Gender_classification.ipynb

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        "# Problem Statement\n",
        "Classifying the person as male or female based on following parameter :\n",
        "1. long_hair - This columns contains 0's and 1's where 1 is \"long hair\" and 0 is \"not long hair\".\n",
        "2. forehead_width_cm - This column is in CM's. This is the width of the forehead.\n",
        "3. forehead_height_cm - This is the height of the forehead and it's in Cm's.\n",
        "4. nose_wide - This column contains 0's and 1's where 1 is \"wide nose\" and 0 is \"not wide nose\".\n",
        "5. nose_long - This column contains 0's and 1's where 1 is \"Long nose\" and 0 is \"not long nose\".\n",
        "6. lips_thin - This column contains 0's and 1's where 1 represents the \"thin lips\" while 0 is \"Not thin lips\".\n",
        "7. distance_nose_to_lip_long - This column contains 0's and 1's where 1 represents the \"long distance between nose and lips\" while 0 is \"short distance between nose and lips\".\n",
        "\n",
        "8. gender(Output variable) - This is either \"Male\" or \"Female\"."
      ],
      "metadata": {
        "id": "wbE2v6M6mEIA"
      }
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          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import all required Libraries"
      ],
      "metadata": {
        "id": "qJgw1RDenWHD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, roc_curve"
      ],
      "metadata": {
        "id": "jQPPFKLMnKHD"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "metadata": {
        "id": "YpbiztDU4pNT"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import the Dataset"
      ],
      "metadata": {
        "id": "VOoSqzdBngjI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gender = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/gender classification/gender_classification_v7.csv')"
      ],
      "metadata": {
        "id": "kwBf__OOo5Z_"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gender.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uy-p3OfVpHCD",
        "outputId": "cf8782ea-a6ab-4d01-e47f-4de1fcdfb5e8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5001, 8)"
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender.head()"
      ],
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          "height": 206
        },
        "id": "vYPz1sFwpJmU",
        "outputId": "189a769e-31cc-418a-8887-2603889d4918"
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      "execution_count": 6,
      "outputs": [
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          "data": {
            "text/plain": [
              "   long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
              "0          1               11.8                 6.1          1          0   \n",
              "1          0               14.0                 5.4          0          0   \n",
              "2          0               11.8                 6.3          1          1   \n",
              "3          0               14.4                 6.1          0          1   \n",
              "4          1               13.5                 5.9          0          0   \n",
              "\n",
              "   lips_thin  distance_nose_to_lip_long  gender  \n",
              "0          1                          1    Male  \n",
              "1          1                          0  Female  \n",
              "2          1                          1    Male  \n",
              "3          1                          1    Male  \n",
              "4          0                          0  Female  "
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      "source": [
        "gender.tail()"
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            "text/plain": [
              "      long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
              "4996          1               13.6                 5.1          0          0   \n",
              "4997          1               11.9                 5.4          0          0   \n",
              "4998          1               12.9                 5.7          0          0   \n",
              "4999          1               13.2                 6.2          0          0   \n",
              "5000          1               15.4                 5.4          1          1   \n",
              "\n",
              "      lips_thin  distance_nose_to_lip_long  gender  \n",
              "4996          0                          0  Female  \n",
              "4997          0                          0  Female  \n",
              "4998          0                          0  Female  \n",
              "4999          0                          0  Female  \n",
              "5000          1                          1    Male  "
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              "      <td>13.2</td>\n",
              "      <td>6.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5000</th>\n",
              "      <td>1</td>\n",
              "      <td>15.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3129137b-a489-449f-ba8e-59e7f0232b09')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3129137b-a489-449f-ba8e-59e7f0232b09 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3129137b-a489-449f-ba8e-59e7f0232b09');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-37914b8d-635b-4c9f-a6dd-2393cb00023b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-37914b8d-635b-4c9f-a6dd-2393cb00023b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-37914b8d-635b-4c9f-a6dd-2393cb00023b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender.sample(6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "RiDHE0h5pOsN",
        "outputId": "509b573b-a4d1-4830-bc66-ccb6a2f18f5a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
              "197           1               11.9                 5.9          0          0   \n",
              "1426          0               12.0                 5.2          0          1   \n",
              "4602          0               11.8                 6.6          1          1   \n",
              "707           1               13.1                 5.3          1          1   \n",
              "3527          1               11.9                 6.7          1          1   \n",
              "356           0               11.6                 6.2          0          0   \n",
              "\n",
              "      lips_thin  distance_nose_to_lip_long  gender  \n",
              "197           1                          0  Female  \n",
              "1426          0                          0  Female  \n",
              "4602          1                          0    Male  \n",
              "707           1                          1    Male  \n",
              "3527          1                          1    Male  \n",
              "356           0                          0  Female  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-db9d5999-28c2-4a95-8e46-d27055fb06bd\" class=\"colab-df-container\">\n",
              "    <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>long_hair</th>\n",
              "      <th>forehead_width_cm</th>\n",
              "      <th>forehead_height_cm</th>\n",
              "      <th>nose_wide</th>\n",
              "      <th>nose_long</th>\n",
              "      <th>lips_thin</th>\n",
              "      <th>distance_nose_to_lip_long</th>\n",
              "      <th>gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>1</td>\n",
              "      <td>11.9</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1426</th>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4602</th>\n",
              "      <td>0</td>\n",
              "      <td>11.8</td>\n",
              "      <td>6.6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707</th>\n",
              "      <td>1</td>\n",
              "      <td>13.1</td>\n",
              "      <td>5.3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3527</th>\n",
              "      <td>1</td>\n",
              "      <td>11.9</td>\n",
              "      <td>6.7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>0</td>\n",
              "      <td>11.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db9d5999-28c2-4a95-8e46-d27055fb06bd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-db9d5999-28c2-4a95-8e46-d27055fb06bd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-db9d5999-28c2-4a95-8e46-d27055fb06bd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-074bf8fc-a060-4a89-a79d-f8b4ed5caaa2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-074bf8fc-a060-4a89-a79d-f8b4ed5caaa2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-074bf8fc-a060-4a89-a79d-f8b4ed5caaa2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender.describe(include = 'all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "mXtdcgN9pjQA",
        "outputId": "32f8a327-9e0f-4ace-fe26-56370da8fcc4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          long_hair  forehead_width_cm  forehead_height_cm    nose_wide  \\\n",
              "count   5001.000000        5001.000000         5001.000000  5001.000000   \n",
              "unique          NaN                NaN                 NaN          NaN   \n",
              "top             NaN                NaN                 NaN          NaN   \n",
              "freq            NaN                NaN                 NaN          NaN   \n",
              "mean       0.869626          13.181484            5.946311     0.493901   \n",
              "std        0.336748           1.107128            0.541268     0.500013   \n",
              "min        0.000000          11.400000            5.100000     0.000000   \n",
              "25%        1.000000          12.200000            5.500000     0.000000   \n",
              "50%        1.000000          13.100000            5.900000     0.000000   \n",
              "75%        1.000000          14.000000            6.400000     1.000000   \n",
              "max        1.000000          15.500000            7.100000     1.000000   \n",
              "\n",
              "          nose_long    lips_thin  distance_nose_to_lip_long  gender  \n",
              "count   5001.000000  5001.000000                5001.000000    5001  \n",
              "unique          NaN          NaN                        NaN       2  \n",
              "top             NaN          NaN                        NaN  Female  \n",
              "freq            NaN          NaN                        NaN    2501  \n",
              "mean       0.507898     0.493101                   0.498900     NaN  \n",
              "std        0.499988     0.500002                   0.500049     NaN  \n",
              "min        0.000000     0.000000                   0.000000     NaN  \n",
              "25%        0.000000     0.000000                   0.000000     NaN  \n",
              "50%        1.000000     0.000000                   0.000000     NaN  \n",
              "75%        1.000000     1.000000                   1.000000     NaN  \n",
              "max        1.000000     1.000000                   1.000000     NaN  "
            ],
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              "\n",
              "  <div id=\"df-8496de37-eafe-4e43-95f9-7cd0da4caa4f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>long_hair</th>\n",
              "      <th>forehead_width_cm</th>\n",
              "      <th>forehead_height_cm</th>\n",
              "      <th>nose_wide</th>\n",
              "      <th>nose_long</th>\n",
              "      <th>lips_thin</th>\n",
              "      <th>distance_nose_to_lip_long</th>\n",
              "      <th>gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5001.000000</td>\n",
              "      <td>5001.000000</td>\n",
              "      <td>5001.000000</td>\n",
              "      <td>5001.000000</td>\n",
              "      <td>5001.000000</td>\n",
              "      <td>5001.000000</td>\n",
              "      <td>5001.000000</td>\n",
              "      <td>5001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.869626</td>\n",
              "      <td>13.181484</td>\n",
              "      <td>5.946311</td>\n",
              "      <td>0.493901</td>\n",
              "      <td>0.507898</td>\n",
              "      <td>0.493101</td>\n",
              "      <td>0.498900</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.336748</td>\n",
              "      <td>1.107128</td>\n",
              "      <td>0.541268</td>\n",
              "      <td>0.500013</td>\n",
              "      <td>0.499988</td>\n",
              "      <td>0.500002</td>\n",
              "      <td>0.500049</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.400000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.200000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.100000</td>\n",
              "      <td>5.900000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>7.100000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8496de37-eafe-4e43-95f9-7cd0da4caa4f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8496de37-eafe-4e43-95f9-7cd0da4caa4f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8496de37-eafe-4e43-95f9-7cd0da4caa4f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-be509136-30c0-4590-9e53-b7677de3ab98\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be509136-30c0-4590-9e53-b7677de3ab98')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-be509136-30c0-4590-9e53-b7677de3ab98 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1uCWsZj6p3r3",
        "outputId": "aa3e0fd4-d71b-4fd0-ec2a-dbce1748eb33"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5001 entries, 0 to 5000\n",
            "Data columns (total 8 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   long_hair                  5001 non-null   int64  \n",
            " 1   forehead_width_cm          5001 non-null   float64\n",
            " 2   forehead_height_cm         5001 non-null   float64\n",
            " 3   nose_wide                  5001 non-null   int64  \n",
            " 4   nose_long                  5001 non-null   int64  \n",
            " 5   lips_thin                  5001 non-null   int64  \n",
            " 6   distance_nose_to_lip_long  5001 non-null   int64  \n",
            " 7   gender                     5001 non-null   object \n",
            "dtypes: float64(2), int64(5), object(1)\n",
            "memory usage: 312.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA"
      ],
      "metadata": {
        "id": "MZxdABcSsEo8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g = gender.copy()     #made a copy because I don't want to make the following changes in original dataset"
      ],
      "metadata": {
        "id": "XnMhVt4EntkP"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_var = ['long_hair', 'nose_wide', 'nose_long',\t'lips_thin',\t'distance_nose_to_lip_long']\n",
        "num_var = ['forehead_width_cm', 'forehead_height_cm']"
      ],
      "metadata": {
        "id": "ZRAW3uqMp_q6"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g[cat_var] = g[cat_var].astype('category')"
      ],
      "metadata": {
        "id": "FGAEQKv4refa"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIh7AJiesOX_",
        "outputId": "7a896af0-03b4-47c6-c4af-6bd4fcc13105"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "long_hair                    category\n",
              "forehead_width_cm             float64\n",
              "forehead_height_cm            float64\n",
              "nose_wide                    category\n",
              "nose_long                    category\n",
              "lips_thin                    category\n",
              "distance_nose_to_lip_long    category\n",
              "gender                         object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in cat_var:\n",
        "  plt.title(i)\n",
        "  sns.countplot(x=i, data = g)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EZQLaOaOq08N",
        "outputId": "52ddfc62-7807-4e58-91e4-a7980807632a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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CAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADG82gQpaam6o477lD9+vUVFBSkfv36affu3W5rTp48qaSkJDVs2FD+/v4aMGCACgoK3Nbk5eWpb9++qlu3roKCgjRhwgSdOXPGbc369et1++23y9fXVy1btlRaWlpVnx4AALhGeDSIMjMzlZSUpC1btig9PV2nT59Wz549VVJSYq8ZN26cPvroIy1btkyZmZk6cOCA+vfvb+8/e/as+vbtq1OnTumzzz7TokWLlJaWpqlTp9prcnNz1bdvX3Xv3l05OTkaO3asRowYoU8//bRazxcAANRMDsuyLE8Pcc7hw4cVFBSkzMxMxcXFqaioSI0bN9aSJUt0//33S5K+++47tWnTRllZWerSpYs++eQT/f73v9eBAwcUHBwsSZo/f74mTZqkw4cPy8fHR5MmTdLKlSv17bff2u/14IMPqrCwUKtWrbrkXC6XSwEBASoqKpLT6ayak5cUNeHtKjs2cC3Lfn6wp0e4annPtPf0CECN1HTqN1V27Cv5/3eNuoeoqKhIktSgQQNJUnZ2tk6fPq2EhAR7TevWrdW0aVNlZWVJkrKystS+fXs7hiQpMTFRLpdLO3bssNf8+hjn1pw7xm+VlpbK5XK5PQAAwPWrxgRRWVmZxo4dqzvvvFO33nqrJCk/P18+Pj4KDAx0WxscHKz8/Hx7za9j6Nz+c/sutsblcunEiRPlZklNTVVAQID9CA8Pr5RzBAAANVONCaKkpCR9++23eueddzw9iiZPnqyioiL7sX//fk+PBAAAqlAtTw8gScnJyVqxYoU2bNigJk2a2NtDQkJ06tQpFRYWul0lKigoUEhIiL1m27Ztbsc79y20X6/57TfTCgoK5HQ65efnV24eX19f+fr6Vsq5AQCAms+jV4gsy1JycrKWL1+utWvXKiIiwm1/VFSUateurYyMDHvb7t27lZeXp5iYGElSTEyMvvnmGx06dMhek56eLqfTqbZt29prfn2Mc2vOHQMAAJjNo1eIkpKStGTJEn344YeqX7++fc9PQECA/Pz8FBAQoOHDhyslJUUNGjSQ0+nUk08+qZiYGHXp0kWS1LNnT7Vt21aPPPKIZs2apfz8fE2ZMkVJSUn2VZ7HHntMc+fO1cSJEzVs2DCtXbtW7777rlauXOmxcwcAADWHR68Qvf766yoqKlJ8fLxCQ0Ptx9KlS+01L730kn7/+99rwIABiouLU0hIiN5//317v7e3t1asWCFvb2/FxMTo4Ycf1uDBg/XMM8/YayIiIrRy5Uqlp6crMjJSs2fP1ptvvqnExMRqPV8AAFAz1aifQ1RT8XOIAM/i5xAB1y9+DhEAAEANQRABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeB4Nog0bNuiee+5RWFiYHA6HPvjgA7f9jz76qBwOh9ujV69ebmuOHj2qQYMGyel0KjAwUMOHD1dxcbHbmu3btys2NlZ16tRReHi4Zs2aVdWnBgAAriEeDaKSkhJFRkZq3rx5F1zTq1cvHTx40H78z//8j9v+QYMGaceOHUpPT9eKFSu0YcMGjRo1yt7vcrnUs2dPNWvWTNnZ2Xr++ec1ffp0LViwoMrOCwAAXFtqefLNe/furd69e190ja+vr0JCQs67b9euXVq1apU+//xzderUSZL06quvqk+fPnrhhRcUFhamxYsX69SpU1q4cKF8fHzUrl075eTk6MUXX3QLJwAAYK4afw/R+vXrFRQUpFatWunxxx/XkSNH7H1ZWVkKDAy0Y0iSEhIS5OXlpa1bt9pr4uLi5OPjY69JTEzU7t27dezYsfO+Z2lpqVwul9sDAABcv2p0EPXq1Utvv/22MjIy9NxzzykzM1O9e/fW2bNnJUn5+fkKCgpye02tWrXUoEED5efn22uCg4Pd1px7fm7Nb6WmpiogIMB+hIeHV/apAQCAGsSjH5ldyoMPPmj/uX379urQoYNatGih9evXq0ePHlX2vpMnT1ZKSor93OVyEUUAAFzHavQVot+66aab1KhRI+3du1eSFBISokOHDrmtOXPmjI4ePWrfdxQSEqKCggK3NeeeX+jeJF9fXzmdTrcHAAC4fl1TQfTjjz/qyJEjCg0NlSTFxMSosLBQ2dnZ9pq1a9eqrKxM0dHR9poNGzbo9OnT9pr09HS1atVKN9xwQ/WeAAAAqJE8GkTFxcXKyclRTk6OJCk3N1c5OTnKy8tTcXGxJkyYoC1btuhf//qXMjIydO+996ply5ZKTEyUJLVp00a9evXSyJEjtW3bNm3evFnJycl68MEHFRYWJkkaOHCgfHx8NHz4cO3YsUNLly7Vyy+/7PaRGAAAMJtHg+iLL77Qbbfdpttuu02SlJKSottuu01Tp06Vt7e3tm/frj/84Q+65ZZbNHz4cEVFRWnjxo3y9fW1j7F48WK1bt1aPXr0UJ8+fdS1a1e3nzEUEBCg1atXKzc3V1FRURo/frymTp3KV+4BAIDNozdVx8fHy7KsC+7/9NNPL3mMBg0aaMmSJRdd06FDB23cuPGK5wMAAGao0BWiu+66S4WFheW2u1wu3XXXXVc7EwAAQLWqUBCtX79ep06dKrf95MmTXIkBAADXnCv6yGz79u32n3fu3On2gw3Pnj2rVatW6cYbb6y86QAAAKrBFQVRx44d7d86f76Pxvz8/PTqq69W2nAAAADV4YqCKDc3V5Zl6aabbtK2bdvUuHFje5+Pj4+CgoLk7e1d6UMCAABUpSsKombNmkmSysrKqmQYAAAAT6jw1+737NmjdevW6dChQ+UCaerUqVc9GAAAQHWpUBD9/e9/1+OPP65GjRopJCREDofD3udwOAgiAABwTalQEP31r3/Vs88+q0mTJlX2PAAAANWuQj+H6NixY/rjH/9Y2bMAAAB4RIWC6I9//KNWr15d2bMAAAB4RIU+MmvZsqX+8pe/aMuWLWrfvr1q167ttn/MmDGVMhwAAEB1qFAQLViwQP7+/srMzFRmZqbbPofDQRABAIBrSoWCKDc3t7LnAAAA8JgK3UMEAABwPanQFaJhw4ZddP/ChQsrNAwAAIAnVCiIjh075vb89OnT+vbbb1VYWHjeX/oKAABQk1UoiJYvX15uW1lZmR5//HG1aNHiqocCAACoTpV2D5GXl5dSUlL00ksvVdYhAQAAqkWl3lS9b98+nTlzpjIPCQAAUOUq9JFZSkqK23PLsnTw4EGtXLlSQ4YMqZTBAAAAqkuFguirr75ye+7l5aXGjRtr9uzZl/wGGgAAQE1ToSBat25dZc8BAADgMRUKonMOHz6s3bt3S5JatWqlxo0bV8pQAAAA1alCN1WXlJRo2LBhCg0NVVxcnOLi4hQWFqbhw4fr+PHjlT0jAABAlapQEKWkpCgzM1MfffSRCgsLVVhYqA8//FCZmZkaP358Zc8IAABQpSr0kdn//u//6r333lN8fLy9rU+fPvLz89MDDzyg119/vbLmAwAAqHIVukJ0/PhxBQcHl9seFBTER2YAAOCaU6EgiomJ0bRp03Ty5El724kTJzRjxgzFxMRU2nAAAADVoUIfmc2ZM0e9evVSkyZNFBkZKUn6+uuv5evrq9WrV1fqgAAAAFWtQkHUvn177dmzR4sXL9Z3330nSXrooYc0aNAg+fn5VeqAAAAAVa1CQZSamqrg4GCNHDnSbfvChQt1+PBhTZo0qVKGAwAAqA4VuofojTfeUOvWrcttb9eunebPn3/VQwEAAFSnCgVRfn6+QkNDy21v3LixDh48eNVDAQAAVKcKBVF4eLg2b95cbvvmzZsVFhZ21UMBAABUpwrdQzRy5EiNHTtWp0+f1l133SVJysjI0MSJE/lJ1QAA4JpToSCaMGGCjhw5oieeeEKnTp2SJNWpU0eTJk3S5MmTK3VAAACAqlahIHI4HHruuef0l7/8Rbt27ZKfn59uvvlm+fr6VvZ8AAAAVa5CQXSOv7+/7rjjjsqaBQAAwCMqdFM1AADA9YQgAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxPBpEGzZs0D333KOwsDA5HA598MEHbvsty9LUqVMVGhoqPz8/JSQkaM+ePW5rjh49qkGDBsnpdCowMFDDhw9XcXGx25rt27crNjZWderUUXh4uGbNmlXVpwYAAK4hHg2ikpISRUZGat68eefdP2vWLL3yyiuaP3++tm7dqnr16ikxMVEnT5601wwaNEg7duxQenq6VqxYoQ0bNmjUqFH2fpfLpZ49e6pZs2bKzs7W888/r+nTp2vBggVVfn4AAODaUMuTb967d2/17t37vPssy9KcOXM0ZcoU3XvvvZKkt99+W8HBwfrggw/04IMPateuXVq1apU+//xzderUSZL06quvqk+fPnrhhRcUFhamxYsX69SpU1q4cKF8fHzUrl075eTk6MUXX3QLJwAAYK4aew9Rbm6u8vPzlZCQYG8LCAhQdHS0srKyJElZWVkKDAy0Y0iSEhIS5OXlpa1bt9pr4uLi5OPjY69JTEzU7t27dezYsWo6GwAAUJN59ArRxeTn50uSgoOD3bYHBwfb+/Lz8xUUFOS2v1atWmrQoIHbmoiIiHLHOLfvhhtuKPfepaWlKi0ttZ+7XK6rPBsAAFCT1dgrRJ6UmpqqgIAA+xEeHu7pkQAAQBWqsUEUEhIiSSooKHDbXlBQYO8LCQnRoUOH3PafOXNGR48edVtzvmP8+j1+a/LkySoqKrIf+/fvv/oTAgAANVaNDaKIiAiFhIQoIyPD3uZyubR161bFxMRIkmJiYlRYWKjs7Gx7zdq1a1VWVqbo6Gh7zYYNG3T69Gl7TXp6ulq1anXej8skydfXV06n0+0BAACuXx4NouLiYuXk5CgnJ0fSLzdS5+TkKC8vTw6HQ2PHjtVf//pX/fOf/9Q333yjwYMHKywsTP369ZMktWnTRr169dLIkSO1bds2bd68WcnJyXrwwQcVFhYmSRo4cKB8fHw0fPhw7dixQ0uXLtXLL7+slJQUD501AACoaTx6U/UXX3yh7t2728/PRcqQIUOUlpamiRMnqqSkRKNGjVJhYaG6du2qVatWqU6dOvZrFi9erOTkZPXo0UNeXl4aMGCAXnnlFXt/QECAVq9eraSkJEVFRalRo0aaOnUqX7kHAAA2h2VZlqeHqOlcLpcCAgJUVFRUpR+fRU14u8qODVzLsp8f7OkRrlreM+09PQJQIzWd+k2VHftK/v9dY+8hAgAAqC4EEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxXo4No+vTpcjgcbo/WrVvb+0+ePKmkpCQ1bNhQ/v7+GjBggAoKCtyOkZeXp759+6pu3boKCgrShAkTdObMmeo+FQAAUIPV8vQAl9KuXTutWbPGfl6r1v8fedy4cVq5cqWWLVumgIAAJScnq3///tq8ebMk6ezZs+rbt69CQkL02Wef6eDBgxo8eLBq166t//qv/6r2cwEAADVTjQ+iWrVqKSQkpNz2oqIi/fd//7eWLFmiu+66S5L01ltvqU2bNtqyZYu6dOmi1atXa+fOnVqzZo2Cg4PVsWNHzZw5U5MmTdL06dPl4+NT3acDAABqoBr9kZkk7dmzR2FhYbrppps0aNAg5eXlSZKys7N1+vRpJSQk2Gtbt26tpk2bKisrS5KUlZWl9u3bKzg42F6TmJgol8ulHTt2XPA9S0tL5XK53B4AAOD6VaODKDo6WmlpaVq1apVef/115ebmKjY2Vj///LPy8/Pl4+OjwMBAt9cEBwcrPz9fkpSfn+8WQ+f2n9t3IampqQoICLAf4eHhlXtiAACgRqnRH5n17t3b/nOHDh0UHR2tZs2a6d1335Wfn1+Vve/kyZOVkpJiP3e5XEQRAADXsRp9hei3AgMDdcstt2jv3r0KCQnRqVOnVFhY6LamoKDAvucoJCSk3LfOzj0/331J5/j6+srpdLo9AADA9euaCqLi4mLt27dPoaGhioqKUu3atZWRkWHv3717t/Ly8hQTEyNJiomJ0TfffKNDhw7Za9LT0+V0OtW2bdtqnx8AANRMNfojs6eeekr33HOPmjVrpgMHDmjatGny9vbWQw89pICAAA0fPlwpKSlq0KCBnE6nnnzyScXExKhLly6SpJ49e6pt27Z65JFHNGvWLOXn52vKlClKSkqSr6+vh88OAADUFDU6iH788Uc99NBDOnLkiBo3bqyuXbtqy5Ytaty4sSTppZdekpeXlwYMGKDS0lIlJibqtddes1/v7e2tFStW6PHHH1dMTIzq1aunIUOG6JlnnvHUKQEAgBqoRgfRO++8c9H9derU0bx58zRv3rwLrmnWrJk+/vjjyh4NAABcR66pe4gAAACqAkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjGdUEM2bN0/NmzdXnTp1FB0drW3btnl6JAAAUAMYE0RLly5VSkqKpk2bpi+//FKRkZFKTEzUoUOHPD0aAADwMGOC6MUXX9TIkSM1dOhQtW3bVvPnz1fdunW1cOFCT48GAAA8zIggOnXqlLKzs5WQkGBv8/LyUkJCgrKysjw4GQAAqAlqeXqA6vDTTz/p7NmzCg4OdtseHBys7777rtz60tJSlZaW2s+LiookSS6Xq0rnPFt6okqPD1yrqvrfXnX4+eRZT48A1EhV+e/73LEty7rkWiOC6EqlpqZqxowZ5baHh4d7YBoAAa8+5ukRAFSV1IAqf4uff/5ZAQEXfx8jgqhRo0by9vZWQUGB2/aCggKFhISUWz958mSlpKTYz8vKynT06FE1bNhQDoejyueFZ7lcLoWHh2v//v1yOp2eHgdAJeLft1ksy9LPP/+ssLCwS641Ioh8fHwUFRWljIwM9evXT9IvkZORkaHk5ORy6319feXr6+u2LTAwsBomRU3idDr5DyZwneLftzkudWXoHCOCSJJSUlI0ZMgQderUSZ07d9acOXNUUlKioUOHeno0AADgYcYE0Z/+9CcdPnxYU6dOVX5+vjp27KhVq1aVu9EaAACYx5ggkqTk5OTzfkQG/Jqvr6+mTZtW7mNTANc+/n3jQhzW5XwXDQAA4DpmxA9mBAAAuBiCCAAAGI8gAgAAxiOIAACA8Qgi4DfmzZun5s2bq06dOoqOjta2bds8PRKASrBhwwbdc889CgsLk8Ph0AcffODpkVCDEETAryxdulQpKSmaNm2avvzyS0VGRioxMVGHDh3y9GgArlJJSYkiIyM1b948T4+CGoiv3QO/Eh0drTvuuENz586V9MuveAkPD9eTTz6pp59+2sPTAagsDodDy5cvt3+dE8AVIuD/nDp1StnZ2UpISLC3eXl5KSEhQVlZWR6cDABQ1Qgi4P/89NNPOnv2bLlf5xIcHKz8/HwPTQUAqA4EEQAAMB5BBPyfRo0aydvbWwUFBW7bCwoKFBIS4qGpAADVgSAC/o+Pj4+ioqKUkZFhbysrK1NGRoZiYmI8OBkAoKoZ9dvugUtJSUnRkCFD1KlTJ3Xu3Flz5sxRSUmJhg4d6unRAFyl4uJi7d27136em5urnJwcNWjQQE2bNvXgZKgJ+No98Btz587V888/r/z8fHXs2FGvvPKKoqOjPT0WgKu0fv16de/evdz2IUOGKC0trfoHQo1CEAEAAONxDxEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAHAVpk+fro4dO150zaOPPqp+/fpVyzwAKoYgAoCr8NRTT7n9/jsA1yZ+lxkAXAV/f3/5+/t7egwAV4krRAA8Ij4+XmPGjNHEiRPVoEEDhYSEaPr06fb+vLw83XvvvfL395fT6dQDDzyggoICe//XX3+t7t27q379+nI6nYqKitIXX3xh79+0aZNiY2Pl5+en8PBwjRkzRiUlJZeca+7cubr11lvt5x988IEcDofmz59vb0tISNCUKVMklf/I7OzZs0pJSVFgYKAaNmyoiRMn6re/IamsrEypqamKiIiQn5+fIiMj9d5771323x2AykcQAfCYRYsWqV69etq6datmzZqlZ555Runp6SorK9O9996ro0ePKjMzU+np6fr+++/1pz/9yX7toEGD1KRJE33++efKzs7W008/rdq1a0uS9u3bp169emnAgAHavn27li5dqk2bNik5OfmSM3Xr1k07d+7U4cOHJUmZmZlq1KiR1q9fL0k6ffq0srKyFB8ff97Xz549W2lpaVq4cKE2bdqko0ePavny5W5rUlNT9fbbb2v+/PnasWOHxo0bp4cffliZmZkV+FsEUCksAPCAbt26WV27dnXbdscdd1iTJk2yVq9ebXl7e1t5eXn2vh07dliSrG3btlmWZVn169e30tLSznvs4cOHW6NGjXLbtnHjRsvLy8s6ceLERecqKyuzGjZsaC1btsyyLMvq2LGjlZqaaoWEhFiWZVmbNm2yateubZWUlFiWZVnTpk2zIiMj7deHhoZas2bNsp+fPn3aatKkiXXvvfdalmVZJ0+etOrWrWt99tln5WZ+6KGHLjobgKrDFSIAHtOhQwe356GhoTp06JB27dql8PBwhYeH2/vatm2rwMBA7dq1S5KUkpKiESNGKCEhQX/729+0b98+e+3XX3+ttLQ0+/4ef39/JSYmqqysTLm5uRedyeFwKC4uTuvXr1dhYaF27typJ554QqWlpfruu++UmZmpO+64Q3Xr1i332qKiIh08eFDR0dH2tlq1aqlTp07287179+r48eO6++673eZ7++233c4BQPXipmoAHnPuI65zHA6HysrKLuu106dP18CBA7Vy5Up98sknmjZtmt555x3dd999Ki4u1ujRozVmzJhyr2vatOkljx0fH68FCxZo48aNuu222+R0Ou1IyszMVLdu3S7vBM+juLhYkrRy5UrdeOONbvt8fX0rfFwAV4crRABqnDZt2mj//v3av3+/vW3nzp0qLCxU27Zt7W233HKLxo0bp9WrV6t///566623JEm33367du7cqZYtW5Z7+Pj4XPL9z91HtGzZMvteofj4eK1Zs0abN2++4P1DAQEBCg0N1datW+1tZ86cUXZ2tv28bdu28vX1VV5eXrnZfn1FDED14goRgBonISFB7du316BBgzRnzhydOXNGTzzxhLp166ZOnTrpxIkTmjBhgu6//35FREToxx9/1Oeff64BAwZIkiZNmqQuXbooOTlZI0aMUL169bRz506lp6dr7ty5l3z/Dh066IYbbtCSJUu0YsUKSb8E0VNPPSWHw6E777zzgq/9j//4D/3tb3/TzTffrNatW+vFF19UYWGhvb9+/fp66qmnNG7cOJWVlalr164qKirS5s2b5XQ6NWTIkKv7ywNQIQQRgBrH4XDoww8/1JNPPqm4uDh5eXmpV69eevXVVyVJ3t7eOnLkiAYPHqyCggI1atRI/fv314wZMyT9EjSZmZn6z//8T8XGxsqyLLVo0cLtW2qXev/Y2FitXLlSXbt2tY/pdDrVqlUr1atX74KvHT9+vA4ePKghQ4bIy8tLw4YN03333aeioiJ7zcyZM9W4cWOlpqbq+++/V2BgoG6//Xb9+c9/ruhfGYCr5LCs3/yADAAAAMNwDxEAADAeQQTAKBs3bnT7uvtvHwDMxEdmAIxy4sQJ/fvf/77g/pYtW1bjNABqCoIIAAAYj4/MAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMb7f3A+5aDHUR5RAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in cat_var:\n",
        "  plt.title(i)\n",
        "  sns.countplot(x=i, data = g, hue = g.gender)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XHAFy-GQtK9x",
        "outputId": "6bb30e52-f867-44a4-d8c6-bea6f1a6ef31"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for j in num_var:\n",
        "  plt.title(j)\n",
        "  sns.boxplot(g[j])\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "lHK_hek64fWG",
        "outputId": "be211ca4-2f6f-4d60-dc1c-ea665b0336a0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for j in num_var:\n",
        "  plt.title(j)\n",
        "  sns.boxplot(x = g[j], y = g.gender)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "OfX28nqQ4fdg",
        "outputId": "c5b94bba-724a-4b50-f578-b2368fea9057"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Output variable"
      ],
      "metadata": {
        "id": "ddsHYGVltc79"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x= 'gender', data = g)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "n3iI5aRo6Cg4",
        "outputId": "3dafca64-2055-4504-b1d7-a4b8d385282a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='gender', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g['gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYUitHo865BO",
        "outputId": "041dc807-4dce-4b67-a2e6-c0c9cf97d7f8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female    2501\n",
              "Male      2500\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing"
      ],
      "metadata": {
        "id": "xxrcERKF7kRD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gender.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMGAWWDo7uOt",
        "outputId": "7849324d-6247-489a-fcf9-2b5d629a29cd"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "long_hair                    0\n",
              "forehead_width_cm            0\n",
              "forehead_height_cm           0\n",
              "nose_wide                    0\n",
              "nose_long                    0\n",
              "lips_thin                    0\n",
              "distance_nose_to_lip_long    0\n",
              "gender                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6fWo_4E7uRM",
        "outputId": "dd3a1c02-566c-4c59-e457-3493e6905549"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5001 entries, 0 to 5000\n",
            "Data columns (total 8 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   long_hair                  5001 non-null   int64  \n",
            " 1   forehead_width_cm          5001 non-null   float64\n",
            " 2   forehead_height_cm         5001 non-null   float64\n",
            " 3   nose_wide                  5001 non-null   int64  \n",
            " 4   nose_long                  5001 non-null   int64  \n",
            " 5   lips_thin                  5001 non-null   int64  \n",
            " 6   distance_nose_to_lip_long  5001 non-null   int64  \n",
            " 7   gender                     5001 non-null   object \n",
            "dtypes: float64(2), int64(5), object(1)\n",
            "memory usage: 312.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender['gender']=gender['gender'].map({'Female':0, 'Male':1})"
      ],
      "metadata": {
        "id": "0VyDv50K-yxE"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gender.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gC6eG5c_Ky2",
        "outputId": "19fd7dbe-8fc1-450b-e98e-0a4fc2859005"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5001 entries, 0 to 5000\n",
            "Data columns (total 8 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   long_hair                  5001 non-null   int64  \n",
            " 1   forehead_width_cm          5001 non-null   float64\n",
            " 2   forehead_height_cm         5001 non-null   float64\n",
            " 3   nose_wide                  5001 non-null   int64  \n",
            " 4   nose_long                  5001 non-null   int64  \n",
            " 5   lips_thin                  5001 non-null   int64  \n",
            " 6   distance_nose_to_lip_long  5001 non-null   int64  \n",
            " 7   gender                     5001 non-null   int64  \n",
            "dtypes: float64(2), int64(6)\n",
            "memory usage: 312.7 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-xxL89sYDIqx",
        "outputId": "a1cdf33d-c52c-43e9-b22f-a10eb54d28b8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
              "0          1               11.8                 6.1          1          0   \n",
              "1          0               14.0                 5.4          0          0   \n",
              "2          0               11.8                 6.3          1          1   \n",
              "3          0               14.4                 6.1          0          1   \n",
              "4          1               13.5                 5.9          0          0   \n",
              "\n",
              "   lips_thin  distance_nose_to_lip_long  gender  \n",
              "0          1                          1       1  \n",
              "1          1                          0       0  \n",
              "2          1                          1       1  \n",
              "3          1                          1       1  \n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-da12757a-f485-489f-ba03-82f5bc9720dc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-da12757a-f485-489f-ba03-82f5bc9720dc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "<style>\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cb593538-8f50-467a-8507-b64f4e1f2022 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## train-test split"
      ],
      "metadata": {
        "id": "DzmXJBZk_-d-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = gender.drop(['gender'], axis=1)\n",
        "y =gender['gender']"
      ],
      "metadata": {
        "id": "l-Y4ElBdE8s1"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 78, stratify= y)"
      ],
      "metadata": {
        "id": "dEB9LofAAELW"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AX6EZiLPAEO5",
        "outputId": "ea18ce8e-d5e8-479f-e024-1c32edb5ed5f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3500, 7)\n",
            "(1501, 7)\n",
            "(3500,)\n",
            "(1501,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.head())\n",
        "print(X_test.head())\n",
        "print(y_train.head())\n",
        "print(y_test.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wDW13lPADwPI",
        "outputId": "7bf19c4f-98ee-4365-8a87-80b1a0c6cd4d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
            "1332          1               12.1                 6.3          1          1   \n",
            "3636          1               13.9                 6.5          0          0   \n",
            "1295          1               14.9                 6.1          1          1   \n",
            "361           1               13.8                 5.5          0          0   \n",
            "2381          0               14.9                 5.3          1          1   \n",
            "\n",
            "      lips_thin  distance_nose_to_lip_long  \n",
            "1332          0                          0  \n",
            "3636          0                          0  \n",
            "1295          1                          1  \n",
            "361           1                          1  \n",
            "2381          1                          0  \n",
            "      long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
            "1383          1               14.1                 6.1          0          0   \n",
            "875           1               13.1                 6.1          0          0   \n",
            "4157          1               13.2                 6.3          0          0   \n",
            "870           1               12.5                 5.8          0          0   \n",
            "114           1               13.8                 5.4          0          0   \n",
            "\n",
            "      lips_thin  distance_nose_to_lip_long  \n",
            "1383          0                          0  \n",
            "875           0                          0  \n",
            "4157          0                          1  \n",
            "870           0                          0  \n",
            "114           0                          0  \n",
            "1332    0\n",
            "3636    0\n",
            "1295    1\n",
            "361     1\n",
            "2381    1\n",
            "Name: gender, dtype: int64\n",
            "1383    0\n",
            "875     0\n",
            "4157    0\n",
            "870     0\n",
            "114     0\n",
            "Name: gender, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## model Training"
      ],
      "metadata": {
        "id": "xFrjbOxv7uTy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### evaluation matrix"
      ],
      "metadata": {
        "id": "BasxA8_pFTcF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_metric(y_train, y_train_pred, y_test, y_test_pred):\n",
        "  print(\"The train accuracy score is\",accuracy_score(y_train,y_train_pred))\n",
        "  print(\"The test accuracy score is\",accuracy_score(y_test,y_test_pred))\n",
        "  print(''' --------------------------------------------\n",
        "                Train Report\n",
        "-------------------------------------------- ''')\n",
        "  print(\"The confusion matrix  is\\n\",confusion_matrix(y_train,y_train_pred))\n",
        "  print(\"The classification report is\\n\",classification_report(y_train,y_train_pred))\n",
        "  print(''' --------------------------------------------\n",
        "                Test Report\n",
        "-------------------------------------------- ''')\n",
        "  print(\"The confusion matrix  is\\n\",confusion_matrix(y_test,y_test_pred))\n",
        "  print(\"The classification report  is\\n\",classification_report(y_test,y_test_pred))"
      ],
      "metadata": {
        "id": "KqnOebOIFaFv"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Logistic regression"
      ],
      "metadata": {
        "id": "PeDdUC8T_IdO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()"
      ],
      "metadata": {
        "id": "4FwwpU457uWV"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(X_train, y_train)\n",
        "\n",
        "lr_train_pred = lr.predict(X_train)\n",
        "lr_test_pred = lr.predict(X_test)\n",
        "\n",
        "eval_metric(y_train, lr_train_pred, y_test, lr_test_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8UHyPSKqL7wT",
        "outputId": "7094a82e-aee0-4bbe-ffee-e55ec2c8bc43"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The train accuracy score is 0.9697142857142858\n",
            "The test accuracy score is 0.9686875416389074\n",
            " --------------------------------------------\n",
            "                Train Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[1698   52]\n",
            " [  54 1696]]\n",
            "The classification report is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97      1750\n",
            "           1       0.97      0.97      0.97      1750\n",
            "\n",
            "    accuracy                           0.97      3500\n",
            "   macro avg       0.97      0.97      0.97      3500\n",
            "weighted avg       0.97      0.97      0.97      3500\n",
            "\n",
            " --------------------------------------------\n",
            "                Test Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[731  20]\n",
            " [ 27 723]]\n",
            "The classification report  is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.97      0.97       751\n",
            "           1       0.97      0.96      0.97       750\n",
            "\n",
            "    accuracy                           0.97      1501\n",
            "   macro avg       0.97      0.97      0.97      1501\n",
            "weighted avg       0.97      0.97      0.97      1501\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### SVM"
      ],
      "metadata": {
        "id": "UCz5AHp37uYr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC()\n",
        "\n",
        "svc_param_grid = {'C':[5,10], 'kernel':['linear', 'rbf'], 'gamma':[0.001, 0.005]}"
      ],
      "metadata": {
        "id": "9AQm6VVuh6j5"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svc_grid = GridSearchCV(estimator = svc, param_grid = svc_param_grid, cv = 5)\n",
        "svc_grid.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "1VKQAzZZh6mt",
        "outputId": "1102fe62-c358-48f6-dc65-de6825cc9db8"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=SVC(),\n",
              "             param_grid={'C': [5, 10], 'gamma': [0.001, 0.005],\n",
              "                         'kernel': ['linear', 'rbf']})"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [5, 10], &#x27;gamma&#x27;: [0.001, 0.005],\n",
              "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [5, 10], &#x27;gamma&#x27;: [0.001, 0.005],\n",
              "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc_train_pred = svc_grid.predict(X_train)\n",
        "svc_test_pred =svc_grid.predict(X_test)\n",
        "\n",
        "eval_metric(y_train, svc_train_pred, y_test, svc_test_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o43dCVeoVOUp",
        "outputId": "bdd11b3d-5d76-4a5c-aeaa-36d240c5898e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The train accuracy score is 0.9702857142857143\n",
            "The test accuracy score is 0.9686875416389074\n",
            " --------------------------------------------\n",
            "                Train Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[1702   48]\n",
            " [  56 1694]]\n",
            "The classification report is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97      1750\n",
            "           1       0.97      0.97      0.97      1750\n",
            "\n",
            "    accuracy                           0.97      3500\n",
            "   macro avg       0.97      0.97      0.97      3500\n",
            "weighted avg       0.97      0.97      0.97      3500\n",
            "\n",
            " --------------------------------------------\n",
            "                Test Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[731  20]\n",
            " [ 27 723]]\n",
            "The classification report  is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.97      0.97       751\n",
            "           1       0.97      0.96      0.97       750\n",
            "\n",
            "    accuracy                           0.97      1501\n",
            "   macro avg       0.97      0.97      0.97      1501\n",
            "weighted avg       0.97      0.97      0.97      1501\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc_grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRG7rRfHVdPe",
        "outputId": "fab53e0b-1d6d-4410-dcff-f242b8a9b9a0"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Desicion Tree"
      ],
      "metadata": {
        "id": "47HCtYD37ub1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dt = DecisionTreeClassifier()\n",
        "\n",
        "dt_param_grid = {'criterion':['gini', 'entropy'], 'max_depth':[5,6,8]}"
      ],
      "metadata": {
        "id": "MvEYRsK-it0F"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt_grid = GridSearchCV(estimator = dt, param_grid =dt_param_grid , cv =5 )\n",
        "dt_grid.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "PY4BEgolit79",
        "outputId": "d0bbfeb1-80ca-48ed-cc14-1477a4930f75"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [5, 6, 8]})"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [5, 6, 8]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [5, 6, 8]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_train_pred = dt_grid.predict(X_train)\n",
        "dt_test_pred = dt_grid.predict(X_test)\n",
        "\n",
        "eval_metric(y_train, dt_train_pred, y_test, dt_test_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8xaZBPF6dnPF",
        "outputId": "8a32fa6c-93cc-46f0-c3d5-0cab5ef9c774"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The train accuracy score is 0.9788571428571429\n",
            "The test accuracy score is 0.966688874083944\n",
            " --------------------------------------------\n",
            "                Train Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[1722   28]\n",
            " [  46 1704]]\n",
            "The classification report is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.98      0.98      1750\n",
            "           1       0.98      0.97      0.98      1750\n",
            "\n",
            "    accuracy                           0.98      3500\n",
            "   macro avg       0.98      0.98      0.98      3500\n",
            "weighted avg       0.98      0.98      0.98      3500\n",
            "\n",
            " --------------------------------------------\n",
            "                Test Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[736  15]\n",
            " [ 35 715]]\n",
            "The classification report  is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.98      0.97       751\n",
            "           1       0.98      0.95      0.97       750\n",
            "\n",
            "    accuracy                           0.97      1501\n",
            "   macro avg       0.97      0.97      0.97      1501\n",
            "weighted avg       0.97      0.97      0.97      1501\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuDh2OIOeKde",
        "outputId": "77d3b77d-050c-47a1-c013-2d3abcabdc55"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini', 'max_depth': 6}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### K Nearest Neigbour"
      ],
      "metadata": {
        "id": "al23eNGv7uej"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kn = KNeighborsClassifier()"
      ],
      "metadata": {
        "id": "wV93BXj7kQzJ"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kn.fit(X_train, y_train)\n",
        "\n",
        "kn_train_pred = kn.predict(X_train)\n",
        "kn_test_pred = kn.predict(X_test)\n",
        "\n",
        "eval_metric(y_train, kn_train_pred, y_test, kn_test_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtIFXZWQkQ-y",
        "outputId": "d7e44f5e-8184-4c72-92c5-e543aacfe435"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The train accuracy score is 0.9748571428571429\n",
            "The test accuracy score is 0.9686875416389074\n",
            " --------------------------------------------\n",
            "                Train Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[1717   33]\n",
            " [  55 1695]]\n",
            "The classification report is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.98      0.98      1750\n",
            "           1       0.98      0.97      0.97      1750\n",
            "\n",
            "    accuracy                           0.97      3500\n",
            "   macro avg       0.97      0.97      0.97      3500\n",
            "weighted avg       0.97      0.97      0.97      3500\n",
            "\n",
            " --------------------------------------------\n",
            "                Test Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[737  14]\n",
            " [ 33 717]]\n",
            "The classification report  is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.98      0.97       751\n",
            "           1       0.98      0.96      0.97       750\n",
            "\n",
            "    accuracy                           0.97      1501\n",
            "   macro avg       0.97      0.97      0.97      1501\n",
            "weighted avg       0.97      0.97      0.97      1501\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest"
      ],
      "metadata": {
        "id": "jRsl7K2KJH60"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier()\n",
        "\n",
        "rf_param_grid = {\"n_estimators\" : [50,100,150],\"max_depth\" : [5,8,10], \"max_features\" : [3, 5, 7], \"min_samples_leaf\" : [2 ,4, 6]}\n"
      ],
      "metadata": {
        "id": "c6GVNIaKkxOb"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_grid = GridSearchCV(estimator = rf, param_grid = rf_param_grid, cv =5 )\n",
        "rf_grid.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "ES6Z0PM7kxXI",
        "outputId": "72318a8d-59d3-4ca7-ea49-9e446aec91ed"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
              "             param_grid={'max_depth': [5, 8, 10], 'max_features': [3, 5, 7],\n",
              "                         'min_samples_leaf': [2, 4, 6],\n",
              "                         'n_estimators': [50, 100, 150]})"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
              "             param_grid={&#x27;max_depth&#x27;: [5, 8, 10], &#x27;max_features&#x27;: [3, 5, 7],\n",
              "                         &#x27;min_samples_leaf&#x27;: [2, 4, 6],\n",
              "                         &#x27;n_estimators&#x27;: [50, 100, 150]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
              "             param_grid={&#x27;max_depth&#x27;: [5, 8, 10], &#x27;max_features&#x27;: [3, 5, 7],\n",
              "                         &#x27;min_samples_leaf&#x27;: [2, 4, 6],\n",
              "                         &#x27;n_estimators&#x27;: [50, 100, 150]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_train_pred = rf_grid.predict(X_train)\n",
        "rf_test_pred = rf_grid.predict(X_test)\n",
        "\n",
        "eval_metric(y_train, rf_train_pred, y_test, rf_test_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHW-9-hqkOee",
        "outputId": "a8ab3a22-d5fe-4ea5-8f7f-6783a054e08a"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The train accuracy score is 0.9777142857142858\n",
            "The test accuracy score is 0.9706862091938707\n",
            " --------------------------------------------\n",
            "                Train Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[1724   26]\n",
            " [  52 1698]]\n",
            "The classification report is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.99      0.98      1750\n",
            "           1       0.98      0.97      0.98      1750\n",
            "\n",
            "    accuracy                           0.98      3500\n",
            "   macro avg       0.98      0.98      0.98      3500\n",
            "weighted avg       0.98      0.98      0.98      3500\n",
            "\n",
            " --------------------------------------------\n",
            "                Test Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[739  12]\n",
            " [ 32 718]]\n",
            "The classification report  is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.98      0.97       751\n",
            "           1       0.98      0.96      0.97       750\n",
            "\n",
            "    accuracy                           0.97      1501\n",
            "   macro avg       0.97      0.97      0.97      1501\n",
            "weighted avg       0.97      0.97      0.97      1501\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8kmEMTBkTc8",
        "outputId": "ac2b56ba-4d94-4a41-95a4-82d74b4f9f2c"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 5, 'max_features': 3, 'min_samples_leaf': 2, 'n_estimators': 150}"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gradient Boost"
      ],
      "metadata": {
        "id": "-KP-uPWNJNE3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gb = GradientBoostingClassifier()\n",
        "gb_param_grid = {'max_depth': [8,10,12,14], 'subsample': [0.8, 0.6], 'max_features':[0.2, 0.3],\n",
        "              'n_estimators': [10, 20, 30]}"
      ],
      "metadata": {
        "id": "yidAplV3lJyi"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gb_grid = GridSearchCV(estimator = gb, param_grid = gb_param_grid, cv = 5)\n",
        "gb_grid.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "IwgpoYexlKc4",
        "outputId": "dd1d362b-96da-40d9-fd05-0286b31b9495"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=GradientBoostingClassifier(),\n",
              "             param_grid={'max_depth': [8, 10, 12, 14],\n",
              "                         'max_features': [0.2, 0.3],\n",
              "                         'n_estimators': [10, 20, 30],\n",
              "                         'subsample': [0.8, 0.6]})"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=GradientBoostingClassifier(),\n",
              "             param_grid={&#x27;max_depth&#x27;: [8, 10, 12, 14],\n",
              "                         &#x27;max_features&#x27;: [0.2, 0.3],\n",
              "                         &#x27;n_estimators&#x27;: [10, 20, 30],\n",
              "                         &#x27;subsample&#x27;: [0.8, 0.6]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=GradientBoostingClassifier(),\n",
              "             param_grid={&#x27;max_depth&#x27;: [8, 10, 12, 14],\n",
              "                         &#x27;max_features&#x27;: [0.2, 0.3],\n",
              "                         &#x27;n_estimators&#x27;: [10, 20, 30],\n",
              "                         &#x27;subsample&#x27;: [0.8, 0.6]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gb_train_pred = gb_grid.predict(X_train)\n",
        "gb_test_pred = gb_grid.predict(X_test)\n",
        "\n",
        "eval_metric(y_train, gb_train_pred, y_test, gb_test_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXuAmLPCmr9x",
        "outputId": "b38e1149-208a-4f16-f83c-89f5f18b6e38"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The train accuracy score is 0.9868571428571429\n",
            "The test accuracy score is 0.9700199866755497\n",
            " --------------------------------------------\n",
            "                Train Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[1738   12]\n",
            " [  34 1716]]\n",
            "The classification report is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99      1750\n",
            "           1       0.99      0.98      0.99      1750\n",
            "\n",
            "    accuracy                           0.99      3500\n",
            "   macro avg       0.99      0.99      0.99      3500\n",
            "weighted avg       0.99      0.99      0.99      3500\n",
            "\n",
            " --------------------------------------------\n",
            "                Test Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[736  15]\n",
            " [ 30 720]]\n",
            "The classification report  is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.98      0.97       751\n",
            "           1       0.98      0.96      0.97       750\n",
            "\n",
            "    accuracy                           0.97      1501\n",
            "   macro avg       0.97      0.97      0.97      1501\n",
            "weighted avg       0.97      0.97      0.97      1501\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gb_grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPzdP9znoCwF",
        "outputId": "0e6756fb-9221-4315-a48f-7639d4648b30"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 8, 'max_features': 0.2, 'n_estimators': 10, 'subsample': 0.8}"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ANN"
      ],
      "metadata": {
        "id": "CwJiqwSD5e8K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_arr = X_train.values\n",
        "X_test_arr = X_test.values"
      ],
      "metadata": {
        "id": "troSCBSB5mM1"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_arr.shape)\n",
        "print(X_test_arr.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6oPcpr1f5-R3",
        "outputId": "34d8f0c7-0ea3-4c6d-8ed5-525da2d222b0"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3500, 7)\n",
            "(1501, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(1, activation='sigmoid', input_shape=(7,)))"
      ],
      "metadata": {
        "id": "Ji7v0o0O5zTa"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "ibVljv_C6IrA"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LSGua8Mq6UHg",
        "outputId": "0e1de205-0477-4ce5-8f16-b85a0f3e36dc"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_6 (Dense)             (None, 1)                 8         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8 (32.00 Byte)\n",
            "Trainable params: 8 (32.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_history = model.fit(X_train_arr, y_train, epochs=100, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AscRxi0q6YYr",
        "outputId": "5168eef2-3475-4dab-c0af-ba5a19d9127e"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "88/88 [==============================] - 1s 4ms/step - loss: 0.8830 - accuracy: 0.4675 - val_loss: 0.7068 - val_accuracy: 0.5200\n",
            "Epoch 2/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.6541 - accuracy: 0.6254 - val_loss: 0.6067 - val_accuracy: 0.7057\n",
            "Epoch 3/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.5637 - accuracy: 0.7743 - val_loss: 0.5627 - val_accuracy: 0.7129\n",
            "Epoch 4/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.4937 - accuracy: 0.8539 - val_loss: 0.4756 - val_accuracy: 0.8614\n",
            "Epoch 5/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.4420 - accuracy: 0.8907 - val_loss: 0.4235 - val_accuracy: 0.8929\n",
            "Epoch 6/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.3981 - accuracy: 0.9046 - val_loss: 0.3992 - val_accuracy: 0.9000\n",
            "Epoch 7/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.3644 - accuracy: 0.9186 - val_loss: 0.3586 - val_accuracy: 0.9071\n",
            "Epoch 8/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.3376 - accuracy: 0.9250 - val_loss: 0.3326 - val_accuracy: 0.9171\n",
            "Epoch 9/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.3139 - accuracy: 0.9361 - val_loss: 0.3107 - val_accuracy: 0.9257\n",
            "Epoch 10/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.2949 - accuracy: 0.9429 - val_loss: 0.3008 - val_accuracy: 0.9257\n",
            "Epoch 11/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.2783 - accuracy: 0.9521 - val_loss: 0.2812 - val_accuracy: 0.9343\n",
            "Epoch 12/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.2644 - accuracy: 0.9504 - val_loss: 0.2752 - val_accuracy: 0.9171\n",
            "Epoch 13/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.2522 - accuracy: 0.9550 - val_loss: 0.2560 - val_accuracy: 0.9414\n",
            "Epoch 14/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.2407 - accuracy: 0.9554 - val_loss: 0.2452 - val_accuracy: 0.9429\n",
            "Epoch 15/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.2313 - accuracy: 0.9568 - val_loss: 0.2377 - val_accuracy: 0.9400\n",
            "Epoch 16/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.2231 - accuracy: 0.9582 - val_loss: 0.2313 - val_accuracy: 0.9443\n",
            "Epoch 17/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.2151 - accuracy: 0.9561 - val_loss: 0.2234 - val_accuracy: 0.9386\n",
            "Epoch 18/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.2089 - accuracy: 0.9579 - val_loss: 0.2163 - val_accuracy: 0.9386\n",
            "Epoch 19/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.2027 - accuracy: 0.9564 - val_loss: 0.2115 - val_accuracy: 0.9414\n",
            "Epoch 20/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1971 - accuracy: 0.9575 - val_loss: 0.2064 - val_accuracy: 0.9414\n",
            "Epoch 21/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1918 - accuracy: 0.9561 - val_loss: 0.2050 - val_accuracy: 0.9429\n",
            "Epoch 22/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1872 - accuracy: 0.9579 - val_loss: 0.1980 - val_accuracy: 0.9443\n",
            "Epoch 23/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1830 - accuracy: 0.9575 - val_loss: 0.1944 - val_accuracy: 0.9429\n",
            "Epoch 24/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1791 - accuracy: 0.9564 - val_loss: 0.1897 - val_accuracy: 0.9386\n",
            "Epoch 25/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1753 - accuracy: 0.9561 - val_loss: 0.1870 - val_accuracy: 0.9357\n",
            "Epoch 26/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1721 - accuracy: 0.9571 - val_loss: 0.1843 - val_accuracy: 0.9357\n",
            "Epoch 27/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1689 - accuracy: 0.9550 - val_loss: 0.1806 - val_accuracy: 0.9386\n",
            "Epoch 28/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1665 - accuracy: 0.9571 - val_loss: 0.1786 - val_accuracy: 0.9429\n",
            "Epoch 29/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1636 - accuracy: 0.9554 - val_loss: 0.1775 - val_accuracy: 0.9371\n",
            "Epoch 30/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1608 - accuracy: 0.9557 - val_loss: 0.1738 - val_accuracy: 0.9357\n",
            "Epoch 31/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1584 - accuracy: 0.9571 - val_loss: 0.1714 - val_accuracy: 0.9371\n",
            "Epoch 32/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1558 - accuracy: 0.9557 - val_loss: 0.1694 - val_accuracy: 0.9357\n",
            "Epoch 33/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1542 - accuracy: 0.9571 - val_loss: 0.1682 - val_accuracy: 0.9414\n",
            "Epoch 34/100\n",
            "88/88 [==============================] - 0s 6ms/step - loss: 0.1520 - accuracy: 0.9557 - val_loss: 0.1658 - val_accuracy: 0.9357\n",
            "Epoch 35/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1490 - accuracy: 0.9586 - val_loss: 0.1657 - val_accuracy: 0.9371\n",
            "Epoch 36/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1479 - accuracy: 0.9550 - val_loss: 0.1623 - val_accuracy: 0.9386\n",
            "Epoch 37/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1459 - accuracy: 0.9561 - val_loss: 0.1608 - val_accuracy: 0.9386\n",
            "Epoch 38/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1449 - accuracy: 0.9557 - val_loss: 0.1596 - val_accuracy: 0.9357\n",
            "Epoch 39/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1432 - accuracy: 0.9575 - val_loss: 0.1607 - val_accuracy: 0.9386\n",
            "Epoch 40/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1416 - accuracy: 0.9554 - val_loss: 0.1581 - val_accuracy: 0.9400\n",
            "Epoch 41/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1400 - accuracy: 0.9554 - val_loss: 0.1555 - val_accuracy: 0.9400\n",
            "Epoch 42/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1390 - accuracy: 0.9564 - val_loss: 0.1544 - val_accuracy: 0.9386\n",
            "Epoch 43/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1375 - accuracy: 0.9546 - val_loss: 0.1564 - val_accuracy: 0.9400\n",
            "Epoch 44/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1357 - accuracy: 0.9571 - val_loss: 0.1522 - val_accuracy: 0.9386\n",
            "Epoch 45/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1353 - accuracy: 0.9561 - val_loss: 0.1527 - val_accuracy: 0.9357\n",
            "Epoch 46/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1336 - accuracy: 0.9568 - val_loss: 0.1510 - val_accuracy: 0.9386\n",
            "Epoch 47/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.1327 - accuracy: 0.9536 - val_loss: 0.1497 - val_accuracy: 0.9371\n",
            "Epoch 48/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1317 - accuracy: 0.9546 - val_loss: 0.1492 - val_accuracy: 0.9386\n",
            "Epoch 49/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1309 - accuracy: 0.9550 - val_loss: 0.1489 - val_accuracy: 0.9357\n",
            "Epoch 50/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1295 - accuracy: 0.9550 - val_loss: 0.1475 - val_accuracy: 0.9400\n",
            "Epoch 51/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.1293 - accuracy: 0.9536 - val_loss: 0.1467 - val_accuracy: 0.9371\n",
            "Epoch 52/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1276 - accuracy: 0.9554 - val_loss: 0.1459 - val_accuracy: 0.9371\n",
            "Epoch 53/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1271 - accuracy: 0.9550 - val_loss: 0.1463 - val_accuracy: 0.9443\n",
            "Epoch 54/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1265 - accuracy: 0.9536 - val_loss: 0.1439 - val_accuracy: 0.9386\n",
            "Epoch 55/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1253 - accuracy: 0.9543 - val_loss: 0.1432 - val_accuracy: 0.9386\n",
            "Epoch 56/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1244 - accuracy: 0.9550 - val_loss: 0.1427 - val_accuracy: 0.9386\n",
            "Epoch 57/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1239 - accuracy: 0.9543 - val_loss: 0.1422 - val_accuracy: 0.9386\n",
            "Epoch 58/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1233 - accuracy: 0.9518 - val_loss: 0.1415 - val_accuracy: 0.9386\n",
            "Epoch 59/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.1223 - accuracy: 0.9557 - val_loss: 0.1409 - val_accuracy: 0.9400\n",
            "Epoch 60/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1219 - accuracy: 0.9536 - val_loss: 0.1405 - val_accuracy: 0.9386\n",
            "Epoch 61/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.1210 - accuracy: 0.9561 - val_loss: 0.1414 - val_accuracy: 0.9371\n",
            "Epoch 62/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1207 - accuracy: 0.9529 - val_loss: 0.1406 - val_accuracy: 0.9357\n",
            "Epoch 63/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1197 - accuracy: 0.9564 - val_loss: 0.1389 - val_accuracy: 0.9371\n",
            "Epoch 64/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1192 - accuracy: 0.9550 - val_loss: 0.1383 - val_accuracy: 0.9400\n",
            "Epoch 65/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.1188 - accuracy: 0.9543 - val_loss: 0.1383 - val_accuracy: 0.9386\n",
            "Epoch 66/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1181 - accuracy: 0.9546 - val_loss: 0.1374 - val_accuracy: 0.9400\n",
            "Epoch 67/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1172 - accuracy: 0.9546 - val_loss: 0.1369 - val_accuracy: 0.9371\n",
            "Epoch 68/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1166 - accuracy: 0.9554 - val_loss: 0.1372 - val_accuracy: 0.9357\n",
            "Epoch 69/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1164 - accuracy: 0.9536 - val_loss: 0.1377 - val_accuracy: 0.9357\n",
            "Epoch 70/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1157 - accuracy: 0.9539 - val_loss: 0.1371 - val_accuracy: 0.9357\n",
            "Epoch 71/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1155 - accuracy: 0.9554 - val_loss: 0.1353 - val_accuracy: 0.9371\n",
            "Epoch 72/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1145 - accuracy: 0.9586 - val_loss: 0.1357 - val_accuracy: 0.9357\n",
            "Epoch 73/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1144 - accuracy: 0.9539 - val_loss: 0.1346 - val_accuracy: 0.9371\n",
            "Epoch 74/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1138 - accuracy: 0.9536 - val_loss: 0.1345 - val_accuracy: 0.9386\n",
            "Epoch 75/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.1136 - accuracy: 0.9550 - val_loss: 0.1343 - val_accuracy: 0.9371\n",
            "Epoch 76/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1129 - accuracy: 0.9554 - val_loss: 0.1358 - val_accuracy: 0.9371\n",
            "Epoch 77/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1128 - accuracy: 0.9532 - val_loss: 0.1337 - val_accuracy: 0.9371\n",
            "Epoch 78/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1124 - accuracy: 0.9550 - val_loss: 0.1330 - val_accuracy: 0.9371\n",
            "Epoch 79/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1117 - accuracy: 0.9557 - val_loss: 0.1327 - val_accuracy: 0.9386\n",
            "Epoch 80/100\n",
            "88/88 [==============================] - 1s 6ms/step - loss: 0.1115 - accuracy: 0.9568 - val_loss: 0.1323 - val_accuracy: 0.9357\n",
            "Epoch 81/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1109 - accuracy: 0.9550 - val_loss: 0.1333 - val_accuracy: 0.9343\n",
            "Epoch 82/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1107 - accuracy: 0.9532 - val_loss: 0.1320 - val_accuracy: 0.9386\n",
            "Epoch 83/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1105 - accuracy: 0.9539 - val_loss: 0.1315 - val_accuracy: 0.9357\n",
            "Epoch 84/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1102 - accuracy: 0.9543 - val_loss: 0.1316 - val_accuracy: 0.9386\n",
            "Epoch 85/100\n",
            "88/88 [==============================] - 1s 6ms/step - loss: 0.1096 - accuracy: 0.9539 - val_loss: 0.1312 - val_accuracy: 0.9371\n",
            "Epoch 86/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1091 - accuracy: 0.9550 - val_loss: 0.1321 - val_accuracy: 0.9343\n",
            "Epoch 87/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1087 - accuracy: 0.9546 - val_loss: 0.1305 - val_accuracy: 0.9386\n",
            "Epoch 88/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1085 - accuracy: 0.9571 - val_loss: 0.1322 - val_accuracy: 0.9371\n",
            "Epoch 89/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1084 - accuracy: 0.9564 - val_loss: 0.1301 - val_accuracy: 0.9357\n",
            "Epoch 90/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1078 - accuracy: 0.9543 - val_loss: 0.1315 - val_accuracy: 0.9357\n",
            "Epoch 91/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1077 - accuracy: 0.9546 - val_loss: 0.1298 - val_accuracy: 0.9371\n",
            "Epoch 92/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1069 - accuracy: 0.9593 - val_loss: 0.1299 - val_accuracy: 0.9443\n",
            "Epoch 93/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.1072 - accuracy: 0.9550 - val_loss: 0.1293 - val_accuracy: 0.9371\n",
            "Epoch 94/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1069 - accuracy: 0.9546 - val_loss: 0.1303 - val_accuracy: 0.9357\n",
            "Epoch 95/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1066 - accuracy: 0.9546 - val_loss: 0.1288 - val_accuracy: 0.9357\n",
            "Epoch 96/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1064 - accuracy: 0.9525 - val_loss: 0.1287 - val_accuracy: 0.9357\n",
            "Epoch 97/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1059 - accuracy: 0.9568 - val_loss: 0.1284 - val_accuracy: 0.9371\n",
            "Epoch 98/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1055 - accuracy: 0.9539 - val_loss: 0.1297 - val_accuracy: 0.9357\n",
            "Epoch 99/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1055 - accuracy: 0.9539 - val_loss: 0.1282 - val_accuracy: 0.9343\n",
            "Epoch 100/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1049 - accuracy: 0.9543 - val_loss: 0.1281 - val_accuracy: 0.9357\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model_history.history.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jTGN4OPm6uIp",
        "outputId": "ad1e18b4-be1a-4f44-d0b5-c0159f1d3a1e"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model_history.history['accuracy'])\n",
        "plt.plot(model_history.history['val_accuracy'])\n",
        "plt.title('accuracy Plot')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "fyo-8fKr6uLZ",
        "outputId": "00d46b99-6b95-43d5-af8e-e5fcea81dd0f"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model_history.history['loss'])\n",
        "plt.plot(model_history.history['val_loss'])\n",
        "plt.title('Loss Function Plot')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "6AgBL6U86uP6",
        "outputId": "9a838dfc-60a9-4c85-df5d-c199aeddb83f"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predicted_probabilities = model.predict(X_test_arr)\n",
        "train_predicted_probabilities = model.predict(X_train_arr)\n",
        "\n",
        "test_pred_class_labels = [0 if val<0.5 else 1 for val in test_predicted_probabilities]\n",
        "train_pred_class_labels = [0 if val<0.5 else 1 for val in train_predicted_probabilities]\n",
        "\n",
        "eval_metric(y_train, train_pred_class_labels, y_test, test_pred_class_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WE1bjib6uSs",
        "outputId": "242c02f2-b9b1-4f36-a514-d560763b9a19"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "47/47 [==============================] - 0s 2ms/step\n",
            "110/110 [==============================] - 0s 2ms/step\n",
            "The train accuracy score is 0.9517142857142857\n",
            "The test accuracy score is 0.9506995336442372\n",
            " --------------------------------------------\n",
            "                Train Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[1661   89]\n",
            " [  80 1670]]\n",
            "The classification report is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.95      0.95      1750\n",
            "           1       0.95      0.95      0.95      1750\n",
            "\n",
            "    accuracy                           0.95      3500\n",
            "   macro avg       0.95      0.95      0.95      3500\n",
            "weighted avg       0.95      0.95      0.95      3500\n",
            "\n",
            " --------------------------------------------\n",
            "                Test Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[717  34]\n",
            " [ 40 710]]\n",
            "The classification report  is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.95      0.95       751\n",
            "           1       0.95      0.95      0.95       750\n",
            "\n",
            "    accuracy                           0.95      1501\n",
            "   macro avg       0.95      0.95      0.95      1501\n",
            "weighted avg       0.95      0.95      0.95      1501\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MLP with 2 layers"
      ],
      "metadata": {
        "id": "hglLK0h36uUX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_model1 = Sequential()\n",
        "\n",
        "mlp_model1.add(Dense(15, activation='tanh', kernel_initializer='normal', input_shape=(7,)))\n",
        "mlp_model1.add(Dense(1, activation='sigmoid', kernel_initializer='normal'))"
      ],
      "metadata": {
        "id": "qRCyTBKy-0Og"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_model1.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "fZXVsNon-0Re"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1_history = mlp_model1.fit(X_train_arr, y_train, epochs=100, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09G-3cqS-0U5",
        "outputId": "9386072b-d780-4e3c-d612-7775c49e044c"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "88/88 [==============================] - 1s 5ms/step - loss: 0.6876 - accuracy: 0.6279 - val_loss: 0.6834 - val_accuracy: 0.9271\n",
            "Epoch 2/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6782 - accuracy: 0.8196 - val_loss: 0.6719 - val_accuracy: 0.9400\n",
            "Epoch 3/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6631 - accuracy: 0.9171 - val_loss: 0.6512 - val_accuracy: 0.8271\n",
            "Epoch 4/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6385 - accuracy: 0.9293 - val_loss: 0.6210 - val_accuracy: 0.9471\n",
            "Epoch 5/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6002 - accuracy: 0.9461 - val_loss: 0.5745 - val_accuracy: 0.9471\n",
            "Epoch 6/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.5446 - accuracy: 0.9539 - val_loss: 0.5103 - val_accuracy: 0.9400\n",
            "Epoch 7/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.4755 - accuracy: 0.9507 - val_loss: 0.4418 - val_accuracy: 0.9543\n",
            "Epoch 8/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.4032 - accuracy: 0.9529 - val_loss: 0.3673 - val_accuracy: 0.9386\n",
            "Epoch 9/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.3357 - accuracy: 0.9604 - val_loss: 0.3099 - val_accuracy: 0.9457\n",
            "Epoch 10/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.2822 - accuracy: 0.9529 - val_loss: 0.2885 - val_accuracy: 0.9443\n",
            "Epoch 11/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.2411 - accuracy: 0.9564 - val_loss: 0.2286 - val_accuracy: 0.9400\n",
            "Epoch 12/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.2112 - accuracy: 0.9543 - val_loss: 0.2112 - val_accuracy: 0.9529\n",
            "Epoch 13/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1862 - accuracy: 0.9536 - val_loss: 0.1869 - val_accuracy: 0.9429\n",
            "Epoch 14/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1691 - accuracy: 0.9539 - val_loss: 0.1723 - val_accuracy: 0.9357\n",
            "Epoch 15/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1563 - accuracy: 0.9507 - val_loss: 0.1619 - val_accuracy: 0.9343\n",
            "Epoch 16/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1437 - accuracy: 0.9611 - val_loss: 0.1561 - val_accuracy: 0.9400\n",
            "Epoch 17/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1385 - accuracy: 0.9489 - val_loss: 0.1481 - val_accuracy: 0.9400\n",
            "Epoch 18/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1307 - accuracy: 0.9543 - val_loss: 0.1434 - val_accuracy: 0.9371\n",
            "Epoch 19/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1252 - accuracy: 0.9504 - val_loss: 0.1392 - val_accuracy: 0.9400\n",
            "Epoch 20/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1213 - accuracy: 0.9543 - val_loss: 0.1375 - val_accuracy: 0.9343\n",
            "Epoch 21/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1173 - accuracy: 0.9546 - val_loss: 0.1338 - val_accuracy: 0.9371\n",
            "Epoch 22/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1135 - accuracy: 0.9511 - val_loss: 0.1316 - val_accuracy: 0.9414\n",
            "Epoch 23/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1096 - accuracy: 0.9564 - val_loss: 0.1476 - val_accuracy: 0.9443\n",
            "Epoch 24/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1085 - accuracy: 0.9557 - val_loss: 0.1423 - val_accuracy: 0.9543\n",
            "Epoch 25/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1070 - accuracy: 0.9579 - val_loss: 0.1273 - val_accuracy: 0.9414\n",
            "Epoch 26/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1060 - accuracy: 0.9514 - val_loss: 0.1270 - val_accuracy: 0.9400\n",
            "Epoch 27/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1038 - accuracy: 0.9550 - val_loss: 0.1272 - val_accuracy: 0.9357\n",
            "Epoch 28/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1003 - accuracy: 0.9564 - val_loss: 0.1636 - val_accuracy: 0.9443\n",
            "Epoch 29/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1026 - accuracy: 0.9532 - val_loss: 0.1253 - val_accuracy: 0.9371\n",
            "Epoch 30/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0988 - accuracy: 0.9557 - val_loss: 0.1264 - val_accuracy: 0.9371\n",
            "Epoch 31/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0970 - accuracy: 0.9614 - val_loss: 0.1233 - val_accuracy: 0.9357\n",
            "Epoch 32/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0969 - accuracy: 0.9561 - val_loss: 0.1308 - val_accuracy: 0.9443\n",
            "Epoch 33/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0963 - accuracy: 0.9557 - val_loss: 0.1248 - val_accuracy: 0.9371\n",
            "Epoch 34/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0954 - accuracy: 0.9543 - val_loss: 0.1221 - val_accuracy: 0.9371\n",
            "Epoch 35/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0962 - accuracy: 0.9557 - val_loss: 0.1375 - val_accuracy: 0.9529\n",
            "Epoch 36/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0952 - accuracy: 0.9529 - val_loss: 0.1321 - val_accuracy: 0.9443\n",
            "Epoch 37/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0968 - accuracy: 0.9575 - val_loss: 0.1285 - val_accuracy: 0.9429\n",
            "Epoch 38/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0935 - accuracy: 0.9589 - val_loss: 0.1285 - val_accuracy: 0.9429\n",
            "Epoch 39/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.0927 - accuracy: 0.9579 - val_loss: 0.1279 - val_accuracy: 0.9429\n",
            "Epoch 40/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0915 - accuracy: 0.9596 - val_loss: 0.1235 - val_accuracy: 0.9457\n",
            "Epoch 41/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0923 - accuracy: 0.9579 - val_loss: 0.1357 - val_accuracy: 0.9514\n",
            "Epoch 42/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0919 - accuracy: 0.9564 - val_loss: 0.1368 - val_accuracy: 0.9443\n",
            "Epoch 43/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0907 - accuracy: 0.9575 - val_loss: 0.1211 - val_accuracy: 0.9386\n",
            "Epoch 44/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0914 - accuracy: 0.9550 - val_loss: 0.1212 - val_accuracy: 0.9386\n",
            "Epoch 45/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0917 - accuracy: 0.9564 - val_loss: 0.1285 - val_accuracy: 0.9429\n",
            "Epoch 46/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0886 - accuracy: 0.9614 - val_loss: 0.1255 - val_accuracy: 0.9400\n",
            "Epoch 47/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0895 - accuracy: 0.9582 - val_loss: 0.1240 - val_accuracy: 0.9400\n",
            "Epoch 48/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0886 - accuracy: 0.9564 - val_loss: 0.1217 - val_accuracy: 0.9357\n",
            "Epoch 49/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0904 - accuracy: 0.9546 - val_loss: 0.1270 - val_accuracy: 0.9400\n",
            "Epoch 50/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0897 - accuracy: 0.9596 - val_loss: 0.1230 - val_accuracy: 0.9357\n",
            "Epoch 51/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0894 - accuracy: 0.9593 - val_loss: 0.1277 - val_accuracy: 0.9400\n",
            "Epoch 52/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0892 - accuracy: 0.9550 - val_loss: 0.1280 - val_accuracy: 0.9400\n",
            "Epoch 53/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0898 - accuracy: 0.9561 - val_loss: 0.1304 - val_accuracy: 0.9443\n",
            "Epoch 54/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0907 - accuracy: 0.9568 - val_loss: 0.1228 - val_accuracy: 0.9357\n",
            "Epoch 55/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0876 - accuracy: 0.9561 - val_loss: 0.1267 - val_accuracy: 0.9471\n",
            "Epoch 56/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0893 - accuracy: 0.9593 - val_loss: 0.1268 - val_accuracy: 0.9400\n",
            "Epoch 57/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0884 - accuracy: 0.9571 - val_loss: 0.1398 - val_accuracy: 0.9443\n",
            "Epoch 58/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0885 - accuracy: 0.9546 - val_loss: 0.1243 - val_accuracy: 0.9357\n",
            "Epoch 59/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0892 - accuracy: 0.9586 - val_loss: 0.1224 - val_accuracy: 0.9400\n",
            "Epoch 60/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0889 - accuracy: 0.9564 - val_loss: 0.1255 - val_accuracy: 0.9414\n",
            "Epoch 61/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0880 - accuracy: 0.9579 - val_loss: 0.1270 - val_accuracy: 0.9471\n",
            "Epoch 62/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0894 - accuracy: 0.9554 - val_loss: 0.1391 - val_accuracy: 0.9443\n",
            "Epoch 63/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0900 - accuracy: 0.9561 - val_loss: 0.1281 - val_accuracy: 0.9414\n",
            "Epoch 64/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0868 - accuracy: 0.9586 - val_loss: 0.1228 - val_accuracy: 0.9386\n",
            "Epoch 65/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0881 - accuracy: 0.9593 - val_loss: 0.1228 - val_accuracy: 0.9386\n",
            "Epoch 66/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0863 - accuracy: 0.9589 - val_loss: 0.1630 - val_accuracy: 0.9443\n",
            "Epoch 67/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0887 - accuracy: 0.9575 - val_loss: 0.1246 - val_accuracy: 0.9429\n",
            "Epoch 68/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0878 - accuracy: 0.9579 - val_loss: 0.1285 - val_accuracy: 0.9414\n",
            "Epoch 69/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0867 - accuracy: 0.9550 - val_loss: 0.1267 - val_accuracy: 0.9471\n",
            "Epoch 70/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0865 - accuracy: 0.9604 - val_loss: 0.1236 - val_accuracy: 0.9343\n",
            "Epoch 71/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0858 - accuracy: 0.9607 - val_loss: 0.1246 - val_accuracy: 0.9371\n",
            "Epoch 72/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0859 - accuracy: 0.9557 - val_loss: 0.1416 - val_accuracy: 0.9443\n",
            "Epoch 73/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0879 - accuracy: 0.9579 - val_loss: 0.1409 - val_accuracy: 0.9443\n",
            "Epoch 74/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0867 - accuracy: 0.9621 - val_loss: 0.1318 - val_accuracy: 0.9443\n",
            "Epoch 75/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0866 - accuracy: 0.9607 - val_loss: 0.1266 - val_accuracy: 0.9371\n",
            "Epoch 76/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.0871 - accuracy: 0.9557 - val_loss: 0.1236 - val_accuracy: 0.9371\n",
            "Epoch 77/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0860 - accuracy: 0.9604 - val_loss: 0.1345 - val_accuracy: 0.9429\n",
            "Epoch 78/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0865 - accuracy: 0.9579 - val_loss: 0.1422 - val_accuracy: 0.9443\n",
            "Epoch 79/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0862 - accuracy: 0.9589 - val_loss: 0.1240 - val_accuracy: 0.9357\n",
            "Epoch 80/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.0867 - accuracy: 0.9564 - val_loss: 0.1368 - val_accuracy: 0.9514\n",
            "Epoch 81/100\n",
            "88/88 [==============================] - 0s 6ms/step - loss: 0.0856 - accuracy: 0.9604 - val_loss: 0.1343 - val_accuracy: 0.9443\n",
            "Epoch 82/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0860 - accuracy: 0.9586 - val_loss: 0.1404 - val_accuracy: 0.9443\n",
            "Epoch 83/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0864 - accuracy: 0.9604 - val_loss: 0.1266 - val_accuracy: 0.9386\n",
            "Epoch 84/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0853 - accuracy: 0.9586 - val_loss: 0.1253 - val_accuracy: 0.9357\n",
            "Epoch 85/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0847 - accuracy: 0.9589 - val_loss: 0.1253 - val_accuracy: 0.9386\n",
            "Epoch 86/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0865 - accuracy: 0.9614 - val_loss: 0.1263 - val_accuracy: 0.9429\n",
            "Epoch 87/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0858 - accuracy: 0.9582 - val_loss: 0.1248 - val_accuracy: 0.9343\n",
            "Epoch 88/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0865 - accuracy: 0.9593 - val_loss: 0.1289 - val_accuracy: 0.9457\n",
            "Epoch 89/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0860 - accuracy: 0.9561 - val_loss: 0.1274 - val_accuracy: 0.9371\n",
            "Epoch 90/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0857 - accuracy: 0.9604 - val_loss: 0.1360 - val_accuracy: 0.9514\n",
            "Epoch 91/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0852 - accuracy: 0.9593 - val_loss: 0.1300 - val_accuracy: 0.9443\n",
            "Epoch 92/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0878 - accuracy: 0.9575 - val_loss: 0.1324 - val_accuracy: 0.9400\n",
            "Epoch 93/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0843 - accuracy: 0.9618 - val_loss: 0.2100 - val_accuracy: 0.9443\n",
            "Epoch 94/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0864 - accuracy: 0.9589 - val_loss: 0.1248 - val_accuracy: 0.9386\n",
            "Epoch 95/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0859 - accuracy: 0.9561 - val_loss: 0.1478 - val_accuracy: 0.9443\n",
            "Epoch 96/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0830 - accuracy: 0.9629 - val_loss: 0.1367 - val_accuracy: 0.9514\n",
            "Epoch 97/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0861 - accuracy: 0.9614 - val_loss: 0.1292 - val_accuracy: 0.9471\n",
            "Epoch 98/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0850 - accuracy: 0.9607 - val_loss: 0.1509 - val_accuracy: 0.9443\n",
            "Epoch 99/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0872 - accuracy: 0.9582 - val_loss: 0.1250 - val_accuracy: 0.9357\n",
            "Epoch 100/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0870 - accuracy: 0.9600 - val_loss: 0.1264 - val_accuracy: 0.9371\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model1_history.history.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yf6b6_cd-0Zg",
        "outputId": "bb1caf2d-58a4-4d73-8636-607e8994925f"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model1_history.history['accuracy'])\n",
        "plt.plot(model1_history.history['val_accuracy'])\n",
        "plt.title('accuracy Plot')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Md4Oq56L-0hL",
        "outputId": "ecab6f18-4c48-4b08-e7ac-d39b84d8156e"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model1_history.history['loss'])\n",
        "plt.plot(model1_history.history['val_loss'])\n",
        "plt.title('Loss Function Plot')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "WalChm5R-0kp",
        "outputId": "9285e638-9b09-40c4-9bed-1f7887db2448"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predicted_probabilities1 = mlp_model1.predict(X_test_arr)\n",
        "train_predicted_probabilities1 = mlp_model1.predict(X_train_arr)\n",
        "\n",
        "test_pred_class_labels1 = [0 if val<0.5 else 1 for val in test_predicted_probabilities1]\n",
        "train_pred_class_labels1 = [0 if val<0.5 else 1 for val in train_predicted_probabilities1]\n",
        "\n",
        "eval_metric(y_train, train_pred_class_labels, y_test, test_pred_class_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uMd_0nYM-0pt",
        "outputId": "fd7cbd35-e0a3-4b4e-973f-73cf0eec5757"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "47/47 [==============================] - 0s 2ms/step\n",
            "110/110 [==============================] - 0s 2ms/step\n",
            "The train accuracy score is 0.9517142857142857\n",
            "The test accuracy score is 0.9506995336442372\n",
            " --------------------------------------------\n",
            "                Train Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[1661   89]\n",
            " [  80 1670]]\n",
            "The classification report is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.95      0.95      1750\n",
            "           1       0.95      0.95      0.95      1750\n",
            "\n",
            "    accuracy                           0.95      3500\n",
            "   macro avg       0.95      0.95      0.95      3500\n",
            "weighted avg       0.95      0.95      0.95      3500\n",
            "\n",
            " --------------------------------------------\n",
            "                Test Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[717  34]\n",
            " [ 40 710]]\n",
            "The classification report  is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.95      0.95       751\n",
            "           1       0.95      0.95      0.95       750\n",
            "\n",
            "    accuracy                           0.95      1501\n",
            "   macro avg       0.95      0.95      0.95      1501\n",
            "weighted avg       0.95      0.95      0.95      1501\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MLP with 3 layers"
      ],
      "metadata": {
        "id": "L6t5fSmHHrat"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_model3 = Sequential()\n",
        "\n",
        "mlp_model3.add(Dense(25, input_shape=(7,), activation='relu', kernel_initializer='normal'))\n",
        "mlp_model3.add(Dense(20, activation='relu', kernel_initializer='normal'))\n",
        "mlp_model3.add(Dense(1, activation='sigmoid', kernel_initializer='normal'))"
      ],
      "metadata": {
        "id": "5X0MNg35av4I"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_model3.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "cy6oJB9Mav6h"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model3_history = mlp_model3.fit(X_train_arr, y_train, epochs=100, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afx3C4b3av9b",
        "outputId": "d8a26809-4775-4ccf-a78d-c1792dbbc5eb"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "88/88 [==============================] - 1s 5ms/step - loss: 0.6926 - accuracy: 0.6075 - val_loss: 0.6925 - val_accuracy: 0.4800\n",
            "Epoch 2/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6922 - accuracy: 0.5675 - val_loss: 0.6921 - val_accuracy: 0.6443\n",
            "Epoch 3/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6918 - accuracy: 0.6643 - val_loss: 0.6916 - val_accuracy: 0.7900\n",
            "Epoch 4/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6913 - accuracy: 0.6682 - val_loss: 0.6912 - val_accuracy: 0.7143\n",
            "Epoch 5/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6907 - accuracy: 0.7471 - val_loss: 0.6905 - val_accuracy: 0.9229\n",
            "Epoch 6/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6901 - accuracy: 0.9261 - val_loss: 0.6896 - val_accuracy: 0.9214\n",
            "Epoch 7/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6891 - accuracy: 0.8107 - val_loss: 0.6885 - val_accuracy: 0.8386\n",
            "Epoch 8/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6880 - accuracy: 0.7714 - val_loss: 0.6872 - val_accuracy: 0.9114\n",
            "Epoch 9/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6864 - accuracy: 0.8029 - val_loss: 0.6853 - val_accuracy: 0.9000\n",
            "Epoch 10/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6843 - accuracy: 0.8764 - val_loss: 0.6827 - val_accuracy: 0.8600\n",
            "Epoch 11/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6813 - accuracy: 0.8493 - val_loss: 0.6790 - val_accuracy: 0.8443\n",
            "Epoch 12/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.6770 - accuracy: 0.8179 - val_loss: 0.6744 - val_accuracy: 0.9400\n",
            "Epoch 13/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.6707 - accuracy: 0.9314 - val_loss: 0.6658 - val_accuracy: 0.9043\n",
            "Epoch 14/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.6606 - accuracy: 0.8529 - val_loss: 0.6552 - val_accuracy: 0.9500\n",
            "Epoch 15/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.6447 - accuracy: 0.9550 - val_loss: 0.6322 - val_accuracy: 0.9429\n",
            "Epoch 16/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.6173 - accuracy: 0.9546 - val_loss: 0.5964 - val_accuracy: 0.9314\n",
            "Epoch 17/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.5704 - accuracy: 0.9568 - val_loss: 0.5467 - val_accuracy: 0.9543\n",
            "Epoch 18/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.4965 - accuracy: 0.9554 - val_loss: 0.4450 - val_accuracy: 0.9371\n",
            "Epoch 19/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.3916 - accuracy: 0.9582 - val_loss: 0.3742 - val_accuracy: 0.9500\n",
            "Epoch 20/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.2892 - accuracy: 0.9561 - val_loss: 0.2503 - val_accuracy: 0.9414\n",
            "Epoch 21/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.2161 - accuracy: 0.9564 - val_loss: 0.2621 - val_accuracy: 0.9371\n",
            "Epoch 22/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1744 - accuracy: 0.9589 - val_loss: 0.1621 - val_accuracy: 0.9343\n",
            "Epoch 23/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.1413 - accuracy: 0.9564 - val_loss: 0.1574 - val_accuracy: 0.9529\n",
            "Epoch 24/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.1320 - accuracy: 0.9571 - val_loss: 0.1447 - val_accuracy: 0.9514\n",
            "Epoch 25/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1195 - accuracy: 0.9589 - val_loss: 0.1384 - val_accuracy: 0.9443\n",
            "Epoch 26/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1146 - accuracy: 0.9554 - val_loss: 0.1257 - val_accuracy: 0.9400\n",
            "Epoch 27/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1079 - accuracy: 0.9571 - val_loss: 0.1292 - val_accuracy: 0.9500\n",
            "Epoch 28/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1026 - accuracy: 0.9586 - val_loss: 0.1562 - val_accuracy: 0.9543\n",
            "Epoch 29/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1008 - accuracy: 0.9589 - val_loss: 0.1209 - val_accuracy: 0.9357\n",
            "Epoch 30/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.1039 - accuracy: 0.9543 - val_loss: 0.1229 - val_accuracy: 0.9357\n",
            "Epoch 31/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0990 - accuracy: 0.9568 - val_loss: 0.1499 - val_accuracy: 0.9443\n",
            "Epoch 32/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0960 - accuracy: 0.9600 - val_loss: 0.1254 - val_accuracy: 0.9500\n",
            "Epoch 33/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0948 - accuracy: 0.9600 - val_loss: 0.1271 - val_accuracy: 0.9514\n",
            "Epoch 34/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0975 - accuracy: 0.9550 - val_loss: 0.4055 - val_accuracy: 0.8029\n",
            "Epoch 35/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0993 - accuracy: 0.9561 - val_loss: 0.1321 - val_accuracy: 0.9514\n",
            "Epoch 36/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0944 - accuracy: 0.9564 - val_loss: 0.1623 - val_accuracy: 0.9443\n",
            "Epoch 37/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0938 - accuracy: 0.9611 - val_loss: 0.1256 - val_accuracy: 0.9400\n",
            "Epoch 38/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0988 - accuracy: 0.9575 - val_loss: 0.1210 - val_accuracy: 0.9357\n",
            "Epoch 39/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0959 - accuracy: 0.9589 - val_loss: 0.1215 - val_accuracy: 0.9357\n",
            "Epoch 40/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0982 - accuracy: 0.9554 - val_loss: 0.1590 - val_accuracy: 0.9443\n",
            "Epoch 41/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.0937 - accuracy: 0.9607 - val_loss: 0.1457 - val_accuracy: 0.9543\n",
            "Epoch 42/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.0922 - accuracy: 0.9586 - val_loss: 0.1530 - val_accuracy: 0.9443\n",
            "Epoch 43/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.0918 - accuracy: 0.9561 - val_loss: 0.1227 - val_accuracy: 0.9357\n",
            "Epoch 44/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0929 - accuracy: 0.9579 - val_loss: 0.1275 - val_accuracy: 0.9414\n",
            "Epoch 45/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0929 - accuracy: 0.9586 - val_loss: 0.1226 - val_accuracy: 0.9386\n",
            "Epoch 46/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0940 - accuracy: 0.9557 - val_loss: 0.2154 - val_accuracy: 0.9471\n",
            "Epoch 47/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0938 - accuracy: 0.9586 - val_loss: 0.1234 - val_accuracy: 0.9329\n",
            "Epoch 48/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0967 - accuracy: 0.9568 - val_loss: 0.1397 - val_accuracy: 0.9443\n",
            "Epoch 49/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0911 - accuracy: 0.9571 - val_loss: 0.1536 - val_accuracy: 0.9543\n",
            "Epoch 50/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0901 - accuracy: 0.9582 - val_loss: 0.1362 - val_accuracy: 0.9514\n",
            "Epoch 51/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0917 - accuracy: 0.9568 - val_loss: 0.1284 - val_accuracy: 0.9471\n",
            "Epoch 52/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0881 - accuracy: 0.9607 - val_loss: 0.1282 - val_accuracy: 0.9429\n",
            "Epoch 53/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0922 - accuracy: 0.9568 - val_loss: 0.1526 - val_accuracy: 0.9443\n",
            "Epoch 54/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0963 - accuracy: 0.9579 - val_loss: 0.1329 - val_accuracy: 0.9414\n",
            "Epoch 55/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0900 - accuracy: 0.9600 - val_loss: 0.1400 - val_accuracy: 0.9443\n",
            "Epoch 56/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0871 - accuracy: 0.9618 - val_loss: 0.1371 - val_accuracy: 0.9514\n",
            "Epoch 57/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0908 - accuracy: 0.9575 - val_loss: 0.1249 - val_accuracy: 0.9329\n",
            "Epoch 58/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0930 - accuracy: 0.9582 - val_loss: 0.1427 - val_accuracy: 0.9443\n",
            "Epoch 59/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0940 - accuracy: 0.9561 - val_loss: 0.1386 - val_accuracy: 0.9514\n",
            "Epoch 60/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0933 - accuracy: 0.9571 - val_loss: 0.1381 - val_accuracy: 0.9443\n",
            "Epoch 61/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0873 - accuracy: 0.9600 - val_loss: 0.1470 - val_accuracy: 0.9443\n",
            "Epoch 62/100\n",
            "88/88 [==============================] - 0s 5ms/step - loss: 0.0912 - accuracy: 0.9582 - val_loss: 0.1879 - val_accuracy: 0.9443\n",
            "Epoch 63/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0860 - accuracy: 0.9643 - val_loss: 0.1363 - val_accuracy: 0.9443\n",
            "Epoch 64/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0925 - accuracy: 0.9554 - val_loss: 0.1251 - val_accuracy: 0.9371\n",
            "Epoch 65/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0930 - accuracy: 0.9571 - val_loss: 0.1316 - val_accuracy: 0.9414\n",
            "Epoch 66/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0897 - accuracy: 0.9582 - val_loss: 0.1326 - val_accuracy: 0.9400\n",
            "Epoch 67/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0907 - accuracy: 0.9582 - val_loss: 0.1425 - val_accuracy: 0.9443\n",
            "Epoch 68/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0938 - accuracy: 0.9589 - val_loss: 0.1355 - val_accuracy: 0.9514\n",
            "Epoch 69/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0856 - accuracy: 0.9586 - val_loss: 0.1255 - val_accuracy: 0.9343\n",
            "Epoch 70/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0907 - accuracy: 0.9582 - val_loss: 0.1265 - val_accuracy: 0.9429\n",
            "Epoch 71/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0884 - accuracy: 0.9564 - val_loss: 0.1528 - val_accuracy: 0.9443\n",
            "Epoch 72/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0854 - accuracy: 0.9596 - val_loss: 0.1335 - val_accuracy: 0.9500\n",
            "Epoch 73/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0957 - accuracy: 0.9579 - val_loss: 0.1285 - val_accuracy: 0.9414\n",
            "Epoch 74/100\n",
            "88/88 [==============================] - 0s 4ms/step - loss: 0.0908 - accuracy: 0.9589 - val_loss: 0.1408 - val_accuracy: 0.9443\n",
            "Epoch 75/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0861 - accuracy: 0.9600 - val_loss: 0.1250 - val_accuracy: 0.9357\n",
            "Epoch 76/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0928 - accuracy: 0.9561 - val_loss: 0.1378 - val_accuracy: 0.9457\n",
            "Epoch 77/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0869 - accuracy: 0.9621 - val_loss: 0.1251 - val_accuracy: 0.9357\n",
            "Epoch 78/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0859 - accuracy: 0.9621 - val_loss: 0.1575 - val_accuracy: 0.9443\n",
            "Epoch 79/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0887 - accuracy: 0.9575 - val_loss: 0.1894 - val_accuracy: 0.9443\n",
            "Epoch 80/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0916 - accuracy: 0.9579 - val_loss: 0.1255 - val_accuracy: 0.9343\n",
            "Epoch 81/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.0897 - accuracy: 0.9579 - val_loss: 0.1263 - val_accuracy: 0.9400\n",
            "Epoch 82/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.0880 - accuracy: 0.9561 - val_loss: 0.2537 - val_accuracy: 0.9414\n",
            "Epoch 83/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0925 - accuracy: 0.9582 - val_loss: 0.1332 - val_accuracy: 0.9500\n",
            "Epoch 84/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0860 - accuracy: 0.9579 - val_loss: 0.2315 - val_accuracy: 0.9429\n",
            "Epoch 85/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0923 - accuracy: 0.9596 - val_loss: 0.1421 - val_accuracy: 0.9443\n",
            "Epoch 86/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0915 - accuracy: 0.9518 - val_loss: 0.1295 - val_accuracy: 0.9471\n",
            "Epoch 87/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0895 - accuracy: 0.9579 - val_loss: 0.1968 - val_accuracy: 0.9529\n",
            "Epoch 88/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0890 - accuracy: 0.9557 - val_loss: 0.1535 - val_accuracy: 0.9443\n",
            "Epoch 89/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0904 - accuracy: 0.9586 - val_loss: 0.1299 - val_accuracy: 0.9414\n",
            "Epoch 90/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0881 - accuracy: 0.9575 - val_loss: 0.1468 - val_accuracy: 0.9514\n",
            "Epoch 91/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0890 - accuracy: 0.9611 - val_loss: 0.1253 - val_accuracy: 0.9343\n",
            "Epoch 92/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0853 - accuracy: 0.9579 - val_loss: 0.1284 - val_accuracy: 0.9471\n",
            "Epoch 93/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.0880 - accuracy: 0.9604 - val_loss: 0.1269 - val_accuracy: 0.9371\n",
            "Epoch 94/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0864 - accuracy: 0.9579 - val_loss: 0.1259 - val_accuracy: 0.9371\n",
            "Epoch 95/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0858 - accuracy: 0.9600 - val_loss: 0.1464 - val_accuracy: 0.9514\n",
            "Epoch 96/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0899 - accuracy: 0.9600 - val_loss: 0.1304 - val_accuracy: 0.9471\n",
            "Epoch 97/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0837 - accuracy: 0.9639 - val_loss: 0.1305 - val_accuracy: 0.9471\n",
            "Epoch 98/100\n",
            "88/88 [==============================] - 0s 2ms/step - loss: 0.0850 - accuracy: 0.9589 - val_loss: 0.1607 - val_accuracy: 0.9443\n",
            "Epoch 99/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0869 - accuracy: 0.9618 - val_loss: 0.1411 - val_accuracy: 0.9443\n",
            "Epoch 100/100\n",
            "88/88 [==============================] - 0s 3ms/step - loss: 0.0854 - accuracy: 0.9625 - val_loss: 0.1399 - val_accuracy: 0.9443\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model3_history.history.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qi-0vO_jawBC",
        "outputId": "38464012-efa7-49d4-8f31-c8de989b8358"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model3_history.history['accuracy'])\n",
        "plt.plot(model3_history.history['val_accuracy'])\n",
        "plt.title('accuracy Plot')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "eeFxj3gZculi",
        "outputId": "461c4392-52c6-4dce-bdb0-a0ed641b9543"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model1_history.history['loss'])\n",
        "plt.plot(model1_history.history['val_loss'])\n",
        "plt.title('Loss Function Plot')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UMVbZDPCc6LI",
        "outputId": "3168bb86-d61c-46c1-8f67-c7df7477c3f3"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predicted_probabilities3 = mlp_model3.predict(X_test_arr)\n",
        "train_predicted_probabilities3 = mlp_model3.predict(X_train_arr)\n",
        "\n",
        "test_pred_class_labels3 = [0 if val<0.5 else 1 for val in test_predicted_probabilities3]\n",
        "train_pred_class_labels3 = [0 if val<0.5 else 1 for val in train_predicted_probabilities3]\n",
        "\n",
        "eval_metric(y_train, train_pred_class_labels, y_test, test_pred_class_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqEoX9SPc6Nm",
        "outputId": "41eabeb8-4c46-4bca-f905-3160e8b93d19"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "47/47 [==============================] - 0s 5ms/step\n",
            "110/110 [==============================] - 1s 6ms/step\n",
            "The train accuracy score is 0.9517142857142857\n",
            "The test accuracy score is 0.9506995336442372\n",
            " --------------------------------------------\n",
            "                Train Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[1661   89]\n",
            " [  80 1670]]\n",
            "The classification report is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.95      0.95      1750\n",
            "           1       0.95      0.95      0.95      1750\n",
            "\n",
            "    accuracy                           0.95      3500\n",
            "   macro avg       0.95      0.95      0.95      3500\n",
            "weighted avg       0.95      0.95      0.95      3500\n",
            "\n",
            " --------------------------------------------\n",
            "                Test Report\n",
            "-------------------------------------------- \n",
            "The confusion matrix  is\n",
            " [[717  34]\n",
            " [ 40 710]]\n",
            "The classification report  is\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.95      0.95       751\n",
            "           1       0.95      0.95      0.95       750\n",
            "\n",
            "    accuracy                           0.95      1501\n",
            "   macro avg       0.95      0.95      0.95      1501\n",
            "weighted avg       0.95      0.95      0.95      1501\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yq6AufQxc6RF"
      },
      "execution_count": 74,
      "outputs": []
    }
  ]
}