artificial_neural_network.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "artificial_neural_network.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/antonio2505/SuperDataScience_Projects_ML/blob/main/artificial_neural_network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lP6JLo1tGNBg"
      },
      "source": [
        "# Artificial Neural Network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWZyYmS_UE_L"
      },
      "source": [
        "### Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3_eS0cV-kCM"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zLg7kKcU-vOY",
        "outputId": "1c405e57-a0c5-4f6b-e4e6-8f60a72c3969"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.5.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1E0Q3aoKUCRX"
      },
      "source": [
        "## Part 1 - Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKWAkFVGUU0Z"
      },
      "source": [
        "### Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sX7Wgre-5V-"
      },
      "source": [
        "dataset = pd.read_csv('Churn_Modelling.csv')\n",
        "\n",
        "x = dataset.iloc[ : , 3:-1].values\n",
        "y= dataset.iloc[ : , -1].values"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2e93ULVqA_S4",
        "outputId": "240f0b5a-0dbb-4c94-a17d-c7c166d81687"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hXWa-j5RA_Me",
        "outputId": "c539c0f9-9e97-4289-d6af-1f444e8c17be"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 0 1 ... 1 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "hsDOC8kHADgF"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "\n",
        "x[ :, 2] = le.fit_transform(x[ :, 2])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20HCJ071BOPF",
        "outputId": "1cf377e0-0a81-44c2-9352-d1cfc563f2a2"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUxGZezpbMcb"
      },
      "source": [
        "One Hot Encoding the \"Geography\" column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s56aDktGB3v2"
      },
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "ct = ColumnTransformer(transformers = [('encoder', OneHotEncoder(), [1])], remainder = 'passthrough')\n",
        "\n",
        "x = np.array(ct.fit_transform(x))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7VLPcl5TEE3g",
        "outputId": "51585cfd-1a1e-4dc5-cfd3-a8b996d2364d"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QAngJVkVFack"
      },
      "source": [
        "### Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCQTb5gTFgc_"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RE_FcHyfV3TQ"
      },
      "source": [
        "### Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCikAyBzGg7W"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-zfEzkRVXIwF"
      },
      "source": [
        "## Part 2 - Building the ANN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvdeScabXtlB"
      },
      "source": [
        "### Initializing the ANN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qplROIxRUhA"
      },
      "source": [
        "ann = tf.keras.models.Sequential()"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rP6urV6SX7kS"
      },
      "source": [
        "### Adding the input layer and the first hidden layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnZGc9mER-_U"
      },
      "source": [
        "ann.add(tf.keras.layers.Dense(units= 6, activation = 'relu'))"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BELWAc_8YJze"
      },
      "source": [
        "### Adding the second hidden layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0ku4vC9SGZL"
      },
      "source": [
        "ann.add(tf.keras.layers.Dense(units= 6, activation = 'relu'))"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OyNEe6RXYcU4"
      },
      "source": [
        "### Adding the output layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-CY_89uSJUz"
      },
      "source": [
        "ann.add(tf.keras.layers.Dense(units= 1, activation = 'sigmoid'))"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JT4u2S1_Y4WG"
      },
      "source": [
        "## Training the ANN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GWlJChhY_ZI"
      },
      "source": [
        "### Compiling the ANN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AmzPs7DVEj9"
      },
      "source": [
        "ann.compile(optimizer= 'adam', loss= 'binary_crossentropy', metrics= ['accuracy'])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QR_G5u7ZLSM"
      },
      "source": [
        "### Training the ANN on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmE9X9yAVZ-J",
        "outputId": "dd98553c-de0a-4ae2-a706-aec0855dd2bd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "ann.fit(x_train, y_train, batch_size= 32, epochs= 100)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "250/250 [==============================] - 1s 1ms/step - loss: 0.5967 - accuracy: 0.7190\n",
            "Epoch 2/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4874 - accuracy: 0.7955\n",
            "Epoch 3/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4504 - accuracy: 0.8034\n",
            "Epoch 4/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4332 - accuracy: 0.8099\n",
            "Epoch 5/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4238 - accuracy: 0.8150\n",
            "Epoch 6/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4160 - accuracy: 0.8191\n",
            "Epoch 7/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4071 - accuracy: 0.8216\n",
            "Epoch 8/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3944 - accuracy: 0.8303\n",
            "Epoch 9/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3812 - accuracy: 0.8388\n",
            "Epoch 10/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3716 - accuracy: 0.8441\n",
            "Epoch 11/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3655 - accuracy: 0.8479\n",
            "Epoch 12/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3613 - accuracy: 0.8487\n",
            "Epoch 13/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3580 - accuracy: 0.8509\n",
            "Epoch 14/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3558 - accuracy: 0.8516\n",
            "Epoch 15/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3539 - accuracy: 0.8520\n",
            "Epoch 16/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3524 - accuracy: 0.8525\n",
            "Epoch 17/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3511 - accuracy: 0.8525\n",
            "Epoch 18/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3503 - accuracy: 0.8537\n",
            "Epoch 19/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3494 - accuracy: 0.8541\n",
            "Epoch 20/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3487 - accuracy: 0.8541\n",
            "Epoch 21/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3480 - accuracy: 0.8560\n",
            "Epoch 22/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3473 - accuracy: 0.8549\n",
            "Epoch 23/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3469 - accuracy: 0.8564\n",
            "Epoch 24/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3461 - accuracy: 0.8551\n",
            "Epoch 25/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3461 - accuracy: 0.8556\n",
            "Epoch 26/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3456 - accuracy: 0.8566\n",
            "Epoch 27/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3451 - accuracy: 0.8568\n",
            "Epoch 28/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3449 - accuracy: 0.8575\n",
            "Epoch 29/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3444 - accuracy: 0.8572\n",
            "Epoch 30/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3441 - accuracy: 0.8569\n",
            "Epoch 31/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3438 - accuracy: 0.8569\n",
            "Epoch 32/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3431 - accuracy: 0.8583\n",
            "Epoch 33/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3434 - accuracy: 0.8580\n",
            "Epoch 34/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3424 - accuracy: 0.8572\n",
            "Epoch 35/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3426 - accuracy: 0.8585\n",
            "Epoch 36/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3421 - accuracy: 0.8594\n",
            "Epoch 37/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3416 - accuracy: 0.8587\n",
            "Epoch 38/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3413 - accuracy: 0.8594\n",
            "Epoch 39/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3410 - accuracy: 0.8593\n",
            "Epoch 40/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3408 - accuracy: 0.8599\n",
            "Epoch 41/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3404 - accuracy: 0.8593\n",
            "Epoch 42/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3403 - accuracy: 0.8596\n",
            "Epoch 43/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3398 - accuracy: 0.8586\n",
            "Epoch 44/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3393 - accuracy: 0.8605\n",
            "Epoch 45/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3396 - accuracy: 0.8608\n",
            "Epoch 46/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3392 - accuracy: 0.8593\n",
            "Epoch 47/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3386 - accuracy: 0.8605\n",
            "Epoch 48/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3390 - accuracy: 0.8616\n",
            "Epoch 49/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3388 - accuracy: 0.8614\n",
            "Epoch 50/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3381 - accuracy: 0.8597\n",
            "Epoch 51/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3385 - accuracy: 0.8597\n",
            "Epoch 52/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3384 - accuracy: 0.8590\n",
            "Epoch 53/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3378 - accuracy: 0.8606\n",
            "Epoch 54/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3379 - accuracy: 0.8620\n",
            "Epoch 55/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3377 - accuracy: 0.8615\n",
            "Epoch 56/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3374 - accuracy: 0.8612\n",
            "Epoch 57/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3376 - accuracy: 0.8615\n",
            "Epoch 58/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3371 - accuracy: 0.8612\n",
            "Epoch 59/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3372 - accuracy: 0.8605\n",
            "Epoch 60/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3369 - accuracy: 0.8615\n",
            "Epoch 61/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3369 - accuracy: 0.8609\n",
            "Epoch 62/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3368 - accuracy: 0.8614\n",
            "Epoch 63/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3361 - accuracy: 0.8616\n",
            "Epoch 64/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3363 - accuracy: 0.8622\n",
            "Epoch 65/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3359 - accuracy: 0.8615\n",
            "Epoch 66/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3356 - accuracy: 0.8616\n",
            "Epoch 67/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3355 - accuracy: 0.8597\n",
            "Epoch 68/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3350 - accuracy: 0.8605\n",
            "Epoch 69/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3352 - accuracy: 0.8609\n",
            "Epoch 70/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3348 - accuracy: 0.8622\n",
            "Epoch 71/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3348 - accuracy: 0.8621\n",
            "Epoch 72/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3350 - accuracy: 0.8618\n",
            "Epoch 73/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3346 - accuracy: 0.8625\n",
            "Epoch 74/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3346 - accuracy: 0.8612\n",
            "Epoch 75/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3342 - accuracy: 0.8618\n",
            "Epoch 76/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3347 - accuracy: 0.8622\n",
            "Epoch 77/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3345 - accuracy: 0.8618\n",
            "Epoch 78/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3341 - accuracy: 0.8615\n",
            "Epoch 79/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3345 - accuracy: 0.8625\n",
            "Epoch 80/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3340 - accuracy: 0.8618\n",
            "Epoch 81/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3337 - accuracy: 0.8616\n",
            "Epoch 82/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3340 - accuracy: 0.8630\n",
            "Epoch 83/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3339 - accuracy: 0.8622\n",
            "Epoch 84/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3338 - accuracy: 0.8616\n",
            "Epoch 85/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3338 - accuracy: 0.8618\n",
            "Epoch 86/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3336 - accuracy: 0.8618\n",
            "Epoch 87/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3339 - accuracy: 0.8610\n",
            "Epoch 88/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3333 - accuracy: 0.8614\n",
            "Epoch 89/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3333 - accuracy: 0.8631\n",
            "Epoch 90/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3334 - accuracy: 0.8618\n",
            "Epoch 91/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3336 - accuracy: 0.8631\n",
            "Epoch 92/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3332 - accuracy: 0.8616\n",
            "Epoch 93/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3331 - accuracy: 0.8622\n",
            "Epoch 94/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3328 - accuracy: 0.8604\n",
            "Epoch 95/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3327 - accuracy: 0.8614\n",
            "Epoch 96/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3329 - accuracy: 0.8625\n",
            "Epoch 97/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3328 - accuracy: 0.8630\n",
            "Epoch 98/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3327 - accuracy: 0.8630\n",
            "Epoch 99/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3328 - accuracy: 0.8620\n",
            "Epoch 100/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3322 - accuracy: 0.8619\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f6f87f6de90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJj5k2MxZga3"
      },
      "source": [
        "## Part 4 - Making the predictions and evaluating the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7yx47jPZt11"
      },
      "source": [
        "### Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xmv1cniBWc-t",
        "outputId": "2815025d-7302-4fcb-dab4-0a53e19f2e84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(ann.predict(sc.transform([[1,0,0,600,1,40,3,60000,2,1,1,50000]]))>0.5)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[False]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-gDVd4YaHr3"
      },
      "source": [
        "y_pred = ann.predict(x_test)\n",
        "y_pred = (y_pred>0.5)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0oyfLWoaEGw"
      },
      "source": [
        "### Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_344bnVZtI9",
        "outputId": "65616cce-86bf-437a-9758-547afa50e103",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1515   80]\n",
            " [ 195  210]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8625"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9aULtiwqagCV"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}