Model-training.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "tgp3cxeZoWZt"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "5CWVvbyCoysQ",
        "outputId": "96359f0e-cf29-4172-a977-ea2405ddf773",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXpbWIxOoWZx"
      },
      "source": [
        "# Reading all the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "3w2e0gOSoWZ4",
        "outputId": "b2e3211a-c900-4b4b-92df-285e693af7ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1. Title: Image Segmentation data\n",
            "\n",
            "2. Source Information\n",
            "   -- Creators: Vision Group, University of Massachusetts\n",
            "   -- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu)\n",
            "   -- Date: November, 1990\n",
            " \n",
            "3. Past Usage: None yet published\n",
            "\n",
            "4. Relevant Information:\n",
            "\n",
            "   The instances were drawn randomly from a database of 7 outdoor \n",
            "   images.  The images were handsegmented to create a classification\n",
            "   for every pixel.  \n",
            "\n",
            "   Each instance is a 3x3 region.\n",
            "\n",
            "5. Number of Instances: Training data: 210  Test data: 2100\n",
            "\n",
            "6. Number of Attributes: 19 continuous attributes\n",
            "\n",
            "7. Attribute Information:\n",
            "\n",
            "    1.  region-centroid-col:  the column of the center pixel of the region.\n",
            "    2.  region-centroid-row:  the row of the center pixel of the region.\n",
            "    3.  region-pixel-count:  the number of pixels in a region = 9.\n",
            "    4.  short-line-density-5:  the results of a line extractoin algorithm that \n",
            "         counts how many lines of length 5 (any orientation) with\n",
            "         low contrast, less than or equal to 5, go through the region.\n",
            "    5.  short-line-density-2:  same as short-line-density-5 but counts lines\n",
            "         of high contrast, greater than 5.\n",
            "    6.  vedge-mean:  measure the contrast of horizontally\n",
            "         adjacent pixels in the region.  There are 6, the mean and \n",
            "         standard deviation are given.  This attribute is used as\n",
            "        a vertical edge detector.\n",
            "    7.  vegde-sd:  (see 6)\n",
            "    8.  hedge-mean:  measures the contrast of vertically adjacent\n",
            "          pixels. Used for horizontal line detection. \n",
            "    9.  hedge-sd: (see 8).\n",
            "    10. intensity-mean:  the average over the region of (R + G + B)/3\n",
            "    11. rawred-mean: the average over the region of the R value.\n",
            "    12. rawblue-mean: the average over the region of the B value.\n",
            "    13. rawgreen-mean: the average over the region of the G value.\n",
            "    14. exred-mean: measure the excess red:  (2R - (G + B))\n",
            "    15. exblue-mean: measure the excess blue:  (2B - (G + R))\n",
            "    16. exgreen-mean: measure the excess green:  (2G - (R + B))\n",
            "    17. value-mean:  3-d nonlinear transformation\n",
            "         of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals\n",
            "         of Interactive Computer Graphics)\n",
            "    18. saturatoin-mean:  (see 17)\n",
            "    19. hue-mean:  (see 17)\n",
            "\n",
            "8. Missing Attribute Values: None\n",
            "\n",
            "9. Class Distribution: \n",
            "\n",
            "   Classes:  brickface, sky, foliage, cement, window, path, grass.\n",
            "\n",
            "   30 instances per class for training data.\n",
            "   300 instances per class for test data.\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "with open(\"/content/drive/MyDrive/Image-Segementation/Image-Segementation-Dataset/segmentation.names\") as data_description:\n",
        "    print(data_description.read())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "myJPNtQEoWZ7",
        "outputId": "4cb4813a-b91a-4906-99ae-c530c13ccdc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    TARGET-CLASS  REGION-CENTROID-COL  REGION-CENTROID-ROW  \\\n",
              "0      BRICKFACE                140.0                125.0   \n",
              "1      BRICKFACE                188.0                133.0   \n",
              "2      BRICKFACE                105.0                139.0   \n",
              "3      BRICKFACE                 34.0                137.0   \n",
              "4      BRICKFACE                 39.0                111.0   \n",
              "..           ...                  ...                  ...   \n",
              "205        GRASS                 36.0                243.0   \n",
              "206        GRASS                186.0                218.0   \n",
              "207        GRASS                197.0                236.0   \n",
              "208        GRASS                208.0                240.0   \n",
              "209        GRASS                223.0                185.0   \n",
              "\n",
              "     REGION-PIXEL-COUNT  SHORT-LINE-DENSITY-5  SHORT-LINE-DENSITY-2  \\\n",
              "0                     9              0.000000                   0.0   \n",
              "1                     9              0.000000                   0.0   \n",
              "2                     9              0.000000                   0.0   \n",
              "3                     9              0.000000                   0.0   \n",
              "4                     9              0.000000                   0.0   \n",
              "..                  ...                   ...                   ...   \n",
              "205                   9              0.111111                   0.0   \n",
              "206                   9              0.000000                   0.0   \n",
              "207                   9              0.000000                   0.0   \n",
              "208                   9              0.111111                   0.0   \n",
              "209                   9              0.000000                   0.0   \n",
              "\n",
              "     VEDGE-MEAN  VEDGE-SD  HEDGE-MEAN  HEDGE-SD  INTENSITY-MEAN  RAWRED-MEAN  \\\n",
              "0      0.277778  0.062963    0.666667  0.311111        6.185185     7.333334   \n",
              "1      0.333333  0.266667    0.500000  0.077778        6.666666     8.333334   \n",
              "2      0.277778  0.107407    0.833333  0.522222        6.111111     7.555555   \n",
              "3      0.500000  0.166667    1.111111  0.474074        5.851852     7.777778   \n",
              "4      0.722222  0.374074    0.888889  0.429629        6.037037     7.000000   \n",
              "..          ...       ...         ...       ...             ...          ...   \n",
              "205    1.888889  1.851851    2.000000  0.711110       13.333333     9.888889   \n",
              "206    1.166667  0.744444    1.166667  0.655555       13.703704    10.666667   \n",
              "207    2.444444  6.829628    3.333333  7.599998       16.074074    13.111111   \n",
              "208    1.055556  0.862963    2.444444  5.007407       14.148149    10.888889   \n",
              "209    0.500000  0.349603    2.388889  2.080776       12.962963    11.555555   \n",
              "\n",
              "     RAWBLUE-MEAN  RAWGREEN-MEAN  EXRED-MEAN  EXBLUE-MEAN  EXGREEN-MEAN  \\\n",
              "0        7.666666       3.555556    3.444444     4.444445     -7.888889   \n",
              "1        7.777778       3.888889    5.000000     3.333333     -8.333333   \n",
              "2        7.222222       3.555556    4.333334     3.333333     -7.666666   \n",
              "3        6.444445       3.333333    5.777778     1.777778     -7.555555   \n",
              "4        7.666666       3.444444    2.888889     4.888889     -7.777778   \n",
              "..            ...            ...         ...          ...           ...   \n",
              "205     12.111111      18.000000  -10.333333    -3.666667     14.000000   \n",
              "206     12.666667      17.777779   -9.111111    -3.111111     12.222222   \n",
              "207     16.666668      18.444445   -8.888889     1.777778      7.111111   \n",
              "208     13.000000      18.555555   -9.777778    -3.444444     13.222222   \n",
              "209      9.777778      17.555555   -4.222222    -9.555555     13.777778   \n",
              "\n",
              "     VALUE-MEAN  SATURATION-MEAN  HUE-MEAN  \n",
              "0      7.777778         0.545635 -1.121818  \n",
              "1      8.444445         0.538580 -0.924817  \n",
              "2      7.555555         0.532628 -0.965946  \n",
              "3      7.777778         0.573633 -0.744272  \n",
              "4      7.888889         0.562919 -1.175773  \n",
              "..          ...              ...       ...  \n",
              "205   18.000000         0.452229  2.368311  \n",
              "206   17.777779         0.401347  2.382684  \n",
              "207   18.555555         0.292729  2.789800  \n",
              "208   18.555555         0.421621  2.392487  \n",
              "209   17.555555         0.445418  1.838850  \n",
              "\n",
              "[210 rows x 20 columns]"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TARGET-CLASS</th>\n",
              "      <th>REGION-CENTROID-COL</th>\n",
              "      <th>REGION-CENTROID-ROW</th>\n",
              "      <th>REGION-PIXEL-COUNT</th>\n",
              "      <th>SHORT-LINE-DENSITY-5</th>\n",
              "      <th>SHORT-LINE-DENSITY-2</th>\n",
              "      <th>VEDGE-MEAN</th>\n",
              "      <th>VEDGE-SD</th>\n",
              "      <th>HEDGE-MEAN</th>\n",
              "      <th>HEDGE-SD</th>\n",
              "      <th>INTENSITY-MEAN</th>\n",
              "      <th>RAWRED-MEAN</th>\n",
              "      <th>RAWBLUE-MEAN</th>\n",
              "      <th>RAWGREEN-MEAN</th>\n",
              "      <th>EXRED-MEAN</th>\n",
              "      <th>EXBLUE-MEAN</th>\n",
              "      <th>EXGREEN-MEAN</th>\n",
              "      <th>VALUE-MEAN</th>\n",
              "      <th>SATURATION-MEAN</th>\n",
              "      <th>HUE-MEAN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BRICKFACE</td>\n",
              "      <td>140.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.277778</td>\n",
              "      <td>0.062963</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.311111</td>\n",
              "      <td>6.185185</td>\n",
              "      <td>7.333334</td>\n",
              "      <td>7.666666</td>\n",
              "      <td>3.555556</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>4.444445</td>\n",
              "      <td>-7.888889</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>0.545635</td>\n",
              "      <td>-1.121818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BRICKFACE</td>\n",
              "      <td>188.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.266667</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.077778</td>\n",
              "      <td>6.666666</td>\n",
              "      <td>8.333334</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>3.888889</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>-8.333333</td>\n",
              "      <td>8.444445</td>\n",
              "      <td>0.538580</td>\n",
              "      <td>-0.924817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BRICKFACE</td>\n",
              "      <td>105.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.277778</td>\n",
              "      <td>0.107407</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.522222</td>\n",
              "      <td>6.111111</td>\n",
              "      <td>7.555555</td>\n",
              "      <td>7.222222</td>\n",
              "      <td>3.555556</td>\n",
              "      <td>4.333334</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>-7.666666</td>\n",
              "      <td>7.555555</td>\n",
              "      <td>0.532628</td>\n",
              "      <td>-0.965946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BRICKFACE</td>\n",
              "      <td>34.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>1.111111</td>\n",
              "      <td>0.474074</td>\n",
              "      <td>5.851852</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>6.444445</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>5.777778</td>\n",
              "      <td>1.777778</td>\n",
              "      <td>-7.555555</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>0.573633</td>\n",
              "      <td>-0.744272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BRICKFACE</td>\n",
              "      <td>39.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.722222</td>\n",
              "      <td>0.374074</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.429629</td>\n",
              "      <td>6.037037</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.666666</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>2.888889</td>\n",
              "      <td>4.888889</td>\n",
              "      <td>-7.777778</td>\n",
              "      <td>7.888889</td>\n",
              "      <td>0.562919</td>\n",
              "      <td>-1.175773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>36.0</td>\n",
              "      <td>243.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.888889</td>\n",
              "      <td>1.851851</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.711110</td>\n",
              "      <td>13.333333</td>\n",
              "      <td>9.888889</td>\n",
              "      <td>12.111111</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>-10.333333</td>\n",
              "      <td>-3.666667</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.452229</td>\n",
              "      <td>2.368311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>186.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>0.744444</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>0.655555</td>\n",
              "      <td>13.703704</td>\n",
              "      <td>10.666667</td>\n",
              "      <td>12.666667</td>\n",
              "      <td>17.777779</td>\n",
              "      <td>-9.111111</td>\n",
              "      <td>-3.111111</td>\n",
              "      <td>12.222222</td>\n",
              "      <td>17.777779</td>\n",
              "      <td>0.401347</td>\n",
              "      <td>2.382684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>197.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.444444</td>\n",
              "      <td>6.829628</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>7.599998</td>\n",
              "      <td>16.074074</td>\n",
              "      <td>13.111111</td>\n",
              "      <td>16.666668</td>\n",
              "      <td>18.444445</td>\n",
              "      <td>-8.888889</td>\n",
              "      <td>1.777778</td>\n",
              "      <td>7.111111</td>\n",
              "      <td>18.555555</td>\n",
              "      <td>0.292729</td>\n",
              "      <td>2.789800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>208.0</td>\n",
              "      <td>240.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.055556</td>\n",
              "      <td>0.862963</td>\n",
              "      <td>2.444444</td>\n",
              "      <td>5.007407</td>\n",
              "      <td>14.148149</td>\n",
              "      <td>10.888889</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>18.555555</td>\n",
              "      <td>-9.777778</td>\n",
              "      <td>-3.444444</td>\n",
              "      <td>13.222222</td>\n",
              "      <td>18.555555</td>\n",
              "      <td>0.421621</td>\n",
              "      <td>2.392487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>223.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.349603</td>\n",
              "      <td>2.388889</td>\n",
              "      <td>2.080776</td>\n",
              "      <td>12.962963</td>\n",
              "      <td>11.555555</td>\n",
              "      <td>9.777778</td>\n",
              "      <td>17.555555</td>\n",
              "      <td>-4.222222</td>\n",
              "      <td>-9.555555</td>\n",
              "      <td>13.777778</td>\n",
              "      <td>17.555555</td>\n",
              "      <td>0.445418</td>\n",
              "      <td>1.838850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>210 rows × 20 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45925403-78a8-4e4e-8e51-8cac810d2fad')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-45925403-78a8-4e4e-8e51-8cac810d2fad button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-45925403-78a8-4e4e-8e51-8cac810d2fad');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-381adbcd-7856-4314-913a-3127eaab3ece\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-381adbcd-7856-4314-913a-3127eaab3ece')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-381adbcd-7856-4314-913a-3127eaab3ece button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 210,\n  \"fields\": [\n    {\n      \"column\": \"TARGET-CLASS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"BRICKFACE\",\n          \"SKY\",\n          \"PATH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"REGION-CENTROID-COL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.10402373702024,\n        \"min\": 1.0,\n        \"max\": 252.0,\n        \"num_unique_values\": 139,\n        \"samples\": [\n          232.0,\n          58.0,\n          21.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"REGION-CENTROID-ROW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58.1396855414602,\n        \"min\": 11.0,\n        \"max\": 250.0,\n        \"num_unique_values\": 139,\n        \"samples\": [\n          184.0,\n          45.0,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"REGION-PIXEL-COUNT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 9,\n        \"max\": 9,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SHORT-LINE-DENSITY-5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.029548507677113485,\n        \"min\": 0.0,\n        \"max\": 0.11111111,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.11111111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SHORT-LINE-DENSITY-2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03007687534278999,\n        \"min\": 0.0,\n        \"max\": 0.22222222,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VEDGE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.158211423890182,\n        \"min\": 0.0,\n        \"max\": 25.5,\n        \"num_unique_values\": 160,\n        \"samples\": [\n          1.7222222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VEDGE-SD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.49594197730217,\n        \"min\": 0.0,\n        \"max\": 572.9964,\n        \"num_unique_values\": 202,\n        \"samples\": [\n          0.7793635\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HEDGE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.7982679462201085,\n        \"min\": 0.0,\n        \"max\": 44.722225,\n        \"num_unique_values\": 164,\n        \"samples\": [\n          5.2222214\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HEDGE-SD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 97.39002304965264,\n        \"min\": 0.0,\n        \"max\": 1386.3292,\n        \"num_unique_values\": 202,\n        \"samples\": [\n          1.186342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INTENSITY-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.677167899526836,\n        \"min\": 0.0,\n        \"max\": 143.44444,\n        \"num_unique_values\": 196,\n        \"samples\": [\n          2.1111112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAWRED-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.540563469991476,\n        \"min\": 0.0,\n        \"max\": 136.88889,\n        \"num_unique_values\": 160,\n        \"samples\": [\n          14.555555\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAWBLUE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.8044469451816,\n        \"min\": 0.0,\n        \"max\": 150.88889,\n        \"num_unique_values\": 175,\n        \"samples\": [\n          17.88889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAWGREEN-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.05700252549078,\n        \"min\": 0.0,\n        \"max\": 142.55556,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          3.6666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EXRED-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.559599057642876,\n        \"min\": -48.22222,\n        \"max\": 5.7777777,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          -14.333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EXBLUE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.761842297569604,\n        \"min\": -9.666667,\n        \"max\": 78.77778,\n        \"num_unique_values\": 168,\n        \"samples\": [\n          45.11111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EXGREEN-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.003746151323178,\n        \"min\": -30.555555,\n        \"max\": 21.88889,\n        \"num_unique_values\": 151,\n        \"samples\": [\n          -18.11111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VALUE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.23518235830037,\n        \"min\": 0.0,\n        \"max\": 150.88889,\n        \"num_unique_values\": 175,\n        \"samples\": [\n          19.88889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SATURATION-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22733330772007826,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 202,\n        \"samples\": [\n          0.2453213\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HUE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5594054046531285,\n        \"min\": -2.5309503,\n        \"max\": 2.8649306,\n        \"num_unique_values\": 202,\n        \"samples\": [\n          -1.9107349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "train = pd.read_csv('/content/drive/MyDrive/Image-Segementation/Image-Segementation-Dataset/segmentation.data')\n",
        "train #All data is of float64 type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "n4rDPMp4oWZ9",
        "outputId": "40bfc6cb-2968-4573-9dbb-efa8ccab9ca6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     TARGET-CLASS  REGION-CENTROID-COL  REGION-CENTROID-ROW  \\\n",
              "0           GRASS                110.0                189.0   \n",
              "1           GRASS                 86.0                187.0   \n",
              "2           GRASS                225.0                244.0   \n",
              "3           GRASS                 47.0                232.0   \n",
              "4           GRASS                 97.0                186.0   \n",
              "...           ...                  ...                  ...   \n",
              "2095       CEMENT                 32.0                158.0   \n",
              "2096       CEMENT                  8.0                162.0   \n",
              "2097       CEMENT                128.0                161.0   \n",
              "2098       CEMENT                150.0                158.0   \n",
              "2099       CEMENT                124.0                162.0   \n",
              "\n",
              "      REGION-PIXEL-COUNT  SHORT-LINE-DENSITY-5  SHORT-LINE-DENSITY-2  \\\n",
              "0                      9              0.000000                   0.0   \n",
              "1                      9              0.000000                   0.0   \n",
              "2                      9              0.000000                   0.0   \n",
              "3                      9              0.000000                   0.0   \n",
              "4                      9              0.000000                   0.0   \n",
              "...                  ...                   ...                   ...   \n",
              "2095                   9              0.000000                   0.0   \n",
              "2096                   9              0.111111                   0.0   \n",
              "2097                   9              0.000000                   0.0   \n",
              "2098                   9              0.000000                   0.0   \n",
              "2099                   9              0.111111                   0.0   \n",
              "\n",
              "      VEDGE-MEAN  VEDGE-SD  HEDGE-MEAN  HEDGE-SD  INTENSITY-MEAN  RAWRED-MEAN  \\\n",
              "0       1.000000  0.666667    1.222222  1.186342       12.925926    10.888889   \n",
              "1       1.111111  0.720082    1.444444  0.750309       13.740741    11.666667   \n",
              "2       3.388889  2.195113    3.000000  1.520234       12.259259    10.333334   \n",
              "3       1.277778  1.254621    1.000000  0.894427       12.703704    11.000000   \n",
              "4       1.166667  0.691215    1.166667  1.005540       15.592592    13.888889   \n",
              "...          ...       ...         ...       ...             ...          ...   \n",
              "2095    0.944445  0.862963    0.833333  0.611111        7.962963     6.333334   \n",
              "2096    1.611111  2.062962    0.333333  0.133333        8.370370     6.666666   \n",
              "2097    0.555555  0.251852    0.777778  0.162963        7.148148     5.555555   \n",
              "2098    2.166667  1.633334    1.388889  0.418518        8.444445     7.000000   \n",
              "2099    1.388889  1.129630    2.000000  0.888889       10.037037     8.000000   \n",
              "\n",
              "      RAWBLUE-MEAN  RAWGREEN-MEAN  EXRED-MEAN  EXBLUE-MEAN  EXGREEN-MEAN  \\\n",
              "0         9.222222      18.666668   -6.111111   -11.111111     17.222221   \n",
              "1        10.333334      19.222221   -6.222222   -10.222222     16.444445   \n",
              "2         9.333334      17.111110   -5.777778    -8.777778     14.555555   \n",
              "3         9.000000      18.111110   -5.111111   -11.111111     16.222221   \n",
              "4        11.777778      21.111110   -5.111111   -11.444445     16.555555   \n",
              "...            ...            ...         ...          ...           ...   \n",
              "2095     11.888889       5.666666   -4.888889    11.777778     -6.888889   \n",
              "2096     12.000000       6.444445   -5.111111    10.888889     -5.777778   \n",
              "2097     10.888889       5.000000   -4.777778    11.222222     -6.444445   \n",
              "2098     12.222222       6.111111   -4.333334    11.333333     -7.000000   \n",
              "2099     14.555555       7.555555   -6.111111    13.555555     -7.444445   \n",
              "\n",
              "      VALUE-MEAN  SATURATION-MEAN  HUE-MEAN  \n",
              "0      18.666668         0.508139  1.910864  \n",
              "1      19.222221         0.463329  1.941465  \n",
              "2      17.111110         0.480149  1.987902  \n",
              "3      18.111110         0.500966  1.875362  \n",
              "4      21.111110         0.442661  1.863654  \n",
              "...          ...              ...       ...  \n",
              "2095   11.888889         0.520578 -1.982834  \n",
              "2096   12.000000         0.484805 -2.044946  \n",
              "2097   10.888889         0.540918 -1.996307  \n",
              "2098   12.222222         0.503086 -1.943449  \n",
              "2099   14.555555         0.479931 -2.029312  \n",
              "\n",
              "[2100 rows x 20 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-46f5dd87-ec17-4567-bf76-bf42a7317423\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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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>TARGET-CLASS</th>\n",
              "      <th>REGION-CENTROID-COL</th>\n",
              "      <th>REGION-CENTROID-ROW</th>\n",
              "      <th>REGION-PIXEL-COUNT</th>\n",
              "      <th>SHORT-LINE-DENSITY-5</th>\n",
              "      <th>SHORT-LINE-DENSITY-2</th>\n",
              "      <th>VEDGE-MEAN</th>\n",
              "      <th>VEDGE-SD</th>\n",
              "      <th>HEDGE-MEAN</th>\n",
              "      <th>HEDGE-SD</th>\n",
              "      <th>INTENSITY-MEAN</th>\n",
              "      <th>RAWRED-MEAN</th>\n",
              "      <th>RAWBLUE-MEAN</th>\n",
              "      <th>RAWGREEN-MEAN</th>\n",
              "      <th>EXRED-MEAN</th>\n",
              "      <th>EXBLUE-MEAN</th>\n",
              "      <th>EXGREEN-MEAN</th>\n",
              "      <th>VALUE-MEAN</th>\n",
              "      <th>SATURATION-MEAN</th>\n",
              "      <th>HUE-MEAN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>110.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.222222</td>\n",
              "      <td>1.186342</td>\n",
              "      <td>12.925926</td>\n",
              "      <td>10.888889</td>\n",
              "      <td>9.222222</td>\n",
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              "      <td>-11.111111</td>\n",
              "      <td>17.222221</td>\n",
              "      <td>18.666668</td>\n",
              "      <td>0.508139</td>\n",
              "      <td>1.910864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>86.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.111111</td>\n",
              "      <td>0.720082</td>\n",
              "      <td>1.444444</td>\n",
              "      <td>0.750309</td>\n",
              "      <td>13.740741</td>\n",
              "      <td>11.666667</td>\n",
              "      <td>10.333334</td>\n",
              "      <td>19.222221</td>\n",
              "      <td>-6.222222</td>\n",
              "      <td>-10.222222</td>\n",
              "      <td>16.444445</td>\n",
              "      <td>19.222221</td>\n",
              "      <td>0.463329</td>\n",
              "      <td>1.941465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>225.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.388889</td>\n",
              "      <td>2.195113</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.520234</td>\n",
              "      <td>12.259259</td>\n",
              "      <td>10.333334</td>\n",
              "      <td>9.333334</td>\n",
              "      <td>17.111110</td>\n",
              "      <td>-5.777778</td>\n",
              "      <td>-8.777778</td>\n",
              "      <td>14.555555</td>\n",
              "      <td>17.111110</td>\n",
              "      <td>0.480149</td>\n",
              "      <td>1.987902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>47.0</td>\n",
              "      <td>232.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.277778</td>\n",
              "      <td>1.254621</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.894427</td>\n",
              "      <td>12.703704</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>18.111110</td>\n",
              "      <td>-5.111111</td>\n",
              "      <td>-11.111111</td>\n",
              "      <td>16.222221</td>\n",
              "      <td>18.111110</td>\n",
              "      <td>0.500966</td>\n",
              "      <td>1.875362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>97.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>0.691215</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>1.005540</td>\n",
              "      <td>15.592592</td>\n",
              "      <td>13.888889</td>\n",
              "      <td>11.777778</td>\n",
              "      <td>21.111110</td>\n",
              "      <td>-5.111111</td>\n",
              "      <td>-11.444445</td>\n",
              "      <td>16.555555</td>\n",
              "      <td>21.111110</td>\n",
              "      <td>0.442661</td>\n",
              "      <td>1.863654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2095</th>\n",
              "      <td>CEMENT</td>\n",
              "      <td>32.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.944445</td>\n",
              "      <td>0.862963</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>7.962963</td>\n",
              "      <td>6.333334</td>\n",
              "      <td>11.888889</td>\n",
              "      <td>5.666666</td>\n",
              "      <td>-4.888889</td>\n",
              "      <td>11.777778</td>\n",
              "      <td>-6.888889</td>\n",
              "      <td>11.888889</td>\n",
              "      <td>0.520578</td>\n",
              "      <td>-1.982834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2096</th>\n",
              "      <td>CEMENT</td>\n",
              "      <td>8.0</td>\n",
              "      <td>162.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.611111</td>\n",
              "      <td>2.062962</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>8.370370</td>\n",
              "      <td>6.666666</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>6.444445</td>\n",
              "      <td>-5.111111</td>\n",
              "      <td>10.888889</td>\n",
              "      <td>-5.777778</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.484805</td>\n",
              "      <td>-2.044946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2097</th>\n",
              "      <td>CEMENT</td>\n",
              "      <td>128.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.555555</td>\n",
              "      <td>0.251852</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>0.162963</td>\n",
              "      <td>7.148148</td>\n",
              "      <td>5.555555</td>\n",
              "      <td>10.888889</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>-4.777778</td>\n",
              "      <td>11.222222</td>\n",
              "      <td>-6.444445</td>\n",
              "      <td>10.888889</td>\n",
              "      <td>0.540918</td>\n",
              "      <td>-1.996307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2098</th>\n",
              "      <td>CEMENT</td>\n",
              "      <td>150.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.166667</td>\n",
              "      <td>1.633334</td>\n",
              "      <td>1.388889</td>\n",
              "      <td>0.418518</td>\n",
              "      <td>8.444445</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>12.222222</td>\n",
              "      <td>6.111111</td>\n",
              "      <td>-4.333334</td>\n",
              "      <td>11.333333</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>12.222222</td>\n",
              "      <td>0.503086</td>\n",
              "      <td>-1.943449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2099</th>\n",
              "      <td>CEMENT</td>\n",
              "      <td>124.0</td>\n",
              "      <td>162.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.388889</td>\n",
              "      <td>1.129630</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>10.037037</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>14.555555</td>\n",
              "      <td>7.555555</td>\n",
              "      <td>-6.111111</td>\n",
              "      <td>13.555555</td>\n",
              "      <td>-7.444445</td>\n",
              "      <td>14.555555</td>\n",
              "      <td>0.479931</td>\n",
              "      <td>-2.029312</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2100 rows × 20 columns</p>\n",
              "</div>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 2100,\n  \"fields\": [\n    {\n      \"column\": \"TARGET-CLASS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"GRASS\",\n          \"PATH\",\n          \"SKY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"REGION-CENTROID-COL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72.85863657812145,\n        \"min\": 1.0,\n        \"max\": 254.0,\n        \"num_unique_values\": 253,\n        \"samples\": [\n          3.0,\n          62.0,\n          181.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"REGION-CENTROID-ROW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57.43142820381204,\n        \"min\": 11.0,\n        \"max\": 251.0,\n        \"num_unique_values\": 238,\n        \"samples\": [\n          102.0,\n          201.0,\n          59.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"REGION-PIXEL-COUNT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 9,\n        \"max\": 9,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SHORT-LINE-DENSITY-5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04102362644537332,\n        \"min\": 0.0,\n        \"max\": 0.33333334,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.11111111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SHORT-LINE-DENSITY-2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.023573107211427188,\n        \"min\": 0.0,\n        \"max\": 0.22222222,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VEDGE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6494530325094483,\n        \"min\": 0.0,\n        \"max\": 29.222221,\n        \"num_unique_values\": 647,\n        \"samples\": [\n          1.4444437\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VEDGE-SD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.989359186186924,\n        \"min\": 0.0,\n        \"max\": 991.7184,\n        \"num_unique_values\": 1794,\n        \"samples\": [\n          0.06296299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HEDGE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.469954474069581,\n        \"min\": 0.0,\n        \"max\": 44.722225,\n        \"num_unique_values\": 702,\n        \"samples\": [\n          0.33333364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HEDGE-SD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.471074088334944,\n        \"min\": -1.5894573e-08,\n        \"max\": 1386.3292,\n        \"num_unique_values\": 1799,\n        \"samples\": [\n          1.3185184\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INTENSITY-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.135291251144125,\n        \"min\": 0.0,\n        \"max\": 143.44444,\n        \"num_unique_values\": 1271,\n        \"samples\": [\n          33.333332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAWRED-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.994537648521025,\n        \"min\": 0.0,\n        \"max\": 137.11111,\n        \"num_unique_values\": 681,\n        \"samples\": [\n          83.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAWBLUE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.51011921688415,\n        \"min\": 0.0,\n        \"max\": 150.88889,\n        \"num_unique_values\": 781,\n        \"samples\": [\n          29.11111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAWGREEN-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.30376811479441,\n        \"min\": 0.0,\n        \"max\": 142.55556,\n        \"num_unique_values\": 691,\n        \"samples\": [\n          120.44444\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EXRED-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.588213926529571,\n        \"min\": -49.666668,\n        \"max\": 9.888889,\n        \"num_unique_values\": 430,\n        \"samples\": [\n          -28.222221\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EXBLUE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.65410749438449,\n        \"min\": -12.444445,\n        \"max\": 82.0,\n        \"num_unique_values\": 636,\n        \"samples\": [\n          2.4444444\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EXGREEN-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.606996083847285,\n        \"min\": -33.88889,\n        \"max\": 24.666666,\n        \"num_unique_values\": 377,\n        \"samples\": [\n          -21.666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VALUE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.900581748332506,\n        \"min\": 0.0,\n        \"max\": 150.88889,\n        \"num_unique_values\": 785,\n        \"samples\": [\n          122.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SATURATION-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22845750588218885,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1899,\n        \"samples\": [\n          0.12738518\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HUE-MEAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5442778266375843,\n        \"min\": -3.0441751,\n        \"max\": 2.9124804,\n        \"num_unique_values\": 1937,\n        \"samples\": [\n          -2.1609125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "test = pd.read_csv('/content/drive/MyDrive/Image-Segementation/Image-Segementation-Dataset/segmentation.test')\n",
        "test #All data is of floeat64 type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "6xrpvAJCoWZ-"
      },
      "outputs": [],
      "source": [
        "test['TARGET-CLASS'].replace({'BRICKFACE':1, 'SKY':2, 'FOLIAGE':3, 'CEMENT':4, 'WINDOW':5, 'PATH':6, 'GRASS':7}, inplace = True )\n",
        "train['TARGET-CLASS'].replace({'BRICKFACE':1, 'SKY':2, 'FOLIAGE':3, 'CEMENT':4, 'WINDOW':5, 'PATH':6, 'GRASS':7}, inplace = True )\n",
        "#Brickface = 1\n",
        "#Sky = 2\n",
        "#Foliage = 3\n",
        "#Cement = 4\n",
        "#Window = 5\n",
        "#Path = 6\n",
        "#Grass = 7"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCwJTvvfoWaA"
      },
      "source": [
        "## Spliting training and testing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "IRPGS4m_oWaB",
        "outputId": "a5a94eac-ed3d-4f6e-9209-5bd01f59837f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2099, 18)\n"
          ]
        }
      ],
      "source": [
        "X_test = test.iloc[1:, 2:]\n",
        "print(X_test.shape)\n",
        "y_test = test.iloc[1:,:1]\n",
        "X_train = train.iloc[1:, 2:]\n",
        "y_train = train.iloc[1:,:1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rE6Gwe9ooWaG"
      },
      "source": [
        "### Applying Support Vector Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "xHBKZC-SoWaG",
        "outputId": "56790e16-4e60-44a8-f200-279ed79fdf94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC()\n",
        "model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "kaqE2xHSoWaH",
        "outputId": "83cc1d8c-c7c8-48b4-8ed6-319a363ff6f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7179609337779895\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import sklearn as sk\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "model_predict = model.predict(X_test)\n",
        "acc = sk.metrics.accuracy_score(y_test, model_predict)\n",
        "print(acc) # Printing accuracy\n",
        "# Confusion Matrics\n",
        "conf_matrix = confusion_matrix(y_test, model_predict)\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d');\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTVe994DoWaJ"
      },
      "source": [
        "### Applying Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "TG6O-ubuoWaK",
        "outputId": "25eaa188-d236-4b0f-ebb2-b5aa6f8b9ef8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model2 = LogisticRegression()\n",
        "model2.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "CNgV5e7yoWaL",
        "outputId": "d9afb4fd-feab-403a-b776-97870e3b4fea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9056693663649357\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model2_predict = model2.predict(X_test)\n",
        "acc2 = sk.metrics.accuracy_score(y_test, model2_predict)\n",
        "print(acc2)\n",
        "conf_matrix = confusion_matrix(y_test, model2_predict)\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMFP6z9poWaM"
      },
      "source": [
        "### Applying K-Nearest Neighbour"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "gUMx57groWaN",
        "outputId": "a37d2d8f-b1e9-4190-ae9a-1de25d2df9bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=7)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "model3 = KNeighborsClassifier(n_neighbors=7)\n",
        "model3.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "By2rJ2y6oWaN",
        "outputId": "197257b2-921e-4a0c-898e-5375706bcbd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8937589328251548\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model3_predict = model3.predict(X_test)\n",
        "acc3 = sk.metrics.accuracy_score(y_test, model3_predict)\n",
        "print(acc3)\n",
        "conf_matrix = confusion_matrix(y_test, model3_predict)\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaoN-5PAoWaO"
      },
      "source": [
        "### Applying Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "b7NCjTHYoWaQ",
        "outputId": "04332030-fcf9-4560-cc8b-cc8e4375f6fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-0cef9be7b3ad>:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  model4.fit(X_train, y_train)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=7)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=7)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model4 = RandomForestClassifier(n_estimators=7)\n",
        "model4.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "ExswVhKmoWaS",
        "outputId": "2812e4e5-e880-44ae-f260-cb6352aa7a52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9299666507860886\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model4_predict = model4.predict(X_test)\n",
        "acc4 = sk.metrics.accuracy_score(y_test, model4_predict)\n",
        "print(acc4)\n",
        "conf_matrix = confusion_matrix(y_test, model4_predict)\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqPtfGIuoWaT"
      },
      "source": [
        "### Applying Adaboost Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "jxuktvcroWaU",
        "outputId": "bf46072d-4942-49f2-b8b0-013bafe0009d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(n_estimators=7)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(n_estimators=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(n_estimators=7)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "model5 = AdaBoostClassifier(n_estimators=7)\n",
        "model5.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "AX84Gud4oWaV",
        "outputId": "7388c00c-dd70-4a78-c268-63bc1fa044d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.4263935207241544\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model5_predict = model5.predict(X_test)\n",
        "acc5 = sk.metrics.accuracy_score(y_test, model5_predict)\n",
        "print(acc5)\n",
        "conf_matrix = confusion_matrix(y_test, model5_predict)\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EaxEDnWIoWaW"
      },
      "source": [
        "### Applying Naive Bayes Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "SyMFXP4WoWaX",
        "outputId": "aad33ebb-265c-4eb5-cdb9-c5cba3c65212",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "model6 = GaussianNB()\n",
        "model6.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "6G1ZpNpioWaY",
        "outputId": "d5bf8e9d-d9e9-4e37-fbec-3cc1f6bfff3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7951405431157694\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model6_predict = model6.predict(X_test)\n",
        "acc6 = sk.metrics.accuracy_score(y_test, model6_predict)\n",
        "print(acc6)\n",
        "conf_matrix = confusion_matrix(y_test, model6_predict)\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "p_aaXE02oWaY",
        "outputId": "a774fb56-dafc-49b9-ab8c-7e147c4e3a7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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8T6pXr5706tVLpy0sLEwaNWqkPK9Ro4a8/PLLRa4jt4Bz+fJlsba2lvfee0+OHz8uGzZsEGdnZ503+b59+4q3t7ds2bJFjhw5Ip06dRI7O7tcA86aNWvE3t5efvzxR7lw4YLs3btXFi5cKCIid+7ckbJly8qECRPk2rVrcu3aNRHRDzjff/+9mJqaytixY+XYsWOSkJCgs1E/uUNnZWXJjh07xNraWkJDQ3XqbtSokezYsUNOnz4t06ZNE61Wq7yB7dq1S0xMTGTatGmSmJgoc+fOldKlS+sFHBsbG2nTpo0cOHBADh06JCIiwcHB0qFDB/njjz/k5MmT8uGHH4qTk5PcuXNHRESqVasmb731lhw/flxOnjwp3377rRKA2rVrJ61bt5bDhw/LmTNn5LvvvpPffvtNRPQDzp9//ikmJiYyYcIESUxMlNjYWLGystIJDd7e3lK6dGmZO3eunDp1SmJiYsTExEROnDiRxxbx7zYxYsQIsbW1lS1btuT4+vr168XS0lIuXbokIrkHnCtXroi7u7tMmzZNee15BpwjR46Im5ubBAUFicg/2/a0adPk4MGDcubMGfn888/F1NRU9u7dq0zTvHlzsbe3l3HjxsnJkydl6dKlotFo5JdffhGRf0JE9erVpVWrVpKQkCC//fab1KpVS+fN/8GDB+Lu7i6vv/66HDlyROLi4qR8+fI6H3jh4eFiZ2cnAwcOlBMnTsjixYsFgISEhMikSZPk5MmTEh0dLebm5so4i+QdcGbMmCH29vaycuVKOXHihIwYMULMzc2V7Tv7Q9jHx0fWrVsnZ8+elatXr8o333wj7u7uStu6deukdOnSsmTJEhERmTZtmnh5ecmOHTvk/PnzsnPnTuUD9ebNmwJAYmNj5dq1a3Lz5k29uhYvXix169YVEZHvvvtOfH19JSsrS0REVq9eLVqtVr788ks5ceKEfPTRR2JnZ6cTcOLi4mTZsmVy/PhxOXbsmPTp00dcXV0lOTlZ6fN0wLl8+bLUr19f5320IPvOq6++KlWqVJEdO3ZIQkKChISESMWKFSUtLU1Ect9XMzIyZN26dQJAEhMT5dq1a3Lv3r0c/055Kcg+VtDtOPv98OjRowJATp8+rbye3Xbq1CkRkXy3ARH9bS8xMVHKly+vE5ISEhJkwYIFcuTIETl58qSMGTNGLC0t5cKFCyIicunSJTExMZF9+/Yp0xw4cEA0Go2cOXOmQLXktT0WBANOMVu7dq04OjqKpaWlNGrUSCIjI5UPShGRBQsWiK2trfI/vuTkZLG2tpYvv/xS6WNlZSVDhgwpcg25BZzRo0eLv7+/8gYkIjJ37lyxtbWVzMxMSU5OFnNzc1mzZo3y+r1798Ta2jrXgDN9+nTx8/NT3iSeltP/vp4OOA0bNpQePXrkuj7NmzcXc3NzsbGxEXNzcwEglpaWsnv3bhERuXDhgpiamsqVK1d0pmvVqpVERkaKiEhoaKi0a9dO5/UePXroBRxzc3OdN/KdO3eKvb29XmD19fVVjpzY2dnpvFk8KSAgQMaNG5fja08HnDfffFNat26t02f48OE6/1P19vaWt956S3melZUlLi4uMn/+/ByXkS08PFwsLCwEgMTFxeX4evY206BBA+ndu7eI5B5w7t69KwsWLJDSpUsrb/bFHXBMTU3FxsZGtFqtABATExNZu3ZtrtO0a9dOPvzwQ+V58+bNpUmTJjp96tWrJyNHjhQRkZ9//lnMzMx0tqOffvpJ581/4cKF4ujoqHMU9YcffhATExO5fv26Uqu3t7dkZmYqffz9/aVp06bK84yMDLGxsZGVK1cqbdnbtY2NjfLIXq6Hh4fe0ZF69erJe++9JyL/BpxZs2bp9PH19dX7gIiOjpaGDRuKiMjgwYOlZcuWOu8JT8ordImINGrUSFlmenq6ODs7K0dOGzZsqNSXLSgoSCfgPC0zM1Ps7Ozku+++U9q8vb3FwsJCbGxsxNLSUgBIUFCQzpHP/PadkydPCgDlPUPkn6MlVlZW8u2334pI4fbVoijoPva0nLbjJ9+PAwMDZcKECcrzyMhIJfiL5L8NiOhue9n7V/v27XN9X89WrVo1mT17tvL8lVdekXfffVd5PnjwYHnppZcKXEt+22N+eA5OMevcuTOuXr2KTZs2oU2bNti+fTtq166tnHz5xhtvIDMzE99++y0AYPXq1TAxMUFoaKgyDymmk2ePHz+Ohg0b6pzz0bhxYzx48ACXL1/G2bNnkZ6ejvr16yuvOzg4wN/fP9d5du3aFY8ePUKFChXQr18/bNiwARkZGYWqKyEhAa1atcqzT48ePZCQkIDdu3fjlVdewUcffYRGjRoBAI4cOYLMzEz4+fnB1tZWefz22284c+YMACAxMVFnvQDoPQcAb29vlClTRnl+6NAhPHjwAE5OTjrzPnfunDLviIgI9O3bF8HBwfjkk0+UdgAYMmQIJk6ciMaNGyMqKgqHDx/OdR2PHz+Oxo0b67Q1btwYp06d0jmht0aNGsq/NRoN3NzccPPmzTzHL3s6Hx8fREVF4cGDB7n2mzJlCpYuXYrjx4/nOb8+ffrAyckJU6ZMyXfZhtCiRQskJCRg7969CA8PR69evdC5c2cAQGZmJqKjoxEQEIDSpUvD1tYWP//8My5evKgzjyfHDgDc3d2VsTt+/Di8vLzg4eGhvN6wYUOd/sePH0dgYCBsbGyUtsaNGyMrKwuJiYlKW7Vq1XR+U8/V1RUBAQHKc1NTUzg5Oen93WbOnImEhATl0bp1ayQnJ+Pq1as5bhtP/43q1q2r/DslJQVnzpxBnz59dLbdiRMnKttoz549kZCQAH9/fwwZMgS//PILCioxMRH79u3DG2+8AQAwMzNDaGgoFi9erIxVUFCQzjRPj+eNGzfQr18/VKpUCQ4ODrC3t8eDBw/0/m7Dhw9HQkICDh8+jLi4OABAu3btlP0iv33n+PHjMDMz06nHyckJ/v7+yhgWZl99VrntYwXdjp/Uo0cPrFixAsA/nx0rV65Ejx49ABRsG8iWve0dOnQI33//PU6ePIm3335bef3BgwcYNmwYqlSpglKlSsHW1hbHjx/Xqa1fv35YuXIlHj9+jLS0NKxYsQK9e/cucC3Psj0CPMn4ubC0tETr1q3x8ccfY8+ePejZs6dyMqS9vT26dOminGwcGxuLbt26wdbWVpnez88PJ06cMErtheXl5YXExETMmzcPVlZWeO+999CsWbNCneBoZWWVbx8HBwdUrFgR9erVw7fffos5c+Zgy5YtAP7Z8UxNTbF//36dD4fjx4/js88+K9T6PPnBlT1vd3d3nfkmJCQgMTERw4cPB/DPpa9//fUX2rVrh61bt6Jq1arYsGEDAKBv3744e/Ys3n77bRw5cgR169bF7NmzC1XT08zNzXWeazQaZGVl5Tudp6cntm/fjitXrqBNmza4f/9+jv2aNWuGkJAQREZG5jk/MzMzTJo0CZ999lmxnVz6JBsbG1SsWBGBgYH46quvsHfvXuXDdNq0afjss88wcuRIbNu2DQkJCQgJCdE7UbyoY1dYOS2nIMt2c3NDxYoVlcfT22N+nuyfHWIXLVqks+0ePXoUv//+OwCgdu3aOHfuHKKjo/Ho0SN069YNXbp0KdCyFi9ejIyMDHh4eMDMzAxmZmaYP38+1q1bh6SkpALNIzw8HAkJCfjss8+wZ88eJCQkwMnJSe/v5uzsjIoVK6JSpUpo2bIlZs2ahT179mDbtm0FWk5BFMe+mpvc9rGCbsdPeuONN5CYmIgDBw5gz549uHTpkvIf5oJsA9mytz1/f3+0a9cO48ePx+rVq3H69GkAwLBhw7BhwwZMnjwZO3fuREJCAgICAnRq69ChA7RaLTZs2IDvvvsO6enpyvZU3NsjwIBjFFWrVkVKSoryvE+fPti1axe+//577NmzB3369NHp/+abb2LLli04ePCg3rzS09N15lUYVapUQXx8vM4Rot27d8POzg5ly5ZFhQoVYG5ujj/++EN5PSkpKd9Luq2srNChQwd8/vnn2L59O+Lj43HkyBEA/1wp9PTlxE+rUaOG8r+ygrC1tcXQoUMxbNgwiAhq1aqFzMxM3Lx5U+fDoWLFinBzcwMA+Pv766wXAL3nOalduzauX78OMzMzvXk7Ozsr/fz8/PDBBx/gl19+weuvv65ztZyXlxfeeecdrF+/Hh9++CEWLVqU47KqVKmC3bt367Tt3r0bfn5+MDU1LfD45MXb2xu//fYbrl+/nmfI+eSTT/Ddd98hPj4+z/l17doV1apVw/jx4w1SX0GZmJhg9OjRGDNmDB49eoTdu3fjtddew1tvvYXAwEBUqFChULciAP4Z/0uXLuHatWtK29MfAlWqVMGhQ4d09sHdu3fDxMQkzyOdz8Le3h4eHh45bhtVq1bNdTpXV1d4eHjg7Nmzettu+fLldeYfGhqKRYsWYfXq1Vi3bh3+/vtvAP8EtZz234yMDHz99deYPn26zofVoUOH4OHhgZUrV6JKlSrYu3evznRPj+fu3bsxZMgQtG3bFtWqVYNWq8Xt27fzHZPs/eHRo0cA8t93qlSpgoyMDJ167ty5g8TERJ0xzG1ftbCwAIB838sKI6d9rCjbcdmyZdG8eXMsX74cy5cvR+vWrZWrVAu6DeTk6THevXs3evbsiU6dOiEgIABubm44f/68zjRmZmYIDw9HbGwsYmNj0b17d+U/sIbYHvNTIn5NXK3u3LmDrl27onfv3qhRowbs7Ozw559/YurUqXjttdeUfs2aNUPFihURFhaGypUrK1+1ZHv//ffxww8/oFWrVoiOjkaTJk2UeU2ZMgWLFy/O92ZZSUlJSEhI0Gnr378/Zs2ahcGDB2PQoEFITExEVFQUIiIiYGJiAjs7O4SHh2P48OEoXbo0XFxcEBUVBRMTk1wvZV6yZAkyMzMRFBQEa2trfPPNN7CysoK3tzeAf+5hsWPHDnTv3h1arVYnFGSLiopCq1at4Ovri+7duyMjIwM//vgjRo4cmev6DRgwANHR0Vi3bh26dOmCHj16ICwsDNOnT0etWrVw69YtxMXFoUaNGmjXrh0GDx6MZs2aYcaMGejQoQO2bt2Kn376Kd9LtIODg9GwYUN07NgRU6dOhZ+fH65evYoffvgBnTp1QrVq1TB8+HB06dIF5cuXx+XLl/HHH38oX528//77eOWVV+Dn54e7d+9i27ZtqFKlSo7L+vDDD1GvXj1ER0cjNDQU8fHxmDNnDubNm5dnjYXl5eWF7du3o0WLFggJCcHmzZv1+gQEBKBHjx74/PPP853fJ598gpCQEIPWWBBdu3bF8OHDMXfuXFSqVAlr167Fnj174OjoiBkzZuDGjRt5BoCnBQcHw8/PD+Hh4Zg2bRqSk5Px0Ucf6fTp0aMHoqKiEB4ejnHjxuHWrVsYPHgw3n77bbi6uhp6FRXDhw9HVFQUfH19UbNmTcTGxiIhIQHLly/Pc7rx48djyJAhcHBwQJs2bZCamoo///wTd+/eRUREBGbMmAF3d3fUqlULJiYmWLNmDdzc3JQbcfr4+CAuLg6NGzeGVquFo6MjAOD777/H3bt30adPH737OHXu3BmLFy/GsGHD0LNnT9StWxeNGzfG8uXL8ddff6FChQpK30qVKmHZsmWoW7cukpOTMXz48ByP6N6/fx/Xr1+HiODSpUsYMWIEypQpo7x35rfvVKpUCa+99hr69euHL774AnZ2dhg1ahQ8PT2V9+a89lVvb29oNBp8//33aNu2LaysrHSOuhdFTvtYUbfj7O0yLS0NM2fO1Hktv20g271793D9+nVkZWXh1KlTmDBhAvz8/JQxqFSpEtavX48OHTpAo9Hg448/zvHoZ9++fZVpng6dz7o95qtIZ+5QgTx+/FhGjRoltWvXFgcHB7G2thZ/f38ZM2aMPHz4UKfv5MmTBYBMnTo113nFxMRIQECAcjlq48aNZcmSJTqXdeckPDxc77JGANKnT58iXSZev359GTVqlNLnyROHN2zYIEFBQWJvby82NjbSoEEDnSt04uPjpUaNGsqJayI5Xya+bt06qVmzplhYWIizs7O8/vrryms5XSYuIjJgwACpVq2aZGZmSlpamowdO1Z8fHzE3Nxc3N3dpVOnTnL48GGl/8KFC8XT01O5THzixIni5uamvJ59mfjTkpOTZfDgweLh4SHm5ubi5eUlPXr0kIsXL0pqaqp0795duUzew8NDBg0aJI8ePRIRkUGDBomvr69otVopU6aMvP3223L79m0RyfsycXNzcylXrpzOVUpPj322wMBAvcudn5bTieeXL1+WSpUqSYMGDaRTp056r587d045MTlbbidbvvzyy8oVN8UhtxPnY2JipEyZMnL58mV57bXXxNbWVlxcXGTMmDESFhamM01O29Frr72mcwVUYmKiNGnSRCwsLMTPz082b95c5MvEn5TTsp/+Wz69nCdlZmbKuHHjxNPTU8zNzXO9TPzgwYN60y5fvlzZtxwdHaVZs2bKLQIWLlwoNWvWFBsbG7G3t5dWrVrp3J5i06ZNUrFiRTEzM9O5TLx9+/Y6lxA/ae/evQJADh06JJMmTRJnZ2extbWV8PBwGTFihM4+duDAAalbt65YWlpKpUqVZM2aNXrj8vTl6mXKlJG2bdvqrWt++072ZeIODg5iZWUlISEhOpeJ57WviohMmDBB3NzcRKPRFPky8fz2sTt37hRpO757965otVqxtrbW2Raz5bUNiIjO+Go0GnF3d5fQ0FDl6qfsWlu0aCFWVlbi5eUlc+bMyfW9uWnTpjleMp5fLfltj/nR/P/KEBVISkoKPD09MX36dL2v0l50/fr1w4kTJ7Bz505jl0JEpAoigkqVKuG9997TOUL0PPArKsrTwYMHceLECdSvXx9JSUmYMGECAOh8xfai+vTTT9G6dWvY2Njgp59+wtKlSw3+9Q8R0X/VrVu3sGrVKly/fh29evV67stnwKF8ffrpp0hMTISFhQXq1KmDnTt35njuzItm3759mDp1Ku7fv48KFSrg888/R9++fY1dFhGRKri4uMDZ2RkLFy5Uztd6nvgVFREREakOLxMnIiIi1WHAISIiItVhwCEiIiLVYcAhIiIi1WHAISLV2b59OzQaDe7du1fgaXx8fDBr1qxiq4mIni8GHCJ67nr27AmNRoN33nlH77WBAwdCo9GgZ8+ez78wIlINBhwiMgovLy+sWrVK+fE+AHj8+DFWrFiBcuXKGbEyIlIDBhwiMoratWvDy8sL69evV9rWr1+PcuXKoVatWkpbamoqhgwZAhcXF1haWqJJkyZ6v/z+448/ws/PD1ZWVmjRooXerxoDwK5du9C0aVNYWVnBy8sLQ4YM0fkV8CeJCMaNG4dy5cpBq9XCw8MDQ4YMMcyKE9FzwYBDREbTu3dvxMbGKs+/+uorvVu6jxgxAuvWrcPSpUtx4MABVKxYESEhIfj7778BAJcuXcLrr7+ODh06ICEhAX379sWoUaN05nHmzBm0adMGnTt3xuHDh7F69Wrs2rULgwYNyrGudevWYebMmfjiiy9w6tQpbNy4EQEBAQZeeyIqVgX+WU4iIgPJ/iXlmzdvilarlfPnz8v58+fF0tJSbt26pfyq94MHD8Tc3FyWL1+uTJuWliYeHh4ydepUERGJjIyUqlWr6sx/5MiROr9y3qdPH+nfv79On507d4qJiYnyS+9P/mr19OnTxc/PT9LS0oppBIiouPEIDhEZTZkyZdCuXTssWbIEsbGxaNeunc7vnJ05cwbp6elo3Lix0mZubo769evj+PHjAIDjx48jKChIZ74NGzbUeX7o0CEsWbIEtra2yiMkJARZWVk4d+6cXl1du3bFo0ePUKFCBfTr1w8bNmxARkaGIVediIoZf2yTiIyqd+/eyldFc+fOLZZlPHjwAAMGDMjxPJqcTmj28vJCYmIitmzZgl9//RXvvfcepk2bht9++w3m5ubFUiMRGRaP4BCRUbVp0wZpaWlIT09HSEiIzmu+vr6wsLDA7t27lbb09HT88ccfqFq1KgCgSpUq2Ldvn850v//+u87z2rVr49ixY6hYsaLew8LCIse6rKys0KFDB3z++efYvn074uPjceTIEUOsMhE9BzyCQ0RGZWpqqnzdZGpqqvOajY0N3n33XQwfPhylS5dGuXLlMHXqVDx8+BB9+vQBALzzzjuYPn06hg8fjr59+2L//v1YsmSJznxGjhyJBg0aYNCgQejbty9sbGxw7Ngx/Prrr5gzZ45eTUuWLEFmZiaCgoJgbW2Nb775BlZWVvD29i6eQSAig+MRHCIyOnt7e9jb2+f42ieffILOnTvj7bffRu3atXH69Gn8/PPPcHR0BPDPV0zr1q3Dxo0bERgYiAULFmDy5Mk686hRowZ+++03nDx5Ek2bNkWtWrUwduxYeHh45LjMUqVKYdGiRWjcuDFq1KiBLVu24LvvvoOTk5NhV5yIio1GRMTYRRAREREZEo/gEBERkeow4BAREZHqMOAQERGR6jDgEBERkeow4BAREZHqMOAQERGR6jDgEBERkeow4BAREZHqMOAQERGR6jDgEBERkeow4BAREZHqMOAQERGR6vwfqHAUo1XEwjUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "data = {'SVC':acc, 'LogisticRegression':acc2, 'KNN':acc3, 'RandomForest':acc4, 'AdaBoost':acc5, 'NaiveBayes':acc6}\n",
        "courses = list(data.keys())\n",
        "values = list(data.values())\n",
        "fig = plt.figure()\n",
        "plt.bar(courses, values, color ='cyan',width = 0.4)\n",
        "plt.xlabel(\"Models\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Change in accuracy based on the model\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "uOclqTVFoWaZ"
      },
      "outputs": [],
      "source": [
        "pickle.dump(model4, open('/content/drive/MyDrive/Image-Segementation/Image-Segementation-Dataset/model.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vb5LxIc1rjNU"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.6"
    },
    "orig_nbformat": 4,
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}