Bank Account Churn Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "seed = 7\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# downloaded from https://www.kaggle.com/filippoo/deep-learning-az-ann\n",
    "churn_df = pd.read_csv('Churn_Modelling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "churnWithoutRowNumber = churn_df.drop(churn_df.columns[[0, 2]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# mapping countries to a specific integer\n",
    "countries = map(lambda x: x[2], churnWithoutRowNumber.values)\n",
    "unique_countries = set(countries)\n",
    "country_hash = {}\n",
    "for i, c in enumerate(unique_countries): country_hash[c] = i # can save this later when getting insight into neural network\n",
    "integerMapping = map(lambda c: country_hash[c], churnWithoutRowNumber['Geography'])\n",
    "countriesAsIntegers = churnWithoutRowNumber\n",
    "countriesAsIntegers['Geography'] = integerMapping\n",
    "\n",
    "#mapping genders as a specific integer\n",
    "genders = map(lambda x: x[3], countriesAsIntegers.values)\n",
    "unique_genders = set(genders)\n",
    "gender_hash = {}\n",
    "for i, g in enumerate(unique_genders): gender_hash[g] = i # can save this later when getting insight into neural network\n",
    "integerMapping = map(lambda g: gender_hash[g], countriesAsIntegers['Gender'])\n",
    "gendersAsIntegers = countriesAsIntegers\n",
    "gendersAsIntegers['Gender'] = integerMapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15634602</td>\n",
       "      <td>619</td>\n",
       "      <td>2</td>\n",
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       "      <td>42</td>\n",
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       "      <td>101348.88</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15647311</td>\n",
       "      <td>608</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15619304</td>\n",
       "      <td>502</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15701354</td>\n",
       "      <td>699</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15737888</td>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerId  CreditScore  Geography  Gender  Age  Tenure    Balance  \\\n",
       "0    15634602          619          2       1   42       2       0.00   \n",
       "1    15647311          608          1       1   41       1   83807.86   \n",
       "2    15619304          502          2       1   42       8  159660.80   \n",
       "3    15701354          699          2       1   39       1       0.00   \n",
       "4    15737888          850          1       1   43       2  125510.82   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0              1          1               1        101348.88       1  \n",
       "1              1          0               1        112542.58       0  \n",
       "2              3          1               0        113931.57       1  \n",
       "3              2          0               0         93826.63       0  \n",
       "4              1          1               1         79084.10       0  "
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gendersAsIntegers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_dataset = gendersAsIntegers.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.56346020e+07,   6.19000000e+02,   2.00000000e+00,\n",
       "         1.00000000e+00,   4.20000000e+01,   2.00000000e+00,\n",
       "         0.00000000e+00,   1.00000000e+00,   1.00000000e+00,\n",
       "         1.00000000e+00,   1.01348880e+05,   1.00000000e+00])"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = clean_dataset[:,0:11].astype(float)\n",
    "\n",
    "Y = clean_dataset[:,11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# encode class values as integers\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(Y)\n",
    "encoded_Y = encoder.transform(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 1, 1, 0])"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# baseline model\n",
    "def create_baseline():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(11, input_dim=11, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimator = KerasClassifier(build_fn=create_baseline, nb_epoch=10, batch_size=20, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = cross_val_score(estimator, X, encoded_Y, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline: 34.27% (23.48%)\n"
     ]
    }
   ],
   "source": [
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standardized: 85.27% (0.94%)\n"
     ]
    }
   ],
   "source": [
    "# Model with standard scaled data\n",
    "numpy.random.seed(seed)\n",
    "estimators = []\n",
    "estimators.append(('standardize', StandardScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_baseline, nb_epoch=10, batch_size=20, verbose=0)))\n",
    "pipeline = Pipeline(estimators)\n",
    "kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)\n",
    "print(\"Standardized: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smaller: 83.95% (1.43%)\n"
     ]
    }
   ],
   "source": [
    "# smaller net\n",
    "def create_smaller():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(5, input_dim=11, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "estimators = []\n",
    "estimators.append(('standardize', StandardScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_smaller, nb_epoch=10, batch_size=20, verbose=0)))\n",
    "pipeline = Pipeline(estimators)\n",
    "kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)\n",
    "print(\"Smaller: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Larger: 85.27% (0.86%)\n"
     ]
    }
   ],
   "source": [
    "def create_larger():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(11, input_dim=11, activation='relu'))\n",
    "    model.add(Dense(5, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "estimators = []\n",
    "estimators.append(('standardize', StandardScaler()))\n",
    "estimators.append(('mlp', KerasClassifier(build_fn=create_larger, nb_epoch=10, batch_size=20, verbose=0)))\n",
    "pipeline = Pipeline(estimators)\n",
    "kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
    "results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)\n",
    "print(\"Larger: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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