{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f049173-8782-4c79-88dc-4874d8a080a4",
   "metadata": {},
   "source": [
    "In typical environments our data would be available in a relational database (or some other common data store) and spread across multiple tables/documents/files. To access it, we would first need to get our credentials and access authorizations and familiarize ourselves with the data schema.\n",
    "\n",
    "In thos notebook, however, things are much simpler: we will just download a single compressed file, housing.tgz, which contains a comma-separated values (CSV) file called `housing.csv` with all the data.\n",
    "\n",
    "It is preferable to create a small function to do that. Having a function that downloads the data is useful in particular if the data changes regularly: we can write a small script that uses the function to fetch the latest data (or we can set up a scheduled job to do that automatically at regular inter‐ vals). Automating the process of fetching the data is also useful if we need to install the dataset on multiple machines.\n",
    "\n",
    "The next cell is the function to fetch the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1e230ca-0f08-4edd-8f99-2a73c692b68f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/QubitPi/handson-ml2/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    os.makedirs(housing_path, exist_ok=True)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()\n",
    "\n",
    "fetch_housing_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c5004f-224e-4690-9e0a-99928b00a53a",
   "metadata": {},
   "source": [
    "Now let's load the data using [pandas](https://pandas.pydata.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a67b5744-0a7b-4289-9735-7f42ec981c0c",
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)\n",
    "\n",
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e24d48-c1f1-4d3b-8747-692124121189",
   "metadata": {},
   "source": [
    "The `info()` method is useful to get a quick description of the data, in particular the total number of rows, each attribute’s type, and the number of nonnull values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2712c11-f764-49ba-84ad-24891d4ebdd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec6f9ff-6b87-4d5d-9a6c-e37cf9c211a6",
   "metadata": {},
   "source": [
    "All attributes are numerical, except the `ocean_proximity` field. Its type is object, so it could hold any kind of Python object. But since we loaded this data from a CSV file, we know that it must be a text attribute. When we looked at the top five rows, we probably noticed that the values in the `ocean_proximity` column were repetitive, which means that it is probably a categorical attribute. We can find out what categories exist and how many districts belong to each category by using the `value_counts()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "187be41d-20da-4dde-a894-4c7fffcabc45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ocean_proximity\n",
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8d1a19-dc2c-4874-9692-971c0e080429",
   "metadata": {},
   "source": [
    "Let’s look at the other fields. The `describe()` method shows a summary of the numerical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "800ee5dd-a6cc-42ae-9377-1e6ff64595c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8baa2199-86a0-4a0f-8228-d524ef238e72",
   "metadata": {},
   "source": [
    "Another quick way to get a feel of the type of data we are dealing with is to plot a histogram for each numerical attribute. A histogram shows the number of instances (on the vertical axis) that have a given value range (on the horizontal axis). We can either plot this one attribute at a time, or we can call the `hist()` method on the whole dataset (as shown in the following code example), and it will plot a histogram for each numerical attribute\n",
    "\n",
    "It should be noted that the median income attribute does not look like it is expressed in US dollars (USD). In fact, the data has been scaled and capped at 15 (actually, 15.0001) for higher median incomes, and at 0.5 (actually, 0.4999) for lower median incomes. The numbers represent roughly tens of thousands of dollars (e.g., 3 actually means about $30,000). __Working with preprocessed attributes is common in Machine Learning, and it is not necessarily a problem, but we should try to understand how the data was computed__.\n",
    "\n",
    "What's indeed a problem is that housing median age and the median house value were also capped. The latter may be a serious problem since it is our target attribute (our labels). Our Machine Learning algorithms may learn that prices never go beyond that limit. We need to check with our business team to see if this is a problem or not. If they tell us that they need precise predictions even beyond $500,000, then we have two options:\n",
    "\n",
    "1. Collect proper labels for the districts whose labels were capped.\n",
    "2. Remove those districts from the training set (and also from the test set, since our system should not be evaluated poorly if it predicts values beyond $500,000).\n",
    "\n",
    "Finally, many histograms are _tail-heavy_: they extend much farther to the right of the median than to the left. This may make it a bit harder for some Machine Learning algorithms to detect patterns. We will try transforming these attributes later on to have more bell-shaped distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac53dd25-846c-4d75-8143-cef48323b571",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c1c14f-3657-4f2f-a2fd-13dd4ea644ab",
   "metadata": {},
   "source": [
    "Now before we look at the data any further, we need to create a test set, put it aside, and __never look at it__.\n",
    "\n",
    "Creating a Test Set\n",
    "-------------------\n",
    "\n",
    "It may sound strange to voluntarily set aside part of the data at this stage. After all, we have only taken a quick glance at the data, and surely we should learn a whole lot more about it before we decide what algorithms to use, right? This is true, but our brain is an amazing pattern detection system, which means that it is highly prone to overfitting: if we look at the test set, we may stumble upon some seemingly interesting pattern in the test data that leads us to select a particular kind of Machine Learning model. When we estimate the generalization error using the test set, our estimate will be too optimistic, and we will launch a system that will not perform as well as expected. This is called _data snooping_ bias.\n",
    "\n",
    "Creating a test set is theoretically simple: pick some instances randomly, typically 20% of the dataset (or less if your dataset is very large), and set them aside:\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "\n",
    "def split_training_and_test_data(data, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    training_indices = shuffled_indices[test_set_size:]\n",
    "    return data.iloc[training_indices], data.iloc(test_indices)\n",
    "\n",
    "training_set, test_set = split_training_and_test_data(housing, 0.2)\n",
    "```\n",
    "\n",
    "Well, this works, but it is not perfect: if we run the program again, it will generate a different test set! Over time, we (or our Machine Learning algorithms) will get to see the whole dataset, which is what we want to avoid.\n",
    "\n",
    "To have a stable train/test split even after updating the dataset, a common solution is to use each instance’s identifier to decide whether or not it should go in the test set (assuming instances have a unique and immutable identifier). For example, we could compute a hash of each instance’s identifier and put that instance in the test set if the hash is lower than or equal to 20% of the maximum hash value. This ensures that the test set will remain consistent across multiple runs, even if we refresh the dataset. The new test set will contain 20% of the new instances, but it will not contain any instance that was previously in the training set.\n",
    "\n",
    "Here is a possible implementation:\n",
    "\n",
    "```python\n",
    "from zlib import crc32\n",
    "\n",
    "def test_set_check(identifier, test_ratio):\n",
    "    return crc32(np.int64(identifier)) & 0xffffffff < test_ratio * 2**32\n",
    "\n",
    "def split_train_test_by_id(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio)) return data.loc[~in_test_set], data.loc[in_test_set]\n",
    "```\n",
    "\n",
    "Unfortunately, the housing dataset does not have an identifier column. The simplest solution is to use the row index as the ID:\n",
    "\n",
    "```python\n",
    "housing_with_id = housing.reset_index() # adds an `index` column\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"index\")\n",
    "```\n",
    "\n",
    "If we use the row index as a unique identifier, we need to make sure that new data gets appended to the end of the dataset and that no row ever gets deleted. If this is not possible, then we can try to use the most stable features to build a unique identifier. For example, a district’s latitude and longitude are guaranteed to be stable for a few million years, so we could combine them into an ID like so\n",
    "\n",
    "```python\n",
    "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"id\")\n",
    "```\n",
    "\n",
    "__Scikit-Learn__ provides a few functions to split datasets into multiple subsets in various ways. The simplest function is `train_test_split()`, which does pretty much the same thing as the function `split_training_and_test_data()`, with a couple of additional features. First, there is a `random_state` parameter that allows us to set the random generator seed. Second, we can pass it multiple datasets with an identical number of rows, and it will split them on the same indices (this is very useful, for example, if we have a separate DataFrame for labels):\n",
    "\n",
    "```python\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "```\n",
    "\n",
    "So far we have considered purely random sampling methods. This is generally fine if our dataset is large enough (especially relative to the number of attributes), but if it is not, we run the risk of introducing a significant sampling bias. We want a sampling that is representative of the whole dataset. For this, we need to use some domain knowledge.\n",
    "\n",
    "Suppose we chatted with experts who told us that the median income is a very important attribute to predict median housing prices. We may want to ensure that the test set is representative of the various categories of incomes in the whole dataset. Since the median income is a continuous numerical attribute, we first need to create an income _category_ attribute. Let’s look at the median income histogram above more closely: most median income values are clustered around 1.5 to 6 (i.e., $15,000–$60,000), but some median incomes go far beyond 6. It is important to have a sufficient number of instances in our dataset for each stratum, or else the estimate of a stratum’s importance may be biased. This means that we should not have too many strata, and each stratum should be large enough. The following code uses the `pd.cut()` function to create an income category attribute with five categories (labeled from 1 to 5): category 1 ranges from 0 to 1.5 (i.e., less than $15,000), category 2 from 1.5 to 3, and so on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f131dcf-c03e-4399-8ba4-f62ada5d31c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "housing[\"income_category\"] = pd.cut(housing[\"median_income\"], bins=[0, 1.5, 3, 4.5, 6, np.inf], labels=[1, 2, 3, 4, 5])\n",
    "housing[\"income_category\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f1c615-b213-466b-abee-9d7c19de786c",
   "metadata": {},
   "source": [
    "We use this to construct a __stratified sampling__, i.e. the number of samples of each median income category follows the same distribution shown above. For this, we can use Scikit-Learn’s `StratifiedShuffleSplit` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ceda4c99-59d2-45a7-abb2-de5f439f0d59",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing[\"income_category\"]):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7011bf50-78f0-44ff-9b97-23d4068d3ca7",
   "metadata": {},
   "source": [
    "Let’s see if this worked as expected. We can start by looking at the income category proportions in the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77658423-0751-4e60-8a6a-a376959304c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_category\n",
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114341\n",
       "1    0.039971\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set[\"income_category\"].value_counts() / len(strat_test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ed91fe-aa12-4324-8061-a6564b0ec213",
   "metadata": {},
   "source": [
    "Now we should remove the income_cat attribute so the data is back to its original state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d57c7c6e-416e-4381-91c3-9b243f610e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (strat_train_set, strat_test_set):\n",
    "    set_.drop(\"income_category\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce861cbc-ba30-4921-a971-5a9840a36120",
   "metadata": {},
   "source": [
    "Visualizing Data\n",
    "----------------\n",
    "\n",
    "First, make sure we have put the test set aside and we are only exploring the training set. Also, if the training set is very large, we may want to sample an exploration set, to make manipulations easy and fast. In our case, the set is quite small, so we can just work directly on the full set. Let’s create a copy so that we can play with it without harming the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63a7efde-fd7d-4dce-94d7-db70ef83127a",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c1c750f-2d09-4e1a-89b1-4b147425ae08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4gMvb0plAjglc9laeA6DCuMRIScGYNjgAizZVuzyvxhuatBJCiNIop0dh2w/XoeCY+FkeMfguQJcbhNrf12jowK+nmkQ3nJSb46HEBZSr2F/BMZ0i+Uhim5JuP1xnN2PGhumK3mRox6JNB7FwQzkWbiizymUSw115Nd7QpJUQQpRGMz0KkVKbRywx+SFCsywP5BWaZw4aDO1Wy2OV9S0oOdFobocgdomqsr4F+6oaoAIQE94LXSbHdhzxFfvjI3UJhm05akxcuFXwd+Bkk6hjMYGcI7lM7s6r8fQmrYQQojQKehQitjaPFGIuQGISpy0vwkyeTE5eoV3jUj7VulZogwPw6KclnPlKtnlFtgGFLSZ/54xForAUUpdgwoID8G721Zi7phhFl/KciqobzcniYcEBuFqgkjIApMZpzUGjI0GLu/NqXJEwTQghnoSCHgVIrZrMRgVgYnI0Xpl+laQLEFflY0u2F2G2AO03npozQHeA8cS6A7wJ2s0XOqDp5Y/mi50A7AMKhhKzYumJ8tpPPLHuAEqON1ndZhkUXje0L2tSOEMbHIAPZ6YCcDxo8ZS8GmcmTBNCiCehnB4FOFJhl3FVjAbLs1Nk1bRhy9lhU61rtcgBsg6QmKUktU0SNpPjwbX8ZanLBHPAw2DLT1FiVqzLyN+ElA3Xe7fNEdo8bwLCe9snEY8coMG23EnmAM7RoIXyagghxLUo6HFQk6EdK/OPOnyc5feO4d2tw1Ygj8EsU6x9KJX3NeIjQwQDtOE2y1zMEpvcwM42oOAKPKQqrG7ATokzRULvYf66EugNHYiNDMaBRVPxyew0zMqIx4KpQ5Cfm4Vv5k+0+h0pEbSITTJnw/eZIIQQYo+Wtxz0xLoDdi0elCRlG3nmkL6CTT5NAsHG8uwxAGC3xOZodMws9SgxK8Z4IK8QqfFafJiTKmp7t9DMzG+1zVYJyBOTozExOZr3OY4mA8vJq3FG+wjqv0UI6Qko6HGAErk8DK78D6m7g9guwimDwnHPNQNRda5VdPdzk8lk3padEBUiuJNKCLPU40gvMTZF1Y3IWppvtezEhXnvuyrqWd+P0WRdg0dMIKBUMrCUvBolq19T/y1CSE9CQY8DlJy18LdNpoG8Lc2WF+HyWj3WFlSjqLrRXJU5Mzkab9wxAi9+Xc46O8F1EVwwdYjge9AGB0Bv6LAKKGyDKa6gS60CZKTpAOiuRzRnTRHWP5oh+Njl2Sm478O9vM1Cy2v1WLTpoKRAwFXJwEpvc3d2+xBCCPEklNMjU2V9i+yt1mweyCu0K2onZncQl4SoEKwvOsnaXf3Fr8uxdnYa8nOzsHpWKtY+lIpZE+LRcCngYbsILtt6hDV/RQUguW8oFs8YiTUPpWGCzXIQ21IPWx5LrwDHPopFNY1WeUN8+U/vCiw9rS2o9thGnI58JmyJTewmhBBfQTM9EjmjCCHD9hu2I7uDxMwIsLVAYMM8Z/Pj4wHA6vHhwQGoqGvBwg1lALpnRDbPGw+dod1qqcd2qci275TchqmWxMzQMOPgKqaYMiictReaqwoGCi2pKbnN3d11ggghxNUo6JHIGUUIGbYXVrH5N2zEXNAWbarGLgnBm67VuvHpyp+Psta8AWAO3PhyRpgloW9+rRU9Bj5rC6pZZ7bmryvFu9lX243Dth7P+KQo3HPNQN4GrXsrzzmlkJ/Y3Bqhz4TJZEL+4TpR4/OUOkGEEOIqFPRIoGTiMh/Lb9iv3zEC01fssro4a3r74407RvAeQ+iC5qeC5PfC5B0xu8CKWIIDMYUQbWe0/rn9mKRxsBkZo+GdoZm7ptguQGu+0InUOC0em5xk1ZqDz8IN5eb/r2TCr5TcGrZk9bSECHQajZi8bLvo8TkSVBNCiDeinB4JlExc5mP5Dfulr8vRfMG64F/zhU68+HW57dOs8NWQuSZOi9ITTZLH1WmRaSxmJklMzkhlfQvKTvFXgxZjxpiBvPcX1TSyjqOoptFqVoTrvLFRKs9Ham4Nk6zO5GTl52YhwE/N2o1+9poi3lo+r99xFTS9rb/7iAmqCSHEG1HQI4HS261t2Ra1czTRlC1hWNPbH8U1jXj7xwrJ47MMxoRnklSCy1bVulbFAslegX6yn2ub/Cu2wrVSCb9yk5OZ6t1MtWy2z0lxTSNmrS7CpKXbcNeqAnz7a63VeF/6+qCsoJoQQrwRLW9JIKbPlSNsdzo5mmhqW0NmZf5R2YUUtcEBiAgONP/MufX80uvmfFQoeEymu7sSruqv4VyqGRPHnpxsOQ5LtuftjP6iOVGbjaMJv47m1ogNHIuqG83nITM5Gn9KG+jWLu+EEOJqNNMjEdssQGZyND6fcy1GDLBu4RDkL7xEwlgyYyTWzk6zyr9wdPcWs6yREBWCuIhgFFXbL/GIpTd02C3lsJ2LsOAANF9gb9bJsJzRSowOFezwLsbSrUc4Wzp8mJMqq10EM5NybUIE72s7mvDraDsLOTOQOyrq8dhn/EtzUra/E0KIN6CZHon4KvB+O3+i1e17K3W8MwSWrk2MtLtNTqIp1y6ge67hz3kRYgTsvv3bngs/lUrUDI/tjNYbd47E9BW7HRrfjop6NBjaOX83jrSLUCLhV2grutyEdQA44aRcM9q9RQjxNRT0yMRVgdfydjFLN0IXTqkXa65dQIb2TtbHM0YO0IhKKGZbymHec/7hOt7nPj0lGbePHmD3/NGx4chMjnZ4ZxwzNrbfjaPtIuQGTWxBaGqcFh/OTIWutQ01DQb4qVR487+/WxWmBC7n1ghVRj5wskn0+xCDdm8RQnwVBT0Ksv02nxgdivTESOyp1HE+h+3CyVfIj+9izVeQsLimkbMgn6a3P36rPS/qPfJ9+xdaZmELeBjLs1Nw3d/z0SSwNCZ3bAy57SLkBk1PrDtgVwupqKYRY1//0Wo3HBsmt2ZnRT1v49OrB4aLeg9i2X4mqRkpIcRXUNCjAL7CcqvuH2s3Q5Aap8WDGfEYPiDM6iIippAfwH0REkponZkRj94BJ+2akfIV42OI+fbvyDKQrrXNoYAHAM5fdOz5YkgJmvjqOgkFPJYeyCvkrblz3dC+doUW5fpkdpo5wKJmpIQQX6MyKbV9xkM1NzcjLCwMer0eGo3jCbNscvIKOS/0zNKE7QwBW+AidByhi1BlfYtVcTpb+blZSIgKsRpLta4Vs1YLt4AQe7FjEp6lXijzD9eJGgefEQM0+Hb+RIeOoSQl3hPD9vNk64TOgNve2yU7cGQ7vpjPNSGEOIszrt800+MgsV2vmX9Nhnbk5BWydjEXOs6iTQd5q/YKbalftOmg3ayRUMy7eMZIjEuMFD27IXcZSIkaSOWnmvHrySaMUni5Ry4lt0YKbSOPjQzGgUVTsbOiHj8fqkNIoB+2/HYWFXX8FaYZw2L6IPfGIeafle7mTgghnoC2rDtIamE5rkTjFzfy7/LaW3lOVKFCvsJ6bBWEhbZLZ6cNknVxY7Z7i32ulErIfF4QOI+uZHTCMQ/W6nnvn5gcjUW3X4Xcm67EBw+M5X2sCkBoUHdRx/JTzbj9vd3IySuE3tChaDd3QgjxFBT0OEhKLR2+Csvltfw7py62d/Hez1yEwoID8PLtw1kfw1VBmKu+jZjt3Epanp2ClEHhDh2j/FQzdsrYBWZZ10gpzqjgvaagmvV2tvELBZLhwQEwtFl/rpjAmJqREkJ8ES1vOUhK8q7Qt+cRAzQo59g2/nnhcd7nWl6EpFZydnQ7t1LCggMwb3KSw3kwD+QVIjVeiw9zUgVzkJyZrMt8NnZV1Cs261NU3Wi1tCQ0frat9iMGaDAvazAeZSlOyATGqkszfdSMlBDiS2imRwFiZ0qETva8rCTO+yrq2Gcg2Kr2yv2WLnVJyhmUmh0pqm5E1tJ8u9o3tvi6m3ORMiu0PDsFE3i2m8thubQkNH625qTfzp+IXoH833eqda0eMwNICCFKoZkeBQjNlLB9G7fEfHuW0zST7SKkRAVhd7k89np0ObivsNHQgTlrirD+0QzW+6Um68qZFWI+G7+ebMILG8s4Z/KkiLzUA03K+G232osJjD1lBpAQQpRCMz0K4popYfs2bokJXKTMciyZMRL5uVl2/boY3vwtvXvs1rMjITK7qBfVNHLOyCiVhM43K8QYNTAc7/4pBYtnjMSSGSPxyew0jBiggdom3YaZucvkmR1auvWIrPFbktLvyxNmAAkhRAkeM9OzZMkSLFy4EE8++STeeecdAMDFixexYMECfPHFF2hra8ONN96IlStX4oorrnDvYCXgK1AHWBeDCwsOEN3F/YqwXpI6rHvTt3S2sUcEB2L8//2Eljb+hG42v53Ss753OUnotsRs4eaaIXr/3rF48ety1tYW5aeaOD83zOs5mmzsSD8yQgjxRh4R9BQVFeGDDz7AqFGjrG5/+umn8d1332H9+vUICwvD448/jhkzZmD3bseaU7qS0Ldx28q8y7NTMHtNkWCVZLG7Z+S2XfAEtmPf/dz1mPH+bhyrl7bD6uOCakwbHWN3u5JJ6Gw9yRicZQou9dViC0wX//C74OtNGtrXoWVMbw6MCSFEDrcvb7W0tOC+++7Dv/71L2i1WvPter0eeXl5eOuttzB58mSMHTsWq1evRkFBAfbu3evGEUsj9ds4s4OJz4gYTY+8OIUFB+Ctu0dLfh7fEpfYZUCh32NkSCDr7XxlCixniCyXjyrrWwRLGDCfGyWWMeUsXzljiz8hhDib22d65s2bh2nTpuGGG27A66+/br59//796OjowA033GC+7corr8SgQYOwZ88ejBs3jvV4bW1taGtrM//c3Ox44qgj5CQVC11g/3bnSMXH6Wpym1gu21oh6/W4ZmLEznYwv0euJaelW46wtmaQM0Mk9Jxh/fqYn8PUZSqsaoAJkFQ9Ww7qx0UI8WZuDXq++OILlJSUoKjIvi7LmTNnEBgYiPDwcKvbr7jiCpw5c4bzmIsXL8Yrr7yi9FAdIjV3gitQUquACUnRGBUb7uwhO40jF02h/Cg+QsuBCVEhMJlM5uRftsCBq1UIAM68Hjl5N0LP6eXfPUHrjgCEL5mb+nERQjyd25a3Tpw4gSeffBKfffYZevXqpdhxFy5cCL1eb/534sQJxY4tF1utFK5dVwy2ZYsJSdFen2TqyA4ooRkQNmy7kWwx/dAmL9uOWauLMGnpNnM7BkZlfQu2HanjfS223VJ8u6SuidOiWtdqt0SUGB2K1HgtuJSe1KPqXKtD51IOMUt1hBDiydw207N//37U1dVhzJgx5tu6urqwY8cOvPfee9iyZQva29vR1NRkNdtz9uxZ9OvXj/O4QUFBCAoKcubQZRObVMws/bwy/SoAcGqSqdxlJrmv5UgTSzmFC7lm1CzfN18j13ezr+atsWSJazaJbaZP09sfxTWN5urTtjM0MzPiUVTNncy+t1In+1za/s7FfgYcSeYmhBBP4Lag5/rrr0dZmXVzyFmzZuHKK6/Ec889h9jYWAQEBOCnn37CH/7wBwDA4cOHcfz4caSnp7tjyE6n5HKF0IXMHUsjjl40E6NDoenlj+aLnbzHUamAq2I0WJ49xnxRLznRiPjIEGiDA0QFMUzwMGt1EUpPNPE+FgAyBnPn0tjmDa3MP4qSGutj2i4RDe+v4X+PAuNhziXzOfBTAfqLnfjn9mMosyiQaHs++T4DQtPC1I+LEOLp3Bb09OnTByNGjLC6LSQkBJGRkebbZ8+ejWeeeQYRERHQaDSYP38+0tPTOZOYvZ0S+RJigxl35GY4WlfmlxONggEPAKQnRuL9+8bCBBNy8gqtzkV47wA0XeBvTWFJTMADAAJllQBczhtim8GxnKExmUyoaTAgNV6Lkpom1gT4tIQI3teKCA60e+9sbM8n22dAbEVxmuUhhHg6t29Z5/P222/j1ltvxR/+8AdkZmaiX79+2LBhg7uH5RRK5UuIyfMQeq11hcedkp8hpQowmxc3lot6HX+1GmGXZnR2HbW+UEsJeKTYU6kTdc6EZrvmf15izi0qqm6Eprf19xKx29Ff/+433irgXNg+b2IrihNCiKdz+5Z1S9u2bbP6uVevXlixYgVWrFjhngG5kBL5EmJzZoRea+GG7mVHR5e72JbY5FYBFlO7hrGjoh4f766UvdNLrs2/nMLtowc4lJf0m817bL7QidQ4LR6bnGR1HktO8Bev5MsHEsNyeUxsRXFCCPF0HhX09GSOLv0A4gMnsQnBcpe7hJbYxFYBtgyapO7cevmbQ5Ier4S3f6zA2z9W8AaLfOUIjCbAaPP4LpMJRTWNdudJqW70XOIjQ7D9cB02HjjF+zjbiuKEEOLJKOjxEI50RrdMVuXDBE5cr2VL7K4qW2Lyhfh2srEFTXxbuD0NW7BoGcCxzXYNj9HwdmC3nekT+zuUyk+lwpi4cMxYuRuNBuGlQEpeJoR4Ewp6PIjUpR+24EAbHAC9ocNqxoAtcGJ7LS5StiI7ui0dYA+aSmqaoA0OQPOFTkUv8s5g+V7ZdosxM0ENhnbzbJfJZMLkZds5j8kWXEj5HYo1PikKv5xsgl4g94mSlwkh3oiCHg8itQEkW3DQfKEDYcEBVt/S2QIny9faW3kOCzdwJwlL+TbvaG4SX9DUaOhAapwWRQLNWD1Fta4VizZV8856WZ4LqTN9zO/wi8LjeH5Dmd39YqXGafFgRjyGDwjDcV0rZq62r5Bui5KXCSHeiIIeDySmiCF3cAA0Gjrwyew0dBpNgoET81o/lJ2V3a3bkqO5SUJB02OTk+CnUiHno0LRY3IXPxUkzXqxzdykDArHPdcM5J0hE9q+vmTGSAzQ9kan0QR/tcrqf20/H5t/4c/huSMlBk9eP4RmeAghXomCHi8lFBx0Gk2YNLSv6OPJ3VVly5HcJEBc0JQQFYLUeC2Kaxrt6uP4q1UwmUzocuEKmAqA5csx71VoDLazXpazb+W1eqwtqEZRdSOKL81scSVIC53zP6UNEv1erh4Yznv/H8YMpICHEOK1PLpOD+me0ck/XGdXA0aJ3V6W5PQH48LWN4yvHYTl+xOq5aMNDsC9/9qLomr7gKdPL39snjce45Ost1An93XuRdo2thkTF467UwfavQdbK38+atXfi5EQFYL1RSc5qzazEXPOuT5Llq4b2hf+avZx+6tVtD2dEOLVVCaTh2eFOqi5uRlhYWHQ6/XQaPhL+3uSJkM75q4ttqq3YvtNPyevkPPbvSd0vObLTeLb1g7AbtaJue+RT/djT6WO8zUzk6Pt8qKqda3mHlfO1ifID+fbusw/dydfd7DO+jC/q5dvH27XC4svqTk/N4tztoXtnEtpOeLIaxNCiJKccf2m5S0P9MuJRtz34T60WFw8AWD30XpzAmxlfQvuTh2ICx2dVoER24yKK5uKWuLLTRLa1s6W0F1Z38Ib8ACwypVhXtuVcf15m9+Z3tCBEJtAiMHk9lgGGZnJ0bg7dSDva/Alg7OdcyktR6ipKCHEl1HQ40GEehx1mbov6ne9X2C1g8ly943lBYnteMl9Q/DMlCHoFeivSBAkJ6ASu63d9gK+r6pB1PFtL8wRIYHQ2uxocxUj7AMhPruPnsOFDv7+YlKWLqWWEFB62ZQQQjwJBT0ehK1XFBvbLdslx5vQO/Ak1o6OsT+ezQWvoq4Vj352OS9EbqsJR7q0y59NEDdjY3thfmLdATQ7qeeW0rouNSRNjdOi5Lh1s1E1gAkiepRZknquxSaiu2v2kBBCHEGJzB6C+UYup6o/W5NI8/EEnrurop4zOZYv8VVMY1MucmcTrk2IFDz2yBiN1UX4cnNVwad6lAcz4u22ohsBdBqNrMnPXOSca66k6AVTk/HtL7W46/0Cc1PUSUu3ISevUNKYCCHEXSjo8RBSe0uxqdZdDk7EHs+I7iWzLyw6qzcZ2pGTV8h5YXO0I7zcbuuJ0aHIGMwd+PirVfh0zjjzGPMP16FQ5JKYpxk+IAwBfmrYbqTaV9kgKrBkiD3XlgGuCSYY2q2X2PZWnsP0FQV4fF2p3Uyj2GCXEELcjZa3PIQSDSQtv7VLPR5T0feaOC1a2jpx+Mx5q/stE1+VSHaVWxfo/fvGYu7aYhRWWwczIQFq/Pep62CCCTl5hS7vsC4XV40f06UA0pacfmh855ptmdJPDXTZTBG280yVye3RRgghrkZBj4dgvpHvErEkZYut8F9idChGDtCgjKeJJZtijhYPlhc2JZJdbdtgACqMS4wUzAcKCw7Afx5Jxy8nmvDkF6Wo1nUHYK0dRrz4dTk6uoyiZnf8VCr06eWPJjfn+rDV+FmenYKdAkGblF1UfO1NmLIHlmwDHrFoZxchxNNR0ONBlmenYM7aIqst6GJwzZA8fN1gPP65sssO1bpWTBra16Gqy4wmQzsWbTooKxl62dYjONFwweq2XUfF50SlDArnDPDcqai6EVPe2oYOgcij+lwrqiKFgwzbhGO2fCelcBU1JIQQT0E5PR4kLDgAM8bw12hh88r0q1iDhOH9lS/GyMziSKm6zEVuMjRXTpFQwLN4xkhztel5k5NEj9PV6lra0XiBf9v6K9/8xptELJSXBSiTR2bpgbxCZP9zLyU1E0I8FgU9Hkf6NiPLBGZLzJKZUkZY7IwyyRinJUeSoeVerMclRmLS0L5IiApBhIz2Gp6IK0gUE1AqkUdma0+lDnPXuqb6NSGESEVBj4cRsy3bFjP7wrbFfMHUIYqN7W93jjT/f0e2rAPi6sdwkXuxnrFyN05cygFatrVC1jE8DV+5AqGAMjE6FCMGKD8bWFjdKLiDjxBC3IGCHg8jtC3bkmUTTq6ljAZDu8NjUqu6c21GxYYDkD5LwxaMOZIMzcxgSU0haTR0YNrynVhXeNxrdneJJaVcgeVj37hjhFPG892vtU45LiGEOIKCHg/0/n1j7ZalMgZHIj3ROhhicmj4Zl2UWMIwmoDzF9rNuRpiL6p8eSVya/Uwx+3oMsoq5Nh8sRMLL23PdyZX5/TGR4aYg0s/gde2DChHx2q7A0iFx3OuxfFgmxBClEZd1j0Y2xZjtiacQl2xF206aLfTSg1I3hqvDQ7AttxJOHCiETN5upZPGhqFuMgQ7K9pxG+15zm7wOsNHXa71dh2b9nuQGLrLu9p/NUqdMqJyiRSq7pzlfzVaqvZK21wAPSGDqvfseW5t6Q3dNjV8XHUJ7PTMFHBfDJCSM9DXdZ7GLaO2ba3iZl1WZ6dgkc+3W/VoVxOKZZGQwceXF0I/QX+b/H5h88BOMd6H7ME9suJRizbWmEV8KTGaa0CHrbCeanxWslb+t3BFQEPAAzuGwKTCXYzfc0XOhBm02SVa3edbR0fPxWQ85H8ZOSw3gEU8BBCPBIFPV5OTG5MWHCAuaWBo9fi0hNNjh3gkhc3luPQaeuqzyXHm8xVnwH2ZOn9Hlhbx51uHzUAy348Ynd7l6k7SP1kdho6jSZRjUEtA2q5wWVokB++fXyC5OcRQogrUE6PlxOTG+NIM1NnKa9t5k2ElluLp6eJ6hPEe3+n0WTepi/FzIx4WeN5//6xiI1Ufis8IYQogYIeHyBUKFDpInTOVq1rFRwzfXC7Lf7+EO/9YlqCsJFb2NJVy3qEECIHLW/5AL7eSk2GdqzMP6rI6yixPCZGZEggQoP4P5pj47RW3b61wQFovtABnr6YPqn5Yif8VIDJBNakZbm9sJgZxJ0V9ZLKUD75eQmgApKi++DpqUMot4cQ4lHoC7MPYduI98S6AyipaWJ9vNRd1WG9A6Dt7fw4eemWI4LLdusfzUB+bpa5rcS23EkYn9QzL7BdJvvEdKktQdgsz05BUt9QSc9pbutC88UulJxowgN5hbjqr9+bC0ISQoi70ZZ1L2O7fRtg3+WUmRyNBVOHYPqK3ZzHSo3Twt9PjX2VOtG7ud6/bwwe/azEkbcgSn5uFiKCA+22UgttaQeAvZU6l9Ti8URqVfcs2PpHMhQ53rrC4w6fS00vf/z68o2KjIcQ0nPQlvUejCuw4StO2NDaxnvMxyYnYUysFrPXFInuON4r0A/D+/fBbzY7r5RWrevuIM61bAdwn5O7U8U1bfVTAcNjNCg71az4+N3FaOru1F51TrgDuxjXJkQ4fIzmi53YWVFPS12EELej5S0vwRXYzFlbxNkSoryW/2LObGeX0nE8PjIE6+amK9rIlOt1GAlRIaw7kOasKcYum4J6u4+ew5qCalGvoekdgIevG+zwWD0RX+8yKRKjQ+0qgcuxseSUAqMhhBDHUNDjBfh6XQnVUhkxQCPY6kFMqwq1CkjuG4q9lTo0GNqxdnYa8nOzkKjAbILV6wCi2lDctaoAxTWNdstyzDlJjdPavW9bjYYOaHr55mSn3F1bbFbdPxYjYrx/aZgQQijo8QKObDn/250jebezA9y1fiwZTUBFXQsWbijDpKXbkP3PvYgIDsSCKcp1cQe6E3I7jUZzny82T6w7IFik8MGMeLv3zabLBMH3ziYk0M/l/bXEsA1otx+uwz9+OoKdDrSYCAsOwLsOJkXfOWaAQ88nhBAl+ObXXB8jp2kos2V51MBw3rwYxvLsFLuk4dQ4LZoudKCirsXu8XsqdZi/rhQLpiZLHpuQfZUNVpWZLTGzXkKGDwjD2tEx2HGkjrelQnxkCJZnp0jKawKA1vYu0Y91pTFx4VienYJdFXWYs6YYFzsvzw5qgwOwed4EWcUDmcBYTn+u0CA/yuchhHgECnq8AHPBkdJkc8ygcKvZHLY+XpbYav2YTCbeZqY7KupxoaNT/BsRybIys+2YxRQtnGAx0zFQG4wRAzT4rbbZqsaQWgVMSIqGNjgA89eVSgp4PFlRdSMmvPkTzl+0D8oaDR24fcUulP6/qbKOvWDqEFlBz6Jbh8t6PUIIURotb3kJtqrLfCYMiUKDgb8xKNA9c5J/uA5V57oTXy2ThvdVNQg+35nNP9mScYVmvcZealrK5P1MXrYd5aea7YoqGk1AR5cRj31Wgl1Hlesu7gnYAh5Go6FD9lKXmM8Tm6H9LucD2X7eCCHElWimx0vYd8JWIeejQs7Hv/1jBd7+sYK1rg3AvwX+8mPdW8KJKxl3RMylmRuL2yzr0zQZ2pH5Zj6aL/LPQu2t1Ln5HbpHyfFGWctNcpZZAWDp1iN4N/tqEZ83QghxLprp8TLMTEzmkGhRCbi7j57D/HWldrdzbYG3fOy1CY5vVZZrRIzGriZPTl5h98yNTcADdC9VfZiTKjrgAdwd0rnPmEFaWc8Tk/DOZkdFPeauKRb8vBFCiLNR0OPFxCx5WebHMPi2wFs+NjE6FBmD5QU+jm4Ff3ySde0gtiBNrerekp+fm4W1s9MQFhyAuWuLRQU8PVVIYHdSsdxlJqnLrIyimkbBzxshhDgbLW95Mcslr82/nMLbP1ZwPpapcAwIJwNbPva5m67kbWXBxdHA42j95R1jXDu2jCag3KKacmV9i1NzjHzBfx4eh5y8QtnLTJafufnrSuwSxOWw/LwRQogz0UyPD0iICsFto2J4H2OZHyOUm2H5WLnJq45auvUI7v3XXugNHaKCNMCxekY9wZt/HIn/++8R0ctMfLNBCVEh+Gz2OEwQaPLqp1IhNZ5/OU3JQoqEEMKHZnp8BNe2dqZej+U3aSmPlZu8qoSCYzo8+tl+vH7HCN7HMRdNd47VG/TT9GKdMbMtESAuyd161mdvpQ4X27vwfdlpFFls/2cKYc5fVyrq88aHrdkuIYRIQUGPD2ErMGhbfVnqYyNCAqHp5e+2PJmCYzqoVCpkDI5EwTGd3f0pseHmmZ7E6FBogwPQyFPNuafSBgcI/g6ZZSa+JHfbgpFNhnYs2nTQuqhlvBYzM+JxVUyYOTiR8tkErAMcbXAA7fwihChCZTKJrHbnpZzRmt7TCVVfFvvYJkM7Ji3d5vYgYsmMkdh0oBZ7Ku2DHkvXxGl9psig0tQAxsZreXOe8nOzBAtS5udmWX1OcvIKOWdw2CpqC3022WaZtMEB0Bs6rHbs8b0GIcQ3OOP6TTM9Pkio+rLYx85ZU+z2gAcA6lsuCgY8AFBCAQ8nI2BuxFpyvIlzmSn/cB3vcSyTjrkSzPkqagt9Ntlmmdg+g3yvQQghXCiRmbD65USjx8yaVNWLS1C2rd1D7JXX6jFmULjVbZbLTFKS3MUmmIvFVUpBydcghPRsNNNDWL24sdzdQzDbUHrK3UPwGRc6jCg53ojNj4+HrrXdbplJqM/bok0Hzbk0UgIkMeTsvqOdX4QQKWimh9g5cLwR5bXNwg8kXqnLBLy4oczcY80WXwFCy+3tXBWa/VQqZFo0fRVLyu47ua9BCOnZKOghdl7a5DmzPMQ5ymqbseNIPWsdnrDgALx8O3tndNsqymwBEt+uLD7cQVR3MrMSr0EI6dloeYtYqaxvsapyTHyXZcNa2y3gYqt22zbCdbSGDvvW9u6xNRjaFXkNQkjPRUEPsUJVjXsm2zo8QlPAtrk0UnYM8uELosKCAyjYIYQ4hIIeYoWqGnfzU6kwJi7cLb281HD9TjRm2er5Db/i4Ck9yjhm+6RWUZZLqSCKEEIsUU4PscKVV9HTjE+Kwoc5qW45F8FBfi59PUtfFJ7gDHgAyqUhhHg3CnqIHb7dO75MBWBEjAb5uVlYOzsNYcEBbjkXhrYuaHr5w9PCzk9mp5nPCyGEeCNa3iJ2LPMqDtbq8cKGMrf13nKliRbJvNsP1+HAySaMGaTFM1OSWSsPO4sR8Mjz3Wn06Y41hJAegIIewonJq5iYFI0/frAbFWd9q/rtNXFaTBvZD70D/XFtYiQSokKwvvg4/vr1QVzsvJxV4+dpUy4OUKsAubELFQIkhHg7CnqIoLDgAHxw/zW8jSi9zfD+fVBcc7nVxjVxWhyta0HTBbY+T64enfOEBvlLnkWyTF627H5OicaEEG9DQQ8RRag9gTcJ6+2Pw2darG7zlD5jziZn2SxlUDhuHdkPd60qsNrNZlvbhxBCPJ3KZPLyK5gAZ7Sm76n0hg67wnHEt2l6cc8MMTNATG0fQghRkjOu3zTTQ0SzTHDeW6nDwg1l7h4ScTK+mSHLlhTeutRFy3WE9Cxu3bL+/vvvY9SoUdBoNNBoNEhPT8cPP/xgvv/MmTN44IEH0K9fP4SEhGDMmDH46quv3DhiAnQnOPcL6yX4ODWAkED31ZwhrlGt874E9yZDO3LyCjF52XbMWl2ESUu3ISevEHqDfU4XIcR3uDXoGThwIJYsWYL9+/ejuLgYkydPxvTp03Hw4EEAQE5ODg4fPozNmzejrKwMM2bMwN13343S0lJ3DptAXOXmXoF+aG3vcsFoXEvTy9+jCzg+mBGHqcOucNnreeOurifWHcDuo+esbrPsIE8I8U1uDXpuu+023HLLLUhOTsaQIUPwxhtvIDQ0FHv37gUAFBQUYP78+UhLS0NiYiJeeuklhIeHY//+/e4cNkF3YvM1cVrexxh8MOABupd87r021i0FHP1UKmQmR/MGXR8X1GDrobMuG4u3LQtV1rdgR0W9XUK+bQd5Qojv8ZiKzF1dXfjiiy/Q2tqK9PR0AEBGRgb+/e9/o6GhAUajEV988QUuXryIrKwszuO0tbWhubnZ6h9xjryZqdCy7NzxUwHJfUPdMCLXOXz2PNbOTkN+bhZWz0p12X9IY+LCcXfqQOTeOMTtVbM1vf3xxh0j3DoGOcR0kCeE+Ca3Bz1lZWUIDQ1FUFAQHnnkEWzcuBHDhw8HAPznP/9BR0cHIiMjERQUhIcffhgbN25EUlIS5/EWL16MsLAw87/Y2FhXvZUeJyw4ANtyJyE13nrGZ3xSNLLTfPu8D72iD4Du/KZJQ/siwAWpS8lXhKCouhGPf16K29/bDQDYPG88Fs9wT+DRfKETL35d7pbXdoTQ0qw3LtcRQsRx++6toUOH4sCBA9Dr9fjyyy8xc+ZMbN++HcOHD8df//pXNDU14X//+x+ioqLw9ddf4+6778bOnTsxcuRI1uMtXLgQzzzzjPnn5uZmCnycKCw4AOsfyUDVuVZU61rNu2AOHPftujdr99TgphH9zT8nX9EH5bXnnfqax+qsZyCYnJRZE+J5n7dg6hD8cqIJ/ztUp+h4uHZvefqOKK6aU67qIE8IcR+Pq9Nzww03YPDgwXj22WeRlJSE8vJyXHXVVVb3JyUlYdWqVaKOR3V63MO2kJ0vys/NMl8gp761HUfqWgSeIY8a3f24uKx9KBU5HxVx3p+fmwWTyeS0itqrZ6Vi0tC+aDK044l1B6zqOHlqAUO2mlOeOlZCeqoeUafHaDSira0NBkP3urtabb0C5+fnB6OR7xJA3K2yvsXnAx4A2FepM7dmcFbAAwCD+4agoo47z+TN/x5GemIkCqsaeGcuMpOjsbOiHkp/y2GWg/h2RHlKAUPLWSim5pTlDCUhxLe5NehZuHAhbr75ZgwaNAjnz5/H559/jm3btmHLli248sorkZSUhIcffhhLly5FZGQkvv76a/z444/49ttv3TlsIkAoUdRXHDl7HvmH63BWf9Gpr8MX8ADAwdpmpA+ORMqgcKt2GuOTorA8O8X88/LsFNzzzz34/Ywyy3C2PbnYKnV7SgHDJkM75qwptjo/zMwOBTuE9ByyE5l37tyJ+++/H+np6Th16hQA4JNPPsGuXbtEH6Ourg45OTkYOnQorr/+ehQVFWHLli2YMmUKAgIC8P333yM6Ohq33XYbRo0ahbVr12LNmjW45ZZb5A6buICYGj6e5rbR/RAgsezOR7urMWt1EZ53c2VqE4CCYzqrC3pqvNZuqSYsOAB9ein3PccyqFJ6R1RlfQvyD9cpsn28ydCOSUu32fVX21VRT3V5COlhZP0F/Oqrr/DAAw/gvvvuQ2lpKdra2gAAer0ef/vb3/D999+LOk5eXh7v/cnJyVSB2QtdThSt95oO5d/8csbdQ1BUSU2T3bKSksuOuVOH4PHJyebjntFf4H282B1RzsgLmru2GI0slZaNgEfMQhFCXEfWTM/rr7+OVatW4V//+hcCAi7/IRo/fjxKSkoUGxzxXsuzUzA+Kdrdw3DIsH7eW2vIttBeZX0Lvvm1VrHjR4UGWbVyWLiBfeu61AKGSldKFhPoOasuj5KzVYQQZcia6Tl8+DAyMzPtbg8LC0NTU5OjYyI+wLI5abWuFZHBgVi69YhXdWg/dMZ5ycmu8uin+xEREoiCYzrRz0mN0+LmEf3w6neHOB/z1f6T2PxLLfZVNvAei1kCE7ON3Rl5QWLyy/zVyrYT8aZdbIT0NLKCnn79+uHo0aOIj4+3un3Xrl1ITExUYlzERyREXb7IMUHQH1buQoOBu3s3UY6YpGU/FTAmTovHJiXBT6VCl8mE+MgQ/O/3Os5gaX9NI+82+sUzRmJcYiS0wQGit4aLyQuSGvSIyS97IK8QQ64IwfqHxysSlHjDLjZCeipZy1tz587Fk08+iX379kGlUqG2thafffYZcnNz8eijjyo9RuJDEqJCkJ02yN3DkMRD+4oqRtM7AMvuuhqrd1Uj56NCc9dxkwlIiQ1nfY5Q0Yh+Yb2QEBUiabnKGZWSmfwyP4Hf4ZGzrRj96lb8dkov+TUsUV8vQjybrKDn+eefx7333ovrr78eLS0tyMzMxJw5c/Dwww9j/vz5So+R+JhBEd6VNOpZ5TuV12jowIL19sFJYVUDOmTWxIqPDJEcAFwOUKwjFEcbm0rJL7ttBfvuU7H5OdTXixDPJivoUalUePHFF9HQ0IDy8nLs3bsX9fX1eO2115QeH/FBB042uXsIsqkB1iarbIRmFzxJUXUja3BSfkp6w96MwZFIiAqRFQB0ByjWjVRt6w1JxeSXrX0oVfCxXUZgffEJ88+WydrMDFhOXiH0LLvBAOrrRYinc6hoR2BgoLk5KCFibT/sPcnMtozonhkZ3r8PfjvNnS/z77nj8I+fKyQlEHuq5L6hOFbXYrWk5adSIbSXH/QX7HOzmNhJTgBgmwCvZKVkseUTdh87h7uu6e7XJzU/h/p6EeLZRAc9M2bMEH3QDRs2yBoM8X2V9S2odXIFY1fgC3g2Pz4eowaGw3/bMcG+Wc6WEhuG3oH+vMHX1QPDeWffKlhabIyJC+fcCr6nUodfTzYhNEj+dyrLBHiliC2aOX5w90yT3N1ky7NT7JK3HZ2tIoQoQ/TyVlhYmPmfRqPBTz/9hOLiYvP9+/fvx08//YSwsDCnDJT4Bm9pUZEcLf+Cq2ttN18w3d0l7okbhuDzueOQn5uFYf36sD4mOMiPNZeGjVrVXe35sUlJvI97YWOZ4O96fdFxlyb2MrMwfPzVKvMsj9z8HGa2Kj83C6tnpSI/NwtrZ6fRdnVCPIDor2KrV682///nnnsOd999N1atWgU/Pz8AQFdXFx577DHqZE54eXKLilhtLwzvH4bnbxmGhKgQ5OQVsi5T8M1yAN3LNp6SsOqnAvIP18FPpcIhju3rBcd02Pz4eAAQrKNkNHXn/wjlK5WfasaFdv6yBCu3V2Ll9kqX1rBZnp2CRz7djz2V9jNf/moVNs8bb/7Z0fwcZ8xWEUIcozKZpO9NiY6Oxq5duzB06FCr2w8fPoyMjAzodJ6Tx+CM1vTEMTl5hdjlAbMglgLUKnz1aAZGWWzR1hs6OGvMzF9Xypm38W721Zi7ttitneZVAEKC/NDS1iXq8atnpWLS0L7mXJoz+otYyNNTbPWsVCzbchjltdyJzlf26yOyTlD3eXNlDZuqc63YW6nD/uoG6C90YOpV/cwzPJa4Al9Xj5eQnsgZ129Zi+6dnZ34/fff7YKe33//HUaZW1xJz8GW8+BuHUYTbl+xG2nxEfhXzjUICw5gTao1mUwoOdGI3BuHAABr3sb8daUoqWly0zvpZgJEBzzA5VkLZnaisp6/GnV8ZAjeuHMkpq/YzfkYsd3cmRyZnRX1GBDeW7BysxKY9ylUM0pMfo7YatOueF+EEH6ygp5Zs2Zh9uzZOHbsGNLSur/t7Nu3D0uWLMGsWbMUHSDxPUwwseNIPXI+KnT3cKwUVjfgj6sK8OMz15lvS4gKgTY4gLW1wOZ546EztJsvZlzJr4zQIDVa2jzniwHXriKxu5BS47WKzWg9kGf9WfCE1g18u8nEtJtoMrRjzppiqw7vfO+LgiNCnEvW8pbRaMTSpUvxj3/8A6dPnwYA9O/fH08++SQWLFhgzvPxBLS85dnu/ddej9zWnRqnxYczU80XJrHLHPmH6zBrdRHncWekxGBDqXKNPx2V3DcUy+4abV7Ws7zoRgQH8raQqKxvwfr9J/D+tkqnjE0NYHDfUDw0IQHjEiM9LggQ+kw0Gdox8c18nL9on9uUGqfF+kczzD9Tvy5C7Dnj+i0r6LEdFACPDSgo6PFsekMHHv1sv13gc/XAMBw46VhLAEdlDI7E53PHobK+BZOXbed8XH5ulvmCLPTYzY+Px+3vcS8JucvQK0JhaO/CicYL5tuYi26Dod1qloPtAu0K6YmRWHX/WI8IAsR8Jp798hfeWTDLzw3lDhFizxnXb1kVmS1pNBoKJohsYcEB5i3VS2aMxOIZI5Gfm4Unpwxx99BQcEyHgqPnJG1dFtoWvXTLEfx77jjFxqiUw2dbrAIeoDtfae7aYiREhWDS0L7mCzRbwT5bDv9hYbGnUsfas8sdhD4Teyt1gst+3/7aPeNH/boIcR1Zf5sSEhKQmJjI+Y8QqRKiQvCntEHIThuEhKgQj9naPntNkeStywumcgdsOyrq0TesF375f1MRH+kZ75FPYXUDdlrM6HBdoG2NidM6ZTxiggCxfbIcIfSZENOBpKGlHQD16yLElWQlMj/11FNWP3d0dKC0tBT//e9/8Ze//EWJcZEejiuR1tUudBhxqumCpNYCDYZ23mNW67or+W77yyT8UHYaj35W4pSxK+WBvELzUpfQBfrpKcm4ffQAzjpHSthXqWPN73FlXgxfondaQgS+3H9S8BiTh/UFQP26CHElWUHPk08+yXr7ihUrrKo0E+KIBVOHoKG1jbcWjCuUHG9kHQtXawEpF7FegZ6T9M9nR0U9Hvl0P964cwTv45iAB2Df7p0SG4aLnUYc4mnjIeT5DWX4vuyMXTAjtU+Wo7i2s3cajSg93sT73PDeAZh4aRmU+nUR4joOJzJbqqysxNVXX21ObvYElMjsfdyVKMslVtvbKt9lxAAN/nbnSIwaGM75HLGJqb+caMT0FQWSxsMcB4DLZ8Lyc7OwaNNBSUm3VedacfCUHmsKqlFUo8z2dtvXk5JsrjTbOk584wCAsN4BWHnvGLQbjebkcL5CmJ6QuE2IO3hMcUIuX375JSIiIpQ8JOmBxCTKupJtgm/5qWYs/v53rPszd0Ky2KaTy7ZWSB6P5XFcXeRxTUE1cqdyF2ZkkxAVgkWbDqJEYPZDCibJ94vC47g2MVJUXoyzCgdatpvIP1zH+9h5WYNRdqoZ9+XtM9/GBDfO6i5PCLlMVtCTkpIClUVzQpPJhDNnzqC+vh4rV65UbHCk5xEq7ucp9lTqOLtsA/xF7RhS3uvf/zgKUX2C7I7DvMbeSh1v2wilfFxQjY8LqrsLMz4+HrrWdsEL9PbDdU77nT5/6T2PHMD/LZAtL8YZOUBCS5uF1Q121botl+CoXxchziUr6Jk+fbpV0KNWqxEdHY2srCxceeWVig2O9Dze0oUd6N6WLHSB4ruISXmv3/xyGsuzU3DgRCM2/3IKYwZpzTkhCVHCDU7//odR+H+by3ChQ5mlMGYmji9XRu4yZay2N16cNgxBAX7wUwE5H3EXe2SUnWJfUufLi3FGDhBffk7KIPZGtZZb0yngIcS5ZAU9L7/8ssLDIKSbp2xVF+OjXVW4ZUR/q1kBKUslUt7rzop6jHl9K7osOlhogwOwed4ExEYGCx7rmoQIHHrtFiz/uQLLth4R/bpc+C7UzDlY+fNRWUtap5ou4PN9J8yBhyO7+LiW3bhm2ZQIQLiWNu+5ZqBVOwpbfEtwhBBlyAp6/Pz8cPr0afTt29fqdp1Oh759+6KrS3yjQ0Is8X1T1vT2h/5CB4zu28Fu5WhdC+asLcL6RzJkLZUw73XX0XrB92QCrAIeAGg0dOCWd3eg7JWbRI952sj+igQ9DMvt40oloBtNMDcgnXjpHM5eU8QbMLD5ZHaaeTbMliM5QEK4ljbFNHElhDiXrOKEXBu+2traEBgY6NCACFmenWLencRIGRSORoPnBDxAdyBSVN2Iu94vwGOflXAulfBZnp2C4THydyWcb+vCzop67Kvi7192sLa7pQcTaPmpxJTPE/b8hjJc9+bP+PVEk+QE9NAg/u36D+QVIudSE9J5k5Mkj62T58Piito4tpWsuc69n0qFzORomuUhxAUkbVl/9913AQBPP/00XnvtNYSGhprv6+rqwo4dO1BdXY3SUs8oFQ/QlnVvZvlNuVrXytvI05MJbZcW2m4tJCI4AA2GDt7HxEcGY/Ws7kTZ47pWTF+xG40Cz3GmT2anYUB4b8H3zeTkvHz7cMnnSOi8u6PfldJb06krO/Flbt+y/vbbbwPonulZtWqVVTf1wMBAxMfHY9WqVYoMjBDLJGAFy0m5nNBSiaPVp4UCnu4xGDBp6TZkJkej02hE8wX7zt+uwAQVE5OjUVnfghEDNPittplzBo/JsVFdmg0Rc47UKmBCkvDMidiyAkpilr52HKlD6Ykmq4R0KagrOyHyyCpOOGnSJGzYsAFarXP66yiJZnp8h7PaGjibmMJ4rpp9UQMwCj7KeYZdEYpZExKwvvikpEKFi2eMRJ9e/t0FDgUaeabGa/FhTqroi78ra+MoFaxQV3bSEzjj+q1oRWZPREGP72BbGsgYHImyk0043+Z5yfNSLkJsFzE1gOExGvQO9BO80PckqXFatLR14vCZ81YBnBrA2Dgt1j+a4a6hCVIiWHFn9WlCXMmty1vPPPMMXnvtNYSEhOCZZ57hfexbb73l8MAIscW1K0Zv6EDqGz+ivcuz4ndNb3+8cQd/ryqAe/u0EUB5bTM2Pz4eL28qR8kJvdX9firAw96yS5Qcb0JaQgQmJEdbnbcJl2ZMPJVS2+SdufOMEF8nOugpLS1FR0f31HtJSYlVcUJCXMm24F9YcADWP5KB6St2u3FU9hoNHViw/gD+8wj/zIPQRUzX2o4N8yag6lwrPt1TjZoGA268qh/uuiZW0SW/0CA/GNq6rGZPVOjepeZJukwm7KnUIT83CwC8pm2DUsEKdWUnRD7RQU9+fr75/2/bts0ZYyFEttGx4UiLj0BhdYO7h2KlsLoRv55s4m1OKvYilhAVgr/edpXVfWzJuHK1sCwRelrAY6la12q1JRzw7N1MSgUr1JWdEPlk1el56KGHcP78ebvbW1tb8dBDDzk8KELk+FfONR5Z0Xn+5/wlHByp38Is+eXnZmH1rFTk52bhk9lpmJEyALeO6ofkvr57AbQMEpoM7cjJK8TkZdsxa3URJi3dhpy8QujduC3flpJ1ethqWTl75xkhvkBWIjNXReZz586hX79+6Ox0z3ZYNpTI3PNUnWvFLf/YgQsd7tynZG3zvPEYFRvOeb/S9VsY+YfrvLa+ERe2xF9v2c2k9O+ZurITX+b2Oj3Nzc0wmUwwmUw4f/48evXqZb6vq6sL33//vV0gRIirJUSFYO/CGxRb9lHCCxvL8O0TEznv17W2YdaEeMzNTECn0ST7Ima7vOOJM1+Osp3RcGYfLaVxJePLRV3ZCZFGUtATHh4OlUoFlUqFIUOG2N2vUqnwyiuvKDY4QuRiLi6/nmjCCxvLUF7L3oXbVcprm1kvvnx1W6TgO44jhQ89TXxkMGZNiEeDod08M3LwNP/vdm/lOY+bDaFghRD3kLS8tX37dphMJkyePBlfffUVIiIizPcFBgYiLi4OMTExThmoXLS8RQDg15OXgp9Tly+QwYF+MLS7rr7P6lmpmDTUeiZUqWUZvuMomezsSZigbs7aItF1jNiWkjw5+ZmQnszty1vXXXcdAKCqqgqxsbFQq2XlQRPicqMGhiMtLgK/nWqGEUCMJghnz7cpdvyQADVaBXKIbHfnKLUsI3ScBkO71ZKKnwrI+cj783x2VtTj9vd2oqbhgujnME1g185O88hWDmwBGAVlhChHUtDDiIuLAwAYDAYcP34c7e3tVvePGjXK8ZERopCt5afx509LrG6rbVYu4AGA9+4fi/jIEMz/vAQHa5uttnpzbSVWqm6L2ONYLqlcE6dFsYQ2EJ7IBEgKeADrgHLRpoN2XeEtgyJXYgvA0hMjoVIBBcd05tvcHZQR4u1kTdXU19fj1ltvRZ8+fXDVVVchJSXF6h8hnqJG12oX8DiDn6o7uPjbjJF2DSS5thIrVbdFznHyZqZC08uP5dE9w95KHXZU1NvlOVkGRXwq61uQf7hO8HFiPbHugF0AtqdSZxXwAJeDMkKIPLJmep566ik0NTVh3759yMrKwsaNG3H27Fm8/vrrWLZsmdJjJES2KW9vc+rx/VSApneA1XJRZnI0Ns8bD52hnXdJQqkic2KPY7tMsvPZ60Xlwzw9JRl9+/TCwg1losbjDYTqyXPNsjljSYxreZKNJ+5II8SbyAp6fv75Z2zatAnXXHMN1Go14uLiMGXKFGg0GixevBjTpk1TepyESLb9cB3anVwyStM7AE02BfCYb+xilkjYkozlFJnjOw7fhXr9IxnYcaSON8fn9tED4Et9idMTI5GWEMH7GK5ZNrYZGUeXxISWJ9lQfy1C5JEV9LS2tprr8Wi1WtTX12PIkCEYOXIkSkqcv5RAiBgHTjY57diDo0Jw/7hBeOXbQ3b3Sfk2rlTdFr7jMDu7LFleqDOH9OWcKRoTF24+nq9sfVep5M2yOasekJxaStRfixB5ZOX0DB06FIcPHwYAjB49Gh988AFOnTqFVatWoX///ooOkBC5rubpd+WoY+daWQMeS9U68fkeCVEhrH2kpOaN2B6HuVAL5a6wtTXQ9PZHUXWjua1DR5cR1yZaz5CMGOB9ZSAKjulQda4Vr98xApre1t/7NL398cYdI1ifJyZhXA6u9hRs5LSsIIRcJmum58knn8Tp06cBAIsWLcJNN92ETz/9FIGBgVizZo2iAyREruuG9kWQvwptne6ZmSg/qccZ/UWMS4yUdJFSMm9E7M4u25milT8fRcnxJqvHFlY1YHxSFPJzs8yzPyaTCZOXbZc0Jk9QrWvF6l3VaL5gvf7ZfKETL35dzrpU5czu5mzLk2y7t6i/FiGOkdV7y5bBYMDvv/+OQYMGISoqSvgJLkTFCXu23RXncF/ePncPA+mJkVh1/1hRQYuSfaQq61t4g5L83Cy7gEzqc9jG6+mCA9Qw8NRVWjxjJGuw6uweX2zLk97cX4tqDBFHuLU44TPPPCP6oG+99ZaswRCitPHJUUiL16JQZMVeZ9lTqROV7Kp03oic3BWp9YO8seIzX8ADwLxTzXaGTanEcy5s7Sm8sWWFJxZ+JASQEPSUloqrDaESsS5NiCu9OG0Ypq8ocPcwsKOiHjsr6u3q+FhSqmChJakXajHLOLbf4C2XxspPNmHZjxWSxuipbHdmKd0w1Fc5Y5cbIUoQHfTk5+c7cxyEOE2DzZZyNmoVYORYnclMjkbujUOga+2uu8NU8pWznPNAXiFS47T4cGYq6zdeZ+SNSL1Q880OpSVEYNGmg6zf4JkZiTIn7ppTmgoA32+Ra4bNG2dfXMWbut6TnoeaZxGfJxRIrLpvDLbnToLWJggJDfLD53OuxdrZaRg1MNy8K4ptp5MURTWNuO7v+dCzBGNcO3mU2LXDtkOMy4KpyRgW08fqtvFJUVCpwPkNvsnQjpy8QrzlRbM8wYHiqlLL3ZnVEzlrlxshSpC1e4sQb8I1c6FWAcNjNBjaX4PYyGCU/r+p2FlRj5LjjRgzSMu5DGU5c7L5l1N4W8ZFvulCB+asLcL6RzLs7nN23gjvuFhyMUbEaPC3O0citJc/a4Iz8w1+7ppiux1fnu5Ce5eox1FdHPGcucuNEEcpsnvLk9HuLQIAekMHb7Kt3CRLoZ1OQth2TzHckTfCtztp1oR4zFrt/d3ZpWDe+8u3D6ddSBI4e5cb6Rmccf2m5S3SIzCzM/m5WRgRo7H74Mtt5Mi1HCU2nf+N737D8p8rWAsQSlmOUoJQIUMxxfN8TVpCBDqNRkxett1cpDEnr5B1aZJcxrYETDWGiCegoIf0KCaTCeW1zbDdsCy2uzYbtj/w18RrRT33f4fqsGzrEUxaug13vV/g0oupbcVnoVwM/cUOu7wnoPuPSKrI9+stlswYifzcLAT4qbGvssHqPup0LszyS8bqWanIz83C2tlptF2duB3l9JAeRc6WcKECa1y7o3LyCiXVrimqaXTJll62vJ2+fQIx3CZx2dbagmroL9gHZWq1Cm/ddTVe/Lrcq2r1AN1LLmxLMH9KG0S7kBRAu9yIp6Ggh/QoUpIspRZYs/0Dvzw7BY98uh97KnV2j+XiiospWw2VuvPtqDvMPk4/lQopg8JRxFHgsdNowjP/OYCXbh3udUFPVEgAzra0m3+2XILZV8X/e7MNkKn6MCGej4Ie0qNIqVDsaIG1sOAArPvzOFSda8Wf1xajoq5F1BjlFCAUi2v2gs/4pCjcc81AFNdwV7UuqmnExtKTjg7P5c62tGPz4+NxsLYZKgDXJkbCBJOoWTomQKbqw4R4Dwp6SI8jZku4kksbJpNJdMADiNvSK3dWQWh5z9KCqUNw66gYJESFYPvhOsHHf1xQI/rYnuT293Zb/awNDuDNrbINkKn6MCHeg4Ie0uOIqVCsZDsIKYGGUAFCR2cVhJb3LOla2s1j4e9U5VsaBZLJLQNkyvshxLvQ7i3SY/FtCVeywJrYQCM1Tiu4pZdvVkEMZnlPzObzqNBA8/+XEiz5ssUzRlrtQqLqw97Fdsci6XncGvS8//77GDVqFDQaDTQaDdLT0/HDDz9YPWbPnj2YPHkyQkJCoNFokJmZiQsXLrhpxKSnULIdBF8tn5iwXlgwdQjyc7Ow/tEM3tkaoTo6Yv+QL89OEbWlftqoGPP/91Or4K+WVqfn6SnJ2Pz4eGTyNFhVA9D08p4J53GJkVY/U/Vh78C0SKF6S8StQc/AgQOxZMkS7N+/H8XFxZg8eTKmT5+OgwcPAugOeG666SZMnToVhYWFKCoqwuOPPw61miaoiPM5WmDN8lsl27EmJkfjhyczMX9ysqggSqlZhbDgAKx/JAMjY7grnKbFa63GdPt7u9DJ1ZGVw+2jB2DUwHBzvZYV2SlIjbMOtiYkR2Pns5PRN9SzE365gl1n9kojynF0hpT4Do9rQxEREYG///3vmD17NsaNG4cpU6bgtddeE/38trY2tLW1mX9ubm5GbGwstaEgskltB8GXd9NgaJfdWkKo5QVfSws2ekMH65b6jMGReP++seZZp+2H6zCTp/2EbYd6oXYDbOdTb+jA6Fe3ih67q/HlTbG1OKHdW55D6f9uiOs4ow2Fx8wrd3V1Yf369WhtbUV6ejrq6uqwb98+3HfffcjIyMCxY8dw5ZVX4o033sCECRM4j7N48WK88sorLhw58XVSC6wJ7eaR+wdWynZ7NrY7viy31O+t1Jm3bNseJ19g51a/sF6obbpo/lloNoztfIYFByAv5xrMXlvM+1qu9Pc/jkJUnyDBAFVMYjxxHyU3JRDv5/aZnrKyMqSnp+PixYsIDQ3F559/jltuuQV79+5Feno6IiIisHTpUlx99dVYu3YtVq5cifLyciQnJ7Mej2Z6iDs5+1ulnFkFR3d8vfvTEbzF00k+d+oQTBsVo8gFX2/owLR3d+Jk0+W8vREDNCg/1Sz7mI6gGRvvRzM93ssnZ3qGDh2KAwcOQK/X48svv8TMmTOxfft2GI3dm2QffvhhzJo1CwCQkpKCn376CR999BEWL17MerygoCAEBQW5bPyEWHL2t0o5swqO1pG5dVQMb9Az7VItHyUuHGHBAdj1/GTz+4sIDsBr3x5y+LhyUb0d7+foDCnxLW7PCA4MDERSUhLGjh2LxYsXY/To0fjHP/6B/v37AwCGDx9u9fhhw4bh+PHj7hgqIYJctZtHbAd2JXZ8JUaHcu6w0vTyd8pFg3l/y7ZWoPR4k+LHF8v2PNGWZ+9EXd8Jw+0zPbaMRiPa2toQHx+PmJgYHD582Or+I0eO4Oabb3bT6Ajh52nfKpWYeaqsb0HzxU7W+5ovdjqtAJ+clhnOcvCUHos2HaRkZS9FeVeE4daZnoULF2LHjh2orq5GWVkZFi5ciG3btuG+++6DSqXCX/7yF7z77rv48ssvcfToUfz1r3/F77//jtmzZ7tz2ITwUvJbpdiZBa7HKTHz5K4CfFIqWTvbmoJqWVueaWbIs4idISW+y60zPXV1dcjJycHp06cRFhaGUaNGYcuWLZgyZQoA4KmnnsLFixfx9NNPo6GhAaNHj8aPP/6IwYMHu3PYhPBS4lul2ORjocdxzTypVcDYOK2ocbmrAJ8nVIH2U6kwJo69wzyz9LXjSPfyoeXvWej3omRHdr5jUed3Qqy5ffeWszkj+5sQZ8vJK7QPVNBdzM8yqZbtcbZ1cth2fDHELtGIeR1n4HtdAA4tf6XGa1FS02SX72QpMzkad6cOxOOfiytil5kcjQVTk/HixnL8Vtts1bPMT6VCWkIEAvzUiiyT8QVWJpio8zvxes64flPQQ4iHEdpiu/nx8Rg1MFzyVty73i/A/ppGuwuxmMDFXQX4+F63WteC6SsKZB/7vXtT8J+ik3bHzp06BDpDu3l2ROg8SyW1mCMXoYDQHUEqIUryyS3rhBBrQrksL2wsw7fzJ0pKUq6sb0FRDfcSjVAyslKJoFKXW/het+GEY32TrooJw9rZMYLviWuJUC7bbh5yOrILdXdnQ53fCaGghxCPI5TLUn6qGVXnWgUfF+R3eZ+CUvWD5Nbj4VqKWTB1CBosZlWkvK7cnB+1CpiQdLkvlpj3tDw7hXOJUClSajg5kuQ9aek2/O2Oq3DvuHjZxyDEW1HQQ4gbsc18JEaHYkSMBuW13FWIq3WtSIkNhzY4AI0cnaLv/XAfPp9zLTKSotzeDZytQOKOinqHlsuKqhoQERKAhlZpMz4TkqIl76SznXHyU6mQ81GhpGMIkfI7cDTJ+4WvD+LbsjNWPdYI6QncXpyQkJ6oydCOu1YVYPKy7Zi1ugiTlm5DTl4h9JcCmDfuHMn7/PjIEDyx7gCaOAIexr0f7kNOXiEiQ4JEdwNXeps1V4FEW2K7XpedbMLghd/huQ1lkgKe5L6h2DxvPNbOTpN9oWe2PGcOiWY9n3KlitxJx+Dq7i7lD3rBMR11GReByg74Fgp6CHGxJkM7Ji3dZrcNevfRevNFaHRsODKTo6G2uaYyQYrpUn6GmAyTnRXdxxWqH9RkaEdOXiFnICaX2KUYsVWi71ixG10yUmtemDYMo2LDpT+RA9v5lOvBjHhFXn94jLRkT7FVuXsiZ/33QNyLlrcIcbE5a4pZl6S6TLBKNGXLI2GClJIT9knJXEzoPm6DoZ03GdnRHl1cpC7F8OW2/LvwuKyAB1B+Cc92yauqvgWvyuwTNnxAmMOvHx8ZApPJJHmnGXUZZ+es/x6Ie1HQQ4gLVda3oJhlF5Ul5iLEt3NJ21v68szeSp05aZetiB3fbiBHdvxI3f3EF5z8eOis5Nf3UwHjk6J5xy+3iJ/l8+IigiUHPUzxQ6aqtZxzbPv7zEyOxs6j9RC70czZ+VzeyJn/PRD3oqCHEBcSs9RjexFiC1L4up5zWbihDD+UnWFNFnZ2d3gxu5/E9Cfr2ydI8muPt0hcrqxvwb4qHc61tCM6NAjD+vfBsq0VkhOquXajXT0wDAdO6kWPTdPbH0XVjZi1ukj0awtZnp2COWuLWKtI27LN5yLdnP3fA3EfCnoIcSGpSz2WMwkmkwk1DQb4qVSyt05zTc87e3eX7axVZEgglm45wrp0x+fI2RZJr3vX2IF4NGswviw5gVXbjqG+pV3wOTsq6vHIp/ux7s/jOB/DtfRxbWIE7466zORo5N44BLrWdqz8+ShKbDrIK7F8EhYcgPWPZODmd3bg0JnznI9LGRROXcY5uHu3I3EeCnoIcaHE6FCMGKBB+Sn+7eja4AC7mQQlcE3Pu6o7vOWslZRih90zNA2CS4O21u8/ifX7T0oe555KHecSBt/SR8ExHTbPG4/XvvvNaqZlRIwGf7tzpDmR2tFikWJ88ed0ZP79Z+gvdNrdd3VsODY+Nt6h4/syV/33QFyPdm8R4mJv3DGC9/74yBA8+mmJ0wvh2VKyO7xYQl2vLXfQLNxQ5rRxsPlq/wnW24WWPnSGdqx/JAP5uVlYPSsV+blZ+PaJiVY7x1zRuT4sOAA7/jIZqXFaq9szk6OxZhYl4gpxx38PxPlopocQFxsdq0VmcjR2VdSz9sEymUzYU6lz6hj8bffCQ7lWE0piW0Zylffyj+HXk812OTZilz74Kj07Y/mELRk7LDgA6x/N8KjfqbfwxP8eiOMo6CHEDfi2o39fftrpr/9AXiFn0qzcVhNK41pGcqVdl2ocWebYKLH0oeTyCV+3deZ36ym/U29E58630PIWIW7AfIu0XAK5XCnY8aaWYoitgOwuYosaDuvfB4OjHGvLwMUI9gJ+Six9KLV8wldPhhBijWZ6CHEjtm+R1yZEuuS1PbnmSJOhHSvzj4p67Mr7xiIhKgRfFB7H807K+zlYq7c6R0osfShxDKonQ4g0NNNDiIdJjA7FsP59XPZ6SiTNKu2JdQdQUtPE+xjbvmFpCRFOG8+agmrW24USscVw5BhCs2F7nZwbRqg3l7ehoIcQD7RkBn/DUSUpXXPE0YuA2AaltktBXE04lVBU3YhfTzRZjdETLnRCCdELN5RRvygnod5c3omCHkI8ELPDS/nL92VqFTBigLQGlXyUugiIzeVho2QTUFvPffWrqPfozIDI9thiAj3K73EOyqXyTiqTSWyHFu/U3NyMsLAw6PV6aDTK/YEnxNn0hg7B1g1KUaL9QU5eIeduJCkVhivrW0Q1zVQDmJAczXrsHUfqkfNRoejXFCs1XouSmibW9/hu9tV2u6iSrwjBDcOuQMbgKExMjpb9umw7tFLjtPhwZioAiPqc5OdmOZzfI7dHma8R+owqca6Jc67fNNNDiIey3OH1/6YNc+prOfoNlWtJyjKhVixm9kII184q5nWdoai6kfM9zl1TjF02gUfF2Va8v60SD+QVIuXVrTihkzeL9cS6A9hpc+yimkZkLc0H0F3dWmhJ1JHcLVrKseaK4pLEOSjoIcTDJUSFIKFvqFNfg7lw7zhSL2tpRumLwIKpyaIfy3ZsqT3OlFBU02hVbNJWo6EDt763U/JxDxxvxI6KetZCBo2GDsxZ292sVCiR25HcLVrKsUa9ubwXBT2EeAFXXcRzPiqU9U1e6YtAg4QZBLZjOzOp2RH6C512MzaMyvoWrCs8ji8Kj1sFnS9tKuc9ZlF1I3492cT5nm13uUml5Cyer3DWuSbOR0EPIV7AHRdxKd/klb4IiA3yAv1UiAgOZL2PLak5PTESGYOt6yBlDI5ESmyY4GulJ0Yq8jv46dBZq5+bDO3I/udec3+x5zeUYdLSbbhz5W78cqKJtzkt44WN3fWJFkxNxrAY63IH45OisGDqENnJ1bSUw456c3knSmQmxEu4MrHZktikTLbxOZIgfd2bP6Om4YLg4zI5kpkZbMX/uG777ZQe7/1cgUNnW6yOkTE4Eu/fNxYAMGdtkVUHdalypw7B45MvL9/l5BVy/k5Dg/zQ0tYl6ripcVqrzu0jYjRYePOV+GBHlUO/E0ra5Ue9uZzHGddvCnoI8TKWf2QXbTro9CBo9axUTBraV/TjlboI6A0dGP3qVlGPVfrCW3WuFXsrdVABuDYx0urY+YfrMGt1kexjW45V7E41MdSAXQPb0F7+0F+wXyrMGByJz+eOE31spXbmWaKdYESIM67f1IaCEC9j2bpieXYKHlxdiFKLwnlKk5qPo1SDxrDgAPx77jjc86+9go+t1rG3W5B7YXWkQzrf7ExafITVcR2pSWTLNom6y2RiDXgAoOCYTlKLCq4GucyymZTzK6ZBKiHOQkEPIV4sLDgAG+eNx12rChxacmEjp+O30q4dHInqJdPw+jcH8eHuas7H2QZm7rywfj5nHBZtKkfpSb3V7ZZLZAx3JlXuq9SJ/t3a9gmLCA7Esq1HMH3FbvNjbM8vV8DJtxNM7qyRN6CZLc9AQQ8hPuClacMwfUWBosf0pKTMl267CkfqWjmXWGwvIs68sArNzugM7dj4+ATeJTK2oIxParwW+6vZt8SnxIbLmumTk9fAzIAxy12WdlbU474P9+KNO0fixa/LrBKweweo8fzNV2JicrRgg1STyeRTwYE3zGz1pICMgh5CfICULd5ifDI7zaEKws7AtcRiG5g5u/O42O35bEtkzMVl5c9HUXK8SfC1mKrTbO89uW8olt09GqMGhrPm3Njm+NgalxjJcy83rvNrAlBe22w1+8O40GHEos2/CbZVmf95CcprLwdLfMGBt1yoPXlmyxsCMqVR0EOID1C6jk+n0fP2N9gusXBd7MRssbZ8ntSLJ7M9X+ysEyB9ZocxNk5rvgDxvXe2oGhCcjQudnSikGXZM91m5kkKR/KQhD5Vv9Vab89nCw686ULt7ADcUWwBGTNjt/zeMR4dTMpFQQ8hPoDrQiyXv9qzivpZEkqUFjsTw9fPSujiKXbWicF2cRHjaH2L1Vi43jtXUMRXRkAuZxTKVKsAo4k9Gds2OPDkmRNbUgNwVxKasZu0dJvHBpOOoKCHEB/BdiGWy9kzPc5cmkiMDoU2OACNLEt+2uAAq4unba8spp/VttxJvH/oxc46AdwXFzEaDR3YWVEveqnRNiiSMk6xEqNDMSJGY7UMJZW/Gui0iHCGx2h4izAywYGnz5zY8uR2FWJm7Dw1mHQEBT2E+AjLC1xxlQ6vfXcIzRc7ZR3LWX+MXbE0UVnfwhrwAN1BBJMsyxWIMP2s1j+SIfhaYrbnO7ot/auSkxioDXboYq5UGQHGG3eOZM3dEavT2J031mk0IT4yBCaTibdeEfN59OSZEzZylkJdRcyMnacGk46gNhSE+JiEqBDclToIv758Iz6ZnYanpyRjZIz4wl4jB2hE/4GrrG+R1N7AFY0rxVwYhR5TVN0o6j2Jef+OLgd9XVqLSUu34a5VBaJ7oUn9vUg1OjYcmcnRcGQVtNNowqShfZEQFSK6jYknz5xw8dR2FYnRoRgxQNzfBV9qNUIzPYT4qMr6FnQaTbh99AA8mJ6ACW/+hPMX+Vsa+KtV+HS2cKVeOTM2zlqasF0qE3NhFFOInm/WQMr7T4wO7d5yXtMIy1VDP5UKw2P6oExEby2gOxDL/Hs+dvyFe+nNlUm+y7NTkLU0n3NWTYhtgCImT8qTZ064OGOJUSlv3DFCVKkLTwwm5aKghxAfw3bhu3pgmGDAAwBjBoULPqayvgVPfFEqaqeNJaWXJvgu8GIujNfEaVFcw13Qke8PvdhkWmaMbIUjxydFIWtolOigBwD0FzowZ00R1j/KvvTmyiRfXWubrICH2YZv+7sWGxxITSL3FEovMSphdKwWmcnR2FVRz1rewJODSbko6CHEx7Bd+A7YVAfmsr+mifMC2WRox5w1xZyBgtCMjdJLE3wXeDEXxryZqawzFX4qYHwSd2d4KTNWbGNUAxgVGw4AePXbQ6LfL6OoppG1iJ+rk3yFgtgHM+IRF9EbG0pOocwiQJ4gsHtMKDjw5JkTb8S3AcIbgkmpKOghxIc4slMI4L5A/nKiEdn/3AtDB1+5u25cMzZKLk0IXeAbDO2sF8bK+haUnGg0/7wtd5Jd1/TxSfwXZbEzVlxjNAI4cKJJsFAfn/nrSqx2O2UmR+Pu1IGixqUUoSB2ZkY8EqJCMGtColMCFE+cOfFGtkGkv1plTjD3xfNLQQ8hPkSpBpbMBbLJ0I5HPy3Bnkqd6OfyzdgotTQhNvBg/jUZ2pGTV8i6FLb+kQxJF2WxM1ZCY3SkKIDt9u7dR8/hQgf/Tj0pM2n/LjyOHw+dRVivAFyTEIFxl4oZMvlTfioVukwmpMZpUXK8STCIpQDF8/WU3xEFPYT4EKUKxzEXyCfWHRAd8HDlalhSamlC6lKZUK6LlD/4idGhSE+MZD0vlpWOnVHEj0uXyYSi6kakDAyza3RqOy5LtkngZSebcMeK3eiyiMi+Kj0FAAjr7Q/9BfvAyrYmki8uiXgTb2nP4S4U9BDiQxytzGz5LV3qUln/8CDRFztHv1VKWSpTMteFuaBc7GBPCrfccc2MUYlikWJd7GRffrTZCc6ZBL77aL1VwGOJLeABgKYLHRg5QINnpg4VdaEVuijTRVseb2rP4U4U9BDiYxypzHxtYoQ5cJG6VBYXESLqj6tSFzWxS2VK7BoT2zur4JjOKoj6U1qsS4OeQ2fOixoXa8+lo/WQ08HEZALKTjXj9W9+w4bHxnM+TuiiTBdtx3hTew53oqCHEB/DtoT0woYywWUqNQB/tdp8gZG6PDOsP3uhs8r6Fnz762mcabqAslq91Rbt5L6heGZKMnoF+rMGQXwBku379FOpcKrJgO/LT2NAeC90mbqXuZTYNSald9aagiqEBPpj66EzqDjrOUXdhBKsHW3ZduxcK1Je24pPH7oWGclRdvcLXZTpoi2ft7XncCcKegjxUZZLSKvuH4s/ripARV0L5+ONgNUfSKnLM7+e0kNv6ICutQ01DQasLajErgod+DZ8VdS14NHPLldjDuvljwfS4zBleD8s23pE1Ld+bXAAHvvsdxw6zT7LMSJGg5TYcPx6Ui9r15jUZb6PC2pEP9aVmOBuX5X4pHSpjCbg3rx9SI3X4sOcy41bhS7KO47US75oOzpj6EvLaN7WnsOdKOghpAcICw7Aj89ch6pzrZjzcRGO8bQnsPwDuTw7BY98ul9UMnPJpWadciv0AoD+Yifeyz+G9/KP2d3H9q2/RteKyUu3ceahADA3xuRLuK2sb8G+Kh3OtbQhKrSX1W6lb36tlf1+PAGTYK4NDrDbweYsRdXWjVuFLsqlJ7iLRALWn0lHl8F8cRnNG9tzuAsFPYT0IAlRIfjXzGtENXcEuoOldX8eh4Kj5zBnTRFvnZ4uExwKeITYfuv/5UQj7lhRIHrrt/5CB5KvCMFVMWEYPzgKd10TiwPHG7FwYxnrLBHXbiVvExYcYM5/ErtEp4RGw+Xq0UIX5ZRYLe/9lp9JR5fBfHEZzdntOXxpVoyCHkJ6GDl/IDOSovDbazdjZ0U9vio5ia9L3Tf7UVitw6JNByXPWBhNQMXZVlScbcXXpbVYuKEMnUbukMldAU+AH8CxOUyWRkMHfjnZ6NKEakZRTSN+OdGIZVsrWO9nPnOZQ6JFfSYdzV3x5dwXZ7Tn8MVZMeqyTkgPJLfz88TkaDwxOdmZQxP02je/YedRxy/gfAGPO219OgupcVpF/zgXSCguqbQXN5ZzzjBZfubEfCbF5K7wcfT5noxJ7M/PzcLqWanIz83C2tlpDgUnfLNi3opmegjpgRwpEuhoLSBHtbQpOA3iYZgigh/OTLX71q4NDkDzhQ7e/CUuuytct6xlq7yWu6HqzSP7ocHQjrDgAOha2zBrQjzmZiZwtkFwNHelJ+S+KFVZ2VdnxSjoIaQHk/sHkm0q3TZRmEi38JYrAbAHpRHBgXbnPDM5Grk3DoGutR0r84+ipMa+JcSYuHDWLu+u0MtfzVkwEQAWbigDYP/ZyeRoSupo7oqzc198ia/uCFOZTG74quZCzc3NCAsLg16vh0bDXkeEECKP7UV59poizi7sRNh796bg1lExvI/hmp3TGzpYg6K7Uwfi8c+9azmCCULYEou53qfYPBNHn99TVNa38G54yM/NcnrQ44zrNwU9hBBF3bliF0pP2Pd/IuKkJ2pxZf8wmEwmXD/sCkxMjpb0fLbO8nwXL0/GXFjZdg/JWZq1PM6JBgNKTzRizCCt5HMshzfugMrJK+ScFXPFTjcKemSgoIcQ19IbOkTX9iHC/FTAm38chYjQIPipYK40LeXCyXbx8gbvZafgP8UnRc/K2AYWzM8RwYF2xS4tZSZHY8HUIWgwtJs7yEutEM7Fm3dAiZkVc2YwR0GPDBT0EOIeVedasbdSBxWAaxMjcf5CB5776lfW/lAhgX741wPXoL+2N/ZW6qBraUPVuVYcqm2GCUDG4Ej8elKPkuNNXnfhdpbkviF4ZsoQVNS1oKG1A9cP64uJydGsF/7fTjfjg+3HrFqAeIPUOK3d75xtpuHA8Ua8tKkc5RbvT9PLH80XHSs7wFzgTTDJDlzcPVuiBLZZNVcEcxT0yEBBDyGepepcK/ZV6nDk7HmYTMDkSxdrIWzfOgk/JS787qBWAWPjtLwJ2Pm5WdAGB4hqBCsXE5wAkBW4eEJejLO4IphzxvWbdm8RQlxK7o4x2x1NL3z1C043tzthhL7DGwMeAAgN8sfdYwfyBj3VulYs2lSNXU4Mgpnt2Xz38W3d9tUdUN68nZ2KExJCvEpCVAgmDe2LVQ+kunsoxEla27rw7+KTvI/xU3U3yOXpZ+sSfAUNfbUukDcXeaSghxDilUbHhmNY/z7uHgZxgi6TCcU1jUiN18JPpbK6T43uXB85RRqdgS9wYeoC2b4HP5UKmcnRHjsbIsSbgzkKegghXmvJjJHuHgJxopkZ8XatKYzo7un11tbDLhsHW/AlNnCR2/LFk3lzMOfWoOf999/HqFGjoNFooNFokJ6ejh9++MHucSaTCTfffDNUKhW+/vpr1w+UEOKRRsdqkemCGivEPa6KCcPa2WlIjddCbX19xUGZO9GWzBiJ/Nws1mNyYQu+xAYuzuiJ5Qm8NZhzayLzwIEDsWTJEiQnJ8NkMmHNmjWYPn06SktLcdVVV5kf984770ClEvnpJIT0KMuzU3B/3l6v245NgMSoENSfb8P5NuuEazWACZdmDCrrW1gTmuXk8qTGafGntEEAgA9z7PubcekOvmJkFURkKNUTy9W46vA40r/PnTxuy3pERAT+/ve/Y/bs2QCAAwcO4NZbb0VxcTH69++PjRs34o477hB9PNqyTkjPcNeqAs7dPneNHYixcVrUt7Rh2dYjLh4ZkUrTyx9/mzESV8WEoVrXilmrixw+Zlhvf+z4y2S7GRbmov3WlsM4WNtsFUx5Wz0dJXlCUUWf3rLe1dWF9evXo7W1Fenp6QAAg8GAe++9FytWrEC/fv1EHaetrQ1tbW3mn5ub6dsfIT0B2zd3tj/SRVWNXlmduCdpvthp7hcWEujn8PFS47X4MCeV9WLNzMCMidXafX68YbnGUVwzOU+sO4DdR89ZPXb30XOYv67Uq4NAtwc9ZWVlSE9Px8WLFxEaGoqNGzdi+PDhAICnn34aGRkZmD59uujjLV68GK+88oqzhksI8VBip9vZOsQTz9Xa3iX5Ob0D1Hj7nqsRFOAnetnFGcs1ntxvi28mR9fa5rV1eIS4fXmrvb0dx48fh16vx5dffokPP/wQ27dvx9GjR7FgwQKUlpYiNDS0e7AqleDyFttMT2xsLC1vEUKsWF7cFm06iF0sNV/81Sp0Gi//icxMjkbujUNwvMGAFzaUeW3xv55C7HKM0sGJJywNCeGrqDxrQjzvkuLqWamYNLSv08fYI9pQ3HDDDRg8eDB69+6Nd999F2r15Q1mXV1dUKvVmDhxIrZt2ybqeJTTQwgRwtbiIjVOiw9npqLB0M76zV9v6MCDq/dRR3kPJpST46zgxB39tqQEbkLtMdY+lIacjwo573dV+wyfzulhGI1GtLW14ZVXXsGcOXOs7hs5ciTefvtt3HbbbW4aHSHEF/EtbYQFB7D+gQ8LDsDGeRPMjVVf3nwQbZ3urg9MLDHLMTuO1LF2p3dG3oqrWzTICdyEKip3mUzITI7mDNy8dWkLcHPQs3DhQtx8880YNGgQzp8/j88//xzbtm3Dli1b0K9fP9bk5UGDBiEhIcENoyWE+Do524qZ50wYHIVb39sF/YUOJ42OyJXz0eWlGiap2Vl5K67utyUncBNTUZkt980XErvdGvTU1dUhJycHp0+fRlhYGEaNGoUtW7ZgypQp7hwWIYRIFhsZjF8WTUXKq1vRaKDAx1MVVTdi/P/9hCV/GMX7OLnBiStbNMidVWIqKgvN5HhjHR4hbg168vLyJD3ew9KPCCHESmV9CwU8XqClrQvzL22J5yI3OBEbUCjBkVklsTM53lpUkYvH5fQQQoi32lelc/cQiEhMOKIGWAsSOnKhd9XSkCOzSt5aUdlRFPQQQohiqF2Ot7FNPVciOHFVQKHErJKvzeQIoaCHEEIUcm1ChLuHQGRQq4DhMRoszx6jaADgioBC6VmlyvoW7KtqgArAtYmRPhcQUdBDCCEKSYwORXpiJPZU0jKXNzGagHIvbVir1KxSk6Eds1YX2tWdyhgciffvG+sxRRUdpRZ+CCGEELFW3T8WmcnR7h6GVwkOUHvExaha1+ruIciWEBWCSUP7yg54Mt/MZy20WXBMh/nr+JO+vYknfM4IIcRnMN+8P59zLfwoxUcUQ4fRLrfGHfzVnvULq6xvQf7hOlSdc24wNmdNMW9LFWb7uy+g5S1CCHGCjKQolPx1Ku7/cC/KaoWXToID1TC0e8Klv+fqNJo8okmoK3t3Vda3oLimUfBxShdVdBcKegghxEnCggPwzRMTUXWuFQdr9VhTUI2iavsLDHNBazC0Y2+lDh/urMSxet/4Zi0kJNCPt5P6m38ciWe/LHPJWFb+fBRFFgEAW6DhiqDIGe0xuOyrahD1OCWLKrqTxzUcVRo1HCWEeBIm4ZTp4M528dQbOvDoZ/tRcMz3E6JVuFwzh03G4EgcrG12ensPbXAAmi90cjYJddXsi1AzUKWafbK9Hy6hQX4of+Umh19Tqh7RcJQQQrwd32yAmG3MYcEB+HzuOFSda0X+73VYV3gcFXUtzhyy2wh963Yk8JszIQEHa/VI7huCPZWNvOeQrZK2ZTuHRZsOumT2xRW9uyrrW/DEF6X4TcSyK9BdwVrpRqnuQkEPIYQoROnZgISoECRMSMBDExJQUHEOf/60GC1t3EtBxNr45Ci8dOtwAED+4TrMWl0k8Ax2eyvPuaxzujN7d0mZ3bHlKzk9tHuLEEIUwpeL4aiM5CiUv3ITVt03BnERvR0+ni94ZsoQ3vstAwShYILPyYYLvPcrudWdqbLsp7LeSeanUiEzOdqhwIPt8ymWr+T0UNBDCCEKYDped9mkSVrOBjCPc2Qb8k0j+2P7s5Oxed54jIhRNk9R62UF6G4bHcMaIKhVwIgB1udGKJjge+8rth3jHUdd80X846cj2CljBoXN8uwUjE+KsrrN0fYYXJ9PIUoEW56EEpkJIUQBQssny+9Nwfqik4onwjKJ0UF+atz34T7BHBkuIYFq/PBkJhb85xerHUzOpFZ1V0OWyjLBWG/osGvDYMnyHLM9NjM5GgumDsH0Fbuljx+A+lJCOkMbHIDN8yYgNlL+zBJDyd5dYpb3/FSApneAVX6Ts7bKi+GM6zcFPYQQogChXTep8VqU1DRx7g5Sgt7Qgbs+KMCRs9KTnlNiw9GnV4Bd80pnykyORkeXEXsrdZzBWsbgSJhMsGrtwXYhrjrXivmfl+C32marQodqFTAhKdrqHFsGE9rgANyft09WGwp/m4CHoQ0OQOn/myr5eM4k9PkErEsneELnddq9RQghHoqv43XKoHDW+jxKJ8KGBQdg69PX4a73CyTP1twwrC/+vvWIw2MQQ60CxsZpzTM1WUvz7XZPqdH9mM/njgMgPOthMplQzrIbyWjqrij864kmjIoNB2C9gy4nr1D0LiZLM8YMwIaSU6z3NRo6sLOiHhM9qB0J1+dTjUvNVu+93Gw1LDjAZ5azbFFODyGEKIQrF2NWRjzv85Tu+fThzFRJ/b/UKmD4gDBFx8DHaAKKqhtRda4VutY21u3iRgBFNY3m3Ceh3lJCW71f2Ghf4JDJc5GzxMYV8DBKjlsHna5qKcGH7fM5ITkan80Z57NBji2a6SGEEIVwdbyurOdfblJ6Z4zlOA7W6vHChjLO3kp+KuCbxyegV6CfomMQQ0ywJ3artNDurPLaZrsZNaFAyRH9Nb2Qf7gOEcGBWLb1iEtaSghRqiO7N6OghxBCFGZbgJBv6Wt8UpTTLjzMOCYmRWPO2iKrJbbQID/85cahmJmRYL4tMzkaOyvqZSdDS7Uy/yhemjaM9zFiA8LE6FCMGKDhzc2xDaAc2cbOx1+twrNfcbfO2HW0Hvfl7cXy7DFuCTrEFMj0VZTITAghLsC1c8iV3/iFvuHrDR0Y/epWl4wFuBz0AbDPNVFdyjURGRg0Gdoxa3UhSk/oOR/D1sIhJ69Q0eRtf7UKXUaT6MDRnbujPB3t3pKBgh5CiCfx5KWFfxcex3MbXNPc09LmeeOx1GYJyJKYwIAveOHbJSe05V2sBzPiMLy/hneGh43SO/h8iTOu35TITAghLiSUkOtO6wqPu+V1dYZ2rJ2dhvzcLIyI0dhdmHZV1GP2miLORGChwntjBoVzFvYLCw7Ay7cPFxxjemIkMgZHchY3fPn2EYjW9BI8ji3b4pXEuSinhxBCSDeV8EOcwV+tQmV9C/ZV6di3nQMormk0F9eznfkRSkieMXYgSk40cs6uCT1/yYyR+FPaINZZIctKyY7kCDna24qvyS25jIIeQgghAIDs1EE4cML1y1sP5BVKevyuo/VW3c2Fgo2FFkt2bEtlQs+/NjESgPDuJ66EdTHk7uBTusmtr6PlLUIIIQCAe9IGwc9Nsz1SWBYcBLj7arFhawArtckn3xLlo1kJkgIeR3tbObPJrS+ioIcQQojZN49P8JoLg2XBQbbCe2y4cmiUavL5QB5/fytbw/r3Qe5U/m7xXMQ2uSWX0fIWIYQQs+EDwpA3K1WwOaUnsCw4aLv0dFZ/Ec/z7ESzzaFRonDfvwuPs/biYqNGd65SeW0zbl+xW9aSlFAukqN5Qr7IWwJ6QgghLuKson3OYFvVmVl6SkuI4H0eVw6NI7vr9lTphB90idHmZzlLUkK/J6UrffsCCnoIIYRYSYwORXJf77hgcl3YpebpKCE9IVL2c+UsSbnjPXo7CnoIIYTYWXrXaHcPgZeYC7tSeTpi3ZM2CH4OXlWlNp919Xv0dpTTQwghxM7oWC2G9e+DQ6fPu3sorMRc2JnCg/uqGqBC99ZzZ89+/GXKUCzZclj286UuSVETUWko6CGEEMJqyYyRmL6iwN3DsKJWAWPjtIJtG9xVv+Z/v9fJfm6vALV5pkdq4NKTm4hKQctbhBBCWI2O1ULTy7O+GxtNQFF1o2Duizvq11TWt6C4plH4gRwudhgxa3URJi3dhpy8QugNHQqOTprK+hbOth/ezLM+zYQQQjxGZX0Lmi92unsYrPi2YzP1a2xZJgsrOSvCtIA4q7+o2DGZAM1VjUiZ9xARHIhlNs1ffanCMwU9hBBCWH37a627h8CJL/dlX1UD73OFAiaxPazYltCU4qwAzVaToR1z1hTzzlC5OgBzJgp6CCGEsGpobXf3EOyoVcCEJPZdW2KDELaASU4OENsSmtKcWWCwydCOSUu3oVFgGc1VAZgrUE4PIYQQVpOG9nX3EOwMjg7l3LUlFITwbXPnywFiy2/hagGhNGcWGJy7tlgw4LEkdTu9J6KZHkIIIayuG9oXAWqgw7Z8sBtV1LVg/rpS8wwMsxzlp1IJzvCkDArHPdcMtJuxEMoBmrxsu/k2ZvZHqAWEo/xUKoxPinLazEplfQuKqqUlXftChWcKegghhHCaPjoGX5Z6Vm7P7qPn8Ohn++GvVovOp0nuG4rimkZz7orl0pWUAIaZ/Xn59uGyxi6WswsMSnnPzg7AXImWtwghhHC67eoB7h6CnS6TCQXHdNglIYH4WF2L1c+W29el9BpjZn9Ul5bKbFtAOHpRjYvojfzcLKydnebU3VJS3rMvVXimoIcQQgin64b2RVhv1y8KZCZHY8mMkbyPEbPqplaxP9YyOZerhxWfal0rawuI4TEa0cdgszx7jEtmVC6/Z/v7tMEB2DxvPFbPSnVJAOZKFPQQQgjh9e3jExHsLz4gUELu1CGCndLFEApCmORctgCGT3xkiLkFRH5uljlAeFfmjIha1R3ojYoNl/V8Obrfc7TVbanxWmzLnYRRseGyu817MsrpIYQQwis2Mhi/vX4L/lt2Gk/9uxQXO527YwkAdIZ2jIoNR2ZyNHYfPWe1S0qt6q7MzOWT2WnoNJoQHxkCk8lklYhsi0nOZethtWjTQbvXZstvsW0BwTZmIROSol2+hNQT+3apTCYn77dzs+bmZoSFhUGv10OjcWzakRBCCPCX9b9g/f6TTn2N/NwsJESFQG/owPx1pXb1czq6jCisamANSGyL6OXkFXIGL3wF97heW6g6Mdvz2Azv3wfzspIwfECYzwcbcjjj+k1BDyGEEEkq61t4Z0/4JF8RghduGS44k2IbjNjORkgJSOQGL1yvLZbl8042GvDJnmrUn2/HmEHhuD89ngIdART0yEBBDyGEKC/7n3uxp1In6TnBgX7Y8/z15kDD0WAEkBaQ9KRlHF9AQY8MFPQQQojyxC7hWNo8bzxroi4FI4SNM67flMhMCCFEMiYJtuDoOTz0caFgcjPfziTbRGBCnIW2rBNCCJEtIykKv79+C1bdNwaaXuzfozMGR/pMcTvi3WimhxBCiMNuGtkfN43sj6pzrdhbqYOupQ1RoUG4NjGSZnGIx6CghxBCiGJoqYp4MlreIoQQQkiPQEEPIYQQQnoECnoIIYQQ0iNQ0EMIIYSQHoGCHkIIIYT0CBT0EEIIIaRHoKCHEEIIIT0CBT2EEEII6REo6CGEEEJIj0BBDyGEEEJ6BJ9vQ2EydXf+bW5udvNICCGEECIWc91mruNK8Pmg5/z58wCA2NhYN4+EEEIIIVKdP38eYWFhihxLZVIyhPJARqMRtbW16NOnD1QqlbuHY9bc3IzY2FicOHECGo3G3cPxWXSeXYPOs2vQeXY+OseuIeY8m0wmnD9/HjExMVCrlcnG8fmZHrVajYEDB7p7GJw0Gg39h+UCdJ5dg86za9B5dj46x64hdJ6VmuFhUCIzIYQQQnoECnoIIYQQ0iNQ0OMmQUFBWLRoEYKCgtw9FJ9G59k16Dy7Bp1n56Nz7BruOs8+n8hMCCGEEALQTA8hhBBCeggKegghhBDSI1DQQwghhJAegYIeQgghhPQIFPS4wBtvvIGMjAwEBwcjPDzc7v5ffvkF2dnZiI2NRe/evTFs2DD84x//4Dze7t274e/vj6uvvtp5g/YySpzjDRs2YMqUKYiOjoZGo0F6ejq2bNnionfgHZT6LG/btg1jxoxBUFAQkpKS8PHHHzt/8F5E6DwDwBNPPIGxY8ciKCiI82/Bli1bMG7cOPTp0wfR0dH4wx/+gOrqaqeN29sodZ5NJhOWLl2KIUOGICgoCAMGDMAbb7zhvIF7GaXOM+Po0aPo06cP57H4UNDjAu3t7bjrrrvw6KOPst6/f/9+9O3bF59++ikOHjyIF198EQsXLsR7771n99impibk5OTg+uuvd/awvYoS53jHjh2YMmUKvv/+e+zfvx+TJk3CbbfdhtLSUle9DY+nxHmuqqrCtGnTMGnSJBw4cABPPfUU5syZQwGmBaHzzHjooYdwzz33sN5XVVWF6dOnY/LkyThw4AC2bNmCc+fOYcaMGc4YsldS4jwDwJNPPokPP/wQS5cuxe+//47NmzcjLS1N6eF6LaXOMwB0dHQgOzsbEydOlDcYE3GZ1atXm8LCwkQ99rHHHjNNmjTJ7vZ77rnH9NJLL5kWLVpkGj16tLID9AFKnGNLw4cPN73yyisKjMy3OHKen332WdNVV11l9Zh77rnHdOONNyo5RJ8g5jxz/S1Yv369yd/f39TV1WW+bfPmzSaVSmVqb29XeKTezZHz/Ntvv5n8/f1Nv//+u3MG50McOc+MZ5991nT//fdL+htkiWZ6PJRer0dERITVbatXr0ZlZSUWLVrkplH5FrZzbMloNOL8+fO8jyHCbM/znj17cMMNN1g95sYbb8SePXtcPTSfNnbsWKjVaqxevRpdXV3Q6/X45JNPcMMNNyAgIMDdw/MZ33zzDRITE/Htt98iISEB8fHxmDNnDhoaGtw9NJ/z888/Y/369VixYoXsY/h8w1FvVFBQgH//+9/47rvvzLdVVFTg+eefx86dO+HvT782R7GdY1tLly5FS0sL7r77bheOzLewneczZ87giiuusHrcFVdcgebmZly4cAG9e/d29TB9UkJCArZu3Yq7774bDz/8MLq6upCeno7vv//e3UPzKZWVlaipqcH69euxdu1adHV14emnn8Yf//hH/Pzzz+4ens/Q6XR48MEH8emnnzrUCJZmemR6/vnnoVKpeP/9/vvvko9bXl6O6dOnY9GiRZg6dSoAoKurC/feey9eeeUVDBkyROm34rFceY5tff7553jllVfwn//8B3379nX0rXg0d57nnsRZ55nLmTNnMHfuXMycORNFRUXYvn07AgMD8cc//hEmHy7E7+rzbDQa0dbWhrVr12LixInIyspCXl4e8vPzcfjwYcVex9O4+jzPnTsX9957LzIzMx06Dk0ZyLRgwQI8+OCDvI9JTEyUdMzffvsN119/Pf785z/jpZdeMt9+/vx5FBcXo7S0FI8//jiA7v/QTCYT/P39sXXrVkyePFnye/B0rjzHlr744gvMmTMH69evt1uG8UWuPs/9+vXD2bNnrW47e/YsNBqNT8/yOOM881mxYgXCwsLw5ptvmm/79NNPERsbi3379mHcuHGKvZYncfV57t+/P/z9/a2+kA4bNgwAcPz4cQwdOlSx1/Ikrj7PP//8MzZv3oylS5cC6N4xZzQa4e/vj3/+85946KGHRB2Hgh6ZoqOjER0drdjxDh48iMmTJ2PmzJl2Wx01Gg3Kysqsblu5ciV+/vlnfPnll0hISFBsHJ7EleeYsW7dOjz00EP44osvMG3aNMVe25O5+jyzLbH8+OOPSE9PV2wMnkjp8yzEYDBArbaezPfz8wPQ/aXJV7n6PI8fPx6dnZ04duwYBg8eDAA4cuQIACAuLs5l43A1V5/nPXv2oKury/zzpk2b8H//938oKCjAgAEDRB+Hgh4XOH78OBoaGnD8+HF0dXXhwIEDAICkpCSEhoaivLwckydPxo033ohnnnkGZ86cAdD9Byo6OhpqtRojRoywOmbfvn3Rq1cvu9t7KkfPMdC9pDVz5kz84x//wLXXXmt+TO/evREWFuaW9+VplDjPjzzyCN577z08++yzeOihh/Dzzz/jP//5D29+VU8jdJ6B7lolLS0tOHPmDC5cuGB+zPDhwxEYGIhp06bh7bffxquvvors7GycP38eL7zwAuLi4pCSkuKmd+ZZlDjPN9xwA8aMGYOHHnoI77zzDoxGI+bNm4cpU6b0qHQEPkqcZ2b2jFFcXMx6bRQkeb8XkWzmzJkmAHb/8vPzTSZT9xY9tvvj4uI4j0lb1q0pcY6vu+461sfMnDnTLe/JEyn1Wc7PzzddffXVpsDAQFNiYqJp9erVLn8vnkzoPJtM3J/Xqqoq82PWrVtnSklJMYWEhJiio6NNt99+u+nQoUOuf0MeSqnzfOrUKdOMGTNMoaGhpiuuuML04IMPmnQ6nevfkIdS6jxbkrtlXWUy+XBGGyGEEELIJbR7ixBCCCE9AgU9hBBCCOkRKOghhBBCSI9AQQ8hhBBCegQKegghhBDSI1DQQwghhJAegYIeQgghhPQIFPQQQgghpEegoIcQ4pCsrCw89dRTPvOaDz74IO644w6nHJsQ4l7Ue4sQ4nU2bNiAgIAA88/x8fF46qmnXB58EUK8CwU9hBCvExER4e4hEEK8EC1vEUIU09jYiJycHGi1WgQHB+Pmm29GRUWF+f6PP/4Y4eHh2LJlC4YNG4bQ0FDcdNNNOH36tPkxnZ2deOKJJxAeHo7IyEg899xzmDlzptWSk+XyVlZWFmpqavD0009DpVJBpVIBAF5++WVcffXVVuN75513EB8fb/65q6sLzzzzjPm1nn32Wdi2IzQajVi8eDESEhLQu3dvjB49Gl9++aUyJ4wQ4lIU9BBCFPPggw+iuLgYmzdvxp49e2AymXDLLbego6PD/BiDwYClS5fik08+wY4dO3D8+HHk5uaa7/+///s/fPbZZ1i9ejV2796N5uZmfP3115yvuWHDBgwcOBCvvvoqTp8+bRVACVm2bBk+/vhjfPTRR9i1axcaGhqwceNGq8csXrwYa9euxapVq3Dw4EE8/fTTuP/++7F9+3bxJ4YQ4hFoeYsQooiKigps3rwZu3fvRkZGBgDgs88+Q2xsLL7++mvcddddAICOjg6sWrUKgwcPBgA8/vjjePXVV83HWb58ORYuXIg777wTAPDee+/h+++/53zdiIgI+Pn5oU+fPujXr5+kMb/zzjtYuHAhZsyYAQBYtWoVtmzZYr6/ra0Nf/vb3/C///0P6enpAIDExETs2rULH3zwAa677jpJr0cIcS8Kegghijh06BD8/f1x7bXXmm+LjIzE0KFDcejQIfNtwcHB5oAHAPr374+6ujoAgF6vx9mzZ5GWlma+38/PD2PHjoXRaFR0vHq9HqdPn7Yar7+/P6655hrzEtfRo0dhMBgwZcoUq+e2t7cjJSVF0fEQQpyPgh5CiEtZ7roCAJVKZZdHowS1Wm13XMtlNjFaWloAAN999x0GDBhgdV9QUJBjAySEuBzl9BBCFDFs2DB0dnZi37595tt0Oh0OHz6M4cOHizpGWFgYrrjiChQVFZlv6+rqQklJCe/zAgMD0dXVZXVbdHQ0zpw5YxX4HDhwwOq1+vfvbzXezs5O7N+/3/zz8OHDERQUhOPHjyMpKcnqX2xsrKj3RAjxHDTTQwhRRHJyMqZPn465c+figw8+QJ8+ffD8889jwIABmD59uujjzJ8/H4sXL0ZSUhKuvPJKLF++HI2NjeZdWWzi4+OxY8cO/OlPf0JQUBCioqKQlZWF+vp6vPnmm/jjH/+I//73v/jhhx+g0WjMz3vyySexZMkSJCcn48orr8Rbb72FpqYm8/19+vRBbm4unn76aRiNRkyYMAF6vR67d++GRqPBzJkzZZ0rQoh70EwPIUQxq1evxtixY3HrrbciPT0dJpMJ33//vd2SFp/nnnsO2dnZyMnJQXp6OkJDQ3HjjTeiV69enM959dVXUV1djcGDByM6OhpA98zTypUrsWLFCowePRqFhYVWu8QAYMGCBXjggQcwc+ZMpKeno0+fPuYEasZrr72Gv/71r1i8eDGGDRuGm266Cd999x0SEhIknBlCiCdQmZyxmE4IIQoxGo0YNmwY7r77brz22mvuHg4hxIvR8hYhxKPU1NRg69atuO6669DW1ob33nsPVVVVuPfee909NEKIl6PlLUKIR1Gr1fj444+RmpqK8ePHo6ysDP/73/8wbNgwdw+NEOLlaHmLEEIIIT0CzfQQQgghpEegoIcQQgghPQIFPYQQQgjpESjoIYQQQkiPQEEPIYQQQnoECnoIIYQQ0iNQ0EMIIYSQHoGCHkIIIYT0CP8f6ctIMNt6k5AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "356ad835-9132-4e83-b36b-edb8b4377813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 13,
     "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": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fde1388-6785-4c49-9145-c0caa69e70c7",
   "metadata": {},
   "source": [
    "Now that’s much better: we can clearly see the high-density areas, namely the Bay Area and around Los Angeles and San Diego, plus a long line of fairly high density in the Central Valley, in particular around Sacramento and Fresno."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aed7042c-7db5-4b4d-b4de-2ae23e08cb16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13bc3a9b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(\n",
    "    kind=\"scatter\",\n",
    "    x=\"longitude\",\n",
    "    y=\"latitude\",\n",
    "    alpha=0.4,\n",
    "    s=housing[\"population\"]/100,\n",
    "    label=\"population\",\n",
    "    figsize=(10,7),\n",
    "    c=\"median_house_value\",\n",
    "    cmap=plt.get_cmap(\"jet\"),\n",
    "    colorbar=True,\n",
    ")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ade7b9d-4f80-4d05-bbb5-3a955790a72e",
   "metadata": {},
   "source": [
    "Now let’s look at the housing prices shown in the figure above. The radius of each circle represents the district’s population (option `s`), and the color represents the price (option `c`). We will use a predefined color map (option `cmap`) called `jet`, which ranges from blue (low values) to red (high prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54c1dae-6490-475c-b9c0-7030786f35d6",
   "metadata": {},
   "source": [
    "### Looking for Correlations\n",
    "\n",
    "Since the dataset is not too large, we can easily compute the standard correlation coefficient (also called Pearson’s r) between every pair of attributes using the `corr()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d3485d8c-3b13-408c-8e6c-f52f1cbb5cd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr(numeric_only=True) # https://github.com/ageron/handson-ml2/issues/614"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d49a940e-acb2-4599-9e0c-364b86607554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.687151\n",
       "total_rooms           0.135140\n",
       "housing_median_age    0.114146\n",
       "households            0.064590\n",
       "total_bedrooms        0.047781\n",
       "population           -0.026882\n",
       "longitude            -0.047466\n",
       "latitude             -0.142673\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcba189f-b4bc-4bf5-ad58-5bb8f5533458",
   "metadata": {},
   "source": [
    "The correlation coefficient ranges from –1 to 1\n",
    "\n",
    "- When the coefficient is close to 1, it means that there is a strong positive correlation\n",
    "- When the coefficient is close to –1, it means that there is a strong negative correlation\n",
    "- Coefficients close to 0 mean that there is no linear correlation\n",
    "\n",
    "> The correlation coefficient only measures linear correlations (“if x goes up, then y generally goes up/down”). It may completely miss out on nonlinear relationships (e.g., “if x is close to 0, then y gener‐ ally goes up”)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62197da4-4fba-4ebf-ba89-b037c058d2ec",
   "metadata": {},
   "source": [
    "Another way to check for correlation between attributes is to use the pandas `scatter_matrix()` function, which plots every numerical attribute against every other numerical attribute. Since there are now 11 numerical attributes, we would get 11 x 11 = 121 plots, which would not fit on a page; so let’s just focus on a few promising attributes that seem most correlated with the median housing value:\n",
    "\n",
    "> Note that the main diagonal (top left to bottom right) would be full of straight lines if pandas plotted each variable against itself, which would not be very useful. So instead pandas displays a histogram of each attribute (other options are available; see the pandas documentation for more details)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9e2b4134-427e-49e4-aa51-16ef8469fa31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: xlabel='median_house_value', ylabel='median_house_value'>,\n",
       "        <Axes: xlabel='median_income', ylabel='median_house_value'>,\n",
       "        <Axes: xlabel='total_rooms', ylabel='median_house_value'>,\n",
       "        <Axes: xlabel='housing_median_age', ylabel='median_house_value'>],\n",
       "       [<Axes: xlabel='median_house_value', ylabel='median_income'>,\n",
       "        <Axes: xlabel='median_income', ylabel='median_income'>,\n",
       "        <Axes: xlabel='total_rooms', ylabel='median_income'>,\n",
       "        <Axes: xlabel='housing_median_age', ylabel='median_income'>],\n",
       "       [<Axes: xlabel='median_house_value', ylabel='total_rooms'>,\n",
       "        <Axes: xlabel='median_income', ylabel='total_rooms'>,\n",
       "        <Axes: xlabel='total_rooms', ylabel='total_rooms'>,\n",
       "        <Axes: xlabel='housing_median_age', ylabel='total_rooms'>],\n",
       "       [<Axes: xlabel='median_house_value', ylabel='housing_median_age'>,\n",
       "        <Axes: xlabel='median_income', ylabel='housing_median_age'>,\n",
       "        <Axes: xlabel='total_rooms', ylabel='housing_median_age'>,\n",
       "        <Axes: xlabel='housing_median_age', ylabel='housing_median_age'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\", \"housing_median_age\"]\n",
    "scatter_matrix(housing[attributes], figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8498e29-a1ed-4916-bee5-eddd079b307f",
   "metadata": {},
   "source": [
    "### Experimenting with Attribute Combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f543a53-3c27-40a9-af5c-6731ec02a9f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"]\n",
    "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n",
    "housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "754a8630-4ebe-48b1-9cbb-376e5fd5b8db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.687151\n",
       "rooms_per_household         0.146255\n",
       "total_rooms                 0.135140\n",
       "housing_median_age          0.114146\n",
       "households                  0.064590\n",
       "total_bedrooms              0.047781\n",
       "population_per_household   -0.021991\n",
       "population                 -0.026882\n",
       "longitude                  -0.047466\n",
       "latitude                   -0.142673\n",
       "bedrooms_per_room          -0.259952\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr(numeric_only=True)\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad7c09b3-c0e7-4dba-9d80-c214f3457f39",
   "metadata": {},
   "source": [
    "We see that the new `bedrooms_per_room` attribute is much more correlated with the median house value than the total number of rooms or bedrooms shown previsouly. Trying out various attribute combinations could also give us useful insights that will help us get a good prototype in the next training iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de3cebe1-30cd-49ce-bd0d-f919020cb04c",
   "metadata": {},
   "source": [
    "Preparing the Data for Machine Learning Algorithms\n",
    "--------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad3a706-209d-47b6-95f5-09aa3e9abdff",
   "metadata": {},
   "source": [
    "After we have gained enough empirical ideas on what our data looks like, it's time to feed the data into a data preprocessing pipeline and use the preprosessed data to train a model.\n",
    "\n",
    "What we mean by \"preprocessing\" is to make sure our training data doesn't contain any value that could potentially trigger a runtime error during model training. For our housing data, we will execute the following 4 preprocessings in order:\n",
    "\n",
    "1. Handling the missing feautre values in dataset\n",
    "2. Handling text and categorical attributes\n",
    "3. [Composing attributes](#Experimenting-with-Attribute-Combinations)\n",
    "4. Feature scaling\n",
    "\n",
    "We will show the code for each of these 4 steps separately below\n",
    "\n",
    "But first let’s separate the predictors and the labels, since we don’t necessarily want to apply the same transformations to the predictors and the target values :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d31963cb-4b75-4db6-8d8c-7845bccfbb21",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3883440a-7b9b-4295-bd06-d986a22ebc4b",
   "metadata": {},
   "source": [
    "### Handling Missing Values\n",
    "\n",
    "Most Machine Learning algorithms cannot work with missing features. We saw earlier that the `total_bedrooms` attribute has some missing values. We have 3 options:\n",
    "\n",
    "1. Get rid of the corresponding districts.\n",
    "2. Get rid of the whole attribute.\n",
    "3. Set the values to some value (zero, the mean, the median, etc.).\n",
    "\n",
    "Scikit-Learn provides a handy class to take care of missing values: `SimpleImputer`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "878889f0-1222-43a1-b64c-4346630ba596",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d4ac939-1cf9-4b2d-a3d5-ad103a775dea",
   "metadata": {},
   "source": [
    "Since the median can only be computed on numberical attributes, we need to create a copy of the data without the text attribute `ocean_proximity` and then fit the data. The result is a plain NumPy array containing the transformed features that are put back into a pandas `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8143ae89-c6ca-40bb-ac9a-260938b755eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)\n",
    "imputer.fit(housing_num)\n",
    "X = imputer.transform(housing_num)\n",
    "housing_train = pd.DataFrame(X, columns=housing_num.columns, index=housing_num.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5797c4e6-8af0-441f-9105-18fead70f5cb",
   "metadata": {},
   "source": [
    "### Handling Text and Categorical Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "155c23c9-49ad-41b2-88c9-0677461774ad",
   "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>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12655</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15502</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14053</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20496</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18125</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5830</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17989</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "12655          INLAND\n",
       "15502      NEAR OCEAN\n",
       "2908           INLAND\n",
       "14053      NEAR OCEAN\n",
       "20496       <1H OCEAN\n",
       "1481         NEAR BAY\n",
       "18125       <1H OCEAN\n",
       "5830        <1H OCEAN\n",
       "17989       <1H OCEAN\n",
       "4861        <1H OCEAN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_categories = housing[[\"ocean_proximity\"]] # https://stackoverflow.com/a/45201152/14312712\n",
    "housing_categories.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d9bd4e-3e74-44ad-bd6e-26ead3dca2a8",
   "metadata": {},
   "source": [
    "We see the only text attribute, `ocean_proximity`, is not random text, but a limited number of possible values, each of which represents a category. Moast machine-learning algorithms perfer working with numbers, so let's convert these categories to numbers. For this, we use scikit-learn's `OrdinalEncoder`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eaa54633-24b4-4f3e-a4d4-b5c4b74863c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [4.],\n",
       "       [1.],\n",
       "       [4.],\n",
       "       [0.],\n",
       "       [3.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "housing_categories_encoded = ordinal_encoder.fit_transform(housing_categories)\n",
    "housing_categories_encoded[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2b862aef-4d5d-49e8-83ce-7f701787197c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba29b83-c64c-4d77-be6e-0907f8c4da75",
   "metadata": {},
   "source": [
    "One issue with this representation is that ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases (e.g., for ordered categories such as “bad,” “average,” “good,” and “excellent”), but it is obviously not the case for the `ocean_proximity` column (for example, categories 0 and 4 are clearly more similar than categories 0 and 1). To fix this issue, a common solution is to create one binary attribute per category:\n",
    "\n",
    "- one attribute equal to 1 when the category is `<1H OCEAN` and 0, otherwise\n",
    "- one attribute equal to 1 when the category is 'INLAND' and 0, otherwise\n",
    "- so on and so forth...\n",
    "\n",
    "This is called __one-hot encoding__, because only one attribute will be equal to 1 (hot) while others will all be 0 (cold) for one row from the housing data. The new attribute are sometimes called _dummy_ attribute. scikit-learn offers a `OneHotEncoder` class to convert categorical values into one-hot vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c25a0f09-14c4-43d1-b68f-afeaaf031817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Compressed Sparse Row sparse matrix of dtype 'float64'\n",
       "\twith 16512 stored elements and shape (16512, 5)>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "categories_encoder = OneHotEncoder()\n",
    "housing_categories_1hot = categories_encoder.fit_transform(housing_categories)\n",
    "housing_categories_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e68f3c-3ea0-453c-bae9-53397de534e6",
   "metadata": {},
   "source": [
    "If a categorical attribute has a large number of possible categories (e.g., country code, profession, species), then one-hot encoding will result in a large number of input features. This may slow down training and degrade performance. If this happens, we may want to replace the categorical input with useful numerical features related to the categories: for example, we could replace the `ocean_proximity` feature with the distance to the ocean (similarly, a country code could be replaced with the country’s population and GDP per capita). Alternatively, we could replace each category with a learnable, low-dimensional vector called an embedding. Each category’s representation would be learned during training. This is an example of _representation learning_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2f9745d-a10d-40e1-b134-cab724bfbb70",
   "metadata": {},
   "source": [
    "### Composing Attributes (Custom Transformers)\n",
    "\n",
    "Before moving on, let's pause for a second for one question: \"How would we bundle up preprocessing steps together?\" This is an important question because at the end of the day the increasing degree of automation is required so that \n",
    "\n",
    "- we could reproduce these transformations easily on any dataset\n",
    "- we will be able to gradually build transformation library with increasing complexity\n",
    "- we are able to perform typerparameter tunning which greatly enhances the quality of Machine Learning outcome (we will briefly touch this in this section)\n",
    "\n",
    "sciki-learn, for this purpose, introduced __transformers__. Let's pick up the attribute composition we have done early and re-do it using such transformer\n",
    "\n",
    "From the `housing.head()` we used earlier, we need and know the indexes of following 4 attributes:\n",
    "\n",
    "1. `total_rooms` = 3\n",
    "2. `total_bedrooms` = 4\n",
    "3. `population` = 5\n",
    "4. `households` = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f826923e-dc67-45f5-8791-893e62e601d5",
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12655</th>\n",
       "      <td>-121.46</td>\n",
       "      <td>38.52</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3873.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>2237.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>2.1736</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15502</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.09</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5320.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>6.3373</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>-119.04</td>\n",
       "      <td>35.37</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14053</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.75</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2.2264</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20496</th>\n",
       "      <td>-118.70</td>\n",
       "      <td>34.28</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3536.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>4.4964</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "12655    -121.46     38.52                29.0       3873.0           797.0   \n",
       "15502    -117.23     33.09                 7.0       5320.0           855.0   \n",
       "2908     -119.04     35.37                44.0       1618.0           310.0   \n",
       "14053    -117.13     32.75                24.0       1877.0           519.0   \n",
       "20496    -118.70     34.28                27.0       3536.0           646.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "12655      2237.0       706.0         2.1736          INLAND  \n",
       "15502      2015.0       768.0         6.3373      NEAR OCEAN  \n",
       "2908        667.0       300.0         2.8750          INLAND  \n",
       "14053       898.0       483.0         2.2264      NEAR OCEAN  \n",
       "20496      1837.0       580.0         4.4964       <1H OCEAN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "17455b3e-e114-41b7-b05c-2a84279534da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "TOTAL_ROOMS_IDX = 3\n",
    "TOTAL_BEDROOMS_IDX = 4\n",
    "POPULATION_IDX = 5\n",
    "HOUSEHOLDS_IDX = 6\n",
    "\n",
    "class CombineAttributesTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room=True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y=None):\n",
    "        return self # do nothing\n",
    "    def transform(self, X):\n",
    "        rooms_per_household = X[:, TOTAL_ROOMS_IDX] / X[:, HOUSEHOLDS_IDX]\n",
    "        population_per_household = X[:, POPULATION_IDX] / X[:, HOUSEHOLDS_IDX]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, TOTAL_BEDROOMS_IDX] / X[:, TOTAL_ROOMS_IDX]\n",
    "            return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "\n",
    "transformer = CombineAttributesTransformer(add_bedrooms_per_room=False)\n",
    "housing_extra_attributes = transformer.transform(housing.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09a4abf-7806-48d2-8b89-e4485cfc6944",
   "metadata": {},
   "source": [
    "In this example the transformer has one hyperparameter, `add_bedrooms_per_room`, set to `True` by default (it is often helpful to provide sensible defaults). This hyperparameter will allow us to easily find out whether adding this attribute helps the Machine Learning algorithms or not. More generally, we can add a hyperparameter to gate any data preparation step that we are not 100% sure about. The more we automate these data preparation steps, the more combinations we can automatically try out, making it much more likely that we will find a great combination (and saving us a lot of time)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8219d25-eaea-4c35-b9ef-bb3dd65ba11e",
   "metadata": {},
   "source": [
    "### Feature Scaling\n",
    "\n",
    "With few exceptions, Machine Learning algorithms don’t perform well when the input numerical attributes have very different scales. There are two common ways to get all attributes to have the same scale: \n",
    "\n",
    "1. min-max scaling\n",
    "2. standardization.\n",
    "\n",
    "Min-max scaling (many people call this _normalization_) is the simplest: values are shifted and rescaled so that they end up ranging from 0 to 1. We do this by subtracting the min value and dividing by the max minus the min. __Scikit-Learn provides a transformer called _MinMaxScaler_ for this__. It has a `feature_range` hyperparameter that lets us change the range if, for some reason, we don’t want 0 ~ 1.\n",
    "\n",
    "Standardization is different: first it subtracts the mean value (so standardized values always have a zero mean), and then it divides by the standard deviation so that the resulting distribution has unit variance. Unlike min-max scaling, standardization does not bound values to a specific range, which may be a problem for some algorithms (e.g., neural networks often expect an input value ranging from 0 to 1). However, standardization is much less affected by outliers. For example, suppose a district had a median income equal to 100 (by mistake). Min-max scaling would then crush all the other values from 0 ~ 15 down to 0 ~ 0.15, whereas standardization would not be much affected. __Scikit-Learn provides a transformer called `StandardScaler` for standardization__."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09969abf-b65c-4782-9091-252588e67077",
   "metadata": {},
   "source": [
    "#### Puting Things Together - Transformation Pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9dc55a6b-d03b-43d4-9ca5-5ff9aa9dd653",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "numerical_pipeline = Pipeline([\n",
    "    (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "    (\"combine_attr_transformer\", CombineAttributesTransformer()),\n",
    "    (\"std_scaler\", StandardScaler())\n",
    "])\n",
    "\n",
    "pipeline = ColumnTransformer([\n",
    "    (\"numerical\", numerical_pipeline, list(housing_num)),\n",
    "    (\"categorical\", OneHotEncoder(), [\"ocean_proximity\"])\n",
    "])\n",
    "\n",
    "housing_prepared = pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf4081c-29d9-41d6-840d-67f654a84cbc",
   "metadata": {},
   "source": [
    "Selecting and Training a Model\n",
    "------------------------------\n",
    "\n",
    "Thanks to all previous steps, we are ready to train a Linear Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0bfcfb2f-1a99-4fc2-ae76-c8268e283777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aadc21b6-636d-4f17-8725-53a5f6a1ed97",
   "metadata": {},
   "source": [
    "Done! We now have a working Linear Regression model. Let’s try it out on a few instances from the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a2296517-adb5-41dd-b223-76ca6c7ea15c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Price: [ 85657.90192014 305492.60737488 152056.46122456 186095.70946094\n",
      " 244550.67966089]\n",
      "Actual Price (Label): [72100.0, 279600.0, 82700.0, 112500.0, 238300.0]\n"
     ]
    }
   ],
   "source": [
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "some_data_prepared = pipeline.transform(some_data)\n",
    "print(\"Predicted Price:\", lin_reg.predict(some_data_prepared))\n",
    "print(\"Actual Price (Label):\", list(some_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6e8ae4-f5b0-4cc0-871c-a42275bbc360",
   "metadata": {},
   "source": [
    "Let’s measure this regression model’s RMSE on the whole train‐ ing set using Scikit-Learn’s `mean_squared_error()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4e9b0235-8421-4bd7-a818-9104506d8790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(68627.87390018745)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d297dbab-fd3f-4f82-ab2e-b1b2036a49f5",
   "metadata": {},
   "source": [
    "Most districts’ `median_housing_values` range between \\\\$120,000 and \\\\$265,000, so a typical prediction error of \\\\$68,628 is not very satisfying. \n",
    "\n",
    "This is an example of a model underfitting the training data. When this happens it can mean that the features do not provide enough information to make good predictions, or that the model is not powerful enough.\n",
    "\n",
    "The main approaches to fix underfitting are to\n",
    "\n",
    "- select a more powerful model\n",
    "- feed the training algorithm with better features, or\n",
    "- reduce the constraints on the model.\n",
    "\n",
    "This model is not regularized, which rules out the last option. We could try to add more features (e.g., the log of the population), but first let’s try a more complex model to see how it does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fb04a327-c4db-446a-9888-dd3a8608a29c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_prepared, housing_labels)\n",
    "\n",
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f6790d-735a-4e8d-92f2-63e618734797",
   "metadata": {},
   "source": [
    "The model has badly overfit the data. Note that we haven't touched test dataset yet. One way to evaluate the Decision Tree model would be to use such validation set. \n",
    "\n",
    "A great alternative is to use scikit-learn’s __K-fold cross-validation__ feature. The following code randomly splits the training set into 10 distinct subsets called __folds__, then it trains and evaluates the Decision Tree model 10 times, picking a different fold for evaluation every time and training on the other 9 folds. The result is an array containing the 10 evaluation scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b624311b-327d-4c35-bafa-ddde38a92a14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [72683.3040288  69837.26399838 68277.46664553 70832.5547978\n",
      " 71319.06682789 76363.6781665  71071.74339147 74590.95581044\n",
      " 68344.11769612 72522.53336355]\n",
      "Mean: 71584.26847264657\n",
      "Standard deviation: 2436.2835068978843\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_labels, scoring=\"neg_mean_squared_error\", cv=10)\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "\n",
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ddd22bc-925e-4ede-bdbf-3f299919e3ab",
   "metadata": {},
   "source": [
    "scikit-learn’s cross-validation features expect a utility function (greater is better) rather than a cost function (lower is better), so the scoring function is actually the opposite of the MSE (i.e., a neg‐ ative value), which is why the preceding code computes `-scores` before calculating the square root."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b521462d-8a78-40bd-8050-c78d5c73aa5a",
   "metadata": {},
   "source": [
    "Now the Decision Tree doesn’t look as good as it did earlier. In fact, it seems to perform worse than the Linear Regression model! Notice that cross-validation allows us to get not only an estimate of the performance of our model, but also a measure of how precise this estimate is (i.e., its standard deviation). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8581559-c6f0-4e00-bf60-8cb7fee6079b",
   "metadata": {},
   "source": [
    "Let’s compute the same scores for the Linear Regression model just to be sure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1bb691b6-37ca-4ffc-8a0e-e2c07111ccc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [71762.76364394 64114.99166359 67771.17124356 68635.19072082\n",
      " 66846.14089488 72528.03725385 73997.08050233 68802.33629334\n",
      " 66443.28836884 70139.79923956]\n",
      "Mean: 69104.07998247063\n",
      "Standard deviation: 2880.3282098180694\n"
     ]
    }
   ],
   "source": [
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9b1d2ed-3d58-4c5c-b76a-112cbe7b0d8c",
   "metadata": {},
   "source": [
    "That’s right: the Decision Tree model is overfitting so badly that it performs worse than the Linear Regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab93b966-0aae-47a1-b72d-e355fd6d1d2f",
   "metadata": {},
   "source": [
    "Building a model on top of many other models is called __Ensemble Learning__, and it is often a great way to push ML algorithms even further. So let's try Random Forests which trains many Decision Trees on random subsets of the features, then averaging out their predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8c11f7d6-aa18-4f47-9b4c-587ec22fbb96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 18736.99179027963\n",
      "Mean: 18736.99179027963\n",
      "Standard deviation: 0.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(housing_prepared, housing_labels)\n",
    "\n",
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "forest_rmse_scores = np.sqrt(forest_mse)\n",
    "\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f23c982-2d26-4dd8-97a5-cf17a3c00053",
   "metadata": {},
   "source": [
    "Fine-Tuning Models\n",
    "------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f21a6f6a-b2a4-4bfc-a166-11a7e24d4240",
   "metadata": {},
   "source": [
    "### Grid Search\n",
    "\n",
    "One option would be to fiddle with the hyperparameters manually, until we find a great combination of hyperparameter values. This would be very tedious work, and we may not have time to explore many combinations.\n",
    "\n",
    "Instead, we should get scikit-learn’s `GridSearchCV` to search for us. All we need to do is tell it which hyperparameters we want it to experiment with and what values to try out, and it will use cross-validation to evaluate all the possible combinations of hyperparameter values. For example, the following code searches for the best combination of hyperparameter values for the `RandomForestRegressor`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "20bb93ca-3995-414f-8ffb-42a792b0eb46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid=[{&#x27;max_features&#x27;: [2, 4, 6, 8],\n",
       "                          &#x27;n_estimators&#x27;: [3, 10, 30]},\n",
       "                         {&#x27;bootstrap&#x27;: [False], &#x27;max_features&#x27;: [2, 3, 4],\n",
       "                          &#x27;n_estimators&#x27;: [3, 10]}],\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_squared_error&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid=[{&#x27;max_features&#x27;: [2, 4, 6, 8],\n",
       "                          &#x27;n_estimators&#x27;: [3, 10, 30]},\n",
       "                         {&#x27;bootstrap&#x27;: [False], &#x27;max_features&#x27;: [2, 3, 4],\n",
       "                          &#x27;n_estimators&#x27;: [3, 10]}],\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: RandomForestRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_features=6, n_estimators=30)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_features=6, n_estimators=30)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {\"n_estimators\": [3, 10, 30], \"max_features\": [2, 4, 6, 8]},\n",
    "    {\"bootstrap\": [False], \"n_estimators\": [3, 10], \"max_features\": [2, 3, 4]},\n",
    "]\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring=\"neg_mean_squared_error\", return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11807640-48bb-4c15-8d7d-396488e83efe",
   "metadata": {},
   "source": [
    "This `param_grid` above tells scikit-learn to first evaluate all 3 × 4 = 12 combinations of `n_estimators` and `max_features` hyperparameter values, then try all 2 × 3 = 6 combinations of hyperparameter values in the second dict, but this time with the `bootstrap` hyperparameter set to `False`\n",
    "\n",
    "As a result, the grid search will explore 12 + 6 = 18 combinations of `RandomForestRegressor` hyperparameter values, train each model `cv=5` times. There will be 18 x 5 = 90 rounds of traninig.\n",
    "\n",
    "When training is done, we get the besst parameter combination and best estimator (best model) by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "660ae70c-11ae-4ee1-8e1b-70d9cb1e8f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=6, n_estimators=30)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_features=6, n_estimators=30)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features=6, n_estimators=30)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5edc50f2-1bbc-4e0c-9483-c05e73d5cb79",
   "metadata": {},
   "source": [
    "And of course the evalueation scores are also available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "83f0d3c2-3f2b-4c58-b4f5-086bd0998587",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64067.80507879894 {'max_features': 2, 'n_estimators': 3}\n",
      "55365.40010888244 {'max_features': 2, 'n_estimators': 10}\n",
      "52614.943465297416 {'max_features': 2, 'n_estimators': 30}\n",
      "60866.31120992179 {'max_features': 4, 'n_estimators': 3}\n",
      "52759.338194426055 {'max_features': 4, 'n_estimators': 10}\n",
      "50463.50988684138 {'max_features': 4, 'n_estimators': 30}\n",
      "59127.31591272698 {'max_features': 6, 'n_estimators': 3}\n",
      "52217.22332006632 {'max_features': 6, 'n_estimators': 10}\n",
      "49876.61688121687 {'max_features': 6, 'n_estimators': 30}\n",
      "59203.17810977277 {'max_features': 8, 'n_estimators': 3}\n",
      "52057.49324565108 {'max_features': 8, 'n_estimators': 10}\n",
      "50128.82371806042 {'max_features': 8, 'n_estimators': 30}\n",
      "61349.43995199318 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54269.32874253172 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "60010.208502793575 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52460.49897168176 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "59091.76415979513 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "52110.10427253255 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e74f5d-91c6-42eb-bb2c-f17f7e56a1d8",
   "metadata": {},
   "source": [
    "### Randomized Search\n",
    "\n",
    "The grid search approach is fine when we are exploring relatively _few_ combinations, like in the previous setup, but when the hyperparameter search space is large, it is often preferable to use `RandomizedSearchCV` instead.\n",
    "\n",
    "This class can be used in much the same way as the `GridSearchCV` class, but instead of trying out all possible combinations, it evaluates a given number of random combinations by selecting a random value for each hyperparameter at every iteration. This approach has two main benefits\n",
    "\n",
    "1. If we let the randomized search run for, say, 1,000 iterations, this approach will explore 1,000 different values for each hyperparameter (instead of just a few values per hyperparameter with the grid search approach)\n",
    "2. Simply by setting the number of iterations, we have more control over the computing budget we want to allocate to hyperparameter search.\n",
    "\n",
    "### Ensemble Methods\n",
    "\n",
    "Another way to fine-tune our system is to try to combine the models that perform best. The group (or \"ensemble\") will often perform better than the best individual model (just like Random Forests perform better than the individual Decision Trees they rely on), especially if the individual models make very different types of errors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64463988-9054-444d-8e2d-41657f698881",
   "metadata": {},
   "source": [
    "### Analyze the Best Models and Their Errors\n",
    "\n",
    "You will often gain good insights on the problem by inspecting the best models. For example, the `RandomForestRegressor` can indicate the relative importance of each attribute for making accurate predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "38e2f33a-3834-42f8-8b8e-6a36cf310204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(np.float64(0.29838853675690946), 'median_income'),\n",
       " (np.float64(0.16994566339437564), 'INLAND'),\n",
       " (np.float64(0.10959517330055447), 'pop_per_hhold'),\n",
       " (np.float64(0.09415457324292802), 'bedrooms_per_room'),\n",
       " (np.float64(0.07501106217207819), 'longitude'),\n",
       " (np.float64(0.06725803061080804), 'latitude'),\n",
       " (np.float64(0.06115437871913175), 'rooms_per_hhold'),\n",
       " (np.float64(0.041230990130423854), 'housing_median_age'),\n",
       " (np.float64(0.01757889188435985), 'population'),\n",
       " (np.float64(0.01742476855033225), 'total_rooms'),\n",
       " (np.float64(0.017066056355938494), 'total_bedrooms'),\n",
       " (np.float64(0.015819055596048247), 'households'),\n",
       " (np.float64(0.008964477764109332), '<1H OCEAN'),\n",
       " (np.float64(0.0037761767019644883), 'NEAR OCEAN'),\n",
       " (np.float64(0.0025326656783163366), 'NEAR BAY'),\n",
       " (np.float64(9.949914172160384e-05), 'ISLAND')]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "\n",
    "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
    "\n",
    "categorical_encoder = pipeline.named_transformers_[\"categorical\"]\n",
    "cat_one_hot_attribs = list(categorical_encoder.categories_[0])\n",
    "attributes = list(housing_num) + extra_attribs + cat_one_hot_attribs\n",
    "\n",
    "sorted(zip(feature_importances, attributes), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c274a52-7393-4516-9fb9-9338ffebcd21",
   "metadata": {},
   "source": [
    "With this information, you may want to try dropping some of the less useful features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76430700-65c0-492f-9bbd-cab1d5ef6051",
   "metadata": {},
   "source": [
    "### Evaluate Model on the Test Set\n",
    "\n",
    "After tweaking our models for a while, we eventually have a system that performs sufficiently well. Now is the time to evaluate the final model on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b0d3b653-f5dd-4502-9f34-346dcb5a3f7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(47827.71188556865)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
    "y_test = strat_test_set[\"median_house_value\"].copy()\n",
    "\n",
    "X_test_prepared = pipeline.transform(X_test)\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  }
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