Churn Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data = pd.read_csv('churn.csv.txt', parse_dates=['last_trip_date','signup_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 datetime64[ns]\n",
      "phone                     49604 non-null object\n",
      "signup_date               50000 non-null datetime64[ns]\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), datetime64[ns](2), float64(6), int64(1), object(2)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "churn_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2014-07-01 00:00:00')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.last_trip_date.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff = churn_data.last_trip_date.max() - datetime.timedelta(30,0,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2014-06-01 00:00:00')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data['churn'] = (churn_data.last_trip_date < cutoff).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>churn</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>0</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>1</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>1</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>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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  \\\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  churn  \n",
       "0                       4             True         46.2      0  \n",
       "1                       0            False         50.0      1  \n",
       "2                       3            False        100.0      1  \n",
       "3                       9             True         80.0      0  \n",
       "4                      14            False         82.4      1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data = churn_data.drop('churn',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_data = churn_data.churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41445 entries, 0 to 49998\n",
      "Data columns (total 12 columns):\n",
      "avg_dist                  41445 non-null float64\n",
      "avg_rating_by_driver      41445 non-null float64\n",
      "avg_rating_of_driver      41445 non-null float64\n",
      "avg_surge                 41445 non-null float64\n",
      "city                      41445 non-null object\n",
      "last_trip_date            41445 non-null datetime64[ns]\n",
      "phone                     41445 non-null object\n",
      "signup_date               41445 non-null datetime64[ns]\n",
      "surge_pct                 41445 non-null float64\n",
      "trips_in_first_30_days    41445 non-null int64\n",
      "luxury_car_user           41445 non-null bool\n",
      "weekday_pct               41445 non-null float64\n",
      "dtypes: bool(1), datetime64[ns](2), float64(6), int64(1), object(2)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "feature_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn_pandas import DataFrameMapper\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper = DataFrameMapper([\n",
    "    (['avg_dist','avg_rating_by_driver','avg_rating_of_driver',\n",
    "      'avg_surge','surge_pct','trips_in_first_30_days','weekday_pct'], StandardScaler()),\n",
    "    ('city',LabelEncoder()),\n",
    "    ('luxury_car_user',LabelEncoder())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrameMapper(default=False, df_out=False,\n",
       "        features=[(['avg_dist', 'avg_rating_by_driver', 'avg_rating_of_driver', 'avg_surge', 'surge_pct', 'trips_in_first_30_days', 'weekday_pct'], StandardScaler(copy=True, with_mean=True, with_std=True)), ('city', LabelEncoder()), ('luxury_car_user', LabelEncoder())],\n",
       "        input_df=False, sparse=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41445 entries, 0 to 49998\n",
      "Data columns (total 13 columns):\n",
      "avg_dist                  41445 non-null float64\n",
      "avg_rating_by_driver      41445 non-null float64\n",
      "avg_rating_of_driver      41445 non-null float64\n",
      "avg_surge                 41445 non-null float64\n",
      "city                      41445 non-null object\n",
      "last_trip_date            41445 non-null datetime64[ns]\n",
      "phone                     41445 non-null object\n",
      "signup_date               41445 non-null datetime64[ns]\n",
      "surge_pct                 41445 non-null float64\n",
      "trips_in_first_30_days    41445 non-null int64\n",
      "luxury_car_user           41445 non-null bool\n",
      "weekday_pct               41445 non-null float64\n",
      "churn                     41445 non-null int32\n",
      "dtypes: bool(1), datetime64[ns](2), float64(6), int32(1), int64(1), object(2)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "churn_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41445, 9)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapper.fit_transform(churn_data).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_selection import SelectKBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ChurnClassifier(object):\n",
    "    def __init__(self,clsf_list):\n",
    "        self.clsf = clsf_list\n",
    "        self.pipelines = []\n",
    "        \n",
    "    def create_pipelines(self,mapper,selector):\n",
    "        for name,est in self.clsf:\n",
    "            pipeline = Pipeline([\n",
    "                ('mapper',mapper),\n",
    "                ('selector',selector),\n",
    "                ('est',est)\n",
    "            ])\n",
    "            self.pipelines.append((name,pipeline))\n",
    "            \n",
    "    def fit(self,trainX,trainY):\n",
    "        for name,pipeline in self.pipelines:\n",
    "            print ('Training ',name)\n",
    "            pipeline.fit(trainX,trainY)\n",
    "        \n",
    "    def score(self,testX,testY):\n",
    "        for name,pipeline in self.pipelines:\n",
    "            s = pipeline.score(testX,testY)\n",
    "            print ('Mode ',name,' -',s,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "clsf = ChurnClassifier([('lr',LogisticRegression()),('dt',DecisionTreeClassifier()),('rf',RandomForestClassifier(n_estimators=10))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "clsf.create_pipelines(mapper,SelectKBest(k=9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('dt', Pipeline(memory=None,\n",
       "      steps=[('mapper', DataFrameMapper(default=False, df_out=False,\n",
       "         features=[(['avg_dist', 'avg_rating_by_driver', 'avg_rating_of_driver', 'avg_surge', 'surge_pct', 'trips_in_first_30_days', 'weekday_pct'], StandardScaler(copy=True, with_mean=True, with_std=True)), ('city', LabelEncoder()), ('lu...      min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "             splitter='best'))]))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clsf.pipelines[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX,testX, trainY, testY = train_test_split(feature_data, target_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training  lr\n",
      "Training  dt\n",
      "Training  rf\n"
     ]
    }
   ],
   "source": [
    "clsf.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode  lr  - 0.6455317506272921 %\n",
      "Mode  dt  - 0.6762208067940552 %\n",
      "Mode  rf  - 0.7199382358618027 %\n"
     ]
    }
   ],
   "source": [
    "clsf.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['filename.pkl']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(clsf, 'filename.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX.to"
   ]
  }
 ],
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