{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7\n",
    "\n",
    "# Part 1 - DT\n",
    "\n",
    "## Capital Bikeshare data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "- Capital Bikeshare dataset from Kaggle: [data](https://github.com/justmarkham/DAT8/blob/master/data/bikeshare.csv), [data dictionary](https://www.kaggle.com/c/bike-sharing-demand/data)\n",
    "- Each observation represents the bikeshare rentals initiated during a given hour of a given day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor, export_graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data and set \"datetime\" as the index\n",
    "bikes = pd.read_csv('../datasets/bikeshare.csv', index_col='datetime', parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"count\" is a method, so it's best to rename that column\n",
    "bikes.rename(columns={'count':'total'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create \"hour\" as its own feature\n",
    "bikes['hour'] = bikes.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>total</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  total  hour  \n",
       "datetime                                                                   \n",
       "2011-01-01 00:00:00        81        0.0       3          13     16     0  \n",
       "2011-01-01 01:00:00        80        0.0       8          32     40     1  \n",
       "2011-01-01 02:00:00        80        0.0       5          27     32     2  \n",
       "2011-01-01 03:00:00        75        0.0       3          10     13     3  \n",
       "2011-01-01 04:00:00        75        0.0       0           1      1     4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>total</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2012-12-19 19:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.58</td>\n",
       "      <td>19.695</td>\n",
       "      <td>50</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>7</td>\n",
       "      <td>329</td>\n",
       "      <td>336</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-12-19 20:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.76</td>\n",
       "      <td>17.425</td>\n",
       "      <td>57</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>10</td>\n",
       "      <td>231</td>\n",
       "      <td>241</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-12-19 21:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.94</td>\n",
       "      <td>15.910</td>\n",
       "      <td>61</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>168</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-12-19 22:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.94</td>\n",
       "      <td>17.425</td>\n",
       "      <td>61</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12</td>\n",
       "      <td>117</td>\n",
       "      <td>129</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-12-19 23:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.12</td>\n",
       "      <td>16.665</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>4</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2012-12-19 19:00:00       4        0           1        1  15.58  19.695   \n",
       "2012-12-19 20:00:00       4        0           1        1  14.76  17.425   \n",
       "2012-12-19 21:00:00       4        0           1        1  13.94  15.910   \n",
       "2012-12-19 22:00:00       4        0           1        1  13.94  17.425   \n",
       "2012-12-19 23:00:00       4        0           1        1  13.12  16.665   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  total  hour  \n",
       "datetime                                                                   \n",
       "2012-12-19 19:00:00        50    26.0027       7         329    336    19  \n",
       "2012-12-19 20:00:00        57    15.0013      10         231    241    20  \n",
       "2012-12-19 21:00:00        61    15.0013       4         164    168    21  \n",
       "2012-12-19 22:00:00        61     6.0032      12         117    129    22  \n",
       "2012-12-19 23:00:00        66     8.9981       4          84     88    23  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **hour** ranges from 0 (midnight) through 23 (11pm)\n",
    "- **workingday** is either 0 (weekend or holiday) or 1 (non-holiday weekday)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.1\n",
    "\n",
    "Run these two `groupby` statements and figure out what they tell you about the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "workingday\n",
       "0    188.506621\n",
       "1    193.011873\n",
       "Name: total, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean rentals for each value of \"workingday\"\n",
    "bikes.groupby('workingday').total.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hour\n",
       "0      55.138462\n",
       "1      33.859031\n",
       "2      22.899554\n",
       "3      11.757506\n",
       "4       6.407240\n",
       "5      19.767699\n",
       "6      76.259341\n",
       "7     213.116484\n",
       "8     362.769231\n",
       "9     221.780220\n",
       "10    175.092308\n",
       "11    210.674725\n",
       "12    256.508772\n",
       "13    257.787281\n",
       "14    243.442982\n",
       "15    254.298246\n",
       "16    316.372807\n",
       "17    468.765351\n",
       "18    430.859649\n",
       "19    315.278509\n",
       "20    228.517544\n",
       "21    173.370614\n",
       "22    133.576754\n",
       "23     89.508772\n",
       "Name: total, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean rentals for each value of \"hour\"\n",
    "bikes.groupby('hour').total.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.2\n",
    "\n",
    "Run this plotting code, and make sure you understand the output. Then, separate this plot into two separate plots conditioned on \"workingday\". (In other words, one plot should display the hourly trend for \"workingday=0\", and the other should display the hourly trend for \"workingday=1\".)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250dd8a9b08>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mean rentals for each value of \"hour\"\n",
    "bikes.groupby('hour').total.mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot for workingday == 0 and workingday == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250dd9aff08>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hourly rental trend for \"workingday=0\"\n",
    "bikes[bikes.workingday==0].groupby('hour').total.mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250dda31308>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hourly rental trend for \"workingday=1\"\n",
    "bikes[bikes.workingday==1].groupby('hour').total.mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250ddd4db48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combine the two plots\n",
    "bikes.groupby(['hour', 'workingday']).total.mean().unstack().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write about your findings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.3\n",
    "\n",
    "Fit a linear regression model to the entire dataset, using \"total\" as the response and \"hour\" and \"workingday\" as the only features. Then, print the coefficients and interpret them. What are the limitations of linear regression in this instance?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercice 7.4\n",
    "\n",
    "Create a Decision Tree to forecast \"total\" by manually iterating over the features \"hour\" and \"workingday\". The algorithm must at least have 6 end nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.5\n",
    "\n",
    "Train a Decision Tree using scikit-learn. Comment about the performance of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - Bagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mashable news stories analysis\n",
    "\n",
    "Predicting if a news story is going to be popular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>timedelta</th>\n",
       "      <th>n_tokens_title</th>\n",
       "      <th>n_tokens_content</th>\n",
       "      <th>n_unique_tokens</th>\n",
       "      <th>n_non_stop_words</th>\n",
       "      <th>n_non_stop_unique_tokens</th>\n",
       "      <th>num_hrefs</th>\n",
       "      <th>num_self_hrefs</th>\n",
       "      <th>num_imgs</th>\n",
       "      <th>...</th>\n",
       "      <th>min_positive_polarity</th>\n",
       "      <th>max_positive_polarity</th>\n",
       "      <th>avg_negative_polarity</th>\n",
       "      <th>min_negative_polarity</th>\n",
       "      <th>max_negative_polarity</th>\n",
       "      <th>title_subjectivity</th>\n",
       "      <th>title_sentiment_polarity</th>\n",
       "      <th>abs_title_subjectivity</th>\n",
       "      <th>abs_title_sentiment_polarity</th>\n",
       "      <th>Popular</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>http://mashable.com/2014/12/10/cia-torture-rep...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>0.732620</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.844262</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.80</td>\n",
       "      <td>-0.487500</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>http://mashable.com/2013/10/18/bitlock-kicksta...</td>\n",
       "      <td>447.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>0.653199</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.135340</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>http://mashable.com/2013/07/24/google-glass-po...</td>\n",
       "      <td>533.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.660377</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.775701</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>http://mashable.com/2013/11/21/these-are-the-m...</td>\n",
       "      <td>413.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>0.497409</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.677350</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.195701</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.071429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>http://mashable.com/2014/02/11/parking-ticket-...</td>\n",
       "      <td>331.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.830357</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-0.175000</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  timedelta  \\\n",
       "0  http://mashable.com/2014/12/10/cia-torture-rep...       28.0   \n",
       "1  http://mashable.com/2013/10/18/bitlock-kicksta...      447.0   \n",
       "2  http://mashable.com/2013/07/24/google-glass-po...      533.0   \n",
       "3  http://mashable.com/2013/11/21/these-are-the-m...      413.0   \n",
       "4  http://mashable.com/2014/02/11/parking-ticket-...      331.0   \n",
       "\n",
       "   n_tokens_title  n_tokens_content  n_unique_tokens  n_non_stop_words  \\\n",
       "0             9.0             188.0         0.732620               1.0   \n",
       "1             7.0             297.0         0.653199               1.0   \n",
       "2            11.0             181.0         0.660377               1.0   \n",
       "3            12.0             781.0         0.497409               1.0   \n",
       "4             8.0             177.0         0.685714               1.0   \n",
       "\n",
       "   n_non_stop_unique_tokens  num_hrefs  num_self_hrefs  num_imgs  ...  \\\n",
       "0                  0.844262        5.0             1.0       1.0  ...   \n",
       "1                  0.815789        9.0             4.0       1.0  ...   \n",
       "2                  0.775701        4.0             3.0       1.0  ...   \n",
       "3                  0.677350       10.0             3.0       1.0  ...   \n",
       "4                  0.830357        3.0             2.0       1.0  ...   \n",
       "\n",
       "   min_positive_polarity  max_positive_polarity  avg_negative_polarity  \\\n",
       "0               0.200000                   0.80              -0.487500   \n",
       "1               0.160000                   0.50              -0.135340   \n",
       "2               0.136364                   1.00               0.000000   \n",
       "3               0.100000                   1.00              -0.195701   \n",
       "4               0.100000                   0.55              -0.175000   \n",
       "\n",
       "   min_negative_polarity  max_negative_polarity  title_subjectivity  \\\n",
       "0                  -0.60              -0.250000                 0.9   \n",
       "1                  -0.40              -0.050000                 0.1   \n",
       "2                   0.00               0.000000                 0.3   \n",
       "3                  -0.40              -0.071429                 0.0   \n",
       "4                  -0.25              -0.100000                 0.0   \n",
       "\n",
       "   title_sentiment_polarity  abs_title_subjectivity  \\\n",
       "0                       0.8                     0.4   \n",
       "1                      -0.1                     0.4   \n",
       "2                       1.0                     0.2   \n",
       "3                       0.0                     0.5   \n",
       "4                       0.0                     0.5   \n",
       "\n",
       "   abs_title_sentiment_polarity  Popular  \n",
       "0                           0.8        1  \n",
       "1                           0.1        0  \n",
       "2                           1.0        0  \n",
       "3                           0.0        0  \n",
       "4                           0.0        0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../datasets/mashable.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 61)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['url', 'Popular'], axis=1)\n",
    "y = df['Popular']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train/test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.6\n",
    "\n",
    "Estimate a Decision Tree Classifier and a Logistic Regression\n",
    "\n",
    "Evaluate using the following metrics:\n",
    "* Accuracy\n",
    "* F1-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.7\n",
    "\n",
    "Estimate 300 bagged samples\n",
    "\n",
    "Estimate the following set of classifiers:\n",
    "\n",
    "* 100 Decision Trees where max_depth=None\n",
    "* 100 Decision Trees where max_depth=2\n",
    "* 100 Logistic Regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.8\n",
    "\n",
    "Ensemble using majority voting\n",
    "\n",
    "Evaluate using the following metrics:\n",
    "* Accuracy\n",
    "* F1-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.9\n",
    "\n",
    "Estimate the probability as %models that predict positive\n",
    "\n",
    "Modify the probability threshold and select the one that maximizes the F1-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.10\n",
    "\n",
    "Ensemble using weighted voting using the oob_error\n",
    "\n",
    "Evaluate using the following metrics:\n",
    "* Accuracy\n",
    "* F1-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.11\n",
    "\n",
    "Estimate te probability of the weighted voting\n",
    "\n",
    "Modify the probability threshold and select the one that maximizes the F1-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7.12\n",
    "\n",
    "Estimate a logistic regression using as input the estimated classifiers\n",
    "\n",
    "Modify the probability threshold such that maximizes the F1-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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