churn_case_study_lu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, \"./data/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
    "\n",
    "import roc\n",
    "from roc import plot_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_raw = pd.read_csv(\"./data/churn.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>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>3.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>15.4</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>46.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.36</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.14</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.13</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.19</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-03-15</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>82.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  \\\n",
       "0      3.67                   5.0                   4.7       1.10   \n",
       "1      8.26                   5.0                   5.0       1.00   \n",
       "2      0.77                   5.0                   4.3       1.00   \n",
       "3      2.36                   4.9                   4.6       1.14   \n",
       "4      3.13                   4.9                   4.4       1.19   \n",
       "\n",
       "             city last_trip_date    phone signup_date  surge_pct  \\\n",
       "0  King's Landing     2014-06-17   iPhone  2014-01-25       15.4   \n",
       "1         Astapor     2014-05-05  Android  2014-01-29        0.0   \n",
       "2         Astapor     2014-01-07   iPhone  2014-01-06        0.0   \n",
       "3  King's Landing     2014-06-29   iPhone  2014-01-10       20.0   \n",
       "4      Winterfell     2014-03-15  Android  2014-01-27       11.8   \n",
       "\n",
       "   trips_in_first_30_days  luxury_car_user  weekday_pct  \n",
       "0                       4             True         46.2  \n",
       "1                       0            False         50.0  \n",
       "2                       3            False        100.0  \n",
       "3                       9             True         80.0  \n",
       "4                      14            False         82.4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 12 columns):\n",
      "avg_dist                  50000 non-null float64\n",
      "avg_rating_by_driver      49799 non-null float64\n",
      "avg_rating_of_driver      41878 non-null float64\n",
      "avg_surge                 50000 non-null float64\n",
      "city                      50000 non-null object\n",
      "last_trip_date            50000 non-null object\n",
      "phone                     49604 non-null object\n",
      "signup_date               50000 non-null object\n",
      "surge_pct                 50000 non-null float64\n",
      "trips_in_first_30_days    50000 non-null int64\n",
      "luxury_car_user           50000 non-null bool\n",
      "weekday_pct               50000 non-null float64\n",
      "dtypes: bool(1), float64(6), int64(1), object(4)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "churn_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "avg_dist                  False\n",
       "avg_rating_by_driver       True\n",
       "avg_rating_of_driver       True\n",
       "avg_surge                 False\n",
       "city                      False\n",
       "last_trip_date            False\n",
       "phone                      True\n",
       "signup_date               False\n",
       "surge_pct                 False\n",
       "trips_in_first_30_days    False\n",
       "luxury_car_user           False\n",
       "weekday_pct               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_raw.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['avg_dist', 'avg_rating_by_driver', 'avg_rating_of_driver', 'avg_surge',\n",
       "       'city', 'last_trip_date', 'phone', 'signup_date', 'surge_pct',\n",
       "       'trips_in_first_30_days', 'luxury_car_user', 'weekday_pct'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_raw.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find y - 'active_flag',  active(=1) or not(=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>15.4</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>46.2</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.36</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.14</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.13</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.19</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-03-15</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>82.4</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  \\\n",
       "0      3.67                   5.0                   4.7       1.10   \n",
       "1      8.26                   5.0                   5.0       1.00   \n",
       "2      0.77                   5.0                   4.3       1.00   \n",
       "3      2.36                   4.9                   4.6       1.14   \n",
       "4      3.13                   4.9                   4.4       1.19   \n",
       "\n",
       "             city last_trip_date    phone signup_date  surge_pct  \\\n",
       "0  King's Landing     2014-06-17   iPhone  2014-01-25       15.4   \n",
       "1         Astapor     2014-05-05  Android  2014-01-29        0.0   \n",
       "2         Astapor     2014-01-07   iPhone  2014-01-06        0.0   \n",
       "3  King's Landing     2014-06-29   iPhone  2014-01-10       20.0   \n",
       "4      Winterfell     2014-03-15  Android  2014-01-27       11.8   \n",
       "\n",
       "   trips_in_first_30_days  luxury_car_user  weekday_pct pulled_data  \\\n",
       "0                       4             True         46.2  2014-07-01   \n",
       "1                       0            False         50.0  2014-07-01   \n",
       "2                       3            False        100.0  2014-07-01   \n",
       "3                       9             True         80.0  2014-07-01   \n",
       "4                      14            False         82.4  2014-07-01   \n",
       "\n",
       "   preceding_data  active_flag  \n",
       "0              14            1  \n",
       "1              57            0  \n",
       "2             175            0  \n",
       "3               2            1  \n",
       "4             108            0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "def active_flag(df, last_trip_date_columns):\n",
    "    # calucate preceding data\n",
    "    df['pulled_data'] = datetime.strptime('2014-7-1', \"%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)\n",
    "\n",
    "# use the function\n",
    "active_flag(churn_raw,'last_trip_date')\n",
    "churn_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 15 columns):\n",
      "avg_dist                  50000 non-null float64\n",
      "avg_rating_by_driver      49799 non-null float64\n",
      "avg_rating_of_driver      41878 non-null float64\n",
      "avg_surge                 50000 non-null float64\n",
      "city                      50000 non-null object\n",
      "last_trip_date            50000 non-null object\n",
      "phone                     49604 non-null object\n",
      "signup_date               50000 non-null object\n",
      "surge_pct                 50000 non-null float64\n",
      "trips_in_first_30_days    50000 non-null int64\n",
      "luxury_car_user           50000 non-null bool\n",
      "weekday_pct               50000 non-null float64\n",
      "pulled_data               50000 non-null datetime64[ns]\n",
      "preceding_data            50000 non-null int64\n",
      "active_flag               50000 non-null int64\n",
      "dtypes: bool(1), datetime64[ns](1), float64(6), int64(3), object(4)\n",
      "memory usage: 5.4+ MB\n"
     ]
    }
   ],
   "source": [
    "churn=churn_raw.copy()\n",
    "churn.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>50000.000000</td>\n",
       "      <td>49799.000000</td>\n",
       "      <td>41878.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.796827</td>\n",
       "      <td>4.778158</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.074764</td>\n",
       "      <td>8.849536</td>\n",
       "      <td>2.278200</td>\n",
       "      <td>60.926084</td>\n",
       "      <td>72.665220</td>\n",
       "      <td>0.376080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.707357</td>\n",
       "      <td>0.446652</td>\n",
       "      <td>0.617338</td>\n",
       "      <td>0.222336</td>\n",
       "      <td>19.958811</td>\n",
       "      <td>3.792684</td>\n",
       "      <td>37.081503</td>\n",
       "      <td>61.718985</td>\n",
       "      <td>0.484405</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",
       "      <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>13.000000</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>54.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.940000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>137.000000</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>181.000000</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  50000.000000          49799.000000          41878.000000  50000.000000   \n",
       "mean       5.796827              4.778158              4.601559      1.074764   \n",
       "std        5.707357              0.446652              0.617338      0.222336   \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%        6.940000              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  50000.000000            50000.000000  50000.000000    50000.000000   \n",
       "mean       8.849536                2.278200     60.926084       72.665220   \n",
       "std       19.958811                3.792684     37.081503       61.718985   \n",
       "min        0.000000                0.000000      0.000000        0.000000   \n",
       "25%        0.000000                0.000000     33.300000       13.000000   \n",
       "50%        0.000000                1.000000     66.700000       54.000000   \n",
       "75%        8.600000                3.000000    100.000000      137.000000   \n",
       "max      100.000000              125.000000    100.000000      181.000000   \n",
       "\n",
       "        active_flag  \n",
       "count  50000.000000  \n",
       "mean       0.376080  \n",
       "std        0.484405  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        1.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>15.4</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>46.2</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.36</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.14</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.13</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.19</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-03-15</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>82.4</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  \\\n",
       "0      3.67                   5.0                   4.7       1.10   \n",
       "1      8.26                   5.0                   5.0       1.00   \n",
       "2      0.77                   5.0                   4.3       1.00   \n",
       "3      2.36                   4.9                   4.6       1.14   \n",
       "4      3.13                   4.9                   4.4       1.19   \n",
       "\n",
       "             city last_trip_date    phone signup_date  surge_pct  \\\n",
       "0  King's Landing     2014-06-17   iPhone  2014-01-25       15.4   \n",
       "1         Astapor     2014-05-05  Android  2014-01-29        0.0   \n",
       "2         Astapor     2014-01-07   iPhone  2014-01-06        0.0   \n",
       "3  King's Landing     2014-06-29   iPhone  2014-01-10       20.0   \n",
       "4      Winterfell     2014-03-15  Android  2014-01-27       11.8   \n",
       "\n",
       "   trips_in_first_30_days  luxury_car_user  weekday_pct pulled_data  \\\n",
       "0                       4             True         46.2  2014-07-01   \n",
       "1                       0            False         50.0  2014-07-01   \n",
       "2                       3            False        100.0  2014-07-01   \n",
       "3                       9             True         80.0  2014-07-01   \n",
       "4                      14            False         82.4  2014-07-01   \n",
       "\n",
       "   preceding_data  active_flag  \n",
       "0              14            1  \n",
       "1              57            0  \n",
       "2             175            0  \n",
       "3               2            1  \n",
       "4             108            0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7375868c18>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "sns.set_palette('muted')\n",
    "\n",
    "fig, axs = plt.subplots(5, 1, figsize = (20,15))\n",
    "sns.countplot(x = 'city',data = churn, ax=axs[0])\n",
    "sns.countplot(x = 'phone', data = churn, ax=axs[1])\n",
    "sns.countplot(x = 'luxury_car_user',data = churn, ax=axs[2])\n",
    "sns.countplot(x = 'avg_rating_of_driver',data = churn, ax=axs[3])\n",
    "sns.countplot(x = 'avg_rating_by_driver',data = churn, ax=axs[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Winterfell        23336\n",
       " Astapor           16534\n",
       " King's Landing    10130\n",
       " Name: city, dtype: int64, 50000)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn.city.value_counts(), churn.city.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    31146\n",
       "True     18854\n",
       "Name: luxury_car_user, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn.luxury_car_user.value_counts() # 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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></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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>active_flag</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Astapor</th>\n",
       "      <th>0</th>\n",
       "      <td>12306</td>\n",
       "      <td>12251</td>\n",
       "      <td>9790</td>\n",
       "      <td>12306</td>\n",
       "      <td>12306</td>\n",
       "      <td>12215</td>\n",
       "      <td>12306</td>\n",
       "      <td>12306</td>\n",
       "      <td>12306</td>\n",
       "      <td>12306</td>\n",
       "      <td>12306</td>\n",
       "      <td>12306</td>\n",
       "      <td>12306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4228</td>\n",
       "      <td>4225</td>\n",
       "      <td>3857</td>\n",
       "      <td>4228</td>\n",
       "      <td>4228</td>\n",
       "      <td>4198</td>\n",
       "      <td>4228</td>\n",
       "      <td>4228</td>\n",
       "      <td>4228</td>\n",
       "      <td>4228</td>\n",
       "      <td>4228</td>\n",
       "      <td>4228</td>\n",
       "      <td>4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">King's Landing</th>\n",
       "      <th>0</th>\n",
       "      <td>3767</td>\n",
       "      <td>3751</td>\n",
       "      <td>3066</td>\n",
       "      <td>3767</td>\n",
       "      <td>3767</td>\n",
       "      <td>3741</td>\n",
       "      <td>3767</td>\n",
       "      <td>3767</td>\n",
       "      <td>3767</td>\n",
       "      <td>3767</td>\n",
       "      <td>3767</td>\n",
       "      <td>3767</td>\n",
       "      <td>3767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6363</td>\n",
       "      <td>6339</td>\n",
       "      <td>5781</td>\n",
       "      <td>6363</td>\n",
       "      <td>6363</td>\n",
       "      <td>6325</td>\n",
       "      <td>6363</td>\n",
       "      <td>6363</td>\n",
       "      <td>6363</td>\n",
       "      <td>6363</td>\n",
       "      <td>6363</td>\n",
       "      <td>6363</td>\n",
       "      <td>6363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Winterfell</th>\n",
       "      <th>0</th>\n",
       "      <td>15123</td>\n",
       "      <td>15029</td>\n",
       "      <td>11850</td>\n",
       "      <td>15123</td>\n",
       "      <td>15123</td>\n",
       "      <td>14977</td>\n",
       "      <td>15123</td>\n",
       "      <td>15123</td>\n",
       "      <td>15123</td>\n",
       "      <td>15123</td>\n",
       "      <td>15123</td>\n",
       "      <td>15123</td>\n",
       "      <td>15123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8213</td>\n",
       "      <td>8204</td>\n",
       "      <td>7534</td>\n",
       "      <td>8213</td>\n",
       "      <td>8213</td>\n",
       "      <td>8148</td>\n",
       "      <td>8213</td>\n",
       "      <td>8213</td>\n",
       "      <td>8213</td>\n",
       "      <td>8213</td>\n",
       "      <td>8213</td>\n",
       "      <td>8213</td>\n",
       "      <td>8213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            avg_dist  avg_rating_by_driver  \\\n",
       "city           active_flag                                   \n",
       "Astapor        0               12306                 12251   \n",
       "               1                4228                  4225   \n",
       "King's Landing 0                3767                  3751   \n",
       "               1                6363                  6339   \n",
       "Winterfell     0               15123                 15029   \n",
       "               1                8213                  8204   \n",
       "\n",
       "                            avg_rating_of_driver  avg_surge  last_trip_date  \\\n",
       "city           active_flag                                                    \n",
       "Astapor        0                            9790      12306           12306   \n",
       "               1                            3857       4228            4228   \n",
       "King's Landing 0                            3066       3767            3767   \n",
       "               1                            5781       6363            6363   \n",
       "Winterfell     0                           11850      15123           15123   \n",
       "               1                            7534       8213            8213   \n",
       "\n",
       "                            phone  signup_date  surge_pct  \\\n",
       "city           active_flag                                  \n",
       "Astapor        0            12215        12306      12306   \n",
       "               1             4198         4228       4228   \n",
       "King's Landing 0             3741         3767       3767   \n",
       "               1             6325         6363       6363   \n",
       "Winterfell     0            14977        15123      15123   \n",
       "               1             8148         8213       8213   \n",
       "\n",
       "                            trips_in_first_30_days  luxury_car_user  \\\n",
       "city           active_flag                                            \n",
       "Astapor        0                             12306            12306   \n",
       "               1                              4228             4228   \n",
       "King's Landing 0                              3767             3767   \n",
       "               1                              6363             6363   \n",
       "Winterfell     0                             15123            15123   \n",
       "               1                              8213             8213   \n",
       "\n",
       "                            weekday_pct  pulled_data  preceding_data  \n",
       "city           active_flag                                            \n",
       "Astapor        0                  12306        12306           12306  \n",
       "               1                   4228         4228            4228  \n",
       "King's Landing 0                   3767         3767            3767  \n",
       "               1                   6363         6363            6363  \n",
       "Winterfell     0                  15123        15123           15123  \n",
       "               1                   8213         8213            8213  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn.groupby(['city','active_flag']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\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>weekday_pct</th>\n",
       "      <th>pulled_data</th>\n",
       "      <th>preceding_data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>luxury_car_user</th>\n",
       "      <th>active_flag</th>\n",
       "      <th></th>\n",
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       "      <td>9307</td>\n",
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       "      <td>9497</td>\n",
       "      <td>9497</td>\n",
       "      <td>9497</td>\n",
       "      <td>9410</td>\n",
       "      <td>9497</td>\n",
       "      <td>9497</td>\n",
       "      <td>9497</td>\n",
       "      <td>9497</td>\n",
       "      <td>9497</td>\n",
       "      <td>9497</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                             avg_dist  avg_rating_by_driver  \\\n",
       "luxury_car_user active_flag                                   \n",
       "False           0               21839                 21696   \n",
       "                1                9307                  9283   \n",
       "True            0                9357                  9335   \n",
       "                1                9497                  9485   \n",
       "\n",
       "                             avg_rating_of_driver  avg_surge   city  \\\n",
       "luxury_car_user active_flag                                           \n",
       "False           0                           17371      21839  21839   \n",
       "                1                            8388       9307   9307   \n",
       "True            0                            7335       9357   9357   \n",
       "                1                            8784       9497   9497   \n",
       "\n",
       "                             last_trip_date  phone  signup_date  surge_pct  \\\n",
       "luxury_car_user active_flag                                                  \n",
       "False           0                     21839  21731        21839      21839   \n",
       "                1                      9307   9261         9307       9307   \n",
       "True            0                      9357   9202         9357       9357   \n",
       "                1                      9497   9410         9497       9497   \n",
       "\n",
       "                             trips_in_first_30_days  weekday_pct  pulled_data  \\\n",
       "luxury_car_user active_flag                                                     \n",
       "False           0                             21839        21839        21839   \n",
       "                1                              9307         9307         9307   \n",
       "True            0                              9357         9357         9357   \n",
       "                1                              9497         9497         9497   \n",
       "\n",
       "                             preceding_data  \n",
       "luxury_car_user active_flag                  \n",
       "False           0                     21839  \n",
       "                1                      9307  \n",
       "True            0                      9357  \n",
       "                1                      9497  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn.groupby(['luxury_car_user','active_flag']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.scatter(churn.luxury_car_user, churn.active_flag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>3.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>iPhone</td>\n",
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       "      <th>1</th>\n",
       "      <td>8.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>Android</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2014-07-01</td>\n",
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       "      <td>176</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.36</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.14</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.13</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.19</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-03-15</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>82.4</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  \\\n",
       "0      3.67                   5.0                   4.7       1.10   \n",
       "1      8.26                   5.0                   5.0       1.00   \n",
       "2      0.77                   5.0                   4.3       1.00   \n",
       "3      2.36                   4.9                   4.6       1.14   \n",
       "4      3.13                   4.9                   4.4       1.19   \n",
       "\n",
       "             city last_trip_date    phone signup_date  surge_pct  \\\n",
       "0  King's Landing     2014-06-17   iPhone  2014-01-25       15.4   \n",
       "1         Astapor     2014-05-05  Android  2014-01-29        0.0   \n",
       "2         Astapor     2014-01-07   iPhone  2014-01-06        0.0   \n",
       "3  King's Landing     2014-06-29   iPhone  2014-01-10       20.0   \n",
       "4      Winterfell     2014-03-15  Android  2014-01-27       11.8   \n",
       "\n",
       "   trips_in_first_30_days  luxury_car_user  weekday_pct pulled_data  \\\n",
       "0                       4             True         46.2  2014-07-01   \n",
       "1                       0            False         50.0  2014-07-01   \n",
       "2                       3            False        100.0  2014-07-01   \n",
       "3                       9             True         80.0  2014-07-01   \n",
       "4                      14            False         82.4  2014-07-01   \n",
       "\n",
       "   preceding_data  active_flag  signup_period  \n",
       "0              14            1            157  \n",
       "1              57            0            153  \n",
       "2             175            0            176  \n",
       "3               2            1            172  \n",
       "4             108            0            155  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creat signup_period - how long be user\n",
    "\n",
    "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",
    "# use the function\n",
    "signup_period(churn_raw,'signup_date')\n",
    "churn_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(151, 181)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(churn_raw.signup_period), max(churn_raw.signup_period)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1. drop unimportant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop drop columns\n",
    "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']\n",
    "churn_raw = drop_data(churn_raw,drop_columns)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "2. classifier 'city'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "#King's Landing -1 , Astapor -2, Winterfell-3\n",
    "churn_raw.replace(\"King's Landing\", 1, inplace=True)\n",
    "churn_raw.replace(\"Astapor\", 2, inplace=True)\n",
    "churn_raw.replace(\"Winterfell\", 3, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>active_flag</th>\n",
       "      <th>signup_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "      <td>15.4</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
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       "      <td>14</td>\n",
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       "      <td>157</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.36</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.13</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.19</td>\n",
       "      <td>3</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>82.4</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>155</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      3.67                   5.0                   4.7       1.10     1   \n",
       "1      8.26                   5.0                   5.0       1.00     2   \n",
       "2      0.77                   5.0                   4.3       1.00     2   \n",
       "3      2.36                   4.9                   4.6       1.14     1   \n",
       "4      3.13                   4.9                   4.4       1.19     3   \n",
       "\n",
       "   surge_pct  trips_in_first_30_days  luxury_car_user  weekday_pct  \\\n",
       "0       15.4                       4             True         46.2   \n",
       "1        0.0                       0            False         50.0   \n",
       "2        0.0                       3            False        100.0   \n",
       "3       20.0                       9             True         80.0   \n",
       "4       11.8                      14            False         82.4   \n",
       "\n",
       "   preceding_data  active_flag  signup_period  \n",
       "0              14            1            157  \n",
       "1              57            0            153  \n",
       "2             175            0            176  \n",
       "3               2            1            172  \n",
       "4             108            0            155  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final cleaned dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 12 columns):\n",
      "avg_dist                  50000 non-null float64\n",
      "avg_rating_by_driver      49799 non-null float64\n",
      "avg_rating_of_driver      41878 non-null float64\n",
      "avg_surge                 50000 non-null float64\n",
      "city                      50000 non-null int64\n",
      "surge_pct                 50000 non-null float64\n",
      "trips_in_first_30_days    50000 non-null int64\n",
      "luxury_car_user           50000 non-null bool\n",
      "weekday_pct               50000 non-null float64\n",
      "preceding_data            50000 non-null int64\n",
      "active_flag               50000 non-null int64\n",
      "signup_period             50000 non-null int64\n",
      "dtypes: bool(1), float64(6), int64(5)\n",
      "memory usage: 4.2 MB\n"
     ]
    }
   ],
   "source": [
    "# Final cleaned dataset\n",
    "churn = churn_raw.copy()\n",
    "churn.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
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       "      <td>5.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.77</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.36</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.13</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.19</td>\n",
       "      <td>3</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>82.4</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>155</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      3.67                   5.0                   4.7       1.10     1   \n",
       "1      8.26                   5.0                   5.0       1.00     2   \n",
       "2      0.77                   5.0                   4.3       1.00     2   \n",
       "3      2.36                   4.9                   4.6       1.14     1   \n",
       "4      3.13                   4.9                   4.4       1.19     3   \n",
       "\n",
       "   surge_pct  trips_in_first_30_days  luxury_car_user  weekday_pct  \\\n",
       "0       15.4                       4             True         46.2   \n",
       "1        0.0                       0            False         50.0   \n",
       "2        0.0                       3            False        100.0   \n",
       "3       20.0                       9             True         80.0   \n",
       "4       11.8                      14            False         82.4   \n",
       "\n",
       "   preceding_data  active_flag  signup_period  \n",
       "0              14            1            157  \n",
       "1              57            0            153  \n",
       "2             175            0            176  \n",
       "3               2            1            172  \n",
       "4             108            0            155  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# True or False to 1 or 0\n",
    "churn= churn.fillna(churn.mean())\n",
    "churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "avg_dist                  False\n",
       "avg_rating_by_driver      False\n",
       "avg_rating_of_driver      False\n",
       "avg_surge                 False\n",
       "city                      False\n",
       "surge_pct                 False\n",
       "trips_in_first_30_days    False\n",
       "luxury_car_user           False\n",
       "weekday_pct               False\n",
       "preceding_data            False\n",
       "active_flag               False\n",
       "signup_period             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.77</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.00</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>100.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>176</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.36</td>\n",
       "      <td>4.9</td>\n",
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       "      <td>1.14</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>True</td>\n",
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       "      <td>1</td>\n",
       "      <td>172</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.13</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>1.19</td>\n",
       "      <td>3</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>82.4</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.56</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.95</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.36</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.37</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177</td>\n",
       "      <td>0</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.28</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.81</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>26.01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.86</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.38</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>95.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.83</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1</td>\n",
       "      <td>30.8</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>80.8</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12.08</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>66.7</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.53</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.31</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11.47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7.74</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.10</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>36.4</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>14.48</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.66</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.05</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.97</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11.25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>155</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>49970</th>\n",
       "      <td>5.62</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49971</th>\n",
       "      <td>4.69</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49972</th>\n",
       "      <td>4.60</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49973</th>\n",
       "      <td>4.07</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49974</th>\n",
       "      <td>4.63</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49975</th>\n",
       "      <td>2.18</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "      <td>91.8</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49976</th>\n",
       "      <td>2.39</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49977</th>\n",
       "      <td>8.71</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>4</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49978</th>\n",
       "      <td>6.02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49979</th>\n",
       "      <td>3.81</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>50.0</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49980</th>\n",
       "      <td>14.42</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49981</th>\n",
       "      <td>5.49</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49982</th>\n",
       "      <td>15.23</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49983</th>\n",
       "      <td>30.39</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49984</th>\n",
       "      <td>3.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49985</th>\n",
       "      <td>1.38</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49986</th>\n",
       "      <td>0.52</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49987</th>\n",
       "      <td>4.24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>80.0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49988</th>\n",
       "      <td>2.53</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49989</th>\n",
       "      <td>0.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49990</th>\n",
       "      <td>3.38</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.08</td>\n",
       "      <td>2</td>\n",
       "      <td>33.3</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>33.3</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49991</th>\n",
       "      <td>1.06</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49992</th>\n",
       "      <td>7.58</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49993</th>\n",
       "      <td>2.53</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>1.11</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>55.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49994</th>\n",
       "      <td>2.25</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2</td>\n",
       "      <td>37.5</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>5.63</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>0.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>3.86</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>4.58</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>167</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>3.49</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.601559</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  city  \\\n",
       "0          3.67                   5.0              4.700000       1.10     1   \n",
       "1          8.26                   5.0              5.000000       1.00     2   \n",
       "2          0.77                   5.0              4.300000       1.00     2   \n",
       "3          2.36                   4.9              4.600000       1.14     1   \n",
       "4          3.13                   4.9              4.400000       1.19     3   \n",
       "5         10.56                   5.0              3.500000       1.00     3   \n",
       "6          3.95                   4.0              4.601559       1.00     2   \n",
       "7          2.04                   5.0              5.000000       1.00     3   \n",
       "8          4.36                   5.0              4.500000       1.00     3   \n",
       "9          2.37                   5.0              4.601559       1.00     3   \n",
       "10         4.28                   4.9              5.000000       1.00     3   \n",
       "11         3.81                   5.0              4.000000       1.00     3   \n",
       "12        20.29                   5.0              4.601559       1.00     3   \n",
       "13         3.04                   5.0              4.000000       1.38     1   \n",
       "14        26.01                   5.0              4.601559       1.00     2   \n",
       "15        13.20                   5.0              4.601559       1.00     3   \n",
       "16        10.86                   3.0              5.000000       1.00     1   \n",
       "17         2.38                   4.8              3.900000       1.00     1   \n",
       "18         6.83                   4.8              4.600000       1.21     1   \n",
       "19        12.08                   5.0              5.000000       1.17     1   \n",
       "20         2.53                   3.0              4.601559       1.00     3   \n",
       "21         3.31                   5.0              4.601559       1.00     2   \n",
       "22        11.47                   5.0              4.601559       1.00     3   \n",
       "23         7.74                   5.0              5.000000       1.00     1   \n",
       "24         2.10                   4.7              4.500000       1.02     2   \n",
       "25        14.48                   5.0              4.601559       1.00     3   \n",
       "26         1.66                   5.0              4.000000       1.00     2   \n",
       "27         3.05                   5.0              5.000000       1.05     3   \n",
       "28         5.97                   5.0              4.601559       1.50     3   \n",
       "29        11.25                   5.0              4.000000       1.00     2   \n",
       "...         ...                   ...                   ...        ...   ...   \n",
       "49970      5.62                   5.0              4.300000       1.00     3   \n",
       "49971      4.69                   5.0              4.601559       1.00     1   \n",
       "49972      4.60                   3.5              4.000000       1.25     3   \n",
       "49973      4.07                   5.0              4.000000       1.00     2   \n",
       "49974      4.63                   5.0              5.000000       2.00     3   \n",
       "49975      2.18                   4.8              3.900000       1.03     3   \n",
       "49976      2.39                   4.0              2.000000       1.00     1   \n",
       "49977      8.71                   5.0              5.000000       1.00     2   \n",
       "49978      6.02                   5.0              4.500000       1.00     3   \n",
       "49979      3.81                   4.8              4.000000       1.00     3   \n",
       "49980     14.42                   5.0              4.601559       1.00     2   \n",
       "49981      5.49                   5.0              4.000000       1.00     2   \n",
       "49982     15.23                   5.0              5.000000       1.00     1   \n",
       "49983     30.39                   5.0              1.000000       1.00     3   \n",
       "49984      3.50                   5.0              4.601559       1.00     3   \n",
       "49985      1.38                   5.0              4.601559       1.00     2   \n",
       "49986      0.52                   5.0              4.000000       1.00     3   \n",
       "49987      4.24                   4.0              4.600000       1.00     3   \n",
       "49988      2.53                   5.0              4.500000       1.00     1   \n",
       "49989      0.00                   5.0              4.601559       1.00     3   \n",
       "49990      3.38                   5.0              4.700000       1.08     2   \n",
       "49991      1.06                   5.0              5.000000       1.25     3   \n",
       "49992      7.58                   5.0              1.000000       1.00     1   \n",
       "49993      2.53                   4.7              4.800000       1.11     2   \n",
       "49994      2.25                   4.5              4.600000       1.44     2   \n",
       "49995      5.63                   4.2              5.000000       1.00     1   \n",
       "49996      0.00                   4.0              4.601559       1.00     2   \n",
       "49997      3.86                   5.0              5.000000       1.00     3   \n",
       "49998      4.58                   3.5              3.000000       1.00     2   \n",
       "49999      3.49                   5.0              4.601559       1.00     2   \n",
       "\n",
       "       surge_pct  trips_in_first_30_days  luxury_car_user  weekday_pct  \\\n",
       "0           15.4                       4             True         46.2   \n",
       "1            0.0                       0            False         50.0   \n",
       "2            0.0                       3            False        100.0   \n",
       "3           20.0                       9             True         80.0   \n",
       "4           11.8                      14            False         82.4   \n",
       "5            0.0                       2             True        100.0   \n",
       "6            0.0                       1            False        100.0   \n",
       "7            0.0                       2            False        100.0   \n",
       "8            0.0                       2            False        100.0   \n",
       "9            0.0                       1            False          0.0   \n",
       "10           0.0                       1             True        100.0   \n",
       "11           0.0                       3            False        100.0   \n",
       "12           0.0                       1            False        100.0   \n",
       "13          50.0                       0            False         50.0   \n",
       "14           0.0                       1            False        100.0   \n",
       "15           0.0                       1             True        100.0   \n",
       "16           0.0                       1             True         50.0   \n",
       "17           0.0                       0             True         95.2   \n",
       "18          30.8                       6             True         80.8   \n",
       "19          33.3                       0            False         66.7   \n",
       "20           0.0                       1            False         50.0   \n",
       "21           0.0                       1            False        100.0   \n",
       "22           0.0                       0            False        100.0   \n",
       "23           0.0                       0             True        100.0   \n",
       "24           9.1                       4            False         36.4   \n",
       "25           0.0                       1             True        100.0   \n",
       "26           0.0                       1            False        100.0   \n",
       "27          20.0                       3            False        100.0   \n",
       "28         100.0                       0            False          0.0   \n",
       "29           0.0                       1            False        100.0   \n",
       "...          ...                     ...              ...          ...   \n",
       "49970        0.0                       2            False          0.0   \n",
       "49971        0.0                       0             True        100.0   \n",
       "49972       50.0                       1            False          0.0   \n",
       "49973        0.0                       2             True         25.0   \n",
       "49974      100.0                       1            False        100.0   \n",
       "49975        4.1                      11            False         91.8   \n",
       "49976        0.0                       0            False        100.0   \n",
       "49977        0.0                       4            False        100.0   \n",
       "49978        0.0                       1            False         50.0   \n",
       "49979        0.0                       0             True         50.0   \n",
       "49980        0.0                       1            False        100.0   \n",
       "49981        0.0                       0             True          0.0   \n",
       "49982        0.0                       0            False        100.0   \n",
       "49983        0.0                       1            False        100.0   \n",
       "49984        0.0                       1            False          0.0   \n",
       "49985        0.0                       1            False        100.0   \n",
       "49986        0.0                       0            False        100.0   \n",
       "49987        0.0                       3            False         80.0   \n",
       "49988        0.0                       1            False         50.0   \n",
       "49989        0.0                       1            False        100.0   \n",
       "49990       33.3                       1             True         33.3   \n",
       "49991      100.0                       0            False          0.0   \n",
       "49992        0.0                       1            False          0.0   \n",
       "49993       11.1                       3             True         55.6   \n",
       "49994       37.5                       1            False         25.0   \n",
       "49995        0.0                       0            False        100.0   \n",
       "49996        0.0                       1            False          0.0   \n",
       "49997        0.0                       0             True        100.0   \n",
       "49998        0.0                       2            False        100.0   \n",
       "49999        0.0                       0            False          0.0   \n",
       "\n",
       "       preceding_data  active_flag  signup_period  \n",
       "0                  14            1            157  \n",
       "1                  57            0            153  \n",
       "2                 175            0            176  \n",
       "3                   2            1            172  \n",
       "4                 108            0            155  \n",
       "5                  25            1            173  \n",
       "6                 157            0            158  \n",
       "7                 153            0            154  \n",
       "8                 150            0            161  \n",
       "9                 177            0            179  \n",
       "10                 32            0            169  \n",
       "11                172            0            176  \n",
       "12                111            0            167  \n",
       "13                 23            1            153  \n",
       "14                154            0            155  \n",
       "15                166            0            167  \n",
       "16                  3            1            171  \n",
       "17                  0            1            170  \n",
       "18                  7            1            154  \n",
       "19                 28            1            180  \n",
       "20                107            0            151  \n",
       "21                 63            0            173  \n",
       "22                 60            0            158  \n",
       "23                 18            1            161  \n",
       "24                 44            0            163  \n",
       "25                179            0            180  \n",
       "26                160            0            166  \n",
       "27                118            0            163  \n",
       "28                 23            1            163  \n",
       "29                124            0            155  \n",
       "...               ...          ...            ...  \n",
       "49970              78            0            166  \n",
       "49971              17            1            170  \n",
       "49972               2            1            170  \n",
       "49973               3            1            181  \n",
       "49974             164            0            177  \n",
       "49975              13            1            171  \n",
       "49976               0            1            161  \n",
       "49977             173            0            176  \n",
       "49978              59            0            165  \n",
       "49979              85            0            158  \n",
       "49980             162            0            177  \n",
       "49981             107            0            156  \n",
       "49982              68            0            161  \n",
       "49983             125            0            155  \n",
       "49984             156            0            156  \n",
       "49985             126            0            156  \n",
       "49986              39            0            161  \n",
       "49987              31            0            155  \n",
       "49988              17            1            162  \n",
       "49989             151            0            158  \n",
       "49990              44            0            169  \n",
       "49991               2            1            174  \n",
       "49992             163            0            164  \n",
       "49993               0            1            179  \n",
       "49994              31            0            179  \n",
       "49995              26            1            157  \n",
       "49996             157            0            158  \n",
       "49997              40            0            151  \n",
       "49998             167            0            168  \n",
       "49999              72            0            164  \n",
       "\n",
       "[50000 rows x 12 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "data = churn.copy()\n",
    "y = data.active_flag\n",
    "X = data.replace(True, 1)\n",
    "X = data.replace(False, 0)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X.luxury_car_user = X.luxury_car_user.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= X.drop('preceding_data',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop('active_flag', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try diff model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier as RF\n",
    "from sklearn.neighbors import KNeighborsClassifier as KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictive model establishment\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "def run_cv(X_train, X_test, y_train, y_test, clf_class,**kwargs):\n",
    "    clf=clf_class(**kwargs)\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy evaluation\n",
    "def accuracy(y_true,y_pred):\n",
    "    return np.mean(y_true==y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support vector machines:\n",
      "0.741\n",
      "Random forest:\n",
      "0.745\n",
      "K-nearest-neighbors\n",
      "0.725\n"
     ]
    }
   ],
   "source": [
    "print(\"Support vector machines:\")\n",
    "print(\"%.3f\"%accuracy(y_test,run_cv(X_train, X_test, y_train, y_test, SVC)))\n",
    "print(\"Random forest:\")\n",
    "print(\"%.3f\"%accuracy(y_test,run_cv(X_train, X_test, y_train, y_test, RF)))\n",
    "print(\"K-nearest-neighbors\")\n",
    "print(\"%.3f\"%accuracy(y_test,run_cv(X_train, X_test, y_train, y_test, KNN)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TFPN\n",
    "def TFPN(y_true,y_pred):\n",
    "    Pall=y[y_true==1]\n",
    "    Nall=y[y_true==0]\n",
    "    TP=pd.Series(np.sum((y_true==1)&(y_pred==1))/len(Pall))\n",
    "    FP=pd.Series(np.sum((y_true==1)&(y_pred==0))/len(Pall))\n",
    "    TN=pd.Series(np.sum((y_true==0)&(y_pred==0))/len(Nall))\n",
    "    FN=pd.Series(np.sum((y_true==0)&(y_pred==1))/len(Nall))\n",
    "    AC=pd.Series(np.mean(y_true==y_pred))\n",
    "    tfpn=pd.concat([TP,FP,TN,FN,AC],axis=1)\n",
    "    tfpn.columns=['TP','FP','TN','FN','AC']\n",
    "    return tfpn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Support vector machines:\")\n",
    "# print(TFPN(y_test,run_cv(X_train, X_test, y_train, y_test, SVC)))\n",
    "# print(\"Random forest:\")\n",
    "# print(TFPN(y_test,run_cv(X_train, X_test, y_train, y_test, RF)))\n",
    "# print(\"K-nearest-neighbors\")\n",
    "# print(TFPN(y_test,run_cv(X_train, X_test, y_train, y_test, KNN)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Threshold selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plot_roc(X, y, RandomForestClassifier, \"Random Forest\", n_estimators=20)\n",
    "plot_roc(X, y, LogisticRegression, \"Logistic Regression\")\n",
    "plot_roc(X, y, DecisionTreeClassifier, \" DecisionTreeClassifier\")\n",
    "plot_roc(X, y, KNeighborsClassifier, \"KNeighborsClassifier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. avg_dist (0.227833)\n",
      "2. avg_rating_by_driver (0.140364)\n",
      "3. avg_rating_of_driver (0.117277)\n",
      "4. avg_surge (0.101436)\n",
      "5. city (0.098945)\n",
      "6. surge_pct (0.076458)\n",
      "7. trips_in_first_30_days (0.072876)\n",
      "8. luxury_car_user (0.072857)\n",
      "9. weekday_pct (0.056497)\n",
      "10. signup_period (0.035458)\n"
     ]
    },
    {
     "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": [
    "features = X.columns\n",
    "\n",
    "forest = RF()\n",
    "forest_fit = forest.fit(X_train, y_train)\n",
    "forest_predictions = forest_fit.predict(X_test)\n",
    "\n",
    "importances = forest_fit.feature_importances_[:10]\n",
    "\n",
    "# std\n",
    "std = np.std([tree.feature_importances_ for tree in forest.estimators_],\n",
    "             axis=0) \n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(10):\n",
    "    print(\"%d. %s (%f)\" % (f + 1, features[f], importances[indices[f]]))\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(features, importances[indices], yerr=std[indices], color=\"r\", align=\"center\")\n",
    "# plt.xticks(range(10), features)\n",
    "# plt.xlim([-1, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.22783317456844934, 0.03545774721959209)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(importances[indices]), min(importances[indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}