ANN_Churn_Modelling.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWZyYmS_UE_L"
      },
      "source": [
        "### Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "2jZpyubHO3Cy"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "3YFTOGEdPCrE",
        "outputId": "0ea43465-276c-4fcf-b3e6-b88c7f49a460"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.15.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1E0Q3aoKUCRX"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKWAkFVGUU0Z"
      },
      "source": [
        "### Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('Churn_Modelling.csv')\n",
        "X = dataset.iloc[:, 3:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "_Eqjv_jIP-wK"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LletgYzqQyuq",
        "outputId": "907e5082-0463-4a11-e19b-82456d362524"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[619 'France' 'Female' ... 1 1 101348.88]\n",
            " [608 'Spain' 'Female' ... 0 1 112542.58]\n",
            " [502 'France' 'Female' ... 1 0 113931.57]\n",
            " ...\n",
            " [709 'France' 'Female' ... 0 1 42085.58]\n",
            " [772 'Germany' 'Male' ... 1 0 92888.52]\n",
            " [792 'France' 'Female' ... 1 0 38190.78]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0VoIUToQ0HZ",
        "outputId": "0b4d7a8f-1ef9-4acd-fd77-811ff44510ac"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 ... 1 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6bQ0UgSU-NJ"
      },
      "source": [
        "### Encoding categorical data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "le5MJreAbW52"
      },
      "source": [
        "Label Encoding the \"Gender\" column"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "X[:, 2] = le.fit_transform(X[:, 2])"
      ],
      "metadata": {
        "id": "r5K5zPRIRKXn"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_Ir2JU4WpCL",
        "outputId": "2a67cb52-53bd-470a-8297-f7f43f215034"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[619 'France' 0 ... 1 1 101348.88]\n",
            " [608 'Spain' 0 ... 0 1 112542.58]\n",
            " [502 'France' 0 ... 1 0 113931.57]\n",
            " ...\n",
            " [709 'France' 0 ... 0 1 42085.58]\n",
            " [772 'Germany' 1 ... 1 0 92888.52]\n",
            " [792 'France' 0 ... 1 0 38190.78]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUxGZezpbMcb"
      },
      "source": [
        "One Hot Encoding the \"Geography\" column"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1])], remainder='passthrough')\n",
        "X = np.array(ct.fit_transform(X))"
      ],
      "metadata": {
        "id": "HhpmOf_QWw_t"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8c2SA3HwW8rV",
        "outputId": "8109e304-039f-4f19-cbdf-b710050a16dc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.0 0.0 0.0 ... 1 1 101348.88]\n",
            " [0.0 0.0 1.0 ... 0 1 112542.58]\n",
            " [1.0 0.0 0.0 ... 1 0 113931.57]\n",
            " ...\n",
            " [1.0 0.0 0.0 ... 0 1 42085.58]\n",
            " [0.0 1.0 0.0 ... 1 0 92888.52]\n",
            " [1.0 0.0 0.0 ... 1 0 38190.78]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHol938cW8zd"
      },
      "source": [
        "### Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "Xln8K_z8XijF"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RE_FcHyfV3TQ"
      },
      "source": [
        "### Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "x46NOFbMX1LX"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-zfEzkRVXIwF"
      },
      "source": [
        "## Building the ANN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvdeScabXtlB"
      },
      "source": [
        "### Initializing the ANN"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann = tf.keras.models.Sequential()"
      ],
      "metadata": {
        "id": "8cVamPUGYgtr"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rP6urV6SX7kS"
      },
      "source": [
        "### Adding the input layer and the first hidden layer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann.add(tf.keras.layers.Dense(units=6, activation='relu'))"
      ],
      "metadata": {
        "id": "c9FKxFp9ZCbC"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BELWAc_8YJze"
      },
      "source": [
        "### Adding the second hidden layer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann.add(tf.keras.layers.Dense(units=6, activation='relu'))"
      ],
      "metadata": {
        "id": "x51GZLcPaHUG"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OyNEe6RXYcU4"
      },
      "source": [
        "### Adding the output layer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "wjW6fk_IaMpU"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JT4u2S1_Y4WG"
      },
      "source": [
        "italicized text## Part 3 - Training the ANN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GWlJChhY_ZI"
      },
      "source": [
        "### Compiling the ANN"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "-gGFnfnmbeMP"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QR_G5u7ZLSM"
      },
      "source": [
        "### Training the ANN on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann.fit(X_train, y_train, batch_size = 32, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ZGdcITVcfNf",
        "outputId": "834aea14-d8a2-4442-b0a7-346582c0577e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "250/250 [==============================] - 1s 1ms/step - loss: 0.5946 - accuracy: 0.7086\n",
            "Epoch 2/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4850 - accuracy: 0.7960\n",
            "Epoch 3/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4533 - accuracy: 0.7960\n",
            "Epoch 4/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4393 - accuracy: 0.7960\n",
            "Epoch 5/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4331 - accuracy: 0.7960\n",
            "Epoch 6/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4299 - accuracy: 0.7960\n",
            "Epoch 7/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4282 - accuracy: 0.7960\n",
            "Epoch 8/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4267 - accuracy: 0.7960\n",
            "Epoch 9/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.4254 - accuracy: 0.7960\n",
            "Epoch 10/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4249 - accuracy: 0.7960\n",
            "Epoch 11/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4237 - accuracy: 0.7960\n",
            "Epoch 12/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4225 - accuracy: 0.7960\n",
            "Epoch 13/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4209 - accuracy: 0.7976\n",
            "Epoch 14/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4178 - accuracy: 0.7997\n",
            "Epoch 15/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4122 - accuracy: 0.8087\n",
            "Epoch 16/100\n",
            "250/250 [==============================] - 0s 999us/step - loss: 0.4024 - accuracy: 0.8150\n",
            "Epoch 17/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3905 - accuracy: 0.8226\n",
            "Epoch 18/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3795 - accuracy: 0.8307\n",
            "Epoch 19/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3703 - accuracy: 0.8395\n",
            "Epoch 20/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3644 - accuracy: 0.8460\n",
            "Epoch 21/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3598 - accuracy: 0.8506\n",
            "Epoch 22/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3564 - accuracy: 0.8529\n",
            "Epoch 23/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3537 - accuracy: 0.8540\n",
            "Epoch 24/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3513 - accuracy: 0.8566\n",
            "Epoch 25/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3496 - accuracy: 0.8569\n",
            "Epoch 26/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3484 - accuracy: 0.8590\n",
            "Epoch 27/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3473 - accuracy: 0.8590\n",
            "Epoch 28/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3458 - accuracy: 0.8590\n",
            "Epoch 29/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3453 - accuracy: 0.8611\n",
            "Epoch 30/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3444 - accuracy: 0.8612\n",
            "Epoch 31/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3432 - accuracy: 0.8612\n",
            "Epoch 32/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3420 - accuracy: 0.8618\n",
            "Epoch 33/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3419 - accuracy: 0.8621\n",
            "Epoch 34/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3409 - accuracy: 0.8621\n",
            "Epoch 35/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3400 - accuracy: 0.8620\n",
            "Epoch 36/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3395 - accuracy: 0.8635\n",
            "Epoch 37/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3390 - accuracy: 0.8630\n",
            "Epoch 38/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3384 - accuracy: 0.8622\n",
            "Epoch 39/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3380 - accuracy: 0.8627\n",
            "Epoch 40/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3375 - accuracy: 0.8629\n",
            "Epoch 41/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3372 - accuracy: 0.8624\n",
            "Epoch 42/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3371 - accuracy: 0.8626\n",
            "Epoch 43/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3362 - accuracy: 0.8634\n",
            "Epoch 44/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3364 - accuracy: 0.8654\n",
            "Epoch 45/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3365 - accuracy: 0.8644\n",
            "Epoch 46/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3358 - accuracy: 0.8643\n",
            "Epoch 47/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3360 - accuracy: 0.8641\n",
            "Epoch 48/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3354 - accuracy: 0.8636\n",
            "Epoch 49/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3353 - accuracy: 0.8646\n",
            "Epoch 50/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3350 - accuracy: 0.8649\n",
            "Epoch 51/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3351 - accuracy: 0.8644\n",
            "Epoch 52/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3347 - accuracy: 0.8650\n",
            "Epoch 53/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3347 - accuracy: 0.8652\n",
            "Epoch 54/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3345 - accuracy: 0.8650\n",
            "Epoch 55/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3342 - accuracy: 0.8649\n",
            "Epoch 56/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3342 - accuracy: 0.8649\n",
            "Epoch 57/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3339 - accuracy: 0.8643\n",
            "Epoch 58/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3338 - accuracy: 0.8645\n",
            "Epoch 59/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3337 - accuracy: 0.8658\n",
            "Epoch 60/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3333 - accuracy: 0.8650\n",
            "Epoch 61/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3332 - accuracy: 0.8660\n",
            "Epoch 62/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3326 - accuracy: 0.8651\n",
            "Epoch 63/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3324 - accuracy: 0.8652\n",
            "Epoch 64/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3322 - accuracy: 0.8662\n",
            "Epoch 65/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3324 - accuracy: 0.8654\n",
            "Epoch 66/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3320 - accuracy: 0.8644\n",
            "Epoch 67/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3320 - accuracy: 0.8644\n",
            "Epoch 68/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3317 - accuracy: 0.8649\n",
            "Epoch 69/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3314 - accuracy: 0.8656\n",
            "Epoch 70/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3318 - accuracy: 0.8646\n",
            "Epoch 71/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3316 - accuracy: 0.8648\n",
            "Epoch 72/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3316 - accuracy: 0.8660\n",
            "Epoch 73/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3309 - accuracy: 0.8665\n",
            "Epoch 74/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3309 - accuracy: 0.8665\n",
            "Epoch 75/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3308 - accuracy: 0.8651\n",
            "Epoch 76/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3305 - accuracy: 0.8669\n",
            "Epoch 77/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3305 - accuracy: 0.8664\n",
            "Epoch 78/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3302 - accuracy: 0.8668\n",
            "Epoch 79/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3302 - accuracy: 0.8648\n",
            "Epoch 80/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3301 - accuracy: 0.8666\n",
            "Epoch 81/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3300 - accuracy: 0.8675\n",
            "Epoch 82/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3296 - accuracy: 0.8666\n",
            "Epoch 83/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3300 - accuracy: 0.8661\n",
            "Epoch 84/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3294 - accuracy: 0.8679\n",
            "Epoch 85/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3292 - accuracy: 0.8670\n",
            "Epoch 86/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3288 - accuracy: 0.8677\n",
            "Epoch 87/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3290 - accuracy: 0.8664\n",
            "Epoch 88/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3284 - accuracy: 0.8664\n",
            "Epoch 89/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3287 - accuracy: 0.8677\n",
            "Epoch 90/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3287 - accuracy: 0.8662\n",
            "Epoch 91/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3289 - accuracy: 0.8680\n",
            "Epoch 92/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3283 - accuracy: 0.8673\n",
            "Epoch 93/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3280 - accuracy: 0.8670\n",
            "Epoch 94/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3277 - accuracy: 0.8664\n",
            "Epoch 95/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3277 - accuracy: 0.8692\n",
            "Epoch 96/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3275 - accuracy: 0.8676\n",
            "Epoch 97/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3277 - accuracy: 0.8669\n",
            "Epoch 98/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3274 - accuracy: 0.8670\n",
            "Epoch 99/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3275 - accuracy: 0.8664\n",
            "Epoch 100/100\n",
            "250/250 [==============================] - 0s 986us/step - loss: 0.3275 - accuracy: 0.8680\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a9995c1baf0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJj5k2MxZga3"
      },
      "source": [
        "## Making the predictions and evaluating the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84QFoqGYeXHL"
      },
      "source": [
        "### Predicting the result of a single observation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhU1LTgPg-kH"
      },
      "source": [
        "**Solution**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ann.predict(sc.transform([[1, 0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])) > 0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pO5kFoNFeIQB",
        "outputId": "72c9f99d-69c1-4067-dd59-f9627a3c6261"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 13ms/step\n",
            "[[False]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7yx47jPZt11"
      },
      "source": [
        "### Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = ann.predict(X_test)\n",
        "y_pred = (y_pred > 0.5)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LOVMvzmgmmC",
        "outputId": "680e695d-800e-41d0-c09b-1f65f96fb24c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 2ms/step\n",
            "[[0 0]\n",
            " [0 1]\n",
            " [0 0]\n",
            " ...\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0oyfLWoaEGw"
      },
      "source": [
        "### Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpFMtdHVhFTo",
        "outputId": "f6de8d41-db3c-4969-dee9-da56aa3b4aaf"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1506   89]\n",
            " [ 190  215]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8605"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    }
  ]
}