churn_customers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split, cross_val_score\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score, confusion_matrix\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../../data/mini.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>occurence</th>\n",
       "      <th>cost</th>\n",
       "      <th>item_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>13891</td>\n",
       "      <td>2018-02-10 17:35:11.000000</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>828115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9081</td>\n",
       "      <td>2017-12-21 17:13:44.000000</td>\n",
       "      <td>499.0</td>\n",
       "      <td>695501</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>470904</td>\n",
       "      <td>2017-10-31 10:39:49.000000</td>\n",
       "      <td>290.0</td>\n",
       "      <td>899821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58500</td>\n",
       "      <td>2018-03-09 20:57:29.000000</td>\n",
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      "text/plain": [
       "   customer_id                   occurence    cost item_id\n",
       "0       416705  2017-05-07 21:58:10.000000   299.0  515274\n",
       "1        13891  2018-02-10 17:35:11.000000  1090.0  828115\n",
       "2         9081  2017-12-21 17:13:44.000000   499.0  695501\n",
       "3       470904  2017-10-31 10:39:49.000000   290.0  899821\n",
       "4        58500  2018-03-09 20:57:29.000000   150.0  518554"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1990712, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['occurence'] = pd.to_datetime(data.occurence, yearfirst = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['year'] = [d.year for d in data.occurence]\n",
    "data['month'] = [d.month for d in data.occurence]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>2017-12-21 17:13:44</td>\n",
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       "      <td>2017</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>470904</td>\n",
       "      <td>2017-10-31 10:39:49</td>\n",
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       "      <td>899821</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58500</td>\n",
       "      <td>2018-03-09 20:57:29</td>\n",
       "      <td>150.0</td>\n",
       "      <td>518554</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
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      ],
      "text/plain": [
       "   customer_id           occurence    cost item_id  year  month\n",
       "0       416705 2017-05-07 21:58:10   299.0  515274  2017      5\n",
       "1        13891 2018-02-10 17:35:11  1090.0  828115  2018      2\n",
       "2         9081 2017-12-21 17:13:44   499.0  695501  2017     12\n",
       "3       470904 2017-10-31 10:39:49   290.0  899821  2017     10\n",
       "4        58500 2018-03-09 20:57:29   150.0  518554  2018      3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['occurence'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   customer_id    cost item_id  year  month\n",
       "0       416705   299.0  515274  2017      5\n",
       "1        13891  1090.0  828115  2018      2\n",
       "2         9081   499.0  695501  2017     12\n",
       "3       470904   290.0  899821  2017     10\n",
       "4        58500   150.0  518554  2018      3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245676"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data['customer_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data['year'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data['year']==2018]"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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       "      <td>2018</td>\n",
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       "      <th>7</th>\n",
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       "   customer_id    cost item_id  year  month\n",
       "1        13891  1090.0  828115  2018      2\n",
       "4        58500   150.0  518554  2018      3\n",
       "5       572181   990.0  656673  2018      5\n",
       "6       616443  2390.0  678768  2018      7\n",
       "7       114804  3990.0  958574  2018      5"
      ]
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data['month']<7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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       "      <th>16</th>\n",
       "      <td>568563</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>969194</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
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      "text/plain": [
       "    customer_id    cost item_id  year  month\n",
       "1         13891  1090.0  828115  2018      2\n",
       "4         58500   150.0  518554  2018      3\n",
       "5        572181   990.0  656673  2018      5\n",
       "7        114804  3990.0  958574  2018      5\n",
       "16       568563  1050.0  969194  2018      6"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_5_months = data[data['month']<=5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "      <td>3990.0</td>\n",
       "      <td>958574</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>422260</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>451858</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    customer_id    cost item_id  year  month\n",
       "1         13891  1090.0  828115  2018      2\n",
       "4         58500   150.0  518554  2018      3\n",
       "5        572181   990.0  656673  2018      5\n",
       "7        114804  3990.0  958574  2018      5\n",
       "17       422260  1100.0  451858  2018      1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_5_months.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_5_months = pd.DataFrame(first_5_months['customer_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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    "first_5_months.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_month = data[data['month']==6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>cost</th>\n",
       "      <th>item_id</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
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       "      <td>1050.0</td>\n",
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       "      <th>83</th>\n",
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       "      <td>2018</td>\n",
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       "      <th>88</th>\n",
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       "      <td>2018</td>\n",
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       "      <th>94</th>\n",
       "      <td>106202</td>\n",
       "      <td>8990.0</td>\n",
       "      <td>961388</td>\n",
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      ],
      "text/plain": [
       "    customer_id    cost item_id  year  month\n",
       "16       568563  1050.0  969194  2018      6\n",
       "83       589411  2490.0  977771  2018      6\n",
       "88        11551   850.0  689153  2018      6\n",
       "93       564748  4499.0  956799  2018      6\n",
       "94       106202  8990.0  961388  2018      6"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "target_month.head()"
   ]
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  {
   "cell_type": "raw",
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_month = pd.DataFrame(target_month['customer_id'].unique())"
   ]
  },
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   "execution_count": 23,
   "metadata": {},
   "outputs": [
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   "execution_count": 24,
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   "outputs": [],
   "source": [
    "target_month['target']= 'Yes'"
   ]
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   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "        0 target\n",
       "0  568563    Yes\n",
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     "execution_count": 25,
     "metadata": {},
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_dataset = pd.merge(first_5_months, target_month, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_dataset['cost'] = data.groupby('customer_id')['cost'].mean()\n",
    "target_dataset['number of transactions'] = data.groupby('customer_id')['cost'].count()\n",
    "target_dataset['total'] = data.groupby('customer_id')['cost'].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "        0 target    cost  number of transactions   total\n",
       "0   13891    NaN     NaN                     NaN     NaN\n",
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(131774, 5)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "target_dataset.shape"
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    {
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      "text/plain": [
       "(131774, 1)"
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     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "first_5_months.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53727, 2)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "target_month.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_dataset['target'] = target_dataset['target'].fillna('No')\n",
    "target_dataset['cost'] = target_dataset['cost'].fillna(target_dataset['cost'].median())\n",
    "target_dataset['number of transactions'] = target_dataset['number of transactions'].fillna(target_dataset['number of transactions'].median())\n",
    "target_dataset['total'] = target_dataset['total'].fillna(target_dataset['total'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "        0 target    cost  number of transactions   total\n",
       "0   13891     No  2240.0                     3.0  7980.0\n",
       "1   58500     No  1713.0                     3.0  5139.0\n",
       "2  572181     No  2240.0                     3.0  7980.0\n",
       "3  114804     No  2240.0                     3.0  7980.0\n",
       "4  422260    Yes  2240.0                     3.0  7980.0"
      ]
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     "execution_count": 37,
     "metadata": {},
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    "target_dataset.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_dataset['target'] = target_dataset['target'].map({\"Yes\":1, \"No\":0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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       "      <td>2240.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114804</td>\n",
       "      <td>0</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>422260</td>\n",
       "      <td>1</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7980.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0  target    cost  number of transactions   total\n",
       "0   13891       0  2240.0                     3.0  7980.0\n",
       "1   58500       0  1713.0                     3.0  5139.0\n",
       "2  572181       0  2240.0                     3.0  7980.0\n",
       "3  114804       0  2240.0                     3.0  7980.0\n",
       "4  422260       1  2240.0                     3.0  7980.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = target_dataset['target']\n",
    "X = target_dataset.drop('target', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_tree = DecisionTreeClassifier(random_state=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6391952016557091"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score(first_tree, X_train, y_train, cv=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_params = {'max_depth': np.arange(1, 11), 'max_features':[.5, .7, 1]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_grid = GridSearchCV(first_tree, tree_params, cv=5, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=17,\n",
       "            splitter='best'),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10]), 'max_features': [0.5, 0.7, 1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7573855443891545, {'max_depth': 1, 'max_features': 0.5})"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_grid.best_score_, tree_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_test_pred = tree_grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7577466926365315"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, tree_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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