ML_classification_modelling.ipynb

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    "colab": {
      "name": "ML_classification_modelling.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Hetvi07/Age_Group_Detection_From_Images/blob/master/ML_classification_modelling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6EAKgQOM1yh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "6514995c-f2e6-4fbc-a797-7e8202fc702c"
      },
      "source": [
        "#@title Mount Google Drive {display-mode: \"form\"}\n",
        "\n",
        "# This code will be hidden when the notebook is loaded.\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uj0ft8tDNFSJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Imports\n",
        "# Please refer to requirements.txt for a full list of all libraries and their versions used in this project.\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# import os\n",
        "# from zipfile import ZipFile\n",
        "import time\n",
        "from datetime import datetime\n",
        "import itertools\n",
        "\n",
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvIq_TUOclMT",
        "colab_type": "text"
      },
      "source": [
        "## Traditional ML: Data Importing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5Xu46GYNTiF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "4247e70f-edb1-479b-f3d7-cc5545a90226"
      },
      "source": [
        "# Importing the table showing the breakdown of age-ranges into classes.\n",
        "\n",
        "combined_classes = pd.read_csv(\"/content/drive/My Drive/Age_classification_with_faces/input_output/combined_faces_classes_summary.csv\")\n",
        "combined_classes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class label</th>\n",
              "      <th>Age-ranges (classes)</th>\n",
              "      <th>No. of images</th>\n",
              "      <th>Class balance (%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1 - 2</td>\n",
              "      <td>3192</td>\n",
              "      <td>9.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3 - 9</td>\n",
              "      <td>2816</td>\n",
              "      <td>8.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>10 - 20</td>\n",
              "      <td>3136</td>\n",
              "      <td>9.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>21 - 25</td>\n",
              "      <td>3474</td>\n",
              "      <td>10.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>26 - 27</td>\n",
              "      <td>3217</td>\n",
              "      <td>9.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>28 - 31</td>\n",
              "      <td>3063</td>\n",
              "      <td>9.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>32 - 36</td>\n",
              "      <td>3086</td>\n",
              "      <td>9.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>37 - 45</td>\n",
              "      <td>3207</td>\n",
              "      <td>9.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>46 - 54</td>\n",
              "      <td>2802</td>\n",
              "      <td>8.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>55 - 65</td>\n",
              "      <td>2796</td>\n",
              "      <td>8.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>66 - 116</td>\n",
              "      <td>2697</td>\n",
              "      <td>8.05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Class label Age-ranges (classes)  No. of images  Class balance (%)\n",
              "0             0                1 - 2           3192               9.53\n",
              "1             1                3 - 9           2816               8.41\n",
              "2             2              10 - 20           3136               9.37\n",
              "3             3              21 - 25           3474              10.37\n",
              "4             4              26 - 27           3217               9.61\n",
              "5             5              28 - 31           3063               9.15\n",
              "6             6              32 - 36           3086               9.22\n",
              "7             7              37 - 45           3207               9.58\n",
              "8             8              46 - 54           2802               8.37\n",
              "9             9              55 - 65           2796               8.35\n",
              "10           10             66 - 116           2697               8.05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PasUG1OoOfrR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Defining a function to return the class labels corresponding to the age-ranges shown above.\n",
        "\n",
        "def class_labels(age):\n",
        "    if 1 <= age <= 2:\n",
        "        return 1\n",
        "    elif 3 <= age <= 9:\n",
        "        return 2\n",
        "    elif 10 <= age <= 20:\n",
        "        return 3\n",
        "    elif 21 <= age <= 25:\n",
        "        return 4\n",
        "    elif 26 <= age <= 27:\n",
        "        return 5\n",
        "    elif 28 <= age <= 31:\n",
        "        return 6\n",
        "    elif 32 <= age <= 36:\n",
        "        return 7\n",
        "    elif 37 <= age <= 45:\n",
        "        return 8\n",
        "    elif 46 <= age <= 54:\n",
        "        return 9\n",
        "    elif 55 <= age <= 65:\n",
        "        return 10\n",
        "    else:\n",
        "        return 11"
      ],
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "SgYt26In9EfC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "9af27d7e-1725-48d0-fedb-0515c8c1833b"
      },
      "source": [
        "# Importing the feature names.\n",
        "\n",
        "feature_names = pd.read_csv(\"/content/drive/My Drive/Age_classification_with_faces/input_output/canny_features_names.csv\")\n",
        "feature_names"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>canny_edge_features</th>\n",
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              "      <th>...</th>\n",
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              "      <th>796</th>\n",
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              "<p>801 rows × 1 columns</p>\n",
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            "text/plain": [
              "    canny_edge_features\n",
              "0             sec1_mean\n",
              "1              sec1_std\n",
              "2             sec2_mean\n",
              "3              sec2_std\n",
              "4             sec3_mean\n",
              "..                  ...\n",
              "796         sec399_mean\n",
              "797          sec399_std\n",
              "798         sec400_mean\n",
              "799          sec400_std\n",
              "800                 age\n",
              "\n",
              "[801 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPQdqIbR9E0r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing the numpy arrays of train and test datasets.\n",
        "\n",
        "train = np.load(\"/content/drive/My Drive/Age_classification_with_faces/input_output/canny_features_age_train.npy\")\n",
        "test = np.load(\"/content/drive/My Drive/Age_classification_with_faces/input_output/canny_features_age_test.npy\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWO2lMHV9Er3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Converting the numpy arrays to pandas dataframe.\n",
        "\n",
        "train_df = pd.DataFrame(train, columns=feature_names[\"canny_edge_features\"])\n",
        "test_df = pd.DataFrame(test, columns=feature_names[\"canny_edge_features\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sia-yuw9EqO",
        "colab_type": "code",
        "colab": {
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          "height": 253
        },
        "outputId": "7189157b-4e9f-4e16-bd0c-5afe06460c16"
      },
      "source": [
        "train_df.head()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>canny_edge_features</th>\n",
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              "      <th>sec399_std</th>\n",
              "      <th>sec400_mean</th>\n",
              "      <th>sec400_std</th>\n",
              "      <th>age</th>\n",
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              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.407308</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.443959</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>...</td>\n",
              "      <td>0.237487</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.407308</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>...</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.384187</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.384187</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.384187</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.237487</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.433013</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.217945</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 801 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "canny_edge_features  sec1_mean  sec1_std  ...  sec400_std   age\n",
              "0                         0.00  0.000000  ...    0.427083  20.0\n",
              "1                         0.00  0.000000  ...    0.000000   9.0\n",
              "2                         0.02  0.140000  ...    0.366606   8.0\n",
              "3                         0.22  0.414246  ...    0.286182  40.0\n",
              "4                         0.09  0.286182  ...    0.000000  21.0\n",
              "\n",
              "[5 rows x 801 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBOUyN0pBw1n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "358ebd25-015d-4bfb-ad85-afb3332bb26b"
      },
      "source": [
        "test_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>canny_edge_features</th>\n",
              "      <th>sec1_mean</th>\n",
              "      <th>sec1_std</th>\n",
              "      <th>sec2_mean</th>\n",
              "      <th>sec2_std</th>\n",
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              "      <th>sec9_mean</th>\n",
              "      <th>sec9_std</th>\n",
              "      <th>sec10_mean</th>\n",
              "      <th>sec10_std</th>\n",
              "      <th>sec11_mean</th>\n",
              "      <th>sec11_std</th>\n",
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              "      <th>sec15_std</th>\n",
              "      <th>sec16_mean</th>\n",
              "      <th>sec16_std</th>\n",
              "      <th>sec17_mean</th>\n",
              "      <th>sec17_std</th>\n",
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              "      <th>sec18_std</th>\n",
              "      <th>sec19_mean</th>\n",
              "      <th>sec19_std</th>\n",
              "      <th>sec20_mean</th>\n",
              "      <th>sec20_std</th>\n",
              "      <th>...</th>\n",
              "      <th>sec381_std</th>\n",
              "      <th>sec382_mean</th>\n",
              "      <th>sec382_std</th>\n",
              "      <th>sec383_mean</th>\n",
              "      <th>sec383_std</th>\n",
              "      <th>sec384_mean</th>\n",
              "      <th>sec384_std</th>\n",
              "      <th>sec385_mean</th>\n",
              "      <th>sec385_std</th>\n",
              "      <th>sec386_mean</th>\n",
              "      <th>sec386_std</th>\n",
              "      <th>sec387_mean</th>\n",
              "      <th>sec387_std</th>\n",
              "      <th>sec388_mean</th>\n",
              "      <th>sec388_std</th>\n",
              "      <th>sec389_mean</th>\n",
              "      <th>sec389_std</th>\n",
              "      <th>sec390_mean</th>\n",
              "      <th>sec390_std</th>\n",
              "      <th>sec391_mean</th>\n",
              "      <th>sec391_std</th>\n",
              "      <th>sec392_mean</th>\n",
              "      <th>sec392_std</th>\n",
              "      <th>sec393_mean</th>\n",
              "      <th>sec393_std</th>\n",
              "      <th>sec394_mean</th>\n",
              "      <th>sec394_std</th>\n",
              "      <th>sec395_mean</th>\n",
              "      <th>sec395_std</th>\n",
              "      <th>sec396_mean</th>\n",
              "      <th>sec396_std</th>\n",
              "      <th>sec397_mean</th>\n",
              "      <th>sec397_std</th>\n",
              "      <th>sec398_mean</th>\n",
              "      <th>sec398_std</th>\n",
              "      <th>sec399_mean</th>\n",
              "      <th>sec399_std</th>\n",
              "      <th>sec400_mean</th>\n",
              "      <th>sec400_std</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>0.04</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.14000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>...</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.384187</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.443959</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.217945</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.237487</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.407308</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>...</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.443959</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 801 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "canny_edge_features  sec1_mean  sec1_std  ...  sec400_std   age\n",
              "0                         0.16  0.366606  ...    0.000000   9.0\n",
              "1                         0.16  0.366606  ...    0.000000  42.0\n",
              "2                         0.09  0.286182  ...    0.000000  40.0\n",
              "3                         0.00  0.000000  ...    0.000000  34.0\n",
              "4                         0.09  0.286182  ...    0.414246   6.0\n",
              "\n",
              "[5 rows x 801 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41rxgOi5AMuS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8b9856c4-2fcd-4c96-aa1b-9e2e9f620def"
      },
      "source": [
        "train_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23440, 801)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOGIm7S6BzaV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fadebb20-13ce-41a5-d323-e5f3e286c56d"
      },
      "source": [
        "test_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10046, 801)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5H6YxD_a9Elz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3e34eae3-ca79-4263-b6b0-3104ac8ac3f8"
      },
      "source": [
        "train_df.dtypes.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([dtype('float64')], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mpdGGGIoB2lV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cfa35662-87fc-424b-f044-8c7a66ed75d5"
      },
      "source": [
        "test_df.dtypes.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([dtype('float64')], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CVOXo1q_9EjM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['age'] = train_df['age'].astype(np.uint8)\n",
        "test_df['age'] = test_df['age'].astype(np.uint8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cLPidSjAQ5K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creating a column of target class values using the function defined above.\n",
        "\n",
        "train_df['target'] = train_df['age'].map(class_labels)\n",
        "test_df['target'] = test_df['age'].map(class_labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDYshOdiAQ2S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "6a2270d3-b9f2-43d2-b825-1ce384c8e36b"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>canny_edge_features</th>\n",
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              "      <th>sec9_std</th>\n",
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              "      <th>sec10_std</th>\n",
              "      <th>sec11_mean</th>\n",
              "      <th>sec11_std</th>\n",
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              "      <th>sec12_std</th>\n",
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              "      <th>sec16_std</th>\n",
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              "      <th>sec18_std</th>\n",
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              "      <th>sec19_std</th>\n",
              "      <th>sec20_mean</th>\n",
              "      <th>sec20_std</th>\n",
              "      <th>...</th>\n",
              "      <th>sec382_mean</th>\n",
              "      <th>sec382_std</th>\n",
              "      <th>sec383_mean</th>\n",
              "      <th>sec383_std</th>\n",
              "      <th>sec384_mean</th>\n",
              "      <th>sec384_std</th>\n",
              "      <th>sec385_mean</th>\n",
              "      <th>sec385_std</th>\n",
              "      <th>sec386_mean</th>\n",
              "      <th>sec386_std</th>\n",
              "      <th>sec387_mean</th>\n",
              "      <th>sec387_std</th>\n",
              "      <th>sec388_mean</th>\n",
              "      <th>sec388_std</th>\n",
              "      <th>sec389_mean</th>\n",
              "      <th>sec389_std</th>\n",
              "      <th>sec390_mean</th>\n",
              "      <th>sec390_std</th>\n",
              "      <th>sec391_mean</th>\n",
              "      <th>sec391_std</th>\n",
              "      <th>sec392_mean</th>\n",
              "      <th>sec392_std</th>\n",
              "      <th>sec393_mean</th>\n",
              "      <th>sec393_std</th>\n",
              "      <th>sec394_mean</th>\n",
              "      <th>sec394_std</th>\n",
              "      <th>sec395_mean</th>\n",
              "      <th>sec395_std</th>\n",
              "      <th>sec396_mean</th>\n",
              "      <th>sec396_std</th>\n",
              "      <th>sec397_mean</th>\n",
              "      <th>sec397_std</th>\n",
              "      <th>sec398_mean</th>\n",
              "      <th>sec398_std</th>\n",
              "      <th>sec399_mean</th>\n",
              "      <th>sec399_std</th>\n",
              "      <th>sec400_mean</th>\n",
              "      <th>sec400_std</th>\n",
              "      <th>age</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.22</td>\n",
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              "      <td>0.24</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>0.20</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.10</td>\n",
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              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
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              "      <td>0.17</td>\n",
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              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.407308</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.443959</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>20</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>...</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.407308</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>...</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.384187</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.384187</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.384187</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.237487</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.433013</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.217945</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 802 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "canny_edge_features  sec1_mean  sec1_std  sec2_mean  ...  sec400_std  age  target\n",
              "0                         0.00  0.000000       0.00  ...    0.427083   20       3\n",
              "1                         0.00  0.000000       0.00  ...    0.000000    9       2\n",
              "2                         0.02  0.140000       0.15  ...    0.366606    8       2\n",
              "3                         0.22  0.414246       0.06  ...    0.286182   40       8\n",
              "4                         0.09  0.286182       0.12  ...    0.000000   21       4\n",
              "\n",
              "[5 rows x 802 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oqp0sLZRCN1u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "6f1cc786-f9e2-497c-b045-6a11c64f19aa"
      },
      "source": [
        "test_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>canny_edge_features</th>\n",
              "      <th>sec1_mean</th>\n",
              "      <th>sec1_std</th>\n",
              "      <th>sec2_mean</th>\n",
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              "      <th>sec9_std</th>\n",
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              "      <th>sec10_std</th>\n",
              "      <th>sec11_mean</th>\n",
              "      <th>sec11_std</th>\n",
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              "      <th>sec15_std</th>\n",
              "      <th>sec16_mean</th>\n",
              "      <th>sec16_std</th>\n",
              "      <th>sec17_mean</th>\n",
              "      <th>sec17_std</th>\n",
              "      <th>sec18_mean</th>\n",
              "      <th>sec18_std</th>\n",
              "      <th>sec19_mean</th>\n",
              "      <th>sec19_std</th>\n",
              "      <th>sec20_mean</th>\n",
              "      <th>sec20_std</th>\n",
              "      <th>...</th>\n",
              "      <th>sec382_mean</th>\n",
              "      <th>sec382_std</th>\n",
              "      <th>sec383_mean</th>\n",
              "      <th>sec383_std</th>\n",
              "      <th>sec384_mean</th>\n",
              "      <th>sec384_std</th>\n",
              "      <th>sec385_mean</th>\n",
              "      <th>sec385_std</th>\n",
              "      <th>sec386_mean</th>\n",
              "      <th>sec386_std</th>\n",
              "      <th>sec387_mean</th>\n",
              "      <th>sec387_std</th>\n",
              "      <th>sec388_mean</th>\n",
              "      <th>sec388_std</th>\n",
              "      <th>sec389_mean</th>\n",
              "      <th>sec389_std</th>\n",
              "      <th>sec390_mean</th>\n",
              "      <th>sec390_std</th>\n",
              "      <th>sec391_mean</th>\n",
              "      <th>sec391_std</th>\n",
              "      <th>sec392_mean</th>\n",
              "      <th>sec392_std</th>\n",
              "      <th>sec393_mean</th>\n",
              "      <th>sec393_std</th>\n",
              "      <th>sec394_mean</th>\n",
              "      <th>sec394_std</th>\n",
              "      <th>sec395_mean</th>\n",
              "      <th>sec395_std</th>\n",
              "      <th>sec396_mean</th>\n",
              "      <th>sec396_std</th>\n",
              "      <th>sec397_mean</th>\n",
              "      <th>sec397_std</th>\n",
              "      <th>sec398_mean</th>\n",
              "      <th>sec398_std</th>\n",
              "      <th>sec399_mean</th>\n",
              "      <th>sec399_std</th>\n",
              "      <th>sec400_mean</th>\n",
              "      <th>sec400_std</th>\n",
              "      <th>age</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.14000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
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              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.16</td>\n",
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              "      <td>0.18</td>\n",
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              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.443959</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.217945</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.237487</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.407308</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>34</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.01</td>\n",
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              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>...</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.443959</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 802 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "canny_edge_features  sec1_mean  sec1_std  sec2_mean  ...  sec400_std  age  target\n",
              "0                         0.16  0.366606       0.24  ...    0.000000    9       2\n",
              "1                         0.16  0.366606       0.18  ...    0.000000   42       8\n",
              "2                         0.09  0.286182       0.11  ...    0.000000   40       8\n",
              "3                         0.00  0.000000       0.08  ...    0.000000   34       7\n",
              "4                         0.09  0.286182       0.19  ...    0.414246    6       2\n",
              "\n",
              "[5 rows x 802 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IgTgTHbodEJA",
        "colab_type": "text"
      },
      "source": [
        "## Traditional ML: Model Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXgcBWZcAQz3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the above train and test dataframes into features (X) and target (y).\n",
        "\n",
        "X_train = train_df.drop(columns=['age', 'target'])\n",
        "y_train = train_df['target']\n",
        "\n",
        "X_test = test_df.drop(columns=['age', 'target'])\n",
        "y_test = test_df['target']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOT5WwGXXCzD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "63505b5b-3347-4d4c-c91d-273a235565b3"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>canny_edge_features</th>\n",
              "      <th>sec1_mean</th>\n",
              "      <th>sec1_std</th>\n",
              "      <th>sec2_mean</th>\n",
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              "      <th>sec6_std</th>\n",
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              "      <th>sec9_std</th>\n",
              "      <th>sec10_mean</th>\n",
              "      <th>sec10_std</th>\n",
              "      <th>sec11_mean</th>\n",
              "      <th>sec11_std</th>\n",
              "      <th>sec12_mean</th>\n",
              "      <th>sec12_std</th>\n",
              "      <th>sec13_mean</th>\n",
              "      <th>sec13_std</th>\n",
              "      <th>sec14_mean</th>\n",
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              "      <th>sec15_std</th>\n",
              "      <th>sec16_mean</th>\n",
              "      <th>sec16_std</th>\n",
              "      <th>sec17_mean</th>\n",
              "      <th>sec17_std</th>\n",
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              "      <th>sec18_std</th>\n",
              "      <th>sec19_mean</th>\n",
              "      <th>sec19_std</th>\n",
              "      <th>sec20_mean</th>\n",
              "      <th>sec20_std</th>\n",
              "      <th>...</th>\n",
              "      <th>sec381_mean</th>\n",
              "      <th>sec381_std</th>\n",
              "      <th>sec382_mean</th>\n",
              "      <th>sec382_std</th>\n",
              "      <th>sec383_mean</th>\n",
              "      <th>sec383_std</th>\n",
              "      <th>sec384_mean</th>\n",
              "      <th>sec384_std</th>\n",
              "      <th>sec385_mean</th>\n",
              "      <th>sec385_std</th>\n",
              "      <th>sec386_mean</th>\n",
              "      <th>sec386_std</th>\n",
              "      <th>sec387_mean</th>\n",
              "      <th>sec387_std</th>\n",
              "      <th>sec388_mean</th>\n",
              "      <th>sec388_std</th>\n",
              "      <th>sec389_mean</th>\n",
              "      <th>sec389_std</th>\n",
              "      <th>sec390_mean</th>\n",
              "      <th>sec390_std</th>\n",
              "      <th>sec391_mean</th>\n",
              "      <th>sec391_std</th>\n",
              "      <th>sec392_mean</th>\n",
              "      <th>sec392_std</th>\n",
              "      <th>sec393_mean</th>\n",
              "      <th>sec393_std</th>\n",
              "      <th>sec394_mean</th>\n",
              "      <th>sec394_std</th>\n",
              "      <th>sec395_mean</th>\n",
              "      <th>sec395_std</th>\n",
              "      <th>sec396_mean</th>\n",
              "      <th>sec396_std</th>\n",
              "      <th>sec397_mean</th>\n",
              "      <th>sec397_std</th>\n",
              "      <th>sec398_mean</th>\n",
              "      <th>sec398_std</th>\n",
              "      <th>sec399_mean</th>\n",
              "      <th>sec399_std</th>\n",
              "      <th>sec400_mean</th>\n",
              "      <th>sec400_std</th>\n",
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              "      <td>0.346987</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.217945</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 800 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "canny_edge_features  sec1_mean  sec1_std  ...  sec400_mean  sec400_std\n",
              "0                         0.00  0.000000  ...         0.24    0.427083\n",
              "1                         0.00  0.000000  ...         0.00    0.000000\n",
              "2                         0.02  0.140000  ...         0.16    0.366606\n",
              "3                         0.22  0.414246  ...         0.09    0.286182\n",
              "4                         0.09  0.286182  ...         0.00    0.000000\n",
              "\n",
              "[5 rows x 800 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFwo3LjqXEbH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "21f4fc8f-218e-4375-cd01-ecde9cba04a4"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23440, 800)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTW_ujg4XESs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "783fcdbf-b7c9-47d7-b57f-be3d4a0b9476"
      },
      "source": [
        "X_test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th>canny_edge_features</th>\n",
              "      <th>sec1_mean</th>\n",
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              "      <th>sec8_std</th>\n",
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              "      <th>sec9_std</th>\n",
              "      <th>sec10_mean</th>\n",
              "      <th>sec10_std</th>\n",
              "      <th>sec11_mean</th>\n",
              "      <th>sec11_std</th>\n",
              "      <th>sec12_mean</th>\n",
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              "      <th>sec13_std</th>\n",
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              "      <th>sec15_mean</th>\n",
              "      <th>sec15_std</th>\n",
              "      <th>sec16_mean</th>\n",
              "      <th>sec16_std</th>\n",
              "      <th>sec17_mean</th>\n",
              "      <th>sec17_std</th>\n",
              "      <th>sec18_mean</th>\n",
              "      <th>sec18_std</th>\n",
              "      <th>sec19_mean</th>\n",
              "      <th>sec19_std</th>\n",
              "      <th>sec20_mean</th>\n",
              "      <th>sec20_std</th>\n",
              "      <th>...</th>\n",
              "      <th>sec381_mean</th>\n",
              "      <th>sec381_std</th>\n",
              "      <th>sec382_mean</th>\n",
              "      <th>sec382_std</th>\n",
              "      <th>sec383_mean</th>\n",
              "      <th>sec383_std</th>\n",
              "      <th>sec384_mean</th>\n",
              "      <th>sec384_std</th>\n",
              "      <th>sec385_mean</th>\n",
              "      <th>sec385_std</th>\n",
              "      <th>sec386_mean</th>\n",
              "      <th>sec386_std</th>\n",
              "      <th>sec387_mean</th>\n",
              "      <th>sec387_std</th>\n",
              "      <th>sec388_mean</th>\n",
              "      <th>sec388_std</th>\n",
              "      <th>sec389_mean</th>\n",
              "      <th>sec389_std</th>\n",
              "      <th>sec390_mean</th>\n",
              "      <th>sec390_std</th>\n",
              "      <th>sec391_mean</th>\n",
              "      <th>sec391_std</th>\n",
              "      <th>sec392_mean</th>\n",
              "      <th>sec392_std</th>\n",
              "      <th>sec393_mean</th>\n",
              "      <th>sec393_std</th>\n",
              "      <th>sec394_mean</th>\n",
              "      <th>sec394_std</th>\n",
              "      <th>sec395_mean</th>\n",
              "      <th>sec395_std</th>\n",
              "      <th>sec396_mean</th>\n",
              "      <th>sec396_std</th>\n",
              "      <th>sec397_mean</th>\n",
              "      <th>sec397_std</th>\n",
              "      <th>sec398_mean</th>\n",
              "      <th>sec398_std</th>\n",
              "      <th>sec399_mean</th>\n",
              "      <th>sec399_std</th>\n",
              "      <th>sec400_mean</th>\n",
              "      <th>sec400_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>0.04</td>\n",
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              "      <td>0.02</td>\n",
              "      <td>0.14000</td>\n",
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              "      <td>0.11</td>\n",
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              "      <td>0.300000</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>...</td>\n",
              "      <td>0.12</td>\n",
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              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.384187</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.443959</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.217945</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.237487</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.407308</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31289</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.170587</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.324962</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.271293</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.346987</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.392301</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.357071</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.099499</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.336303</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.255147</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.375633</td>\n",
              "      <td>...</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.427083</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.443959</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.286182</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.195959</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.420833</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.312890</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.414246</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 800 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "canny_edge_features  sec1_mean  sec1_std  ...  sec400_mean  sec400_std\n",
              "0                         0.16  0.366606  ...         0.00    0.000000\n",
              "1                         0.16  0.366606  ...         0.00    0.000000\n",
              "2                         0.09  0.286182  ...         0.00    0.000000\n",
              "3                         0.00  0.000000  ...         0.00    0.000000\n",
              "4                         0.09  0.286182  ...         0.22    0.414246\n",
              "\n",
              "[5 rows x 800 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8hkfwvIXEJv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2b57f175-a0f2-41c8-e19f-e005a7a79992"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10046, 800)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MSUfFwfXD_g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "23ca7c76-1b22-4b64-9146-96fbab20787f"
      },
      "source": [
        "# Checking the distribution of classes in y_train.\n",
        "\n",
        "y_train.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4     2432\n",
              "5     2252\n",
              "8     2245\n",
              "1     2234\n",
              "3     2195\n",
              "7     2160\n",
              "6     2144\n",
              "2     1971\n",
              "9     1962\n",
              "10    1957\n",
              "11    1888\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GYw9YCxOXcxk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "a6c32391-1efc-41d4-d5dd-ca5ade8e4271"
      },
      "source": [
        "# Checking the distribution of classes to ensure it is same as y_test.\n",
        "\n",
        "y_train.value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4     0.103754\n",
              "5     0.096075\n",
              "8     0.095776\n",
              "1     0.095307\n",
              "3     0.093643\n",
              "7     0.092150\n",
              "6     0.091468\n",
              "2     0.084087\n",
              "9     0.083703\n",
              "10    0.083490\n",
              "11    0.080546\n",
              "Name: target, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUIPTQbOXcmd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "d7521890-1a81-4f96-a968-e83e14ae4b9d"
      },
      "source": [
        "# Checking the distribution of classes to ensure it is same as y_train.\n",
        "\n",
        "y_test.value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4     0.103723\n",
              "5     0.096058\n",
              "8     0.095760\n",
              "1     0.095361\n",
              "3     0.093669\n",
              "7     0.092176\n",
              "6     0.091479\n",
              "2     0.084113\n",
              "9     0.083615\n",
              "10    0.083516\n",
              "11    0.080530\n",
              "Name: target, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGmOW4-xXcXD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Scaling X_train to the standard scale.\n",
        "\n",
        "ss = StandardScaler()\n",
        "X_train_sc = ss.fit_transform(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Wu5yNOxXcNr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Transforming X_test to the same scale.\n",
        "\n",
        "X_test_sc = ss.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aSqCHX1qc1sN",
        "colab_type": "text"
      },
      "source": [
        "## Traditional ML: Classification Modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yh7NjMHiayLS",
        "colab_type": "text"
      },
      "source": [
        "### *GridSearchCV* with *RandomForestClassifier*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVcPGxaPXcF3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creating a RandomForestClassifier object.\n",
        "\n",
        "rfc = RandomForestClassifier(# class_weight='balanced_subsample', \n",
        "                             # n_estimators=200,\n",
        "                             # max_depth=5,\n",
        "                             ccp_alpha=0,\n",
        "                             min_samples_split=2,\n",
        "                             min_samples_leaf=1,\n",
        "                             random_state=42\n",
        "                            )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjtKvOF-a2Kg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Establishing ranges of hyperparameters of RandomForestClassifier for GridSearchCV.\n",
        "\n",
        "rfc_params = {'n_estimators' : [50, 100, 200],\n",
        "              'max_depth' : [5, 7, 9],\n",
        "              # 'ccp_alpha' : [0, 0.001, 0.01, 0.1, 1, 10],\n",
        "              # 'min_samples_split' : [2, 5, 10, 15, 20],\n",
        "              # 'min_samples_leaf' : [2, 3, 4, 5, 6]\n",
        "             }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53hQAD7_cN9j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creating a GridSearchCV object for the RandomForestClassifier object defined above.\n",
        "\n",
        "rfc_gs = GridSearchCV(rfc, param_grid=rfc_params, n_jobs=-1, cv=5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QVC2fdLXb5K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "7920b97a-7c09-45dc-b0e9-f540c9f37f2a"
      },
      "source": [
        "# Fitting X_train_sc and y_train on GridSearchCV object with RandomForestClassifier defined above.\n",
        "\n",
        "rfc_gs.fit(X_train_sc, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0,\n",
              "                                              class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              max_samples=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators=100, n_jobs=None,\n",
              "                                              oob_score=False, random_state=42,\n",
              "                                              verbose=0, warm_start=False),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'max_depth': [5, 7, 9],\n",
              "                         'n_estimators': [50, 100, 200]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3cm_jceDdA6Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b9076712-dba7-41c4-f827-ce22fe5f3e51"
      },
      "source": [
        "# Best combination of hyperparameters suggested by GridSearchCV.\n",
        "\n",
        "rfc_gs.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 9, 'n_estimators': 200}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhK2Nsua7XAf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e7e8e27a-7d89-4d1d-95f6-4ef6b432fd46"
      },
      "source": [
        "# Best accuracy score obtained by the above combination of hyperparameters.\n",
        "\n",
        "rfc_gs.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.39266211604095563"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_PS9U7AbXbt4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a6cc31db-c1bd-421b-f82f-3c804074e690"
      },
      "source": [
        "# Scoring the model on training dataset.\n",
        "# Training Accuracy\n",
        "\n",
        "rfc_train_acc = rfc_gs.score(X_train_sc, y_train)\n",
        "rfc_train_acc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6679180887372014"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJgeCilJXbjj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Estimated Testing Accuracy\n",
        "\n",
        "# rfc_est_test_acc = cross_val_score(rfc_gs, X_train_sc, y_train, cv=5).mean()\n",
        "# rfc_est_test_acc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgqlqgUjXbZ2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7c9d96ae-37c6-46b7-a644-bd47dffc95ac"
      },
      "source": [
        "# Actual Testing Accuracy\n",
        "\n",
        "rfc_test_acc = rfc_gs.score(X_test_sc, y_test)\n",
        "rfc_test_acc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.39806888313756716"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMM6x4xjdD03",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "3aa2273d-bc47-47ca-8b43-fef5b92cc707"
      },
      "source": [
        "# Summary scores from GridSearchCV with RandomForestClassifier.\n",
        "\n",
        "print(\"RandomForestClassifier summary of accuracy scores:\")\n",
        "print(f\"GridSearchCV best accuracy (cv=5) = {round(rfc_gs.best_score_, 3)}\")\n",
        "print(\"\\nUsing GridSearchCV best params suggested,\")\n",
        "print(f\"Training accuracy = {round(rfc_train_acc, 3)}\")\n",
        "# print(f\"Est. Test accuracy (cv=5) = {round(rfc_est_test_acc , 3)}\")\n",
        "print(f\"Testing accuracy = {round(rfc_test_acc, 3)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RandomForestClassifier summary of accuracy scores:\n",
            "GridSearchCV best accuracy (cv=5) = 0.393\n",
            "\n",
            "Using GridSearchCV best params suggested,\n",
            "Training accuracy = 0.668\n",
            "Testing accuracy = 0.398\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZl8e_uqXD07",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Generating predictions on testing dataset using the model above.\n",
        "\n",
        "rfc_pred = rfc_gs.predict(X_test_sc)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJX6bMeAAMiR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "227d2be2-0009-4911-bbc0-968e0aad9c3c"
      },
      "source": [
        "len(rfc_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10046"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kncpN4qcAMcJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "02dd2df2-0902-4c56-de9d-5ed88c395366"
      },
      "source": [
        "len(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10046"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6Ts2MXVAMXc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1393ee25-e8d1-48ee-dd1f-13ec2370faea"
      },
      "source": [
        "# Generating a confusion matrix based on above predictions.\n",
        "\n",
        "conf_mat_rfc = confusion_matrix(y_test, rfc_pred)\n",
        "conf_mat_rfc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[858,  19,   5,  35,  11,   0,   4,  11,   6,   1,   8],\n",
              "       [218, 369,  76,  93,  17,   1,   2,  36,  19,   6,   8],\n",
              "       [ 70,  39, 398, 254,  39,   0,  21,  58,  29,  12,  21],\n",
              "       [ 42,  30,  49, 647,  70,  16,  29,  84,  36,  20,  19],\n",
              "       [ 77,  13,  30, 395, 197,   8,  50, 136,  27,  18,  14],\n",
              "       [ 30,  23,  39, 371, 122,  42,  36, 159,  47,  19,  31],\n",
              "       [ 41,  19,  48, 280,  98,  11,  91, 186,  68,  48,  36],\n",
              "       [ 34,  12,  33, 188,  96,   9,  31, 359,  98,  59,  43],\n",
              "       [ 25,  14,  47,  99,  49,   5,  23, 159, 262,  67,  90],\n",
              "       [ 32,  13,  35,  91,  50,   3,  11, 137,  88, 263, 116],\n",
              "       [ 22,   6,  36,  29,  19,   3,   5,  97,  37,  42, 513]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-HKiyywLAMTd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Defining a function to plot the confusion matrix in a grid for easier visualization.\n",
        "\n",
        "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion Matrix', export_as='confusion_matrix', cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    # print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title, fontsize=16)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True labels', fontsize=14)\n",
        "    plt.xlabel('Predicted labels', fontsize=14)\n",
        "\n",
        "    # Exporting plot image in PNG format.\n",
        "    plt.savefig(f'/content/drive/My Drive/Age_classification_with_faces/plot_images/{export_as}.png', bbox_inches='tight');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaFs_wbSAMNg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "28996c31-7b73-48ab-dc78-784d4b7dc0a1"
      },
      "source": [
        "# Plotting the NORMALIZED confusion matrix using the function defined above.\n",
        "\n",
        "cm_plot_labels = combined_classes['Age-ranges (classes)']\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "plot_confusion_matrix(conf_mat_rfc, cm_plot_labels, normalize=True,\n",
        "                      title=\"Confusion Matrix based on predictions from\\nRandomForestClassifier model using Canny Edge features\",\n",
        "                      export_as=\"rfc_canny_conf_mat_norm\"\n",
        "                     )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0VffwdiAMK_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "37ddef16-2808-43f3-bc12-a1bede341050"
      },
      "source": [
        "# Plotting the confusion matrix using the function defined above.\n",
        "\n",
        "cm_plot_labels = combined_classes['Age-ranges (classes)']\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "plot_confusion_matrix(conf_mat_rfc, cm_plot_labels, normalize=False,\n",
        "                      title=\"Confusion Matrix based on predictions from\\nRandomForestClassifier model using Canny Edge features\",\n",
        "                      export_as=\"rfc_canny_conf_mat\"\n",
        "                     )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZibaNyUd3Wd",
        "colab_type": "text"
      },
      "source": [
        "The ***RandomForestClassifier* model** above, with a training accuracy of 66.8% and a testing accuracy of 39.8%, clearly shows that it is **over-fitting and not generalizing well on unseen testing data**.\n",
        "\n",
        "The **normalized confusion matrix** above also show this clearly — even though the accuracy values are somewhat high for the younger age-ranges (of 1–2, 3–9, 10–20 and 21–25) and for the older age ranges (of 66–116), there is a **presence of significant misclassification for the middle age-ranges of 26–65**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kanUFUGCfeHn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Saving the RandomForestClassifier model from above in a pickle file for possible use later.\n",
        "\n",
        "rfc_pickle = f\"/content/drive/My Drive/Age_classification_with_faces/input_output/rfc_canny_model_acc_{round(rfc_test_acc, 3)}\"\n",
        "pickle.dump(rfc_gs, open(rfc_pickle, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRbZxe-pMz3X",
        "colab_type": "text"
      },
      "source": [
        "### *GridSearchCV* with *SVC*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ugy32vM-Iepr",
        "colab": {}
      },
      "source": [
        "# Creating a SVC object.\n",
        "\n",
        "svc = SVC(# class_weight='balanced',\n",
        "          # C=1.0,\n",
        "          # kernel='rbf',\n",
        "          # degree=3,\n",
        "          random_state=42\n",
        "         )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RBp-KF7qIepy",
        "colab": {}
      },
      "source": [
        "# Establishing ranges of hyperparameters of SVC for GridSearchCV.\n",
        "\n",
        "svc_params = {'C' : [0.001, 1],\n",
        "              'kernel' : ['rbf', 'poly', 'linear'],\n",
        "              'degree' : [3, 5]\n",
        "             }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ma8cs63NIep3",
        "colab": {}
      },
      "source": [
        "# Creating a GridSearchCV object for the SVC object defined above.\n",
        "\n",
        "svc_gs = GridSearchCV(svc, param_grid=svc_params, n_jobs=-1, cv=5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "r0GGmqkIIep6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "2d9e0738-6fe9-425f-a70e-9d25ce986079"
      },
      "source": [
        "# Fitting X_train_sc and y_train on GridSearchCV object with SVC defined above.\n",
        "\n",
        "svc_gs.fit(X_train_sc, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
              "                           class_weight=None, coef0=0.0,\n",
              "                           decision_function_shape='ovr', degree=3,\n",
              "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
              "                           probability=False, random_state=42, shrinking=True,\n",
              "                           tol=0.001, verbose=False),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'C': [0.001, 1], 'degree': [3, 5],\n",
              "                         'kernel': ['rbf', 'poly', 'linear']},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3mOHtKFAIep_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "696d0d20-ad3f-47b7-dc7b-43254c401efd"
      },
      "source": [
        "# Best combination of hyperparameters suggested by GridSearchCV.\n",
        "\n",
        "svc_gs.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1, 'degree': 3, 'kernel': 'poly'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_a3b5sMSIeqE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8a0ae6a1-ef9b-4a17-8712-5cb2c4c3a6bb"
      },
      "source": [
        "# Best accuracy score obtained by the above combination of hyperparameters.\n",
        "\n",
        "svc_gs.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4899744027303754"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "79pHqV1sIeqH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4825a8f2-22b2-42c0-a82f-177d4c98ef53"
      },
      "source": [
        "# Scoring the model on training dataset.\n",
        "# Training Accuracy\n",
        "\n",
        "svc_train_acc = svc_gs.score(X_train_sc, y_train)\n",
        "svc_train_acc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9286689419795222"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "p3ARfNIHIeqM",
        "colab": {}
      },
      "source": [
        "# Estimated Testing Accuracy\n",
        "\n",
        "# svc_est_test_acc = cross_val_score(svc_gs, X_train_sc, y_train, cv=5).mean()\n",
        "# svc_est_test_acc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jerm3xXVIeqT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7488fc09-d520-4faf-a9a1-60a948b545ff"
      },
      "source": [
        "# Actual Testing Accuracy\n",
        "\n",
        "svc_test_acc = svc_gs.score(X_test_sc, y_test)\n",
        "svc_test_acc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5337447740394187"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WJ5cRmSXIeqY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "79debcf2-9948-43c9-893a-4ea1e3f3a53f"
      },
      "source": [
        "# Summary scores from GridSearchCV with SVC.\n",
        "\n",
        "print(\"SVC summary of accuracy scores:\")\n",
        "print(f\"GridSearchCV best accuracy = {round(svc_gs.best_score_, 3)}\")\n",
        "print(\"\\nUsing GridSearchCV best params suggested,\")\n",
        "print(f\"Training accuracy = {round(svc_train_acc, 3)}\")\n",
        "# print(f\"Est. Test accuracy (cv=5) = {round(svc_est_test_acc , 3)}\")\n",
        "print(f\"Testing accuracy = {round(svc_test_acc, 3)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SVC summary of accuracy scores:\n",
            "GridSearchCV best accuracy = 0.49\n",
            "\n",
            "Using GridSearchCV best params suggested,\n",
            "Training accuracy = 0.929\n",
            "Testing accuracy = 0.534\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CLOvdsivIeqd",
        "colab": {}
      },
      "source": [
        "# Generating predictions on testing dataset using the model above.\n",
        "\n",
        "svc_pred = svc_gs.predict(X_test_sc)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wtC766v1Ieqg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5ca0db15-4906-4d76-90a4-b645b78e70cf"
      },
      "source": [
        "len(svc_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10046"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LwlT4yZlIeql",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5530c4c9-5a29-4b95-9ae3-73cf126c36ca"
      },
      "source": [
        "len(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10046"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lLC1lT4JIeqp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c68c2f3b-12db-498c-ce02-72d29d12be11"
      },
      "source": [
        "# Generating a confusion matrix based on above predictions.\n",
        "\n",
        "conf_mat_svc = confusion_matrix(y_test, svc_pred)\n",
        "conf_mat_svc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[881,  19,   1,  10,  12,   3,  19,  12,   0,   0,   1],\n",
              "       [ 61, 588,  34,  70,  20,   9,  36,  24,   2,   1,   0],\n",
              "       [  8,  36, 533, 219,  43,  18,  36,  32,   7,   4,   5],\n",
              "       [  2,   4,  34, 709, 110,  59,  64,  51,   3,   2,   4],\n",
              "       [  4,   9,  12, 293, 400,  61,  98,  63,   6,  10,   9],\n",
              "       [  0,   6,  13, 267, 140, 258, 114,  91,  16,   6,   8],\n",
              "       [  1,   5,   9, 177, 133,  64, 334, 159,  30,   8,   6],\n",
              "       [  2,   3,  10,  98, 105,  48, 146, 485,  38,  15,  12],\n",
              "       [  2,   9,  13,  51,  54,  21,  92, 213, 320,  47,  18],\n",
              "       [  2,   5,  13,  57,  43,  17,  96, 172,  57, 332,  45],\n",
              "       [  0,   6,  15,  18,  22,  11,  42,  97,  32,  44, 522]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TXVRkxYGIeqx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "085f1099-d82a-4564-eff8-7baf3690522f"
      },
      "source": [
        "# Plotting the NORMALIZED confusion matrix using the function defined above.\n",
        "\n",
        "cm_plot_labels = combined_classes['Age-ranges (classes)']\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "plot_confusion_matrix(conf_mat_svc, cm_plot_labels, normalize=True,\n",
        "                      title=\"Confusion Matrix based on predictions from\\nSVC model using Canny Edge features\",\n",
        "                      export_as=\"svc_canny_conf_mat_norm\"\n",
        "                     )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "V4aDr8s1Ieq0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "6c67bbb5-cd00-4fb0-94ef-f5590a030623"
      },
      "source": [
        "# Plotting the confusion matrix using the function defined above.\n",
        "\n",
        "cm_plot_labels = combined_classes['Age-ranges (classes)']\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "plot_confusion_matrix(conf_mat_svc, cm_plot_labels, normalize=False,\n",
        "                      title=\"Confusion Matrix based on predictions from\\nSVC model using Canny Edge features\",\n",
        "                      export_as=\"svc_canny_conf_mat\"\n",
        "                     )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-6yEvYtftzl",
        "colab_type": "text"
      },
      "source": [
        "As with the *RandomForestClassifier* model above, the ***SVC* model**, with a training accuracy of 92.9% and a testing accuracy of 53.4%, also shows that it is **over-fitting and not generalizing well on unseen testing data**. Even though the training and testing accuracies are better with *SVC* than with *RandomForestClassifier*, the **degree of over-fit is significantly worse than *RandomForestClassifier***.\n",
        "\n",
        "The **normalized confusion matrix** above also show the same trend — even though the accuracy values are somewhat high for the younger age-ranges (of 1–2, 3–9, 10–20 and 21–25) and for the older age ranges (of 66–116), there is a **presence of significant misclassification for the middle age-ranges of 26–65**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFpFMjikYc9v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Saving the SVC model from above in a pickle file for possible use later.\n",
        "\n",
        "svc_pickle = f\"/content/drive/My Drive/Age_classification_with_faces/input_output/svc_canny_model_acc_{round(svc_test_acc, 3)}\"\n",
        "pickle.dump(svc_gs, open(svc_pickle, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VWd3NJVg9T5",
        "colab_type": "text"
      },
      "source": [
        "## Traditional ML: Summary and Limitations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4iV6GOFJhpb2",
        "colab_type": "text"
      },
      "source": [
        "![summary_table_traditional_ml.png](https://drive.google.com/uc?export=view&id=1kc7idDm-1QmSTfdmk592NpMXWPU8moGu)\n",
        "\n",
        "As is clear from the above summary of scores, **modelling using the traditional machine learning methodology may not be the best way to approach this problem**. I will now try modelling with deep learning and see if it provides a better result.\n",
        "\n",
        "There are, of course, a multitude of methods that could still be utilized to improve the above accuracy scores and reduce the degree of over-fit in the models. For instance, better differentiating features could be extracted from the images using some other more complicated techniques, or other classifiers could be utilized to see whether they perform better in this case."
      ]
    }
  ]
}