ANN1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ANN1",
      "provenance": [],
      "authorship_tag": "ABX9TyP/zu4s2umKJ7mqNvtIPQFj",
      "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/vedantpople4/ML_Projects/blob/master/ANN1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Revp3uOmKl0R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "4e62c499-4f43-4d5e-e066-69ba1df51d16"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsL2JGvzK3P_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "eff45359-b3e1-4a35-d6a4-0d5d13c54f14"
      },
      "source": [
        "df = pd.read_csv('Churn_Modelling.csv')\n",
        "df.head()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   RowNumber  CustomerId   Surname  ...  IsActiveMember EstimatedSalary Exited\n",
              "0          1    15634602  Hargrave  ...               1       101348.88      1\n",
              "1          2    15647311      Hill  ...               1       112542.58      0\n",
              "2          3    15619304      Onio  ...               0       113931.57      1\n",
              "3          4    15701354      Boni  ...               0        93826.63      0\n",
              "4          5    15737888  Mitchell  ...               1        79084.10      0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ll6pMJ4TML66",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "outputId": "3f9fd968-913b-46d4-c0d4-359ae36d750c"
      },
      "source": [
        "x = df.iloc[:,3:-1].values\n",
        "y = df.iloc[:,-1].values\n",
        "print(x)\n",
        "print(\"\\n\")\n",
        "print(y)"
      ],
      "execution_count": 8,
      "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",
            "\n",
            "\n",
            "[1 0 1 ... 1 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1HsuabFMgK1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "95b8608b-4cb6-4695-d9d4-dee4f04c9f79"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "x[:, 2] = le.fit_transform(x[:, 2])\n",
        "print(x)"
      ],
      "execution_count": 12,
      "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": "code",
      "metadata": {
        "id": "bVDwHqUcMv4o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "387cc382-c218-48ad-817e-7860bd020d2d"
      },
      "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))\n",
        "print(x)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.0 0.0 1.0 ... 1 1 101348.88]\n",
            " [1.0 0.0 0.0 ... 0 1 112542.58]\n",
            " [1.0 0.0 1.0 ... 1 0 113931.57]\n",
            " ...\n",
            " [1.0 0.0 1.0 ... 0 1 42085.58]\n",
            " [0.0 1.0 0.0 ... 1 0 92888.52]\n",
            " [1.0 0.0 1.0 ... 1 0 38190.78]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcXa6n97NAJc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "ab01b546-951f-40bd-f8ea-537637d908d5"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x = sc.fit_transform(x)\n",
        "print(x)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.57873591 -0.57873591  0.99720391 ...  0.64609167  0.97024255\n",
            "   0.02188649]\n",
            " [ 0.57873591 -0.57873591 -1.00280393 ... -1.54776799  0.97024255\n",
            "   0.21653375]\n",
            " [ 0.57873591 -0.57873591  0.99720391 ...  0.64609167 -1.03067011\n",
            "   0.2406869 ]\n",
            " ...\n",
            " [ 0.57873591 -0.57873591  0.99720391 ... -1.54776799  0.97024255\n",
            "  -1.00864308]\n",
            " [-1.72790383  1.72790383 -1.00280393 ...  0.64609167 -1.03067011\n",
            "  -0.12523071]\n",
            " [ 0.57873591 -0.57873591  0.99720391 ...  0.64609167 -1.03067011\n",
            "  -1.07636976]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qq8W1-1_O77N",
        "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)\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MtGwTwmIPSR9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ann = tf.keras.models.Sequential()\n",
        "ann.add(tf.keras.layers.Dense(units=6,activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=6,activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=1,activation='sigmoid'))\n",
        "ann.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGeGaH8PQP7r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "07a750a9-c962-4a92-ff94-82015321d650"
      },
      "source": [
        "ann.fit(x_train,y_train,batch_size=32,epochs=100)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.6584 - accuracy: 0.6299\n",
            "Epoch 2/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4761 - accuracy: 0.7945\n",
            "Epoch 3/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4502 - accuracy: 0.7981\n",
            "Epoch 4/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4336 - accuracy: 0.8041\n",
            "Epoch 5/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4167 - accuracy: 0.8108\n",
            "Epoch 6/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3989 - accuracy: 0.8223\n",
            "Epoch 7/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3848 - accuracy: 0.8322\n",
            "Epoch 8/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3762 - accuracy: 0.8384\n",
            "Epoch 9/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3706 - accuracy: 0.8415\n",
            "Epoch 10/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3668 - accuracy: 0.8421\n",
            "Epoch 11/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3639 - accuracy: 0.8432\n",
            "Epoch 12/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3623 - accuracy: 0.8451\n",
            "Epoch 13/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3602 - accuracy: 0.8454\n",
            "Epoch 14/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3588 - accuracy: 0.8480\n",
            "Epoch 15/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3573 - accuracy: 0.8480\n",
            "Epoch 16/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3560 - accuracy: 0.8489\n",
            "Epoch 17/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3546 - accuracy: 0.8520\n",
            "Epoch 18/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3538 - accuracy: 0.8518\n",
            "Epoch 19/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3526 - accuracy: 0.8530\n",
            "Epoch 20/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3519 - accuracy: 0.8524\n",
            "Epoch 21/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3513 - accuracy: 0.8546\n",
            "Epoch 22/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3507 - accuracy: 0.8560\n",
            "Epoch 23/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3503 - accuracy: 0.8544\n",
            "Epoch 24/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3506 - accuracy: 0.8549\n",
            "Epoch 25/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3496 - accuracy: 0.8568\n",
            "Epoch 26/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3491 - accuracy: 0.8556\n",
            "Epoch 27/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3486 - accuracy: 0.8546\n",
            "Epoch 28/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3482 - accuracy: 0.8554\n",
            "Epoch 29/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3479 - accuracy: 0.8558\n",
            "Epoch 30/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3477 - accuracy: 0.8560\n",
            "Epoch 31/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3475 - accuracy: 0.8551\n",
            "Epoch 32/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3473 - accuracy: 0.8551\n",
            "Epoch 33/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3465 - accuracy: 0.8562\n",
            "Epoch 34/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3466 - accuracy: 0.8553\n",
            "Epoch 35/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3463 - accuracy: 0.8581\n",
            "Epoch 36/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3463 - accuracy: 0.8570\n",
            "Epoch 37/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3458 - accuracy: 0.8553\n",
            "Epoch 38/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3458 - accuracy: 0.8565\n",
            "Epoch 39/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3450 - accuracy: 0.8568\n",
            "Epoch 40/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3450 - accuracy: 0.8569\n",
            "Epoch 41/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3447 - accuracy: 0.8586\n",
            "Epoch 42/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3445 - accuracy: 0.8566\n",
            "Epoch 43/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3447 - accuracy: 0.8580\n",
            "Epoch 44/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3443 - accuracy: 0.8580\n",
            "Epoch 45/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3440 - accuracy: 0.8580\n",
            "Epoch 46/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3435 - accuracy: 0.8590\n",
            "Epoch 47/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3434 - accuracy: 0.8596\n",
            "Epoch 48/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3436 - accuracy: 0.8597\n",
            "Epoch 49/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3433 - accuracy: 0.8594\n",
            "Epoch 50/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3427 - accuracy: 0.8596\n",
            "Epoch 51/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3428 - accuracy: 0.8589\n",
            "Epoch 52/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3422 - accuracy: 0.8586\n",
            "Epoch 53/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3422 - accuracy: 0.8586\n",
            "Epoch 54/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3417 - accuracy: 0.8602\n",
            "Epoch 55/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3414 - accuracy: 0.8604\n",
            "Epoch 56/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3417 - accuracy: 0.8591\n",
            "Epoch 57/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3408 - accuracy: 0.8610\n",
            "Epoch 58/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3411 - accuracy: 0.8602\n",
            "Epoch 59/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3407 - accuracy: 0.8604\n",
            "Epoch 60/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3408 - accuracy: 0.8606\n",
            "Epoch 61/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3406 - accuracy: 0.8599\n",
            "Epoch 62/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3402 - accuracy: 0.8611\n",
            "Epoch 63/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3403 - accuracy: 0.8611\n",
            "Epoch 64/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3396 - accuracy: 0.8611\n",
            "Epoch 65/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3397 - accuracy: 0.8619\n",
            "Epoch 66/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3395 - accuracy: 0.8619\n",
            "Epoch 67/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3393 - accuracy: 0.8637\n",
            "Epoch 68/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3390 - accuracy: 0.8608\n",
            "Epoch 69/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3386 - accuracy: 0.8627\n",
            "Epoch 70/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3385 - accuracy: 0.8633\n",
            "Epoch 71/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3385 - accuracy: 0.8618\n",
            "Epoch 72/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3383 - accuracy: 0.8631\n",
            "Epoch 73/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3380 - accuracy: 0.8624\n",
            "Epoch 74/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3378 - accuracy: 0.8622\n",
            "Epoch 75/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3378 - accuracy: 0.8649\n",
            "Epoch 76/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3368 - accuracy: 0.8627\n",
            "Epoch 77/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3370 - accuracy: 0.8633\n",
            "Epoch 78/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3369 - accuracy: 0.8624\n",
            "Epoch 79/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3369 - accuracy: 0.8635\n",
            "Epoch 80/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3361 - accuracy: 0.8633\n",
            "Epoch 81/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3363 - accuracy: 0.8635\n",
            "Epoch 82/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3358 - accuracy: 0.8636\n",
            "Epoch 83/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3356 - accuracy: 0.8635\n",
            "Epoch 84/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3357 - accuracy: 0.8634\n",
            "Epoch 85/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3353 - accuracy: 0.8634\n",
            "Epoch 86/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3351 - accuracy: 0.8635\n",
            "Epoch 87/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3352 - accuracy: 0.8634\n",
            "Epoch 88/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3345 - accuracy: 0.8630\n",
            "Epoch 89/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3351 - accuracy: 0.8635\n",
            "Epoch 90/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3347 - accuracy: 0.8648\n",
            "Epoch 91/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3348 - accuracy: 0.8649\n",
            "Epoch 92/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3341 - accuracy: 0.8639\n",
            "Epoch 93/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3345 - accuracy: 0.8635\n",
            "Epoch 94/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3345 - accuracy: 0.8636\n",
            "Epoch 95/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3340 - accuracy: 0.8631\n",
            "Epoch 96/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3341 - accuracy: 0.8654\n",
            "Epoch 97/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3344 - accuracy: 0.8629\n",
            "Epoch 98/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3337 - accuracy: 0.8633\n",
            "Epoch 99/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3338 - accuracy: 0.8631\n",
            "Epoch 100/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3335 - accuracy: 0.8635\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f5b97f6bb00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SPF013RQoa9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "126cc404-0420-47e4-ba37-eafe10257247"
      },
      "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))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0]\n",
            " [0 1]\n",
            " [0 0]\n",
            " ...\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CC5euZTaQ1Y-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f48909ef-f5df-47bb-a7f9-30a05a436ec2"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1504   91]\n",
            " [ 190  215]]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}