chris-churn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>city</th>\n",
       "      <th>last_trip_date</th>\n",
       "      <th>phone</th>\n",
       "      <th>signup_date</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>luxury_car_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.94</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-05-03</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.06</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-05-21</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-05-13</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.51</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-04-22</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.87</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-04-21</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.78</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-12</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.87</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.41</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.96</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-18</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-05-12</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.42</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-06-28</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-06-28</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-16</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6.74</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.02</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-05-30</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>9.1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>54.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.27</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-05-15</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.72</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-04-13</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.94</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-03-06</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17.34</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.31</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.02</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-05-30</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>2.6</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "      <td>47.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.69</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-04-21</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.96</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-02-17</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11.44</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-06-09</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.05</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-12</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.70</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-05-23</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3.89</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-04-13</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.70</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39970</th>\n",
       "      <td>6.65</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-06-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>66.7</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39971</th>\n",
       "      <td>13.45</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39972</th>\n",
       "      <td>1.95</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-02-14</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39973</th>\n",
       "      <td>4.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-06-26</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39974</th>\n",
       "      <td>1.74</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-17</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>95.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39975</th>\n",
       "      <td>1.16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-18</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39976</th>\n",
       "      <td>3.21</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-06-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>2.8</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "      <td>55.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39977</th>\n",
       "      <td>2.05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-06-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39978</th>\n",
       "      <td>5.59</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-04-12</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39979</th>\n",
       "      <td>10.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39980</th>\n",
       "      <td>4.48</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-05-11</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39981</th>\n",
       "      <td>1.13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-01-11</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-11</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39982</th>\n",
       "      <td>1.99</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.15</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-04-25</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39983</th>\n",
       "      <td>9.14</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39984</th>\n",
       "      <td>2.07</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-04-11</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39985</th>\n",
       "      <td>15.95</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39986</th>\n",
       "      <td>12.10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-06-28</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39987</th>\n",
       "      <td>3.29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39988</th>\n",
       "      <td>4.53</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-05-30</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>18.8</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39989</th>\n",
       "      <td>2.67</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-05-23</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>11.1</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>88.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39990</th>\n",
       "      <td>9.45</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39991</th>\n",
       "      <td>3.01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-06-21</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39992</th>\n",
       "      <td>2.71</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-02-15</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39993</th>\n",
       "      <td>6.89</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39994</th>\n",
       "      <td>3.88</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-06-27</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39995</th>\n",
       "      <td>2.06</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.08</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-04-02</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>90.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39996</th>\n",
       "      <td>2.05</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-05-09</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>85.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39997</th>\n",
       "      <td>3.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-06-24</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39998</th>\n",
       "      <td>3.49</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-02-09</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39999</th>\n",
       "      <td>4.25</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-06-27</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  \\\n",
       "0          6.94                   5.0                   5.0       1.00   \n",
       "1          8.06                   5.0                   5.0       1.00   \n",
       "2         21.50                   4.0                   NaN       1.00   \n",
       "3          9.46                   5.0                   NaN       2.75   \n",
       "4         13.77                   5.0                   NaN       1.00   \n",
       "5         14.51                   5.0                   NaN       1.00   \n",
       "6          4.87                   4.7                   4.0       1.00   \n",
       "7          1.78                   4.0                   5.0       1.00   \n",
       "8          6.87                   5.0                   4.7       1.00   \n",
       "9          2.41                   5.0                   3.0       1.00   \n",
       "10         3.96                   5.0                   NaN       2.00   \n",
       "11        17.04                   5.0                   NaN       1.00   \n",
       "12         5.42                   4.7                   5.0       1.00   \n",
       "13         5.02                   5.0                   5.0       1.00   \n",
       "14         4.28                   5.0                   4.0       1.15   \n",
       "15         3.40                   5.0                   5.0       1.00   \n",
       "16         6.74                   4.8                   3.7       1.02   \n",
       "17         7.27                   4.8                   4.0       1.00   \n",
       "18         2.72                   4.7                   4.5       1.00   \n",
       "19         5.94                   5.0                   5.0       1.00   \n",
       "20        17.34                   5.0                   5.0       1.00   \n",
       "21         3.31                   4.8                   3.7       1.02   \n",
       "22         2.69                   4.3                   5.0       1.00   \n",
       "23         6.00                   5.0                   NaN       1.00   \n",
       "24         4.96                   5.0                   5.0       1.00   \n",
       "25        11.44                   4.6                   4.7       1.00   \n",
       "26         0.05                   5.0                   5.0       1.00   \n",
       "27         3.70                   4.6                   5.0       1.00   \n",
       "28         3.89                   5.0                   4.0       1.00   \n",
       "29         4.70                   4.8                   3.3       1.00   \n",
       "...         ...                   ...                   ...        ...   \n",
       "39970      6.65                   5.0                   4.0       1.75   \n",
       "39971     13.45                   5.0                   NaN       1.00   \n",
       "39972      1.95                   3.5                   5.0       1.50   \n",
       "39973      4.67                   5.0                   5.0       1.00   \n",
       "39974      1.74                   4.3                   4.9       1.01   \n",
       "39975      1.16                   5.0                   NaN       1.00   \n",
       "39976      3.21                   4.7                   4.8       1.01   \n",
       "39977      2.05                   4.0                   5.0       1.00   \n",
       "39978      5.59                   5.0                   5.0       1.25   \n",
       "39979     10.50                   5.0                   3.5       1.00   \n",
       "39980      4.48                   4.0                   NaN       1.00   \n",
       "39981      1.13                   5.0                   5.0       1.75   \n",
       "39982      1.99                   4.8                   4.8       1.15   \n",
       "39983      9.14                   4.0                   NaN       1.00   \n",
       "39984      2.07                   4.5                   5.0       1.00   \n",
       "39985     15.95                   5.0                   4.0       1.00   \n",
       "39986     12.10                   5.0                   4.0       1.00   \n",
       "39987      3.29                   5.0                   4.5       1.50   \n",
       "39988      4.53                   4.6                   4.7       1.41   \n",
       "39989      2.67                   4.9                   4.3       1.06   \n",
       "39990      9.45                   4.8                   5.0       1.00   \n",
       "39991      3.01                   5.0                   5.0       1.00   \n",
       "39992      2.71                   3.0                   3.0       1.25   \n",
       "39993      6.89                   5.0                   5.0       1.00   \n",
       "39994      3.88                   4.8                   5.0       1.00   \n",
       "39995      2.06                   4.8                   4.3       1.08   \n",
       "39996      2.05                   5.0                   5.0       1.00   \n",
       "39997      3.04                   5.0                   5.0       1.00   \n",
       "39998      3.49                   4.3                   3.3       1.50   \n",
       "39999      4.25                   4.7                   5.0       1.00   \n",
       "\n",
       "                 city last_trip_date    phone signup_date  surge_pct  \\\n",
       "0             Astapor     2014-05-03  Android  2014-01-12        0.0   \n",
       "1             Astapor     2014-01-26  Android  2014-01-25        0.0   \n",
       "2          Winterfell     2014-05-21   iPhone  2014-01-02        0.0   \n",
       "3          Winterfell     2014-01-10  Android  2014-01-09      100.0   \n",
       "4          Winterfell     2014-05-13   iPhone  2014-01-31        0.0   \n",
       "5             Astapor     2014-04-22   iPhone  2014-01-29        0.0   \n",
       "6             Astapor     2014-04-21  Android  2014-01-18        0.0   \n",
       "7          Winterfell     2014-01-12   iPhone  2014-01-11        0.0   \n",
       "8             Astapor     2014-01-30  Android  2014-01-01        0.0   \n",
       "9          Winterfell     2014-01-29   iPhone  2014-01-28        0.0   \n",
       "10         Winterfell     2014-01-19   iPhone  2014-01-18      100.0   \n",
       "11         Winterfell     2014-05-12  Android  2014-01-26        0.0   \n",
       "12            Astapor     2014-06-28   iPhone  2014-01-05        0.0   \n",
       "13            Astapor     2014-01-19  Android  2014-01-19        0.0   \n",
       "14         Winterfell     2014-06-28  Android  2014-01-14       20.0   \n",
       "15            Astapor     2014-01-16  Android  2014-01-16        0.0   \n",
       "16         Winterfell     2014-05-30   iPhone  2014-01-25        9.1   \n",
       "17         Winterfell     2014-05-15   iPhone  2014-01-07        0.0   \n",
       "18            Astapor     2014-04-13   iPhone  2014-01-10        0.0   \n",
       "19            Astapor     2014-03-06   iPhone  2014-01-26        0.0   \n",
       "20         Winterfell     2014-01-15  Android  2014-01-14        0.0   \n",
       "21     King's Landing     2014-05-30   iPhone  2014-01-28        2.6   \n",
       "22         Winterfell     2014-04-21   iPhone  2014-01-15        0.0   \n",
       "23         Winterfell     2014-01-10   iPhone  2014-01-07        0.0   \n",
       "24         Winterfell     2014-02-17   iPhone  2014-01-26        0.0   \n",
       "25            Astapor     2014-06-09   iPhone  2014-01-25        0.0   \n",
       "26         Winterfell     2014-01-12  Android  2014-01-12        0.0   \n",
       "27         Winterfell     2014-05-23   iPhone  2014-01-04        0.0   \n",
       "28            Astapor     2014-04-13   iPhone  2014-01-03        0.0   \n",
       "29            Astapor     2014-01-19  Android  2014-01-06        0.0   \n",
       "...               ...            ...      ...         ...        ...   \n",
       "39970      Winterfell     2014-06-29   iPhone  2014-01-23       66.7   \n",
       "39971  King's Landing     2014-02-26  Android  2014-01-08        0.0   \n",
       "39972         Astapor     2014-02-14   iPhone  2014-01-25      100.0   \n",
       "39973         Astapor     2014-06-26   iPhone  2014-01-06        0.0   \n",
       "39974         Astapor     2014-06-11   iPhone  2014-01-17        4.5   \n",
       "39975         Astapor     2014-01-18   iPhone  2014-01-18        0.0   \n",
       "39976      Winterfell     2014-06-29   iPhone  2014-01-07        2.8   \n",
       "39977         Astapor     2014-06-29   iPhone  2014-01-18        0.0   \n",
       "39978         Astapor     2014-04-12  Android  2014-01-27       50.0   \n",
       "39979      Winterfell     2014-01-25   iPhone  2014-01-20        0.0   \n",
       "39980         Astapor     2014-05-11   iPhone  2014-01-21        0.0   \n",
       "39981  King's Landing     2014-01-11  Android  2014-01-11      100.0   \n",
       "39982      Winterfell     2014-04-25   iPhone  2014-01-06       20.0   \n",
       "39983         Astapor     2014-01-31   iPhone  2014-01-30        0.0   \n",
       "39984      Winterfell     2014-04-11   iPhone  2014-01-18        0.0   \n",
       "39985      Winterfell     2014-01-15   iPhone  2014-01-15        0.0   \n",
       "39986         Astapor     2014-06-28   iPhone  2014-01-22        0.0   \n",
       "39987      Winterfell     2014-02-01   iPhone  2014-01-05       50.0   \n",
       "39988      Winterfell     2014-05-30   iPhone  2014-01-06       18.8   \n",
       "39989         Astapor     2014-05-23  Android  2014-01-15       11.1   \n",
       "39990      Winterfell     2014-01-25   iPhone  2014-01-23        0.0   \n",
       "39991         Astapor     2014-06-21   iPhone  2014-01-01        0.0   \n",
       "39992  King's Landing     2014-02-15  Android  2014-01-26      100.0   \n",
       "39993      Winterfell     2014-01-04  Android  2014-01-03        0.0   \n",
       "39994         Astapor     2014-06-27  Android  2014-01-16        0.0   \n",
       "39995      Winterfell     2014-04-02  Android  2014-01-26        9.5   \n",
       "39996  King's Landing     2014-05-09   iPhone  2014-01-08        0.0   \n",
       "39997      Winterfell     2014-06-24  Android  2014-01-04        0.0   \n",
       "39998         Astapor     2014-02-09   iPhone  2014-01-08       40.0   \n",
       "39999         Astapor     2014-06-27   iPhone  2014-01-18        0.0   \n",
       "\n",
       "       trips_in_first_30_days  luxury_car_user  weekday_pct  \n",
       "0                           0            False        100.0  \n",
       "1                           2             True          0.0  \n",
       "2                           1             True        100.0  \n",
       "3                           1            False        100.0  \n",
       "4                           0            False        100.0  \n",
       "5                           0             True        100.0  \n",
       "6                           0            False         66.7  \n",
       "7                           3            False          0.0  \n",
       "8                           3            False         66.7  \n",
       "9                           1            False        100.0  \n",
       "10                          1            False          0.0  \n",
       "11                          0             True        100.0  \n",
       "12                          2             True         33.3  \n",
       "13                          1            False          0.0  \n",
       "14                          2            False         40.0  \n",
       "15                          1             True        100.0  \n",
       "16                          2            False         54.5  \n",
       "17                          1            False         75.0  \n",
       "18                          1            False         33.3  \n",
       "19                          1             True        100.0  \n",
       "20                          1             True        100.0  \n",
       "21                          9            False         47.4  \n",
       "22                          0            False          0.0  \n",
       "23                          1            False        100.0  \n",
       "24                          1            False        100.0  \n",
       "25                          6             True         31.4  \n",
       "26                          1            False          0.0  \n",
       "27                          2            False         40.0  \n",
       "28                          0            False          0.0  \n",
       "29                          4            False         25.0  \n",
       "...                       ...              ...          ...  \n",
       "39970                       2            False         33.3  \n",
       "39971                       0             True        100.0  \n",
       "39972                       2            False        100.0  \n",
       "39973                       0             True         62.5  \n",
       "39974                       9             True         95.5  \n",
       "39975                       1             True          0.0  \n",
       "39976                       9            False         55.6  \n",
       "39977                       0            False          0.0  \n",
       "39978                       1            False         50.0  \n",
       "39979                       3             True         66.7  \n",
       "39980                       1             True         50.0  \n",
       "39981                       1            False          0.0  \n",
       "39982                       3            False         50.0  \n",
       "39983                       1             True        100.0  \n",
       "39984                       2            False         87.5  \n",
       "39985                       1             True        100.0  \n",
       "39986                       2            False         75.0  \n",
       "39987                       2            False        100.0  \n",
       "39988                       7            False         75.0  \n",
       "39989                       3             True         88.9  \n",
       "39990                       4             True         75.0  \n",
       "39991                       0            False          0.0  \n",
       "39992                       1             True          0.0  \n",
       "39993                       1            False        100.0  \n",
       "39994                       2            False        100.0  \n",
       "39995                       8            False         90.5  \n",
       "39996                       2            False         85.7  \n",
       "39997                       3             True         33.3  \n",
       "39998                       5            False         60.0  \n",
       "39999                       2             True         42.9  \n",
       "\n",
       "[40000 rows x 12 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('churn_train.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "def active_flag(df, last_trip_date_columns):\n",
    "   # calucate preceding data\n",
    "   df['pulled_data'] = datetime.strptime('2014-6-1', \"%Y-%m-%d\") # temp pulled_data columns\n",
    "   df['preceding_data'] = df['pulled_data'] - pd.to_datetime(df[last_trip_date_columns])\n",
    "   df['preceding_data'] = df['preceding_data'].astype(str).str[:-24].astype(int)\n",
    "\n",
    "\n",
    "   # if active or not\n",
    "   df['active_flag'] = np.where(df['preceding_data'] <= 30, 1, 0)\n",
    "\n",
    "   # drop temp columns\n",
    "   df.drop('pulled_data',axis=1)\n",
    "\n",
    "active_flag(df1,'last_trip_date')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(['pulled_data','phone','city'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(['signup_date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(['last_trip_date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>luxury_car_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "      <th>preceding_data</th>\n",
       "      <th>active_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.94</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.06</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  surge_pct  \\\n",
       "0      6.94                   5.0                   5.0       1.00        0.0   \n",
       "1      8.06                   5.0                   5.0       1.00        0.0   \n",
       "2     21.50                   4.0                   NaN       1.00        0.0   \n",
       "3      9.46                   5.0                   NaN       2.75      100.0   \n",
       "4     13.77                   5.0                   NaN       1.00        0.0   \n",
       "\n",
       "   trips_in_first_30_days  luxury_car_user  weekday_pct  preceding_data  \\\n",
       "0                       0            False        100.0              29   \n",
       "1                       2             True          0.0             126   \n",
       "2                       1             True        100.0              11   \n",
       "3                       1            False        100.0             142   \n",
       "4                       0            False        100.0              19   \n",
       "\n",
       "   active_flag  \n",
       "0            1  \n",
       "1            0  \n",
       "2            1  \n",
       "3            0  \n",
       "4            1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>weekday_pct</th>\n",
       "      <th>preceding_data</th>\n",
       "      <th>active_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>39838.000000</td>\n",
       "      <td>33472.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.791302</td>\n",
       "      <td>4.777434</td>\n",
       "      <td>4.601697</td>\n",
       "      <td>1.074956</td>\n",
       "      <td>8.857342</td>\n",
       "      <td>2.280700</td>\n",
       "      <td>60.874382</td>\n",
       "      <td>42.768575</td>\n",
       "      <td>0.524375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.708056</td>\n",
       "      <td>0.448088</td>\n",
       "      <td>0.614810</td>\n",
       "      <td>0.222427</td>\n",
       "      <td>20.014008</td>\n",
       "      <td>3.811289</td>\n",
       "      <td>37.089619</td>\n",
       "      <td>61.794023</td>\n",
       "      <td>0.499412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.420000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.300000</td>\n",
       "      <td>-16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.880000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>66.700000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.930000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>160.960000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           avg_dist  avg_rating_by_driver  avg_rating_of_driver     avg_surge  \\\n",
       "count  40000.000000          39838.000000          33472.000000  40000.000000   \n",
       "mean       5.791302              4.777434              4.601697      1.074956   \n",
       "std        5.708056              0.448088              0.614810      0.222427   \n",
       "min        0.000000              1.000000              1.000000      1.000000   \n",
       "25%        2.420000              4.700000              4.300000      1.000000   \n",
       "50%        3.880000              5.000000              4.900000      1.000000   \n",
       "75%        6.930000              5.000000              5.000000      1.050000   \n",
       "max      160.960000              5.000000              5.000000      8.000000   \n",
       "\n",
       "          surge_pct  trips_in_first_30_days   weekday_pct  preceding_data  \\\n",
       "count  40000.000000            40000.000000  40000.000000    40000.000000   \n",
       "mean       8.857342                2.280700     60.874382       42.768575   \n",
       "std       20.014008                3.811289     37.089619       61.794023   \n",
       "min        0.000000                0.000000      0.000000      -30.000000   \n",
       "25%        0.000000                0.000000     33.300000      -16.000000   \n",
       "50%        0.000000                1.000000     66.700000       24.000000   \n",
       "75%        8.300000                3.000000    100.000000      107.000000   \n",
       "max      100.000000              125.000000    100.000000      151.000000   \n",
       "\n",
       "        active_flag  \n",
       "count  40000.000000  \n",
       "mean       0.524375  \n",
       "std        0.499412  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        1.000000  \n",
       "75%        1.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.fillna(df1.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plotting' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-1585315ac2a6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'plotting' is not defined"
     ]
    }
   ],
   "source": [
    "plotting.scatter_matrix(df1,figsize=(15,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  surge_pct  \\\n",
       "0      6.94                   5.0              5.000000       1.00        0.0   \n",
       "1      8.06                   5.0              5.000000       1.00        0.0   \n",
       "2     21.50                   4.0              4.601697       1.00        0.0   \n",
       "3      9.46                   5.0              4.601697       2.75      100.0   \n",
       "4     13.77                   5.0              4.601697       1.00        0.0   \n",
       "\n",
       "   trips_in_first_30_days  luxury_car_user  weekday_pct  preceding_data  \\\n",
       "0                       0            False        100.0              29   \n",
       "1                       2             True          0.0             126   \n",
       "2                       1             True        100.0              11   \n",
       "3                       1            False        100.0             142   \n",
       "4                       0            False        100.0              19   \n",
       "\n",
       "   active_flag  \n",
       "0            1  \n",
       "1            0  \n",
       "2            1  \n",
       "3            0  \n",
       "4            1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[\"luxury_car_user\"] *= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  surge_pct  \\\n",
       "0      6.94                   5.0              5.000000       1.00        0.0   \n",
       "1      8.06                   5.0              5.000000       1.00        0.0   \n",
       "2     21.50                   4.0              4.601697       1.00        0.0   \n",
       "3      9.46                   5.0              4.601697       2.75      100.0   \n",
       "4     13.77                   5.0              4.601697       1.00        0.0   \n",
       "\n",
       "   trips_in_first_30_days  luxury_car_user  weekday_pct  preceding_data  \\\n",
       "0                       0                0        100.0              29   \n",
       "1                       2                1          0.0             126   \n",
       "2                       1                1        100.0              11   \n",
       "3                       1                0        100.0             142   \n",
       "4                       0                0        100.0              19   \n",
       "\n",
       "   active_flag  \n",
       "0            1  \n",
       "1            0  \n",
       "2            1  \n",
       "3            0  \n",
       "4            1  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(df1, figsize=(15,15))\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40000 entries, 0 to 39999\n",
      "Data columns (total 10 columns):\n",
      "avg_dist                  40000 non-null float64\n",
      "avg_rating_by_driver      40000 non-null float64\n",
      "avg_rating_of_driver      40000 non-null float64\n",
      "avg_surge                 40000 non-null float64\n",
      "surge_pct                 40000 non-null float64\n",
      "trips_in_first_30_days    40000 non-null int64\n",
      "luxury_car_user           40000 non-null int64\n",
      "weekday_pct               40000 non-null float64\n",
      "preceding_data            40000 non-null int64\n",
      "active_flag               40000 non-null int64\n",
      "dtypes: float64(6), int64(4)\n",
      "memory usage: 3.1 MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df1.iloc[:,:9]\n",
    "y = df1.iloc[:,9:]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/newton/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/newton/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([7.93419932e-04, 1.45094441e-02, 2.33481007e-04, 1.50300004e-02,\n",
       "       3.02461216e-02, 5.68720086e-03, 8.07333600e-04, 9.58153027e-03,\n",
       "       9.23111468e-01])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "base_test = model.predict(X_test)\n",
    "# accuracy of test set\n",
    "model.score(X_test, y_test)\n",
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the preceding_data varable variable\n",
    "X = df1.iloc[:,:8]\n",
    "y = df1.iloc[:,9:]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/newton/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/newton/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7127"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remake the model\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "base_test = model.predict(X_test)\n",
    "# accuracy of test set\n",
    "model.score(X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.38384157, 0.11270961, 0.07198242, 0.05982159, 0.06559706,\n",
       "       0.13642356, 0.01984746, 0.14977674])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>luxury_car_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.791302</td>\n",
       "      <td>4.777434</td>\n",
       "      <td>4.601697</td>\n",
       "      <td>1.074956</td>\n",
       "      <td>8.857342</td>\n",
       "      <td>2.280700</td>\n",
       "      <td>0.376150</td>\n",
       "      <td>60.874382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.708056</td>\n",
       "      <td>0.447180</td>\n",
       "      <td>0.562407</td>\n",
       "      <td>0.222427</td>\n",
       "      <td>20.014008</td>\n",
       "      <td>3.811289</td>\n",
       "      <td>0.484424</td>\n",
       "      <td>37.089619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.420000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.880000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.930000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>160.960000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           avg_dist  avg_rating_by_driver  avg_rating_of_driver     avg_surge  \\\n",
       "count  40000.000000          40000.000000          40000.000000  40000.000000   \n",
       "mean       5.791302              4.777434              4.601697      1.074956   \n",
       "std        5.708056              0.447180              0.562407      0.222427   \n",
       "min        0.000000              1.000000              1.000000      1.000000   \n",
       "25%        2.420000              4.700000              4.500000      1.000000   \n",
       "50%        3.880000              5.000000              4.700000      1.000000   \n",
       "75%        6.930000              5.000000              5.000000      1.050000   \n",
       "max      160.960000              5.000000              5.000000      8.000000   \n",
       "\n",
       "          surge_pct  trips_in_first_30_days  luxury_car_user   weekday_pct  \n",
       "count  40000.000000            40000.000000     40000.000000  40000.000000  \n",
       "mean       8.857342                2.280700         0.376150     60.874382  \n",
       "std       20.014008                3.811289         0.484424     37.089619  \n",
       "min        0.000000                0.000000         0.000000      0.000000  \n",
       "25%        0.000000                0.000000         0.000000     33.300000  \n",
       "50%        0.000000                1.000000         0.000000     66.700000  \n",
       "75%        8.300000                3.000000         1.000000    100.000000  \n",
       "max      100.000000              125.000000         1.000000    100.000000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like variables 1,2,6 and 8 influence the model most.\n",
    "#1 avg_dist - between 0 and 20\n",
    "#2 avg_rating_by_driver - mostly between 4.5 and 5.0\n",
    "#6 trips in first 30 days - mostly between 0 and 12\n",
    "#8 weekday_pct - between 0 and 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 7402.,   398.,  1259.,  1945.,  1630.,  4882.,  3825.,  2624.,\n",
       "         2106., 13929.]),\n",
       " array([  0.,  10.,  20.,  30.,  40.,  50.,  60.,  70.,  80.,  90., 100.]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 98,
     "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": [
    "plt.hist(X['weekday_pct'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tuning the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/newton/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 288 candidates, totalling 864 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  61 tasks      | elapsed:    7.8s\n",
      "[Parallel(n_jobs=-1)]: Done 211 tasks      | elapsed:   35.2s\n",
      "[Parallel(n_jobs=-1)]: Done 461 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done 811 tasks      | elapsed:  5.0min\n",
      "[Parallel(n_jobs=-1)]: Done 864 out of 864 | elapsed:  6.1min finished\n",
      "/home/newton/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:740: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  self.best_estimator_.fit(X, y, **fit_params)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best rf_model: RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=4, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=40, n_jobs=None,\n",
      "            oob_score=False, random_state=1, verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "random_forest_grid = {'max_depth': [None],\n",
    "                      'max_features': ['sqrt'],\n",
    "                      'min_samples_split': [2],\n",
    "                      'min_samples_leaf': [4],\n",
    "                      'bootstrap': [True],\n",
    "                      'n_estimators': [40],\n",
    "                      'random_state': [1]}\n",
    "\n",
    "\n",
    "rf_gridsearch = GridSearchCV(RandomForestClassifier(),\n",
    "                             random_forest_grid,\n",
    "                             n_jobs=-1,\n",
    "                             verbose=True,\n",
    "                             scoring='neg_mean_squared_error')\n",
    "\n",
    "rf_gridsearch.fit(X_train, y_train)\n",
    "best_rf_model = rf_gridsearch.best_estimator_\n",
    "print (\"best rf_model: {}\".format(best_rf_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load in raw data\n",
    "df_train = pd.read_csv('churn_train.csv')\n",
    "df_test = pd.read_csv('churn_test.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean the train data\n",
    "def active_flag(df, last_trip_date_columns):\n",
    "    # calucate preceding data\n",
    "    df['pulled_data'] = datetime.strptime('2014-7-1', \"%Y-%m-%d\") # temp pulled_data columns\n",
    "    df['preceding_data'] = df['pulled_data'] - pd.to_datetime(df[last_trip_date_columns])\n",
    "    df['preceding_data'] = df['preceding_data'].astype(str).str[:-24].astype(int)\n",
    "    \n",
    "    \n",
    "    # if active or not\n",
    "    df['active_flag'] = np.where(df['preceding_data'] <= 30, 1, 0)\n",
    "    \n",
    "    # drop temp columns\n",
    "    df.drop('pulled_data',axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>city</th>\n",
       "      <th>last_trip_date</th>\n",
       "      <th>phone</th>\n",
       "      <th>signup_date</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>luxury_car_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.94</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-05-03</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.06</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge     city  \\\n",
       "0      6.94                   5.0                   5.0        1.0  Astapor   \n",
       "1      8.06                   5.0                   5.0        1.0  Astapor   \n",
       "\n",
       "  last_trip_date    phone signup_date  surge_pct  trips_in_first_30_days  \\\n",
       "0     2014-05-03  Android  2014-01-12        0.0                       0   \n",
       "1     2014-01-26  Android  2014-01-25        0.0                       2   \n",
       "\n",
       "   luxury_car_user  weekday_pct  \n",
       "0            False        100.0  \n",
       "1             True          0.0  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean the data\n",
    "last_trip_date_columns = 'last_trip_date'\n",
    "active_flag(df_train, last_trip_date_columns)\n",
    "active_flag(df_test, last_trip_date_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>city</th>\n",
       "      <th>last_trip_date</th>\n",
       "      <th>phone</th>\n",
       "      <th>signup_date</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>luxury_car_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "      <th>pulled_data</th>\n",
       "      <th>preceding_data</th>\n",
       "      <th>active_flag</th>\n",
       "      <th>signup_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.94</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-05-03</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.06</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-05-21</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-05-13</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  city  \\\n",
       "0      6.94                   5.0                   5.0       1.00     2   \n",
       "1      8.06                   5.0                   5.0       1.00     2   \n",
       "2     21.50                   4.0                   NaN       1.00     3   \n",
       "3      9.46                   5.0                   NaN       2.75     3   \n",
       "4     13.77                   5.0                   NaN       1.00     3   \n",
       "\n",
       "  last_trip_date    phone signup_date  surge_pct  trips_in_first_30_days  \\\n",
       "0     2014-05-03  Android  2014-01-12        0.0                       0   \n",
       "1     2014-01-26  Android  2014-01-25        0.0                       2   \n",
       "2     2014-05-21   iPhone  2014-01-02        0.0                       1   \n",
       "3     2014-01-10  Android  2014-01-09      100.0                       1   \n",
       "4     2014-05-13   iPhone  2014-01-31        0.0                       0   \n",
       "\n",
       "   luxury_car_user  weekday_pct pulled_data  preceding_data  active_flag  \\\n",
       "0            False        100.0  2014-07-01              59            0   \n",
       "1             True          0.0  2014-07-01             156            0   \n",
       "2             True        100.0  2014-07-01              41            0   \n",
       "3            False        100.0  2014-07-01             172            0   \n",
       "4            False        100.0  2014-07-01              49            0   \n",
       "\n",
       "   signup_period  \n",
       "0            170  \n",
       "1            157  \n",
       "2            180  \n",
       "3            173  \n",
       "4            151  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def signup_period(df, signup_date):\n",
    "    # calucate preceding data\n",
    "    df['signup_period'] = df['pulled_data'] - pd.to_datetime(df[signup_date])\n",
    "    df['signup_period'] = df['signup_period'].astype(str).str[:-24].astype(int)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "signup_period(df_train,'signup_date')\n",
    "signup_period(df_test,'signup_date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "drop unimportant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_data(df, drop_columns, axis=1):\n",
    "    return df.drop(drop_columns,axis)\n",
    "\n",
    "drop_columns = ['pulled_data', 'last_trip_date','signup_date','phone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train2 = drop_data(df_train, drop_columns)\n",
    "df_test2 = drop_data(df_test, drop_columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Handle city column\n",
    "df_train2.replace(\"King's Landing\", 1, inplace=True)\n",
    "df_train2.replace(\"Astapor\", 2, inplace=True)\n",
    "df_train2.replace(\"Winterfell\", 3, inplace=True)\n",
    "df_test2.replace(\"King's Landing\", 1, inplace=True)\n",
    "df_test2.replace(\"Astapor\", 2, inplace=True)\n",
    "df_test2.replace(\"Winterfell\", 3, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Handle nans\n",
    "df_train_final = df_train2.fillna(df_train.mean())\n",
    "df_test_final = df_test2.fillna(df_test.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Handle true/false column\n",
    "df_train_final[\"luxury_car_user\"] *= 1\n",
    "df_test_final[\"luxury_car_user\"] *= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40000 entries, 0 to 39999\n",
      "Data columns (total 12 columns):\n",
      "avg_dist                  40000 non-null float64\n",
      "avg_rating_by_driver      40000 non-null float64\n",
      "avg_rating_of_driver      40000 non-null float64\n",
      "avg_surge                 40000 non-null float64\n",
      "city                      40000 non-null int64\n",
      "surge_pct                 40000 non-null float64\n",
      "trips_in_first_30_days    40000 non-null int64\n",
      "luxury_car_user           40000 non-null int64\n",
      "weekday_pct               40000 non-null float64\n",
      "preceding_data            40000 non-null int64\n",
      "active_flag               40000 non-null int64\n",
      "signup_period             40000 non-null int64\n",
      "dtypes: float64(6), int64(6)\n",
      "memory usage: 3.7 MB\n"
     ]
    }
   ],
   "source": [
    "df_train_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_train_final.drop(['active_flag'], axis=1)\n",
    "y = df_train_final['active_flag']\n",
    "X_test = df_test_final.drop(['active_flag'], axis=1)\n",
    "y_test = df_test_final['active_flag']\n",
    "X.drop(['preceding_data'],axis=1, inplace=True)\n",
    "X_test.drop(['preceding_data'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 288 candidates, totalling 864 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/newton/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    9.0s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   44.0s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:  3.9min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  6.9min\n",
      "[Parallel(n_jobs=-1)]: Done 864 out of 864 | elapsed:  9.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best rf_model: RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=4, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=80, n_jobs=None,\n",
      "            oob_score=False, random_state=1, verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "#Now the mnodel\n",
    "random_forest_grid = {'max_depth': [3, None],\n",
    "                      'max_features': ['sqrt', 'log2', None],\n",
    "                      'min_samples_split': [2, 4],\n",
    "                      'min_samples_leaf': [1, 2, 4],\n",
    "                      'bootstrap': [True, False],\n",
    "                      'n_estimators': [10, 20, 40, 80],\n",
    "                      'random_state': [1]}\n",
    "\n",
    "\n",
    "rf_gridsearch = GridSearchCV(RandomForestClassifier(),\n",
    "                             random_forest_grid,\n",
    "                             n_jobs=-1,\n",
    "                             verbose=True,\n",
    "                             scoring='neg_mean_squared_error')\n",
    "\n",
    "rf_gridsearch.fit(X, y)\n",
    "best_rf_model = rf_gridsearch.best_estimator_\n",
    "print (\"best rf_model: {}\".format(best_rf_model))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>city</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>luxury_car_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "      <th>preceding_data</th>\n",
       "      <th>signup_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.94</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>59</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.06</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.601697</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>41</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601697</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>172</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601697</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>49</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  city  \\\n",
       "0      6.94                   5.0              5.000000       1.00     2   \n",
       "1      8.06                   5.0              5.000000       1.00     2   \n",
       "2     21.50                   4.0              4.601697       1.00     3   \n",
       "3      9.46                   5.0              4.601697       2.75     3   \n",
       "4     13.77                   5.0              4.601697       1.00     3   \n",
       "\n",
       "   surge_pct  trips_in_first_30_days  luxury_car_user  weekday_pct  \\\n",
       "0        0.0                       0                0        100.0   \n",
       "1        0.0                       2                1          0.0   \n",
       "2        0.0                       1                1        100.0   \n",
       "3      100.0                       1                0        100.0   \n",
       "4        0.0                       0                0        100.0   \n",
       "\n",
       "   preceding_data  signup_period  \n",
       "0              59            170  \n",
       "1             156            157  \n",
       "2              41            180  \n",
       "3             172            173  \n",
       "4              49            151  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/newton/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:    5.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'max_depth': [None], 'max_features': ['sqrt'], 'min_samples_split': [2], 'min_samples_leaf': [4], 'bootstrap': [True], 'n_estimators': [80], 'random_state': [1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=True)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest_grid_2 = {'max_depth': [None],\n",
    "                      'max_features': ['sqrt'],\n",
    "                      'min_samples_split': [2],\n",
    "                      'min_samples_leaf': [4],\n",
    "                      'bootstrap': [True],\n",
    "                      'n_estimators': [80],\n",
    "                      'random_state': [1]}\n",
    "\n",
    "\n",
    "rf_gridsearch_2 = GridSearchCV(RandomForestClassifier(),\n",
    "                             random_forest_grid_2,\n",
    "                             n_jobs=-1,\n",
    "                             verbose=True,\n",
    "                             scoring='neg_mean_squared_error')\n",
    "\n",
    "rf_gridsearch_2.fit(X, y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=4, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=80, n_jobs=None,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "r2 = RandomForestClassifier(max_depth=None,max_features='sqrt',min_samples_split=2,\n",
    "                           min_samples_leaf=4,bootstrap=True,n_estimators=80,\n",
    "                            random_state=1)\n",
    "r2.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7705"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_test = r2.predict(X_test)\n",
    "# accuracy of test set\n",
    "r2.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.13941668 0.14999281 0.0553585  0.07976889 0.11667072 0.10647196\n",
      " 0.07019816 0.04937712 0.14067693 0.09206822]\n"
     ]
    }
   ],
   "source": [
    "print(r2.feature_importances_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7faa7dc1d1d0>]"
      ]
     },
     "execution_count": 180,
     "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": [
    "plt.plot(range(1,11), r2.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>city</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>luxury_car_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "      <th>signup_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.48</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.81</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.95</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.92</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  city  \\\n",
       "0      2.48                   5.0              5.000000        1.0     3   \n",
       "1     10.81                   5.0              5.000000        1.0     3   \n",
       "2     12.95                   5.0              5.000000        1.0     2   \n",
       "3      3.92                   5.0              4.601011        1.0     3   \n",
       "4      1.46                   5.0              4.500000        1.0     2   \n",
       "\n",
       "   surge_pct  trips_in_first_30_days  luxury_car_user  weekday_pct  \\\n",
       "0        0.0                       2                1        100.0   \n",
       "1        0.0                       3                1        100.0   \n",
       "2        0.0                       1                1        100.0   \n",
       "3        0.0                       0                0          0.0   \n",
       "4        0.0                       2                0        100.0   \n",
       "\n",
       "   signup_period  \n",
       "0            176  \n",
       "1            176  \n",
       "2            163  \n",
       "3            173  \n",
       "4            175  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
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}