Churn model.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "D2IliGyI62B0"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_fyxUHb7GuH",
        "outputId": "2c8a495d-cff8-49f1-b9c2-fa63165b0043"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.15.0\n"
          ]
        }
      ],
      "source": [
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "LLvV2K-o7KAp"
      },
      "outputs": [],
      "source": [
        "# Importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "gHRHobkK7Sxn"
      },
      "outputs": [],
      "source": [
        "# Importing the dataset\n",
        "dataset = pd.read_csv('Churn_Modelling.csv')\n",
        "X = dataset.iloc[:, 3:13]\n",
        "y = dataset.iloc[:, 13]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "T2yS0VL67Ufn"
      },
      "outputs": [],
      "source": [
        "#Create dummy variables\n",
        "geography=pd.get_dummies(X[\"Geography\"],drop_first=True)\n",
        "gender=pd.get_dummies(X['Gender'],drop_first=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "Co2rxaFR7Wgo"
      },
      "outputs": [],
      "source": [
        "## Concatenate the Data Frames\n",
        "\n",
        "X=pd.concat([X,geography,gender],axis=1)\n",
        "\n",
        "## Drop Unnecessary columns\n",
        "X=X.drop(['Geography','Gender'],axis=1)\n",
        "\n",
        "# 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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "GNZrZXy37ZLs"
      },
      "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": "code",
      "execution_count": 32,
      "metadata": {
        "id": "uwN3bgie7bvT"
      },
      "outputs": [],
      "source": [
        "# Part 2 - Now let's make the ANN!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "rL_a7vWu7dj1"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LeakyReLU,PReLU,ELU\n",
        "from tensorflow.keras.layers import Dropout\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "VDJwvrbZ7kj4"
      },
      "outputs": [],
      "source": [
        "# Initialising the ANN\n",
        "classifier = Sequential()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "RSZQrU-g7z4N"
      },
      "outputs": [],
      "source": [
        "# Adding the input layer and the first hidden layer\n",
        "classifier.add(Dense(units=11,activation='relu'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "byszZMvw8RcQ"
      },
      "outputs": [],
      "source": [
        "# Adding the input layer and the first hidden layer\n",
        "classifier.add(Dense(units=6,activation='relu'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "Mk2slmAX8XUI"
      },
      "outputs": [],
      "source": [
        "# Adding the input layer and the first hidden layer\n",
        "classifier.add(Dense(units=1,activation='relu'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "F2udThHo8cFt"
      },
      "outputs": [],
      "source": [
        "classifier.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3qairJAs8fIc",
        "outputId": "0730a4ec-193f-41ae-daff-4ccc7673b142"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "536/536 [==============================] - 2s 2ms/step - loss: 2.1012 - accuracy: 0.7820 - val_loss: 1.8105 - val_accuracy: 0.7781\n",
            "Epoch 2/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 1.3996 - accuracy: 0.7675 - val_loss: 1.1294 - val_accuracy: 0.7603\n",
            "Epoch 3/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.9268 - accuracy: 0.7569 - val_loss: 0.7968 - val_accuracy: 0.7573\n",
            "Epoch 4/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.7136 - accuracy: 0.7621 - val_loss: 0.7196 - val_accuracy: 0.7705\n",
            "Epoch 5/50\n",
            "536/536 [==============================] - 1s 1ms/step - loss: 0.6452 - accuracy: 0.7705 - val_loss: 0.6440 - val_accuracy: 0.7800\n",
            "Epoch 6/50\n",
            "536/536 [==============================] - 1s 1ms/step - loss: 0.5642 - accuracy: 0.7778 - val_loss: 0.5771 - val_accuracy: 0.7902\n",
            "Epoch 7/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.5213 - accuracy: 0.7854 - val_loss: 0.5465 - val_accuracy: 0.7952\n",
            "Epoch 8/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.5023 - accuracy: 0.7964 - val_loss: 0.5265 - val_accuracy: 0.7970\n",
            "Epoch 9/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4856 - accuracy: 0.8039 - val_loss: 0.5119 - val_accuracy: 0.7986\n",
            "Epoch 10/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4749 - accuracy: 0.8074 - val_loss: 0.5053 - val_accuracy: 0.8012\n",
            "Epoch 11/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4643 - accuracy: 0.8095 - val_loss: 0.5012 - val_accuracy: 0.8035\n",
            "Epoch 12/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4529 - accuracy: 0.8078 - val_loss: 0.4964 - val_accuracy: 0.8042\n",
            "Epoch 13/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4470 - accuracy: 0.8108 - val_loss: 0.4901 - val_accuracy: 0.8095\n",
            "Epoch 14/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4392 - accuracy: 0.8128 - val_loss: 0.4696 - val_accuracy: 0.8118\n",
            "Epoch 15/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4324 - accuracy: 0.8164 - val_loss: 0.4634 - val_accuracy: 0.8137\n",
            "Epoch 16/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4301 - accuracy: 0.8196 - val_loss: 0.4593 - val_accuracy: 0.8137\n",
            "Epoch 17/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4230 - accuracy: 0.8248 - val_loss: 0.4689 - val_accuracy: 0.8160\n",
            "Epoch 18/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4176 - accuracy: 0.8270 - val_loss: 0.4601 - val_accuracy: 0.8148\n",
            "Epoch 19/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4135 - accuracy: 0.8298 - val_loss: 0.4833 - val_accuracy: 0.8198\n",
            "Epoch 20/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4087 - accuracy: 0.8332 - val_loss: 0.4480 - val_accuracy: 0.8190\n",
            "Epoch 21/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4027 - accuracy: 0.8345 - val_loss: 0.4317 - val_accuracy: 0.8194\n",
            "Epoch 22/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3928 - accuracy: 0.8377 - val_loss: 0.4371 - val_accuracy: 0.8266\n",
            "Epoch 23/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3851 - accuracy: 0.8414 - val_loss: 0.4464 - val_accuracy: 0.8266\n",
            "Epoch 24/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3825 - accuracy: 0.8433 - val_loss: 0.4196 - val_accuracy: 0.8243\n",
            "Epoch 25/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4336 - accuracy: 0.8274 - val_loss: 0.4724 - val_accuracy: 0.8058\n",
            "Epoch 26/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4067 - accuracy: 0.8173 - val_loss: 0.4365 - val_accuracy: 0.8126\n",
            "Epoch 27/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3911 - accuracy: 0.8237 - val_loss: 0.4227 - val_accuracy: 0.8164\n",
            "Epoch 28/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3846 - accuracy: 0.8304 - val_loss: 0.4295 - val_accuracy: 0.8186\n",
            "Epoch 29/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3789 - accuracy: 0.8354 - val_loss: 0.4228 - val_accuracy: 0.8254\n",
            "Epoch 30/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3773 - accuracy: 0.8412 - val_loss: 0.4176 - val_accuracy: 0.8319\n",
            "Epoch 31/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3680 - accuracy: 0.8464 - val_loss: 0.4230 - val_accuracy: 0.8376\n",
            "Epoch 32/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3653 - accuracy: 0.8490 - val_loss: 0.4062 - val_accuracy: 0.8417\n",
            "Epoch 33/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3596 - accuracy: 0.8545 - val_loss: 0.4166 - val_accuracy: 0.8425\n",
            "Epoch 34/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3639 - accuracy: 0.8545 - val_loss: 0.4446 - val_accuracy: 0.8432\n",
            "Epoch 35/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3632 - accuracy: 0.8533 - val_loss: 0.4169 - val_accuracy: 0.8440\n",
            "Epoch 36/50\n",
            "536/536 [==============================] - 1s 1ms/step - loss: 0.3662 - accuracy: 0.8552 - val_loss: 0.4290 - val_accuracy: 0.8444\n",
            "Epoch 37/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3604 - accuracy: 0.8509 - val_loss: 0.4136 - val_accuracy: 0.8436\n",
            "Epoch 38/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3617 - accuracy: 0.8543 - val_loss: 0.4067 - val_accuracy: 0.8448\n",
            "Epoch 39/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3545 - accuracy: 0.8571 - val_loss: 0.3954 - val_accuracy: 0.8421\n",
            "Epoch 40/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3598 - accuracy: 0.8543 - val_loss: 0.3937 - val_accuracy: 0.8395\n",
            "Epoch 41/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3605 - accuracy: 0.8552 - val_loss: 0.4103 - val_accuracy: 0.8455\n",
            "Epoch 42/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3552 - accuracy: 0.8567 - val_loss: 0.4225 - val_accuracy: 0.8489\n",
            "Epoch 43/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3829 - accuracy: 0.8406 - val_loss: 0.4372 - val_accuracy: 0.8118\n",
            "Epoch 44/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3878 - accuracy: 0.8360 - val_loss: 0.4218 - val_accuracy: 0.8232\n",
            "Epoch 45/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3682 - accuracy: 0.8459 - val_loss: 0.4188 - val_accuracy: 0.8398\n",
            "Epoch 46/50\n",
            "536/536 [==============================] - 1s 1ms/step - loss: 0.3610 - accuracy: 0.8472 - val_loss: 0.4139 - val_accuracy: 0.8395\n",
            "Epoch 47/50\n",
            "536/536 [==============================] - 1s 1ms/step - loss: 0.3575 - accuracy: 0.8475 - val_loss: 0.4148 - val_accuracy: 0.8413\n",
            "Epoch 48/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3529 - accuracy: 0.8492 - val_loss: 0.4163 - val_accuracy: 0.8459\n",
            "Epoch 49/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3547 - accuracy: 0.8530 - val_loss: 0.4224 - val_accuracy: 0.8489\n",
            "Epoch 50/50\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3503 - accuracy: 0.8537 - val_loss: 0.4188 - val_accuracy: 0.8455\n"
          ]
        }
      ],
      "source": [
        "model_history=classifier.fit(X_train,y_train,validation_split=0.33,batch_size=10,epochs=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9t_M4wICTTz",
        "outputId": "18fa935a-8009-407f-de61-7f6a83669624"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        }
      ],
      "source": [
        "# list all data in history\n",
        "\n",
        "print(model_history.history.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "MMOyiwzgBlLY",
        "outputId": "aeb29600-3c1f-41aa-9e29-05acf6011ed8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "# summarize history for accuracy\n",
        "plt.plot(model_history.history['accuracy'])\n",
        "plt.plot(model_history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "QH141RsQCEr0",
        "outputId": "4566dca0-b002-4f59-b6e8-f8654b414283"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# summarize history for loss\n",
        "plt.plot(model_history.history['loss'])\n",
        "plt.plot(model_history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "OYqCIIkDCd8S"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "63/63 [==============================] - 0s 860us/step\n"
          ]
        }
      ],
      "source": [
        "# Part 3 - Making the predictions and evaluating the model\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "y_pred = (y_pred > 0.5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cm3EeOGTCi3-",
        "outputId": "f3cfe210-0811-4c4e-ce72-4100b32bb79f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1533,   62],\n",
              "       [ 225,  180]], dtype=int64)"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "FgfRKzzOClmP"
      },
      "outputs": [],
      "source": [
        "# Calculate the Accuracy\n",
        "from sklearn.metrics import accuracy_score\n",
        "score=accuracy_score(y_pred,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_C5EETrCpSj",
        "outputId": "6e495c58-0149-453b-b0d6-592606fe5c80"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8565\n"
          ]
        }
      ],
      "source": [
        "print(score)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Untitled63.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.11.1"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}