exercises/E7-DecisionTrees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 7\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": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\albah\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor, export_graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data and set \"datetime\" as the index\n",
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/bikeshare.csv'\n",
    "bikes = pd.read_csv(url, index_col='datetime', parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create \"hour\" as its own feature\n",
    "bikes['hour'] = bikes.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
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       "      <td>40</td>\n",
       "      <td>1</td>\n",
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       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
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       "      <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",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>9.84</td>\n",
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       "      <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",
       "      <th>2011-01-01 04:00:00</th>\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>2012-12-19 19:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>15.58</td>\n",
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       "      <td>50</td>\n",
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       "      <td>7</td>\n",
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       "      <td>336</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19 20:00:00</th>\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",
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       "      <td>241</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-19 21:00:00</th>\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",
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       "      <th>2012-12-19 22:00:00</th>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "      <td>6.0032</td>\n",
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       "      <td>22</td>\n",
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       "      <th>2012-12-19 23:00:00</th>\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",
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      ],
      "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": 6,
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "workingday\n",
       "0    188.506621\n",
       "1    193.011873\n",
       "Name: total, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean rentals for each value of \"workingday\"\n",
    "bikes.groupby('workingday').total.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8fbac82b0>"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8fa8976a0>"
      ]
     },
     "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": [
    "# hourly rental trend for \"workingday=0\"\n",
    "bikes[bikes.workingday==0].groupby('hour').total.mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8fb9ca7f0>"
      ]
     },
     "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=1\"\n",
    "bikes[bikes.workingday==1].groupby('hour').total.mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8fbce9ac8>"
      ]
     },
     "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": [
    "# 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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}