ANNs_Day1_Code.ipynb

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  "metadata": {
    "colab": {
      "name": "ANNs Day1 Code.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x44omTCoY-22",
        "colab_type": "text"
      },
      "source": [
        "ANN in Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnJSqqsf0Qup",
        "colab_type": "text"
      },
      "source": [
        "Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6pcFc9R0EPB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import keras \n",
        "from keras.layers import Dense\n",
        "from keras.models import Sequential\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QzZAvfgq0FHf",
        "colab_type": "text"
      },
      "source": [
        "Importing the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExhGklLBZB83",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.read_csv('drive/My Drive/DL Bootcamp/Churn_Modelling.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RetK-fvJ0xCR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsv_HjL2CY64",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsBWwyVUCnPT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=df.iloc[:,3:13]\n",
        "y=df.iloc[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9IeqlspDaZ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "type(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BA91tQT-DcTQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "type(y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytB_ygwjDdlA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCQ-GxFvDkQy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X['Geography'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTA612YEEyn4",
        "colab_type": "text"
      },
      "source": [
        "Categorical to Continuous"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV_3qXZRD_qj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=pd.get_dummies(X,columns=['Geography','Gender'],drop_first=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cE0g5kmlGhHA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X.head(5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ka7r3sN0HadY",
        "colab_type": "text"
      },
      "source": [
        "Scaling the Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9xVMwUzGlV9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sc=StandardScaler()\n",
        "X=sc.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZNF2a7LH15l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "InqN76f7IE4h",
        "colab_type": "text"
      },
      "source": [
        "Train-Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hp57hI9MH3A-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xs63X-MwJLog",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBXijH9UJVp7",
        "colab_type": "text"
      },
      "source": [
        "Creating the model architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "doAIpE6tJRJv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=Sequential()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNYQRAuoJfAj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(Dense(units=6,activation='relu',input_dim=11))\n",
        "model.add(Dense(units=6,activation='relu'))\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nkoZ4TLJtAp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6G-n8GAoKk75",
        "colab_type": "text"
      },
      "source": [
        "Training our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HdfEUbWJwY6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n646QD6OK1Z5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.fit(X_train,y_train,batch_size=8,epochs=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMk264bLLGAK",
        "colab_type": "text"
      },
      "source": [
        "Making Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yZkwNsKK_ZS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred=model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HHD5X7O2LM0e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred=y_pred>0.5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jH9jh6f6LOEu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fiAu8pX0LVrg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2ooKmemLuLF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(1536+187) / (1536+187+59+218)"
      ],
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "01bzqTPgMTpf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
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}