ANN_Churn_Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "tuNw1_MoF8Qa",
    "outputId": "9eb89507-7f0a-40cf-dfe8-f903309630f9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qvW7ACPwHShq"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hOUrCmGTHwBx"
   },
   "outputs": [],
   "source": [
    "dataset=pd.read_csv(\"Churn_Modelling.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pt3YQn9HIY2I"
   },
   "outputs": [],
   "source": [
    "#Creating matrix of features\n",
    "X=dataset.iloc[:,3:13].values\n",
    "y=dataset.iloc[:,13].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "qIdTjm0aIWsD",
    "outputId": "8f8f20e8-0ae1-4ff9-dabd-c5536a9ee928"
   },
   "outputs": [
    {
     "data": {
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       "\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>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",
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       "    <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",
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       "    <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",
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       "      <td>0</td>\n",
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       "      <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",
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      ],
      "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": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7vNZ5jsRO7Pl"
   },
   "source": [
    "***Data Preprocessing***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HM9D72JAKlP4"
   },
   "outputs": [],
   "source": [
    "# Encoding categorical data\n",
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aRm6Nt9KN6v-"
   },
   "outputs": [],
   "source": [
    "labelencoder_X_1 = LabelEncoder()\n",
    "X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])\n",
    "labelencoder_X_2 = LabelEncoder()\n",
    "X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xt0MsXOBOEno"
   },
   "outputs": [],
   "source": [
    "onehotencoder = OneHotEncoder(categorical_features = [1])\n",
    "X = onehotencoder.fit_transform(X).toarray()\n",
    "#To avoid Dummy Variable Trap\n",
    "X = X[:, 1:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mDCTQ8MQOUvq"
   },
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hSxDSHSLOfw9"
   },
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9rUxU1WuQq5r"
   },
   "source": [
    "**Building ANN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ljQtQjcXPbMu"
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MqGkvsgkQltC"
   },
   "outputs": [],
   "source": [
    "# Initialising the ANN\n",
    "classifier = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3451
    },
    "colab_type": "code",
    "id": "e6jizdY-XlEC",
    "outputId": "339ffbba-0fbe-4d45-f2e6-b3b7f87ee408"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras/models.py:981: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  warnings.warn('The `nb_epoch` argument in `fit` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
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      "Epoch 2/100\n",
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      "Epoch 4/100\n",
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      "Epoch 5/100\n",
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      "Epoch 7/100\n",
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      "Epoch 8/100\n",
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      "Epoch 9/100\n",
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      "Epoch 10/100\n",
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      "Epoch 12/100\n",
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      "Epoch 13/100\n",
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      "Epoch 14/100\n",
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      "Epoch 37/100\n",
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      "Epoch 38/100\n",
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      "Epoch 39/100\n",
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      "Epoch 60/100\n",
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      "Epoch 61/100\n",
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      "Epoch 62/100\n",
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      "Epoch 63/100\n",
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      "Epoch 64/100\n",
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      "Epoch 65/100\n",
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      "Epoch 70/100\n",
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      "Epoch 71/100\n",
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      "Epoch 72/100\n",
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      "Epoch 73/100\n",
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      "Epoch 77/100\n",
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      "Epoch 78/100\n",
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      "Epoch 79/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3360 - acc: 0.8629\n",
      "Epoch 80/100\n",
      "8000/8000 [==============================] - 1s 120us/step - loss: 0.3367 - acc: 0.8644\n",
      "Epoch 81/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3358 - acc: 0.8635\n",
      "Epoch 82/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3361 - acc: 0.8637\n",
      "Epoch 83/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3362 - acc: 0.8625\n",
      "Epoch 84/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3358 - acc: 0.8624\n",
      "Epoch 85/100\n",
      "8000/8000 [==============================] - 1s 120us/step - loss: 0.3362 - acc: 0.8630\n",
      "Epoch 86/100\n",
      "8000/8000 [==============================] - 1s 120us/step - loss: 0.3356 - acc: 0.8640\n",
      "Epoch 87/100\n",
      "8000/8000 [==============================] - 1s 118us/step - loss: 0.3364 - acc: 0.8639\n",
      "Epoch 88/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3359 - acc: 0.8651\n",
      "Epoch 89/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3350 - acc: 0.8611\n",
      "Epoch 90/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3359 - acc: 0.8625\n",
      "Epoch 91/100\n",
      "8000/8000 [==============================] - 1s 121us/step - loss: 0.3359 - acc: 0.8627\n",
      "Epoch 92/100\n",
      "8000/8000 [==============================] - 1s 122us/step - loss: 0.3354 - acc: 0.8640\n",
      "Epoch 93/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3357 - acc: 0.8624\n",
      "Epoch 94/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3362 - acc: 0.8636\n",
      "Epoch 95/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3355 - acc: 0.8632\n",
      "Epoch 96/100\n",
      "8000/8000 [==============================] - 1s 118us/step - loss: 0.3357 - acc: 0.8627\n",
      "Epoch 97/100\n",
      "8000/8000 [==============================] - 1s 121us/step - loss: 0.3351 - acc: 0.8632\n",
      "Epoch 98/100\n",
      "8000/8000 [==============================] - 1s 121us/step - loss: 0.3353 - acc: 0.8624\n",
      "Epoch 99/100\n",
      "8000/8000 [==============================] - 1s 119us/step - loss: 0.3348 - acc: 0.8662\n",
      "Epoch 100/100\n",
      "8000/8000 [==============================] - 1s 120us/step - loss: 0.3354 - acc: 0.8619\n"
     ]
    }
   ],
   "source": [
    "# Adding the input layer and the first hidden layer\n",
    "classifier.add(Dense(units=6,kernel_initializer=\"glorot_normal\", activation = 'relu', input_dim = 11))\n",
    "\n",
    "# Adding the second hidden layer\n",
    "classifier.add(Dense(units = 6, kernel_initializer=\"glorot_normal\", activation = 'relu'))\n",
    "\n",
    "# Adding the output layer\n",
    "classifier.add(Dense(units = 1, kernel_initializer=\"glorot_normal\", activation = 'sigmoid'))\n",
    "\n",
    "# Compiling the ANN\n",
    "classifier.compile(optimizer = 'SGD', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "\n",
    "# Fitting the ANN to the Training set\n",
    "history=classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BLzFfGn-X-Ea"
   },
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)\n",
    "\n",
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cr = classification_report(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "HbGK9XHDeaFb",
    "outputId": "ea5694d8-584e-4937-ea33-c24a2cf38457"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1493,  102],\n",
       "       [ 185,  220]])"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "RYlN9umA0MGI",
    "outputId": "6c4b9eeb-4f94-44d3-f0c7-13e6a4f6ada4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'             precision    recall  f1-score   support\\n\\n          0       0.89      0.94      0.91      1595\\n          1       0.68      0.54      0.61       405\\n\\navg / total       0.85      0.86      0.85      2000\\n'"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "2oF7PD9Z5kWc",
    "outputId": "871ffaf3-90c5-464c-b6b9-4dd6b946a61f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'acc'])"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 379
    },
    "colab_type": "code",
    "id": "qd4GT9YFVDqN",
    "outputId": "5323220b-2136-483e-fc47-972e8d224079"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Loss')"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RAHeUC5LRmfEQRaCq3ip1KURENET1K9T37t2LTz/9FADwu9/9Drfffju2b98e0sIiDdfV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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc17ab7390>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Loss\")\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Untitled0.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}