs6BYj1JAOuQtT1p+jI2Eg9T0xr7TQwoXRPEQlJwTFbn0x0tBvQ6iSfHDyMcU8cNI+I4MXgdQ8cNwkQZ0VnYIzI9IqPiRoBG1oxxA0nEoXQ3HXBMAVIwUvI4eWj2okbFC9k7WQcpQMZEcUza1tk9sXCYHsBoeX4Da66w+988crCt/fodFi7OiPbat9vueKEMp+OEmnQqVjIrZcV+paJyyLpj4Ez3tKwpONSDpFilrgvWFJxF5550i4ylY5kat+6YIGUaiUpdY7I6WvbZPVHjKVv6Og7d7JYISbc8WVJKivUAUxOsmlZY19IFVsriYLlOzZfIo3FrLSEZS8c2dX6zY5y0pScenMZ5G6947Jmo8ZTN/R2fN03A8JSHpesM9aSp+YdykdKWjibgYNGjQwcOXhARxpJs2qAvbeIGccvQc0yNiVpA1QsxNY3T1+XaXtSJYklv1kavB4xXPXKOiaNrhHGcvE6Z5FNmx6F6/Vmb09bl+e3OCaSUOKaZ5NGFETKWaJrO1lW5pHhvg3YWA3qzDnnH5EAxJIxDNK0RAigTY1JKyUDOpDfrdNTfehQTA1nHwDZi3DAmjiWaJnCMRMwjlEm7wxc1pJQMF+vU/IjBnI0XRpRrIbIXcm0+/7QFFEkOD7vPtnldZjqUka/GFrBwGGDSbvmiDKfjgJp0KlYyK2nFfiXzWM4h69YEvDkpMXWNh0Yqc3oClpvcu2hMekRjZb5pGzRfswjp3m6JkHTLk2XoGrpg3kKpcZx4MY5GNGWpFzMlzfM1TSt7+utFeFqqXsjBUh3bSAQHSvWQEImBIJ8yKLnJ+1UvJJ9qf/Jp6Bo520ATkooXkbJ0HCGIZPJaF5KcbWDoWkeLOoau0eOYFByTiarHaNlviUOsylv0pm0EnZ83yzR46omDBGHMw6MV/AACQoQQpB2dDb0ZnrChB1M/dG23sxjQmzZZnbc5WKoTxjFhfOiYBMlcbXXe6Vg+vS9tYusaRc/HEBq2pbVEJ6IIPD+kYFv0pc1Zixr1IKJYC8inkvvF0DRCGRJFksE2Dbih3s4MPbtNVcZ22zUJo/Zyp9ptd7xYHr8MjyHUpFOxklEFWhXHgm5NwMMoZsoNKNUDvEaR1umegIoXkk+Zy0buvepHeEHE49f3TAv5SuS9B3I2PSkLL4g6HoduiZB0y5NlGxprC2n2TtWZrPtkLQNdS5L3K36IBIZ60h1P5Jos9WJm1Y/wg5gnNM7b4bLsveksnh93fN6makGj0KpkvBqSSxmYuiCIkteGLvHDmKlawFBP+/1dU3BY35tif9El7xjU/AgPiYYg7xhM1SXrex3WFJIJeLuLOodk5BOFwcNz3or1gK2rMouSpz95TQ4/itk6mOZ3+8qU6yFeFLFtdYbVhQyb+jOkpi0KtLMYEMRJHlnWNqkHiSHWFIeQUpIyDTKWTtDh42FtIUXaMZis+bhExEHLbkqELRBkHIO1hdSsRY0oloRSYjb6HcYxhhDoeiIW0Q5rCg5TNX9OI3euBYOK36YceZvtmqgCuIqOUZNOxUpHFWhVHAu6NQHXNcFkxaPmR6ztSbe224aOndXZP1UjjjtPVu8WzXEYzNn0Z61ZIgNxDGMVr+Nx6JYISbc8Waahs3kgg6bBaMmn7IWHCspKOKEvxca+TMf1gKA7i5nTz1tfxpol6hHLxZ03XSQiCJZlUNCTHBwvSNQFB3IW9TAijGfWNWqHnGNyzpYBfnjXAbwgTnKwWjlkAY6h85TNAx3njzVl5A8+PJ4YcwWHVMai7kc8NFKlJ21y1qbeRcnTb+zPcPe+Ig+P1ijXA/woIgglO8ZqrC5kWFOYqbDYzmJAEMaEkWRtj4NAMFUPCCOJoQt6UyYRkjCWBEcqyDQHmpYUio2lbNVyahpOMWBokqxjtCTILUPjQLHOpoEMuiYwhCCIYmxDp+wGDORsokgyXKq39f07Rqs49uSsBYH5Fgzqfnt64LHsbBzW5W0enFjYcFqX71yi/liiZjbHEDXpVKx0VIFWxbGgu/WsFpikLaOq9YePw+Gy1kEULXocuiFC0i1P1vTCumvyDqNlDz+SWLpgMGejaWJRhXW7tZg567xZM3/Pg3Bx5y2XMklZGlU/ojdv4gZ6S2zBMTUqkyFZW28796WJEIKzN/dR8QLu2D3JRM1veVp6UwZPOKGXszd3XidLSslk1Wd13mZVzqZYCyjWAwwhOGl1FiFgquYvSkZ+31SNWx4aZ9d4HV2X6JqGoUvGqwG/fmSMjQNpTlqV72gxoB5EGLpgIOsgRGL4NgXDJRIpRUslsRPqfkjdTz5j6A2TvyHq0XS/ld2AO/ZMkbZMSm7A3ok6w0WXE1dlyToGB0sulqHhNGpqPTxa4cBUe6p6RTdkMGfPWBA4oT/N7vHanAsGFa+93+98h4a0YbbXvt12xws1Oz+GqEmnYqWjCrQqjgXdmoBHsaQ/a6HXBaNlLxFb0DSCOKZUTwq09qY7T1bvFt2u67XUIiTd8mQdXlg3mdgefWHd6YuZc8mGL3Yxs1vnzTF1Tlyd5Xd7ijx0sIJpaOhakvsVhDG2pXPiquycz+aFKKRMztqU1CZ64GCFmh+StgxOXpPlrE19iw5ZnKj4rOtJY+pwoOThehGOrTOUtwkiFjW+URTxH3fsY+9UjZ60gR/ExEgsLcnVmqj5/Oy+UQqOgW0YbS0GSClBSmxTR0pImRr1IEbK5PrLWDp+KNH1pO5VOzTD4HaMVpis+aTtZN9uENMsweWYGlLAeMXn4ZEy525dRW/GIu+YPDBS5t7hMr0ZE4GgHkTomsb9I2UePlhhf9Ftqx9BGKEJ0VoQ2D9V4393jpM2jRme9+b7/TkHnUTpfj5MAY9bnW/r+5v0pNq7P9ttd7xQhtMxRE06FSsdVaBVcSzoZihZ3jHJN+rRzM49sVrtlgPHoq7XUouQdEtOvxuFdZuLmX4Us3uiytg0T9ZAzmZNPrWoxczp523/VI2UaSyJoWcaOpv6s+ydcNnn1/GjuDW7FQj6MzYb+7OLDlncPV4j55g87cTBVkHZehCye7xG3jEXHbI4WfPZvm+KnRP11nnb1Jfi1LU9aJroeHx3jtf43Z5ikuPmRcRSNkLfJFossA2NiYrHqpzD49bkF1wMaIas7Z2sISSMVnwKjkbGNtGEwNA0dE0SiKSYbGGeqKG59jlR8bljT5GaFxIRY2sGWefQ/RbFEi8MW4WSm3PD/qzNuRmLneNVBnIWfRssfrNzgtGyn8iY61BItXffysOOPWUa3D9c5gkn9MzZfkt/KqlZdQTLyTEF63s6E50wzfYES9ptd7xQhtMxRE06FSsdVaB1Nqq0QHfodijZ1sHsrNyT4ZK77J7BK7GuV9fl9JeosK6ha7hhxAMHy4yVXWJEy8A5WHI5WPI4oT+96FDIE/rT3LpjnPuHy/ihxDIEG/pTnNph3lTzGeMHIWEU05812djnMFz08KIIW9cZ6rEpezGGLkh3KBM9I2SxJ9XyvJmWoCeTWrRMv6FrTNQ8bnlojL0TLhKJJiSxFOydqLNzosb5Jw50PL6jZY+xit9SLpzuIG6mJ/phgBfGCy4KzKg7lrU5c2MvP3/gII+M1nDDCNvQMHWNgmOycTDDmRt7Otpnb9qiL2siBIQBCCMiCpLjbeY4xTFYhsAwZo6tEIKhQoqKG1AUIatzDievyVNxQx48aCDimF/vKh+xLwIYys+cbwpB4j2b51yW/RhN0+aXsQR0XWekGrDliN8+E7vN09xuu+OFMpyOIWrSqXg0sBInct1ClRboLt0MJRsuufSmLWxLJ4hihkvusn0Gr8S6XkvtyepGYd20qVGs+dx7oMRAziZnGRiaIGyo9d17oERP2uzYEGn2d/d4jbSZKMA1xQCklB15cKY/YypeyJ6JGn4UM1qJEJrAlBpCExTrIavyDpamUQtish3MPrsl058yBPftL7F9f4l8yiTvHKpDVnZDtu8vMZCxuPyJ69reJySiBFU/JJYxhtZY5GiqhQBhnBTvXUi8YK4ct560iRtI/CDCj+Ik10smC9u9dYtCyjzifTfXPnO2idm4XoNQ4hMhSAwYLalpm9zTc4ytqSc5bVU/ZHU+hWPqpEydYj1gz9iMw56Xw9X3pEwMtWieVYdiLSCKjzx2QRhSqrcnItGk2mbzdtsdL5ThdIxRk07Fo4GVOJFbalRpgZWJegavPLpZ16tYC0g16upIDv0hIWUlhlXVj8g57Rsizf6Olj1iKWcZIm7oLdhfKSUHii537S3ihRFr8g6GnoTyl2oBI2UPQ9dactZ5R6cnYxPJzkMLuyXTP1L22DVRxTI0UpaBoWkIktC3lGXgBhE7xquMlD2GpuXaLERvygAkVTeikNExNYEgEW4I4mR7LqU12s3P4YJdsYy5d3+Jih+i6YI4grqXGDlNFbo7dk+ybXUu8ci0sU+AjGWQMjXKbuIds3QwDa2V7xRLSOkaGXN2f4PGmEto5ccLEk9UGC9sNAmg5M60ROpByIb+FK4fwRzDPlnzWEj/wgvBDWYq5M0VfdEckzCKKdfbq8/Urqrf8UIZTseBQsrk9LV5Ris+bhDhmDqDWWveG1HROSp8StFNVnJpgZV0b3TLo7fSnsGPdc9mtxRpS/UAL4rZtqZAxQ2ouCF1JDqC3rTJ+t40JdenVA86kuKu+hG7J2pMVH2iOJ5liOiahjEu2DyQnbO/xXrAzrEKt+2aZKziM5i1iWKZ1FSq+RTdgCCMkBIyto4uBJEU7ByrYOuiYzn96TL9Qz0pvDBqCBEkuTYHpuqLkunfN1Wn7IZs7EsTSXCDqCG2AFnLIGfpjFU99k3VOzKchKaTtnWqfpzUWzL0Vl2vehih6RppW0NoRw65PVywa6IacPe+UsPjkhi6GImh54eSiWrArx4Z55nbVrNmjnSLufYJSTieZeg4ukakS6IYwkgihCBl6chYouka8RzDO1nzGcxb1LxoRn581jZItXGtS+Bg2SOKZSu6KeuYnLquwO7x2pzRT4YQLGQiR5IZsuxzPaMsMzGUvSAijCX7i+3Jp9e8hSXLjyfKcDoOzHWBHXwM/Qh2m8f6JONY8Fgf45VaWmAlnbduevRW0jNYeTa7rEgrE2WznlQaL4xa8t62kdRJarNUzgyCMOJAsY6MJYO5QxPsZr2wkXKd4WKdIIzgsOdD83yPVzyCULKuN4UmBKNln7Gyx4Giy3jZR2hQ9X0kJrqmoQdJHaN8ykwU4jpG4IYxeyZqVN2QqGFAZhyDMJZkrM6fY80wMlPXKNgGYSSJZRKeZuiCmhe2Qhg7QQMGMg660Kj6YaJSF0tEI1cxkzHoTVsstAxyuGBXseaxb7JG1QsQCAIZEUWJgINt6AgJj4zCeNmd13CaSwQsisHQdHpSJn4UUfFloz4T5FMGQtPQgIPFOqtyqVkpHCevzrN7ojYrPz5tGQuq3wkgiCRjFW+WZz3vmHN63nsz7RlkVS/xDM31jJqs+ty6YwKAx6/vYTBn47QpWrLYQtbHiuXzi/4YQf0Idhc1vt1HjfHKLC2wks5bNz16ahxmf0c3PJBLud/pk1Hb0GaJeixWkTafMulJm0xW/cb4NpQhGv2crPr0ZkzyHV4PfiSpexE98yyspMzEc+RHMw2c6ed7IGszUvawDR1NCAZzOg8dLDNacnH9iCBOnkGRBEMIpATD1BivJB6yfKp9ZbIoltiWRnki8br1Z20ypo4XJIZU1kkkvTuV6V/bk6IvbTFW9dhom5iHVeYdq3r0ZyzW9sxthMxH2jYYaoQu9oYGFS8kiMHUEk+MYegMZmzSCyxaHS7YVfZCyq6PF0k0GqWWhCCWgrofIiWEMqbozu8RmU8ELG1p1H1wQ0naFtiGAZJEwMLR6c/a9Gcdql44Z/iwEGJWfnyPc8gQ0WjkS9HMpUsMKl3A+Vv6ePKm3ln34Xwh9//78Ehb5yGK5ZzPKCkTJcVETVIyVffpz1oU7PYMp7yzvE2T5d27RxkrObxnJaDGt/uoMU5YaaUFVtp565ZHT43DTLrlgVzq/TYno4+MVpAySV5v5gwV0oli2dZV2Y7VELO2wbahAv/9wAh37Z0kmOYRMXWBYxk8aWNfx2Nr6YK0ZVAPkvNyuCerHkSkbQPrMENi+vmOSY4viGLsxkq9oQsm6z6xBD+M8EKJF0UYQsM0NbKazmTVo9phqJOuCTw/Imcb9KUtym5I1Q3RhGBjXwY/ivCDqONQvdV5h7M39XPjvcMcKNXpSR0KWZyqB4SR5OxN/azOdyZrXUhbnLg6S2l3wFTNp+JGhBIMkYiSrHJMTlyTXVA2/HDBrjgI8cIYL4gxTR1BEk4XyxghaRW+1Y+QWTSXCFjaTIxfP2ostkmBF8RoAixTI4olaUvj7E295NP2nIsNc+Vmogkc85CYQtOubfZOAClbYzCfmteIn0vEpd3TrIm5n1H1IMkdzDdyzIrVgLGUR7HNwsFCP/7P3iOhDKdjyEoN71kpqPHtPmqME1ZaaYGVdt665dFT43CIbnneurFfIQS9GYuDD3tM1QKGCg55MxEXeOBghZ50Urx1MYV11/elEAJ2jdcAAUKSVCeVbBvKN97vvN7SUMFh13iNu/ZP4ocgZYwQGpYBg5kU63tSs+otTT/fQkA+bTJe8bCzSTtdSxL7gzAxorK2nuTIIAhCyWQtRKAtrBgw92i0FhC8MG559GxD40CxnoxJh2iaxsVnrGG86nH/gTKTVZ+mK0cTgiee0Mvvnb6m49zCrG2wrjfNf903QtWLQAgMEQMaFS/CrAWs60m3dQ9PN0geHC4RS4gE6FJi6xqGLoilxI8lccMyCRcIhTzcyKn6AXGcGOW9WRtbS0Q9YglhnCgM6iJR1TtSnw/3EFm6pD/nIEsuTVt5euijY0BPxqJDRyGRFAuq9YlGu7meUVEsCaXE1BMBjIrv8dDBClW/vY4E8fJYcJyP4//L8BhiJYb3rCTU+HYfNcYJK620wEo7b93y6KlxSJjueVuTd6gHEVUvRNcEa/IOw6XF1e3plkdPSslk1Wd13mEgazFW9ijWAyxd8LjVGTShMVXzWd/TmZEjpWTvRB0pBScMZAgi2QrVs3SBbNQb6nS/GUunN2tx2+5Jxso+dT+iOaVN2ToaGk/c1DNrYeXw8z1USFHzI8YqLjnHJAwkYQhBFGHqFiDwAolAEotEcMCPwlZ9o3aJYkl/1kKvC8YqPvmUkYRAxjFjlURMoDdtdhyqB7ChN83Lz93Irx4a5Z79Zap+QMYyOWNdjqdsHWRDb/uiEE2klBwsuUSxxNI1YgmSRFkvKT8kGal4SCnbOm9Ng2Sy7JK2dTRfYjT2GzTCKS1NQzNiLD2p69TuPqt+xO7xCqt7UoRCEgWSWMZIRJKLJhPjxjaTULxC2j7ifqd7iFbnHQqORRBK6p5PPTjkMU1ZgpRlUbCtjkPfBnI2GkfOndIa7eZ6RiX5W4m3FCkp1gIKaZO+tAXUFvz+nmWwaHUklnfvHmWstPCelYYa3+6jxvgQK0nWeqWdt2559LqVK9MtujUOTc+bpWs8PFqZFfq2WM9btzx6zf32pEwmah5SJKVDpUjOU0/KXNR+K17IfQeKZG2dE1clCyFBFGPqGjnHYLjocv9wkdPX5TtS1QOoeiFTtQBL1xnsczCNxCtUrAdM1YLEU3IYh5/vrG2wdTDLgWKdUi1gX6mGpglsoSFEMkmOYwkiKaJq6ok0eT3szPA3dI28k4gFTNZ8irWAigwxhGAwZ7XO52Lvi7xjctKaHIahU/ciUrbOloE0+Q7HtMlI2eP+gxWylkHGNgjCmBiJhsA0NISE+w6UGSl784o4HI4QgnzaopCyphWGnW50SSzDbCgktne/NY0cQ9NwDI1tgzn2TNaYqAZEMkYXgv5Mot4YxJKa314oW5N8yqI3YzBeqaMJDdMkKQolErEJQdzI0Ws/3w1gMGvjmIJqML+h7JiCwaw95zMqZeoU0iajZR8/ihBCMpC1GMi2d743DXZuTB9LlOF0DFlp4T0rDTW+3UeN8UxWSj2rlXbeuuXR61auTLfo1jiEUUzJDZiqB7h+TD51qDDpaNmn5Ib0pjuv29Mtj97h/U1yZWyCKGasHFB2o0X1t9QwYgopkz2TNaaqIWEcY2gaPRmDnG0yWQ06liOveCF7J2qs63UwNI2Keyjhf2N/mjCO2TtRpeKFM/Y71/lOmTrre1MMa4KT1+S5e2+JWhAiEYRhnAgYAIaRSD9bpo7VYS7S9OfDloEMk7UAP4yxDI3etMnBsrfo58P00M31PelphYt9Kl5xUaGbExWf4WIdxxBouqAUS4hBaIK0pRFFkuFinYmK37bhBDCQtVhXSHGgoRHiRVEr9s02DIRMBC8Gsp0ZImnLQBeCyVrAYNYh55hJYV0hcAydyYZHJt2hcqFtaGRtEy9IpMY1TTS6KwkiiQgEWdvsWKUu55j0ZGzcKXdOWXIN6M3Y5Bxz3mdUb9pi90QN149xTANd07Cthc+zAHqznYmFHGuWjeH0N3/zN7zzne/kLW95Cx/72MfmbHPdddfxmte8ZsY227ZxXfcY9PDoWWnhPSsNNb7dR43xbOZKrl1urMTz1g2PXrdyZbpJN8ZB1wTjFZ+aH7J2Wv0c29AZzOnsn6ohG564TuiWZ3N6f5McnEadIZFMdpvS3532F6AeRoyPeRTdRDWtmXsyXvPIO4lHo1OaBtmaQgrH0OeUOT9YdOc0yOY735sHM5zQl+Km7Qepl2KCKEJqAiETz1soYyxNJ2vpOB1OwJvPh+Giy693TiTRijRsBgGb+jOLej50K3TTj5Lz7/nyUBFYIQjCGM9P6k9JIfCjzjw4PRmbJ6wv4O+O8LwIJ9ZahqmpCWxb5/HrC/RkjhxOdzj5lEHWMdkxUUVWJTU/bhlOaUtDaIL1femWmEK7eGFM2YsQOsQhhEGcKOkhMXSB0BPPp9ehB9IydAYyBuW6RtWLZwR+CiBja/RnDKxGjt581+xTtvRR9yLu3ldktOwhSIyOI0mXOAYUOhyHY82y6N2tt97KZz7zGR7/+Mcv2Dafz3P//fe3Xi+nH7h2WEnhPSsRNb7dR43xymQlnrel9uhNz5UZzNmUagElN8AQgpPWZBGIReXKdJvueDYXyFcRneezdNezKfHCiL2TNSrT6gxlHYMwjsm0KXU8nZyTKN/tGKvSl7FJW9MKqfoRO8eqnLgqS24x8siNUyOEwDFnfl4uULxovvM9UXHJpAyiYp2UqYOUSJHUF0Jo+FFMNmXQczQTT5mcetHU4z6KS6xboZt9aRNiycGKR18myfWiIdggkYyUfVbl7KRdB2Rtg3NOHOBAyeP+4SKTbkAUxOimRl/KZGtPhnNPHOh4oSy5BgSlWkCxHoCQjRpTgqmaIJ8ycUyt89IKNZ+RUp0gbOTlGYc+HwNBKBku1SnW/I4KDGcdA9PQsU0DXUS4YdxS6XcMDcPQsQyd7LT7Yr5rVkpJ2tY5MOWStcAyBGE4/7PF1LWOPW/HmuPeu0qlwpVXXsnnPvc53v/+9y/YXgjBmjVrjkHPusdKCe9Zqajx7T5qjFcmK/G8LaVHrzmRW9eTwjYbOU6RRNeTHCcviJeVqt50lnIcoljSm7XR60FLeMDQtIbCV0AuldQu6lQMYLpnc/9UrVHHJZnT1oOQrGMuynMRxRLH0imOJzLZ+ZSJoQvCSLJ7vEbaMRjqSS1KvEA0JtyChq3YNByaDRahUJdPmfSkLCarPqme2edsqubTm7KOWB9qrvMdxJCxDfK2ST2MiKVo9U8TkLeTcK+gQ22TpmcI4JwtfUmoXhBjmY1QvZK3KM9Qt0I3TUMnbetEJclIyUNOM/KFFAiRhMcdrlq4EEIIetImdT8iiGRi4Js6iEQkou5HrZpKnTBR8bh/uMRUzUdCIh+vg64nUuReEHGw5CUFkWnf2CvVAyYrPlEckzINNCFa91ssJW4QMlVN6np1QiTB0ARxnKgrGvqh8mZRLNHiOFlgOOzemOuaFUJwylCBKIa9E1WCBe7RIIoXdR8fS457Buwb3/hGLr30Up797Ge31b5SqbBx40Y2bNjAC17wAu65554jtvc8j1KpNONvOdC8wHrSFlnbWNYTl5WIGt/uo8Z4ZfJYPm8z5J4RpE2DnGOSNg0EYtmp6nULQ9focUw29Kbpz9q4fsRU3cf1IwZyNht60/Q45qLEAAopkxP601T9kDv2TPLrRya4Y88ktSDkhP70okMLXT/CMjQsU7C/WOeR0Sr7i3VsK5HM9hZRZ6jshqQtg80DGSRQCyIqXkgtiJACNg1kSFk65SMUPJ2LrG1w8lCOMJaMlj28MCKOE4/ZaNkjjCUnr811bAiHkcTQdHozJoWUiWPpWLqGY+n0pEx6MyaGphMePqNdgOliIY+MVtk5WmX3ZI2do1UeGa1i6VprQaETpoduzsViQzejODFqdD2R844jSRxBHEnCOEbXIW3pHU/A4zjm9l1T1PyIgmMAojGWgkLKoOZH3LF7ijhu//lQrAf8Zsc4uyfrZB2DtYUUecdE13VMTWdtIUXOMdgzUWWi6nfU35Ib4kcSTRNoAoI4xgtjgjipEaVpAi+UlDq8futemMiba4kUu2wsKDQNMoTAj5J27dCMdjC0eEGjvh7CeLneUX+PNcd1Se1f/uVfuO2227j11lvban/yySfzhS98gcc//vEUi0X+/u//nvPPP5977rmH9evXz/mZa6+9lmuuuWYpu61QKBSKFchKUxfsFtND6k5clZ3leRsuukclBrB7vEbGMnjCCT3oQhBJietH7B6vkXfMRRlPtSCiWA/IWAYn9JtoIpnUNZXqejsMy2qS5HU5VNwgUTuLY3RNoz9jkXGMjlfrIVmcOG1tgYobsnO8SrEetHKGNAGnDOU5dWgxRZYlslEfJ2cLTD1OjCld4Bgamtb8ls4Mhm6JhUy/ztYUnFnX2WJDNyXghpKUaWDrMW4oZ4SSaZrAi+KOnYUjZY/bd00wUXMZrySGooxihK5R80L6szG375rgmdtWzSk6IaWc4c1Pmxq7xquMlF2khKxtknVM0nZigHhhIkSStQ0qfshUrTPDSRdgaOBHEEmJqWktj1Mkk+QvQ0/adUIsJaW6D40wu0RNvynTn3xXseYnRlSbFFImNb+96+fegxUu6azLx5TjZjjt2bOHt7zlLfzkJz/BcdqrGn3eeedx3nnntV6ff/75nHLKKXzmM5/hfe9735yfeec738nb3va21utSqcSGDRuOrvMKhUKhWHGsNHXBbjFDLGSqjmPp6AiCOGaq6i86pG66GMDaw3Mq0ixaDCCMkmR6XZAonOmHcpGCKGwIOnTuKcynTHrSJjU/YkNfhlX52YVfEznnzo2yQsrknC39rM7b7J9y8RoS+Ot6HTb2ZxdlPBq6hq4J/FA2CuFKJJIgTAqRpsxEve1oxDeWUixkuujErx4eb1iOtHKnNg0sTnTCDxueY11HNw0cS7YKv+qieV3IeT1d8zFR8bj/YJn9U4nHI2UZWI6BH0mqXkjVCwkjyURltsx5sR7Myh91LK1RF8vCNnR0DdwwqQVlaAJ0QckNyNo6jqElll8HFNImjm0Q1gOiCCKiaQVwRaLaZxsUOlxUqPkRfqMIstQ0dO3QXkOZhPD5UdyxfHqlzUWIcq3zxYpjyXEznH77298yMjLCk570pNa2KIr47//+bz75yU/ieR76Alr5pmnyxCc+kYceemjeNrZtY9udKaAoFI8WDl8BW+75LApFNzk8B6dpMEQkHpHFGgwrkWZI3a07xrl/uIwfSixDsKEvzamLkIiG7okB+JFExrBpIIsXRJTdsKVK1peysPIOXhDhdxiilrUNtg0V+M2OccarHjnHxDF1wjhmvNoIqVtTWHRuWSFl8vj1PWxdtTTPYEtPQrLKXoBj6KTTWksF0A9jyl6A0FJYnboYgG6Ihcz8/DS7SSSet8WS2BwCXciGQIHW8uhBTLkeoDfC1zqh7gcMl1zCSDKQs4li2cr36UknhZeHSy51f+bEfrrkem/aakmu752ssWeyxqb+NL1pi4oXkjESEY+gkUdn6xphDKtyKdb1dCbDvaaQSKPX3BChacg4UW/UBI3XMYNZqyNJdki8Vc2zHUfyMFU92dLiiGRnhmlvpj0Z93bbHS+Om+H0rGc9i7vuumvGtte85jVs27aNv/zLv1zQaILE0Lrrrrv4/d///W51U6FYscy1AtaXtdjYvzwV1BSKY0E3DIaVyPSQujM39KJpSRHVehAuOqSuW2IAli5IWwaRlKzvTeOFMz1DoxWPtG10bDAIITh1bZ6yG7BzrEqpFhzyiGiNkLq1+aMypJdS1MOPJHEsydmJGmC9HrY8DEn4nkEcy44NyG6JhUwXnTh3c/+sgtPDJXdRHkiJIGMbSAklNyAIAyQxAg3T1CikbDKWgexQEnCylhRAFiLJf/OjuFXHydKTMLikhMOh3J4jSa6v7UnxyFhSr+vUoRy37pzADyMMXbSKFnthjCMMnryll9X59qKvpn/Hxr4Mo0UPLwoRmkiETQRAhGMabOzPzhmWfCR0TWuJpei6aJybRoUoKYmiRBVQ1zrzbJ62Nt+6veZDa7Rbzhw3wymXy3H66afP2JbJZOjv729tf+UrX8m6deu49tprAXjve9/Lueeey4knnsjU1BQf+tCH2LVrF6973euOef8ViuXMfCtgw0WXshsuquigQvFooGkwpE2dk4ZyrYkRsTyqHJyVxJFC6nqxFh1S160cMtPQGSo4HCy7jdAng5SpE8QxYxUfUxeszjkdq6jBYSF1RRcviLFNjXU9iw+p6xZhJIlIJvG6phFOyzExhEBveJ86FYdoioUUHJPJmk+pFhDKEEMIBnI2PSkrqcHT4Xmb7oEUQsySmV6sB7InZdCbNpmoemQdHT8UxOhoJOGFfhTRl0ktSpbd0DWqXoAbNgQWSCb6bhAhhCRjz7wejuRlTZk663pS7Juq86SNvRwo1tl+oIwbhsSN2roZ2+DxJ/Zw8WlDaB0aIkEMAzmboZ4Ueydr+FGMlBKBwNZ1hnpS9GetjlUW06aObWjUGrloYRwTy8SDZ2g6NSSWqZHu0CDryTg4BtSOoCnhGEm75czy0ls9jN27d8+4kCYnJ/mjP/ojhoeH6e3t5ayzzuKWW27h1FNPPY69VCiWF90qOqhQrHSa98Zo2SOKk8T3IIoxdY3BnIUbLk52eaXRrZC6buWQZSydDf1pgjipJ1OsBVQaE/vBnIUQcMJAetG5aUsdUtc9JEKCYWjkTL0lBND8txY0ZuMdyiJ0Syykm3LkAxmLneOCuhehGxo6yVHXvBDH0unPWB0b0oM5qyHDDSDxpw2jTjLGhpZcc+0coxCC9b1pRksew0UvqedkQBTrBDLGEIKetIFtLk6MxtTADUNMXWPrYAY3iImkRBdJflUUC7wwpNPda0KjN2vhBRH1ICSMaS0wGVpM2tDpzVhoorMdSySGrkM4f26UoevIjmU9ji3LynC6+eabj/j6ox/9KB/96EePXYcUihVItyZFCsVKp+pH7J6osW+yxmjFIwwlMRINwXCpzmDWxtAFmweyj+p7Y/pkT0o5K4RqsRPaGaITxTq9aaulzjZZ88nYxqJyyKbvt+IG9GfsJakPdfh3LPdzbugajqURRDp+FDNZ9QklGAL6shYZUydlaR17hqaP73DRpTdtYVs6QZREKSz2vHVTxdK2k0K/JQG1IEbGEqEJ0o5B3tZxFlFEtT9jJTmPcRIeaohDDmkpk1BJQwj6p+XgLHSMtqGxeSDF7XuKHCx5ZB0LQ4vRNMjYJv0Zk/1TLj9/YIQrzj6hI6+TH0k8P8axEkM/iiRxI8dJ1wVVP8Lz445DN9O2Tm/a5mDJQ0Qxti5aOWSxkAhdSwpGd1h0uuKGeEcwmgD8MKLSoXz6sWZ5PyUUCkXHdGuVT6FY6QRhxI7RCjvHq0n+QgxCA9nIaSm7QaJStrUfltkkeimFXpqTvcmqz2TNp1gLCGUyKSykTXrT1qIntM2aLYfnVw71OJzQt/j8yln7jZL9ru1NHdV+VxK2odGXsdg9Ucf1Y3RdYJFMaMfKAY6lsXEggz3Ps/9INMd351hlyUIWu+WBDKOkdpNjmgg0HCPRk9MR2IaObSZGRKe/ccV6iGNq2EbTu6QhNIGMJUEUYxvgmBrFekizAE47x5h1LCarHhpQD2MiErGTsuvjBhEpU+e2XZM8c9vqjoQcwiiRM0+ZOuPlgMm635LT701b9KZNdF3reBxyjoGhJcIVfb2pRL1RSoQQmIag6kYYImnXCRNVH28BIT43ouN6Vsea5fXLoFAojhpVq0ahmBsvjNk1VmWi5pO1TVL2IVnruh8xUfURjXbLiaUWeslYOpapceuOCVKmMatuz+6JGk/Z0ndUoW9nrCssuaJnt/a7UjB0DV1oyFiCjIkioOkLkCBjsSg58ulIKXGDiJqXmCJxh4IQ0+mWB9KPJNUgxDQEfiywNaOlLqgJksl9EHbsaSl5Eaahs3kgx2TVo+IlnlgN6M2Y9KZtwlhSmjb7b+cYTU2ye6pOyU3U/lKGjq4LokjihhFVPySQ8Zwy50dCiES4YaTs4XoRPSkDXdeIokRZMIhjCo38sk6oBzFpy0jEKoQkNhpKiA2xiKxlkrIM6kFMoYP9jpXdJW13vFCGk0LxKEPVqlEo5iYII0puSBRD3jlkcGiawHQ0ivWAkhsSLBBOcizpltCLaCmOxYmUd5gklrc0r+TRGSPdCn1bCSF101lKT6GUkpofYhmJ4t14xSeIJaYm6M8m10Y9CBvnsTOK9YBfPzLOzrEqyMQTW3VDRkseI2Wfc7b0L+o664YH0tTACyLCMObEwSxBGBOR5CGZhsbu8Sp+EHWc25O3dWxTI44l63tTFBvPCl2DgmNQa3jh8oeFqC10jA+PlBivBEgZkTVMKv6hUDS7cc4mKuB2+NzJ2jo1P1H/y6UNKm5ELEM0oZFLG1T9CDeIyHYYUueFMb1pk4xjMF7yCOWhAsOGEPTlbGxddLzANP24l6Ld8WLlPH0UCkVbdGuVTzE3qlbWyqEeSgxdkNcNym5AytTRNEEcJ3k+OdtAIqmHyyM5uVtCL1U/wgsiThzM8tBohV1jNXwpsYRgdY/DiYNJvSSVB3l0LLWnsOyGVLyQyVpAuR5gmxqOEMRSMlb2yaUkZTek7IbkU+3XwpFSsn1/iXsPlEhbOrmU2frNKLsB9x4okXNMzt3St6hn21J7CoMYHEMndmCq5mPpGjTu46ofknNMbEPvWE2uP+ewtsfhvuFK8jxwTCxd4EeS8VpAJGHbmiz9udmqb0c6RiEhjmPcICKWMxU9vDBZuHAs0XGprHoQ0yz964cxWcdAFxDJJFcIBJGMO/YMOaae1DQzdHpTJqNlDy+S2LpgMGeTNg3cMOpc5rzN072oMmTHEPVEVCgehXQrz0AxE1Ura2WRNjVyKRMZSwxdo+ZHyDCZw+QcAz+K0TVBepEqV0tNt4Rewiim5AZM1RPj8aShHEIkNVq8IGas6hNJlQd5NHTDUxhLyYGiix9G5BqfbYpk2KaOF0QMl1ziDj1OFS/kvgNFDE0wkD1kFNiGjp3V2T9V4/7hIqevy5NzFvdcW0pPoaUL+jI2Eo9i3efAlEsoYwyh0Z81yTkWfVm747peg1mLzf0Z9ky6mAK8MMILAAF528SXki0DWQazc9+P8x2jZWjYuka5LgmjxOAVCCTJ/SYlFFLavHnJ8+EGEZpIPGAVL6RU91tiFpaukbcNNKHhBp15sgazFuv7Uty9r0ghZWLqOkJEGFqi5DhcrnPG+p55x2E+7DZVDtttd7xQhpNC8SjlsZ4P0G1UrayVRyFtcUJfml3jNWxDI2ubrdyIIIoIYskJfWkK8xgqx5puCb3ommC84lPzZ9dxwoH9UzVkQ2XvscJSh9R1w1PoBRGleoCuaazO25S9sCWnn7MN9k+5lOoBXocT5VI9YKoWsLowd/2c3ozFwWKy78UaTkuJaegUUgY7Jyp4fpxI0jdEHGpehNACtjqZjuXI66Fk80CW8WrASLHerCSbICWrCik29Weoh5JsB7u2TR3H1nFcDTeKKLlRS2zB0hPvWcpKRC06IZbgh3HyOZH8P5YSTSSFoS1Db2zraLdomsa2oTy/2THJvskSvRkTU9Nxw4Dt+2v0ZmxOXpPruO5Uqk2lw3bbHS+Wd+8exajwHsWxYKXlA6wUVK2slUnWNnjiCb1UvaQ+SRBGhypcajCQtXnixt5lc890V+hlgdlUp3FDK5il9hx3y1PoRzGWruOFPvcOl5JJcQyalng1spaBpVuJYmSnHOExJeWR3z/WpE0NXRdICaYpODDlEsgYU2gM5q1EEc/QOvYch1FMb8bmWaesZvu+KXZO1PHCOJEU70+xbagHXRMdL1TYjb4EUqJpgrR2KFwvBgIpSVlax2qIBUfH0GC0FtCftajIqGU4ZSydsYrPul6dgtOZQSalJIySPK+Jqsc9+0qtXLq1vSnW96aI4kNKe+0ymLPRgSOZ9Xqj3XJmefw6PMZQ4T0KxcpG1cpamQghOG1tgYobsmOsghfGrXQDx9DYNJDl1KHlY+x2S+gliiW9WRu9HjBWcck5JoaWCA6U3YBcyiSfMomOQlFtpdANz3G3PIWGrqNrMFkLcIMYyxSYuiBCUnEjwkgmUvJ6Z9dDPmXSk7KYrPqkemY/r6ZqPr0pi/xRzE+WcrG4FsREMfihZKzkITTQhCBGMjzlMZB3CCNJLYjJ2u0bI82FChCs700RxEmdsJRpsK4n1TJsFrNQYWiJYRRFSSFYhAYNCXVdB0N0Hp4WSkE+ZbJvqs6+oktvysIxNfxQsq/oYumCnGMSdij0UvUj7hsuMVxyyZo6p64ttOo4BY1w0HsPlDqud9eTMtsynHqW+TxY/aIfY1R4j0Kx8lG1slYuhZTJOVv6WZ232T/ltlaU1/Uuvl5Nt+iW0Iuha/Q4JgXHZLLmU6oFhDLEEIKBnE1PykKwuAniSmK653hNwaEeRFS9EF0XrCk4DBfdRXmOu+UpHMwY1IIIP4roSZvUvZgA0BH0pHVKboAbRgxmOpvaZW2Dk4dy3LpjgtGyi20cql/khcmz7uS1uUUvAi31YnEQRgyX6nhBSChjPFciiRFo2KbADUIOluqJR7mDPjdl+n/+wCiuHyNFcg9W/Yh79ldwrBpPP3mw44UKP0pWZwqOSRDTEImQaELDMfVE/U/QsXy6qYFl6Aw0PDSlWogbxugieZ41c986Tdn0g5D7DpQo1XxyjknNj1qFwtO2QbHmc/+BEs/etqqj8Y3imIV+EeNGu+WMMpyOISq8R6F4dKBqZa1sCimTx6/vYeuq5R8u3Q2hl+merK2DGSZrAX4YYxkavWmTgyXvMVGyoOk5NnWNh0YqDQMyKQScbxQCXoznuFuewolaiIbEEBrFmo9h6GgyqbFTrPlJEr+UTNRCerPt77fpiR0pudy5Z4qqHxHHSVhZxtJ5woaeRXtim4vFFTfAsXQcXSOSkgNT9UUvFif12GpU3JCetIU5vVBtHFN1Q3aN1RZVj63mRYyUPISA/qyNY2i4Ycx4xaPkQtXtvFSB1pDxBkHa0rAaXkKd5DciCJPfi05TCoMYbFNjTT6FAIZ6aIUdEyceIsvQOlYXnKqHHCx6RDKmWA+xdIGhJfXDivWQMI4ZLnpM1UP6OrjOdk26CwUIIxvtLuisy8cUZTgdQ1R4j0Lx6EDVylIcS5Za6KXpyRouuvz6kYlkttLM9RKwaSDzmChZEEYxU27QElTIOYdkuMcrHhUvJJ8yO/Ycd8tTWPVCJMlk2Y/jGYaBLgS2qSEb7RZD2jJYlbWYqoX4cYylafRkDNKLTNZvLhaPlj2iOGbnWBU/klh64tmsB/GiFouDMKLYkAfPOSZhJIkbxVkdS6dUDynWg47rsVW8kD0TNdb2pjA0QdUNqfghOoIT+tMEkWTvRI2KF3YkkiERmGaimueGEaauYQiNWEq8KMIxdExDQ3aYSGbpgv60jWMk57vqhkR6YpBlswYyhoxjdKwuKJB4YbMGlGCqnhQC1jVB1koU/BxTRyxoBs1Ek7L1iebjhsNey0a75YyanR9DVHiPQvHoQNXKWtmsxDzTrgm9iGl2k6DjVe+VjK4JJiseNT+aoS44XYY7juNFqQt2w1MoBLhBjNAEA2mbMI5bIVSGpuE3avZ0+thpGjhjFQ8vijlY8fDCCNvQSdkaYxVv0fXCdk/U2D9VZ7Tk4scNN4iA4aLLYN7B0EXHuTLNemxCCPZM1FsS3M1/U6ZA10TH9dhK9YCpus9QwcExdbwwahXAtQ2duh8xUu5cXbDg6KSMJEphY3+GihcSyaReUdY2GCt7pE2tYxEH09AZKjgcLLsEUUzKtFuCDRKJqWuszjkdqwtKEu9XxY8Yq/gzhEHGJDiWTtrSOzb0TFNvqZgePsttvtZE0m45owynY4gK71EoHj2oWlkrE5VnemiiDHDu5n7qwaEV5ZSpM1xaXG7PymSB4+swsX46S+0pzDsGQkAYxqzpTRHLxNOiiUQcYe9kFc00yDudTe2qfsT9B0rcuWeK0ZKHZSVzFTeM+N2eEoN5D1Pr3MAJwogdYxV2jdbwElUEhAay4dksuSGxjAm29neUK5M2k9ygiVoIIhGAFNMsJy+S9Dvm4uqxNWwtgcAxZvap9R0dEsqkcKwbxPhhRF/KQtcFUSSp+AF9GYuBrN2xiEPG0tnQn6boBlTckINFt1XIek3BIeeYnDCQ7jjyoeDoaLqgWPUJG7Xdmga6bSZ1oYYKdseG3vqeFIYGR3IEGnrSbjmjDKdjiArvUSgeXahaWSsLlWeaMD1sXAgxKxTrsRI2HsWS/qyFXheJKIKpzSgEnHVMetNHpy64lJ5CL5QM5BzCWDJWdsmlLCwd/AjKdY+MZdCfs/E69LT4Qcide6fYN1WnN2ORNnV0IYikpKZH7JuqYxqCZ53SmRiAF8bsGq8xXvXIpUwcS0fXBFEscYOIiWqiiNdpLlI+ZZJLmYyUXfoydpLj1FD4DuKYiapHzjE7VgHMp0x60ua86oKTVZ/eTOf7tXTBUCGNZehM1XxGyh6RK9F1wVDeIZ+26F9EwV4hBL0Zi7IbUvMjNg1mpoWa+liNHLBOn2VBDFEkW0Ikpq6jk9iMFS/A0nXCSHacO2XoIhETPILhJETSbjnz6H0iLkNUeI9C8ehD1cpaOag80wQVNp5g6Bp5x0QXgoeqFXaNVVsr9qsLDut6UmRsY9lEgaQtg6G8Q9bUmagFTNZ8Ko2Qr1V5h96GQdFpTtJkLWDvZB3b0Mnbh4wCQwjytkbNi9g7UWeyFtCXnbtI7lwkuUg+Ukpy0/araQLTTgQuSjW/41wkIRKDo1QPiOKYIIpaniyhwWDOYU3e6XgulbUNtg0V+M2O8Tll+sNYcvKaQsfPhmZInR9F1LyQgYzV8vAaukbOSc5rxyF1/z97bx4m2Vnf937es9de1T29zKoZLTNCEmKRAIkQ4xtIbCBgEuzHcbiW7OvLg/PEhIRrHowf49iYgOMlMr6OARuwneQSHIiNn5tLHi4Xm80GIyFjhBEaLSPNjKa7p9faz/7eP05Xd/V0jzRVXadVp/v9PI+eVlfXnH771Dnv+a3fn5Sstn1myg5TJTsRN4klpqZx5nAJgWCt43OsmhvoXPhhsk8KJJae/P1JZlNi64mM+mrbxx/wc6t3Q3Qh0JA7qutpJL169e5wPXp7xf59MowpqrxHsV+I45jFlo8bRDimzlTRGniSuEKxl/Q7DFLKbSVqB8lhUGXjSRWIber87cU1cqbG6dkSmiaI46Q5/rHFFi89NTk2VSCVvMmJQwWeXGwxVbYJI9lngAs6fsR1hwpU8gOq1AURYSxxzGTGUE9sQVuP/ht6ku3ygsEM5W4oMTQNy9ZoeAF5U0/mLckkm1G0DWLJwL1IUSw5Wsuh6YL51S7BhmEPpqYxU81xpOIMnCkUQnDLkTJNN+DcUovLDQ+JTMr2TMHzDpe55Uh5YIesYOnUihZ/N1dH0wV5YWyWLGqJKMVEyRr4OusFgo5Wc9imluxnUZLJypk6XhAPFQhaafk03BBd15LPT9/M6PlRTBgnansrLZ8Tk9e+XhknJaWWLpPj9ZVXCpkcXxMiOS9jjHKcngNUeY8i61xY7XD/uWUurnTxQ4llCI5N5HjJqUmO1/LPfgCF4jmg5zCstn1WOh6Xm0m02zR0pksWE3n7wDgMqmw8oV/nK5l5oxHE8Xq5W5xYdGNC0TZ40fEazW6IG0YYAkzWxT0ETBZNXni8NnBGxNA1io5BHCeqcXFf35eGxLZ0is7gmbe8qVHJmcQycaAabkAowRCszzSK0TQxcC9SL1NYdkymChaLTW9DrW+qZDNRsDfeNyiVnMnzNpyn9kZg8NhEgecdKQ8d3BYIbEOn4pjJh7UhGy5xw3ioXrr+QJBAkDeN5IJYZ9hAUBBFhHGMrWsUHJOg79+XLZO2GxCuZ/oGwTR1LEND0zQESfC1dxo0LVGENDQlDqG4Cqq8R5FVLqx2+Oy351jrBByuOOSsRG3o0YU2i02f195+WDlPirGkN+Dyq48u0Q3CxC5ej/wuNLrkTIO/f/rQvncYVNl4QtuP8IOYFxyrstrxqXcCWuuDgKdKFrV8Ec+Px6Z088qMiBfILRmRk4eKQ2VEZitJqdcjc82kz0vKjQyDFFDvBDzvcInZyrWX6QFU8hYnJvKcvdyiEwTrGSBJJAV1L8DQNM4cKlC5Suns1eh3/G+cLnG0lt8mbjKs41/vBpxf7lC0DV56chJNgziGbhByfrlD2TGv2XmSUtL2k3LFta7PDVMFHl9MMllBJDF1wUzZ4cbpIl4QDXydpZU5NnUN29CJpEQXoG8pIZTouoYuBOaAxz1ec5gs2iw2XfKWmZTryUTZQwM6fsBkMcfx2mDX2V7z3O8ECoUiM8RxzP3nllnrBJyeKW28XnI0So7J2YUmDzy5wtGKo8r2FGNJ20vUp4QQHCpa2KaGF8QstXwanZC2N/iAyyySdtl4z2gc56qKXsR+qmRTy5vbBgFLBEstb6xKNys5k5ddP8lM2eZS3cULYmxT42jV4brJ4lCfW9E2uK5W4NH5JqamUbA3M29tLyIQMScmCgM7j0Xb4KbZEg/PN+i40bpaX5IF6bgRBQdumi0NfNx+x3++4VLLW9iWThDFzDfcoR3/fvGYfnl6gBrWQOIx/SMP1roB352rI0icvtMz5Q2HzAsjlts+wRCZobQyx5NFh8Nlh0v1Li0vJIxkz7/B1AWaEByuOEwO0O8GUMzZvOBYlW8+tUrLC3AMDU1PSmPdMKaWt3nBsQrFnD3Qcfca5TgpFIprZrHlc3Gly+GrRB4PVxwuLHdYbCUNqwrFONHyQi6udDhaczA0jZYb0vLWB1xO5AjjmIsr7YEHXGaVtMrGszInq790s5dxCqXEEILVvEktb41l6WYlZ3L7sSo3TI/mc+sEMYcrDi85OcmTyy26fkSHEIGg4OjceqTCbNmhE8QU7cHORdE2OVbL4/ohQV/vlFkSOJZBwRruekjD8R+VeMyVIw9sU+NbF2LW2gGHqzkmi/1KlubQ88L6HchLax1yprGRKewGIUXHHMqBrBYszhwuM1fv0vVDhNA2MkNBFFGyTc7MlqkWBssUThUt7rphEj9Kxj8sND3CMMYwdK6v5ZkpO9x94yGmioMdd69RjpNCobhm3CDCDyW5q0SwHFPHDxOpWYViFIwyc9HoBqx1AmYrORxjfcDluiqZbei4YcRCffABl1lm1GXjWZqT1SvdvP/cCjnToJwzNkoWF5s+51c6vPT6ibEs3Rzl5xZGMbapc9cNk5w8lOPimosXRNimzvFqjulyDjeIBs6ItP0IL4h46alJllsul9Zc3DDGMTSO1hwmCs5QJWo9Ru34bxGPQW4TW7iWnqGdRh60/RhLE5QcAzeIWG555GpXrHPIeWGVnMmJyTz3n1vmkfnmRs/x8ckctwx5rxUsnUNFG8cyEEIQREmJJQhM3cA2daZK9sD3haZpvOTUJItNn5mihdB0YhmjCQ0ZR0yUctx5cmLsq1WU46RQKK4Zx9SxDEHXjyg52zc3N4iwDLFjvbVCMSipZC7W7RMhBI659REo5ebPFYOTxTlZYv0DF+siELI3ALUnCrGLAbhZoZd5s3SNm6bLHKsVtvQMeWG84ZgMQs8RyVk6miawTD0p9zISp8HUtaEcsn5G6UD2zsNK22el7bHUJzpxaF104tkykDtlreIYynkLzQ1peyGrbT+ZH6ZpNLrhruaF9XqyCpbBC05UN+ZvuX40cE9W/9/gRRFHaznqHZ+2FxHFMbqmUXB0KjkLL0wc3p3sgGfieC3Pa28/3Ccupa07eiXuPDmRif5o5TgpFIprZqpocWwix6ML7R0j8nN1l9OzxbFPtSvGnzQyF+WcSTVnXXXA5VrHp5azBh5wqUjI2pysXkbk9nVxiEYnIFwXhzhUsqnmrF1lRNJklOMgruyVuXIO1LC9Moau4QUR51fahBFUcyambhNEMUvNJPs7U3LGphSyJ0//5bOXN0oLe7GU+XoXxzL4vtPTz3gedpqRpuuComVQsAzqnYD5ZpeVtk/JNtdFSJL7ZdDzsCVQUc1tZMhMQ6OWTz7PYQIVjW5AvRtQy5sIASWHDRESQ0/20bVuMHRm/ngtz9GKk9lxJuO1EygUirGmP9V+dqHJ4YqDY+q4QcRc3aWaNzORaleMN2llLoq2wZnDJe4/t8Ji00tKs9ab4BvdMBlweWTwZnVFQtYG6/aLQ0wWrW2lWXHMrsUh0hDJGPU4iLRUFvOmhhdFLDb9LWJCtqEzVdI5u9CkVrAGliPvZ9Tnt+UFXFzt0O6JWQhBKCW+H1NwdNp+8Iz/fielu5ypU86bLLc8Zio2OUvjxukSRcfYlQpgL1Bh6hqPXW6tO/5Jj155vUdvmECFlNDoBBi64MapIh0v2sj4522dxZZHoxtsZGeHQQhBwTawDQ1D18YmA30tqKeDQqEYiO2p9uTBfXq2mJlUu2K8SStzIYTg1iMVWm7Ik8tt6t2A3gxGTZAMuDw8PmVkWaPfaLQNbduA4XEbrHulkXvlHJwgina13jRKTdMaB5GG2EIniLGMpB9mqeVScsx1Vb2YphswVbIxdW0o0QkY/flteSGPzjfXS9IgiCThundQcHR0TePR+SYvOzV51UzLTkp3AsHhSo6OH3F+pcvJyTwTBYswlrtSAQyjmDU3yfx4QUTJMTcc3uWWR8sLKefMgR1/Y105r+tHLDY9On5EjERDkPeTQKlj6Bj6cPtkvRvw5FJriyrkkYrDyUPDqULuNcpxUigUA5P1VLtivEkzc7FFznnNxVs38o/WhpdzViT0jMYnFltIyRaVusp62c8N08WxEVtIcxBwGqWmaY+DSENswTF0zsyWWGi420ohp4sO3SF7nNI4v/VOIghi6QLDMmh0IyIZowuNkqMThJKnVjrUO/5VHaerZe8MLenFOlxJ+qSeWunset/RNcFqK3Fs+uXTbUPHLupDq/XZhsZE0eS7l7oIBJW8Rc7Q8EPJfN0llpLbjpaxr7I/PxP1bsBfP7HMuaUmLW8zw3t+ucVCw+Nl10+O/R6sHCeFQjEUmqYpyXFFKqQ12LHHqOWcFQlCCGoFi4XHvY2MSNlMlMTOLrSo5k3uODkxNuc5rRK1tEpN92IcRBpiC5auceN0cVsppBfEyb28m96eEZ7fThBT7wZoInm+5W0dXTOIYknTjYjjmFgm73smrpa9m6nYzJRs1twAL4hBsKtyt4Rn+fuGEDdJ9kOTcs4kjiV+GBFGEMuk3FlokLfMoT63715q8PVzyyw2XVwv2ughc2ydhWbikN51/fjsETuhHCeFQqFQjBVpZgJ6jFqGW5EYRqvtxGifKtk0OgENN8AQSSmvQLDW8TlWzY2NYTTO84CupH8chJRym5z+uI2D2CY6cUUp5LD3cVrnN2cIwljihzFHq0kpXRDGaEJQsg2eXutimxo549mv3Suzdx0/4onFFh0/4lDB3siQLTQ8Wl40VIYsimWiztcVLDZdbDPpFZJS4gXxrtT6HFPjeC2PLpJje7HE1gTTZWddKXHwoFXLC/naE0s8vtDC0DWqOQvTEAShZK0b8HinRcle4raj5bEeB6GeGgqFQqEYK9LKBCjSpWfQHq3msE1txwzDOKnq9UhzHtBODFtq2hsHsdryccOIlhsSIdERFB0Dx9CfdRxEGmIVVyOt+zit82saOpW8yfxalwtrHaQUGz2QQkhkLCnnbEzj2hy9XnBGSsn5lQ4dPxpphszQNcqOiS4Ej7aaPHa5uyGffrSa42g1R8E2Bs4MRbGkVkzUDy/VXdA0LJLJxavdJJNcLdgDO2RrHZ+HL9VBSmZK9vpnlJQczpRsnl7t8N25BmvPUAo5DozPzqVQKBQKxTppZAIU6dJv0ArEtgzDuKnq9ZNGidqoS02nihYTRYu/fmKZQ0Vn3SgWhJFktROw1Gpy9w2TVx0HkcpctGehdx8/udQaWU9hWufXNjRmSw7zdZeuF2EZGrqWzGEK1ocEz5adgXt70sqQ9eTT//rcMq4fYho6hi4RIsnsfudS/Vnl03eid36DKJEgtzSBMEDGgIAwkkOd3+W2T9MNqeZNVjpJf7SUIETiROZsnbVOwHLb5/hEYaBj7yXKcVIoFArFWDLqTIAiXdLuTcsKaZWaCiGYLeewdJ2mG2AZGppIMjhNN8DSdaZLO5dBpiGmMDBi8+tuenvSOr+GrpG3dA4VLaJ1Rb2eYW8Iga4LCpY+9CBgy9CQUm5Tm9xNQKHlBVxueAhgsmTjGBpuGLPc9Gi40bPKp+9E3tTww4iWF/H8Y5WkJDQGXUtKQh9daBFE8cAy8pYuQAqWWz45M5Ei1zVBFEs6XkTHDzF1LXnfGKMcJ4VCoVCMLVnrRdrLUqhxYy9607JAWiVqbT+RR/8HN0/z2GKLy3WXFSmxhOC6yTzXHypiaGJb5iItMYVrod9hG1VvT5qlvDlL58REPlGsawcbmblaIekVyg1x7fYCCqttn9WOv01tspa3hgootLyQiytdjtRyGBqstkPaXoCh6ZyYzBPEkovLXVpeOFDpWyeIsXWdqZLFUtOnnDPIGcm8u6Wmz1TJwtIGl5Gv5S3ytsblZoBj6oSxJFp3TiUSN4yo5I2rZubGhew8jRQKhUKhGGOei1KocaLfoL201sGxdHQEERLXjyg65oHpTUuj1LSXuTg2kefYRI7VToAfxFimRi1vIqXYcWBvWqViz0aaDlsaJYC93h5d13D9iOohG7GeHfPCCMfSKecGF1soWDqWqXH/uRVypo5talgiyT4tNj3Or3R46fUTAwcUGt2AtW7yuTZdHzY06iQSSTVnsdr1aXSDgRyncL0s8cxsmcsNl3onoLUuIz9VspgqObhDyMgXbINjtTxrHZ+WG+BH8caKrfVM07FagcKYB8rGe3UKhUKhUGSAXmS95QY4lo6ja0RSMrfW3btSqDGgkjM5MZnn/nPLPDLf3BiQfXwizy0H5Bz0GHWp6ZWlkJMFe8vP3XDngb1pzkV7JvodNoncJhYyModtRCWAhq5RdUwqjslqx6fRCQjiJDM0Vbap5izE+vsGX6LADWLWOsH6xG1gvWfIMfWhZMMBXD+i5baJpaDomBs9b2udkHo3wBxinle/jPwNU8VtpYVeGG9cz4MQSzh5qMD5lQ5LDY+8ZaBpgjiWdIOYQyWL6ybzDCECuKcox0mhUCgUil3Qi6wvNj1iKZlbczdKccp5k24Qp1YKNW7UuwHnlzsULIMXHq+hrTfXd4OQ88sdyo55oJynUZaaDlsKOUjv2ShLTXsOmx/FXFjtrA/A3bwvZkrO0A5bGiWA/ed3p7lT83V3aPn05bZH0dZxw2jTbxKgCSjaOsttb2AHsuQYRFKy3Aq4fqq48bqlC6x8Moh6qmxTcga7/rbJyFtb//2wJbe6lqgUnp4pcbjisNj0CGKJqQlOl20KtokmGHhg716jHCeFQqFQKHZB2484v9Jhpe0TxTElx9zouVhueeiahrEsOHWomKl+rUHpL806Us1v+VkNayS9NAe5h2zY3p5rdbjCKOahp1sjKzU1dA03TO6Nne6LtU7AdNkemwG4/ed3vu5Sy1vYlk4QJSIaw/ZOBWHEXL2LqQuef6Sybf7WYstlvt4lCCMYYH/oDZxuuCFrXZ+8ZWBoySyqjh9ulHAOut50x0EIKjmTM7Mlmm5IEMaYhkbJMZivu0Nn3vaS/buDKxQKhUKxB/QMIxlLpkqbhpxt6NhFncvN7lCGUdZIu5fmoPeQwXC9U9diCFfzFn93qTFS1b2eOtti0+P0TGnj9d59cXahSa1gDqzOluZ1lkZvmh9Jul5ENW8hhMAxt64pZxqsdXz8aLAatSiWHK/lMTSN+UaXthtslgBqcMOhIoerzlADcNM4D/0De5dbAeWcQck2CeKY5Vawq4G9e8n+3cEVCkWqHOTIryL7xHHMYiuZJeKYOlNFC22IfgDYahjtxLCGUdZIs5dmLOS0x4Rheqee0RCu5XlqpTPyDE6/Otti06OcMzC1RJ2t0Q2HVmdLu2dr1L1pli7IWwbdINpRpKEbRORtY2AZ7t4A3LJjMlWyuNz0CcII09CZKdkbjuWw8v9p9Oj11ttTF+wXndjtevcK5TgpFIqBUZFfRZa5sNrhG08sc26xjRtGOIbOqakCL71+kuO1/LMf4ArSMoz2glEGQNKa4/RcymnvJ65mCKeVwUlLnW0v5oWNsjfNNHQOVxwWmu6ODqSpC2ZKDqYxWM/Qlp6sqRJHq1tFHOYbw/Vk9ZNWj971hwqJKmQYYxlJSeFC08vEuALlOCkUioFQkV9Flrmw2uFPvnmRCysdbFPH0gWr3YD5x5e5uNrln95xbGDnKS3DKG1GHQBJa47TcyWnPa7Uu8E2Ge4jVYeTh55dhnsnQzitDE5a6mz919lsxdkm4jBu88IKls7xyTxBHCMl2xxIIeDEofxQA5H75f+F0IilRBOCVelRylm7lv8fZWClt975ustfP7mSDBdmXURdwMnJQibGFez/HUahUIyMLEd+VWmhIo5jvvTIZR673ORQ0aFgGxvyvW0v5LHLTb5y9jL/7CUnBirbS8swSpM0AiBpNZXvhZx2VvaHejfgr59Y5txSCz+QxEg0BOdX2iw0PF52/eTAn1taGZy01Nn6DfCvP768Td775KHxMsD774uWGzBZ2JwP1Q3CXc03q+RMagWTB55c5txSZ0Ns4fqpPK++ZXZXQcxUK0skCAmi73PLCspxUigU10xWI7+qtFABcLnp8d2nGxTsrdPpe/K93SDk7y42uHyzx+wOGZOrkaZhlAZ7MZh0lE3laZdmZWV/kFLyd5fqfOvCKt0gxA8BGYPQsIzk7yjlDO46NTnQ55ZWpjBddbbeL2GbvPc4su2+iJLr7EgtN/R9AUkG/Stnl3D9mFuOlLENDS+MWW35fOXsEiXHHKr8OI3ASm/fAbjrhskd5d7HNfDaz/hYNgqFYux5rgYp7gZVWqjosdr2aXgBJ2p5JJIglERSoguBaQiqeYuLqx1W2/5AjhOkZxilQdoBkFE3ladl2EO29oeWF/Ktp1a5tOYSyRgSnwm5rqKmi+Tntx2p7NhrdzXSdHDScKS3GOCnJreVAM43xtMAH/V9Eccx959bZq0TcHq2vOVnU0WHswtNHnhyhaMVZ6AMelqBlS37jiT5Dzb+f1wDr1cyvitTKBRjx1405Y6SLJcWKkaPrgl0IWj7IfVuQNuPkFIihKBg6Rh68vNhBzCO2jBKi70IgIyyqTwtwz5r+0O94/PoYoumF5I3dUxdSxwnAUEc0/RDzl5uUe/4AzlOkI6D03/sUd4X/Qa4EGJbCeA4G+CjvC8WWz4XV7ocrjg7/vxwxeHCcofFls9Meef37ESaYiEbA5FXkoHhfiSxdMFUyWa6nBu7wOtOjNcVpVAoxpo0I79pkNXSQkU6zFYcKjmDxy63mCja5E0dXQgiKWl4ISstjxunC8xexRC5FkZpGKVF1gIgkI5hn7X9oe1HLDc9DA0CTdB0g6SxnsTZk1HESiui7UdDHT9Nx3+U94XqeUtwgwg/lOSu8rx1TB0/lLjBYNdD//mVyG0ldbsRC/GCiLMLDRYbPlKwUdI8X/eYKrtcN1EYq31nJ577nUChUGSGPalZHyFZLC1UpEfRNjgxUeTsQou2F2JqAtav37YXEsWSExPFsTCS0yRrAZAeozbss7Y/CAlBJGn5EflYYGqgaQIZS1peRMcPcXQdsYtxYcrxz07Pm2PqWIag60eUnO1/qxtEWIbY8Rw9E73zu9L2We34NDoBoZQYQlDOm9Ty1lDnN29qrHV9Hp5rcqhkU7IMDE0QxpKWH/LwXJNq3hp4IPJeM953h0KhGDvSLOkYNVmMrCvSoxPEzFYdXnJygieXOzS6IRKJQFCwDW49XGam4gw8kDNrZC0A0s8oDfus7Q85S8MxNOrdgDjySeYpJzknXSSDmCtlk5w1HutNiy1y5GVnW4/TQel5mypaHJvI8ehCe8fSzLm6y+nZIlPFnTOqV6Ng6dimzjfOLZO3dEqOubE/LLc8Lqx0eOmpyaHGCqx1A3KWviFD3vtPADlLZ62TZHh3cgTHBeU4KRSKgclKL0dWI+uKdAijGMfQueuGQ1w32eHpuosXxNimxrGqw2w5T3eIgZxZJEsBkLTI2v5gGjqlnMl802WtG7JVwzkp2So75tjNCxs1W+TIzy2PbB5Q1nreNE3jJacmWWz6nF1ocrji4Jg6bhAxV3ep5k3uPDkxkDBED7mu3BDH4AURQRgTS0nc2xqHSGs2usnA25tnyzTdgLYb0kWiI6jmTY5V89Rdn0Y3GLhHby9RjpNCoRiKLJR0ZDmyrhg9/QM5T8+WOT5Z2FK77wVxkmUYkwxDP2n0XGQlAJIWWdsfLF1gr5cx5SwdHbHhMcRIYsA2NSx9PNa7J4xwHlDWet4AjtfyvPb2w9x/bpmLK138UGIZgtOzRe48OTGUFHnbj/CDmBunijy+2OL8chtfSiwhmKk43DhVxPPj4c6DBMfUqORyNN2QIIoxdY2SY+CFMfXuwMvdc8bjk1coFIqUUJF1RY9tAzlNA/o+/nHLMPRIs+ciCwGQNMnS/uBHSR5gsmgjgDCWSbZFgKEJJInv4EfDNzllQRQhrXlAWet563G8ludoxWGx5eMGEY6pM1W0hso0QXIeGm7AWjfAMXVOz5YQ6710Xhiz1PaJ5ODnoZwzqeZN5usumgZrnXDjOqvmDeIYJgom5TG653bi4O6WCoXiwHDQI+uKhKxlGCBbPRdZJSv7QxjF6JrgaMVBCMFaJyCMIwxNp1YwiWKJpomhDfusiCJskSNHbAuADJsZylrPWz+apg0kOf5M6JpgueXT8UOOVLdnrC6tdZDrPWWDULQNjk3kefD803S8CMvQ0XWIIriwHJG3dW4/fmzsAznXtLoXvehF17yBPPjgg7takEKhUKTBQY+sKxKylGHIWs9FlsnC/iCEoJIz6XoRax0fNDDQQYOWG1LJW+Tt4Ry+LDnoaWWGstbz1s/oM4XPkrUcUrpRrNdSSilxwxARrsvz9A/DHXOuaZd44xvfmPIyFAqFQqHYzkHu7cliz4UiPZJSJ4vLjSZoYMYCYYKMSZwnL+RoLTdwqVPWHPS0MkNZzEjD6DOFUSypFW30bsBSy6XkmBiaRhjHNN2AUi4pp4viwbyclhdyYaXNZMGi64VcXHPxoxBLNzhWdZgsWFxcbdPywuyLQ/zbf/tv016HQqFQKPrIQq9B2mSxt2eUn1tWey4U6VCwdCqOSRTHHCo6mIZAQxAjCULJUsulkjOHkol+Jge9mje5uNplIm9RyVvP+V6UZmYoSxlpSCdTaOgaVcek4ph9c5xCDCE4VLKp5izE+vsGodENeHKlzWMLDS6uugSxBAmuCHj0ckgnCJGC/amqt7a2xqc//Wkef/xx3vnOdzIxMcGDDz7IzMwMR48eHfUaFQqF4kCRlV6DNMlS6VCPUX9uWe65UIyeThBTzZncPFthsdml3o4IJRgC8rbOzbMVKo458ByyfgddSrllLlIUSy6tdbmw0sFb//3P9V6UdmYoKxnptDKF/Y7pjdPFHcU3hnFM4zjme5caPLXcwdA1SvbmANy2H/H4Yoc4Tt43zgy8237729/m9OnT/Pt//+/5jd/4DdbW1gD4kz/5E9797ncPvZBf/dVfRQjBv/7X//oZ3/epT32Km2++GcdxeP7zn89nP/vZoX+nQqFQjBs9h2FurYsmBI6poQnB3FqX7zxdp94Nnuslps6VBoFj6uvnIjEI2l7I+ZU2Uo5PQXzvc5uvuxRsg6mSTcE2mK+7Q39uPQNmteMjkXSCkKYbJJFZJKsdn8mSNZY9F4rRE0YxtqlzdMLBjWKerntcXGnzdN3DDWOO1hxsUx84A9lz0FfbPo8vtnj4UoPvzjV48PwaX3l0kaeW2+Rtg5ny7q/pUdHLDM1WHNpeyFLLS/aLqjOSoEovI13NWxRtY+ycJhislHcQeo5p77PWEOQtAw2xsb8N45h2/ZC5hksYQ61gY5kGmq5jmQa1gk0Uw1zDpeuHAx13rxnYcXrHO97BT/zET/Doo4/iOJsKHq997Wv58pe/PNQi7r//fj7ykY9w++23P+P7/uqv/oof+7Ef46d+6qf4m7/5G974xjfyxje+ke985ztD/V6FQqEYJ3oOw2LTpeNHPLHY4nvzTZ5YbNHxIxab7tg5DGmQlkGQFmk5ej0DBuDrjy/zt0+t8Z1Ldf72qTW+/vgywFj2XGSROI5ZaLg8tdxmoeGOZdTb0DVWOz73n1slCCSnp4u88ESN09NF/EBy/5OrrHb8gTOQBUvHMjX+9uIai00fx9Kp5k3aXsBi02Op5VOyDQqWMVbBi15m6M5TE9x5ssadpya47cj4ZaLTIs1S3jQc04WGRxyBYwq8MCKMIsI4+eqFEY4piOLkfePMwKV6PSfnSo4ePcr8/PzAC2i1Wrz5zW/m93//93nf+973jO/94Ac/yA/+4A/yzne+E4Bf+ZVf4fOf/zy/8zu/w4c//OGBf7dCoVCME20/4sJyh5W2TxhBOWdslKAsNn0MHcylDqcOFfe1GEDWenv6Hb0rS51ypj4aEQeRCE5pQCxgQCXgPSdLPXoXVjvbBogem8jxklOTQw0QTYucIbi01mG56XPLkfLmDyyo5i2+e6nBfL1Lzhj8PPfUzsS6vJkXxLTdiJylE+8wWHZchEmyoIaYFmmX8o66ZDGUYJkapibo+hGRjJEkl5auaRQsgyCWhGMeFxz4arNtm0ajse31s2fPMjU1NfAC/uW//Je87nWv49WvfvWzOk5f+9rXeMc73rHltR/4gR/gM5/5zFX/jed5eN6m97rT2hUKhWIcCMKIubpLJCXTpc2Mvm3oTJV0Ljdd5houQRjBPjYWstbb03P0/Cjm4mqHeicglBJDCCp5k6mSM5Sjt2XQ56nJbQ7ZfGO4QZ9pk2aP3qgdsgurHT777TnWOgGHKw45S6frRzy60Gax6fPa2w+PjfO01A7wgpjZis1q16doGRt9SC0/ZLZi0/UjltoBM+VrL99s+xFeEHH7sSorbY/Flk+zG9DwfI5P5Kk4Fn4Q0w0i8lay74xL8CJLDvqo2Qv59FE6psdrOUqOwWrLx7EMwpgNx8nQdLp+SK1ocby2/W8ZJwY+G294wxt473vfy3/7b/8NSE7q+fPnede73sWb3vSmgY71yU9+kgcffJD777//mt4/Pz/PzMzMltdmZmaeMdP1gQ98gF/+5V8eaF0KheK54yA/CP1I0vFDqlcpUcuZOmsdHz8a85DcLsnaPBVD1/CCiPMr7R0zhasdn5mSM7Cjt2XQpxAbRmuPcYn695OmqMeoHbI4jrn/3DJrnYDTM6WN10uORskxObvQ5IEnVzhacdC0595Jd4MITWhcP5VjrePTdENiKdGEYCJnUc6ZLDY93GCwEtae45/r3U8SNCHQhUYQxhi6wAvjLfLT4xC8yKKIziifb1mTTz85mee6iTwLdRct2rxGJeBHEX4Uc2oyz8nJ8QhUXI2Bd9rf/M3f5Id/+IeZnp6m2+3yyle+kvn5ee6++27+3b/7d9d8nAsXLvD2t7+dz3/+81t6pUbNu9/97i1ZqkajwfHjx1P7fQqFYniy+CAcJZYuyNk6brDzHItuEJK3dSx9+AdhFhzTrBkEeVPDiyIWm/4WA7yXKTy70KRWsMibgxmZW9TOkNvUrcYl6t8jzXlAaThkiy2fiytdDlccpJQbzoGuCWxD43DF4cJyh8WWz0w5PTvlWnFMHWu9DO9YLb9tvS0vxDLEjlnaZ8LQNdww4vxKhyiOqeZNDhUtJDBfd/EiyWTBRu+rD32ugxdKdTOh14v05FKLS2suXhhjGxpHaw7XTRbH6hy4EdxytMIjCy0WG10QyRxdKQAJU+UcNx+p4EZQHI840I4MvLRKpcLnP/95vvrVr/Ltb3+bVqvFi1/8Yl796lcPdJxvfvObXL58mRe/+MUbr0VRxJe//GV+53d+B8/z0PWtN+Ts7CwLCwtbXltYWGB2dvaqv8e2bWzbHmhtCoVi78nig3DUmEZiYF5ueDsOHjR1nemyjWkMZ6xkyTFNe57KKB3IThBjGTpTJXvHz22qZGPq2sAy0b2SxZW23zdPJSkBLOdNannrOY/695PWwN60HDI3iPDXGyourna2ZHBKjkElZ+GHcuAMTlpMFS2OTeR4dKHN6ZnSNgdpru5yerbIVHHn83818qaGH0YsNr0tjv/hikMUS84ttsmZAlMXuEH0nAcvsjawF/bo+SY2v46jflAQRgRRzLGqQywj6p2QaH1fr+QNjlYdwige+1L0oVf2ile8gle84hVD/+JXvepVPPTQQ1te+8mf/Eluvvlm3vWud21zmgDuvvtuvvCFL2yRLP/85z/P3XffPfQ6FArFc08WH4RpULB0TkzkCSNJFCdlXkEUY+oa02ULTWhcN5kfKsqbRcc0rXkqo3YgwyjGMXTOzJZYaLjbBkZOFx26QTRwZqhg6dimzjfOLZO3dEqOuZF5W255XFjp8NJTk2NTspiWqEdaDplj6sQy5onFFkITFK3NuTKr3YCVtk/e0gfO4KSFpmm85NQki02fswtNDlccHFPHDZLeyGre5M6TEwOXFXaCGFvXmSpZLDa9pNRU09A1Qd7SOTbhEEXw9FqXkm0+58Ng90KMZZSBlb3KxB4q2Bv7+kLDo+VFY7Wve2HMU0ttvFAyVbApmCahjDGERt7S8EO5/vPxyKBfjWu6on77t3/7mg/4r/7Vv7qm95VKJW677bYtrxUKBSYnJzdev+eeezh69Cgf+MAHAHj729/OK1/5Sn7zN3+T173udXzyk5/kgQce4Pd+7/eueX0KhWI0jPLBkpZhlDV6JWrzdZdzS138SCJlIjqw2PQ4dag4VJQ3y47pqFWz0nAge5khS9d2HBjpBXHSEzJEZkiShI6lFL0Xtn4vxie03C/qYRvaNoN22L6YtByyQwUT29R4YrGzRaXO1AW1XKJSd+vREocK42F4Ahyv5Xnt7Ye3qQCeni1y58mJoYQsevOhzsyWudxwqXcCWuuO/7Fajhcdr7LWDXj+sQqTRfs5L+9NS4ylx6gDK1nLxF75O0b1nA/CiKWWz0rHx9Y1EAJDM0BKuoHEi3wMXSQZpzHmmj6h++67b8v3i4uLdDodqtUqAGtra+Tzeaanp6/ZcboWzp8/vyVy8vKXv5xPfOIT/MIv/AI///M/z0033cRnPvOZbQ6YQqFIlzQi9lmSn94LhBDYhkATEMvdyU8rxzSh39CYrSRZoLYXouuC2YrDfH04lborxSzypgF9t8Gw/SBtP8IPYl5wrMpqx99i0E6VLGr5Ip4fj83n1jsPTyy2kJJtBq0QcMN0ceDzkJbKYjeUHKnmudz0Ob/S4VBx05FeaiXDhWcrObqhpDgeSScgcZ6OVhwWWz5uEOGYOlNFa2gBi37H/4ap4jaH1wtjirFksmiPxXWWlhgLpBNY2dKruEOGbNwysT1G/Zzv+jEdN8QPIqSU6/PuJDFJ2WwQxnS8kK4/3s/5azqT586d2/j/T3ziE/zu7/4uH/vYxzhz5gwAjzzyCG95y1t461vfuqvFfPGLX3zG7wF+5Ed+hB/5kR/Z1e9RKBTDk2bEPivy02mRlvy0ckwTeoaGqWs8drm1Y8/QMIZGWmIWvc9tqmQzUbC2XQ+xhKWWNzafmxCCWsFi4XFvQ967bBq4QcTZhRbVvMkdJycGPg/9juls2dl2HoZ1TMMoppa3eOWZKR6+VOdywyOIJKYuODGZ4+bZMpoQuzq/aYmxCCEo2Aa2oWHo2q6Ouc3xv0K98bkWgriStMRY0srg9J5vq+u9ilcGFIbtVUxzX0/jOR8jCWKJaWgYmkajGyKJEST3BQaEkSRmfLLoOzGwC/qe97yHT3/60xtOE8CZM2e47777+OEf/mHe/OY3j3SBCoVifEjrwZI1+em0SEt+WjmmCWEUs+YGNLoBXhBt6xlqeSHlnDmUoZGGmMWVn9uV10MQRmP1uUkpWW0nCnRTJZtGJ6DhBhgiKSUTCNY6PsequYH2h/4S1q+fW0bKpA9eAkLAycnCUI5p7/wWbJPvPzPNaifAD2MsQ6OWN/FDSdsLhz6/aYmxjPq4WVOxTEuMJa0MTsHSsUyN+8+tkDONbRmy8ysdXnr9xNhkYtN6zmuAYQhCXwIxZWdzDpkbRgSxpGgIxmM3uzoDO05zc3OEYbjt9SiKtineKRSK/UVaD5asPbjTIq0IonJME3RNsNry6PgRR6qbvSC2oWMXdS6tdYjjeIvs8iCMWswia59bb384Ws1hm9qOvV67LgmVSVuX0ICYTSWxIbjy/E4Wtirwrna6Q5/ftMRYesdtuQGOpePoGpGUzK11d3XcLbLWdRcviLFNjaPV0chajzLzlpYYS5oZHLF+oQrR61lky/fIwc9FWvtDWs/5nG1QsAzabohl6kgJ4fqJsE0dESTHy41BOegzMfDqXvWqV/HWt76Vj370oxtS4t/85jf5F//iXwwsSa5QKLJFmg+WtOWns0BaEUTlmPbzLH/jEAbMlqOPUMwia59b//4gENt6vYbdH7aUsN4wuc0hG7Y3La3zm1bEvnfcxaZHFMecW+psqG5OlSy6QTwakRe5+XUUstajzpClJcaS1v7b9iO8IOL29V7FKx29as7CC6KxCTim9Zw3dY1DRWtjkLIXxkgp1/t5NbANJovJ3zDODLy7f/zjH+fee+/lzjvvxDSTCz4MQ37gB36Aj370oyNfoEKhGB/SLvlKS346K2zp5VgXL+g3CHaTYVCOKUSxZLJooXcFi00X20x6Q6SUeEFM0TGp5c2NB/s4kKV5VmkanhslrDs4ZLtpgk/j/KYVsW/7yZDap1c7LLV95LpoTCxhoelyqGBh6IJTh4oDn4ctstbF0clap5F5S0uMJa0MTn+v4mTR2ravx/HwvYp7USJ8JcPex7ahcd2hAn4Ys9IJiGNJJEEXiXZoLW9y3WQxcaLGmIEdp6mpKT772c9y9uxZHn74YYQQ3HzzzZw+fTqN9SkUijFiL0qHRi0/nSW29HI8vpxEfvtKkk4eGq6Xo8dBd0wNXaPsmOhC8GiryaOLLkEYYxoaxyoOR6s5CrYxNj1DPbIyzyptwzMtcZNRn9+01huEEecWW1xa6+KYBkXbwNAFYZT0Yl1Y7SCRBDdMDjRANO0M2aiPm1amJa3jbutVvMLRC6Ld9SpmpUTYNHRmyg4XlruYWoiZMxBIJAIhIWcazOxiwPteMbR1cvr0aW666SaAA/PQVSjSUkjKClkrHco0os9vEruTI99y2APsmPYGyv71uRW6foitaViWQCBY7QR851KD7zs9NTY9Q/1kYZ7VXhmeVzIKcZNRnt+01uuFMXNr7np0fjObZekCK2/RrYfMrboDDxBNM0OWllx2WpnYNI6btYBjWvdx3kzuC9MQzFYdFhv+hi01XUkEkUxdG1gNca8Z6iz/p//0n/j1X/91Hn30USBxot75znfy4z/+4yNdnEIxTqSlkJQ1VMlXeqQlR67YpO2FXG64CAGTRRvb1BLRgpZH0w1pe9vFj/YbaQ7OzKrhOUrSzLzFSPSrzGvShUYk47ERRchapjCt42Yx4JjGfdwJYqJIIgR4bsRsxUHTII6h4wbkcwZhGA+shrjXDOw4/Yf/8B94z3vew8/8zM/w9/7e3wPgq1/9Kj/90z/N0tIS/+bf/JuRL1KheK5JSyEpq1RyJrcdKY9sAKMiIS05ckVCywu5sNLmSNXB1LXEUXJDNCG4bqKAH0VcXG3T8kJKzv69n9MenJmm4XlprYNj6egIIiSuH1F0zLEyPNMsJas6JkEsWe36FK1NOeeWH+KYGqY2+HlOK0OWtUxhmsfNYsBx1PdxEEbUuyEl2ySOJUtNjzCOMTSNyaJFyTapuyFBGA1UarrXDLyy//P//D/50Ic+xD333LPx2hve8AZuvfVWfumXfkk5Top9R5rR2ayyU/Zt4QBm30aNGlSbLo1ukAxmreZwDB0vjDcyerah4YYRC3WXRjfY147TXlxnaRieJybz3H9umUfmm/ihxDIExyfy3DKGgas0DOVyzmSm4rDS9jcc/1hKNCGYyFn4UcRk0aK8S7GFKxk3sYWsksUe01Hex34kWWl7dP0IS9c4XMn1OpyQxHT9iJWWhx+NjzjPTgw1x+nlL3/5ttdf/vKXMzc3N5JFKRTjRNrR2ayhsm/poQbV7gHrNooQYts5lpJnVSvfD2TxOqt3A84vdyhYBi88Xtso8ekGIeeXO5Qdc+z2nVEbykXb4ObDFb5xbhnH1KjkcpuqkGFEjMaZ2crAz6GsiS1kmYPcY2pq4IaJc1TMmXT8RBlSCMhbOs2uj162GfMWp8EH9N544438t//237a9/sd//McbYhEKxX5CZQE2uTL75pg62roBeriSo+2FnF9pI0cx/OMA0ovQrnb8HX++2vGZLFkHJkI7aso5k2rOYrW98/ld6/jUcoNH7LNG1q6z/n3nSDVPrWBRyVnUChZHqvmx3nd6hnI1b1G0jV05CUIIbjlS5nmHy4DAC2LcMMILYhCC5x0uc8uR8lC/o5chm604tL2QpZaX7PNVZ1fBsLSOm1WklLS8kLWOT8sLx/KaTYtg3UTqhjHzDRdNQM7U0ATMN1zcMEb2vW9cGdjt/eVf/mV+9Ed/lC9/+csbPU5/+Zd/yRe+8IUdHSqFIutkMTrbY9QqgCr7li4qQpsuRdvgzOES959bYbHpUc4ZmJpGEMc0uiFhLDlzpLTvr92sXWdq39mkkjN52fWTzJRtLtVdvCDGNjWOVh2umyzuyhFJq3c1iyVqaXDQBaZ6maScqVF0TLp+RDeM0BAcrjg0ugGi733jysA7zJve9Cb++q//mvvuu4/PfOYzADzvec/jG9/4Bi960YtGvT6F4jknq3Xa9W7Ak0stLq0l8rS2oXGk6nDy0PAPV5V9S58sNhFnBSEEtx6p0HJDnlxuU19/UEsSuffnHS5zy+GD0auYpetM7TtbqeRMbj9W5Ybp9Od6jap39SCXqMFmiXvLDXAsHUfXiKRkbq17YErcgxhsU6dWsBGAldeSzXd9E54o2Fimvv8yTgB33HEH/+W//JdRr+VAcdDnAWWJrEVnIdmk//qJZZ5cbm+ZLn9htcNCw+Nl10+O1URxxVZUhDY9tkTs+4IKR2u7j9hnjaxcZ2rf2U5ac70OsmGfFr1S08WmRywlc2suoZQYQlDOm3SD+EAITFm6YKJgIaTPatdntRUQyhhDaEyUTCqOxWTBwtLH+xwcXPf/OeSgp2uzSJais1JK/u5SnYfnGuRMg0rO2HD0Gt2Qh+calHIGd52aHHiTzmr2LYsc9AhtmqQVsc8iWbjO1L6znVEGX5Vhny5tP+L8SoeVtk8Ux5Qcc+OZvNzy0DUNY1lw6lBx7O/F3WAaOpWcyVPLHXw/5lDJRheCSEq6XkhThJyaKmAa430fX/MnpOvX9odEUTT0Yg4CSpEsu2QlOtvyQh6Za2JogqmSvfG6behMlXQurUU8cqnJbUcqA0suZzH7plDsRBYchiwzSsNe7TtbGXXwVRn26RKEEXP1LjKWTJU2HX/b0LGLOpebXebr3bGfX7Rb8maSOZYSbEtjqeURRhJDF0wWLKRMym7zY97kdM2fkJSS6667jnvvvVf1Mg2JmgeUfbJgbDW6AWtdn5mys+PPq3mLy83hZ9VkKfumUCj2njSqKrK674y6LD+Nkjpl2KeLH0m6XkT1KuImOdNgreOP/fyi3dIJYnptiH4gOVLJYRqCIJQ0ugG2pRNGkk4QU7TH13m65jvgG9/4Bh/72Mf44Ac/yKlTp/jf/rf/jTe/+c3UarU017evUMpAij3jGfZfIZ7559dCVrJvCoVib0mzqiLNfSeNvuNRO5BpldQpwz5dLF2Qtwy6QbRjsLIbRORtY+x7e3ZLEEZJULdkAYKWG27cFycm80gk9a4/9g76Nbt0d955Jx/60IeYm5vjHe94B3/6p3/KsWPH+Gf/7J/x+c9/Ps017huUMpBiLyjnTKp586qzalbbPrWCuetZNaOcT6JQKLJPf1XFbNkhlpK2FxJLyWzZGcm8pTT2nXo34KGn6zxwboUHnlzlgXMrPPR0nXo32NUxv/N0nfm6S8E2mCrZFGyD+brLd4Y8dn9J3XLLw7F0ankLx9JZbnmstH2eWu7Q9gdrmeg37HfioBj2aWEaOocrDqYuWGx6ybDiOBlavNj0MHXB4bIz9r09u2XTQbc5WstxpJZjtuRwpJbjaC1HLW/T8aKxd9AHzoU5jsP/+r/+r3zhC1/gO9/5DpcvX+YHf/AHWVlZSWN9+4p+ZaCdOIjKQIrR05suH8aSpZaLF0ZE65v0UstNZtUMMV1eoVAonoleVYWlazy+2OLhSw2+O9fg4UsNHl9sYenaRlXFuJCGg5OWA9krqQujiMmCjZTQ9SOkhMmCTRBFmyV1A6AM+3QpWDrHJ/NMFCymShauH7Ha9XH9iKmSxUTB4sSh/L4XN+k56Ksdn4urXS6tdplvulxa7XJxtctq28+Egz6U5XTx4kX+8A//kD/8wz+k0+nwzne+k3K5POq17TuUMpBiL+hNl2+6AU8utWl0giREEgMau5our1AoFFcjjGIabsBaN8D142TA8LrIwGLTp+GG1PLm2FRVpNV33O9APna5yWLTw48klp4I9kwU7KHK8nsRe9vUubjaoekmzpgmBCXHwDb1oSL2PcM+iGOkhHonoCVDDCGYKlkIwYEw7NOiX9yk5QZMFmyEIHF8g5CiYx4IcRPT0CnnDZ5caeP6EZNFm4Kp4wUxF1Y6OJbO4dr4O+jXfMf6vs+f/umf8rGPfYyvfOUrvOY1r+G3fuu3eM1rXnPNinsHHaUMpNgr0pwur1AoFDuha4Lllk/HDzlSzW+8vqno2UHGEl0bj2dcWn3HPQdyruEyv+oiBRuG8nzdY6bqcKTiDOxAWrpAE4JzS21ylk7RMjA0QRhLVrsB3brLsVpu4Ii9MuzTZ5u4SZT09hyp5cZa3GSU9FT1LF1jesKm6Ya03RBNCK6bKLDa9feXqt7hw4cplUrce++9/O7v/i7T09MAtNvtLe9TmadnJqvKQIrsoWbVKBSKZ2P0ogjPku0Q49O/kFbfsa4Jnl7tcm6pTcExKPU5OC0/5InLraEcSEPX1oeZxyB1JGz8h0xe1wVDlfv3bJMnl1oq2JYSB11UqRPE2LrOsVqOIJRUaiaaEMRS4gZx4vRr2v5R1VtdXWV1dZVf+ZVf4X3ve9+2n0spEUKoOU7XwEG/eRR7Rxbk0xUKxXPDqFXfolhSK9ro3YCllkvJMTE0jTCOaboBpVwiShPF4+E89fcdO+b2yplh+46llKy0Pfwo4lhuM/Nm6oJazmK17bHa8YYSychZOrNlB0PTaLkhXSQ6glrepOTo5EZRTic3v+5Cx0OxAwf5mRxGMbapc2a2zOWGS70T4MUxhhBMl22mSg5uEI1NKe/VuOZP7y/+4i/SXMeB4yDfPAqFQqF4bklDNtzQNaqOScUxWe34NDoB4XqvzKGSTTVnIRguI5IGafUdN90QoQkmizZrXZ98X8ap44dMFm0QgqYbUs7tXCa4ExuOqa7h+hGVnLVRUueFEY5lDe2Y9l8Ph4r2xvWw0PBoedGuZOQVCtgMVFi6xvWHCqx2AvwwxjI0ankTP5IbyYRx5pot91e+8pVprkOhUCgUij0jjbk9WSEtUYR+R+TG6SLdICKKJLouyJl64qCMkQBSmn3HOUOnkjNpekkfRy8zVM2bFCyDxhBqfTs5pkGczHGaKg/vmKZ1PVz5Ow7q/aZI6O0PTyy2NkRIenPIVvMmQsAN08Wx2R+uhkp5KBQKheJAMeoStayRlihCvyMyX3ep5S1sSyeI4g2573ETGUij77g3S6/jRxyfyK+PhABdS4Qy5ta6Q83SS8sxTet66HHQ7zdFghCCWsFi4XGP1bbPZHEzUPHIfJNaweKOkxNjtT/shHKcFAqFQjG2jDpSnUaJWtZIcxh7FgWQRt133Jul941zyyy3PEqOiWMkvV7LLW/oWXppOaZpXg/qflP0kFKy2vYpOQZuEPHkYhtfSiwhmKk6lByDtY7PsWpurJ0n5TgpFArFLlAlKOkx6kh1lkuSRnnctEQRemRFAGnnc7p7syjNWXppOKZpimSkfb8pskPbj7iw3MENInKmzunDJYQQSCnxghg3iDi/1OHUoeJYawCM78oUCoVizFElKOmRRqQ6qyVJoz7uXgxjH3cBpLTv3TRn6Y3aMU3rekj7fssiBznQFoQRc3WXSEqmS87WHzpwueky13AJwgjG+HoYeGV/8Ad/wI/+6I+Sz+ef/c0KhUKxT+kZ9i03IGcaOKYgjmFuratKUHZJWpHq/pIkidzWIzKOJUlpHPegD2Pfq/KxnoMzW8nhBhGOqTNVtNC03auGjdIx7b8eLq11yJnGSAbgplkCmEUOeqDNjxJVyWreSrJMYUQkQRdJ71/O1Fnr+PjReGvgD3zX/dzP/Rxvf/vb+ZEf+RF+6qd+ipe//OVprEuhUCjGlp5hv9h0kRLm1twNdaBK3qQbRJxfMVQJypCkFanulSSttP0+uezkcyvnTWp5a9clSbMVh24Q0fZCdF0wW3GYr7tDOXppljplsRdpFOxl+dhOhvLCiAzlUWcuKjmTE5N57j+3zCPzTfxQYhmC45M5bhnSkUy7JDRLqF4vsHRBztZZ63isdXxabki0rjZZdAwkkoJtYOnj/cwc2HF6+umn+b//7/+bP/zDP+T7v//7uf766/nJn/xJ7r33XmZnZ9NYo0KhUOyaURoavVrtlbZPGEE5Z2xE7BebPoYOZgZqtceVtCLVBUvHNnW+cW6ZvKVTcsyNz2255XFhpcNLT00OXZJk6hqPXW7t6JAN4+ilXeqUlV6kUbJX5WNpGsppZC7q3YDzyx0KlsELTlTRhSCSEtePOL/coeyYAx97L0pCs4Dq9UowDZ1qzuLJpQ5eEHOoaOEYOn4Yc36li21qvLCaxzTG+3oY2M03DIN/8k/+CX/2Z3/GhQsXeMtb3sL/9X/9X5w4cYI3vOEN/Nmf/RlxfDDSruOIlJKWFybevBcONZlcodhv1LsBDz1d54FzKzzw5CoPnFvhoafr1IeYpQKbtdpBJJkq2diGjiYEtqEzVbIJIrlZq60YmP5I9U7sJlItSfZEKUXvha3fi8H3zDCKWXMDLqx2WG55OJZOLW/hWHrikK12WHODgR29vSh16pV8VfMWRdvY14Yb7M05vdJQdsxkf+gZym0v5PxKe6jnc88h66noTZVsCrbBfN3lO0Puaf3rPVLNM5G3qeQsJvI2R6r5odfbKwEs2AZz9S5uEBHFEjeImKt3931JaI9BnPX9TN7U1iX5NY5P5IhjSdsPiWPJickctqFh6IK8Od4ZyF2FQmdmZnjFK17B2bNnOXv2LA899BD33nsvtVqNP/iDP+D7v//7R7RMxbVw0OtnFYqdSCPy21+rvRNZqdUeJaPM6KXZrO4HMS84VmW141PvBLRkmAwQLVnU8kU8Px4426BrgtWWR8ePOFLd7P+1DR27qHNprUMcx+jaYOdDlTqNnr04p2lltdLKXKSZhTuoJaH9qF6vhE4QY+s6x2o5wkhSzZtbVPWO1QwsTaMTxBTt8d3ThnKcFhYW+M//+T/zB3/wBzzxxBO88Y1v5H/8j//Bq1/9atrtNu9973u59957eeqpp0a9XsVVUPWzir0mC+pAaRkavVptNwgpOdvvq24Qkrf1sa/VHhWjDtqkJV7QM2CmSjYTBSsRh4glupaIQ8QSllrekAbMs6xFDn4tqFKn0XO1c9oTC7m02uVoLb+rqHdahnJaDk7ahv1BLAntp99Ztw1t275zUAIgYRRjmzpnZstcbrjUOwGBjNcDVzZTJQc3iMbegRzYcXr961/P5z73OU6fPs1b3vIW7rnnHiYmJjZ+XigU+D/+j/+DX//1Xx/pQhVXR9XPKvaarGQ30zI0TCO5ty43PJZaLiXHxNCSAZdNN8DUdabL9tjXao+CtII2ezGvJm9t/cyDMBrKgIliyWTRQu8KFpte0vOmaQRxTKObqJLV8iZRPFyp00FVv0uDnc6pH8acX21zac3F0jQsXeM7lxpD72dpZbXScnD2Igs37vL0adJz1p9YbCEl1Pt6ICt5EyHghunivg+A9K4zS9e4Yaq4zYH0wnjDsR5nBr6Kp6en+dKXvsTdd9991fdMTU1x7ty5XS1Mce2oWQmKvSRL2c00RQZOTOQJI0ks5boYQFLydahkIxBcN5nf9w/CtIM2WZlXY+gaZcek7JhXKQG0Nt43KKrUafT0n9PzKx2eWGwRRJJj1RzHJvJYurar/SzN6ywNB0dlNtNFCEGtYLHwuMdaJ+BwxaFsGrhBxNmFFtW8yR0nJ/Z9AOTK6+zKwFVWrrOBreiPfexjz/oeIQTXXXfdUAtSDI6qn1XsFVnLbqZlaPRHrVtukGQbEEQkKlTDzj3JGnsRtElrXs0oMzj9BsFOkdT5hrsrg+CglzqlQSVnctuRMh0/JAjzHKnmyJmb53Q3+9leXGejdHBUZjNdpJSstn1myg5TJZtGJ6DhBhhCcHq2iECw1vE5Vs2N3TkeZUn+lfPCHEvP5HNzoKdRs9nk7NmznDlzhmKxyIMPPshv/dZv0e12eeMb38ib3/zmtNapeAZUA7Fir8hadjPNSOqVmQA3Tu6zI7XcgckEZDFok0YG50rDs2cQBHHMWscfiUFwkEud0qITxLh+zLFafsfek3ETRUjTwVGZzfToPTePVnPYprZt8LYXxGP13OyRRkn+VeeFTeSHnhe211zzJ/TlL3+Zf/yP/zGtVotarcZ//a//lR/+4R/m6NGj6LrOn/zJn9DpdHjLW96S5noVO6DS7Iq9ImuGctqR1IOeCchq0CaNz20/GAQHjd5+5kcxF1c723pPpkrO2IkipOngHPT9LC36n5sCQd40oO9jGrfnJqRXkt8/L+yFx2toGsRxIqg07LywveaaHadf+IVf4Ed+5Ed473vfy8c//nF+9Ed/lJ/5mZ/h/e9/PwDve9/7+I//8T8qx+k5QKXZFXtFFg3ltCOpBzkTkOWgzag/t/1gEBw0DF3DCyLOr7R3HGS92vGZKTljJ4qQpoOTtf0sC+quWXtuplWSf+W8sH5qWGNX6n81hLzGiWbVapWvf/3r3Hzzzfi+Ty6X48EHH+QFL3gBAI899hgvetGLaDabqS54tzQaDSqVCvV6nXK5/FwvZ6TslFadLFkqza4YGVJKHlofvriToTxX73K46ozlxpeFB2wWuTIyeWXQZpzEQvoZ5fWQ5fviIBPHMZ/51tM8utDm9Exp28/PLjQ5PVvkh15wBE0bD6O2h9rPsqPumrX9oeWFPHBuhYJt7OjouUFE2wu589TEQE52WscdBYP4Bte8skajsSE7blkW+XyeUmlzoymVSnQ6nSGXrBgFKs2uSJssZzezFknNCmln9NIwEEdtcO1F758ylEdPJ4ixDJ2pkr3jWIGpko2pj99Azqw4DGmSJXXXrD030yrJz1qp/9W45h1cCLHlQ73ye8V4oIxDRdqoJmLFlaQVtEnDQEzD4ErbIFCGcjqEUYxj6JyZLbHQcLeNFZguOnTHbCBn7/ptuQGOpePoGpGUzK11x85hSIusqbtCtp6baZUWZq1k8Wpcs4UtpeRVr3oVhpH8k06nw+tf/3osK4mwhWGYzgoVCsXYobKbiitJo2do1A5OWgZXv0Gwk2rWbgyCtCPrBzmT1T+Q88bp4o5qZ8EYDeTsXb+LTY8ojjm31CGIYkxdY6pk0Q3isXMY0iBr6q49svLcTKt3Ncs9sf1c8xX1b//tv93y/Q/90A9te8+b3vSm3a9IoRgBB9kYUCiyTloOTr/BJZHbDOVhDa6eQfDEYrtvIHKizlbOm2hCcMN0YWCDIO3I+kHPZG0byHmF2tm4GXJtP+L8SoenVzsstX2kBE1ALGGh6XKoYGHoglOHimPlMIyavSj5SsuGyEJVUFqlhVfOccqZBkKAlImIzr6b43Sl46RQjCsH3RjYC9Q5VqRJWhHlfvnpC6udbQ7OzJDy00IIagWLhceXWesEHK44VCyDrh/x6EKLat7kjpO1gQ2CNCPrWeoRSYus9Z4EYcS5xRaX1rrYho5l6BvqjX4YcWG1g0QS3DAJY26c74a0S77q3YAnl1pcqrt4QZJFPlJxOHmouO/viR5plRb2xjZ844llHnq6jhtEOKbO9VOFzIxt2L93luJA0l//nTMNHFMQxxyo+u9+0mqsP+gGlyJd0oooG7qGGyZR+yiOKTnmhqG83PJY6wRMl+2BDS4pJattn5myzXTJpt4JqHcDDCE4PVNECFjr+Byr5ga6/9I6D/2ZrJmyzWonYLUTYxkaM2WbhYZ3IEq+IFu9J14YM7fm4oUxQgjWugFSghCQt3T8MGZuNfn5fibNkq96N+Cvn1jmyaU2SBAayBguLHdYaHi87PrJsbom0iSN0sJ6N+DhSw2WWh6WrmPpGiBYbHo8fKmRibENynFS7Bs2679dpIS5NXfLMMNuEHF+xTgQxgCkkxXKYlOuYitZKGNNK6KcNzX8MGKx6W2Rn7YNHbuoc3ahSa1gkjcHO24vM3S0msc21nucYomurffKhPFQmaG0zkNvvUEU88VHLnO54RFEElMXTJdtbpwujWWPSFpkpfckjGLcMKLeDQgjSc7U0TVBFEtabkjbCyHPWIlZpEFamUIpJd+91ODhuQZ5S6eU2wysNN2Ah+calByTu66fGLtrIwtceX6ny1Ymz+/+3xEVB4a2H3FhucNK299xmKGhg7nU2ff135BeViirTbmKhKyUWKYVUe4EMbauM1WyWGx6yR6haQRxTKMbMlWysLTB5af7M0NCCPLW1mt/2MxQWuchjGLmGl2+N9ek5YZU8iZFRxBEkvPLXZbbAc87XNr3Bng/Weg9ARDrkzelAARI1r+ufy+uaTJn9ullCp9canFpPQtnGxpHaw7XTQ5XUtfyQr43V8fQBIeKzsbrvcDKpbUOj8zXue1omZIzfPBx3B30HqN+XvSf38mCjRdGdIMIXcBkwcYPu7s+v3vB+O8SCsU1EoQRc3WXSEqmS1s3vamSzuWmy1zDJQijfV3/nWZWaL/MYTiIZKnEMq2IchjF2KbOmdkylxsu9U5Aa11+eqpkMVVycIeQn04rM5TWedAEPH65xWLTpZa3WGn7SCkRQlCwdJaaLk8YGtp42nMHFkPXsE2NqjBxTJ22H218bhXbxNIEjqmNjQrgniE2v8pdOI6NbsBaJ2Cm4iClxAsjIgm6SOyIWsFioe7S6AZDGfZZCVxBOs+L3vmt5EwurnZouSEREh1B0TEo2gar7WDo87tX7F/rUXHg8CNJxw+pXiUbkjN11jo+frS/Q3JpZoX2yxyGg0YWSyzT6D3pl5++fqrAaifAD2IsU6OWN/FDuREJHoQ0ey7SOA8dP2KtE+D6EQ09JG/q6EIQSUnDC3H9iLW2T8ePKG//cxTPEbahcaSa5+m1DqYuqOXNjR4nKSWm1DhcScpF9zv9hv2hgr1h2C80PFpeNHwgSIAbxCw1vW2GfcE2N520Xax33ANXaT4vvDDi6XpIHEPBNjB0QRhJVjsBa90ASx+P588zcU1W02//9m9f8wH/1b/6V0Mv5iCRpXRtVrB0Qc7WcYNwx2hFNwjJ23ombszdkGZWaL/MYThoZLXEspIzue1ImcWWv6G+NFW00LThDMNnkg1f2YVseNrqbKPuwUmyaokMu5CAXC/1kkmpl6YlZXtuEA11fEU6mIbOyakCfhRxbrGNG8TESDQEjqVx6lCBk1MFTGN/779pGfblnImta3xvvk7BMrF0galpyDgx7C+udTg1WaA8JvPj0iKtsQ0lxyCKJcstj1OHigRRjBdKdAHVvMm5pRbTRYeSMz7PoJ24ptXdd999W75fXFyk0+lQrVYBWFtbI5/PMz09PZDj9KEPfYgPfehDPPnkkwDceuut/OIv/iKvec1rdnz/H/7hH/KTP/mTW16zbRvXda/5d44DWUrXZgnTSDahyw2PpZZLyTExNI0wThoPTV1numzv+4dKmlmhrMn3KhKyWmK50165sIu9Mi3ZcEhfnW2UPTixBATMlB1imWSgZJhkLkqOQTln0PKi5H0HhCwEMwuWzmTBJohgsmTR9eNNERJLS14v2Ps+cJVWIKhg6VTyJo2nk2ys7FPVEwL8KKaStwY+v1kLXKU5tmGiYLHY8nnscnOLaiECdE2jVjTH7r67kmv6hM6dO7fx/5/4xCf43d/9XT72sY9x5swZAB555BHe8pa38Na3vnWgX37s2DF+9Vd/lZtuugkpJX/0R3/ED/3QD/E3f/M33HrrrTv+m3K5zCOPPLLx/bif4CvJUro2axQsnRMTecJI9kWTk/6FQyUbgeC6yfy+f6iknRXKknyvIiGLJZZp7JVpyYb3yIo6WzVnMFGwqHcCTkzkCSJJJCW6EJi64PxKh8miRTX33Btxe0GWgpkSiRBJxZihaeiaRCAQJMb9btUh0nIgR3nctAJBnSDGMnRKjsVyy8c2NfQYIgm+HzNRTAKFuxGPGeV60yKtsQ1RLKkWLHKmzkIjTM6v3Dy/U2WTat4iGvOIzcC74nve8x4+/elPbzhNAGfOnOG+++7jh3/4h3nzm998zcd6/etfv+X7f/fv/h0f+tCH+PrXv35Vx0kIwezs7KDLHguylq7NGv3ZkJYbMFm00BFESFw/ysxU6t2yF1mhrBiIioSslVimtVemJRveTxbU2SzT4HmHy3zrwhoXVrscKvY5pg0fx9K5ebaMZY733zEKshTMbPsRK22fom3Q9UJEoqmXtN3I5Lpbbg1//ablQI76uGkFgoIwYq3rc7yW42jVYa0dEMoYQySZEBDUu/7AAlNZC1ylNbZB1wSuHzFVspgpWSw2ffw4wtJ0pqYtYsALIvQxV6UZ+M6am5sjDMNtr0dRxMLCwtALiaKIT33qU7Tbbe6+++6rvq/VanHdddcRxzEvfvGLef/7339VJwvA8zw8z9v4vtFoDL3G3ZK1dG0WuTIb4sbJhnSkljtQ2ZC9yAplwUBUJOyFMz3KiHJae2VasuFZo2DpnJkt4UcxS013yxynE5M5Jos2Nx8ujY0jnRZZC2YmyrFdwiiimjdZbW+WUFXzJkEUMV/vDqUcm5YDmcZx0woE+ZGk60VU8xZFx2CmEhHFoGuJ09Byw6EEprIWuOof23C54eKYGpoQxFLiBvHQYxsSxHoJpIZpaBALTE0ghIaM4/Vmy/FmYKvnVa96FW9961v56Ec/yotf/GIAvvnNb/Iv/sW/4NWvfvXAC3jooYe4++67cV2XYrHIn/7pn3LLLbfs+N4zZ87w8Y9/nNtvv516vc5v/MZv8PKXv5y/+7u/49ixYzv+mw984AP88i//8sDrSoOspWuzisqGJKjzoOgnTWd61BHltPbKrEV+06LfkZ7Mm9w8W96izlbKWQciO5+1YKYfSVZbPm0/RBMaxZy5oUq21g2JZYwXxAMb9mk5kGkdN61AkKUnwZRuEFFyTBxj62feDSLytjGwwFTWeoN7YxuO1fJ8b67B+eU2vpRYQjBTcTh5qIyuiYH33yiW5CyN5kpIqxsyWbKpGBpuGHNhpUPBMThay+2/Ur2Pf/zj3Hvvvdx5552YZvJADMOQH/iBH+CjH/3owAs4c+YM3/rWt6jX63z605/m3nvv5Utf+tKOztPdd9+9JRv18pe/nOc973l85CMf4Vd+5Vd2PP673/1u3vGOd2x832g0OH78+MDrHAX9D+2dykQOykN7L1DZkAR1HhT9pOFMpxFRTsvByVrkN02u5khPlqwDk53PWjDT1BKp7JYbcd3k5qxCSxdYeYunltsYmsaAFVSpOZBpOqZpBIISgSmHhabLYtPFNjSEJpCxxAtjTF0wU3KGEpjKUm+woWt4QcRC08UxdU7Plrach6fXOsyUnIH3X10TdP2kZ6qWN1lth7S9AEPTOTGZJ4glrh/vv1K9qakpPvvZz3L27Fm+973vAXDzzTdz+vTpoRZgWRY33ngjAHfccQf3338/H/zgB/nIRz7yrP/WNE1e9KIX8dhjj131PbZtY9v2UGsbNZtSuC2khHqfUkklbyIE3DBdPBAPbYVC8dwwSmc6rYhyWg5O1iK/aXPQs9JZy0AGMTimRiwNVrs+RctA1wRRLGn5ISXHwDY0ggH9vLQcyLQd01FfvwVL5/hknrobsOR6PLW0mWmZrTgUHZsTh4YXmMrK/ZY3NbwoYrHpb+lx6pH0OFkD9zglyPXevPX5B+tfZe/1XYqb7AVDPz1PnjyJlJIbbrgBwxhdRDuO4y09Sc9EFEU89NBDvPa1rx3Z70+TTSlcb0MKt2wauEHE2Q0p3Imxu4kUCoViJ9KKKKfp4GQp8rsXZC0rPcpeun4HfbbibJtXM24ZSEsX1IoWjhcCgqYbEkuJJgQTOYuYmMIQpWRpOZB74ZiO8vrt2WhNN6TjR8lMrA01OR/LDanmrV3ZaFm433rqglMle8fRLlMleyh1wSiWOJZOfTmk7UVbRGkurLjkbZ3D1X1YqtfpdHjb297GH/3RHwFw9uxZrr/+et72trdx9OhRfu7nfu6aj/Xud7+b17zmNZw4cYJms8knPvEJvvjFL/K5z30OgHvuuYejR4/ygQ98AID3vve93HXXXdx4442sra3x67/+6zz11FP87//7/z7on/GcsCmF6zBVsml0AhruuhTubBGB2JUUrkKhUOwl/RHlWMasdgL8MMYyNGp5c1cR5TQdnKxEfhVbGXUvXc9Bn6+7fP3x5SQArgHrc2VOHiqMVQayf1ZhGEVUcomtIKXECyMM3RpqVmFaGd6slcbuZKOFscTUNM4cLh0YGy2MYhwjEZBZaLjbRrtMF5Mgw6D7ek9Vr5IzmShYtN2Qth+ik4jShJHcn6p67373u/nbv/1bvvjFL/KDP/iDG6+/+tWv5pd+6ZcGcpwuX77MPffcw9zcHJVKhdtvv53Pfe5z/MN/+A8BOH/+/JYJ8aurq7zlLW9hfn6eWq3GHXfcwV/91V9dVUxi3NiUws1hm9q26JYX7F4KV5EuWRiSqFDsFb2I8sXVDo9dbm5RZ5su29w4XaJgGUNHlCs5k9uOlFls+bhBhGPqTBWtLc+FYclC5FexSeqy4aLPbxIwjrbbTrMKgzjGEIKpsjP0rMK0MrxZK43Nso02Stukt69busaN08Udz0Ow/nsGR2AbGoerObxwq2rh3Fp3f6rqfeYzn+GP//iPueuuu7Z8KLfeeiuPP/74QMf62Mc+9ow//+IXv7jl+/vuu4/77rtvoN8xTmyRwkWQNw3o2+fHrRFVsZUsDUnsoRw9RZoULJ0gjvnzhy8DUMmbFB1BEEnOL3c5v9zlNbcfHjqivNM9tzCiey6O41QcsrQ4yPdyfy/dbDmJdre9EF0TzJYd5hvurlTfAO46NblNsGnY4175O0b1uaU5qzCtDG+WSmOzaqON2ja5MlN45XkYNlMYxTK5ZruCpaZPOWeQMzSCOGap6VNcF43Yd6V6i4uLTE9Pb3u93W4fmE18WLLWiKrYJEtDEntk0dFTZAspJZcbLm0/xNIFlxsRmoBYJhF7P0p+LqUc+PmQ5j13YbXD/eeWubjSxQ8lliE4NpHjJacmOV7LD3XMHmk4OAf9Xu5lAixd4/HF1jZhpVGovu0012u3cuRpfG5pzipMq4Q1K6WxWbTR0tgn08oUGrpG2TEpOyarHZ96J6C1XgI4VbI2emXH6fzuxMA7wZ133sn/8//8P7ztbW8D2DhxH/3oR59xcK0ie/W+ioSsDUmEbDp6iuyx2PJ5ejV5sLa8kN7lL0RScVHLm1xc6bDYSvoGrpU077kLqx0+++25DYGenKXT9SMeXWiz2PR57e2Hh3ae0jCU1b2cZAIabsBaN8D1Y8o5Y8OQW2z6NNyQWt7cleqblHJbxmk3GYY0P7c0HZG0SlizUBqbNRstzX0yjUxh//m9Yaq4Y4Z3nM7v1Rj4Kn7/+9/Pa17zGr773e8ShiEf/OAH+e53v8tf/dVf8aUvfSmNNe4bslbvq0jI2pDELDp6imzS9UOWWj45S+PGcpEgkkRSoguBqQuW2x5LLZ+uHw503LTuuTiOuf/cMmudYIvMbsnRKDkmZxeaPPDkCkcrzsBle2kYyupeTtA1wXLLp+OHHKluOrW2oTNV0rm01kGuG2CD0MswrLb9jQj4lZmsYTIMe/G5ZcER6ScLpaZZs9HStk1G7aD3n9/5hkstb2FbyQzT+YY7duf3agycD3vFK17Bt771LcIw5PnPfz7/7//7/zI9Pc3XvvY17rjjjjTWuK/oefGzFYe2F7LU8pLNteociMhhFsnakMRBNlOFYjfEEvwoxtA0hBBYhkbO1JMeASEwdQ0/ihm0ZD2te26x5XNxpcvhioOUEne9V8YNIqSUHK44XFhOMmSDcKWh7Jg6mhAbhnLbCzm/0kbKwU6Eupf7eZZzN8T8l4KlY5kaf3txjcWmj2Pp1PIWjqWz2PT524tr2JY2cARcfW5bqXcDHnq6zgPnVnjgyVUeOLfCQ0/XqXeD53pp28iSjbYXtknPQa/mLYq2MZKyzayc36sxVLjihhtu4Pd///dHvZYDQ1bqfRUJWat7zpqjp8gu1ZzBRMFirRNQzpkEUUwkQRfJdbbWSRrYq7nBHjVp3XNuEOGHiYF9cbWzZQ5OyTGo5Cz8MHGoBiGtyK+6lxOiWFIr2ujdYMe5MqWcSTk3XFO5IHnuinXHq+fb9r4fRuVLfW6bZLHUNCs2WtZskx5ZOb9XY2DHSdd15ubmtglELC8vMz09TRQdjAjKbslamv0gk7W65/7NdCdJ1XHdTBXZwzINnne4zP3nlvn2hTUsQ0fXIYrADyMmixY3z5axzMH2urTuOcfUiWXME4sthCYoWgaGJghjyWo3YKXtk7f0HY2QZyKtXpmsGkajxtA1qo5JZb2p/Mq5MtWchWDwpvK2H+EFEbcfq171uF4QDezwqs8tIculplmw0bJmm+wXBr4qrlZq4HkelrVztE2hyDJZq3vubaZPLLY3Zn306vbLeRNNCG6YLqjNVLFrCpbOsVqeh+fqdIOIbhATRBKEoGDrFB2DY7XxmStzqGBimxpPLHa45Uh543VTF9RyFt+91ODWoyUOFQaLgKfVK6MMo4T+87DTXJn5+nBN5T2Hd6pkM1m0th03jmGp5Q3s8KrPLSFr/cFZI2u2SY+sq4Re85X627/920DyQX30ox+lWCxu/CyKIr785S9z8803j36FCsUYkKVZFEIIagWLhceXN5TDKpaxrhzWopo3ueNkbew206yShabnNJFIKjmLmbKDkAKJRCCQQuIG8VC9J5DOPdcNJUeqeS43fc6vdDhU3CwdWmr5TJYsZis5uqGkOIBN2+uVuf/cCjnT2Kb6dn6lw0uvnxgbBzJrbGkqr1/RVF4fvqn8yszQlfNqgigayuHtX++ltQ6OpY9s3lKWUCWL6ZMl2wSyWbp5JdfsOPUGz0op+fCHP4yubz4ALMvi5MmTfPjDHx79CvcpB93YyiJZqcuVUrLa9pkp20yXbOqdgHo3wBCC0zNFhIC1js+xam7s1p41sh452y1tP8IPYl6wXuq0U6bF8+OxUXUKo5ha3uKVZ6Z4+FKdyw2PIJKYuuDEZI6bZ8toQgxlyPV6ZUDihhFBGBMh2RA1GKJXBrJnGKVF2vLIo84MVXImJybz3H9umUfmmxvzwo5P5LklA8bhKFAli3tDlmyTrJZu9nPNT7Jz584B8L/8L/8Lf/Inf0KtVkttUfudg25sZZks1D33yiOOVvPYhrat38IL45GURxx0538/RM52S3+p00TB2natxXK4Uqd+RnnP9Qy5gm3y/WemWe0E+GGMZWjU8iZ+KGl74dC9MjdOF3nscpOnljcdspmyw43TxaF6ZXpkxTBKmzTlkUed0at3A84vd8ibOmdmy5uZWBlzfrlD2TH3/f6gShb3jizZJrW8tWMvaFZKNwde2V/8xV+ksY4DgzK2FGnTXx4hhCBvbb3NR1EecdCd//0SOdst20qdrrjWgnC4Uqe06DfkZteH31q6hq4LhBCsdryhe2XW3IBGNyBnGpyesdE0iGPwwojltk8wIlngg86oz0Mamaze/rDYdJES6h1vSybWDV3Orxj7fn9QpaaKfnq2iR/FXFhpc7npE0QRpq4zXbKYLucyUbo58O7zpje9iZe+9KW8613v2vL6r/3ar3H//ffzqU99amSL228oY0uxF6RdHqGcf9X03CNrEeWeITdfd/n648tJFZ0GxICAk4cKQxlyuiZYbXl0/GjLgNYEk0trHeI4HnhAaz8HPcObJqPOZLX9iAvLHVbaPmHEtp43QwdzqcOpQ8V9vT+AKjVVbGLoGl4QcXa+yWKruzEaAmCh0WGq7nHdZH5sAm1XY+A79stf/jK/9Eu/tO3117zmNfzmb/7mKNa0b1HGlmIvSNOYVc5/gmp6Tsh0RFn0+U0CduHTbB6Q5B7xwmhjnpVtrN9nQ/Y4gcrw7gWjzGQFYcRc3SWSkumSs/G6behMlXQuN13mGi5BGMEBeNarUtPsMsqATd7UWO36fPviGqauIQUIkcxOExLm1jyqBYO8uc8cp1artaPsuGmaNBqNkSxqv6KMLcVekKYxq5z/hCw3PY86c5GliHLP8Qe469Tkthr7+YY7lOMfxZLJooVfj3no0lqSweplsjSYKeWYKFpDDWhVGd7s4UeSjh9Svco+mTN11jo+fjSc4mQWUaWm2WPUAZu2H7HY9OgGEUEcU8lZWLrAjyT1rk8YSRYaHm0/ouSM37Ozx8BX8fOf/3z++I//mF/8xV/c8vonP/lJbrnllpEtbD+SZWNLkS3SMmaV85+QtRK1HmllLrISUe53/Hfq/xvW8Td0DV0IojiGWCSRVJIkk4ghljG6GHxvVxnebGLpgpyt4wYhJWf7fdUNQvK2jqWrz0wxnvQCNi03wLF0HF0jkpK5te7QAZt6J2Ch7jJTtjF0ja4f0Q1jNASzFQc/jFmou9Q7wY73zbgwsOP0nve8h3/6T/8pjz/+OP/gH/wDAL7whS/wX//rf1X9Tc9CVo0tRTap5ExuO1JmseXjBhGOqTNVtNC04R1z5fwnZLFELe3MRRYiyv2O/06qTsM6/nlTw4simm7E849V8MJ447i2ofHo5RZ+HA9cgqIyvNnENBLH9nLDY6nlUnJMDE0jjGOabpA0w5dtTEM96xXjx6a4iUcsJXNr7oa4STlv0g3ioQI2HT+kG0TMlB0KtkEQxRslzaau0fJCLjdcOn6Y4l+3ewbeaV//+tfzmc98hve///18+tOfJpfLcfvtt/P//X//H6985SvTWOO+IYvGliK77JRdWNhldkE5/5tksUTtoGcueo7/atu/6typYRz/ThBjGTpTJZvltkfJMXFMnTCOWW57TJVsTF2jE8QU7Ws/tsrwZpOCpXNiIk8YSWIpaXQCQhliCMGhko1AcN1k/kDsk4rs0fYjzq8k4iZRHFNyzA1bdbnloWsaxrIYWNwkb+nkTJ22H1GwjSQlLyW9/tCOH5EzdfJjfl8MFaJ63etex+te97pRr+VAkCVjS5Fd0souKOd/K1ksUduJg5K5KFg6lqlx/7kVcqaObWhYuoaMJYtNj/MrHV56/cRQcuSOoXNmtsRCw91mKE8XHbpBNLCDozK82aR/n2y5AZNFCx1BhMT1I4qOeaD2SUW2SMRNushYMlXaDLTZho5d1Lnc7DJf7w4sblLJW1w3meeRhSaPd31imYjy9L5qmuDMbInKVZ5T48L+fUKOMVkxthTZpD+7MFtODLa2F6JrgtmyM3QDfA/l/G8lcyVqrJeoRRJd312JWhYRCNwgZq0bJGpOJOp6QoBj6EOp3/UcHEvXuHG6uO38ekGcODkDOjgqw5tdrtwn3Thxco/Ucgdyn1SkyyhFf/xI0vWiZxA3MYYSNynaBjfNlPnupSZtP8JeDwZFErp+RN4yuGm6PPbP02ta3cTEBGfPnuXQoUPUarVn/DBWVlZGtrj9TBaMLUU26WUXLF3j8cXWjuVIu80uKOc/W/QM+5X1ErVG3zVR3kWJWtZo+xHLbY+ireMGEUKC6JvjVLR1ltvewPfGlQ5O3jSgzy4e1sFRGd5sk+Y+qeZ6KXqMWvTH0hPhnG4QXUXcJCJvG0OJmxRsg2MTebp+mJSyItEQGIYgZxpJCd+Yc00rvO+++yiVSgD81m/9VprrUSgUuySMYhpuwFo3wPXjbcMXG25ILW/uOrugnP90GaVhVLB0bFPnG+eWyVv6tpr1CysdXnpqct9nLnolKKYu1kUcIqIYdC0pQ1lsukOVoKTp4KgMb7ZJY59Uc70UPdJQv0vETRwWmi6LTS+xITSNII5pdENMXTBTcgYWN2n7EV4Q8dJTE6y0XZ5ec/GCGNvUOFp1mCg4eEE09iXj17Sye++9d8f/VygU44euCZZbPh0/5Eg1v/F6b/jipbUOcl3xazeoiGd61LsBTy61uLTm4oUxtqFxpOpw8lBxaMNIkpRVxDF4QUQQxsRSEvf8Z7G7mTJZuB76S1AEAsfY+ggctgQF0nVwVIZX0UPN9VL0SEv9rmDpHJ/ME8QxUiYy4q31ns2pkoUQcOLQ4OImvZLxnKUTxxI/iOkGEQKIY4mla0P1gu411+Q4DTLYtlwuD70YhWJUZMGIS5dnMfx2aSSriGd61LsBf/3EMk8ut5F9zbMXVjssNDxedv3kwOe47Uf4QcyNU0UeX2xxfrmNLyWWEMxUHG6cKuL58dCRvqxcD2mWoIBycBTpotQxFf2kpX63TdykYCPWBfC6QTi0uImha7hhxN9eWOXJ5Q5dP2K9w5SnVjqcnOxy5nB57EvGr+lMVqvVaz5BURTtakEHBWXYp0dWjLi0iGJJrWijd4MdZ4iUciblnEkUD+c89ZcG5EwDxxTEMbsqDejnIN8bUkr+7lKdh+ca5EyDSl+ZZaMb8vBcg1LO4K5TkwOdk/7yTcfUOT1bQmgCGUu8MGap7RPJ4cQhshQBT6sEpR9VmqVIC6WOqegnLfU72CGDHsldi5vkTY25tS73P7lC3jKo5s2N58VaJ+D+J1eo5s2B593tNdd0Jv/iL/5i4/+ffPJJfu7nfo6f+Imf4O677wbga1/7Gn/0R3/EBz7wgXRWuc/YKMOpb9Z3HqnsrgxHkZAlIy4tDF2j6phUHLNPCGBTGrmasxDr7xuUzdIAFynZUhpQyZt0g4jzK8bQEc+DbiC2vJBH5poYmmCqZG+8vllmGfHIpSa3HakMNFn9auWbPYYt38xaBDytEpQ0UXvaVg5yYEWpYyr6SUv9rseoM+gtL+TCSgddExScJCgoEJi6RsEx8MIkg9byQsq58ZUkvybHqX+w7Xvf+17+w3/4D/zYj/3YxmtveMMbeP7zn8/v/d7vqR6oZ2GjDGepDeuKTjKGC8vDl+EoErJmxKVFv8LXTtLI83V3aAnjth9xYTkpDQgjtglPGDqYS52BSwNAGYgAjW7AWtdnpuzs+PNq3uJy06XRDQZynBJGX76ZtQh4fwlKs+uTr+aIpUQTAiljSjlrrFTq1J62lYMeWFHqmIp+0i49htFm0OfrLvVuwI3TRcIoCYBIKRFCULFNajmTtU7AfN0da8dp4Lvra1/7Gnfeeee21++8806+8Y1vjGRR+xUpJd+91ODhuQZCQDlvMlGwKedNhICH5xp891IDKXfXf3JQGcSI28/0jMOCbTBfd9FINlcNwXzd3ZXCV1Ia4BJEkqmSjW3oaEKsZ0Rsgkgy13CT0oABuHL2VCwlbS8klpLZskPbCzm/0j4Y98Yz/IlCPPPPr0avfLOUM1lquetqchIvjJJyzpxJtWAPXL7ZHwHfiXGMgFdyJicm83SCiEfmG/zd0w0emW/QDSNOTObHygBXe9omvcBKbw+bKtkbe9x3nq5T7wbP9RJ3REpJywtZ6/i0vHBXe1hPHfPbF9dYbnk4lk4tb+FYOsstj29fXMM29bHKmCrSo1d6bOqCxaaHF0bE6/v6YtPD1AWHy7srPR4lUSyJYknBMpguOxyr5jlSzXOsmmeqnNzPvfeMMwO7kcePH+f3f//3+bVf+7Utr3/0ox/l+PHjI1vYfqTlhXxvro6hCQ4VNyPKvXrUS2sdHpmvc9vR8hDR5HTJQnlEFo24tEhL4cuPJB0/pJq3iNd7pvxYYmmCkmOSM/WhSgP2YvZUFijnTKp5k9W2T666/e9cbfvUCkmP2iCkVb7Zi4D7YYxjbn84B1E8dhHwejfg/HKHgmXwghNVdCGIpMT1I84vdyg75tg4T2pPS8hq5i2NDFlPHVP2BjWvb7Ub3+9S+EeRHbJWelwrWJQdg5WOz5FKHsvYeq+udnwqOYNaYXyzTTCE43Tffffxpje9if/5P/8nL3vZywD4xje+waOPPsp//+//feQL3E80ugFrnYCZys5lOLWCxUJ92DKc9MhKeUQWjbg0SUPhy9IFOVtnvt5hrRuw0goIZYwhNCaKicE5WbQHLg3Yq9lT407RNrj5cIVvnFveUdgjjCVnZisDO49plW9eOfj1SoYd/JoW/Qb4tl6vPGNngKs9LaE/87ZTb884BlbSENHpqWO+4FiV1Y6/zVCu5XenjqnIFmmp36XFdMnm1qNVvv7EEmtdn7xlYGiCME4Csm0v5O4bDjHd1987jgx8Z732ta/l7NmzfOhDH+J73/seAK9//ev56Z/+aZVxuhae4fqV8pl//lyQpb6TrBlxe8GoFb5MQ8fWdR5ZaNHxI6aLNhXTwA1izi11yFs6r6zkBy4N2KvZU+OOEIJbjpRpugFPLrVpdIKkoDoGNHje4TK3HCkP/CDcMqB1rYtj6egIgjhmre0P/YBNc/Brj1Fmu7PWk6X2tIRe5s2PYi6sdrb19syUnLHKvKUlotM7D1Mlm4mClTiQ6/tiztSJJSy1vLE5D3tBFqph0iQN9bu00DSN7zszxVLL48JyBz+I0Q1BFErcMOLG6RJ///QUmjbegaChngzHjx/n/e9//6jXsu8p50yqOYvVto9T0fHCeGPTsw2NtY5PLWcNXIaTFlkrj9gLI+6gkzMEqx0PCZw8lKfpRjS8CF0TnDqUZ77hUe/65IxhznG6s6eyQiVn8rLrJ5kp21uUN49WHa6bHF55s9fbc/+5ZR6Zb+KHEssQHJ/Ic8suAiBpDn4ddbY7a6pkak9L6M1/Ob/S2XFezVonYLpsj03mbauIjsQ2NSyhIaVkselh6GIoEZ3+DKS9Q/nmQclA9shKNUzaZGl+3PFann96xzG+8cQy55bauEFEqWBww1SVO09Ncry2XfV13BjKcfrKV77CRz7yEZ544gk+9alPcfToUf7zf/7PnDp1ile84hWjXuO+oWgbnDlc4itnF3noYh0pkgSTJLEJHUvjxSdrYxHphOxFZyFdI04BS+0AL4iZLjm03IBEA0ISRtB0Q6ZLDl0/YqkdMFO+9ih42rOnskYlZ3L7sSo3TI/uQdjf2/PC4zU0DeI4KenYbW9PGg/uNLLdWVQlS3tPy0LEPm9q+OsN7zfNFPHCiG4QoWswWbR5dKFFrTA+8196IjptPwRgqekRIdFJZJjxIZbuwPN1ehnIJxZbGz0t/ZksIeCG6eK+z0DC1lJIx9JxdI1IypHNE8waacyPg3T2h+O1PIdfYPG9hRaNbkA5Z3LzTBHDGA878tkYeJX//b//d378x3+cN7/5zTz44IN4ngdAvV7n/e9/P5/97GdHvsj9ghBJdNcxdS43XAxdoAlBLCVRJKkWChyr5cfmoZXVxuQsRV+yhhtE+FGSAel4YOgCIQRSgkBimRp+FOMGg6l8pTl7KquM8kH4TL09NayRZI/TWu8os909VbJvnFsmb+nbMhcXVjq89NTk2Bmeae1pWYnYd4IYW9cp2gYPPb22Ubra+3qo4GBpGp0gpmg/93uEH0mWOx6d9Wx83jIwdEEYSerdgCiWuFE0sIiOECLphX48ybIdrjiUTQM3iDi70KKaN7nj5MS+f9ZtlkJ6xFJuKYUs5026QTxW1TBZJa394cJqh/vPLXNxpbtR+XB2oclL9mvG6X3vex8f/vCHueeee/jkJz+58frf+3t/j/e9730jXdx+Q0rJatvnUMlGIrm06tINYixD4/hkjkMlm7WOz7Fqbixu9iw3JqcVfTno2IZGxwuJgRumSwShJJISXQhMQzBX7xIJdiwjeSbSnD2lyF72+Grr7ZXXGZrg4mqXk5OFgYV0sqpKNuo9LUv9q2EUE0iJYQiEhFiIpFpDgLb+eiDHJ4hnauAFMS035LrJwsbrli6wchZPLbeTcr0BH509G2Km7DBVsml0AhpugCEEp2eLCMRY2RBp0faTss2Vtr9j6aauaRjLYqh5goqEtPaHC6sdPvvtuQ3HP2fpdP2IRxfaLDZ9Xnv74bF3nga+oh555BG+7/u+b9vrlUqFtbW1Uaxp39K72b0gJm8a3HqkslEu44URXhDz1PJww0PTQDUmK64kb+kUbIPFlodAbJMTdYOI6aJDfsBror+XY77uUstb2JZOEMW7nj2lyF72eKf1trykz6fRCfDjmLYbUnYMbjtaveYHuFIlS9iL/tU4jlls+bhBhGPqTBWtoZu+dU2w2vIIw5hbj1RouiF+FGPpGiXHYL7hstb2xkY8JojBMTQiR9/oReuph7W9kKKjY+sawYC3Wy+gcLSawza1bQEmL4jHKgCSFkkpZBcZS6ZKm9dvb7TL5WaX+Xp34FJIRUJa+0Mcx9x/bpm1TsDpmdLG6yVHo+SYnF1o8sCTKxytOGMtEDHwFTU7O8tjjz3GyZMnt7z+1a9+leuvv35U69qXXO1mTzDH7mZXjcmKK4llUkMfxJLzy20mSza2oeGFMctNj0rO4vrpAsO0Iqn+tPTIWvb4yvW2vJDHF1t0/ZCSY2JJDSlhueXznafr1xz9VKpkCWlnIHcqxTk2kdtlKY7ADSKeXu9hiaVEE4KSYxBEMQXruX9m9rB0wUTBxjZ1BNByQ7rrPU61vEksoegYQ41t6AUUBIK8aUDfZT9uAZC08CNJ14uoXuX6zZnGUPMEFQlp7Q+LLZ+LK10OX2Ukz+GKw4XlDoutJKs6rgy807zlLW/h7W9/Ox//+McRQnDp0iW+9rWv8bM/+7O85z3vSWON+4Ys3uzKmFX0Y+gas+UcJdvk8cUWC3WXFSmxhOC6yTzXTxWT6OqQBrjqT0uH/uzxbNnZ5jCMW/Z4y3orDnP1Ll0/3Bgcvtj0mC7ZnJwsMN9wrzn6eaVDlr/C2A7CaKwcyLRIMwOZRilOFEtylkZzJaTVDZks2eQNHTeME8ETx+BoLTc24jGmoXO44rDQdAkjSSVnIjSBjCVeGGPogpmSM/DYhqwFQNLC0pO+sW4Q7Viq2w0i8vbgjqkiIa39wQ0i/FCSs3SklNuUpR1Txw/lwD3Se83AjtPP/dzPEccxr3rVq+h0Onzf930ftm3zsz/7s7ztbW9LY437hqze7Fk0ZrOgFJVFegatH8W88vQUq50AP0z69Gp5k4Wmx2TJ2pUBrvrTRk8vezxfd/n6ueV1MY91RU8BJycLY5U97s92P7nUZqnhUcwZeGFEoxuSt3RmK0kfxyDRT1V+nJCWAZ5WKY6uCbp+0ssykbdouSEtP0RHcGIyTxBLXD8em1K9gqVzfDJPEMcb6ndBGK+XhCZDSk8cyu/7gdNp0e+YLja9ZGC6phHEMY1uiDmkY6pISGt/cEwdy0hUTb0g2pY5ttd/vtPvHCcGsk6iKOIv//Iv+Zf/8l/yzne+k8cee4xWq8Utt9xCsVhMa437hizf7FkyZrOiFJVF+g3ahaZHLW9RzSflmwtNT5VvZgGZ6B+InirZmH5UvWz33z1d5/HFFkIDU9OYKlnMVnIb+9Eg0U9VfpyQlgGebimOxDE0DldyeGFEJEEXSV/LXL27a1GPUQbb+q+zZtcnX81tGIhSxpRy1tgOnM4COzmm/b2KwzqmioS0KhSmihaTRYuvP7HCoZJN0drs/VvtBixdbnH3DZNMFXeuyhoXBrKEdV3nH/2jf8TDDz9MtVrllltuSWtd+xJ1s6dPlpSiskrPoH1yqTXSAa2K9Og1+wLcdcPkjqqF4yjfW8mZPP9YhXo3wDY0CrZBztxq0A4a/VTlx+kZ4P2lODsxbClO/5y35bZHyTFx9GTO23Lb2/WctzSCbZsDp7tcXGltDpyezHHrZH7XA6cP8v57pWOaqzl9EvVyaMdUkZBWhYIQgumyg6VrNLshtq4hRLLvNLshlq4xXXbG/nMbOIVw22238cQTT3Dq1Kk01rOvSSsKpUjYC6UoxRXIza9yPNoLFDvQ3+y7U1P5uMmR91O0k/6V+bq7rScJhsuOZLH8eNSk4UD2SnG6fkTJ2e7IukE0VClOmnPe0gq29Q+cfsGJKroQRFLi+tGuB05vcID3362O6aYIyfGJPLceG94xVVzBCCsU2n6EqWn8g+dN8+hCg0trLl4osQ3B8YkcN06XMIQYy+dQP0PNcfrZn/1ZfuVXfoU77riDQqGw5eflcnlki9uPpBWFUmRvVk1W6Tc0DhXtDUNjoeHR8iKV1RtDsiZH3k9a2ZEslR+nxagdyKmixbGJHI8utHfs452ru5yeLQ5cipPWnLe0gm1bjlvNbazXNDRqeWtXGd7e/ttyA/K2TskxiKRkvu4eqP233zF94fHaxmiXbhCOzjHNEKMsNU2rQqH3HKrmLY5PFNBEoshrGxpHazkqOQs3iMbyOdTPwE+N1772tQC84Q1v2HLCpJQIIYii8VbDeK7ZkyjUASXLxmFWUFm9bJJ1NS5VXpceo3QgNU3jJacmWWz6nF1ocrji4Jg6bhAxV3ep5k3uPDkx8IyWtOa8pRVs6x3X1DUeu9xaz5BJDCEo582hj9vbfxebHrGUzK25W47bDeIDsf/2P4eOVLcqNNawDtxzaNSlpmlVKBi6hhdEnF9pE0YwXXb6BhcH1LsBMyVnbJ9DPQbeLf/iL/4ijXUcCNKMQimybxxmAZXVyyb7QY1Llddlg+O1PK+9/fC2OU6nZ4vceXJi6DlOaTjPaQXbwihmzQ1odAO8dRXdTQPRo+WFlHPmwMdt+xHnVzqstH2iON52XF3TMJYFpw4V9/X+q55Dm6RRaprWfZE3NbwoYrHpb1HdtA2dqZLO2YUmtYJF3hxvG22gK0pKyZEjR/B9nzNnzmAY+/uCHDVpRaEUCfvBOBx3VFYvm+wXNS5VXpcNjtfyHK04LLZ83CDCMXWmitbAmaYrGbXznFawTdcEqy2Pjh9tyYjYho5d1Lm01iGOB5dPD8KIuXoXGUumSpvPuN5xLze7zNe7BGEE+/g+Uc+hhLQqQNK6LzpBjGXoTJVsllouJcfE0BKRl6YbMFWyMXWNThBTtMfXebrmlZ07d47bb7+dm2++mdtvv50bbriBBx54IM217Tt6UagLqx2WWx6OpVPLWziWznLL48JqhzU32Pc3e1r0jMOCbTBX7+KuS2gmZSLdzBiH40z/hroTKqs3vvQi9rMVh7YXstTy1rPfzoHpi1DsHZqmMVN2uG6ywEx5sLlNz0TPea7mLYq2sav9vBdsW+34O/58tePvYi7ds6xLDr5uP5J0vQjH3NkpypkGHS/Cj/a3UoR6DiUMknkbhP77QiLpBCFNN6AThEjk0PdFGMU4hs6Z2RITBYu1bsBcvctaN2CyaHFmpoRj6GNvA19zSOKd73wnYRjyX/7Lf8FxHH7jN36Dt771rXzzm99Mc337irSiUIpNVC9Euqis3t6RxhBnVe6mUGySViY2iiWTRQu9K3ac2Vh0TGr5weXTLV2Qtwy66+V/V9INIvK2gaXv7/tZPYcS0sq89cuRf+3xJbxQbugY2EZSbIzc1AAAVm5JREFUCjrMfdHv8Eop8YMId/3/41jiZ8ThvWbH6atf/Sqf/vSnecUrXgHAXXfdxbFjx2i329uU9RTPxOijUIqtpGkcpmHMZon9UvI17qQ5xFmVuykUm6QRbDN0jbJjUl6XT79yZmMvQzCogWgaOocrDgtNl8sNF8fU0IQglhI3iDENwUzJwTT2t8OgnkMJ/Y6IbWrb1O92m3nr+CHzay4dPyJGoiHIW/oQw6sTCpaObep8+ewibhghJWgCOn7EajfAMTp83+mpsXd4r/npefnyZW666aaN7w8fPkwul+Py5ctqptM1klYUSrGdNIzDNI3ZLKGyeumihjhnm4MeXMkiow629WdEbphal0+PJbq2LufcGE4+vWDpHJ/MU3cDWq7H+eU2vpRYQjBTcSjlbE4cyo+94TkK1HNo8zp7YrFNLOW2vnlNCG6YLgwl0//dSw2eWu5wuOpgGzqaJohjiRdGPLXc4buXGtx1/cTA90jLC7jccBHAZMnGNjW8IGa56dEgoO0HAx3vueCaLUshBK1Wi1xuMy2qaRrNZpNGo7HxmprjdHXSikIp0kcZs1tRJV/poOTes40KrmSXUQbbtsinN66QT28ML58uhKBWsGi6IV0/4uShIqYhCELJStuj6YZU89aB2RsOenVJ73pYeHyZtU7A4YpDxTLo+hGPLrSo5k3uOFkbwrkJ+d5cHUMTW0RIelxa6/DIfJ3bjpZ3LBl9puNeXOlypJrIkDfdkLYboq3fL34UcXG5S8sLBzruXnPNu4SUktOnT2977UUvetHG/6s5Ts9MWlEoRbooY3ZnpJS0vXBDNStvagfq708DJbObXVRwRdFPGhkRKSWrbZ+ZssNUyU4yDLHE1AWnD5cQCNY6PsequQOzFx/k6pLN68FmumRT7ySzkAwhOD1TRAiGuh4a3YC1TsBMZeeSvFrBYqHu0ugGAzk4jW7AWtfncDWHY+h4YbxhA9tGUmp4uTn4cfeaa77a1Pym3ZNWFEqRLsqY3c6F1c62OS3HJnK85NTk0HNaFEpmN6uo4IpiJ0adEek9i45Wczv2tCQlTwfrWTTqzFCWAiCb10N+w/HoD8Z74S6uh2c4hVI+88+fkfVOFCHENqlzITZ/Ps5c85l85StfmeY6DgyqLjd7KGN2KxdWO3z223MbpQE5S18vDWiz2PR57e2HlfM0JGqIczZRwRXF1RhlRqT/WSQQ5E0D+kyGg/YsGnVmKGsBkC3Xg0gUF/sZ9noo50yqOYvVtk+uuv3aXev41HIW5QHPcTlnUs2bVz3uatunVjAHPu5eo56+zwG9KNSdpya482SNO09NcNuR8YliKLaiZkZsEscx959L6qlvmi5i6kljp6lr3DRdZK0T8MCTK8TxwXhwj5p058oo0kIFVxR7wV48i6SUtLyQtY5PywuRcjxTAL3M0Hw9qdaZKtkUbIP5ust3nq5T7w4uMpDWXKS0SOt6KNoGZw6XCGPJYtPDC6MNYYjFpkcYS84cKQ0cECjaBjcfrhDGkqWWixcmGTIvjFhquclxZytjH1wa79XtY5QkcHZQMyM2WWz5XFzpUs2bXFhps9IOiOIYXdOYKCTRpAvLHRZb/tCSpQcZJbObTVSmMPtkQQwg7WdRlnp70sgMZS0Aktb1IITg1iMVWm7Ik8tt6t2AXhWdJuB5h8vccnjwrJsQgluOlGm6AU8utWl0giR9EwPa+nGPlMfuvruS53QX/9CHPsTtt99OuVymXC5z99138z//5/98xn/zqU99iptvvhnHcXj+85/PZz/72T1areKg0jNmC7bBXL2Lu15H7AYRc/XugTJm3SCi2Q25tNbhscU2Kx2fhhuy0vF5bLHN06sdmt1EMEIxHL1y3tmKQ9sLWWp5iYFQdcaqvl6xicoUZpt6N+Chp+s8cG6FB55c5YFzKzw0ZNYiTdJ8FqWRwUmLtDJDWasuSfN6qORMXnb9JC87NcGJiTzTZYcTE3ledv0EL7t+cujn0MZxr5/g+KE80yWH44fy3LXL4+4lz2nK49ixY/zqr/4qN910E1JK/uiP/ogf+qEf4m/+5m+49dZbt73/r/7qr/ixH/sxPvCBD/CP//E/5hOf+ARvfOMbefDBB7ntttueg79AcVBQvWkJtqGx2vVYbvuUbJOcqaNrgiiWdIOIC6sdDhUs7KtE7BTXhpJ7T59RZhhUpjC7ZEkMANJT68tqb89ODJsZymJ1SZq2SSVncvuxKjdMj/Y5lNZx9wohx6yAdWJigl//9V/np37qp7b97Ed/9Edpt9v8j//xPzZeu+uuu3jhC1/Ihz/84Ws6fqPRoFKpUK/X1cwpxcBkoZwjTeodj1/8s79jbs3lppnStp8/utDkaC3HL73hFip5+zlYoULx7KRVkrTTcSdL1oEKrmQJKSUPrWdZdjKU5+rdJNM7Jg5DP6N8FrW8kAfOrVCwjR1LTd0gou2F3HlqYugWg6ys90pH+soAyLg50j0Oum2yWwbxDQa+A/7JP/knO34YQggcx+HGG2/kn//zf86ZM2cGOm4URXzqU5+i3W5z99137/ier33ta7zjHe/Y8toP/MAP8JnPfOaqx/U8D8/zNr7vH9arUAzKQe9Na3kRtYKFF0bMN1yqORNTFwSRZK0bUCuaVPImLS+iooT1FGNImhkGlSnMFllWQ0xLrW8ndtvbM+pARZqZoaxWlxx022QvGbieplKp8Od//uc8+OCDCCEQQvA3f/M3/Pmf/zlhGPLHf/zHvOAFL+Av//Ivr+l4Dz30EMViEdu2+emf/mn+9E//lFtuuWXH987PzzMzM7PltZmZGebn5696/A984ANUKpWN/44fP37tf2yKZEW5RqG4koqTpNlnyhZtP2Sp5dP2Q2YrNrcfq1IZ48F1ioNNf0nSbMUhJhniHCM3esrOr7R3tR/3DJhq3qJoG8ppGmOyJgaQFmn29qTRO5V237FSPlY8EwO7p7Ozs/zzf/7P+Z3f+R00LbmJ4jjm7W9/O6VSiU9+8pP89E//NO9617v46le/+qzHO3PmDN/61reo1+t8+tOf5t577+VLX/rSVZ2nQXn3u9+9JUvVaDSec+cpK8o1CsWV9OYwdPyIO66boOmGBFEiR15ykodhFuYwKA4mvQyDqWs8drlFoxMQSokhBOW8OdYZBsXoUWqICWllcNLsnUo7M6QyOIqrMfBV8bGPfYy//Mu/3HCaADRN421vexsvf/nLef/738/P/MzP8Pf//t+/puNZlsWNN94IwB133MH999/PBz/4QT7ykY9se+/s7CwLCwtbXltYWGB2dvaqx7dtG9sen16LrDWipo2qy80WvTkM3zi3zErbp+SYFG2TMI5ZafuZmcOgOJiEUcyaG9DoBnhBRMkxN3oYllseLS+knDP3fYZBkZBFMYCrsZtnaVriJmmXQqrSWMVzwcBXahiGfO973+P06dNbXv/e975HFCXSj47jDH3hxnG8pSepn7vvvpsvfOEL/Ot//a83Xvv85z9/1Z6ocSNryjVpozJv2WO/zGFQHEx0TbDa8uj4EUeqm014tqFjF3UurXWI4xhdU9fvQWC/qCGO4lnay+A8udTiUt3FC2JsU+No1eG6yeJQz+S9KIVUmaGErAWhs7befga+2n78x3+cn/qpn+Lnf/7neclLXgLA/fffz/vf/37uueceAL70pS/tKCd+Je9+97t5zWtew4kTJ2g2m3ziE5/gi1/8Ip/73OcAuOeeezh69Cgf+MAHAHj729/OK1/5Sn7zN3+T173udXzyk5/kgQce4Pd+7/cG/TOeE7LciDpqVOYtu/TmMMyU7ZE9YBWKvSN5OEsp8cKYKJbomtiU0JfZeHgrRkNWxQB6pPIslZtfd9N+rUoh94asBaGztt4rGdg6v++++5iZmeHXfu3XNsrmZmZm+Df/5t/wrne9C4B/9I/+ET/4gz/4rMe6fPky99xzD3Nzc1QqFW6//XY+97nP8Q//4T8E4Pz581tKAl/+8pfziU98gl/4hV/g53/+57npppv4zGc+k5kZTqoRNUFl3rJPmnMYshyJUow3USyZLFr4jZiHLtaRInGjJCAkzFQdJooWUXxwxHrU/Zbdkq9RPkv7HbBDRXvDAVtoeLS8aCgHbD+VQo4rWQtCZ229O7GrOU49ae8szUN6Luc47cWshCygzoPiamQ9EqUYb1peyJe+d5mnljsstl2IQWgg10tNpwoO103meeXN0wdi71H3W3aRUnK56XH/uRXKTiLac6VzdK3P0jTnWWV1LlIWyNocsnFeb6pznPrJksM0DvRHX2bLDt11CU1dE+RM/cBEX7KceVPR2fTIaiRKXRPZIW9qeFFE0wt5/pEqXhgRSdBF0uf06OUWfhyTN/d/6VBW7zfFpsN7frnN9+ZaTBQTRcjZSm6Lg3Stz9L+NoI4jlntBPhhjGVo1HapNpn1UshxJmvtH1lb79UYeGULCwv87M/+LF/4whe4fPnytnkXPYEIxXZ6jajzdZevn1tGyr4yEQEnJwuZaETdLVmte1bR2fTIavmmuiayRSeIsQydqZLNctuj5Jg4ukYYxyy3PaZKNqau0QliivZ47T+jJKv3m2Krw1vKmUwUTAxNY7Hp0/Yibpgubhid1/os7QUzVzs+35trsFB38aXEEoKZisOZ2TK6JoYOZma1FHLcyVoQOmvrvRoDO04/8RM/wfnz53nPe97D4cOH1YU/LDKpqRc9RbIDdBqzWPesorPpksVIlLomskcYxTiGzpnZEgsNd32OU4ghBIdKNtPFpBJgHB/co8xs7sX9pjKxo+dKh1ciqRYsllseUyWHxabHfL3LDVNFhBDX/Cw1dI3VtsffPr2G68VMlmwcU8MNYs4vd7jccnnB0equgplK/W70ZC0InbX1Xo2Br+KvfvWrfOUrX+GFL3xhCsvZ3/Q2PYC7bphMSvUiia4npXrzdfdARPmyJgGrorPpk7VIlLomsknvwW3pGjdOF7ftwf9/e/ceJUdZ5w38W1Vd1Zfpnp5L5prrJJCQkIS7IWFfgwsKgizxuMiyCMFFF90gBGQRlVXUc4wICnjkYFBJdtllWWQBFRENYMJCAgYMmkAMIeQCZGYyM5npnr53Vz3vH53uzGTuk67uerq/n3PmwHRXd56peuqp5/dck2kr+/B22IO70D2bdt9v7Im1x7EBrwIFLUEvYikT3ZEEPLqGw9EU6qvSSGTMcT9LvS4FB0NxdIfTWNB6dApGlaGiqt6Ftw6G0RFIwOtiWeYksjVCy5bekUz46TB9+vQhw/NofAYWegoU+HQXAh4dPt0FBcqgVr5ylxv33Bz0IJrMoDuSzFZCazyOa6mfSOssTc7AlqjhOK0linlCTrkHd28sNWwZ3BtLoT5gOOrBnevZ7AglUOV2oSHgRpXbhY5QAjs+CCEUT0/4O+283+xIL2UNF/D63S7MafCj3u9GxrTQG00jnEhP6FnaHU0jkbHQFHSjL55CyrRgCYGUaaEvnkJT0I1Y2kR3lNfOSXKN0FVuF9pDcSSOzJtPpE20h+KOa4SWLb0jmXCpeO+99+K2227Dvn37bEhOeZOtVd1uuXHPZ7bV4cxZtTizrQ4LW50VNAG8biMRQiCSzKAvlkIkmTmuBpWBFdrhOK1Cyzwhp5Ee3PF0Bnu7IxBCoL7KXepk5h3bs+nRNaiKku/ZjCYzOHA4OuF7z677za70UtZIAa/f7cIJjX7MafLjpBY/zprgszSRNqFBxZzG7D58qbSJ/mQaqbSJGp+OOQ1+aFCRSFdOQ1Ahn292kqkRGpAvvcOZ8FC9yy+/HLFYDHPmzIHP54OuD/4jDx8+XLDElZtyGd9ZSDKMe+Z1G6rQQ3FkG77JPCGvY1f56kuk0RtJQkE2D7/d0Y/uSNIRw8rsmotk1/0m41xFmYw21EmBgmTawswpVWgMuCd07Ty6BsOlAAKYXufLrjZpAZqaXW0yksjAcCnDlnXlSLahprItviFbeo814ZLr3nvvtSEZlWHQcuRBz5Dx9bKM76w05TIut1DsWhTB7mVrCzlZnXlCbrkHd3soge3vh6D63Wiu9sCta45a4MPOnk077jf2xNrLroC3wW9gWp0XuzujmOsJwOMaXDVsDyUwt9mPBv/wAXE5kXXRHxkaoQeSLb0DTTjVK1eutCMdFWHgcuRb9nQjmREQQkBRFLhdCtqm+B3Vqk5ZsvWGDFTola3sXhTBrpaoSu8hG4irnR3VHUlCUYC2Kf78a05a4MPuns1C32/F6Imt9PxrR8CrqirOaqtHV38Kb3f2oyXogUfXjsw9SaDGp+PMWXVQ1fLuQeeiP8Uj8308rsApHA7nN7sNh8OjHstNcccWS2XQ0ZdALGXCgoAKBT5DQ1O1p9RJoxHIuImfHcMNijEUp9AtUbL2kNkhFE9jX3cEB0MJJNMW3LqK1qAHs6b4HZleO8kwrKwYPZuFvN/sTq9sQ6jsEvTqWNhaja5ICom0CY+uocFvHFdgM73Wh4sWt2Dr3h68fziOVEbAcCmY2+zHmbPqML3WV8C/wJlkKBPKgez38biufG1tLdrb29HY2Iiamppho8Jczwk3wB2ZEAJvHQxjf08MLTUeuF0aVFWBZQkkMyb298Tw1sEwzp5dJ03kXUlkGpdrV7Ag21AcWXvI7BCKp/Hquz3Y1x3N7iOnAsIC3uuJoTOcxJLZ9VI8tApFhrwsW8+mnemVdQiVHYareHYWoOI5vdaHqUFPQQMymchQJsiuHO7jcQVOL7zwAurq6gAAf/jDH2xNUDmLJDP4a3sILlVBQ2Boa9zBvhh2dYSwcGo1Ah5nZ5xKJcO4XDuDBdkWRZCxh8wOuUabne1h+AwNAa+er9D2J9LY2R5GwKNXVKONLHlZtp5NO9LLIVRH2V3xVFW1Yke/yFImyKpc7uNxPe2XL1+e//+2tjZMnz59yB8lhMB7771X2NSVmXA8jb5YGk3B4Qul2ioDnaEEwvE0AyeaNDuDBdkWRWALYtbARpsp/qPlj9ulwe3XKrLRRqa8LFPPJlD49HIIVVa5VDydSqYyQUblch9POGxua2tDV1fXkNcPHz6Mtra2giSqrI1Slgkx+vtE42FnsCDbBnaybaxrl1yjTW3VCA+sKiO7cWYFbU4qW16WTa4ntsZnwO92Hdd5ZANIFjfethfLBHuVy3084ZAuN5fpWJFIBB5PZXbvjle1V0eN10BvNAVvzdBT3xdLodZroNphQy9ILsVYiWvh1OCQRQam1ngws95ZiwywBXEANtoMIcswONknUx8vDqHKKpeKp5PJtC2GbMrlPh534HTzzTcDyEbk//Zv/waf7+gKK6Zp4tVXX8Wpp55a8ASWE7/bhXktAWzdexhd/UlUe13QVRVpy0I4nkHGEpjXGnB0FyU5X1GDBXH0v07cWF22yfV2YaPNUKZpYv/hOKLJDKoMDTNnBCGgOK4yUw6TqY8XG0CyyqXi6XSybIshm3K5j8ddQ9+2bRuAbLS8fft2GMbRrmLDMHDKKafglltuKXwKy4iiKDi5NYhIIoO93REc6k9ACEBRAI9LxfyWaixo4dhkOj52BwsDK3JT/O58Ra4znEQkaTquIscWRDbaHOut9hA2vNmBvd0xpDMWdJeKtik+fPTkZixoCZY6eXmc05LFBpCscql4ykCWbTFkUi73sSLExNqJP/vZz+K+++6Tdr+mcDiMYDCIUChUsr/hvd4Y/vhuN/Z2xZBIW/DoKmY3VOGs2fUVsVcCFcdwrVv1AeO4ggUhBLZ/EBrxwd0eiqOlxuPIipwdAY5MLYj55ch7otkGG2Q7DFUFmFlfVTHLkb/VHsJ/bN6PvmgaU2s9qHJriCZNfNCbQE2VjquXzXRM8BRJZvDa3sOocruG7WFIpE1Ekxmc2VZXEUGvHWWabI6tgB9b8ayECrhsZH5u2sGJ9/FEYoMJl7Tr1q2bdMIom2EO9MTgd+v4UFs9VBWwLCCezuBATwzVHp2FHhWEHcMNZF4Vp9JbEINeHUtm16Op2o2DfQkkMxbcLhVTa503N80upmliw5sd6IumsaD16MMx6NUQ9Bp462AYz7/ViXmNfmha6VvtOadlMNlWF7SDLPPy6CiZn5t2kP0+ntQVeu211/DYY4/hwIEDSKVSg9574oknCpKwcjRw2EVrzeCepVoYFTPsgoqn0MECK3JZsg6hCnp1LJ5WgzmNcj6wjtf+w3Hs7Y5haq0HAgJp04IpAE3J5t2ptR7s6Ypi/+E4Zjf4S51czmkZhgz7ptkt6NWxsLW6YjeqlQ2fm0PJfB9P+C579NFHsWzZMuzcuRNPPvkk0uk03nzzTbzwwgsIBp0xvMGpuJQoya4Yy3sLIRBJZtAXSyGSzGCCo4mLQuZ7uZDLRMsmmswgnbHg0hR0hhP4oDeOg30xfNAbR2c4AU1VkM5YiCYzpU4qgKNzWnpjqWHf742lUB8wKmpOiwzlw0B2pDcUT2PHwTB2Hgxjd2cEOw+GseNgGKEK2k5AJtwWo7xMONz77ne/i3vuuQerVq1CIBDAfffdh7a2Nlx33XVoaWmxI41lg60OJDu7JyfLMmeI97KcqtwuCAXY3xOF4dLg0TVoqgLTylZuD0eTUFUVVQ5pCS2XydSFIkv5kGNHemUbIkxc1KPcTDi83bNnDy6++GIA2dX0otEoFEXBTTfdhAcffLDgCSwnA1sdLGGhJ5pEe18cPdEkLGGx1YEcz84NAnMVgo5QAlVuFxoCblS5XegIJbDjg5CjWlPZgiinGbUeBL0udIRSCLh16KoKFQp0VUXAraMjlEKtz4UZtc7ZkzA3p6U56EE0mUF3JJkdIlrjqahKskzlA2BPeo8dIuzRNaiKkh8iHE1mcOBw1PG9cJWGG+uWlwk3q9XW1qK/vx8AMHXqVOzYsQOLFi1CX18fYrFYwRNYTnKtDjs+CKGrP4lDoQRSQsBQFDQGPWgIuLFoWpCtDuRodkxOlm3OEFsQ5ZQwgflN1egKJ/BuVwQNATc8uopE2kJXfxJT/C7Ma6xGwgT8zuh0AiD/ZOrjNbB8aK72IH5kNUFNVdBc7UFHOOGo8sGu8oyLDMiLi3qUjwnfWR/+8IexYcMGLFq0CJdddhluvPFGvPDCC9iwYQPOO+88O9JYNhRFgUtT8Jf3Q+iNptBY7UatriGetvDWwTBqqwycOqPGEQU/0WgKXZGzu0JQ6KXIOYSqeAp57TKmhdZaHz5xylS8+m4P3uuN5/dxmj3FhzPb6hHw6I4cYinzZOrjlSsfDE3Fnq4IQrE0MkLApSgI+nTHBQx2lWfFGCIsw750sqr0BpByMeES5sc//jESiQQA4Otf/zp0XcfmzZvxqU99CrfffnvBE1hOLMvCX9vD0FQFU/wGusLJfOE/xW8AioJdHf04qSnA1XHI8QpZkbOzQmDXvAi2INqv0NcuN8SyxmvgjBk1qKtyI57OwKu70FbvzVd0OcTSWTKmhXAijb54GvGkCY+uwq2psCyBQ+EkwokMan3OCXjtKs/sXmVRtjlkMqrkBpByMeGrV1dXl/9/VVVx22235X+Px+OFSVWZ6oqk8E5nBC4VUBT1SBe+AKBAKAJCCOzu6EfXnBSaqp0zxp7IbnZVCOyeSM0WRPvYce2qDA2GrmLr3sPw6i5Mr/PlewoPxzL4INSHD82u4xBLh9FUBT2RFA5Hk9A1Fd2RJCwhoCoKAh4XIsk0hCWgqc647+wqz+wcIsxFJ4jGpyDNaslkEj/84Q/R1tZWiK8rW/FUBl2RFFIZC+mMhb54CofjKfTFU0hnrOw4+2gK8ZQzlsIlKhY7ll0u1kTqSl7e2y52XjsF2eujKOLIv4VBv0Pw+jlRIp1BZziJ3ngaHl1DtUeHR9fQG0+jM5xEPOOc5+bA8kwIgVgqg/5EGrFUdjnyyS4jb9ciA1x0gmj8xt3jlEwmcccdd2DDhg0wDAO33norVqxYgXXr1uHrX/86NE3DTTfdZGdapWcJIJbMwBQWdE2D1zi6FG44kUHaNKEpKiyWTVRh7JgzxInU8rLr2kVTJpJpE4un1aA3lkI4lkZGZLLDpQNu1HgNJNMm84TDZEwLGSGgadmwVwz4UQBomoKMBccM1cuVZx2hBF7Z2wMhkE+3ogCz6qsmPQfSjiHCLCuJxm/cd8A3vvENrF27Fueffz42b96Myy67DJ/97Gfxyiuv4Ic//CEuu+wyaBqHN4wm6MkGSj39GcxpODoUT1UV6B4Ve7qSaKp2I+jheaTKU+gKAfdakpdd1y73vQ0BN+r9BuJpE6aZrZB7dQ2WBXRHkswTDpMyBYQFzKqrQiJjIprIIA4BDQpqfDqaNA+SGRMp04GtjgJQBKCoACwABejQLPQQYZaV8uOiHsUz7sDpF7/4Bf7jP/4Df/d3f4cdO3Zg8eLFyGQy+POf/8yLM04ZoWBKIPuwPhiKwe92QVNVmJaFSDKDgMeFer+BDIeKHDcWInIqZIXA7onUZB+7rt2x3+vTXcCAeDxtmswTBVLIMtjQFHjdGoQlML3Oh2TGhGkBmgq4XRq6+hPwuTUYmjPK+NzQNwA4e079kAC9I3T8y6cXcpEBlpVy46IexTXuu+7999/HGWecAQBYuHAh3G43brrpJlZGJ8DQFLQGfQCA93vjONibQEZYcCkqGqoNtAa9aK3xOabwlxULEbkVqkLAvZbkZde1Y54ojkKXwborO9fmUDiJnkgSAY8Oj0tFxrLQE0lC1zQ0Vruhu5xx3QYOfVOgDAnQnTb0jfeFvLioR/GN+441TROGcXT8q8vlgt/vtyVR5Up3aQh6Xdh32ELAcKFxqhuaosAUAvGEiZRlIehxOabwlxELEcrhXkvysuvaMU/Yz67VEGfU+ZAxBSwhhsxNU6BgZr3PMRV72Ya+8b6Qk2wbx5eLcQdOQghcc801cLvdAIBEIoEvfOELqKqqGnTcE088UdgUlhGfrkLTFBiaioYpbvRGM8hYJlyqhplTPOiLp+ByqfDp7A6fDBYi5aGQQ3y415K87Lp2dueJSh4mbFcZPLBiH0mkUe83oEGBCYFEyoTfozuqYi/j0LfcfbGvO4KDoQSSaQtuXcXUGg9m1vtZVjoQF/UojXGfyZUrVw76/TOf+UzBE1PuYmkLhktDfZUbXZEk0pYFYQFpmDgcE2jwu6FrKmJpC363cwpUWbAQkZ8dwyxl3WupkivgOXZdu6BXx8LWanRFUkikTXh0DQ1+47g3Hq/0YcJ2lsHHBrwJKxt8tNZ6HdcIIv3QN3H0v1yB3Llk69ksF+MuudatW2dnOipCLpPrmgIFCgxVg+ICxJGVdlyawkx+HFiIyM3OYZay7dZe6RXwgey4dsOd387jPL8cJmx/GWxnI0ghGypkHPo2MP9O8bvz+bcznEQkaVZE/pWNjD2b5UCemkQZ0FQFvZEk0qbAomnBISsDtffF0RdNOmb3c9mwEJEXh1kexQq4vew4v8XIvzL0QBajDC5WIF2Inm5Zhgkz/8qpGD2bvG5DMXAquiO71kOBxzXM6edS5JMm/fCICsZhllkMIO1l1/m1O//K0gMpYxlsZ0OFLD1kA/OvECK7fLoloKnZ5dMrJf/Kxu6eTV634ZVvDcSBTEtkJ7XGFXT1J+DWVSiKAiEEkmkLfo+OWp8O0+Kg4smQcXgEZXGYZRYDSHvZdX4H5l8BMWTfnuPJvzL1QMpWBhejoUKGHrJc/k2ZFt7vjSEUSyMjBFyKgqBPR0PAUxH5V0Z29Wzyuo2MT94icmkqqj06NEXBO9EI9ndHkRIChqKgKejB1Bovqtyu4xrGUOndqjINj6CjOMwyiwGkvQYFOMO0rE/2/Oby7+FoCr2x1JHlsrMVz2qfjlqfMan8K2MPpExlsIwNFXZUaF2aimTaxIHDUWRMoNrryge8Xf3ZPN0U8FRE/pVRoXs2ed1G54ySoEJUGRrcuoY/v98Hr65ibnMAqqrAsgSSGRPvdEXwobb6SQ9jYLdqlqyrqFUyGYf42IEBpL1y57f3SIBzbMv6ZAOcXNn+x7098BkaAh49X/HsiSTx3uHYpMr2YlTs7Whsk6UMlq2hwq4KrU9XkTRNdPWnMLcpkH/d7dLQENDwdmc/aquMCW+VImNgKqtC9mzyuo2u8v7iEhP5dT5VuHUNuqoibVlIZgQAC1AmN0yP3aqDybaKWqWTbYiPXRhA2qvK0GDoKrbuPQyv7hrSsn7gcAwfml03qfObK9tFbp7qkaI8//skyna7K/Z2NrbJUAbL1lBhV4U2t1VKQ8CN7kgCAY8Ol6oiY1noT6TREJjcVimyBaaUxes2OmeXamUmmjKRSls4ZVpNvrUzcmT384aAgVqfH8mUNeFCj92q8qv0IZaAXEN87MIA0n5KboEeJRfoYNDvk1mgx66y3c6KPRvb5GuosKtCmzEteFwa5jUH0BlOHBlqms2/UwJuNPo9iKfNSQ9hlSUwpSxet9ExcCqiXKHXEHCj1qejN5ZGKmPBcKmo9ekQUNAdSU64cGK3qtwqbYjlaEGiLEN87MQA0j7RlIlk2sTiIwHOsRXEGq+BZNqc9OIQDQE36qqMIXOnLIFJle12VezZ2JYlW0OFXRXa3PcamooTGv1DFjdJpq3sd09iCKtMgSll8bqNjrXoIhptfH3vcYyvZ7eqvCqt1Xc8QaIMQ3zsxgDSHgMDnHq/MaSCaFmTC3COrdD6jMH5N50xJ1W221WxZ2PbUTI1VNhVoT32e326CxjwZ0/2e2ULTCmL12105V0iOoxd4+vZrSqnSmv1LXWQKNtwSAaQhTckwDmmgpg2Jxfg2NlCa0fFno1tg8nSUGFXhdbOirJMgSkdxes2Mj6Vi8yO8fXsVpVTJbX6ljpIrLThkDQ8u8pKu1toC12xZ2PbULI0VNhVobWzoixLYEqD8boNz/mlRBmxa3w9u1XlVEmtvqUMEkvd00XOMbCsPNgXg8fQoEGBCYFEyoTfozu2Zb2QFXs2tsnNrgqtnRVlWQJTGozXbSiejSKya3w9wG5VGVVSq2+pgsRS93SR8wS9OmbU+7B1bw92dfQjlREwXAqm1/mw4DiDaFlaaNnYJj+7KrSsKNtLtiHjNBTvjiIaWFF2D7OR3PFWlGV5aFNWJbX6lipIHNjTJSCGNFY4dQNRsk8onsaBnhiqDBdOnV4LVQUsC4inMzjQE0O1Rz+u4EmWiicb24iKi0PGy4PzS/cykqsov9sVhSXEkaF62VX1qn06VEXBnMaq46ooy/LQpspq9S1VkJjr6UqZFt7rjQ2555oCHsduIEqFN7AHsrXGN+i9WhgV1wPJxjai4uCQ8fLBGnYRKYqC2ioDnXt60BdLoyXoQdBwIZ4ysbszghqfjjNm1VbUQ6vSW+srpdW3VEGiS1ORyJg4cDgG07IQ8Oj5f7cnkkRfLI3Gajc3EK0QlbQgy3ixsY3IXhwyXl5YWhaREAK90RSaqt1oDLgRiqURiqfhUhTMbfJDUYC+WArTarwVcfOwtT6rUlp9SxEk+nQVqYyJrv4k5jYF8q+7XRrcfg1vd/ajtkqHb5ihs6Phg1BOlbQgCxE5AxtsyguvUBHlbp6pNT64XeqQ3eWTGatibh621g9WKa2+xQ4SY2kLbk1DQ8BAV38yu3eaqiJtWQjHM2gIGDBUFbG0Bb97/METH4RyqqQFWYjIGdhgU15K+nRYs2YNzjrrLAQCATQ2NmLFihXYtWvXqJ9Zv349FEUZ9OPxeIqU4uMz8OZRFAU+w4WAR4fPcEFRlIq5eY5trffoGlRFybfWR5MZHDgchchtckVlJRck1vgM+N0uW3tkMqYFt65hXnM1GgIGEikTvfEUEikTDQEDc5uq4da1Cd9zxXgQCiEQSWbQF0shkszwfiiA3Fy73lhq2Pd7YynUB4yyWJCFiJxhYIPNcNhgI5eSNoVu2rQJq1atwllnnYVMJoOvfe1r+NjHPoa33noLVVVVI36uurp6UIAly1CYQavqDdPjVCk3TzFWOiMCjt5zhqZiToN/2F7eXM/XZL7Xrp4LDmO1RyUtyEJEzjBwcaTmoGdInaecVtCtBCWtlT777LODfl+/fj0aGxvx+uuv48Mf/vCIn1MUBc3NzXYnr+COrqoXgRBAaMAKX0GfDkUB5jT6y/7msXulM6KcY1fz8xmDi7zJPrDsXCWQw1jtVSkLshCRM+QabDpCCbyypwcQyI73sgAowKwpVY5tsKn0BbyG46jm/FAoBACoq6sb9bhIJIKZM2fCsiycfvrp+O53v4uTTz552GOTySSSyWT+93A4XLgET9DRVfWS6I2mUO8/2tq5q6MftVUGzphVV/aZ0s6VzogGsquHwa7v5aITxVEpC7IQkcMoA+ImBVAdXOTYOfJB5oDMMYGTZVlYvXo1zjnnHCxcuHDE4+bNm4eHHnoIixcvRigUwt13341ly5bhzTffxLRp04Ycv2bNGnzrW9+yM+njlltVL+BxIZE2sa8ripQQMBQFTTUeBDyuilhVz66VzoiGY1cPgx3fW4xFJ2R+YBVSpSzIQkSllWsQA4Cz2+qHDBnvCCcc1yBm58gH2YeiK8IhM46/+MUv4re//S1eeumlYQOgkaTTacyfPx9XXHEFvvOd7wx5f7gep+nTpyMUCqG6urogaR+vSDKDTX89hM7+BDKmgFvPLhIhhEAybcGlKWgKeLD8pMayfqAPPg8YstKZS0NFnAcqLrsChkJ+b18shdf29aIh4IY6zHeYlkB3JIkzZ9WiZoTgajSyP7CIyF5sWCm8SDKD1/YeRpXbNeyc2ETaRDSZwZltdY6o8wghsP2D0IhD0dtDcbTUeCYV6I0UkOVGapRqKHo4HEYwGBxXbFD6KwTg+uuvx9NPP40XX3xxQkETAOi6jtNOOw3vvPPOsO+73W643e5CJPO4pTMm2kMJmEKgIeBGMmPCtABNUxDw6OjqT6I9nEA6YwIOuHnsMnCls0PhBEKxNCIiA5eioCFgoCHgQSJtOnKOEx8q8rKrh6GQ32vnohOcO0VUfDI9M9iwYg/ZliO3a+TDwKHozdXZRTKiyQw0VUFztceRPW/DKWntXAiBL33pS3jyySexceNGtLW1Tfg7TNPE9u3bcdFFF9mQwsJKmQKxVAZul4YDh6Poi2XyhWmNzwWvy4VYMoOU6YhOQNvYtdKZ3fhQIbvZtegE504RFZ9Mzww2rNhHtv3j7Ar0cgGZoanY0xUZskCaLCsqlzRlq1atwiOPPIJf/vKXCAQC6OjoAAAEg0F4vdmH+9VXX42pU6dizZo1AIBvf/vbOPvss3HCCSegr68Pd911F/bv34/Pfe5zJfs7xsvQFKiKgrc7+5EyLUAAigoIC+iJJGFoKtqmVMHQyrviMmhpzuqhe3A5cWlOPlSoGOxadIIb9hIVl0zPDDas2MvOVVjtYFeglzEthBNp9MXTSKSs7DSNI8+3rv4UwokMan26Y3reRlLSJ+QDDzwAADj33HMHvb5u3Tpcc801AIADBw5AVY9enN7eXnz+859HR0cHamtrccYZZ2Dz5s1YsGBBsZI9aS4tO4+nO5KEpgJVbh2aqiAjBKLJNMIWMK3Oe1ytDjIMCxi0NOfeHggBKMiuNKMowKx6Zy3NyYcKFZMdi07INlSEhpKhbKcs2Z4ZbFixl2z7x9kV6Gmqgp5ICrFUBq01vvzrbpeGhoCGg30xiCOjj5ys5EP1xrJx48ZBv99zzz245557bEqRvYQQiCUzcLsUqKqC7v4kTGFBU1QEfTosy0I8lRnXeRmOTMMC8gSgHOl5y+1p4DRc6YyKrdDLZcs2VIQGk7Jst4kMZaVsgQgbVuwn0/5x9gZ6Y9RvFedPVSn9HVtB+hPZOU2qml2Ou8HvhktTkDEFIqk0DJeGdMZCfyKDau/EVsyScVgAAJw9p37ILtodIWdNELT7ocJKEQ2nkItOFGOoiAwV2oFkSa9MZbvdZCkrZQtE2LBSHDLtH2dHoGdaArV+N7R4Gt2RBAIeHS5VRcay0J9II+DVUe3VYVrODp4YOBWREALxTG7DVwOxlAlTAKqmoCXoRSqTfX+iPU4yDwtQoMCnu4AB96DTWuO40hmNRoYKuN1DRWSp0OaE4mns647gYCiBZNqCW1fRGvRg1hS/o9IrW9k+UKHvC5nKStkCEdnm4MhMpv3j7Bj5UOPREfTo6I2lEI6lkTmyovKUgBs1XgPKkeOcTI6rVyZcmgoVCqAKNAU8SJsCphDQFAW6pqA9HIeGiRemHBZgL650RiORKWCwa6iITBVaIJveV9/twb7u6KAFet7riaEznMSS2fWOSa9sZXtOoe8L2cpK2QIR2ebgUPHYNfLhhEb/sKONnHRfjMQ5JW0FcLtUtNR4cLAvjr54GlVuF9xHgoS+eBoA0FLrgXuEgGIksgUisrXGcaUzGo5sAQNQ+BZE2Sq0Qgi8dTCMne1h+AwNAa+ev5f7E2nsbA8j4NFx9uw6R6RXtrIdsOe+kK2slDEQkWkOTjHIMJJANgPvi45QArU+A25DQ9rMlg9OvC+GU/oSpoLoLg1tDX6oioKuSBK9sSSElW3xNDQV02t9mDmlCrprYtG2bIGIbK1xAFc6G04lP1hkCxgGKmQLomwV2kgyg7+2h+BSFUzxH90Kwe3S4PZnV3Xa1RHCwqnVCHhKX1GUrWy3674oRllZ6PJMxkBEpjk4dpJpJIFsZLwvjlX6J1kFqTI0zKjzIRRPI21ZONibQMo0YWgaGgM6aqoMzKz3TThgkC0QkbE1DuBKZwNV+oNFtoDBLrIF/+F4Gn2xNJqCQ/ePA4DaKgOdoQTC8bQjAifZyna77gu7y0q7yjMZAxGZ5uDYQcaRBLKR8b4YqHLvjhJQFAW1VQYiiQySKQsnNPnhdqlIZiz0RlKIJDKo8RkTzjwyBiKytjrIttKZHfhgkS9gsIuUwf8oxaAQo79fbLKV7XbdF3aWlXaXZ5UeiMhE5pEEVDy8m4tICIHeaApN1W40BtwIxdJImwKGqmJucwCKAvTFUphW453wTSljICJ7q8Pxkq1SBPDBkiNlwGAD2YL/aq+OGq+B3mgK3pqhj7++WAq1XgPVDiovZSrb7bov7CorWZ7RQBxJUByyj1jhlS+i3E05tcYHQ1PQG0sjlbFguFTU+nSkTHFcN6WMgUilt8bJVCkC+GDJ4b5IWbIF/363C/NaAti69zC6+pOo9rqgqyrSloVwPIOMJTCvNeC4vCtL2W7nfWFHWcnyjAbiSAL7lcOIFZYERZS7KVOmhfd7EwjF0sgIAZeioNenoyHgOe6bstIDERnJUikC+GDJ4b5IR8kU/CuKgpNbg4gkMtjXE0UonoaC7F72qgLMb6nGghZn9i7IULbbfV8UuqxkeUYDcSSBvcqlh9fZpXCZcWkqkmkTBw5HkTGRbe088lDp6k+hN5ZCU8DDm7ICyVApAvhgGYj7Ih0lU/Af9OpYMrseTdVuHOxLIJmx4HapmFrrwcx6Z22AKyO7A+lClpUsz2gg2YYey6Zcenidm7Iy5NNVJE0TXf0pzG0K5F93uzQ0BDS83dmP2ioDPp2FNDkTHyyDVfq+SAPJEvwD2eu2eFoN5jQ6P9CTkSyBNMszGki2oceyKZceXjmecmUilrZguDQ0BNzojiQQ8OhwqSoyVnbzxYaAG7qmIpa24HczeCLn4YNlKLv2RRJCZHdWtwQ0NbuzupNb5GSYkzWQTIGejGQ4vyzP6FgyDT2WTbn08Dq7VCszGdOCx6VhXnMAneEEwrE0MiIDl6JgSsCNRr8H8bTp+GibKhsfLPYZPA8yNmgeZLBA8yDtINOcLKKBWJ7JzY4GG1l6TGVTLj28DJyKKBdtG5qKExr92dZkU0DTsq3JybSVjbgdHm0T8cFiDxnnQco4J4toIJZncrKzwUaGHlPZlEsPr3OevhUgF233xlLDvt8bS6E+YDg+2iYCjj5YanwG/G6X4ws7GQycB9kQcMPt0qAqypF5kG509aeQsizHzIM8dk6WR8+mNzcnK5rM4MDhKIQQpU4q0ahYnskl12DTEUqgyu1CQ8CNKrcLHaEEdnwQQiieLnUSaRi5Ht7moAfRZAbdkWT2+VHjkaaRjeF0EeWi7Y5QAq/s6TmyBi4AC4ACzJpSJUW0TUT2kG0eZLmskkQkG9nmFBaSzIvokPw9vHySlYoyIG5SsnuIEFFlk20eZLmskkQkk1A8jX3dkUHL6bfWeDBrSmUsp88GG/nJPBRSzlRLKtdKAgBnt9UPWTGrI5xgKwlRBZNtHmS5rJJEJItQPI1X3+3Bvp4ohMg2uloCeK83hs5wEktm15d98MQGGyolPs2KaGAriaIo8BkuBDw6fEZ2PPXAVhIipxNCIJLMoC+WQiSZ4TyWAhg4D1KBAp9+pIzQXVCgOG4eJOdtEhWPEAJvHgxhZ3sYEAqCXh11Ve5soCQU7GwP4632UNmXxQMbbIbDBhuyE3ucioitJFQuuPy0PWRbdUi29BLJLJLMYFd7P1yqgoaAO/96dvEYDQf7TOw62I+FrUEEPOVbDpfLstYkJwZORcRhLVQOuPy0vWTbV0a29BLJKhxPoy+eQlO1Z9j3a3wGDvUnEI6nyzpwYoMNlRIDpyJiKwnJjqsZDcUNGOVLL5G0RhmFpyijv19O2GBDpcLAqYgGtpIc7IvBq7ugKIAQQDydgd+js5WEHI2rGQ3GDRiPki29RLKp9uqo8enojabgrRl6r/VGU6it0lFdIUEDG2yoFPiUK7KgV8eMeh+27u3Bro5+pDIChkvB9HovFnCIEzkc5+kdxSGLRFRMfrcLJ7UE8ce9PcPu85axBOY1ByuqAYMNNlRszG1FFoqncaAnhirDhVNm1EBTFJhCIJEycaAnhmqPzsoWORbn6WVxyCIRFZuiKFjQWo3+RBr7uqMIx9JHNoMEoALzW6qxoLWaZQ6RjRg4FdHAylZz0IPeWBqxtAnDpaI56EFnOMnKFjka5+llccgiEZVC0Ktjyex6NFW7cTCUQDJtwa2rmFrjwcz6ytgAl6iU+EQvolxlK21a2LjrEA6Fk0ibArqmoLHajRMaA6xskaNxNaMsDlkkolIJenUsnlaDOY2c20NUbKydF1HGtNAejuOv7f2IJk1M8Rtw6yqSaQvvHU6gJ5rG/JYAK1vkaFzNiEMWiai0OLeHqDR41xWRqgB7DkUQiqUxu8Gff91nqJhR58K7XRG82xWFykYjcrhKX82IQxaJiIgqDwOnIoqlTMRSGXhHqEx5DQ2xZBqxlInqoXUxIkep5BZPDlkkIiKqPJVZ6ymRZMZCla5DUYHeeAp+wwVNVWBaApFUJlsJFdnjiJzOjo1fZcIhi0RERJWFgVMReXQNAa8Lbl1DMm2iP5GBJQRURUGd14Chq0imzWHnTBA5iZ0bv8qk0ocsEhERVRIGTkXU4Dcwrc6L3Z1RnNjoRzJjwbQENFWB26Vi96EI5jb70eAffoljIifgxq+DVfKQRSIiokrCJZ+KSFVVnNVWjxqfjt2HIkibFgxXdl7E7kMR1Ph0nDmrDqrKy0LOdOzGrx5dg6oo+Y1fo8kMDhyOQghR6qQSERERFRSbSYtseq0PFy1uwda9PXj/cBypjIDhUjC32Y8zZ9Vheq2v1EkkGhE3fiUiIqJKxZpNCUyv9WFq0IOuSAqJI3OaGvwGe5rI8bjxKxEREVUqBk4loqoqmqo9pU4G0YRw41ciIiKqVKzdENG45TZ+7Y2lhn2/N5ZCfcDgxq9ERERUdhg4EdG45TZ+rXK70B6KI5E2YVoCibSJ9lCcG78SERFR2eJQPSKaEG78WhyVvsEwERGR0zBwIqIJ48av9uIGw0RERM7DwImIJoUbv9qDGwwTERE5E+c4ERE5BDcYJiIici4GTkQ0KUIIRJIZ9MVSiCQzrMwXwEQ2GCYiIqLi4jgbIpowzsGxBzcYJiIici4GTjQuXOGLcjgHxz7cYJiIiMi5GDjRmNi7QDnHzsHJyc3BaQ/FceBwFAtbgwysJyG3wXBHKDHo/Ob0xlJoqfFwg2EiIqISYOBEo2LvAg00kTk4XHFv4nIbDPcnMmgPxVHrM6BrKtKmhd5YihsMExERlRDHe9CIuMIXHYtzcOyX22C4OehBNJlBdySZvQdrPGyoICIiKiE2CZeIDHOG2LtAx+IcnOLgBsNERETOw9puCcgyZ4i9C3QszsEpHm4wTERE5Cx8KheZTHOG2LtAx+IcHCIiIqpUrPEW0cA5Q81BDywIRJMZWBD5+QxOmjOU613ojaWGfb83lkJ9wGDvQoXhHBwiIiKqROxxKqLcnCFdU/HOoQjCsTQyQsClKKj26Y6bM8TeBRoJ5+AQERFRpSlpj9OaNWtw1llnIRAIoLGxEStWrMCuXbvG/NwvfvELnHTSSfB4PFi0aBGeeeaZIqT2+GVMC32JNN7rjaEnkoTH0FDrM+AxNPREknivN4a+RNpRc4bYu0Ajyc3BqfEZ8LtdDJqIiIiorJU0cNq0aRNWrVqFV155BRs2bEA6ncbHPvYxRKPRET+zefNmXHHFFbj22muxbds2rFixAitWrMCOHTuKmPLJ0VQFvZEk+uNpTPF74HZll/d2uzRM8XvQH0+jL5qEpjqrAprrXTizrQ5nzqrFmW11WNjKoImIiIiIKocinDKhBkBXVxcaGxuxadMmfPjDHx72mMsvvxzRaBRPP/10/rWzzz4bp556Kn7yk5+M+W+Ew2EEg0GEQiFUV1cXLO3j0Z9I4/HX3kcslUFrjW/I+wf7YqgyXPjUmdMQ8DAoISIiIiKy00RiA0ctDhEKhQAAdXV1Ix6zZcsWnH/++YNeu+CCC7Bly5Zhj08mkwiHw4N+SsW0BOr9BgJeHV39SSQzJixLIJkx0dWfhN+jo85vwLQcE8sSEREREREcFDhZloXVq1fjnHPOwcKFC0c8rqOjA01NTYNea2pqQkdHx7DHr1mzBsFgMP8zffr0gqZ7IlyaimqPjhm1PjQEDCRSJnrjKSRSJhoCBmbW+VDt0bm8NxERERGRw5R+6bYjVq1ahR07duCll14q6Pd+9atfxc0335z/PRwOlyx4Grh56JwGP+JpE6YloKkKvLqGjnCCm4cSERERETmQIwKn66+/Hk8//TRefPFFTJs2bdRjm5ub0dnZOei1zs5ONDc3D3u82+2G2+0uWFqPx8DlvTvCCdT6DLgNDWnTQkc4weW9iYiIiIgcqqRjwoQQuP766/Hkk0/ihRdeQFtb25ifWbp0KZ5//vlBr23YsAFLly61K5kFxeW9iYiIiIjkU9Iep1WrVuGRRx7BL3/5SwQCgfw8pWAwCK/XCwC4+uqrMXXqVKxZswYAcOONN2L58uX4wQ9+gIsvvhiPPvooXnvtNTz44IMl+zsmipuHEhERERHJpaQ9Tg888ABCoRDOPfdctLS05H/+53/+J3/MgQMH0N7env992bJleOSRR/Dggw/ilFNOweOPP46nnnpq1AUlnIibhxIRERERycNR+zgVQyn3cSIiIiIiIueQdh8nIiIiIiIiJ2LgRERERERENAYGTkRERERERGNg4ERERERERDQGBk5ERERERERjYOBEREREREQ0hpJugEtERFQKQghuQk5ERBPCwImIiCpKKJ7G/p4oDkdSyFgCLlVBnd/AzPoqBL16qZNHREQOxcCJiCoCexgIyAZNOz4IIZrMoNZnwHCpSGUsdIQS6E9ksHBqkMETERENi4ETEZU99jAQkA2e9/dEEU1m0BL05l/36Bpagl60h+I4cDiKha1BBtVERDQEA6cSYes3UXGwh4FyoikThyMp1PqMYd+v9Rno6U8hmjLhd/PxSEREg/HJUAJs/SYqDvYw0EAZ00LGEjBcwy8oq2sqMpZAxrSKnDIiIpIBlyMvslzrd0cogSq3Cw0BN6rcLnSEEtjxQQiheLrUSSQqGxPpYaDy59JUuFQFqczwgVHatOBSFbg0PhqJiGgoPh2K6NjWb4+uQVWUfOt3NJnBgcNRCCFKnVSissAeBhqoytBQ5zfQG0sN+35vLIX6gIEqQytyyoiISAYMnIqIrd9ExcUeBhpIURTMrK9ClduF9lAcibQJ0xJIpE20h+Kocrswo66KwzaJiGhYrC0UEVu/iYqLPQx0rKBXx8KpQTQHPYgmM+iOJLOjAGo8XCiEiIhGxcUhimhg67dHH1pRY+s3UWHlehj6Exm0h+Ko9RnQNRVp00JvLMUehgoV9OpYNDXIlU2JiGhCGDgVUa71uyOUQHO1B/Ejw0Q0VYFX19AbS6GlxsPWb6ICyvUwHLuSZUuNBzPquJJlpVIUhUuOExHRhPCpUUS51u+OUAKv7O2BEIACQABQFGBWfRVbv4lswB4GIiIiOl4MnEpFAIoAFBWAhWwERUS2YQ8DERERHQ/WIoootxw5AJw9pz47VM8U0LTsUL2OUIKbcRIRERERORADpyIauBy5AgU+3QUMmF4xcDlytowTERERETkHl28rIi5HTkREREQkJwZORcTNOImIiIiI5MQaehFxM04iIiIiIjkxcCqi3HLkVW4X2kNxJI7s45RIm2gPxbkZJxERERGRQ3EFgiLjZpxERERERPJh4FQC3IyTiIiIiEguDJxKhJtxEhERERHJg3OciIiIiIiIxsDAiYiIiIiIaAwMnIiIiIiIiMbAwImIiIiIiGgMDJyIiIiIiIjGwMCJiIiIiIhoDAyciIiIiIiIxsDAiYiIiIiIaAwMnIiIiIiIiMbAwImIiIiIiGgMDJyIiIiIiIjGwMCJiIiIiIhoDAyciIiIiIiIxuAqdQKKTQgBAAiHwyVOCRERERERlVIuJsjFCKOpuMCpv78fADB9+vQSp4SIiIiIiJygv78fwWBw1GMUMZ7wqoxYloWDBw8iEAhAUZSSpiUcDmP69Ol47733UF1dXdK0lCOeX/vxHNuL59dePL/24vm1F8+vvXh+7eWk8yuEQH9/P1pbW6Gqo89iqrgeJ1VVMW3atFInY5Dq6uqSZ5pyxvNrP55je/H82ovn1148v/bi+bUXz6+9nHJ+x+ppyuHiEERERERERGNg4ERERERERDQGBk4l5Ha78c1vfhNut7vUSSlLPL/24zm2F8+vvXh+7cXzay+eX3vx/NpL1vNbcYtDEBERERERTRR7nIiIiIiIiMbAwImIiIiIiGgMDJyIiIiIiIjGwMCJiIiIiIhoDAycbPTiiy/ikksuQWtrKxRFwVNPPTXmZzZu3IjTTz8dbrcbJ5xwAtavX297OmU10fO7ceNGKIoy5Kejo6M4CZbMmjVrcNZZZyEQCKCxsRErVqzArl27xvzcL37xC5x00knweDxYtGgRnnnmmSKkVj6TOb/r168fkn89Hk+RUiyXBx54AIsXL85vrrh06VL89re/HfUzzLvjN9Hzy7x7fL73ve9BURSsXr161OOYhydnPOeXeXj87rjjjiHn6qSTThr1M7LkXQZONopGozjllFNw//33j+v4vXv34uKLL8ZHPvIRvPHGG1i9ejU+97nP4Xe/+53NKZXTRM9vzq5du9De3p7/aWxstCmFctu0aRNWrVqFV155BRs2bEA6ncbHPvYxRKPRET+zefNmXHHFFbj22muxbds2rFixAitWrMCOHTuKmHI5TOb8Atld1gfm3/379xcpxXKZNm0avve97+H111/Ha6+9hr/927/FpZdeijfffHPY45l3J2ai5xdg3p2srVu3Yu3atVi8ePGoxzEPT854zy/APDwRJ5988qBz9dJLL414rFR5V1BRABBPPvnkqMfceuut4uSTTx702uWXXy4uuOACG1NWHsZzfv/whz8IAKK3t7coaSo3hw4dEgDEpk2bRjzm05/+tLj44osHvbZkyRJx3XXX2Z086Y3n/K5bt04Eg8HiJarM1NbWip/97GfDvse8e/xGO7/Mu5PT398vTjzxRLFhwwaxfPlyceONN454LPPwxE3k/DIPj983v/lNccopp4z7eJnyLnucHGTLli04//zzB712wQUXYMuWLSVKUXk69dRT0dLSgo9+9KN4+eWXS50caYRCIQBAXV3diMcwD0/eeM4vAEQiEcycORPTp08fs4WfskzTxKOPPopoNIqlS5cOewzz7uSN5/wCzLuTsWrVKlx88cVD8uZwmIcnbiLnF2Aenojdu3ejtbUVs2fPxpVXXokDBw6MeKxMeddV6gTQUR0dHWhqahr0WlNTE8LhMOLxOLxeb4lSVh5aWlrwk5/8BGeeeSaSySR+9rOf4dxzz8Wrr76K008/vdTJczTLsrB69Wqcc845WLhw4YjHjZSHOY9sdOM9v/PmzcNDDz2ExYsXIxQK4e6778ayZcvw5ptvYtq0aUVMsRy2b9+OpUuXIpFIwO/348knn8SCBQuGPZZ5d+Imcn6Zdyfu0UcfxZ/+9Cds3bp1XMczD0/MRM8v8/D4LVmyBOvXr8e8efPQ3t6Ob33rW/h//+//YceOHQgEAkOOlynvMnCiijFv3jzMmzcv//uyZcuwZ88e3HPPPXj44YdLmDLnW7VqFXbs2DHqGGWavPGe36VLlw5q0V+2bBnmz5+PtWvX4jvf+Y7dyZTOvHnz8MYbbyAUCuHxxx/HypUrsWnTphEr9zQxEzm/zLsT89577+HGG2/Ehg0buACBDSZzfpmHx+/jH/94/v8XL16MJUuWYObMmXjsscdw7bXXljBlx4+Bk4M0Nzejs7Nz0GudnZ2orq5mb5NNPvShDzEYGMP111+Pp59+Gi+++OKYrWoj5eHm5mY7kyi1iZzfY+m6jtNOOw3vvPOOTamTm2EYOOGEEwAAZ5xxBrZu3Yr77rsPa9euHXIs8+7ETeT8Hot5d3Svv/46Dh06NGg0hGmaePHFF/HjH/8YyWQSmqYN+gzz8PhN5vwei3l4/GpqajB37twRz5VMeZdznBxk6dKleP755we9tmHDhlHHjNPxeeONN9DS0lLqZDiSEALXX389nnzySbzwwgtoa2sb8zPMw+M3mfN7LNM0sX37dubhcbIsC8lkctj3mHeP32jn91jMu6M777zzsH37drzxxhv5nzPPPBNXXnkl3njjjWEr9czD4zeZ83ss5uHxi0Qi2LNnz4jnSqq8W+rVKcpZf3+/2LZtm9i2bZsAIH74wx+Kbdu2if379wshhLjtttvEVVddlT/+3XffFT6fT/zrv/6r2Llzp7j//vuFpmni2WefLdWf4GgTPb/33HOPeOqpp8Tu3bvF9u3bxY033ihUVRXPPfdcqf4ER/viF78ogsGg2Lhxo2hvb8//xGKx/DFXXXWVuO222/K/v/zyy8Llcom7775b7Ny5U3zzm98Uuq6L7du3l+JPcLTJnN9vfetb4ne/+53Ys2ePeP3118U//MM/CI/HI958881S/AmOdtttt4lNmzaJvXv3ir/85S/itttuE4qiiN///vdCCObd4zXR88u8e/yOXfWNebiwxjq/zMPj9+Uvf1ls3LhR7N27V7z88svi/PPPF1OmTBGHDh0SQsiddxk42Si3/PWxPytXrhRCCLFy5UqxfPnyIZ859dRThWEYYvbs2WLdunVFT7csJnp+77zzTjFnzhzh8XhEXV2dOPfcc8ULL7xQmsRLYLhzC2BQnly+fHn+fOc89thjYu7cucIwDHHyySeL3/zmN8VNuCQmc35Xr14tZsyYIQzDEE1NTeKiiy4Sf/rTn4qfeAn80z/9k5g5c6YwDEM0NDSI8847L1+pF4J593hN9Pwy7x6/Yyv2zMOFNdb5ZR4ev8svv1y0tLQIwzDE1KlTxeWXXy7eeeed/Psy511FCCGK179FREREREQkH85xIiIiIiIiGgMDJyIiIiIiojEwcCIiIiIiIhoDAyciIiIiIqIxMHAiIiIiIiIaAwMnIiIiIiKiMTBwIiIiIiIiGgMDJyIiIiIiojEwcCIioknbuHEjFEVBX19fqZNCRERkKwZORERl7pprroGiKFAUBbquo62tDbfeeisSicSEvufcc8/F6tWrB722bNkytLe3IxgMFjDFQ+UCtGN/br/9dlv/3fFYv359Pj2apqG2thZLlizBt7/9bYRCoQl91759+6AoCt544w17EjuC9evXo6ampqj/JhGRbFylTgAREdnvwgsvxLp165BOp/H6669j5cqVUBQFd95553F9r2EYaG5uLlAqx7Zr1y5UV1fnf/f7/UOOMU0TiqJAVYvXNlhdXY1du3ZBCIG+vj5s3rwZa9aswbp16/Dyyy+jtbW1aGkhIiJ7sMeJiKgCuN1uNDc3Y/r06VixYgXOP/98bNiwIf9+T08PrrjiCkydOhU+nw+LFi3Cf//3f+ffv+aaa7Bp0ybcd999+d6Vffv2DRmql+u5+N3vfof58+fD7/fjwgsvRHt7e/67MpkMbrjhBtTU1KC+vh5f+cpXsHLlSqxYsWLMv6OxsRHNzc35H7/fn/83f/WrX2HBggVwu904cOAAent7cfXVV6O2thY+nw8f//jHsXv37vx35T739NNPY968efD5fPj7v/97xGIx/Pu//ztmzZqF2tpa3HDDDTBNc9R0KYqC5uZmtLS0YP78+bj22muxefNmRCIR3Hrrrfnjnn32WfzN3/xN/m//xCc+gT179uTfb2trAwCcdtppUBQF5557LgBg69at+OhHP4opU6YgGAxi+fLl+NOf/pT/nBACd9xxB2bMmAG3243W1lbccMMN+feTySRuueUWTJ06FVVVVViyZAk2btwIINub99nPfhahUCh/be+4444xrwURUaVh4EREVGF27NiBzZs3wzCM/GuJRAJnnHEGfvOb32DHjh3453/+Z1x11VX44x//CAC47777sHTpUnz+859He3s72tvbMX369GG/PxaL4e6778bDDz+MF198EQcOHMAtt9ySf//OO+/Ef/3Xf+V7Y8LhMJ566qnj+ptisRjuvPNO/OxnP8Obb76JxsZGXHPNNXjttdfwq1/9Clu2bIEQAhdddBHS6fSgz/3oRz/Co48+imeffRYbN27EJz/5STzzzDN45pln8PDDD2Pt2rV4/PHHJ5ymxsZGXHnllfjVr36VD7yi0ShuvvlmvPbaa3j++eehqio++clPwrIsAMif7+eeew7t7e144oknAAD9/f1YuXIlXnrpJbzyyis48cQTcdFFF6G/vx8A8L//+7+45557sHbtWuzevRtPPfUUFi1alE/L9ddfjy1btuDRRx/FX/7yF1x22WW48MILsXv3bixbtgz33nsvqqur89d24PUiIqIjBBERlbWVK1cKTdNEVVWVcLvdAoBQVVU8/vjjo37u4osvFl/+8pfzvy9fvlzceOONg475wx/+IACI3t5eIYQQ69atEwDEO++8kz/m/vvvF01NTfnfm5qaxF133ZX/PZPJiBkzZohLL710xLTk/p2qqqpBP93d3fl/84033sgf//bbbwsA4uWXX86/1t3dLbxer3jsscdGTOt1110nfD6f6O/vz792wQUXiOuuu27EtK1bt04Eg8Fh33vggQcEANHZ2Tns+11dXQKA2L59uxBCiL179woAYtu2bSP+e0IIYZqmCAQC4te//rUQQogf/OAHYu7cuSKVSg05dv/+/ULTNPHBBx8Mev28884TX/3qV8f8G4iIKItznIiIKsBHPvIRPPDAA4hGo7jnnnvgcrnwqU99Kv++aZr47ne/i8ceewwffPABUqkUkskkfD7fhP8tn8+HOXPm5H9vaWnBoUOHAAChUAidnZ340Ic+lH9f0zScccYZ+V6X0fzf//0fAoFA/vfa2loA2blWixcvzr++c+dOuFwuLFmyJP9afX095s2bh507d46Y1qamJsyaNWvQ3KmmpqZ8+idKCAEgO5QPAHbv3o1vfOMbePXVV9Hd3Z3/mw8cOICFCxeO+D2dnZ24/fbbsXHjRhw6dAimaSIWi+HAgQMAgMsuuwz33nsvZs+ejQsvvBAXXXQRLrnkErhcLmzfvh2maWLu3LmDvjOZTKK+vn5SfxcRUSVi4EREVAGqqqpwwgknAAAeeughnHLKKfj5z3+Oa6+9FgBw11134b777sO9996LRYsWoaqqCqtXr0YqlZrwv6Xr+qDfFUXJBxDHq62tbdjV37xebz44mYjh0jrca+MJ6oazc+dOVFdX5wOUSy65BDNnzsRPf/pTtLa2wrIsLFy4cMzzvHLlSvT09OC+++7DzJkz4Xa7sXTp0vznpk+fjl27duG5557Dhg0b8C//8i+46667sGnTJkQiEWiahtdffx2apg363uEW1yAiouFxjhMRUYVRVRVf+9rXcPvttyMejwMAXn75ZVx66aX4zGc+g1NOOQWzZ8/G22+/PehzhmGMuUjCWILBIJqamrB169b8a6ZpDlrooBDmz5+PTCaDV199Nf9aT08Pdu3ahQULFhT03xrJoUOH8Mgjj2DFihVQVTX/799+++0477zzMH/+fPT29g76TG7e2bHn+eWXX8YNN9yAiy66CCeffDLcbje6u7sHHeP1enHJJZfgRz/6ETZu3IgtW7Zg+/btOO2002CaJg4dOoQTTjhh0E9uRcRCXFsionLHwImIqAJddtll0DQN999/PwDgxBNPxIYNG7B582bs3LkT1113HTo7Owd9ZtasWXj11Vexb9++QcPMJupLX/oS1qxZg1/+8pfYtWsXbrzxRvT29k6qx2gkJ554Ii699FJ8/vOfx0svvYQ///nP+MxnPoOpU6fi0ksvLdi/kyOEQEdHB9rb27Fz50489NBDWLZsGYLBIL73ve8ByA4rrK+vx4MPPoh33nkHL7zwAm6++eZB39PY2Aiv14tnn30WnZ2d+X2gTjzxRDz88MPYuXMnXn31VVx55ZXwer35z61fvx4///nPsWPHDrz77rv4z//8T3i9XsycORNz587FlVdeiauvvhpPPPEE9u7diz/+8Y9Ys2YNfvOb3wDIXttIJILnn38e3d3diMViBT9HRESyY+BERFSBXC4Xrr/+enz/+99HNBrF7bffjtNPPx0XXHABzj33XDQ3Nw9ZHvyWW26BpmlYsGABGhoa8vNrJuorX/kKrrjiClx99dVYunQp/H4/LrjgAng8ngL8ZUetW7cOZ5xxBj7xiU9g6dKlEELgmWeeGTIUrxDC4TBaWlowdepULF26FGvXrsXKlSuxbds2tLS0AMj29D366KN4/fXXsXDhQtx000246667Bn2Py+XCj370I6xduxatra35IO/nP/85ent7cfrpp+Oqq67CDTfcgMbGxvznampq8NOf/hTnnHMOFi9ejOeeew6//vWv80ME161bh6uvvhpf/vKXMW/ePKxYsQJbt27FjBkzAGQ3Mv7CF76Ayy+/HA0NDfj+979f8HNERCQ7RRRq4DkREdEkWJaF+fPn49Of/jS+853vlDo5REREw+LiEEREVFT79+/H73//eyxfvhzJZBI//vGPsXfvXvzjP/5jqZNGREQ0Ig7VIyKiolJVFevXr8dZZ52Fc845B9u3b8dzzz2H+fPnlzppREREI+JQPSIiIiIiojGwx4mIiIiIiGgMDJyIiIiIiIjGwMCJiIiIiIhoDAyciIiIiIiIxsDAiYiIiIiIaAwMnIiIiIiIiMbAwImIiIiIiGgMDJyIiIiIiIjG8P8BDMaHml7DAyUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test, Y_pred, alpha=0.2)\n",
    "plt.xlabel('Rating From Dataset')\n",
    "plt.ylabel('Rating Predicted By Model')\n",
    "plt.rcParams[\"figure.figsize\"] = (10,6) # Custom figure size in inches\n",
    "plt.title(\"Rating From Dataset Vs Rating Predicted By Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the Gradient Boosting here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error = 0.5519593529266892\n",
      "R-Squared = 0.4509842026276053\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "reg = GradientBoostingRegressor(random_state=0)\n",
    "reg.fit(X_train, Y_train)\n",
    "Y_pred = reg.predict(X_test)\n",
    "print(f\"Mean Squared Error = {mean_squared_error(Y_test, Y_pred)}\")\n",
    "print(f\"R-Squared = {r2_score(Y_test, Y_pred)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicted against actual Y plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rating From Dataset Vs Rating Predicted By Model')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test, Y_pred, alpha=0.2)\n",
    "plt.xlabel('Rating From Dataset')\n",
    "plt.ylabel('Rating Predicted By Model')\n",
    "plt.rcParams[\"figure.figsize\"] = (10,6) # Custom figure size in inches\n",
    "plt.title(\"Rating From Dataset Vs Rating Predicted By Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further Evaluations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how well the model fits the data when it comes to prediction of the Car_Make level ratings. For that we need to keep the Car_Make in our dataset datarame; fit the regression on individual reviews and then calulate the average mean squared error and R-squared in the Car_Make level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment.score</th>\n",
       "      <th>emotion.sadness</th>\n",
       "      <th>emotion.joy</th>\n",
       "      <th>emotion.fear</th>\n",
       "      <th>emotion.disgust</th>\n",
       "      <th>emotion.anger</th>\n",
       "      <th>Rating\\r</th>\n",
       "      <th>Car_Make</th>\n",
       "      <th>Review_Content</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Review_Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 sweet R32</th>\n",
       "      <td>0.649825</td>\n",
       "      <td>0.151543</td>\n",
       "      <td>0.532162</td>\n",
       "      <td>0.067859</td>\n",
       "      <td>0.018501</td>\n",
       "      <td>0.112994</td>\n",
       "      <td>4.875</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>1 sweet R32: I was looking into buying a Subaru WRX \\rSTI, but after two test drives in each \\rand reading as many \\rRoad&amp;Track,Car&amp;Driver,and any other \\rinfo I could find I desided to go with \\rthe R32. I traded in my 2003 GTI VR6 \\rthat had 29,000 miles on it. That was a \\rgreat car but this is a whole new \\rbeast. Once you own an all wheel drive \\rthere is just no going back. This car \\rhandles like a dream, the seats are the \\rbest I've ever been in. Cabin is put \\rtogether very well and the pipes are \\rcrazy. The climit control is awsome, \\rheated seats are so sweet on those cold \\rwinter days. I live in the central \\rvalley of California so these tire are \\rthe best. If there was one thing I \\rwould change(give me a spare tire)!!!!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002 Trans Am/Sunset Orange Metallic</th>\n",
       "      <td>0.148035</td>\n",
       "      <td>0.176322</td>\n",
       "      <td>0.465210</td>\n",
       "      <td>0.257064</td>\n",
       "      <td>0.032842</td>\n",
       "      <td>0.038908</td>\n",
       "      <td>4.625</td>\n",
       "      <td>pontiac</td>\n",
       "      <td>2002 Trans Am/Sunset Orange Metallic: This Is Pontiac's most exciting vehicle \\rof all time.It has so much performance \\rthat it is a big disapointment that it \\rwill be discontinued this year.The only \\rarea that this vehicle does not excell \\rin would be the fuel economy \\rdepartment.I guess that if you can \\rafford one of these dream cars, you \\rreally dony worry about how far it will \\rtravel on a tankfull of gas.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42 days of driving 8 days in the shop</th>\n",
       "      <td>-0.054126</td>\n",
       "      <td>0.206478</td>\n",
       "      <td>0.563466</td>\n",
       "      <td>0.114506</td>\n",
       "      <td>0.010082</td>\n",
       "      <td>0.082325</td>\n",
       "      <td>3.375</td>\n",
       "      <td>chrysler</td>\n",
       "      <td>42 days of driving 8 days in the shop : I was given the sebring for my 20th wedding anniversary. I have been in love with it for years and finally got it. After 42 days I blew most of the electrical system. It has been at the dealer for 8 days and they can not find the problem. Right now I am not very happy.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A great little car</th>\n",
       "      <td>0.503785</td>\n",
       "      <td>0.278575</td>\n",
       "      <td>0.470586</td>\n",
       "      <td>0.063823</td>\n",
       "      <td>0.015218</td>\n",
       "      <td>0.039688</td>\n",
       "      <td>4.875</td>\n",
       "      <td>kia</td>\n",
       "      <td>A great little car: Bought my Spectra about one year ago, currently has about 18,000 miles on it. I have had absolutely no problems with it. I had cruise control added at the time of purchase, other than that it's stock. This is my daily driver, it's comfortable, reliable and gets decent mileage. The Spectra happens to be my second Kia, I have a Sedona van that has been to the dealer several times (however everything was covered by the warranty) it currently has 58,000 miles on it.  The Spectra's a great handling car.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AWESOME FUN MY LITTLE TIGER</th>\n",
       "      <td>0.986029</td>\n",
       "      <td>0.007629</td>\n",
       "      <td>0.628312</td>\n",
       "      <td>0.013015</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.024782</td>\n",
       "      <td>5.000</td>\n",
       "      <td>fiat</td>\n",
       "      <td>AWESOME FUN MY LITTLE TIGER:  Abarth is ultimately more fun than my old mustang or Z a little power house that doesn't shy away from a fight love the engine growl and the kick more room than you think awesome bang for the buck .Fun the most fun than any car I have ever own worth every penny a pleasure to drive.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I LOVE my Focus</th>\n",
       "      <td>0.621983</td>\n",
       "      <td>0.074019</td>\n",
       "      <td>0.589196</td>\n",
       "      <td>0.111722</td>\n",
       "      <td>0.008124</td>\n",
       "      <td>0.066092</td>\n",
       "      <td>4.750</td>\n",
       "      <td>ford</td>\n",
       "      <td>I LOVE my Focus: I LOVE my Focus. I've had it about 2 \\ryears. It drives great, looks good, \\rgets great gas milage and never slows \\rdown. I'm even thinking of getting \\ranother one on my next car purchase!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Looks Good But Hunk Of Junk</th>\n",
       "      <td>-0.984622</td>\n",
       "      <td>0.144671</td>\n",
       "      <td>0.061358</td>\n",
       "      <td>0.060613</td>\n",
       "      <td>0.050494</td>\n",
       "      <td>0.116835</td>\n",
       "      <td>2.875</td>\n",
       "      <td>maserati</td>\n",
       "      <td>Looks Good But Hunk Of Junk: This car is strictly \"looks only\", it is not reliable or even close to it.I have already sank $13,760 in repairs at only 23K miles.This is totally unacceptable for a $140K car when new.I am taking it to the auction next week to \"unload\" before it can empty my wallet again.But if you want a sharp car that sits good in the driveway - this is it!Just don't drive it anywhere!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr  TACOMA</th>\n",
       "      <td>0.633803</td>\n",
       "      <td>0.122766</td>\n",
       "      <td>0.825653</td>\n",
       "      <td>0.034777</td>\n",
       "      <td>0.023124</td>\n",
       "      <td>0.030344</td>\n",
       "      <td>5.000</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Mr  TACOMA: Great truck. The Handling is pretty  \\rnice and the engine is stronger. The V6 \\rwith 3100 pounds can really make this \\rtruck move.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veracruz</th>\n",
       "      <td>0.591816</td>\n",
       "      <td>0.106981</td>\n",
       "      <td>0.524371</td>\n",
       "      <td>0.091482</td>\n",
       "      <td>0.012344</td>\n",
       "      <td>0.054493</td>\n",
       "      <td>4.750</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>Veracruz: This is a crossover with the ride of a cruse ship. The car has so many bells and whistles. Have it one week and already over 1100 miles. Finding wonderful things about it every day. Could be the best car ever.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>You will pay for that warranty</th>\n",
       "      <td>-0.373583</td>\n",
       "      <td>0.396306</td>\n",
       "      <td>0.110458</td>\n",
       "      <td>0.056980</td>\n",
       "      <td>0.021192</td>\n",
       "      <td>0.119030</td>\n",
       "      <td>2.750</td>\n",
       "      <td>kia</td>\n",
       "      <td>You will pay for that warranty: Own a 2002 KIA Sedona EX. I complained about lights going dim while under warranty. Kia checked, said everything within parameters. Guess what, 3000 miles out of warranty alternator died. KIA says it's on you now. 63,000 miles and they want $565.00 to repair; that includes alternator, belts and labor. It's not a repair you can do either, seems AC lines are in the way. Do you think KIA planned it? Ask them about changing spark plugs the rear 3, seems you need to remove the air intake manifold? That will require new gaskets? Not sure of that cost. I hope to dump this Sedona by then! Think twice before you buy, they will get you to pay for that supposedly free 5year/60000 bumper to bumper warranty. RIPOFF.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         sentiment.score  emotion.sadness  \\\n",
       "Review_Title                                                                \n",
       " 1 sweet R32                                    0.649825         0.151543   \n",
       " 2002 Trans Am/Sunset Orange Metallic           0.148035         0.176322   \n",
       " 42 days of driving 8 days in the shop         -0.054126         0.206478   \n",
       " A great little car                             0.503785         0.278575   \n",
       " AWESOME FUN MY LITTLE TIGER                    0.986029         0.007629   \n",
       " I LOVE my Focus                                0.621983         0.074019   \n",
       " Looks Good But Hunk Of Junk                   -0.984622         0.144671   \n",
       " Mr  TACOMA                                     0.633803         0.122766   \n",
       " Veracruz                                       0.591816         0.106981   \n",
       " You will pay for that warranty                -0.373583         0.396306   \n",
       "\n",
       "                                         emotion.joy  emotion.fear  \\\n",
       "Review_Title                                                         \n",
       " 1 sweet R32                                0.532162      0.067859   \n",
       " 2002 Trans Am/Sunset Orange Metallic       0.465210      0.257064   \n",
       " 42 days of driving 8 days in the shop      0.563466      0.114506   \n",
       " A great little car                         0.470586      0.063823   \n",
       " AWESOME FUN MY LITTLE TIGER                0.628312      0.013015   \n",
       " I LOVE my Focus                            0.589196      0.111722   \n",
       " Looks Good But Hunk Of Junk                0.061358      0.060613   \n",
       " Mr  TACOMA                                 0.825653      0.034777   \n",
       " Veracruz                                   0.524371      0.091482   \n",
       " You will pay for that warranty             0.110458      0.056980   \n",
       "\n",
       "                                         emotion.disgust  emotion.anger  \\\n",
       "Review_Title                                                              \n",
       " 1 sweet R32                                    0.018501       0.112994   \n",
       " 2002 Trans Am/Sunset Orange Metallic           0.032842       0.038908   \n",
       " 42 days of driving 8 days in the shop          0.010082       0.082325   \n",
       " A great little car                             0.015218       0.039688   \n",
       " AWESOME FUN MY LITTLE TIGER                    0.001452       0.024782   \n",
       " I LOVE my Focus                                0.008124       0.066092   \n",
       " Looks Good But Hunk Of Junk                    0.050494       0.116835   \n",
       " Mr  TACOMA                                     0.023124       0.030344   \n",
       " Veracruz                                       0.012344       0.054493   \n",
       " You will pay for that warranty                 0.021192       0.119030   \n",
       "\n",
       "                                         Rating\\r    Car_Make  \\\n",
       "Review_Title                                                    \n",
       " 1 sweet R32                                4.875  Volkswagen   \n",
       " 2002 Trans Am/Sunset Orange Metallic       4.625     pontiac   \n",
       " 42 days of driving 8 days in the shop      3.375    chrysler   \n",
       " A great little car                         4.875         kia   \n",
       " AWESOME FUN MY LITTLE TIGER                5.000        fiat   \n",
       " I LOVE my Focus                            4.750        ford   \n",
       " Looks Good But Hunk Of Junk                2.875    maserati   \n",
       " Mr  TACOMA                                 5.000      Toyota   \n",
       " Veracruz                                   4.750     hyundai   \n",
       " You will pay for that warranty             2.750         kia   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Review_Content  \n",
       "Review_Title                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n",
       " 1 sweet R32                              1 sweet R32: I was looking into buying a Subaru WRX \\rSTI, but after two test drives in each \\rand reading as many \\rRoad&Track,Car&Driver,and any other \\rinfo I could find I desided to go with \\rthe R32. I traded in my 2003 GTI VR6 \\rthat had 29,000 miles on it. That was a \\rgreat car but this is a whole new \\rbeast. Once you own an all wheel drive \\rthere is just no going back. This car \\rhandles like a dream, the seats are the \\rbest I've ever been in. Cabin is put \\rtogether very well and the pipes are \\rcrazy. The climit control is awsome, \\rheated seats are so sweet on those cold \\rwinter days. I live in the central \\rvalley of California so these tire are \\rthe best. If there was one thing I \\rwould change(give me a spare tire)!!!!!  \n",
       " 2002 Trans Am/Sunset Orange Metallic                                                                                                                                                                                                                                                                                                                                             2002 Trans Am/Sunset Orange Metallic: This Is Pontiac's most exciting vehicle \\rof all time.It has so much performance \\rthat it is a big disapointment that it \\rwill be discontinued this year.The only \\rarea that this vehicle does not excell \\rin would be the fuel economy \\rdepartment.I guess that if you can \\rafford one of these dream cars, you \\rreally dony worry about how far it will \\rtravel on a tankfull of gas.  \n",
       " 42 days of driving 8 days in the shop                                                                                                                                                                                                                                                                                                                                                                                                                                                           42 days of driving 8 days in the shop : I was given the sebring for my 20th wedding anniversary. I have been in love with it for years and finally got it. After 42 days I blew most of the electrical system. It has been at the dealer for 8 days and they can not find the problem. Right now I am not very happy.   \n",
       " A great little car                                                                                                                                                                                                                                                         A great little car: Bought my Spectra about one year ago, currently has about 18,000 miles on it. I have had absolutely no problems with it. I had cruise control added at the time of purchase, other than that it's stock. This is my daily driver, it's comfortable, reliable and gets decent mileage. The Spectra happens to be my second Kia, I have a Sedona van that has been to the dealer several times (however everything was covered by the warranty) it currently has 58,000 miles on it.  The Spectra's a great handling car.  \n",
       " AWESOME FUN MY LITTLE TIGER                                                                                                                                                                                                                                                                                                                                                                                                                                                                   AWESOME FUN MY LITTLE TIGER:  Abarth is ultimately more fun than my old mustang or Z a little power house that doesn't shy away from a fight love the engine growl and the kick more room than you think awesome bang for the buck .Fun the most fun than any car I have ever own worth every penny a pleasure to drive.  \n",
       " I LOVE my Focus                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      I LOVE my Focus: I LOVE my Focus. I've had it about 2 \\ryears. It drives great, looks good, \\rgets great gas milage and never slows \\rdown. I'm even thinking of getting \\ranother one on my next car purchase!    \n",
       " Looks Good But Hunk Of Junk                                                                                                                                                                                                                                                                                                                                                                       Looks Good But Hunk Of Junk: This car is strictly \"looks only\", it is not reliable or even close to it.I have already sank $13,760 in repairs at only 23K miles.This is totally unacceptable for a $140K car when new.I am taking it to the auction next week to \"unload\" before it can empty my wallet again.But if you want a sharp car that sits good in the driveway - this is it!Just don't drive it anywhere!!  \n",
       " Mr  TACOMA                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Mr  TACOMA: Great truck. The Handling is pretty  \\rnice and the engine is stronger. The V6 \\rwith 3100 pounds can really make this \\rtruck move.   \n",
       " Veracruz                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Veracruz: This is a crossover with the ride of a cruse ship. The car has so many bells and whistles. Have it one week and already over 1100 miles. Finding wonderful things about it every day. Could be the best car ever.  \n",
       " You will pay for that warranty               You will pay for that warranty: Own a 2002 KIA Sedona EX. I complained about lights going dim while under warranty. Kia checked, said everything within parameters. Guess what, 3000 miles out of warranty alternator died. KIA says it's on you now. 63,000 miles and they want $565.00 to repair; that includes alternator, belts and labor. It's not a repair you can do either, seems AC lines are in the way. Do you think KIA planned it? Ask them about changing spark plugs the rear 3, seems you need to remove the air intake manifold? That will require new gaskets? Not sure of that cost. I hope to dump this Sedona by then! Think twice before you buy, they will get you to pay for that supposedly free 5year/60000 bumper to bumper warranty. RIPOFF.   "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_colwidth\", 10000)\n",
    "agg_merged_keywords_review_df = merged_keywords_review_df.drop_duplicates(['Review_Title','sentiment.score']).groupby([\"Review_Title\"]).agg({\n",
    "                       'sentiment.score': 'mean',\n",
    "                       'emotion.sadness': 'mean',\n",
    "                       'emotion.joy': 'mean',\n",
    "                       'emotion.fear': 'mean',\n",
    "                       'emotion.disgust': 'mean',\n",
    "                       'emotion.anger': 'mean',\n",
    "                       'Rating\\r': 'first',\n",
    "                       'Car_Make': 'first',\n",
    "                       'Review_Content': 'first'\n",
    "})\n",
    "agg_merged_keywords_review_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = agg_merged_keywords_review_df.sample(frac=0.75, random_state=0)\n",
    "test_set = agg_merged_keywords_review_df.drop(train_set.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Make\n",
       "AMGeneral          2\n",
       "Acura            154\n",
       "AlfaRomeo         60\n",
       "AstonMartin       55\n",
       "Audi             144\n",
       "BMW              143\n",
       "Bentley          102\n",
       "Bugatti            7\n",
       "Buick            134\n",
       "Cadillac         140\n",
       "Chevrolet        157\n",
       "GMC              133\n",
       "Honda            143\n",
       "Toyota           130\n",
       "Volkswagen       152\n",
       "chrysler         136\n",
       "dodge            142\n",
       "ferrari          111\n",
       "fiat             142\n",
       "ford             138\n",
       "genesis           48\n",
       "hummer           149\n",
       "hyundai          142\n",
       "infiniti         134\n",
       "isuzu            137\n",
       "jaguar           128\n",
       "jeep             127\n",
       "kia              126\n",
       "lamborghini       54\n",
       "land-rover       141\n",
       "lexus            125\n",
       "lincoln          138\n",
       "lotus            102\n",
       "maserati         136\n",
       "maybach           15\n",
       "mazda            137\n",
       "mclaren            1\n",
       "mercedes-benz    133\n",
       "mercury          131\n",
       "mini             142\n",
       "mitsubishi       118\n",
       "nissan           125\n",
       "pontiac          132\n",
       "porsche          136\n",
       "ram              152\n",
       "rolls-royce       23\n",
       "subaru           129\n",
       "suzuki           121\n",
       "tesla            100\n",
       "volvo            135\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.groupby(\"Car_Make\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train = train_set.dropna().iloc[:, :6].values.reshape(-1, 6)  # values converts it into a numpy array\n",
    "X_test = test_set.dropna().iloc[:, :6].values.reshape(-1, 6)  # values converts it into a numpy array\n",
    "Y_train = train_set.dropna()['Rating\\r'].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
    "Y_test = test_set.dropna()['Rating\\r'].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = GradientBoostingRegressor(random_state=0)  # create object for the class\n",
    "reg.fit(X_train, Y_train)  # fit the model on the training data\n",
    "Y_pred = reg.predict(X_test) # make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "predicted_y_with_na = np.zeros(len(test_set.index), dtype=object)\n",
    "predicted_y_with_na[~test_set.isna().any(axis=1)] = Y_pred\n",
    "test_set['Predicted_Y'] = predicted_y_with_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>emotion.sadness</th>\n",
       "      <th>emotion.joy</th>\n",
       "      <th>emotion.fear</th>\n",
       "      <th>emotion.disgust</th>\n",
       "      <th>emotion.anger</th>\n",
       "      <th>sentiment.score</th>\n",
       "      <th>Rating\\r</th>\n",
       "      <th>Predicted_Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car_Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMGeneral</th>\n",
       "      <td>0.233502</td>\n",
       "      <td>0.416527</td>\n",
       "      <td>0.149416</td>\n",
       "      <td>0.030530</td>\n",
       "      <td>0.065171</td>\n",
       "      <td>0.021626</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>4.264005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acura</th>\n",
       "      <td>0.186803</td>\n",
       "      <td>0.467307</td>\n",
       "      <td>0.134082</td>\n",
       "      <td>0.020744</td>\n",
       "      <td>0.064254</td>\n",
       "      <td>0.330192</td>\n",
       "      <td>4.538690</td>\n",
       "      <td>4.455716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AlfaRomeo</th>\n",
       "      <td>0.179048</td>\n",
       "      <td>0.434307</td>\n",
       "      <td>0.101048</td>\n",
       "      <td>0.030507</td>\n",
       "      <td>0.088323</td>\n",
       "      <td>0.268080</td>\n",
       "      <td>4.187500</td>\n",
       "      <td>4.435494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AstonMartin</th>\n",
       "      <td>0.161465</td>\n",
       "      <td>0.532924</td>\n",
       "      <td>0.093979</td>\n",
       "      <td>0.030943</td>\n",
       "      <td>0.063027</td>\n",
       "      <td>0.470149</td>\n",
       "      <td>4.613636</td>\n",
       "      <td>4.631018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Audi</th>\n",
       "      <td>0.196609</td>\n",
       "      <td>0.490965</td>\n",
       "      <td>0.092430</td>\n",
       "      <td>0.021402</td>\n",
       "      <td>0.059860</td>\n",
       "      <td>0.303431</td>\n",
       "      <td>4.453431</td>\n",
       "      <td>4.421119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>0.191940</td>\n",
       "      <td>0.474563</td>\n",
       "      <td>0.086499</td>\n",
       "      <td>0.024038</td>\n",
       "      <td>0.072675</td>\n",
       "      <td>0.243201</td>\n",
       "      <td>4.468750</td>\n",
       "      <td>4.354955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bentley</th>\n",
       "      <td>0.187771</td>\n",
       "      <td>0.528449</td>\n",
       "      <td>0.089768</td>\n",
       "      <td>0.028513</td>\n",
       "      <td>0.057980</td>\n",
       "      <td>0.441103</td>\n",
       "      <td>4.239583</td>\n",
       "      <td>4.57587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bugatti</th>\n",
       "      <td>0.188314</td>\n",
       "      <td>0.587727</td>\n",
       "      <td>0.055366</td>\n",
       "      <td>0.023677</td>\n",
       "      <td>0.054105</td>\n",
       "      <td>0.430882</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>4.718716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buick</th>\n",
       "      <td>0.260452</td>\n",
       "      <td>0.392718</td>\n",
       "      <td>0.099129</td>\n",
       "      <td>0.025511</td>\n",
       "      <td>0.086554</td>\n",
       "      <td>0.088404</td>\n",
       "      <td>4.162736</td>\n",
       "      <td>4.075432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cadillac</th>\n",
       "      <td>0.218803</td>\n",
       "      <td>0.429976</td>\n",
       "      <td>0.102534</td>\n",
       "      <td>0.030479</td>\n",
       "      <td>0.069962</td>\n",
       "      <td>0.274075</td>\n",
       "      <td>4.395408</td>\n",
       "      <td>4.335975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chevrolet</th>\n",
       "      <td>0.200984</td>\n",
       "      <td>0.441252</td>\n",
       "      <td>0.102457</td>\n",
       "      <td>0.037943</td>\n",
       "      <td>0.074343</td>\n",
       "      <td>0.173096</td>\n",
       "      <td>4.104730</td>\n",
       "      <td>4.23044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC</th>\n",
       "      <td>0.218755</td>\n",
       "      <td>0.416075</td>\n",
       "      <td>0.111135</td>\n",
       "      <td>0.033378</td>\n",
       "      <td>0.067579</td>\n",
       "      <td>0.071438</td>\n",
       "      <td>4.089912</td>\n",
       "      <td>4.138499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honda</th>\n",
       "      <td>0.191337</td>\n",
       "      <td>0.418301</td>\n",
       "      <td>0.117569</td>\n",
       "      <td>0.029766</td>\n",
       "      <td>0.063076</td>\n",
       "      <td>0.118789</td>\n",
       "      <td>3.832386</td>\n",
       "      <td>4.130003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota</th>\n",
       "      <td>0.199025</td>\n",
       "      <td>0.431638</td>\n",
       "      <td>0.104728</td>\n",
       "      <td>0.027125</td>\n",
       "      <td>0.070333</td>\n",
       "      <td>0.154561</td>\n",
       "      <td>4.350543</td>\n",
       "      <td>4.243079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volkswagen</th>\n",
       "      <td>0.190767</td>\n",
       "      <td>0.429078</td>\n",
       "      <td>0.126892</td>\n",
       "      <td>0.031965</td>\n",
       "      <td>0.071180</td>\n",
       "      <td>0.130390</td>\n",
       "      <td>4.396875</td>\n",
       "      <td>4.17746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrysler</th>\n",
       "      <td>0.234828</td>\n",
       "      <td>0.398700</td>\n",
       "      <td>0.116330</td>\n",
       "      <td>0.035023</td>\n",
       "      <td>0.075663</td>\n",
       "      <td>0.086065</td>\n",
       "      <td>4.140957</td>\n",
       "      <td>4.168451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dodge</th>\n",
       "      <td>0.218513</td>\n",
       "      <td>0.408767</td>\n",
       "      <td>0.119761</td>\n",
       "      <td>0.026407</td>\n",
       "      <td>0.076249</td>\n",
       "      <td>0.100277</td>\n",
       "      <td>4.133929</td>\n",
       "      <td>4.163826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ferrari</th>\n",
       "      <td>0.159649</td>\n",
       "      <td>0.539798</td>\n",
       "      <td>0.108343</td>\n",
       "      <td>0.019731</td>\n",
       "      <td>0.082763</td>\n",
       "      <td>0.463863</td>\n",
       "      <td>4.767241</td>\n",
       "      <td>4.530158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiat</th>\n",
       "      <td>0.203202</td>\n",
       "      <td>0.401303</td>\n",
       "      <td>0.100537</td>\n",
       "      <td>0.030897</td>\n",
       "      <td>0.076235</td>\n",
       "      <td>0.087065</td>\n",
       "      <td>3.818878</td>\n",
       "      <td>4.134359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>0.238288</td>\n",
       "      <td>0.362188</td>\n",
       "      <td>0.121460</td>\n",
       "      <td>0.028063</td>\n",
       "      <td>0.088316</td>\n",
       "      <td>0.078135</td>\n",
       "      <td>4.040094</td>\n",
       "      <td>4.005134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genesis</th>\n",
       "      <td>0.211237</td>\n",
       "      <td>0.430926</td>\n",
       "      <td>0.078043</td>\n",
       "      <td>0.031972</td>\n",
       "      <td>0.057856</td>\n",
       "      <td>0.156253</td>\n",
       "      <td>4.608696</td>\n",
       "      <td>4.316763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hummer</th>\n",
       "      <td>0.181750</td>\n",
       "      <td>0.502888</td>\n",
       "      <td>0.126320</td>\n",
       "      <td>0.027008</td>\n",
       "      <td>0.055950</td>\n",
       "      <td>0.297203</td>\n",
       "      <td>4.404605</td>\n",
       "      <td>4.462752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai</th>\n",
       "      <td>0.220990</td>\n",
       "      <td>0.393721</td>\n",
       "      <td>0.096735</td>\n",
       "      <td>0.027251</td>\n",
       "      <td>0.079915</td>\n",
       "      <td>0.161732</td>\n",
       "      <td>4.109375</td>\n",
       "      <td>4.14899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infiniti</th>\n",
       "      <td>0.200538</td>\n",
       "      <td>0.469661</td>\n",
       "      <td>0.090667</td>\n",
       "      <td>0.024671</td>\n",
       "      <td>0.059761</td>\n",
       "      <td>0.322187</td>\n",
       "      <td>4.566860</td>\n",
       "      <td>4.393907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isuzu</th>\n",
       "      <td>0.201127</td>\n",
       "      <td>0.404813</td>\n",
       "      <td>0.122677</td>\n",
       "      <td>0.028856</td>\n",
       "      <td>0.101334</td>\n",
       "      <td>0.205943</td>\n",
       "      <td>4.220238</td>\n",
       "      <td>4.306578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jaguar</th>\n",
       "      <td>0.163703</td>\n",
       "      <td>0.556705</td>\n",
       "      <td>0.086785</td>\n",
       "      <td>0.025675</td>\n",
       "      <td>0.055537</td>\n",
       "      <td>0.375661</td>\n",
       "      <td>4.584091</td>\n",
       "      <td>4.497573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jeep</th>\n",
       "      <td>0.253255</td>\n",
       "      <td>0.396047</td>\n",
       "      <td>0.104165</td>\n",
       "      <td>0.025074</td>\n",
       "      <td>0.079216</td>\n",
       "      <td>0.106891</td>\n",
       "      <td>4.108607</td>\n",
       "      <td>4.15378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kia</th>\n",
       "      <td>0.266675</td>\n",
       "      <td>0.394792</td>\n",
       "      <td>0.111849</td>\n",
       "      <td>0.025904</td>\n",
       "      <td>0.070382</td>\n",
       "      <td>0.141621</td>\n",
       "      <td>4.141827</td>\n",
       "      <td>4.12064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamborghini</th>\n",
       "      <td>0.127094</td>\n",
       "      <td>0.629176</td>\n",
       "      <td>0.082221</td>\n",
       "      <td>0.029788</td>\n",
       "      <td>0.052919</td>\n",
       "      <td>0.657044</td>\n",
       "      <td>4.725000</td>\n",
       "      <td>4.665769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land-rover</th>\n",
       "      <td>0.274524</td>\n",
       "      <td>0.355115</td>\n",
       "      <td>0.109827</td>\n",
       "      <td>0.034846</td>\n",
       "      <td>0.086169</td>\n",
       "      <td>0.080642</td>\n",
       "      <td>3.848837</td>\n",
       "      <td>4.0177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lexus</th>\n",
       "      <td>0.202997</td>\n",
       "      <td>0.439800</td>\n",
       "      <td>0.100562</td>\n",
       "      <td>0.031977</td>\n",
       "      <td>0.076374</td>\n",
       "      <td>0.231606</td>\n",
       "      <td>4.306122</td>\n",
       "      <td>4.294433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lincoln</th>\n",
       "      <td>0.213433</td>\n",
       "      <td>0.466320</td>\n",
       "      <td>0.112260</td>\n",
       "      <td>0.027192</td>\n",
       "      <td>0.075736</td>\n",
       "      <td>0.163414</td>\n",
       "      <td>4.269231</td>\n",
       "      <td>4.264042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lotus</th>\n",
       "      <td>0.158467</td>\n",
       "      <td>0.457916</td>\n",
       "      <td>0.137350</td>\n",
       "      <td>0.023458</td>\n",
       "      <td>0.080445</td>\n",
       "      <td>0.307135</td>\n",
       "      <td>4.702381</td>\n",
       "      <td>4.490183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maserati</th>\n",
       "      <td>0.174925</td>\n",
       "      <td>0.523992</td>\n",
       "      <td>0.087822</td>\n",
       "      <td>0.037616</td>\n",
       "      <td>0.071795</td>\n",
       "      <td>0.311256</td>\n",
       "      <td>4.431250</td>\n",
       "      <td>4.394369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maybach</th>\n",
       "      <td>0.178194</td>\n",
       "      <td>0.515520</td>\n",
       "      <td>0.077657</td>\n",
       "      <td>0.015687</td>\n",
       "      <td>0.072357</td>\n",
       "      <td>0.633714</td>\n",
       "      <td>4.958333</td>\n",
       "      <td>4.733771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mazda</th>\n",
       "      <td>0.203442</td>\n",
       "      <td>0.444971</td>\n",
       "      <td>0.111021</td>\n",
       "      <td>0.026170</td>\n",
       "      <td>0.062387</td>\n",
       "      <td>0.230268</td>\n",
       "      <td>4.479651</td>\n",
       "      <td>4.318612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes-benz</th>\n",
       "      <td>0.227015</td>\n",
       "      <td>0.387488</td>\n",
       "      <td>0.105910</td>\n",
       "      <td>0.026781</td>\n",
       "      <td>0.085045</td>\n",
       "      <td>0.091385</td>\n",
       "      <td>4.095745</td>\n",
       "      <td>4.094125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercury</th>\n",
       "      <td>0.200664</td>\n",
       "      <td>0.462219</td>\n",
       "      <td>0.105958</td>\n",
       "      <td>0.025067</td>\n",
       "      <td>0.063838</td>\n",
       "      <td>0.246360</td>\n",
       "      <td>4.311224</td>\n",
       "      <td>4.390451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mini</th>\n",
       "      <td>0.218531</td>\n",
       "      <td>0.443708</td>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.026429</td>\n",
       "      <td>0.071672</td>\n",
       "      <td>0.167861</td>\n",
       "      <td>4.036184</td>\n",
       "      <td>4.190949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mitsubishi</th>\n",
       "      <td>0.175781</td>\n",
       "      <td>0.481554</td>\n",
       "      <td>0.118347</td>\n",
       "      <td>0.025169</td>\n",
       "      <td>0.070454</td>\n",
       "      <td>0.300219</td>\n",
       "      <td>4.346698</td>\n",
       "      <td>4.417798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nissan</th>\n",
       "      <td>0.241268</td>\n",
       "      <td>0.373916</td>\n",
       "      <td>0.111114</td>\n",
       "      <td>0.034341</td>\n",
       "      <td>0.079956</td>\n",
       "      <td>0.102161</td>\n",
       "      <td>4.247093</td>\n",
       "      <td>4.119348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pontiac</th>\n",
       "      <td>0.190257</td>\n",
       "      <td>0.430994</td>\n",
       "      <td>0.110610</td>\n",
       "      <td>0.026520</td>\n",
       "      <td>0.078449</td>\n",
       "      <td>0.165777</td>\n",
       "      <td>4.375000</td>\n",
       "      <td>4.221752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>porsche</th>\n",
       "      <td>0.145226</td>\n",
       "      <td>0.510727</td>\n",
       "      <td>0.093447</td>\n",
       "      <td>0.026101</td>\n",
       "      <td>0.080697</td>\n",
       "      <td>0.382931</td>\n",
       "      <td>4.662500</td>\n",
       "      <td>4.552274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram</th>\n",
       "      <td>0.240626</td>\n",
       "      <td>0.367544</td>\n",
       "      <td>0.108349</td>\n",
       "      <td>0.040745</td>\n",
       "      <td>0.076267</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>3.861111</td>\n",
       "      <td>4.113366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rolls-royce</th>\n",
       "      <td>0.260943</td>\n",
       "      <td>0.412009</td>\n",
       "      <td>0.080448</td>\n",
       "      <td>0.037039</td>\n",
       "      <td>0.072188</td>\n",
       "      <td>0.321649</td>\n",
       "      <td>4.843750</td>\n",
       "      <td>4.508778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subaru</th>\n",
       "      <td>0.202121</td>\n",
       "      <td>0.470115</td>\n",
       "      <td>0.103731</td>\n",
       "      <td>0.020184</td>\n",
       "      <td>0.070802</td>\n",
       "      <td>0.301044</td>\n",
       "      <td>4.257212</td>\n",
       "      <td>4.327014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suzuki</th>\n",
       "      <td>0.206990</td>\n",
       "      <td>0.410432</td>\n",
       "      <td>0.111564</td>\n",
       "      <td>0.033440</td>\n",
       "      <td>0.075242</td>\n",
       "      <td>0.114764</td>\n",
       "      <td>4.235119</td>\n",
       "      <td>4.255248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tesla</th>\n",
       "      <td>0.296216</td>\n",
       "      <td>0.379065</td>\n",
       "      <td>0.064811</td>\n",
       "      <td>0.024911</td>\n",
       "      <td>0.066818</td>\n",
       "      <td>0.154607</td>\n",
       "      <td>4.673387</td>\n",
       "      <td>4.284923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volvo</th>\n",
       "      <td>0.204267</td>\n",
       "      <td>0.433600</td>\n",
       "      <td>0.113805</td>\n",
       "      <td>0.024111</td>\n",
       "      <td>0.071134</td>\n",
       "      <td>0.220652</td>\n",
       "      <td>4.380814</td>\n",
       "      <td>4.281185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              emotion.sadness emotion.joy emotion.fear emotion.disgust  \\\n",
       "                         mean        mean         mean            mean   \n",
       "Car_Make                                                                 \n",
       "AMGeneral            0.233502    0.416527     0.149416        0.030530   \n",
       "Acura                0.186803    0.467307     0.134082        0.020744   \n",
       "AlfaRomeo            0.179048    0.434307     0.101048        0.030507   \n",
       "AstonMartin          0.161465    0.532924     0.093979        0.030943   \n",
       "Audi                 0.196609    0.490965     0.092430        0.021402   \n",
       "BMW                  0.191940    0.474563     0.086499        0.024038   \n",
       "Bentley              0.187771    0.528449     0.089768        0.028513   \n",
       "Bugatti              0.188314    0.587727     0.055366        0.023677   \n",
       "Buick                0.260452    0.392718     0.099129        0.025511   \n",
       "Cadillac             0.218803    0.429976     0.102534        0.030479   \n",
       "Chevrolet            0.200984    0.441252     0.102457        0.037943   \n",
       "GMC                  0.218755    0.416075     0.111135        0.033378   \n",
       "Honda                0.191337    0.418301     0.117569        0.029766   \n",
       "Toyota               0.199025    0.431638     0.104728        0.027125   \n",
       "Volkswagen           0.190767    0.429078     0.126892        0.031965   \n",
       "chrysler             0.234828    0.398700     0.116330        0.035023   \n",
       "dodge                0.218513    0.408767     0.119761        0.026407   \n",
       "ferrari              0.159649    0.539798     0.108343        0.019731   \n",
       "fiat                 0.203202    0.401303     0.100537        0.030897   \n",
       "ford                 0.238288    0.362188     0.121460        0.028063   \n",
       "genesis              0.211237    0.430926     0.078043        0.031972   \n",
       "hummer               0.181750    0.502888     0.126320        0.027008   \n",
       "hyundai              0.220990    0.393721     0.096735        0.027251   \n",
       "infiniti             0.200538    0.469661     0.090667        0.024671   \n",
       "isuzu                0.201127    0.404813     0.122677        0.028856   \n",
       "jaguar               0.163703    0.556705     0.086785        0.025675   \n",
       "jeep                 0.253255    0.396047     0.104165        0.025074   \n",
       "kia                  0.266675    0.394792     0.111849        0.025904   \n",
       "lamborghini          0.127094    0.629176     0.082221        0.029788   \n",
       "land-rover           0.274524    0.355115     0.109827        0.034846   \n",
       "lexus                0.202997    0.439800     0.100562        0.031977   \n",
       "lincoln              0.213433    0.466320     0.112260        0.027192   \n",
       "lotus                0.158467    0.457916     0.137350        0.023458   \n",
       "maserati             0.174925    0.523992     0.087822        0.037616   \n",
       "maybach              0.178194    0.515520     0.077657        0.015687   \n",
       "mazda                0.203442    0.444971     0.111021        0.026170   \n",
       "mercedes-benz        0.227015    0.387488     0.105910        0.026781   \n",
       "mercury              0.200664    0.462219     0.105958        0.025067   \n",
       "mini                 0.218531    0.443708     0.096154        0.026429   \n",
       "mitsubishi           0.175781    0.481554     0.118347        0.025169   \n",
       "nissan               0.241268    0.373916     0.111114        0.034341   \n",
       "pontiac              0.190257    0.430994     0.110610        0.026520   \n",
       "porsche              0.145226    0.510727     0.093447        0.026101   \n",
       "ram                  0.240626    0.367544     0.108349        0.040745   \n",
       "rolls-royce          0.260943    0.412009     0.080448        0.037039   \n",
       "subaru               0.202121    0.470115     0.103731        0.020184   \n",
       "suzuki               0.206990    0.410432     0.111564        0.033440   \n",
       "tesla                0.296216    0.379065     0.064811        0.024911   \n",
       "volvo                0.204267    0.433600     0.113805        0.024111   \n",
       "\n",
       "              emotion.anger sentiment.score  Rating\\r Predicted_Y  \n",
       "                       mean            mean      mean        mean  \n",
       "Car_Make                                                           \n",
       "AMGeneral          0.065171        0.021626  4.833333    4.264005  \n",
       "Acura              0.064254        0.330192  4.538690    4.455716  \n",
       "AlfaRomeo          0.088323        0.268080  4.187500    4.435494  \n",
       "AstonMartin        0.063027        0.470149  4.613636    4.631018  \n",
       "Audi               0.059860        0.303431  4.453431    4.421119  \n",
       "BMW                0.072675        0.243201  4.468750    4.354955  \n",
       "Bentley            0.057980        0.441103  4.239583     4.57587  \n",
       "Bugatti            0.054105        0.430882  4.750000    4.718716  \n",
       "Buick              0.086554        0.088404  4.162736    4.075432  \n",
       "Cadillac           0.069962        0.274075  4.395408    4.335975  \n",
       "Chevrolet          0.074343        0.173096  4.104730     4.23044  \n",
       "GMC                0.067579        0.071438  4.089912    4.138499  \n",
       "Honda              0.063076        0.118789  3.832386    4.130003  \n",
       "Toyota             0.070333        0.154561  4.350543    4.243079  \n",
       "Volkswagen         0.071180        0.130390  4.396875     4.17746  \n",
       "chrysler           0.075663        0.086065  4.140957    4.168451  \n",
       "dodge              0.076249        0.100277  4.133929    4.163826  \n",
       "ferrari            0.082763        0.463863  4.767241    4.530158  \n",
       "fiat               0.076235        0.087065  3.818878    4.134359  \n",
       "ford               0.088316        0.078135  4.040094    4.005134  \n",
       "genesis            0.057856        0.156253  4.608696    4.316763  \n",
       "hummer             0.055950        0.297203  4.404605    4.462752  \n",
       "hyundai            0.079915        0.161732  4.109375     4.14899  \n",
       "infiniti           0.059761        0.322187  4.566860    4.393907  \n",
       "isuzu              0.101334        0.205943  4.220238    4.306578  \n",
       "jaguar             0.055537        0.375661  4.584091    4.497573  \n",
       "jeep               0.079216        0.106891  4.108607     4.15378  \n",
       "kia                0.070382        0.141621  4.141827     4.12064  \n",
       "lamborghini        0.052919        0.657044  4.725000    4.665769  \n",
       "land-rover         0.086169        0.080642  3.848837      4.0177  \n",
       "lexus              0.076374        0.231606  4.306122    4.294433  \n",
       "lincoln            0.075736        0.163414  4.269231    4.264042  \n",
       "lotus              0.080445        0.307135  4.702381    4.490183  \n",
       "maserati           0.071795        0.311256  4.431250    4.394369  \n",
       "maybach            0.072357        0.633714  4.958333    4.733771  \n",
       "mazda              0.062387        0.230268  4.479651    4.318612  \n",
       "mercedes-benz      0.085045        0.091385  4.095745    4.094125  \n",
       "mercury            0.063838        0.246360  4.311224    4.390451  \n",
       "mini               0.071672        0.167861  4.036184    4.190949  \n",
       "mitsubishi         0.070454        0.300219  4.346698    4.417798  \n",
       "nissan             0.079956        0.102161  4.247093    4.119348  \n",
       "pontiac            0.078449        0.165777  4.375000    4.221752  \n",
       "porsche            0.080697        0.382931  4.662500    4.552274  \n",
       "ram                0.076267        0.000294  3.861111    4.113366  \n",
       "rolls-royce        0.072188        0.321649  4.843750    4.508778  \n",
       "subaru             0.070802        0.301044  4.257212    4.327014  \n",
       "suzuki             0.075242        0.114764  4.235119    4.255248  \n",
       "tesla              0.066818        0.154607  4.673387    4.284923  \n",
       "volvo              0.071134        0.220652  4.380814    4.281185  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_grouped_test_set = (\n",
    "    test_set[sentiment_cols + ['Car_Make', 'Rating\\r', 'Predicted_Y']]\n",
    "    .groupby('Car_Make')\n",
    "    .agg(['mean']))\n",
    "agg_grouped_test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment.score</th>\n",
       "      <th>emotion.sadness</th>\n",
       "      <th>emotion.joy</th>\n",
       "      <th>emotion.fear</th>\n",
       "      <th>emotion.disgust</th>\n",
       "      <th>emotion.anger</th>\n",
       "      <th>Rating\\r</th>\n",
       "      <th>Review_Content</th>\n",
       "      <th>Predicted_Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car_Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMGeneral</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acura</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AlfaRomeo</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AstonMartin</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Audi</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>15</td>\n",
       "      <td>51</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>16</td>\n",
       "      <td>48</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bentley</th>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bugatti</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buick</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>19</td>\n",
       "      <td>53</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cadillac</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chevrolet</th>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC</th>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>21</td>\n",
       "      <td>57</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honda</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>18</td>\n",
       "      <td>44</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota</th>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>11</td>\n",
       "      <td>46</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volkswagen</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>40</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrysler</th>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>19</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dodge</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>16</td>\n",
       "      <td>42</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ferrari</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiat</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>11</td>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>53</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genesis</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hummer</th>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>16</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infiniti</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isuzu</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>16</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jaguar</th>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>13</td>\n",
       "      <td>55</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jeep</th>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>20</td>\n",
       "      <td>61</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kia</th>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>20</td>\n",
       "      <td>52</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamborghini</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land-rover</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>21</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lexus</th>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lincoln</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lotus</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maserati</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maybach</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mazda</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes-benz</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>19</td>\n",
       "      <td>47</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercury</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mini</th>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>38</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mitsubishi</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>53</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nissan</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pontiac</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>porsche</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram</th>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rolls-royce</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subaru</th>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>16</td>\n",
       "      <td>52</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suzuki</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>16</td>\n",
       "      <td>42</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tesla</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volvo</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>15</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               sentiment.score  emotion.sadness  emotion.joy  emotion.fear  \\\n",
       "Car_Make                                                                     \n",
       "AMGeneral                    3                3            3             3   \n",
       "Acura                       42               42           42            42   \n",
       "AlfaRomeo                   16               16           16            16   \n",
       "AstonMartin                 33               33           33            33   \n",
       "Audi                        51               51           51            51   \n",
       "BMW                         48               48           48            48   \n",
       "Bentley                     36               36           36            36   \n",
       "Bugatti                      1                1            1             1   \n",
       "Buick                       53               53           53            53   \n",
       "Cadillac                    49               49           49            49   \n",
       "Chevrolet                   37               37           37            37   \n",
       "GMC                         57               57           57            57   \n",
       "Honda                       44               44           44            44   \n",
       "Toyota                      46               46           46            46   \n",
       "Volkswagen                  40               40           40            40   \n",
       "chrysler                    46               47           47            47   \n",
       "dodge                       42               42           42            42   \n",
       "ferrari                     29               29           29            29   \n",
       "fiat                        49               49           49            49   \n",
       "ford                        53               53           53            53   \n",
       "genesis                     23               23           23            23   \n",
       "hummer                      38               38           38            38   \n",
       "hyundai                     48               48           48            48   \n",
       "infiniti                    43               43           43            43   \n",
       "isuzu                       42               42           42            42   \n",
       "jaguar                      55               55           55            55   \n",
       "jeep                        61               61           61            61   \n",
       "kia                         52               52           52            52   \n",
       "lamborghini                 20               20           20            20   \n",
       "land-rover                  43               43           43            43   \n",
       "lexus                       48               49           49            49   \n",
       "lincoln                     39               39           39            39   \n",
       "lotus                       21               21           21            21   \n",
       "maserati                    40               40           40            40   \n",
       "maybach                      3                3            3             3   \n",
       "mazda                       43               43           43            43   \n",
       "mercedes-benz               47               47           47            47   \n",
       "mercury                     49               49           49            49   \n",
       "mini                        38               38           38            38   \n",
       "mitsubishi                  53               53           53            53   \n",
       "nissan                      43               43           43            43   \n",
       "pontiac                     40               40           40            40   \n",
       "porsche                     40               40           40            40   \n",
       "ram                         34               36           36            36   \n",
       "rolls-royce                  4                4            4             4   \n",
       "subaru                      52               52           52            52   \n",
       "suzuki                      42               42           42            42   \n",
       "tesla                       31               31           31            31   \n",
       "volvo                       43               43           43            43   \n",
       "\n",
       "               emotion.disgust  emotion.anger  Rating\\r  Review_Content  \\\n",
       "Car_Make                                                                  \n",
       "AMGeneral                    3              3         2               3   \n",
       "Acura                       42             42        12              42   \n",
       "AlfaRomeo                   16             16         4              16   \n",
       "AstonMartin                 33             33        10              33   \n",
       "Audi                        51             51        15              51   \n",
       "BMW                         48             48        16              48   \n",
       "Bentley                     36             36        14              36   \n",
       "Bugatti                      1              1         1               1   \n",
       "Buick                       53             53        19              53   \n",
       "Cadillac                    49             49        15              49   \n",
       "Chevrolet                   37             37        17              37   \n",
       "GMC                         57             57        21              57   \n",
       "Honda                       44             44        18              44   \n",
       "Toyota                      46             46        11              46   \n",
       "Volkswagen                  40             40        15              40   \n",
       "chrysler                    47             47        19              47   \n",
       "dodge                       42             42        16              42   \n",
       "ferrari                     29             29         7              29   \n",
       "fiat                        49             49        11              49   \n",
       "ford                        53             53        18              53   \n",
       "genesis                     23             23         3              23   \n",
       "hummer                      38             38        15              38   \n",
       "hyundai                     48             48        16              48   \n",
       "infiniti                    43             43        13              43   \n",
       "isuzu                       42             42        16              42   \n",
       "jaguar                      55             55        13              55   \n",
       "jeep                        61             61        20              61   \n",
       "kia                         52             52        20              52   \n",
       "lamborghini                 20             20         7              20   \n",
       "land-rover                  43             43        21              43   \n",
       "lexus                       49             49        15              49   \n",
       "lincoln                     39             39        16              39   \n",
       "lotus                       21             21         9              21   \n",
       "maserati                    40             40        13              40   \n",
       "maybach                      3              3         2               3   \n",
       "mazda                       43             43        14              43   \n",
       "mercedes-benz               47             47        19              47   \n",
       "mercury                     49             49        18              49   \n",
       "mini                        38             38        15              38   \n",
       "mitsubishi                  53             53        18              53   \n",
       "nissan                      43             43        18              43   \n",
       "pontiac                     40             40        14              40   \n",
       "porsche                     40             40        10              40   \n",
       "ram                         36             36        10              36   \n",
       "rolls-royce                  4              4         3               4   \n",
       "subaru                      52             52        16              52   \n",
       "suzuki                      42             42        16              42   \n",
       "tesla                       31             31         5              31   \n",
       "volvo                       43             43        15              43   \n",
       "\n",
       "               Predicted_Y  \n",
       "Car_Make                    \n",
       "AMGeneral                3  \n",
       "Acura                   41  \n",
       "AlfaRomeo               16  \n",
       "AstonMartin             32  \n",
       "Audi                    47  \n",
       "BMW                     44  \n",
       "Bentley                 32  \n",
       "Bugatti                  1  \n",
       "Buick                   51  \n",
       "Cadillac                47  \n",
       "Chevrolet               37  \n",
       "GMC                     55  \n",
       "Honda                   42  \n",
       "Toyota                  43  \n",
       "Volkswagen              36  \n",
       "chrysler                47  \n",
       "dodge                   39  \n",
       "ferrari                 26  \n",
       "fiat                    48  \n",
       "ford                    52  \n",
       "genesis                 22  \n",
       "hummer                  38  \n",
       "hyundai                 47  \n",
       "infiniti                41  \n",
       "isuzu                   42  \n",
       "jaguar                  48  \n",
       "jeep                    60  \n",
       "kia                     50  \n",
       "lamborghini             16  \n",
       "land-rover              43  \n",
       "lexus                   46  \n",
       "lincoln                 36  \n",
       "lotus                   21  \n",
       "maserati                38  \n",
       "maybach                  3  \n",
       "mazda                   42  \n",
       "mercedes-benz           45  \n",
       "mercury                 48  \n",
       "mini                    33  \n",
       "mitsubishi              51  \n",
       "nissan                  43  \n",
       "pontiac                 39  \n",
       "porsche                 40  \n",
       "ram                     36  \n",
       "rolls-royce              4  \n",
       "subaru                  48  \n",
       "suzuki                  41  \n",
       "tesla                   31  \n",
       "volvo                   42  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get the number of reviews per Car Name:\n",
    "test_set.groupby('Car_Make').nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared = 0.5833818585164156\n",
      "Mean Squared Error = 0.03221873091669909\n"
     ]
    }
   ],
   "source": [
    "# r2_score for predicted y and target y avg per group!\n",
    "agg_r2_score = r2_score(agg_grouped_test_set['Rating\\r'], agg_grouped_test_set['Predicted_Y'])\n",
    "print(f\"R-Squared = {agg_r2_score}\")\n",
    "agg_mse = mean_squared_error(agg_grouped_test_set['Rating\\r'], agg_grouped_test_set['Predicted_Y'])\n",
    "print(f\"Mean Squared Error = {agg_mse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the mean_squared error shows when it comes to the average the model has fitted the data moderately well. The R-squareds shows a moderate effect size indicates that ~44% of the variability in the Rating cannot be explained by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Rating\\r</th>\n",
       "      <th>Predicted_Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car_Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMGeneral</th>\n",
       "      <td>4.833333</td>\n",
       "      <td>4.264005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acura</th>\n",
       "      <td>4.538690</td>\n",
       "      <td>4.455716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AlfaRomeo</th>\n",
       "      <td>4.187500</td>\n",
       "      <td>4.435494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AstonMartin</th>\n",
       "      <td>4.613636</td>\n",
       "      <td>4.631018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Audi</th>\n",
       "      <td>4.453431</td>\n",
       "      <td>4.421119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>4.468750</td>\n",
       "      <td>4.354955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bentley</th>\n",
       "      <td>4.239583</td>\n",
       "      <td>4.57587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bugatti</th>\n",
       "      <td>4.750000</td>\n",
       "      <td>4.718716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buick</th>\n",
       "      <td>4.162736</td>\n",
       "      <td>4.075432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cadillac</th>\n",
       "      <td>4.395408</td>\n",
       "      <td>4.335975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chevrolet</th>\n",
       "      <td>4.104730</td>\n",
       "      <td>4.23044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC</th>\n",
       "      <td>4.089912</td>\n",
       "      <td>4.138499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honda</th>\n",
       "      <td>3.832386</td>\n",
       "      <td>4.130003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota</th>\n",
       "      <td>4.350543</td>\n",
       "      <td>4.243079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volkswagen</th>\n",
       "      <td>4.396875</td>\n",
       "      <td>4.17746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrysler</th>\n",
       "      <td>4.140957</td>\n",
       "      <td>4.168451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dodge</th>\n",
       "      <td>4.133929</td>\n",
       "      <td>4.163826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ferrari</th>\n",
       "      <td>4.767241</td>\n",
       "      <td>4.530158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiat</th>\n",
       "      <td>3.818878</td>\n",
       "      <td>4.134359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>4.040094</td>\n",
       "      <td>4.005134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genesis</th>\n",
       "      <td>4.608696</td>\n",
       "      <td>4.316763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hummer</th>\n",
       "      <td>4.404605</td>\n",
       "      <td>4.462752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai</th>\n",
       "      <td>4.109375</td>\n",
       "      <td>4.14899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infiniti</th>\n",
       "      <td>4.566860</td>\n",
       "      <td>4.393907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isuzu</th>\n",
       "      <td>4.220238</td>\n",
       "      <td>4.306578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jaguar</th>\n",
       "      <td>4.584091</td>\n",
       "      <td>4.497573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jeep</th>\n",
       "      <td>4.108607</td>\n",
       "      <td>4.15378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kia</th>\n",
       "      <td>4.141827</td>\n",
       "      <td>4.12064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamborghini</th>\n",
       "      <td>4.725000</td>\n",
       "      <td>4.665769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land-rover</th>\n",
       "      <td>3.848837</td>\n",
       "      <td>4.0177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lexus</th>\n",
       "      <td>4.306122</td>\n",
       "      <td>4.294433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lincoln</th>\n",
       "      <td>4.269231</td>\n",
       "      <td>4.264042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lotus</th>\n",
       "      <td>4.702381</td>\n",
       "      <td>4.490183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maserati</th>\n",
       "      <td>4.431250</td>\n",
       "      <td>4.394369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maybach</th>\n",
       "      <td>4.958333</td>\n",
       "      <td>4.733771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mazda</th>\n",
       "      <td>4.479651</td>\n",
       "      <td>4.318612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes-benz</th>\n",
       "      <td>4.095745</td>\n",
       "      <td>4.094125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercury</th>\n",
       "      <td>4.311224</td>\n",
       "      <td>4.390451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mini</th>\n",
       "      <td>4.036184</td>\n",
       "      <td>4.190949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mitsubishi</th>\n",
       "      <td>4.346698</td>\n",
       "      <td>4.417798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nissan</th>\n",
       "      <td>4.247093</td>\n",
       "      <td>4.119348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pontiac</th>\n",
       "      <td>4.375000</td>\n",
       "      <td>4.221752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>porsche</th>\n",
       "      <td>4.662500</td>\n",
       "      <td>4.552274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram</th>\n",
       "      <td>3.861111</td>\n",
       "      <td>4.113366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rolls-royce</th>\n",
       "      <td>4.843750</td>\n",
       "      <td>4.508778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subaru</th>\n",
       "      <td>4.257212</td>\n",
       "      <td>4.327014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suzuki</th>\n",
       "      <td>4.235119</td>\n",
       "      <td>4.255248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tesla</th>\n",
       "      <td>4.673387</td>\n",
       "      <td>4.284923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volvo</th>\n",
       "      <td>4.380814</td>\n",
       "      <td>4.281185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Rating\\r Predicted_Y\n",
       "                   mean        mean\n",
       "Car_Make                           \n",
       "AMGeneral      4.833333    4.264005\n",
       "Acura          4.538690    4.455716\n",
       "AlfaRomeo      4.187500    4.435494\n",
       "AstonMartin    4.613636    4.631018\n",
       "Audi           4.453431    4.421119\n",
       "BMW            4.468750    4.354955\n",
       "Bentley        4.239583     4.57587\n",
       "Bugatti        4.750000    4.718716\n",
       "Buick          4.162736    4.075432\n",
       "Cadillac       4.395408    4.335975\n",
       "Chevrolet      4.104730     4.23044\n",
       "GMC            4.089912    4.138499\n",
       "Honda          3.832386    4.130003\n",
       "Toyota         4.350543    4.243079\n",
       "Volkswagen     4.396875     4.17746\n",
       "chrysler       4.140957    4.168451\n",
       "dodge          4.133929    4.163826\n",
       "ferrari        4.767241    4.530158\n",
       "fiat           3.818878    4.134359\n",
       "ford           4.040094    4.005134\n",
       "genesis        4.608696    4.316763\n",
       "hummer         4.404605    4.462752\n",
       "hyundai        4.109375     4.14899\n",
       "infiniti       4.566860    4.393907\n",
       "isuzu          4.220238    4.306578\n",
       "jaguar         4.584091    4.497573\n",
       "jeep           4.108607     4.15378\n",
       "kia            4.141827     4.12064\n",
       "lamborghini    4.725000    4.665769\n",
       "land-rover     3.848837      4.0177\n",
       "lexus          4.306122    4.294433\n",
       "lincoln        4.269231    4.264042\n",
       "lotus          4.702381    4.490183\n",
       "maserati       4.431250    4.394369\n",
       "maybach        4.958333    4.733771\n",
       "mazda          4.479651    4.318612\n",
       "mercedes-benz  4.095745    4.094125\n",
       "mercury        4.311224    4.390451\n",
       "mini           4.036184    4.190949\n",
       "mitsubishi     4.346698    4.417798\n",
       "nissan         4.247093    4.119348\n",
       "pontiac        4.375000    4.221752\n",
       "porsche        4.662500    4.552274\n",
       "ram            3.861111    4.113366\n",
       "rolls-royce    4.843750    4.508778\n",
       "subaru         4.257212    4.327014\n",
       "suzuki         4.235119    4.255248\n",
       "tesla          4.673387    4.284923\n",
       "volvo          4.380814    4.281185"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_grouped_test_set[['Rating\\r', 'Predicted_Y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emotion.sadness  mean    float64\n",
       "emotion.joy      mean    float64\n",
       "emotion.fear     mean    float64\n",
       "emotion.disgust  mean    float64\n",
       "emotion.anger    mean    float64\n",
       "sentiment.score  mean    float64\n",
       "Rating\\r         mean    float64\n",
       "Predicted_Y      mean     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_grouped_test_set.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0.51x + 2.10\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as pylab\n",
    "\n",
    "# plot the data itself\n",
    "pylab.plot(agg_grouped_test_set['Rating\\r'],agg_grouped_test_set['Predicted_Y'],'o')\n",
    "pylab.xlabel('Rating From Dataset')\n",
    "pylab.ylabel('Rating Predicted By Model')\n",
    "\n",
    "# calc the trendline\n",
    "z = np.polyfit(np.squeeze(agg_grouped_test_set['Rating\\r']),\n",
    "               np.squeeze(agg_grouped_test_set['Predicted_Y'].astype(float)), 1)\n",
    "p = np.poly1d(z)\n",
    "pylab.plot(agg_grouped_test_set['Predicted_Y'],p(agg_grouped_test_set['Predicted_Y']),\"r--\")\n",
    "pylab.title(\"Rating From Dataset Vs Rating Predicted By Model\")\n",
    "# the trendline equation:\n",
    "print (\"y = %.2fx + %.2f\"%(z[0],z[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above results suggest a clear better fit for the model in average; showing that the Gradient Boosting Regressor model explains 74% of the fitted Car Make level Rating in the regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "In this notebook we demonstrated how Text Extensions for Pandas can be used to perform Sentiment Analysis tasks. We started by loading our car reviews and passing it through Watson NLU service. We extracted the keywords and their corresponding sentiment and fine-grained emotion using the Watson NLU service. We used Text Extensions for Pandas to convert the Watson NLU output to pandas dataframe and calculated the reveiw-level sentiment and emotion. Using the resulted Pandas dataframe, we showed the correlation of Watson NLU's extracted features and user's Rating first and then developed the Univariate/Multivariate Regression, Random Forest, and Gradient Boosting models for predicting the Ratings for a given review. Finally we evaluated the ability of the model for predicting the sentiment for each car make.\n",
    "\n",
    "This notebook also demonstrates how easy it is to use IBM Watson NLU, Pandas, Scikit Learn together to conduct exploratory analysis or predcition on your data."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}