drycoffeebeansM.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "diLtAkU0eJH8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "82so9kD8gTtK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "LaLqTYzSgW0L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "UtFBxYn7gZN5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/Dry_Bean.csv')"
      ],
      "metadata": {
        "id": "O66L7aTeg4Si"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df.head()"
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      },
      "execution_count": null,
      "outputs": [
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              "    Area  Perimeter  MajorAxisLength  MinorAxisLength  AspectRation  \\\n",
              "0  28395    610.291       208.178117       173.888747      1.197191   \n",
              "1  28734    638.018       200.524796       182.734419      1.097356   \n",
              "2  29380    624.110       212.826130       175.931143      1.209713   \n",
              "3  30008    645.884       210.557999       182.516516      1.153638   \n",
              "4  30140    620.134       201.847882       190.279279      1.060798   \n",
              "\n",
              "   Eccentricity  ConvexArea  EquivDiameter    Extent  Solidity  roundness  \\\n",
              "0      0.549812       28715     190.141097  0.763923  0.988856   0.958027   \n",
              "1      0.411785       29172     191.272751  0.783968  0.984986   0.887034   \n",
              "2      0.562727       29690     193.410904  0.778113  0.989559   0.947849   \n",
              "3      0.498616       30724     195.467062  0.782681  0.976696   0.903936   \n",
              "4      0.333680       30417     195.896503  0.773098  0.990893   0.984877   \n",
              "\n",
              "   Compactness  ShapeFactor1  ShapeFactor2  ShapeFactor3  ShapeFactor4  Class  \n",
              "0     0.913358      0.007332      0.003147      0.834222      0.998724  SEKER  \n",
              "1     0.953861      0.006979      0.003564      0.909851      0.998430  SEKER  \n",
              "2     0.908774      0.007244      0.003048      0.825871      0.999066  SEKER  \n",
              "3     0.928329      0.007017      0.003215      0.861794      0.994199  SEKER  \n",
              "4     0.970516      0.006697      0.003665      0.941900      0.999166  SEKER  "
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              "      <th>Area</th>\n",
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              "      <th>0</th>\n",
              "      <td>28395</td>\n",
              "      <td>610.291</td>\n",
              "      <td>208.178117</td>\n",
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              "      <td>1.197191</td>\n",
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              "      <td>0.003147</td>\n",
              "      <td>0.834222</td>\n",
              "      <td>0.998724</td>\n",
              "      <td>SEKER</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0.953861</td>\n",
              "      <td>0.006979</td>\n",
              "      <td>0.003564</td>\n",
              "      <td>0.909851</td>\n",
              "      <td>0.998430</td>\n",
              "      <td>SEKER</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29380</td>\n",
              "      <td>624.110</td>\n",
              "      <td>212.826130</td>\n",
              "      <td>175.931143</td>\n",
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              "      <td>0.003048</td>\n",
              "      <td>0.825871</td>\n",
              "      <td>0.999066</td>\n",
              "      <td>SEKER</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30008</td>\n",
              "      <td>645.884</td>\n",
              "      <td>210.557999</td>\n",
              "      <td>182.516516</td>\n",
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              "      <td>0.498616</td>\n",
              "      <td>30724</td>\n",
              "      <td>195.467062</td>\n",
              "      <td>0.782681</td>\n",
              "      <td>0.976696</td>\n",
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              "      <td>0.003215</td>\n",
              "      <td>0.861794</td>\n",
              "      <td>0.994199</td>\n",
              "      <td>SEKER</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>620.134</td>\n",
              "      <td>201.847882</td>\n",
              "      <td>190.279279</td>\n",
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              "      <td>0.003665</td>\n",
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              "      <td>0.999166</td>\n",
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\"column\": \"EquivDiameter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.17712014871188,\n        \"min\": 161.2437642,\n        \"max\": 569.3743583,\n        \"num_unique_values\": 12011,\n        \"samples\": [\n          247.2463893,\n          240.8237141,\n          478.8490747\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04908636684396477,\n        \"min\": 0.555314717,\n        \"max\": 0.866194641,\n        \"num_unique_values\": 13535,\n        \"samples\": [\n          0.753012629,\n          0.813903525,\n          0.710475845\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Solidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004660379169838444,\n        \"min\": 0.919246157,\n        \"max\": 0.9946775,\n        \"num_unique_values\": 13522,\n        \"samples\": [\n          0.989451562,\n          0.988288395,\n          0.990392287\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roundness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05951988794941905,\n        \"min\": 0.489618256,\n        \"max\": 0.9906854,\n        \"num_unique_values\": 13540,\n        \"samples\": [\n          0.938825243,\n          0.905049768,\n          0.921551042\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Compactness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06171346311682394,\n        \"min\": 0.640576759,\n        \"max\": 0.987302969,\n        \"num_unique_values\": 13543,\n        \"samples\": [\n          0.86341771,\n          0.810085068,\n          0.819260795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0011279982248070585,\n        \"min\": 0.002778013,\n        \"max\": 0.010451169,\n        \"num_unique_values\": 13521,\n        \"samples\": [\n          0.008601217,\n          0.006711799,\n          0.008063532\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005958748528937666,\n        \"min\": 0.000564169,\n        \"max\": 0.003664972,\n        \"num_unique_values\": 13506,\n        \"samples\": [\n          0.001092201,\n          0.001521882,\n          0.000939109\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.098996150476599,\n        \"min\": 0.410338584,\n        \"max\": 0.974767153,\n        \"num_unique_values\": 13543,\n        \"samples\": [\n          0.745490141,\n          0.656237818,\n          0.67118825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004366457746634138,\n        \"min\": 0.947687403,\n        \"max\": 0.99973253,\n        \"num_unique_values\": 13532,\n        \"samples\": [\n          0.993185175,\n          0.997895985,\n          0.990881262\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"SEKER\",\n          \"BARBUNYA\",\n          \"SIRA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m6fqdoOThSJY",
        "outputId": "4bae5441-d369-440e-eada-e85d58291810"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Area', 'Perimeter', 'MajorAxisLength', 'MinorAxisLength',\n",
              "       'AspectRation', 'Eccentricity', 'ConvexArea', 'EquivDiameter', 'Extent',\n",
              "       'Solidity', 'roundness', 'Compactness', 'ShapeFactor1', 'ShapeFactor2',\n",
              "       'ShapeFactor3', 'ShapeFactor4', 'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zkmumrxAlUAi",
        "outputId": "76f469ac-454b-4694-ad79-f4f2f14730a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13611 entries, 0 to 13610\n",
            "Data columns (total 17 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Area             13611 non-null  int64  \n",
            " 1   Perimeter        13611 non-null  float64\n",
            " 2   MajorAxisLength  13611 non-null  float64\n",
            " 3   MinorAxisLength  13611 non-null  float64\n",
            " 4   AspectRation     13611 non-null  float64\n",
            " 5   Eccentricity     13611 non-null  float64\n",
            " 6   ConvexArea       13611 non-null  int64  \n",
            " 7   EquivDiameter    13611 non-null  float64\n",
            " 8   Extent           13611 non-null  float64\n",
            " 9   Solidity         13611 non-null  float64\n",
            " 10  roundness        13611 non-null  float64\n",
            " 11  Compactness      13611 non-null  float64\n",
            " 12  ShapeFactor1     13611 non-null  float64\n",
            " 13  ShapeFactor2     13611 non-null  float64\n",
            " 14  ShapeFactor3     13611 non-null  float64\n",
            " 15  ShapeFactor4     13611 non-null  float64\n",
            " 16  Class            13611 non-null  object \n",
            "dtypes: float64(14), int64(2), object(1)\n",
            "memory usage: 1.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4IWzxo1-lwLF",
        "outputId": "a6bd95ab-e24b-4065-9045-c4abe76833e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Area               0\n",
              "Perimeter          0\n",
              "MajorAxisLength    0\n",
              "MinorAxisLength    0\n",
              "AspectRation       0\n",
              "Eccentricity       0\n",
              "ConvexArea         0\n",
              "EquivDiameter      0\n",
              "Extent             0\n",
              "Solidity           0\n",
              "roundness          0\n",
              "Compactness        0\n",
              "ShapeFactor1       0\n",
              "ShapeFactor2       0\n",
              "ShapeFactor3       0\n",
              "ShapeFactor4       0\n",
              "Class              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no missing values"
      ],
      "metadata": {
        "id": "na7h_S4AoyAZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "[features for features in df.columns if df[features].isnull().sum()>0]\n",
        "df['Class'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjtJUzknmcI4",
        "outputId": "494a3667-3386-4944-af4f-acfe470de8e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['SEKER', 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ', 'SIRA', 'DERMASON'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Class'].value_counts(sort=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mcm5AMtEryBf",
        "outputId": "8a18460f-e79e-450f-85c5-27aad0970a3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class\n",
              "SEKER       2027\n",
              "BARBUNYA    1322\n",
              "BOMBAY       522\n",
              "CALI        1630\n",
              "HOROZ       1928\n",
              "SIRA        2636\n",
              "DERMASON    3546\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "Eo1STSsUvYMf",
        "outputId": "7411b700-1d3d-484b-f997-3d3def47d16b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Area  Perimeter  MajorAxisLength  MinorAxisLength  AspectRation  \\\n",
              "0      28395    610.291       208.178117       173.888747      1.197191   \n",
              "1      28734    638.018       200.524796       182.734419      1.097356   \n",
              "2      29380    624.110       212.826130       175.931143      1.209713   \n",
              "3      30008    645.884       210.557999       182.516516      1.153638   \n",
              "4      30140    620.134       201.847882       190.279279      1.060798   \n",
              "...      ...        ...              ...              ...           ...   \n",
              "13606  42097    759.696       288.721612       185.944705      1.552728   \n",
              "13607  42101    757.499       281.576392       190.713136      1.476439   \n",
              "13608  42139    759.321       281.539928       191.187979      1.472582   \n",
              "13609  42147    763.779       283.382636       190.275731      1.489326   \n",
              "13610  42159    772.237       295.142741       182.204716      1.619841   \n",
              "\n",
              "       Eccentricity  ConvexArea  EquivDiameter    Extent  Solidity  roundness  \\\n",
              "0          0.549812       28715     190.141097  0.763923  0.988856   0.958027   \n",
              "1          0.411785       29172     191.272751  0.783968  0.984986   0.887034   \n",
              "2          0.562727       29690     193.410904  0.778113  0.989559   0.947849   \n",
              "3          0.498616       30724     195.467062  0.782681  0.976696   0.903936   \n",
              "4          0.333680       30417     195.896503  0.773098  0.990893   0.984877   \n",
              "...             ...         ...            ...       ...       ...        ...   \n",
              "13606      0.765002       42508     231.515799  0.714574  0.990331   0.916603   \n",
              "13607      0.735702       42494     231.526798  0.799943  0.990752   0.922015   \n",
              "13608      0.734065       42569     231.631261  0.729932  0.989899   0.918424   \n",
              "13609      0.741055       42667     231.653247  0.705389  0.987813   0.907906   \n",
              "13610      0.786693       42600     231.686223  0.788962  0.989648   0.888380   \n",
              "\n",
              "       Compactness  ShapeFactor1  ShapeFactor2  ShapeFactor3  ShapeFactor4  \\\n",
              "0         0.913358      0.007332      0.003147      0.834222      0.998724   \n",
              "1         0.953861      0.006979      0.003564      0.909851      0.998430   \n",
              "2         0.908774      0.007244      0.003048      0.825871      0.999066   \n",
              "3         0.928329      0.007017      0.003215      0.861794      0.994199   \n",
              "4         0.970516      0.006697      0.003665      0.941900      0.999166   \n",
              "...            ...           ...           ...           ...           ...   \n",
              "13606     0.801865      0.006858      0.001749      0.642988      0.998385   \n",
              "13607     0.822252      0.006688      0.001886      0.676099      0.998219   \n",
              "13608     0.822730      0.006681      0.001888      0.676884      0.996767   \n",
              "13609     0.817457      0.006724      0.001852      0.668237      0.995222   \n",
              "13610     0.784997      0.007001      0.001640      0.616221      0.998180   \n",
              "\n",
              "          Class  \n",
              "0         SEKER  \n",
              "1         SEKER  \n",
              "2         SEKER  \n",
              "3         SEKER  \n",
              "4         SEKER  \n",
              "...         ...  \n",
              "13606  DERMASON  \n",
              "13607  DERMASON  \n",
              "13608  DERMASON  \n",
              "13609  DERMASON  \n",
              "13610  DERMASON  \n",
              "\n",
              "[13611 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-abcde7c4-91c2-4494-8e4f-fcc1564b86c9\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Perimeter</th>\n",
              "      <th>MajorAxisLength</th>\n",
              "      <th>MinorAxisLength</th>\n",
              "      <th>AspectRation</th>\n",
              "      <th>Eccentricity</th>\n",
              "      <th>ConvexArea</th>\n",
              "      <th>EquivDiameter</th>\n",
              "      <th>Extent</th>\n",
              "      <th>Solidity</th>\n",
              "      <th>roundness</th>\n",
              "      <th>Compactness</th>\n",
              "      <th>ShapeFactor1</th>\n",
              "      <th>ShapeFactor2</th>\n",
              "      <th>ShapeFactor3</th>\n",
              "      <th>ShapeFactor4</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28395</td>\n",
              "      <td>610.291</td>\n",
              "      <td>208.178117</td>\n",
              "      <td>173.888747</td>\n",
              "      <td>1.197191</td>\n",
              "      <td>0.549812</td>\n",
              "      <td>28715</td>\n",
              "      <td>190.141097</td>\n",
              "      <td>0.763923</td>\n",
              "      <td>0.988856</td>\n",
              "      <td>0.958027</td>\n",
              "      <td>0.913358</td>\n",
              "      <td>0.007332</td>\n",
              "      <td>0.003147</td>\n",
              "      <td>0.834222</td>\n",
              "      <td>0.998724</td>\n",
              "      <td>SEKER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28734</td>\n",
              "      <td>638.018</td>\n",
              "      <td>200.524796</td>\n",
              "      <td>182.734419</td>\n",
              "      <td>1.097356</td>\n",
              "      <td>0.411785</td>\n",
              "      <td>29172</td>\n",
              "      <td>191.272751</td>\n",
              "      <td>0.783968</td>\n",
              "      <td>0.984986</td>\n",
              "      <td>0.887034</td>\n",
              "      <td>0.953861</td>\n",
              "      <td>0.006979</td>\n",
              "      <td>0.003564</td>\n",
              "      <td>0.909851</td>\n",
              "      <td>0.998430</td>\n",
              "      <td>SEKER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29380</td>\n",
              "      <td>624.110</td>\n",
              "      <td>212.826130</td>\n",
              "      <td>175.931143</td>\n",
              "      <td>1.209713</td>\n",
              "      <td>0.562727</td>\n",
              "      <td>29690</td>\n",
              "      <td>193.410904</td>\n",
              "      <td>0.778113</td>\n",
              "      <td>0.989559</td>\n",
              "      <td>0.947849</td>\n",
              "      <td>0.908774</td>\n",
              "      <td>0.007244</td>\n",
              "      <td>0.003048</td>\n",
              "      <td>0.825871</td>\n",
              "      <td>0.999066</td>\n",
              "      <td>SEKER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30008</td>\n",
              "      <td>645.884</td>\n",
              "      <td>210.557999</td>\n",
              "      <td>182.516516</td>\n",
              "      <td>1.153638</td>\n",
              "      <td>0.498616</td>\n",
              "      <td>30724</td>\n",
              "      <td>195.467062</td>\n",
              "      <td>0.782681</td>\n",
              "      <td>0.976696</td>\n",
              "      <td>0.903936</td>\n",
              "      <td>0.928329</td>\n",
              "      <td>0.007017</td>\n",
              "      <td>0.003215</td>\n",
              "      <td>0.861794</td>\n",
              "      <td>0.994199</td>\n",
              "      <td>SEKER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30140</td>\n",
              "      <td>620.134</td>\n",
              "      <td>201.847882</td>\n",
              "      <td>190.279279</td>\n",
              "      <td>1.060798</td>\n",
              "      <td>0.333680</td>\n",
              "      <td>30417</td>\n",
              "      <td>195.896503</td>\n",
              "      <td>0.773098</td>\n",
              "      <td>0.990893</td>\n",
              "      <td>0.984877</td>\n",
              "      <td>0.970516</td>\n",
              "      <td>0.006697</td>\n",
              "      <td>0.003665</td>\n",
              "      <td>0.941900</td>\n",
              "      <td>0.999166</td>\n",
              "      <td>SEKER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <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>13606</th>\n",
              "      <td>42097</td>\n",
              "      <td>759.696</td>\n",
              "      <td>288.721612</td>\n",
              "      <td>185.944705</td>\n",
              "      <td>1.552728</td>\n",
              "      <td>0.765002</td>\n",
              "      <td>42508</td>\n",
              "      <td>231.515799</td>\n",
              "      <td>0.714574</td>\n",
              "      <td>0.990331</td>\n",
              "      <td>0.916603</td>\n",
              "      <td>0.801865</td>\n",
              "      <td>0.006858</td>\n",
              "      <td>0.001749</td>\n",
              "      <td>0.642988</td>\n",
              "      <td>0.998385</td>\n",
              "      <td>DERMASON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13607</th>\n",
              "      <td>42101</td>\n",
              "      <td>757.499</td>\n",
              "      <td>281.576392</td>\n",
              "      <td>190.713136</td>\n",
              "      <td>1.476439</td>\n",
              "      <td>0.735702</td>\n",
              "      <td>42494</td>\n",
              "      <td>231.526798</td>\n",
              "      <td>0.799943</td>\n",
              "      <td>0.990752</td>\n",
              "      <td>0.922015</td>\n",
              "      <td>0.822252</td>\n",
              "      <td>0.006688</td>\n",
              "      <td>0.001886</td>\n",
              "      <td>0.676099</td>\n",
              "      <td>0.998219</td>\n",
              "      <td>DERMASON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13608</th>\n",
              "      <td>42139</td>\n",
              "      <td>759.321</td>\n",
              "      <td>281.539928</td>\n",
              "      <td>191.187979</td>\n",
              "      <td>1.472582</td>\n",
              "      <td>0.734065</td>\n",
              "      <td>42569</td>\n",
              "      <td>231.631261</td>\n",
              "      <td>0.729932</td>\n",
              "      <td>0.989899</td>\n",
              "      <td>0.918424</td>\n",
              "      <td>0.822730</td>\n",
              "      <td>0.006681</td>\n",
              "      <td>0.001888</td>\n",
              "      <td>0.676884</td>\n",
              "      <td>0.996767</td>\n",
              "      <td>DERMASON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13609</th>\n",
              "      <td>42147</td>\n",
              "      <td>763.779</td>\n",
              "      <td>283.382636</td>\n",
              "      <td>190.275731</td>\n",
              "      <td>1.489326</td>\n",
              "      <td>0.741055</td>\n",
              "      <td>42667</td>\n",
              "      <td>231.653247</td>\n",
              "      <td>0.705389</td>\n",
              "      <td>0.987813</td>\n",
              "      <td>0.907906</td>\n",
              "      <td>0.817457</td>\n",
              "      <td>0.006724</td>\n",
              "      <td>0.001852</td>\n",
              "      <td>0.668237</td>\n",
              "      <td>0.995222</td>\n",
              "      <td>DERMASON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13610</th>\n",
              "      <td>42159</td>\n",
              "      <td>772.237</td>\n",
              "      <td>295.142741</td>\n",
              "      <td>182.204716</td>\n",
              "      <td>1.619841</td>\n",
              "      <td>0.786693</td>\n",
              "      <td>42600</td>\n",
              "      <td>231.686223</td>\n",
              "      <td>0.788962</td>\n",
              "      <td>0.989648</td>\n",
              "      <td>0.888380</td>\n",
              "      <td>0.784997</td>\n",
              "      <td>0.007001</td>\n",
              "      <td>0.001640</td>\n",
              "      <td>0.616221</td>\n",
              "      <td>0.998180</td>\n",
              "      <td>DERMASON</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13611 rows × 17 columns</p>\n",
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\"column\": \"EquivDiameter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.17712014871188,\n        \"min\": 161.2437642,\n        \"max\": 569.3743583,\n        \"num_unique_values\": 12011,\n        \"samples\": [\n          247.2463893,\n          240.8237141,\n          478.8490747\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04908636684396477,\n        \"min\": 0.555314717,\n        \"max\": 0.866194641,\n        \"num_unique_values\": 13535,\n        \"samples\": [\n          0.753012629,\n          0.813903525,\n          0.710475845\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Solidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004660379169838444,\n        \"min\": 0.919246157,\n        \"max\": 0.9946775,\n        \"num_unique_values\": 13522,\n        \"samples\": [\n          0.989451562,\n          0.988288395,\n          0.990392287\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roundness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05951988794941905,\n        \"min\": 0.489618256,\n        \"max\": 0.9906854,\n        \"num_unique_values\": 13540,\n        \"samples\": [\n          0.938825243,\n          0.905049768,\n          0.921551042\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Compactness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06171346311682394,\n        \"min\": 0.640576759,\n        \"max\": 0.987302969,\n        \"num_unique_values\": 13543,\n        \"samples\": [\n          0.86341771,\n          0.810085068,\n          0.819260795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0011279982248070585,\n        \"min\": 0.002778013,\n        \"max\": 0.010451169,\n        \"num_unique_values\": 13521,\n        \"samples\": [\n          0.008601217,\n          0.006711799,\n          0.008063532\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005958748528937666,\n        \"min\": 0.000564169,\n        \"max\": 0.003664972,\n        \"num_unique_values\": 13506,\n        \"samples\": [\n          0.001092201,\n          0.001521882,\n          0.000939109\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.098996150476599,\n        \"min\": 0.410338584,\n        \"max\": 0.974767153,\n        \"num_unique_values\": 13543,\n        \"samples\": [\n          0.745490141,\n          0.656237818,\n          0.67118825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004366457746634138,\n        \"min\": 0.947687403,\n        \"max\": 0.99973253,\n        \"num_unique_values\": 13532,\n        \"samples\": [\n          0.993185175,\n          0.997895985,\n          0.990881262\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"SEKER\",\n          \"BARBUNYA\",\n          \"SIRA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "le.fit(df['Class'])\n",
        "df['Class']=le.transform(df['Class'])\n"
      ],
      "metadata": {
        "id": "-kq_sMGPsXcw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "id": "9U1vmwoQvaOR",
        "outputId": "77bbdc13-3c57-4df2-be14-5894082527d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Area  Perimeter  MajorAxisLength  MinorAxisLength  \\\n",
              "Area             1.000000   0.966722         0.931834         0.951602   \n",
              "Perimeter        0.966722   1.000000         0.977338         0.913179   \n",
              "MajorAxisLength  0.931834   0.977338         1.000000         0.826052   \n",
              "MinorAxisLength  0.951602   0.913179         0.826052         1.000000   \n",
              "AspectRation     0.241735   0.385276         0.550335        -0.009161   \n",
              "Eccentricity     0.267481   0.391066         0.541972         0.019574   \n",
              "ConvexArea       0.999939   0.967689         0.932607         0.951339   \n",
              "EquivDiameter    0.984968   0.991380         0.961733         0.948539   \n",
              "Extent           0.054345  -0.021160        -0.078062         0.145957   \n",
              "Solidity        -0.196585  -0.303970        -0.284302        -0.155831   \n",
              "roundness       -0.357530  -0.547647        -0.596358        -0.210344   \n",
              "Compactness     -0.268067  -0.406857        -0.568377        -0.015066   \n",
              "ShapeFactor1    -0.847958  -0.864623        -0.773609        -0.947204   \n",
              "ShapeFactor2    -0.639291  -0.767592        -0.859238        -0.471347   \n",
              "ShapeFactor3    -0.272145  -0.408435        -0.568185        -0.019326   \n",
              "ShapeFactor4    -0.355721  -0.429310        -0.482527        -0.263749   \n",
              "Class           -0.475252  -0.507638        -0.455175        -0.458492   \n",
              "\n",
              "                 AspectRation  Eccentricity  ConvexArea  EquivDiameter  \\\n",
              "Area                 0.241735      0.267481    0.999939       0.984968   \n",
              "Perimeter            0.385276      0.391066    0.967689       0.991380   \n",
              "MajorAxisLength      0.550335      0.541972    0.932607       0.961733   \n",
              "MinorAxisLength     -0.009161      0.019574    0.951339       0.948539   \n",
              "AspectRation         1.000000      0.924293    0.243301       0.303647   \n",
              "Eccentricity         0.924293      1.000000    0.269255       0.318667   \n",
              "ConvexArea           0.243301      0.269255    1.000000       0.985226   \n",
              "EquivDiameter        0.303647      0.318667    0.985226       1.000000   \n",
              "Extent              -0.370184     -0.319362    0.052564       0.028383   \n",
              "Solidity            -0.267754     -0.297592   -0.206191      -0.231648   \n",
              "roundness           -0.766979     -0.722272   -0.362083      -0.435945   \n",
              "Compactness         -0.987687     -0.970313   -0.269922      -0.327650   \n",
              "ShapeFactor1         0.024593      0.019920   -0.847950      -0.892741   \n",
              "ShapeFactor2        -0.837841     -0.860141   -0.640862      -0.713069   \n",
              "ShapeFactor3        -0.978592     -0.981058   -0.274024      -0.330389   \n",
              "ShapeFactor4        -0.449264     -0.449354   -0.362049      -0.392512   \n",
              "Class               -0.116332     -0.200356   -0.477459      -0.481099   \n",
              "\n",
              "                   Extent  Solidity  roundness  Compactness  ShapeFactor1  \\\n",
              "Area             0.054345 -0.196585  -0.357530    -0.268067     -0.847958   \n",
              "Perimeter       -0.021160 -0.303970  -0.547647    -0.406857     -0.864623   \n",
              "MajorAxisLength -0.078062 -0.284302  -0.596358    -0.568377     -0.773609   \n",
              "MinorAxisLength  0.145957 -0.155831  -0.210344    -0.015066     -0.947204   \n",
              "AspectRation    -0.370184 -0.267754  -0.766979    -0.987687      0.024593   \n",
              "Eccentricity    -0.319362 -0.297592  -0.722272    -0.970313      0.019920   \n",
              "ConvexArea       0.052564 -0.206191  -0.362083    -0.269922     -0.847950   \n",
              "EquivDiameter    0.028383 -0.231648  -0.435945    -0.327650     -0.892741   \n",
              "Extent           1.000000  0.191389   0.344411     0.354212     -0.141616   \n",
              "Solidity         0.191389  1.000000   0.607150     0.303766      0.153388   \n",
              "roundness        0.344411  0.607150   1.000000     0.768086      0.230273   \n",
              "Compactness      0.354212  0.303766   0.768086     1.000000     -0.009394   \n",
              "ShapeFactor1    -0.141616  0.153388   0.230273    -0.009394      1.000000   \n",
              "ShapeFactor2     0.237956  0.343559   0.782824     0.868939      0.469197   \n",
              "ShapeFactor3     0.347624  0.307662   0.763126     0.998686     -0.008320   \n",
              "ShapeFactor4     0.148502  0.702163   0.472149     0.484436      0.248619   \n",
              "Class           -0.031184  0.321283   0.384972     0.156204      0.391907   \n",
              "\n",
              "                 ShapeFactor2  ShapeFactor3  ShapeFactor4     Class  \n",
              "Area                -0.639291     -0.272145     -0.355721 -0.475252  \n",
              "Perimeter           -0.767592     -0.408435     -0.429310 -0.507638  \n",
              "MajorAxisLength     -0.859238     -0.568185     -0.482527 -0.455175  \n",
              "MinorAxisLength     -0.471347     -0.019326     -0.263749 -0.458492  \n",
              "AspectRation        -0.837841     -0.978592     -0.449264 -0.116332  \n",
              "Eccentricity        -0.860141     -0.981058     -0.449354 -0.200356  \n",
              "ConvexArea          -0.640862     -0.274024     -0.362049 -0.477459  \n",
              "EquivDiameter       -0.713069     -0.330389     -0.392512 -0.481099  \n",
              "Extent               0.237956      0.347624      0.148502 -0.031184  \n",
              "Solidity             0.343559      0.307662      0.702163  0.321283  \n",
              "roundness            0.782824      0.763126      0.472149  0.384972  \n",
              "Compactness          0.868939      0.998686      0.484436  0.156204  \n",
              "ShapeFactor1         0.469197     -0.008320      0.248619  0.391907  \n",
              "ShapeFactor2         1.000000      0.872971      0.529932  0.335122  \n",
              "ShapeFactor3         0.872971      1.000000      0.484274  0.167673  \n",
              "ShapeFactor4         0.529932      0.484274      1.000000  0.165033  \n",
              "Class                0.335122      0.167673      0.165033  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-efd91d05-4abf-46b9-8e0c-7f667461634e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Perimeter</th>\n",
              "      <th>MajorAxisLength</th>\n",
              "      <th>MinorAxisLength</th>\n",
              "      <th>AspectRation</th>\n",
              "      <th>Eccentricity</th>\n",
              "      <th>ConvexArea</th>\n",
              "      <th>EquivDiameter</th>\n",
              "      <th>Extent</th>\n",
              "      <th>Solidity</th>\n",
              "      <th>roundness</th>\n",
              "      <th>Compactness</th>\n",
              "      <th>ShapeFactor1</th>\n",
              "      <th>ShapeFactor2</th>\n",
              "      <th>ShapeFactor3</th>\n",
              "      <th>ShapeFactor4</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Area</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.966722</td>\n",
              "      <td>0.931834</td>\n",
              "      <td>0.951602</td>\n",
              "      <td>0.241735</td>\n",
              "      <td>0.267481</td>\n",
              "      <td>0.999939</td>\n",
              "      <td>0.984968</td>\n",
              "      <td>0.054345</td>\n",
              "      <td>-0.196585</td>\n",
              "      <td>-0.357530</td>\n",
              "      <td>-0.268067</td>\n",
              "      <td>-0.847958</td>\n",
              "      <td>-0.639291</td>\n",
              "      <td>-0.272145</td>\n",
              "      <td>-0.355721</td>\n",
              "      <td>-0.475252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perimeter</th>\n",
              "      <td>0.966722</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977338</td>\n",
              "      <td>0.913179</td>\n",
              "      <td>0.385276</td>\n",
              "      <td>0.391066</td>\n",
              "      <td>0.967689</td>\n",
              "      <td>0.991380</td>\n",
              "      <td>-0.021160</td>\n",
              "      <td>-0.303970</td>\n",
              "      <td>-0.547647</td>\n",
              "      <td>-0.406857</td>\n",
              "      <td>-0.864623</td>\n",
              "      <td>-0.767592</td>\n",
              "      <td>-0.408435</td>\n",
              "      <td>-0.429310</td>\n",
              "      <td>-0.507638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MajorAxisLength</th>\n",
              "      <td>0.931834</td>\n",
              "      <td>0.977338</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.826052</td>\n",
              "      <td>0.550335</td>\n",
              "      <td>0.541972</td>\n",
              "      <td>0.932607</td>\n",
              "      <td>0.961733</td>\n",
              "      <td>-0.078062</td>\n",
              "      <td>-0.284302</td>\n",
              "      <td>-0.596358</td>\n",
              "      <td>-0.568377</td>\n",
              "      <td>-0.773609</td>\n",
              "      <td>-0.859238</td>\n",
              "      <td>-0.568185</td>\n",
              "      <td>-0.482527</td>\n",
              "      <td>-0.455175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MinorAxisLength</th>\n",
              "      <td>0.951602</td>\n",
              "      <td>0.913179</td>\n",
              "      <td>0.826052</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.009161</td>\n",
              "      <td>0.019574</td>\n",
              "      <td>0.951339</td>\n",
              "      <td>0.948539</td>\n",
              "      <td>0.145957</td>\n",
              "      <td>-0.155831</td>\n",
              "      <td>-0.210344</td>\n",
              "      <td>-0.015066</td>\n",
              "      <td>-0.947204</td>\n",
              "      <td>-0.471347</td>\n",
              "      <td>-0.019326</td>\n",
              "      <td>-0.263749</td>\n",
              "      <td>-0.458492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AspectRation</th>\n",
              "      <td>0.241735</td>\n",
              "      <td>0.385276</td>\n",
              "      <td>0.550335</td>\n",
              "      <td>-0.009161</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.924293</td>\n",
              "      <td>0.243301</td>\n",
              "      <td>0.303647</td>\n",
              "      <td>-0.370184</td>\n",
              "      <td>-0.267754</td>\n",
              "      <td>-0.766979</td>\n",
              "      <td>-0.987687</td>\n",
              "      <td>0.024593</td>\n",
              "      <td>-0.837841</td>\n",
              "      <td>-0.978592</td>\n",
              "      <td>-0.449264</td>\n",
              "      <td>-0.116332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Eccentricity</th>\n",
              "      <td>0.267481</td>\n",
              "      <td>0.391066</td>\n",
              "      <td>0.541972</td>\n",
              "      <td>0.019574</td>\n",
              "      <td>0.924293</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.269255</td>\n",
              "      <td>0.318667</td>\n",
              "      <td>-0.319362</td>\n",
              "      <td>-0.297592</td>\n",
              "      <td>-0.722272</td>\n",
              "      <td>-0.970313</td>\n",
              "      <td>0.019920</td>\n",
              "      <td>-0.860141</td>\n",
              "      <td>-0.981058</td>\n",
              "      <td>-0.449354</td>\n",
              "      <td>-0.200356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ConvexArea</th>\n",
              "      <td>0.999939</td>\n",
              "      <td>0.967689</td>\n",
              "      <td>0.932607</td>\n",
              "      <td>0.951339</td>\n",
              "      <td>0.243301</td>\n",
              "      <td>0.269255</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.985226</td>\n",
              "      <td>0.052564</td>\n",
              "      <td>-0.206191</td>\n",
              "      <td>-0.362083</td>\n",
              "      <td>-0.269922</td>\n",
              "      <td>-0.847950</td>\n",
              "      <td>-0.640862</td>\n",
              "      <td>-0.274024</td>\n",
              "      <td>-0.362049</td>\n",
              "      <td>-0.477459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EquivDiameter</th>\n",
              "      <td>0.984968</td>\n",
              "      <td>0.991380</td>\n",
              "      <td>0.961733</td>\n",
              "      <td>0.948539</td>\n",
              "      <td>0.303647</td>\n",
              "      <td>0.318667</td>\n",
              "      <td>0.985226</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.028383</td>\n",
              "      <td>-0.231648</td>\n",
              "      <td>-0.435945</td>\n",
              "      <td>-0.327650</td>\n",
              "      <td>-0.892741</td>\n",
              "      <td>-0.713069</td>\n",
              "      <td>-0.330389</td>\n",
              "      <td>-0.392512</td>\n",
              "      <td>-0.481099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Extent</th>\n",
              "      <td>0.054345</td>\n",
              "      <td>-0.021160</td>\n",
              "      <td>-0.078062</td>\n",
              "      <td>0.145957</td>\n",
              "      <td>-0.370184</td>\n",
              "      <td>-0.319362</td>\n",
              "      <td>0.052564</td>\n",
              "      <td>0.028383</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.191389</td>\n",
              "      <td>0.344411</td>\n",
              "      <td>0.354212</td>\n",
              "      <td>-0.141616</td>\n",
              "      <td>0.237956</td>\n",
              "      <td>0.347624</td>\n",
              "      <td>0.148502</td>\n",
              "      <td>-0.031184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solidity</th>\n",
              "      <td>-0.196585</td>\n",
              "      <td>-0.303970</td>\n",
              "      <td>-0.284302</td>\n",
              "      <td>-0.155831</td>\n",
              "      <td>-0.267754</td>\n",
              "      <td>-0.297592</td>\n",
              "      <td>-0.206191</td>\n",
              "      <td>-0.231648</td>\n",
              "      <td>0.191389</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.607150</td>\n",
              "      <td>0.303766</td>\n",
              "      <td>0.153388</td>\n",
              "      <td>0.343559</td>\n",
              "      <td>0.307662</td>\n",
              "      <td>0.702163</td>\n",
              "      <td>0.321283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>roundness</th>\n",
              "      <td>-0.357530</td>\n",
              "      <td>-0.547647</td>\n",
              "      <td>-0.596358</td>\n",
              "      <td>-0.210344</td>\n",
              "      <td>-0.766979</td>\n",
              "      <td>-0.722272</td>\n",
              "      <td>-0.362083</td>\n",
              "      <td>-0.435945</td>\n",
              "      <td>0.344411</td>\n",
              "      <td>0.607150</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.768086</td>\n",
              "      <td>0.230273</td>\n",
              "      <td>0.782824</td>\n",
              "      <td>0.763126</td>\n",
              "      <td>0.472149</td>\n",
              "      <td>0.384972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Compactness</th>\n",
              "      <td>-0.268067</td>\n",
              "      <td>-0.406857</td>\n",
              "      <td>-0.568377</td>\n",
              "      <td>-0.015066</td>\n",
              "      <td>-0.987687</td>\n",
              "      <td>-0.970313</td>\n",
              "      <td>-0.269922</td>\n",
              "      <td>-0.327650</td>\n",
              "      <td>0.354212</td>\n",
              "      <td>0.303766</td>\n",
              "      <td>0.768086</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.009394</td>\n",
              "      <td>0.868939</td>\n",
              "      <td>0.998686</td>\n",
              "      <td>0.484436</td>\n",
              "      <td>0.156204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ShapeFactor1</th>\n",
              "      <td>-0.847958</td>\n",
              "      <td>-0.864623</td>\n",
              "      <td>-0.773609</td>\n",
              "      <td>-0.947204</td>\n",
              "      <td>0.024593</td>\n",
              "      <td>0.019920</td>\n",
              "      <td>-0.847950</td>\n",
              "      <td>-0.892741</td>\n",
              "      <td>-0.141616</td>\n",
              "      <td>0.153388</td>\n",
              "      <td>0.230273</td>\n",
              "      <td>-0.009394</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.469197</td>\n",
              "      <td>-0.008320</td>\n",
              "      <td>0.248619</td>\n",
              "      <td>0.391907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ShapeFactor2</th>\n",
              "      <td>-0.639291</td>\n",
              "      <td>-0.767592</td>\n",
              "      <td>-0.859238</td>\n",
              "      <td>-0.471347</td>\n",
              "      <td>-0.837841</td>\n",
              "      <td>-0.860141</td>\n",
              "      <td>-0.640862</td>\n",
              "      <td>-0.713069</td>\n",
              "      <td>0.237956</td>\n",
              "      <td>0.343559</td>\n",
              "      <td>0.782824</td>\n",
              "      <td>0.868939</td>\n",
              "      <td>0.469197</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.872971</td>\n",
              "      <td>0.529932</td>\n",
              "      <td>0.335122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ShapeFactor3</th>\n",
              "      <td>-0.272145</td>\n",
              "      <td>-0.408435</td>\n",
              "      <td>-0.568185</td>\n",
              "      <td>-0.019326</td>\n",
              "      <td>-0.978592</td>\n",
              "      <td>-0.981058</td>\n",
              "      <td>-0.274024</td>\n",
              "      <td>-0.330389</td>\n",
              "      <td>0.347624</td>\n",
              "      <td>0.307662</td>\n",
              "      <td>0.763126</td>\n",
              "      <td>0.998686</td>\n",
              "      <td>-0.008320</td>\n",
              "      <td>0.872971</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.484274</td>\n",
              "      <td>0.167673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ShapeFactor4</th>\n",
              "      <td>-0.355721</td>\n",
              "      <td>-0.429310</td>\n",
              "      <td>-0.482527</td>\n",
              "      <td>-0.263749</td>\n",
              "      <td>-0.449264</td>\n",
              "      <td>-0.449354</td>\n",
              "      <td>-0.362049</td>\n",
              "      <td>-0.392512</td>\n",
              "      <td>0.148502</td>\n",
              "      <td>0.702163</td>\n",
              "      <td>0.472149</td>\n",
              "      <td>0.484436</td>\n",
              "      <td>0.248619</td>\n",
              "      <td>0.529932</td>\n",
              "      <td>0.484274</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.165033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <td>-0.475252</td>\n",
              "      <td>-0.507638</td>\n",
              "      <td>-0.455175</td>\n",
              "      <td>-0.458492</td>\n",
              "      <td>-0.116332</td>\n",
              "      <td>-0.200356</td>\n",
              "      <td>-0.477459</td>\n",
              "      <td>-0.481099</td>\n",
              "      <td>-0.031184</td>\n",
              "      <td>0.321283</td>\n",
              "      <td>0.384972</td>\n",
              "      <td>0.156204</td>\n",
              "      <td>0.391907</td>\n",
              "      <td>0.335122</td>\n",
              "      <td>0.167673</td>\n",
              "      <td>0.165033</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      const quickchartButtonEl =\n",
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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Solidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4023730088471431,\n        \"min\": -0.3039695683823133,\n        \"max\": 1.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          -0.19658453791194178,\n          -0.3039695683823133,\n          -0.297592310946913\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roundness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6045368671357316,\n        \"min\": -0.7669792980904121,\n        \"max\": 1.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          -0.3575303383740047,\n          -0.547646671599879,\n          -0.72227246236476\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Compactness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6326829160809985,\n        \"min\": -0.9876872990588927,\n        \"max\": 1.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          -0.2680668252854951,\n          -0.40685686307000285,\n          -0.9703130216151127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5890613132263336,\n        \"min\": -0.9472041674932353,\n        \"max\": 1.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          -0.8479583280028486,\n          -0.8646229455087555,\n          0.019919682125316245\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7172021363273564,\n        \"min\": -0.8601407904438397,\n        \"max\": 1.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          -0.6392907452214683,\n          -0.7675923296091915,\n          -0.8601407904438397\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6332995252526258,\n        \"min\": -0.9810576972682232,\n        \"max\": 1.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          -0.27214484769919034,\n          -0.40843529638058734,\n          -0.9810576972682232\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48825567398110037,\n        \"min\": -0.4825271772554347,\n        \"max\": 1.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          -0.3557214347445865,\n          -0.4293097790899208,\n          -0.4493537361178914\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335513270606065,\n        \"min\": -0.5076384666053644,\n        \"max\": 1.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          -0.4752523276565528,\n          -0.5076384666053644,\n          -0.20035645134371133\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ob6Gm5KO1JPb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='Class',y='Area', data=df)"
      ],
      "metadata": {
        "id": "e2KKQbYbKby2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "86269dd2-a548-4348-f847-7b46a529b1f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Class', ylabel='Area'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df['Class'][0],y='Area', data=df)"
      ],
      "metadata": {
        "id": "3YC5NBFJ1-h3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "47936054-a0bf-45a1-92c3-ac8fb96d1a1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Area'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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OS+sAhD9CFQAEgNvUADgaoQoAAuB2uy2tAxD+CFUAEIAjR45YWgcg/BGqACAALKkA4GiEKgAIQOvWrS2tAxD+CFUAEADu/QfgaIQqAAjA/v37La0DEP4IVQAAABYIaqiaPXu2Lr30UrVr105xcXEaOnSoduzY4Vdz6NAh5eTk6Oyzz9ZZZ52lYcOGqbKy0q+mvLxcQ4YMUZs2bRQXF6epU6cec8XNunXrdPHFFysqKkrnn3++8vLyjuln/vz5OvfccxUdHa2MjAxt2rTplHsBAAAtU1BDVUFBgXJyclRcXKz8/HwdPnxYgwYNUk1NjVkzadIkLV++XO+8844KCgpUUVGhW265xRz3er0aMmSI6urqtGHDBi1evFh5eXmaMWOGWVNWVqYhQ4bo6quv1pYtWzRx4kTdcccd+uCDD8yat956S7m5uZo5c6Y++eQTXXjhhcrKytKePXsa3QuAloOJ6gCOZjMMwwh2E/X27t2ruLg4FRQUaMCAAXK73erUqZPeeOMN/eEPf5AkffHFF+rRo4eKiorUv39/rVq1SjfccIMqKioUHx8vSVq4cKHuu+8+7d27V5GRkbrvvvu0cuVKbdu2zfxZI0aMUFVVlVavXi1JysjI0KWXXqrnn39ekuTz+ZScnKy7775b999/f6N6ORmPxyOn0ym32y2Hw2HpcwegaV1//fU6cODASevatGmj999/vwk6AnCmNPb1O6TmVNWvPNyhQwdJUklJiQ4fPqyBAweaNd27d1dKSoqKiookSUVFRerTp48ZqCQpKytLHo9H27dvN2saHqO+pv4YdXV1Kikp8aux2+0aOHCgWdOYXo5WW1srj8fjtwFoHrxer6V1AMJfyIQqn8+niRMn6vLLL1fv3r0lSS6XS5GRkYqNjfWrjY+Pl8vlMmsaBqr68fqxX6vxeDw6ePCgfvzxR3m93uPWNDzGyXo52uzZs+V0Os0tOTm5kc8GgFDn8/ksrQMQ/kImVOXk5Gjbtm168803g92KZaZNmya3221u3333XbBbAmCRw4cPW1oHIPy1CnYDkjRhwgStWLFChYWF6ty5s/l4QkKC6urqVFVV5XeGqLKyUgkJCWbN0Vfp1V+R17Dm6Kv0Kisr5XA4FBMTo4iICEVERBy3puExTtbL0aKiohQVFXUKzwSAcGG32xt1FspuD5n3rgDOsKD+tRuGoQkTJmjp0qVau3atUlNT/cb79eun1q1ba82aNeZjO3bsUHl5uTIzMyVJmZmZ2rp1q99Vevn5+XI4HOrZs6dZ0/AY9TX1x4iMjFS/fv38anw+n9asWWPWNKYXAC1HYy824aIUoOUI6pmqnJwcvfHGG3rvvffUrl07c26S0+lUTEyMnE6nxowZo9zcXHXo0EEOh0N33323MjMzzavtBg0apJ49e+q2227TnDlz5HK5NH36dOXk5Jhnie688049//zzuvfee/XnP/9Za9eu1dtvv62VK1eaveTm5mrUqFG65JJLdNlll+npp59WTU2NRo8ebfZ0sl4AtByNvXA6hC6wBnCGBTVUvfDCC5Kkq666yu/xV199Vbfffrskad68ebLb7Ro2bJhqa2uVlZWlBQsWmLURERFasWKFxo8fr8zMTLVt21ajRo3SrFmzzJrU1FStXLlSkyZN0jPPPKPOnTvrpZdeUlZWllkzfPhw7d27VzNmzJDL5VJ6erpWr17tN3n9ZL0AaDmYUwXgaCG1TlVzxzpVQPMxePBgHTx48KR1MTExWrVqVRN0BOBMCct1qgAgXERGRlpaByD8EaoAIABH31/0dOsAhD9CFQAEgDNVAI5GqAIAALAAoQoAAlBdXW1pHYDwR6gCgAAwpwrA0QhVAAAAFiBUAQAAWIBQBQABsNlsltYBCH+EKgAIAKEKwNEIVQAQAJ/PZ2kdgPBHqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALBAUENVYWGhbrzxRiUlJclms2nZsmV+47fffrtsNpvfdt111/nV7Nu3T9nZ2XI4HIqNjdWYMWO0f/9+v5rS0lL99re/VXR0tJKTkzVnzpxjennnnXfUvXt3RUdHq0+fPnr//ff9xg3D0IwZM5SYmKiYmBgNHDhQX331lTVPBAAACHtBDVU1NTW68MILNX/+/BPWXHfdddq9e7e5/fWvf/Ubz87O1vbt25Wfn68VK1aosLBQ48aNM8c9Ho8GDRqkLl26qKSkRE8++aQefvhhLVq0yKzZsGGDRo4cqTFjxujTTz/V0KFDNXToUG3bts2smTNnjp599lktXLhQGzduVNu2bZWVlaVDhw5Z+IwAAIBwZTMMwwh2E5Jks9m0dOlSDR061Hzs9ttvV1VV1TFnsOp9/vnn6tmzpz7++GNdcsklkqTVq1fr+uuv1/fff6+kpCS98MILevDBB+VyuRQZGSlJuv/++7Vs2TJ98cUXkqThw4erpqZGK1asMI/dv39/paena+HChTIMQ0lJSZo8ebKmTJkiSXK73YqPj1deXp5GjBjRqN/R4/HI6XTK7XbL4XCc6lMEIIRcddVVja5dt27dGesDwJnX2NfvkJ9TtW7dOsXFxalbt24aP368fvrpJ3OsqKhIsbGxZqCSpIEDB8put2vjxo1mzYABA8xAJUlZWVnasWOHfv75Z7Nm4MCBfj83KytLRUVFkqSysjK5XC6/GqfTqYyMDLMGAAC0bK2C3cCvue6663TLLbcoNTVVX3/9tR544AENHjxYRUVFioiIkMvlUlxcnN/3tGrVSh06dJDL5ZIkuVwupaam+tXEx8ebY+3bt5fL5TIfa1jT8BgNv+94NcdTW1ur2tpac9/j8ZzKrw8AAMJISIeqhh+r9enTR3379tV5552ndevW6dprrw1iZ40ze/ZsPfLII8FuAwAANIGQ//ivobS0NHXs2FE7d+6UJCUkJGjPnj1+NUeOHNG+ffuUkJBg1lRWVvrV1O+frKbheMPvO17N8UybNk1ut9vcvvvuu1P6fQGELru9cf98NrYOQPgLq7/277//Xj/99JMSExMlSZmZmaqqqlJJSYlZs3btWvl8PmVkZJg1hYWFOnz4sFmTn5+vbt26qX379mbNmjVr/H5Wfn6+MjMzJUmpqalKSEjwq/F4PNq4caNZczxRUVFyOBx+G4DmITo62tI6AOEvqKFq//792rJli7Zs2SLplwnhW7ZsUXl5ufbv36+pU6equLhY33zzjdasWaPf//73Ov/885WVlSVJ6tGjh6677jqNHTtWmzZt0j/+8Q9NmDBBI0aMUFJSkiTp1ltvVWRkpMaMGaPt27frrbfe0jPPPKPc3Fyzj3vuuUerV6/W3Llz9cUXX+jhhx/W5s2bNWHCBEm/XJk4ceJEPfbYY/rb3/6mrVu36k9/+pOSkpL8rlYE0HIcPHjQ0joA4S+oc6o2b96sq6++2tyvDzqjRo3SCy+8oNLSUi1evFhVVVVKSkrSoEGD9OijjyoqKsr8ntdff10TJkzQtddeK7vdrmHDhunZZ581x51Opz788EPl5OSoX79+6tixo2bMmOG3ltVvfvMbvfHGG5o+fboeeOABXXDBBVq2bJl69+5t1tx7772qqanRuHHjVFVVpSuuuEKrV6/mXSjQQjV2NZoQWbUGQBMIaJ2qd999V2+//bbKy8tVV1fnN/bJJ59Y1lxzwzpVQPPBOlVAy3HG1ql69tlnNXr0aMXHx+vTTz/VZZddprPPPlu7du3S4MGDT6tpAACAcHXKoWrBggVatGiRnnvuOUVGRuree+9Vfn6+/vKXv8jtdp+JHgEg5HD1H4CjnfJfe3l5uX7zm99IkmJiYlRdXS1Juu222465Lx8ANFetWjVuSmpj6wCEv1MOVQkJCdq3b58kKSUlRcXFxZJ+uXKPCZkAWoqGt76yog5A+DvlUHXNNdfob3/7myRp9OjRmjRpkn73u99p+PDhuvnmmy1vEAAAIByc8nnpRYsWyefzSZJycnJ09tlna8OGDbrpppv0b//2b5Y3CAChqOGCwlbUAQh/pxyq7Ha738TLESNG+N2jDwBaAtapAnC0gC5L+Z//+R/98Y9/VGZmpn744QdJ0muvvab169db2hwAAEC4OOVQ9V//9V/KyspSTEyMPv30U9XW1kqS3G63/v3f/93yBgEgFLGkAoCjnfJf+2OPPaaFCxfqxRdfVOvWrc3HL7/8clZTB9BieL1eS+sAhL9TDlU7duzQgAEDjnnc6XSqqqrKip4AIOQxUR3A0QJap2rnzp3HPL5+/XqlpaVZ0hQAAEC4OeVQNXbsWN1zzz3auHGjbDabKioq9Prrr2vKlCkaP378megRAEIOi38CONopL6lw//33y+fz6dprr9WBAwc0YMAARUVFacqUKbr77rvPRI8AEHL69u2rzZs3N6oOQMtwSqHK6/XqH//4h3JycjR16lTt3LlT+/fvV8+ePXXWWWedqR4BIOS0a9fO0joA4e+UQlVERIQGDRqkzz//XLGxserZs+eZ6gsAQtqePXssrQMQ/k55TlXv3r21a9euM9ELAISN+jX6rKoDEP4CWqdqypQpWrFihXbv3i2Px+O3AUBLUFdX57eflJSkzp07Kykp6VfrADRfpzxR/frrr5ck3XTTTbLZbObjhmHIZrOx0B2AFuHQoUN++xUVFY2qA9B8nXKo+uijj044tnXr1tNqBgDCBTdUBnC0Uw5VV155pd9+dXW1/vrXv+qll15SSUmJJkyYYFlzABCqOnXqpL179zaqDkDLEPCdPgsLCzVq1CglJibqP//zP3XNNdeouLjYyt4AIGSlpqZaWgcg/J3SmSqXy6W8vDy9/PLL8ng8+td//VfV1tZq2bJlLK8AoEVp7Fwp5lQBLUejz1TdeOON6tatm0pLS/X000+roqJCzz333JnsDQBCFqEKwNEafaZq1apV+stf/qLx48frggsuOJM9AUDI69ixo6V1AMJfo89UrV+/XtXV1erXr58yMjL0/PPP68cffzyTvQFAyOrevbv59WWXXaZhw4bphhtu0LBhw3TZZZcdtw5A89boM1X9+/dX//799fTTT+utt97SK6+8otzcXPl8PuXn5ys5OZl7XAFoMfbv329+vXnzZm3atMnct9vtx60D0Lyd8tV/bdu21Z///GetX79eW7du1eTJk/XEE08oLi5ON91005noEQBCTmxsrCQpMTHxuOP1j9fXAWj+Tnmdqoa6deumOXPmaPbs2Vq+fLleeeUVq/oCgJBWP1fK5XIpIyND55xzjmpraxUVFaUffvhBGzdu9KsD0PzZDJb7bTIej0dOp1Nut1sOhyPY7QA4DV6vV9nZ2ebftMvlMscSExPlcDjk8Xi0ZMkSRUREBLFTAKersa/fp3WmCgBaqoiICN11112aOXOm+vfvr+HDhysqKkq1tbXatGmTiouL9cgjjxCogBaEM1VNiDNVQPNTWFioBQsWHHOmavz48RowYEAQOwNglca+fhOqmhChCmievF6vSktLtW/fPnXo0EF9+/blDBXQjPDxHwA0kYiICF100UXBbgNAkAV8Q2UAAAD8H0IVAACABfj4DwBOE3OqAEiEKgA4LYWFhZo/f74qKyvNx+Lj45WTk8PVf0ALw8d/ABCgwsJCzZgxwy9QSVJlZaVmzJihwsLCIHUGIBgIVQAQAK/XqyeeeEKS/w2UG+7/x3/8h7xeb5P3BiA4CFUAEIBPPvlEBw4ckCS1auU/k6J+v6amRp988kmT9wYgOAhVABCADz74wNI6AOGPieoAEICGt6Xp16+f/vjHPyo1NVVlZWVasmSJioqKjqkD0LxxpgoAAhAZGSlJioqK0qOPPqpevXqpTZs26tWrlx599FFzvP6/AJo/QhUABKB9+/aSpNraWk2fPl3bt2/XgQMHtH37dk2fPl11dXV+dQCaPz7+A4AAJCQkmF9v3LhRxcXF5r7NZjtuHYDmjTNVABCAiy++2PzaMAy/sYb7DesANG+EKgAIQHp6umJjYyUdO2+qfj82Nlbp6elN3BmAYCFUAUAAIiIilJub+6s1ubm53AMQaEEIVQAQoAEDBmjWrFnmGat67du316xZs7j3H9DCEKoA4DQdfZuahhPVAbQcXP0HAAEqLCzUzJkzlZGRocsvv1x1dXWKjIzUDz/8oJkzZ+qRRx7hbBXQgtiMoy9bwRnj8XjkdDrldrvlcDiC3Q6A0+D1epWdnS273a7du3f7XfFns9mUmJgowzC0ZMkS5lUBYa6xr9+cqQKAAJSWlp7wFjSGYaiiosKsu+iii5qyNQBBwpwqAAjAnj17LK0DEP44UwUAAdi2bZv59aWXXqqYmBhVV1erXbt2OnjwoD7++GOzLisrK1htAmhChCoACMCuXbsk/XLlX32Aashut8vn85l1AJo/QhUABKCmpkaS5PP5ZLPZ5HA4zCDl8Xjk8/n86gA0f4QqAAjAueeeq2+++UbSLxPT3W73CesAtAxMVAeAANTW1lpaByD8EaoAIAAHDhywtA5A+AtqqCosLNSNN96opKQk2Ww2LVu2zG/cMAzNmDFDiYmJiomJ0cCBA/XVV1/51ezbt0/Z2dlyOByKjY3VmDFjtH//fr+a0tJS/fa3v1V0dLSSk5M1Z86cY3p555131L17d0VHR6tPnz56//33T7kXAC1HeXm5pXUAwl9QQ1VNTY0uvPBCzZ8//7jjc+bM0bPPPquFCxdq48aNatu2rbKysnTo0CGzJjs7W9u3b1d+fr5WrFihwsJCjRs3zhz3eDwaNGiQunTpopKSEj355JN6+OGHtWjRIrNmw4YNGjlypMaMGaNPP/1UQ4cO1dChQ/0umW5MLwBaDs5UAThayNymxmazaenSpRo6dKikX84MJSUlafLkyZoyZYokye12Kz4+Xnl5eRoxYoQ+//xz9ezZUx9//LEuueQSSdLq1at1/fXX6/vvv1dSUpJeeOEFPfjgg3K5XIqMjJQk3X///Vq2bJm++OILSdLw4cNVU1OjFStWmP30799f6enpWrhwYaN6aQxuUwM0H9dcc415hd+vsdvtWrt2bRN0BOBMaezrd8jOqSorK5PL5dLAgQPNx5xOpzIyMlRUVCRJKioqUmxsrBmoJGngwIGy2+3auHGjWTNgwAAzUElSVlaWduzYoZ9//tmsafhz6mvqf05jejme2tpaeTwevw1A89DY96Mh8r4VQBMI2VBVf0+t+Ph4v8fj4+PNMZfLpbi4OL/xVq1aqUOHDn41xztGw59xopqG4yfr5Xhmz54tp9NpbsnJySf5rQGEi7Zt2/rtd+rUSd26dVOnTp1+tQ5A8xWyoao5mDZtmtxut7l99913wW4JgEX69evnt793717t2LFDe/fu/dU6AM1XyC7+mZCQIEmqrKxUYmKi+XhlZaXS09PNmqNvVnrkyBHt27fP/P6EhARVVlb61dTvn6ym4fjJejmeqKgoRUVFNer3BQJx6NAhri4LkjZt2jS67ssvvzzD3eB4UlJSFB0dHew20IKEbKhKTU1VQkKC1qxZYwYXj8ejjRs3avz48ZKkzMxMVVVVqaSkxHw3uHbtWvl8PmVkZJg1Dz74oA4fPqzWrVtLkvLz89WtWze1b9/erFmzZo0mTpxo/vz8/HxlZmY2uhcgGMrLy/2udkXoWbVqlVatWhXsNlqkRYsWqWvXrsFuAy1IUEPV/v37tXPnTnO/rKxMW7ZsUYcOHZSSkqKJEyfqscce0wUXXKDU1FQ99NBDSkpKMq8Q7NGjh6677jqNHTtWCxcu1OHDhzVhwgSNGDFCSUlJkqRbb71VjzzyiMaMGaP77rtP27Zt0zPPPKN58+aZP/eee+7RlVdeqblz52rIkCF68803tXnzZnPZBZvNdtJegGBISUnxWx4ETcfn82nKlCnHrIvXULt27fTkk0/KbmemRTCkpKQEuwW0NEYQffTRR4akY7ZRo0YZhmEYPp/PeOihh4z4+HgjKirKuPbaa40dO3b4HeOnn34yRo4caZx11lmGw+EwRo8ebVRXV/vV/POf/zSuuOIKIyoqyjjnnHOMJ5544phe3n77baNr165GZGSk0atXL2PlypV+443p5WTcbrchyXC73af0fQBCU0FBgXHllVcav/vd74wrr7zS3Or3CwoKgt0iAAs09vU7ZNapaglYpwpofgoLCzV//ny/eZkJCQm66667NGDAgCB2BsAqjX39JlQ1IUIV0Dx5vV69//77mjt3riZPnqzrr79eERERwW4LgEXCfvFPAAgXERER6tatmySpW7duBCqghSJUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABggVbBbgDhqbKyUm63O9htACHj22+/9fsvgF84nU7Fx8cHu40mYTMMwwh2Ey2Fx+OR0+mU2+2Ww+EIdjsBq6ys1B9v+5MO19UGuxUAQIhrHRmlJa/9v7AOVo19/eZMFU6Z2+3W4bpaHUy7Ur5oZ7DbAQCEKPsht7SrQG63O6xDVWMRqhAwX7RTvrYdg90GAAAhgYnqAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWCOlQ9fDDD8tms/lt3bt3N8cPHTqknJwcnX322TrrrLM0bNgwVVZW+h2jvLxcQ4YMUZs2bRQXF6epU6fqyJEjfjXr1q3TxRdfrKioKJ1//vnKy8s7ppf58+fr3HPPVXR0tDIyMrRp06Yz8jsDAIDwFNKhSpJ69eql3bt3m9v69evNsUmTJmn58uV65513VFBQoIqKCt1yyy3muNfr1ZAhQ1RXV6cNGzZo8eLFysvL04wZM8yasrIyDRkyRFdffbW2bNmiiRMn6o477tAHH3xg1rz11lvKzc3VzJkz9cknn+jCCy9UVlaW9uzZ0zRPAgAACHkhH6patWqlhIQEc+vY8Zd7zbndbr388st66qmndM0116hfv3569dVXtWHDBhUXF0uSPvzwQ3322WdasmSJ0tPTNXjwYD366KOaP3++6urqJEkLFy5Uamqq5s6dqx49emjChAn6wx/+oHnz5pk9PPXUUxo7dqxGjx6tnj17auHChWrTpo1eeeWVpn9CAABASAr5UPXVV18pKSlJaWlpys7OVnl5uSSppKREhw8f1sCBA83a7t27KyUlRUVFRZKkoqIi9enTx+/O2FlZWfJ4PNq+fbtZ0/AY9TX1x6irq1NJSYlfjd1u18CBA82aE6mtrZXH4/HbAABA8xTSoSojI0N5eXlavXq1XnjhBZWVlem3v/2tqqur5XK5FBkZqdjYWL/viY+Pl8vlkiS5XC6/QFU/Xj/2azUej0cHDx7Ujz/+KK/Xe9ya+mOcyOzZs+V0Os0tOTn5lJ8DAAAQHloFu4FfM3jwYPPrvn37KiMjQ126dNHbb7+tmJiYIHbWONOmTVNubq657/F4CFYAADRTIX2m6mixsbHq2rWrdu7cqYSEBNXV1amqqsqvprKyUgkJCZKkhISEY64GrN8/WY3D4VBMTIw6duyoiIiI49bUH+NEoqKi5HA4/DYAANA8hVWo2r9/v77++mslJiaqX79+at26tdasWWOO79ixQ+Xl5crMzJQkZWZmauvWrX5X6eXn58vhcKhnz55mTcNj1NfUHyMyMlL9+vXzq/H5fFqzZo1ZAwAAENKhasqUKSooKNA333yjDRs26Oabb1ZERIRGjhwpp9OpMWPGKDc3Vx999JFKSko0evRoZWZmqn///pKkQYMGqWfPnrrtttv0z3/+Ux988IGmT5+unJwcRUVFSZLuvPNO7dq1S/fee6+++OILLViwQG+//bYmTZpk9pGbm6sXX3xRixcv1ueff67x48erpqZGo0ePDsrzAgAAQk9Iz6n6/vvvNXLkSP3000/q1KmTrrjiChUXF6tTp06SpHnz5slut2vYsGGqra1VVlaWFixYYH5/RESEVqxYofHjxyszM1Nt27bVqFGjNGvWLLMmNTVVK1eu1KRJk/TMM8+oc+fOeumll5SVlWXWDB8+XHv37tWMGTPkcrmUnp6u1atXHzN5HQAAtFw2wzCMYDfRUng8HjmdTrnd7rCeX/Xll19q3Lhxqul5k3xtOwa7HQBAiLLX/Ki2n/1NixYtUteuXYPdTsAa+/od0meqENrsB6uC3QIAIIS1tNcJQhUCFlNWGOwWAAAIGYQqBOxg6gD5YmKD3QYAIETZD1a1qDfghCoEzBcTy5wqAAD+V0gvqQAAABAuCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAEW/0TA7IfcwW4BABDCWtrrBKEKp8zpdKp1ZJS0qyDYrQAAQlzryCg5nc5gt9EkCFU4ZfHx8Vry2v+T292y3oEAv+bbb7/V448/rgcffFBdunQJdjtAyHA6nYqPjw92G02CUIWAxMfHt5g/EuBUdOnSRV27dg12GwCCgInqAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFigVbAbABC4Q4cOqby8PNhtQNK3337r918EX0pKiqKjo4PdBloQQhUQxsrLyzVu3Lhgt4EGHn/88WC3gP+1aNEide3aNdhtoAUhVAFhLCUlRYsWLQp2G0BISklJCXYLaGEIVUAYi46O5p04AIQIJqoDAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYoFWwG2hJDMOQJHk8niB3AgAAGqv+dbv+dfxECFVNqLq6WpKUnJwc5E4AAMCpqq6ultPpPOG4zThZ7IJlfD6fKioq1K5dO9lstmC3A8BCHo9HycnJ+u677+RwOILdDgALGYah6upqJSUlyW4/8cwpQhUAWMDj8cjpdMrtdhOqgBaKieoAAAAWIFQBAABYgFAFABaIiorSzJkzFRUVFexWAAQJc6oAAAAswJkqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgA4DQ8//LBsNpvf1r1792C3BSAIuPcfAJymXr166e9//7u536oV/7QCLRF/+QBwmlq1aqWEhIRgtwEgyPj4DwBO01dffaWkpCSlpaUpOztb5eXlwW4JQBCw+CcAnIZVq1Zp//796tatm3bv3q1HHnlEP/zwg7Zt26Z27doFuz0ATYhQBQAWqqqqUpcuXfTUU09pzJgxwW4HQBPi4z8AsFBsbKy6du2qnTt3BrsVAE2MUAUAFtq/f7++/vprJSYmBrsVAE2MUAUAp2HKlCkqKCjQN998ow0bNujmm29WRESERo4cGezWADQxllQAgNPw/fffa+TIkfrpp5/UqVMnXXHFFSouLlanTp2C3RqAJsZEdQAAAAvw8R8AAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGCB/w/XSCLyM+HffwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "each bean category has many outliers, so first we will have to handle them by feature scalling and for that we have to decide which type of encoding\n",
        "\n"
      ],
      "metadata": {
        "id": "R-jOXGVs9Oga"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "newdf=df.copy()\n",
        "newdf['Class'].unique()"
      ],
      "metadata": {
        "id": "nXqxJhXU4ubF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50c62d71-03fd-4303-dd25-6bbf9cbeb925"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 1, 2, 4, 6, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "newdf.iloc[:,:-1]=scaler.fit_transform(newdf.iloc[:,:-1])\n",
        "newdf.head()\n",
        "#Scaled everything except target class"
      ],
      "metadata": {
        "id": "nr1YMr0dBpQs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "8ad715a6-6b16-4fd8-f6cd-36cc330cd99c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Area  Perimeter  MajorAxisLength  MinorAxisLength  AspectRation  \\\n",
              "0 -0.840749  -1.143319        -1.306598        -0.631153     -1.565053   \n",
              "1 -0.829188  -1.013924        -1.395911        -0.434445     -1.969784   \n",
              "2 -0.807157  -1.078829        -1.252357        -0.585735     -1.514291   \n",
              "3 -0.785741  -0.977215        -1.278825        -0.439290     -1.741618   \n",
              "4 -0.781239  -1.097384        -1.380471        -0.266663     -2.117993   \n",
              "\n",
              "   Eccentricity  ConvexArea  EquivDiameter    Extent  Solidity  roundness  \\\n",
              "0     -2.185720   -0.841451      -1.063341  0.289087  0.367614   1.423867   \n",
              "1     -3.686040   -0.826102      -1.044217  0.697477 -0.462907   0.231054   \n",
              "2     -2.045336   -0.808704      -1.008084  0.578195  0.518417   1.252865   \n",
              "3     -2.742211   -0.773975      -0.973337  0.671260 -2.241767   0.515049   \n",
              "4     -4.535028   -0.784286      -0.966080  0.476020  0.804772   1.874992   \n",
              "\n",
              "   Compactness  ShapeFactor1  ShapeFactor2  ShapeFactor3  ShapeFactor4  Class  \n",
              "0     1.839116      0.680786      2.402173      1.925723      0.838371      5  \n",
              "1     2.495449      0.367967      3.100894      2.689702      0.771138      5  \n",
              "2     1.764843      0.603129      2.235091      1.841356      0.916755      5  \n",
              "3     2.081715      0.401718      2.515075      2.204250     -0.197985      5  \n",
              "4     2.765330      0.118268      3.270983      3.013462      0.939640      5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fbcb573f-fee0-43b1-8500-be4b05db9d1c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Perimeter</th>\n",
              "      <th>MajorAxisLength</th>\n",
              "      <th>MinorAxisLength</th>\n",
              "      <th>AspectRation</th>\n",
              "      <th>Eccentricity</th>\n",
              "      <th>ConvexArea</th>\n",
              "      <th>EquivDiameter</th>\n",
              "      <th>Extent</th>\n",
              "      <th>Solidity</th>\n",
              "      <th>roundness</th>\n",
              "      <th>Compactness</th>\n",
              "      <th>ShapeFactor1</th>\n",
              "      <th>ShapeFactor2</th>\n",
              "      <th>ShapeFactor3</th>\n",
              "      <th>ShapeFactor4</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.840749</td>\n",
              "      <td>-1.143319</td>\n",
              "      <td>-1.306598</td>\n",
              "      <td>-0.631153</td>\n",
              "      <td>-1.565053</td>\n",
              "      <td>-2.185720</td>\n",
              "      <td>-0.841451</td>\n",
              "      <td>-1.063341</td>\n",
              "      <td>0.289087</td>\n",
              "      <td>0.367614</td>\n",
              "      <td>1.423867</td>\n",
              "      <td>1.839116</td>\n",
              "      <td>0.680786</td>\n",
              "      <td>2.402173</td>\n",
              "      <td>1.925723</td>\n",
              "      <td>0.838371</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.829188</td>\n",
              "      <td>-1.013924</td>\n",
              "      <td>-1.395911</td>\n",
              "      <td>-0.434445</td>\n",
              "      <td>-1.969784</td>\n",
              "      <td>-3.686040</td>\n",
              "      <td>-0.826102</td>\n",
              "      <td>-1.044217</td>\n",
              "      <td>0.697477</td>\n",
              "      <td>-0.462907</td>\n",
              "      <td>0.231054</td>\n",
              "      <td>2.495449</td>\n",
              "      <td>0.367967</td>\n",
              "      <td>3.100894</td>\n",
              "      <td>2.689702</td>\n",
              "      <td>0.771138</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.807157</td>\n",
              "      <td>-1.078829</td>\n",
              "      <td>-1.252357</td>\n",
              "      <td>-0.585735</td>\n",
              "      <td>-1.514291</td>\n",
              "      <td>-2.045336</td>\n",
              "      <td>-0.808704</td>\n",
              "      <td>-1.008084</td>\n",
              "      <td>0.578195</td>\n",
              "      <td>0.518417</td>\n",
              "      <td>1.252865</td>\n",
              "      <td>1.764843</td>\n",
              "      <td>0.603129</td>\n",
              "      <td>2.235091</td>\n",
              "      <td>1.841356</td>\n",
              "      <td>0.916755</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.785741</td>\n",
              "      <td>-0.977215</td>\n",
              "      <td>-1.278825</td>\n",
              "      <td>-0.439290</td>\n",
              "      <td>-1.741618</td>\n",
              "      <td>-2.742211</td>\n",
              "      <td>-0.773975</td>\n",
              "      <td>-0.973337</td>\n",
              "      <td>0.671260</td>\n",
              "      <td>-2.241767</td>\n",
              "      <td>0.515049</td>\n",
              "      <td>2.081715</td>\n",
              "      <td>0.401718</td>\n",
              "      <td>2.515075</td>\n",
              "      <td>2.204250</td>\n",
              "      <td>-0.197985</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.781239</td>\n",
              "      <td>-1.097384</td>\n",
              "      <td>-1.380471</td>\n",
              "      <td>-0.266663</td>\n",
              "      <td>-2.117993</td>\n",
              "      <td>-4.535028</td>\n",
              "      <td>-0.784286</td>\n",
              "      <td>-0.966080</td>\n",
              "      <td>0.476020</td>\n",
              "      <td>0.804772</td>\n",
              "      <td>1.874992</td>\n",
              "      <td>2.765330</td>\n",
              "      <td>0.118268</td>\n",
              "      <td>3.270983</td>\n",
              "      <td>3.013462</td>\n",
              "      <td>0.939640</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fbcb573f-fee0-43b1-8500-be4b05db9d1c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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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-fbcb573f-fee0-43b1-8500-be4b05db9d1c 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-fbcb573f-fee0-43b1-8500-be4b05db9d1c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f8ac490f-489a-4d9d-98ad-146275dd0e66')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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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-f8ac490f-489a-4d9d-98ad-146275dd0e66 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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\"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MajorAxisLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06290808348197359,\n        \"min\": -1.395911106904106,\n        \"max\": -1.252356605783403,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -1.395911106904106,\n          -1.3804709099824553,\n          -1.252356605783403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MinorAxisLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14396809408704056,\n        \"min\": -0.6311530382735442,\n        \"max\": -0.26666336374606375,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.43444457918299023,\n          -0.26666336374606375,\n          -0.58573461521174\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 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     ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48068247523826785,\n        \"min\": -0.19798533229602513,\n        \"max\": 0.9396398936236829,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.771138313820243,\n          0.9396398936236829,\n          0.9167550573022227\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=newdf.iloc[:,:-1]\n",
        "y=newdf.iloc[:,-1]\n",
        "#Split into dependent and independent variables"
      ],
      "metadata": {
        "id": "7yGJhqtTENPI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(\n",
        "    x, y, test_size=0.20, random_state=42)\n",
        "#train test split\n",
        "from sklearn.svm import SVC\n",
        "classifier=SVC(kernel='linear',random_state=0)\n",
        "classifier.fit(x_train,y_train)\n",
        "pred=classifier.predict(x_test)\n",
        "#to find the accuracy of the model\n",
        "accuracy=classifier.score(x_test,y_test)\n",
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SoC1DHBQGBN8",
        "outputId": "0c0e226b-6ba1-4287-85e9-1a3aef87e3d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9283878075651855"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1=df.iloc[:,:-1]\n",
        "y1=df.iloc[:,-1]\n",
        "#train test split on unstandardized model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "x1_train, x1_test, y1_train, y1_test = train_test_split(x1, y1, test_size=0.20, random_state=42)\n",
        "#train test split\n",
        "classifier1=SVC(kernel='linear',random_state=0)\n",
        "classifier1.fit(x1_train,y1_train)\n",
        "pred1=classifier.predict(x1_test)\n",
        "#to find the accuracy of the model\n",
        "accuracy1=classifier1.score(x1_test,y1_test)\n",
        "accuracy1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EB2aMmFaPTT0",
        "outputId": "22acb4cf-ca36-4005-ba46-60dabe0ce8ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9173705471905986"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###after comparing the accuracy of the model before and after standardization we found out that there wasn't much difference in accuracy of the model"
      ],
      "metadata": {
        "id": "4a7qLsfRdBDp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "cm=confusion_matrix(y_test,pred)\n",
        "#displaying confusion matrix\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=classifier.classes_)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "-xUnjJZsGoZX",
        "outputId": "f620eaae-bc02-4e0a-b045-535c0a9a402d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7e061017fa60>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ERGYe6dJzXB",
        "outputId": "aaad9da5-45e9-411d-f290-c241ec93d598"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.92      0.92       261\n",
            "           1       1.00      1.00      1.00       117\n",
            "           2       0.94      0.94      0.94       317\n",
            "           3       0.91      0.91      0.91       671\n",
            "           4       0.97      0.97      0.97       408\n",
            "           5       0.97      0.94      0.96       413\n",
            "           6       0.87      0.89      0.88       536\n",
            "\n",
            "    accuracy                           0.93      2723\n",
            "   macro avg       0.94      0.94      0.94      2723\n",
            "weighted avg       0.93      0.93      0.93      2723\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accu=accuracy_score(y_test,pred)\n",
        "#calculating error rate\n",
        "error_rate=1-accu\n",
        "error_rate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfXOip7kKWf5",
        "outputId": "4c096853-f5b1-4b3e-ab6e-de7a5a9ae485"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07161219243481454"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "neigh = KNeighborsClassifier(n_neighbors=5)\n",
        "neigh.fit(x_train, y_train)\n",
        "pred2=neigh.predict(x_test)\n",
        "accuracy2=neigh.score(x_test,y_test)\n",
        "accuracy2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJookR_hgdea",
        "outputId": "71cd7eac-f2d9-4df1-ac4f-0a8fe9cd3f5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9232464193903782"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accu2=accuracy_score(y_test,pred2)\n",
        "accu2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IkU7okdggh2G",
        "outputId": "d925b5f7-18a9-4c95-c1bb-f69c8f923854"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9232464193903782"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "WE ARE CHECKING ACCURACY OF MODEL BEFOR STANDARDIZATION USING KNN AND\n",
        "WE FOUND OUT THAT THERE IS A SIGNIFICANT DIFFERENCE IN ACCURACY."
      ],
      "metadata": {
        "id": "XbxYvjvJoE5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "neigh = KNeighborsClassifier(n_neighbors=5)\n",
        "neigh.fit(x1_train, y1_train)\n",
        "predic=neigh.predict(x1_test)\n",
        "accuracy=neigh.score(x1_test,y1_test)\n",
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzVH7HWfm9Hp",
        "outputId": "9e0961c9-3073-4efb-b746-eed017e33f4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7194271024605214"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CONCLUSION- WE SHOULD SCALE INCASE OF USING KNN IN THIS CASE"
      ],
      "metadata": {
        "id": "rg3sHNNJohkj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "6vSLTxVZ-6z2",
        "outputId": "f5c5adc9-2947-4f82-8b8f-4c5c47ac8e42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Area  Perimeter  MajorAxisLength  MinorAxisLength  AspectRation  \\\n",
              "0      28395    610.291       208.178117       173.888747      1.197191   \n",
              "1      28734    638.018       200.524796       182.734419      1.097356   \n",
              "2      29380    624.110       212.826130       175.931143      1.209713   \n",
              "3      30008    645.884       210.557999       182.516516      1.153638   \n",
              "4      30140    620.134       201.847882       190.279279      1.060798   \n",
              "...      ...        ...              ...              ...           ...   \n",
              "13606  42097    759.696       288.721612       185.944705      1.552728   \n",
              "13607  42101    757.499       281.576392       190.713136      1.476439   \n",
              "13608  42139    759.321       281.539928       191.187979      1.472582   \n",
              "13609  42147    763.779       283.382636       190.275731      1.489326   \n",
              "13610  42159    772.237       295.142741       182.204716      1.619841   \n",
              "\n",
              "       Eccentricity  ConvexArea  EquivDiameter    Extent  Solidity  roundness  \\\n",
              "0          0.549812       28715     190.141097  0.763923  0.988856   0.958027   \n",
              "1          0.411785       29172     191.272751  0.783968  0.984986   0.887034   \n",
              "2          0.562727       29690     193.410904  0.778113  0.989559   0.947849   \n",
              "3          0.498616       30724     195.467062  0.782681  0.976696   0.903936   \n",
              "4          0.333680       30417     195.896503  0.773098  0.990893   0.984877   \n",
              "...             ...         ...            ...       ...       ...        ...   \n",
              "13606      0.765002       42508     231.515799  0.714574  0.990331   0.916603   \n",
              "13607      0.735702       42494     231.526798  0.799943  0.990752   0.922015   \n",
              "13608      0.734065       42569     231.631261  0.729932  0.989899   0.918424   \n",
              "13609      0.741055       42667     231.653247  0.705389  0.987813   0.907906   \n",
              "13610      0.786693       42600     231.686223  0.788962  0.989648   0.888380   \n",
              "\n",
              "       Compactness  ShapeFactor1  ShapeFactor2  ShapeFactor3  ShapeFactor4  \\\n",
              "0         0.913358      0.007332      0.003147      0.834222      0.998724   \n",
              "1         0.953861      0.006979      0.003564      0.909851      0.998430   \n",
              "2         0.908774      0.007244      0.003048      0.825871      0.999066   \n",
              "3         0.928329      0.007017      0.003215      0.861794      0.994199   \n",
              "4         0.970516      0.006697      0.003665      0.941900      0.999166   \n",
              "...            ...           ...           ...           ...           ...   \n",
              "13606     0.801865      0.006858      0.001749      0.642988      0.998385   \n",
              "13607     0.822252      0.006688      0.001886      0.676099      0.998219   \n",
              "13608     0.822730      0.006681      0.001888      0.676884      0.996767   \n",
              "13609     0.817457      0.006724      0.001852      0.668237      0.995222   \n",
              "13610     0.784997      0.007001      0.001640      0.616221      0.998180   \n",
              "\n",
              "       Class  \n",
              "0          5  \n",
              "1          5  \n",
              "2          5  \n",
              "3          5  \n",
              "4          5  \n",
              "...      ...  \n",
              "13606      3  \n",
              "13607      3  \n",
              "13608      3  \n",
              "13609      3  \n",
              "13610      3  \n",
              "\n",
              "[13611 rows x 17 columns]"
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              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Perimeter</th>\n",
              "      <th>MajorAxisLength</th>\n",
              "      <th>MinorAxisLength</th>\n",
              "      <th>AspectRation</th>\n",
              "      <th>Eccentricity</th>\n",
              "      <th>ConvexArea</th>\n",
              "      <th>EquivDiameter</th>\n",
              "      <th>Extent</th>\n",
              "      <th>Solidity</th>\n",
              "      <th>roundness</th>\n",
              "      <th>Compactness</th>\n",
              "      <th>ShapeFactor1</th>\n",
              "      <th>ShapeFactor2</th>\n",
              "      <th>ShapeFactor3</th>\n",
              "      <th>ShapeFactor4</th>\n",
              "      <th>Class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28395</td>\n",
              "      <td>610.291</td>\n",
              "      <td>208.178117</td>\n",
              "      <td>173.888747</td>\n",
              "      <td>1.197191</td>\n",
              "      <td>0.549812</td>\n",
              "      <td>28715</td>\n",
              "      <td>190.141097</td>\n",
              "      <td>0.763923</td>\n",
              "      <td>0.988856</td>\n",
              "      <td>0.958027</td>\n",
              "      <td>0.913358</td>\n",
              "      <td>0.007332</td>\n",
              "      <td>0.003147</td>\n",
              "      <td>0.834222</td>\n",
              "      <td>0.998724</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28734</td>\n",
              "      <td>638.018</td>\n",
              "      <td>200.524796</td>\n",
              "      <td>182.734419</td>\n",
              "      <td>1.097356</td>\n",
              "      <td>0.411785</td>\n",
              "      <td>29172</td>\n",
              "      <td>191.272751</td>\n",
              "      <td>0.783968</td>\n",
              "      <td>0.984986</td>\n",
              "      <td>0.887034</td>\n",
              "      <td>0.953861</td>\n",
              "      <td>0.006979</td>\n",
              "      <td>0.003564</td>\n",
              "      <td>0.909851</td>\n",
              "      <td>0.998430</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29380</td>\n",
              "      <td>624.110</td>\n",
              "      <td>212.826130</td>\n",
              "      <td>175.931143</td>\n",
              "      <td>1.209713</td>\n",
              "      <td>0.562727</td>\n",
              "      <td>29690</td>\n",
              "      <td>193.410904</td>\n",
              "      <td>0.778113</td>\n",
              "      <td>0.989559</td>\n",
              "      <td>0.947849</td>\n",
              "      <td>0.908774</td>\n",
              "      <td>0.007244</td>\n",
              "      <td>0.003048</td>\n",
              "      <td>0.825871</td>\n",
              "      <td>0.999066</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30008</td>\n",
              "      <td>645.884</td>\n",
              "      <td>210.557999</td>\n",
              "      <td>182.516516</td>\n",
              "      <td>1.153638</td>\n",
              "      <td>0.498616</td>\n",
              "      <td>30724</td>\n",
              "      <td>195.467062</td>\n",
              "      <td>0.782681</td>\n",
              "      <td>0.976696</td>\n",
              "      <td>0.903936</td>\n",
              "      <td>0.928329</td>\n",
              "      <td>0.007017</td>\n",
              "      <td>0.003215</td>\n",
              "      <td>0.861794</td>\n",
              "      <td>0.994199</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30140</td>\n",
              "      <td>620.134</td>\n",
              "      <td>201.847882</td>\n",
              "      <td>190.279279</td>\n",
              "      <td>1.060798</td>\n",
              "      <td>0.333680</td>\n",
              "      <td>30417</td>\n",
              "      <td>195.896503</td>\n",
              "      <td>0.773098</td>\n",
              "      <td>0.990893</td>\n",
              "      <td>0.984877</td>\n",
              "      <td>0.970516</td>\n",
              "      <td>0.006697</td>\n",
              "      <td>0.003665</td>\n",
              "      <td>0.941900</td>\n",
              "      <td>0.999166</td>\n",
              "      <td>5</td>\n",
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              "      <td>42097</td>\n",
              "      <td>759.696</td>\n",
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              "      <td>0.765002</td>\n",
              "      <td>42508</td>\n",
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              "      <td>0.714574</td>\n",
              "      <td>0.990331</td>\n",
              "      <td>0.916603</td>\n",
              "      <td>0.801865</td>\n",
              "      <td>0.006858</td>\n",
              "      <td>0.001749</td>\n",
              "      <td>0.642988</td>\n",
              "      <td>0.998385</td>\n",
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              "      <th>13607</th>\n",
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              "      <th>13610</th>\n",
              "      <td>42159</td>\n",
              "      <td>772.237</td>\n",
              "      <td>295.142741</td>\n",
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\"column\": \"EquivDiameter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.17712014871188,\n        \"min\": 161.2437642,\n        \"max\": 569.3743583,\n        \"num_unique_values\": 12011,\n        \"samples\": [\n          247.2463893,\n          240.8237141,\n          478.8490747\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04908636684396477,\n        \"min\": 0.555314717,\n        \"max\": 0.866194641,\n        \"num_unique_values\": 13535,\n        \"samples\": [\n          0.753012629,\n          0.813903525,\n          0.710475845\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Solidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004660379169838444,\n        \"min\": 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            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(y_test))\n",
        "print(type(pred))\n",
        "print(type(x_test))"
      ],
      "metadata": {
        "id": "Kv9d8xiK-75_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "866c0f58-4be8-431f-bf20-f0c712e4cd93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "<class 'numpy.ndarray'>\n",
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=pd.Series(pred,index= x_test.index,name='y_pred')\n",
        "df_new=pd.concat([x_test,y_test.rename('y_test'),y_pred],axis=1)\n",
        "df_new.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "0EnKtoFNuyDC",
        "outputId": "ce1b582e-e81a-435e-a538-152c0aa0d361"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Area  Perimeter  MajorAxisLength  MinorAxisLength  AspectRation  \\\n",
              "1488  -0.365218  -0.532278        -0.699519         0.111983     -1.330113   \n",
              "2611   0.518320   1.089549         0.586132         0.791512     -0.106143   \n",
              "749   -0.518101  -0.683994        -0.836862        -0.175910     -1.236665   \n",
              "99    -0.678828  -0.965544        -1.139157        -0.278714     -1.663886   \n",
              "11298 -0.788571  -0.969347        -0.961919        -0.917663     -0.432975   \n",
              "\n",
              "       Eccentricity  ConvexArea  EquivDiameter    Extent  Solidity  roundness  \\\n",
              "1488      -1.592944   -0.372549      -0.352918  0.437396  1.064426   1.597939   \n",
              "2611       0.169698    0.550947       0.704943  0.365695 -3.127637  -2.516723   \n",
              "749       -1.391983   -0.521807      -0.566452 -0.103759  0.648568   1.240429   \n",
              "99        -2.482813   -0.684870      -0.805076  0.412487  1.254524   1.972582   \n",
              "11298     -0.165137   -0.787511      -0.977908  0.724802 -0.043581   0.394278   \n",
              "\n",
              "       Compactness  ShapeFactor1  ShapeFactor2  ShapeFactor3  ShapeFactor4  \\\n",
              "1488      1.498162     -0.370573      1.153692      1.541930      0.989265   \n",
              "2611     -0.064090     -1.007778     -0.625351     -0.102226     -1.995235   \n",
              "749       1.358954     -0.000975      1.263821      1.387803      0.726306   \n",
              "99        1.997703      0.133443      2.168058      2.107283      1.008149   \n",
              "11298     0.344776      1.223497      0.858970      0.309933      0.104866   \n",
              "\n",
              "       y_test  y_pred  \n",
              "1488        5       5  \n",
              "2611        0       0  \n",
              "749         5       5  \n",
              "99          5       5  \n",
              "11298       3       3  "
            ],
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              "  <div id=\"df-c1dd052d-131c-4129-8212-6e4b17853bd2\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Perimeter</th>\n",
              "      <th>MajorAxisLength</th>\n",
              "      <th>MinorAxisLength</th>\n",
              "      <th>AspectRation</th>\n",
              "      <th>Eccentricity</th>\n",
              "      <th>ConvexArea</th>\n",
              "      <th>EquivDiameter</th>\n",
              "      <th>Extent</th>\n",
              "      <th>Solidity</th>\n",
              "      <th>roundness</th>\n",
              "      <th>Compactness</th>\n",
              "      <th>ShapeFactor1</th>\n",
              "      <th>ShapeFactor2</th>\n",
              "      <th>ShapeFactor3</th>\n",
              "      <th>ShapeFactor4</th>\n",
              "      <th>y_test</th>\n",
              "      <th>y_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1488</th>\n",
              "      <td>-0.365218</td>\n",
              "      <td>-0.532278</td>\n",
              "      <td>-0.699519</td>\n",
              "      <td>0.111983</td>\n",
              "      <td>-1.330113</td>\n",
              "      <td>-1.592944</td>\n",
              "      <td>-0.372549</td>\n",
              "      <td>-0.352918</td>\n",
              "      <td>0.437396</td>\n",
              "      <td>1.064426</td>\n",
              "      <td>1.597939</td>\n",
              "      <td>1.498162</td>\n",
              "      <td>-0.370573</td>\n",
              "      <td>1.153692</td>\n",
              "      <td>1.541930</td>\n",
              "      <td>0.989265</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2611</th>\n",
              "      <td>0.518320</td>\n",
              "      <td>1.089549</td>\n",
              "      <td>0.586132</td>\n",
              "      <td>0.791512</td>\n",
              "      <td>-0.106143</td>\n",
              "      <td>0.169698</td>\n",
              "      <td>0.550947</td>\n",
              "      <td>0.704943</td>\n",
              "      <td>0.365695</td>\n",
              "      <td>-3.127637</td>\n",
              "      <td>-2.516723</td>\n",
              "      <td>-0.064090</td>\n",
              "      <td>-1.007778</td>\n",
              "      <td>-0.625351</td>\n",
              "      <td>-0.102226</td>\n",
              "      <td>-1.995235</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>-0.518101</td>\n",
              "      <td>-0.683994</td>\n",
              "      <td>-0.836862</td>\n",
              "      <td>-0.175910</td>\n",
              "      <td>-1.236665</td>\n",
              "      <td>-1.391983</td>\n",
              "      <td>-0.521807</td>\n",
              "      <td>-0.566452</td>\n",
              "      <td>-0.103759</td>\n",
              "      <td>0.648568</td>\n",
              "      <td>1.240429</td>\n",
              "      <td>1.358954</td>\n",
              "      <td>-0.000975</td>\n",
              "      <td>1.263821</td>\n",
              "      <td>1.387803</td>\n",
              "      <td>0.726306</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>-0.678828</td>\n",
              "      <td>-0.965544</td>\n",
              "      <td>-1.139157</td>\n",
              "      <td>-0.278714</td>\n",
              "      <td>-1.663886</td>\n",
              "      <td>-2.482813</td>\n",
              "      <td>-0.684870</td>\n",
              "      <td>-0.805076</td>\n",
              "      <td>0.412487</td>\n",
              "      <td>1.254524</td>\n",
              "      <td>1.972582</td>\n",
              "      <td>1.997703</td>\n",
              "      <td>0.133443</td>\n",
              "      <td>2.168058</td>\n",
              "      <td>2.107283</td>\n",
              "      <td>1.008149</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11298</th>\n",
              "      <td>-0.788571</td>\n",
              "      <td>-0.969347</td>\n",
              "      <td>-0.961919</td>\n",
              "      <td>-0.917663</td>\n",
              "      <td>-0.432975</td>\n",
              "      <td>-0.165137</td>\n",
              "      <td>-0.787511</td>\n",
              "      <td>-0.977908</td>\n",
              "      <td>0.724802</td>\n",
              "      <td>-0.043581</td>\n",
              "      <td>0.394278</td>\n",
              "      <td>0.344776</td>\n",
              "      <td>1.223497</td>\n",
              "      <td>0.858970</td>\n",
              "      <td>0.309933</td>\n",
              "      <td>0.104866</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      fill: #174EA6;\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      fill: #D2E3FC;\n",
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              "        document.querySelector('#df-c1dd052d-131c-4129-8212-6e4b17853bd2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-c1dd052d-131c-4129-8212-6e4b17853bd2');\n",
              "        const dataTable =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "      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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "df_new",
              "summary": "{\n  \"name\": \"df_new\",\n  \"rows\": 2723,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0406049198867302,\n        \"min\": -1.1027952178607532,\n        \"max\": 5.921583945703651,\n        \"num_unique_values\": 2658,\n        \"samples\": [\n          4.430775622718362,\n          -0.2382184683623419,\n          -0.08093588834156747\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Perimeter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0235689115030853,\n        \"min\": -1.5394234225008048,\n        \"max\": 4.622751121660969,\n        \"num_unique_values\": 2714,\n        \"samples\": [\n          -0.5486534345545556,\n          -1.1755568163126544,\n          -0.6638195254550452\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MajorAxisLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0183506833676423,\n        \"min\": -1.5726259030658107,\n        \"max\": 4.6743937626488545,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          -0.9291100274903495,\n          -0.8317639035328883,\n          -0.31619601488054266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MinorAxisLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0338743718816932,\n        \"min\": -1.555818834076004,\n        \"max\": 5.4942748343155206,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          -0.024949042053749,\n          0.18382911544299083,\n          -0.35477800509808344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AspectRation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.008232355043125,\n        \"min\": -2.173282865728862,\n        \"max\": 3.4340071744360667,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          -1.5708599152438976,\n          -1.6264045768235378,\n          -0.04216910683193911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Eccentricity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0059191510580596,\n        \"min\": -4.93676140048858,\n        \"max\": 1.7449006448854243,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          -2.202277841768642,\n          -2.3661724607057955,\n          0.22788530449325511\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ConvexArea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 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\"max\": 2.057906461560355,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          0.5296251555381019,\n          0.530913666534861,\n          1.0757647770623184\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Solidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0064318440387303,\n        \"min\": -8.674236825008805,\n        \"max\": 1.4541173873364939,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          0.9758273591979291,\n          0.6403279542703755,\n          -0.7598020496334087\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roundness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006326807123593,\n        \"min\": -4.500886774769131,\n        \"max\": 1.9725823719860645,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          1.7165223826115157,\n          1.429127564191612,\n          0.02311056477576924\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Compactness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.006032298440466,\n        \"min\": -2.5811673264002737,\n        \"max\": 2.866176148006009,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          1.8481395996958514,\n          1.9368114834364747,\n          -0.09397531149178394\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0051499562358224,\n        \"min\": -3.287264680697546,\n        \"max\": 2.746540052080355,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          -0.20020138180709057,\n          -0.45298766163320414,\n          0.28996544581737904\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0053395723506109,\n        \"min\": -1.9329908725382114,\n        \"max\": 3.117033523891411,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          1.697394699415881,\n          1.597104856606589,\n          -0.043692555936193034\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.005449026524695,\n        \"min\": -2.356254916400231,\n        \"max\": 3.135879359235509,\n        \"num_unique_values\": 2722,\n        \"samples\": [\n          1.9360018588441537,\n          2.0373419664567605,\n          -0.13184820546273726\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ShapeFactor4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0209492972852643,\n        \"min\": -8.66424017145925,\n        \"max\": 1.0358600928229726,\n        \"num_unique_values\": 2721,\n        \"samples\": [\n          0.8433260223796484,\n          0.9382661896520218,\n          -1.2267247076258538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y_test\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5,\n          0,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y_pred\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5,\n          0,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.reset_index(level=None,drop=True,inplace=True)\n",
        "df_new.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Qm44nZp1xTKY",
        "outputId": "feb6de56-b97f-4886-c349-926966d6f83f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Area  Perimeter  MajorAxisLength  MinorAxisLength  AspectRation  \\\n",
              "0 -0.365218  -0.532278        -0.699519         0.111983     -1.330113   \n",
              "1  0.518320   1.089549         0.586132         0.791512     -0.106143   \n",
              "2 -0.518101  -0.683994        -0.836862        -0.175910     -1.236665   \n",
              "3 -0.678828  -0.965544        -1.139157        -0.278714     -1.663886   \n",
              "4 -0.788571  -0.969347        -0.961919        -0.917663     -0.432975   \n",
              "\n",
              "   Eccentricity  ConvexArea  EquivDiameter    Extent  Solidity  roundness  \\\n",
              "0     -1.592944   -0.372549      -0.352918  0.437396  1.064426   1.597939   \n",
              "1      0.169698    0.550947       0.704943  0.365695 -3.127637  -2.516723   \n",
              "2     -1.391983   -0.521807      -0.566452 -0.103759  0.648568   1.240429   \n",
              "3     -2.482813   -0.684870      -0.805076  0.412487  1.254524   1.972582   \n",
              "4     -0.165137   -0.787511      -0.977908  0.724802 -0.043581   0.394278   \n",
              "\n",
              "   Compactness  ShapeFactor1  ShapeFactor2  ShapeFactor3  ShapeFactor4  \\\n",
              "0     1.498162     -0.370573      1.153692      1.541930      0.989265   \n",
              "1    -0.064090     -1.007778     -0.625351     -0.102226     -1.995235   \n",
              "2     1.358954     -0.000975      1.263821      1.387803      0.726306   \n",
              "3     1.997703      0.133443      2.168058      2.107283      1.008149   \n",
              "4     0.344776      1.223497      0.858970      0.309933      0.104866   \n",
              "\n",
              "   y_test  y_pred  \n",
              "0       5       5  \n",
              "1       0       0  \n",
              "2       5       5  \n",
              "3       5       5  \n",
              "4       3       3  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-436ba133-02a3-4eb9-9abe-faf3960db91a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Perimeter</th>\n",
              "      <th>MajorAxisLength</th>\n",
              "      <th>MinorAxisLength</th>\n",
              "      <th>AspectRation</th>\n",
              "      <th>Eccentricity</th>\n",
              "      <th>ConvexArea</th>\n",
              "      <th>EquivDiameter</th>\n",
              "      <th>Extent</th>\n",
              "      <th>Solidity</th>\n",
              "      <th>roundness</th>\n",
              "      <th>Compactness</th>\n",
              "      <th>ShapeFactor1</th>\n",
              "      <th>ShapeFactor2</th>\n",
              "      <th>ShapeFactor3</th>\n",
              "      <th>ShapeFactor4</th>\n",
              "      <th>y_test</th>\n",
              "      <th>y_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.365218</td>\n",
              "      <td>-0.532278</td>\n",
              "      <td>-0.699519</td>\n",
              "      <td>0.111983</td>\n",
              "      <td>-1.330113</td>\n",
              "      <td>-1.592944</td>\n",
              "      <td>-0.372549</td>\n",
              "      <td>-0.352918</td>\n",
              "      <td>0.437396</td>\n",
              "      <td>1.064426</td>\n",
              "      <td>1.597939</td>\n",
              "      <td>1.498162</td>\n",
              "      <td>-0.370573</td>\n",
              "      <td>1.153692</td>\n",
              "      <td>1.541930</td>\n",
              "      <td>0.989265</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.518320</td>\n",
              "      <td>1.089549</td>\n",
              "      <td>0.586132</td>\n",
              "      <td>0.791512</td>\n",
              "      <td>-0.106143</td>\n",
              "      <td>0.169698</td>\n",
              "      <td>0.550947</td>\n",
              "      <td>0.704943</td>\n",
              "      <td>0.365695</td>\n",
              "      <td>-3.127637</td>\n",
              "      <td>-2.516723</td>\n",
              "      <td>-0.064090</td>\n",
              "      <td>-1.007778</td>\n",
              "      <td>-0.625351</td>\n",
              "      <td>-0.102226</td>\n",
              "      <td>-1.995235</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.518101</td>\n",
              "      <td>-0.683994</td>\n",
              "      <td>-0.836862</td>\n",
              "      <td>-0.175910</td>\n",
              "      <td>-1.236665</td>\n",
              "      <td>-1.391983</td>\n",
              "      <td>-0.521807</td>\n",
              "      <td>-0.566452</td>\n",
              "      <td>-0.103759</td>\n",
              "      <td>0.648568</td>\n",
              "      <td>1.240429</td>\n",
              "      <td>1.358954</td>\n",
              "      <td>-0.000975</td>\n",
              "      <td>1.263821</td>\n",
              "      <td>1.387803</td>\n",
              "      <td>0.726306</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.678828</td>\n",
              "      <td>-0.965544</td>\n",
              "      <td>-1.139157</td>\n",
              "      <td>-0.278714</td>\n",
              "      <td>-1.663886</td>\n",
              "      <td>-2.482813</td>\n",
              "      <td>-0.684870</td>\n",
              "      <td>-0.805076</td>\n",
              "      <td>0.412487</td>\n",
              "      <td>1.254524</td>\n",
              "      <td>1.972582</td>\n",
              "      <td>1.997703</td>\n",
              "      <td>0.133443</td>\n",
              "      <td>2.168058</td>\n",
              "      <td>2.107283</td>\n",
              "      <td>1.008149</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.788571</td>\n",
              "      <td>-0.969347</td>\n",
              "      <td>-0.961919</td>\n",
              "      <td>-0.917663</td>\n",
              "      <td>-0.432975</td>\n",
              "      <td>-0.165137</td>\n",
              "      <td>-0.787511</td>\n",
              "      <td>-0.977908</td>\n",
              "      <td>0.724802</td>\n",
              "      <td>-0.043581</td>\n",
              "      <td>0.394278</td>\n",
              "      <td>0.344776</td>\n",
              "      <td>1.223497</td>\n",
              "      <td>0.858970</td>\n",
              "      <td>0.309933</td>\n",
              "      <td>0.104866</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.to_csv('REPORT.csv',header=True,index=True)"
      ],
      "metadata": {
        "id": "i1bBxKMcyRdQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vEU7kVnV1F6V"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}