ICR_Test1.ipynb

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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "dgUq51x2YBVD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train=pd.read_csv('/content/train.csv')"
      ],
      "metadata": {
        "id": "nOhXMeAYDCIv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "POMj-VN-MiHo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test=pd.read_csv('/content/test.csv')"
      ],
      "metadata": {
        "id": "W-kVSY6XDJ3j"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "train.columns"
      ],
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        "outputId": "7c8d1f4a-95d3-41f7-f837-499b5e6829c5"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "Index(['Id', 'AB', 'AF', 'AH', 'AM', 'AR', 'AX', 'AY', 'AZ', 'BC', 'BD ', 'BN',\n",
              "       'BP', 'BQ', 'BR', 'BZ', 'CB', 'CC', 'CD ', 'CF', 'CH', 'CL', 'CR', 'CS',\n",
              "       'CU', 'CW ', 'DA', 'DE', 'DF', 'DH', 'DI', 'DL', 'DN', 'DU', 'DV', 'DY',\n",
              "       'EB', 'EE', 'EG', 'EH', 'EJ', 'EL', 'EP', 'EU', 'FC', 'FD ', 'FE', 'FI',\n",
              "       'FL', 'FR', 'FS', 'GB', 'GE', 'GF', 'GH', 'GI', 'GL', 'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
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          "height": 299
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        "id": "V37kheWzjb3x",
        "outputId": "fb5e343d-ffc3-4c22-d508-cbb6e5bdddc4"
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      "execution_count": null,
      "outputs": [
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              "             Id        AB          AF          AH         AM        AR  \\\n",
              "0  000ff2bfdfe9  0.209377  3109.03329   85.200147  22.394407  8.138688   \n",
              "1  007255e47698  0.145282   978.76416   85.200147  36.968889  8.138688   \n",
              "2  013f2bd269f5  0.470030  2635.10654   85.200147  32.360553  8.138688   \n",
              "3  043ac50845d5  0.252107  3819.65177  120.201618  77.112203  8.138688   \n",
              "4  044fb8a146ec  0.380297  3733.04844   85.200147  14.103738  8.138688   \n",
              "\n",
              "         AX        AY         AZ          BC  ...        FL        FR  \\\n",
              "0  0.699861  0.025578   9.812214    5.555634  ...  7.298162   1.73855   \n",
              "1  3.632190  0.025578  13.517790    1.229900  ...  0.173229   0.49706   \n",
              "2  6.732840  0.025578  12.824570    1.229900  ...  7.709560   0.97556   \n",
              "3  3.685344  0.025578  11.053708    1.229900  ...  6.122162   0.49706   \n",
              "4  3.942255  0.054810   3.396778  102.151980  ...  8.153058  48.50134   \n",
              "\n",
              "         FS         GB          GE            GF         GH         GI  \\\n",
              "0  0.094822  11.339138   72.611063   2003.810319  22.136229  69.834944   \n",
              "1  0.568932   9.292698   72.611063  27981.562750  29.135430  32.131996   \n",
              "2  1.198821  37.077772   88.609437  13676.957810  28.022851  35.192676   \n",
              "3  0.284466  18.529584   82.416803   2094.262452  39.948656  90.493248   \n",
              "4  0.121914  16.408728  146.109943   8524.370502  45.381316  36.262628   \n",
              "\n",
              "          GL  Class  \n",
              "0   0.120343      1  \n",
              "1  21.978000      0  \n",
              "2   0.196941      0  \n",
              "3   0.155829      0  \n",
              "4   0.096614      1  \n",
              "\n",
              "[5 rows x 58 columns]"
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        "id": "7rBDWBfmj11y",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 617 entries, 0 to 616\n",
            "Data columns (total 58 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
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            " 13  BQ      557 non-null    float64\n",
            " 14  BR      617 non-null    float64\n",
            " 15  BZ      617 non-null    float64\n",
            " 16  CB      615 non-null    float64\n",
            " 17  CC      614 non-null    float64\n",
            " 18  CD      617 non-null    float64\n",
            " 19  CF      617 non-null    float64\n",
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            " 25  CW      617 non-null    float64\n",
            " 26  DA      617 non-null    float64\n",
            " 27  DE      617 non-null    float64\n",
            " 28  DF      617 non-null    float64\n",
            " 29  DH      617 non-null    float64\n",
            " 30  DI      617 non-null    float64\n",
            " 31  DL      617 non-null    float64\n",
            " 32  DN      617 non-null    float64\n",
            " 33  DU      616 non-null    float64\n",
            " 34  DV      617 non-null    float64\n",
            " 35  DY      617 non-null    float64\n",
            " 36  EB      617 non-null    float64\n",
            " 37  EE      617 non-null    float64\n",
            " 38  EG      617 non-null    float64\n",
            " 39  EH      617 non-null    float64\n",
            " 40  EJ      617 non-null    object \n",
            " 41  EL      557 non-null    float64\n",
            " 42  EP      617 non-null    float64\n",
            " 43  EU      617 non-null    float64\n",
            " 44  FC      616 non-null    float64\n",
            " 45  FD      617 non-null    float64\n",
            " 46  FE      617 non-null    float64\n",
            " 47  FI      617 non-null    float64\n",
            " 48  FL      616 non-null    float64\n",
            " 49  FR      617 non-null    float64\n",
            " 50  FS      615 non-null    float64\n",
            " 51  GB      617 non-null    float64\n",
            " 52  GE      617 non-null    float64\n",
            " 53  GF      617 non-null    float64\n",
            " 54  GH      617 non-null    float64\n",
            " 55  GI      617 non-null    float64\n",
            " 56  GL      616 non-null    float64\n",
            " 57  Class   617 non-null    int64  \n",
            "dtypes: float64(55), int64(1), object(2)\n",
            "memory usage: 279.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#EJ -> object datatype"
      ],
      "metadata": {
        "id": "sPWrc2wfkE3m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming 'train' is your training dataset and 'Class' is the column containing the class labels\n",
        "class_counts = train['Class'].value_counts()\n",
        "\n",
        "print(\"Class Distribution:\")\n",
        "print(class_counts)\n",
        "\n",
        "# Create a pie chart to visualize the class distribution\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.pie(class_counts, labels=class_counts.index, autopct='%1.1f%%')\n",
        "plt.title('Class Distribution')\n",
        "plt.show()\n",
        "\n",
        "# Calculate the class imbalance ratio\n",
        "imbalance_ratio = class_counts.min() / class_counts.max()\n",
        "\n",
        "print(\"Imbalance Ratio: {:.2f}\".format(imbalance_ratio))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "xfIbfY6gkJIq",
        "outputId": "c0e914dc-2362-48a7-8b40-d1f8cf928fbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class Distribution:\n",
            "0    509\n",
            "1    108\n",
            "Name: Class, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Imbalance Ratio: 0.21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dataset -> highly unbalanced"
      ],
      "metadata": {
        "id": "-mud3uqtk4au"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jXjB36qQk-w3",
        "outputId": "c4869a30-a054-4625-c55b-4450716265a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       count          mean           std          min          25%  \\\n",
              "AB     617.0      0.477149      0.468388     0.081187     0.252107   \n",
              "AF     617.0   3502.013221   2300.322717   192.593280  2197.345480   \n",
              "AH     617.0    118.624513    127.838950    85.200147    85.200147   \n",
              "AM     617.0     38.968552     69.728226     3.177522    12.270314   \n",
              "AR     617.0     10.128242     10.518877     8.138688     8.138688   \n",
              "AX     617.0      5.545576      2.551696     0.699861     4.128294   \n",
              "AY     617.0      0.060320      0.416817     0.025578     0.025578   \n",
              "AZ     617.0     10.566447      4.350645     3.396778     8.129580   \n",
              "BC     617.0      8.053012     65.166943     1.229900     1.229900   \n",
              "BD     617.0   5350.388655   3021.326641  1693.624320  4155.702870   \n",
              "BN     617.0     21.419492      3.478278     9.886800    19.420500   \n",
              "BP     617.0    231.322223    183.992505    72.948951   156.847239   \n",
              "BQ     557.0     98.328737     96.479371     1.331155    27.834425   \n",
              "BR     617.0   1218.133238   7575.293707    51.216883   424.990642   \n",
              "BZ     617.0    550.632525   2076.371275   257.432377   257.432377   \n",
              "CB     615.0     77.104151    159.049302    12.499760    23.317567   \n",
              "CC     614.0      0.688801      0.263994     0.176874     0.563688   \n",
              "CD     617.0     90.251735     51.585130    23.387600    64.724192   \n",
              "CF     617.0     11.241064     13.571133     0.510888     5.066306   \n",
              "CH     617.0      0.030615      0.014808     0.003184     0.023482   \n",
              "CL     617.0      1.403761      1.922210     1.050225     1.050225   \n",
              "CR     617.0      0.742262      0.281195     0.069225     0.589575   \n",
              "CS     617.0     36.917590     17.266347    13.784111    29.782467   \n",
              "CU     617.0      1.383792      0.538717     0.137925     1.070298   \n",
              "CW     617.0     27.165653     14.645993     7.030640     7.030640   \n",
              "DA     617.0     51.128326     21.210888     6.906400    37.942520   \n",
              "DE     617.0    401.901299    317.745623    35.998895   188.815690   \n",
              "DF     617.0      0.633884      1.912384     0.238680     0.238680   \n",
              "DH     617.0      0.367002      0.112989     0.040995     0.295164   \n",
              "DI     617.0    146.972099     86.084419    60.232470   102.703553   \n",
              "DL     617.0     94.795377     28.243187    10.345600    78.232240   \n",
              "DN     617.0     26.370568      8.038825     6.339496    20.888264   \n",
              "DU     616.0      1.802900      9.034721     0.005518     0.005518   \n",
              "DV     617.0      1.924830      1.484555     1.743070     1.743070   \n",
              "DY     617.0     26.388989     18.116679     0.804068    14.715792   \n",
              "EB     617.0      9.072700      6.200281     4.926396     5.965392   \n",
              "EE     617.0      3.064778      2.058344     0.286201     1.648679   \n",
              "EG     617.0   1731.248215   1790.227476   185.594100  1111.160625   \n",
              "EH     617.0      0.305107      1.847499     0.003042     0.003042   \n",
              "EL     557.0     69.582596     38.555707     5.394675    30.927468   \n",
              "EP     617.0    105.060712     68.445620    78.526968    78.526968   \n",
              "EU     617.0     69.117005    390.187057     3.828384     4.324656   \n",
              "FC     616.0     71.341526    165.551545     7.534128    25.815384   \n",
              "FD     617.0      6.930086     64.754262     0.296850     0.296850   \n",
              "FE     617.0  10306.810737  11331.294051  1563.136688  5164.666260   \n",
              "FI     617.0     10.111079      2.934025     3.583450     8.523098   \n",
              "FL     616.0      5.433199     11.496257     0.173229     0.173229   \n",
              "FR     617.0      3.533905     50.181948     0.497060     0.497060   \n",
              "FS     615.0      0.421501      1.305365     0.067730     0.067730   \n",
              "GB     617.0     20.724856      9.991907     4.102182    14.036718   \n",
              "GE     617.0    131.714987    144.181524    72.611063    72.611063   \n",
              "GF     617.0  14679.595398  19352.959387    13.038894  2798.992584   \n",
              "GH     617.0     31.489716      9.864239     9.432735    25.034888   \n",
              "GI     617.0     50.584437     36.266251     0.897628    23.011684   \n",
              "GL     616.0      8.530961     10.327010     0.001129     0.124392   \n",
              "Class  617.0      0.175041      0.380310     0.000000     0.000000   \n",
              "\n",
              "               50%           75%            max  \n",
              "AB        0.354659      0.559763       6.161666  \n",
              "AF     3120.318960   4361.637390   28688.187660  \n",
              "AH       85.200147    113.739540    1910.123198  \n",
              "AM       20.533110     39.139886     630.518230  \n",
              "AR        8.138688      8.138688     178.943634  \n",
              "AX        5.031912      6.431634      38.270880  \n",
              "AY        0.025578      0.036845      10.315851  \n",
              "AZ       10.461320     12.969516      38.971568  \n",
              "BC        1.229900      5.081244    1463.693448  \n",
              "BD     4997.960730   6035.885700   53060.599240  \n",
              "BN       21.186000     23.657700      29.307300  \n",
              "BP      193.908816    247.803462    2447.810550  \n",
              "BQ       61.642115    134.009015     344.644105  \n",
              "BR      627.417402    975.649259  179250.252900  \n",
              "BZ      257.432377    257.432377   50092.459300  \n",
              "CB       42.554330     77.310097    2271.436167  \n",
              "CC        0.658715      0.772206       4.103032  \n",
              "CD       79.819104     99.813520     633.534408  \n",
              "CF        9.123000     13.565901     200.967526  \n",
              "CH        0.027860      0.034427       0.224074  \n",
              "CL        1.050225      1.228445      31.688153  \n",
              "CR        0.730800      0.859350       3.039675  \n",
              "CS       34.835130     40.529401     267.942823  \n",
              "CU        1.351665      1.660617       4.951507  \n",
              "CW       36.019104     37.935832      64.521624  \n",
              "DA       49.180940     61.408760     210.330920  \n",
              "DE      307.509595    507.896200    2103.405190  \n",
              "DF        0.238680      0.238680      37.895013  \n",
              "DH        0.358023      0.426348       1.060404  \n",
              "DI      130.050630    165.836955    1049.168078  \n",
              "DL       96.264960    110.640680     326.236200  \n",
              "DN       25.248800     30.544224      62.808096  \n",
              "DU        0.251741      1.058690     161.355315  \n",
              "DV        1.743070      1.743070      25.192930  \n",
              "DY       21.642456     34.058344     152.355164  \n",
              "EB        8.149404     10.503048      94.958580  \n",
              "EE        2.616119      3.910070      18.324926  \n",
              "EG     1493.817413   1905.701475   30243.758780  \n",
              "EH        0.085176      0.237276      42.569748  \n",
              "EL       71.949306    109.125159     109.125159  \n",
              "EP       78.526968    112.766654    1063.594578  \n",
              "EU       22.641144     49.085352    6501.264480  \n",
              "FC       36.394008     56.714448    3030.655824  \n",
              "FD        1.870155      4.880214    1578.654237  \n",
              "FE     7345.143424  10647.951650  143224.682300  \n",
              "FI        9.945452     11.516657      35.851039  \n",
              "FL        3.028141      6.238814     137.932739  \n",
              "FR        1.131000      1.512060    1244.227020  \n",
              "FS        0.250601      0.535067      31.365763  \n",
              "GB       18.771436     25.608406     135.781294  \n",
              "GE       72.611063    127.591671    1497.351958  \n",
              "GF     7838.273610  19035.709240  143790.071200  \n",
              "GH       30.608946     36.863947      81.210825  \n",
              "GI       41.007968     67.931664     191.194764  \n",
              "GL        0.337827     21.978000      21.978000  \n",
              "Class     0.000000      0.000000       1.000000  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-91cae0fc-a9d3-4982-8f4a-fc575686df82\">\n",
              "    <div 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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AB</th>\n",
              "      <td>617.0</td>\n",
              "      <td>0.477149</td>\n",
              "      <td>0.468388</td>\n",
              "      <td>0.081187</td>\n",
              "      <td>0.252107</td>\n",
              "      <td>0.354659</td>\n",
              "      <td>0.559763</td>\n",
              "      <td>6.161666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AF</th>\n",
              "      <td>617.0</td>\n",
              "      <td>3502.013221</td>\n",
              "      <td>2300.322717</td>\n",
              "      <td>192.593280</td>\n",
              "      <td>2197.345480</td>\n",
              "      <td>3120.318960</td>\n",
              "      <td>4361.637390</td>\n",
              "      <td>28688.187660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AH</th>\n",
              "      <td>617.0</td>\n",
              "      <td>118.624513</td>\n",
              "      <td>127.838950</td>\n",
              "      <td>85.200147</td>\n",
              "      <td>85.200147</td>\n",
              "      <td>85.200147</td>\n",
              "      <td>113.739540</td>\n",
              "      <td>1910.123198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AM</th>\n",
              "      <td>617.0</td>\n",
              "      <td>38.968552</td>\n",
              "      <td>69.728226</td>\n",
              "      <td>3.177522</td>\n",
              "      <td>12.270314</td>\n",
              "      <td>20.533110</td>\n",
              "      <td>39.139886</td>\n",
              "      <td>630.518230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR</th>\n",
              "      <td>617.0</td>\n",
              "      <td>10.128242</td>\n",
              "      <td>10.518877</td>\n",
              "      <td>8.138688</td>\n",
              "      <td>8.138688</td>\n",
              "      <td>8.138688</td>\n",
              "      <td>8.138688</td>\n",
              "      <td>178.943634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AX</th>\n",
              "      <td>617.0</td>\n",
              "      <td>5.545576</td>\n",
              "      <td>2.551696</td>\n",
              "      <td>0.699861</td>\n",
              "      <td>4.128294</td>\n",
              "      <td>5.031912</td>\n",
              "      <td>6.431634</td>\n",
              "      <td>38.270880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AY</th>\n",
              "      <td>617.0</td>\n",
              "      <td>0.060320</td>\n",
              "      <td>0.416817</td>\n",
              "      <td>0.025578</td>\n",
              "      <td>0.025578</td>\n",
              "      <td>0.025578</td>\n",
              "      <td>0.036845</td>\n",
              "      <td>10.315851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AZ</th>\n",
              "      <td>617.0</td>\n",
              "      <td>10.566447</td>\n",
              "      <td>4.350645</td>\n",
              "      <td>3.396778</td>\n",
              "      <td>8.129580</td>\n",
              "      <td>10.461320</td>\n",
              "      <td>12.969516</td>\n",
              "      <td>38.971568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BC</th>\n",
              "      <td>617.0</td>\n",
              "      <td>8.053012</td>\n",
              "      <td>65.166943</td>\n",
              "      <td>1.229900</td>\n",
              "      <td>1.229900</td>\n",
              "      <td>1.229900</td>\n",
              "      <td>5.081244</td>\n",
              "      <td>1463.693448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BD</th>\n",
              "      <td>617.0</td>\n",
              "      <td>5350.388655</td>\n",
              "      <td>3021.326641</td>\n",
              "      <td>1693.624320</td>\n",
              "      <td>4155.702870</td>\n",
              "      <td>4997.960730</td>\n",
              "      <td>6035.885700</td>\n",
              "      <td>53060.599240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BN</th>\n",
              "      <td>617.0</td>\n",
              "      <td>21.419492</td>\n",
              "      <td>3.478278</td>\n",
              "      <td>9.886800</td>\n",
              "      <td>19.420500</td>\n",
              "      <td>21.186000</td>\n",
              "      <td>23.657700</td>\n",
              "      <td>29.307300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BP</th>\n",
              "      <td>617.0</td>\n",
              "      <td>231.322223</td>\n",
              "      <td>183.992505</td>\n",
              "      <td>72.948951</td>\n",
              "      <td>156.847239</td>\n",
              "      <td>193.908816</td>\n",
              "      <td>247.803462</td>\n",
              "      <td>2447.810550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BQ</th>\n",
              "      <td>557.0</td>\n",
              "      <td>98.328737</td>\n",
              "      <td>96.479371</td>\n",
              "      <td>1.331155</td>\n",
              "      <td>27.834425</td>\n",
              "      <td>61.642115</td>\n",
              "      <td>134.009015</td>\n",
              "      <td>344.644105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BR</th>\n",
              "      <td>617.0</td>\n",
              "      <td>1218.133238</td>\n",
              "      <td>7575.293707</td>\n",
              "      <td>51.216883</td>\n",
              "      <td>424.990642</td>\n",
              "      <td>627.417402</td>\n",
              "      <td>975.649259</td>\n",
              "      <td>179250.252900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BZ</th>\n",
              "      <td>617.0</td>\n",
              "      <td>550.632525</td>\n",
              "      <td>2076.371275</td>\n",
              "      <td>257.432377</td>\n",
              "      <td>257.432377</td>\n",
              "      <td>257.432377</td>\n",
              "      <td>257.432377</td>\n",
              "      <td>50092.459300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CB</th>\n",
              "      <td>615.0</td>\n",
              "      <td>77.104151</td>\n",
              "      <td>159.049302</td>\n",
              "      <td>12.499760</td>\n",
              "      <td>23.317567</td>\n",
              "      <td>42.554330</td>\n",
              "      <td>77.310097</td>\n",
              "      <td>2271.436167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CC</th>\n",
              "      <td>614.0</td>\n",
              "      <td>0.688801</td>\n",
              "      <td>0.263994</td>\n",
              "      <td>0.176874</td>\n",
              "      <td>0.563688</td>\n",
              "      <td>0.658715</td>\n",
              "      <td>0.772206</td>\n",
              "      <td>4.103032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CD</th>\n",
              "      <td>617.0</td>\n",
              "      <td>90.251735</td>\n",
              "      <td>51.585130</td>\n",
              "      <td>23.387600</td>\n",
              "      <td>64.724192</td>\n",
              "      <td>79.819104</td>\n",
              "      <td>99.813520</td>\n",
              "      <td>633.534408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CF</th>\n",
              "      <td>617.0</td>\n",
              "      <td>11.241064</td>\n",
              "      <td>13.571133</td>\n",
              "      <td>0.510888</td>\n",
              "      <td>5.066306</td>\n",
              "      <td>9.123000</td>\n",
              "      <td>13.565901</td>\n",
              "      <td>200.967526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CH</th>\n",
              "      <td>617.0</td>\n",
              "      <td>0.030615</td>\n",
              "      <td>0.014808</td>\n",
              "      <td>0.003184</td>\n",
              "      <td>0.023482</td>\n",
              "      <td>0.027860</td>\n",
              "      <td>0.034427</td>\n",
              "      <td>0.224074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CL</th>\n",
              "      <td>617.0</td>\n",
              "      <td>1.403761</td>\n",
              "      <td>1.922210</td>\n",
              "      <td>1.050225</td>\n",
              "      <td>1.050225</td>\n",
              "      <td>1.050225</td>\n",
              "      <td>1.228445</td>\n",
              "      <td>31.688153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CR</th>\n",
              "      <td>617.0</td>\n",
              "      <td>0.742262</td>\n",
              "      <td>0.281195</td>\n",
              "      <td>0.069225</td>\n",
              "      <td>0.589575</td>\n",
              "      <td>0.730800</td>\n",
              "      <td>0.859350</td>\n",
              "      <td>3.039675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CS</th>\n",
              "      <td>617.0</td>\n",
              "      <td>36.917590</td>\n",
              "      <td>17.266347</td>\n",
              "      <td>13.784111</td>\n",
              "      <td>29.782467</td>\n",
              "      <td>34.835130</td>\n",
              "      <td>40.529401</td>\n",
              "      <td>267.942823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CU</th>\n",
              "      <td>617.0</td>\n",
              "      <td>1.383792</td>\n",
              "      <td>0.538717</td>\n",
              "      <td>0.137925</td>\n",
              "      <td>1.070298</td>\n",
              "      <td>1.351665</td>\n",
              "      <td>1.660617</td>\n",
              "      <td>4.951507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CW</th>\n",
              "      <td>617.0</td>\n",
              "      <td>27.165653</td>\n",
              "      <td>14.645993</td>\n",
              "      <td>7.030640</td>\n",
              "      <td>7.030640</td>\n",
              "      <td>36.019104</td>\n",
              "      <td>37.935832</td>\n",
              "      <td>64.521624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DA</th>\n",
              "      <td>617.0</td>\n",
              "      <td>51.128326</td>\n",
              "      <td>21.210888</td>\n",
              "      <td>6.906400</td>\n",
              "      <td>37.942520</td>\n",
              "      <td>49.180940</td>\n",
              "      <td>61.408760</td>\n",
              "      <td>210.330920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DE</th>\n",
              "      <td>617.0</td>\n",
              "      <td>401.901299</td>\n",
              "      <td>317.745623</td>\n",
              "      <td>35.998895</td>\n",
              "      <td>188.815690</td>\n",
              "      <td>307.509595</td>\n",
              "      <td>507.896200</td>\n",
              "      <td>2103.405190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DF</th>\n",
              "      <td>617.0</td>\n",
              "      <td>0.633884</td>\n",
              "      <td>1.912384</td>\n",
              "      <td>0.238680</td>\n",
              "      <td>0.238680</td>\n",
              "      <td>0.238680</td>\n",
              "      <td>0.238680</td>\n",
              "      <td>37.895013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DH</th>\n",
              "      <td>617.0</td>\n",
              "      <td>0.367002</td>\n",
              "      <td>0.112989</td>\n",
              "      <td>0.040995</td>\n",
              "      <td>0.295164</td>\n",
              "      <td>0.358023</td>\n",
              "      <td>0.426348</td>\n",
              "      <td>1.060404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DI</th>\n",
              "      <td>617.0</td>\n",
              "      <td>146.972099</td>\n",
              "      <td>86.084419</td>\n",
              "      <td>60.232470</td>\n",
              "      <td>102.703553</td>\n",
              "      <td>130.050630</td>\n",
              "      <td>165.836955</td>\n",
              "      <td>1049.168078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DL</th>\n",
              "      <td>617.0</td>\n",
              "      <td>94.795377</td>\n",
              "      <td>28.243187</td>\n",
              "      <td>10.345600</td>\n",
              "      <td>78.232240</td>\n",
              "      <td>96.264960</td>\n",
              "      <td>110.640680</td>\n",
              "      <td>326.236200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DN</th>\n",
              "      <td>617.0</td>\n",
              "      <td>26.370568</td>\n",
              "      <td>8.038825</td>\n",
              "      <td>6.339496</td>\n",
              "      <td>20.888264</td>\n",
              "      <td>25.248800</td>\n",
              "      <td>30.544224</td>\n",
              "      <td>62.808096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DU</th>\n",
              "      <td>616.0</td>\n",
              "      <td>1.802900</td>\n",
              "      <td>9.034721</td>\n",
              "      <td>0.005518</td>\n",
              "      <td>0.005518</td>\n",
              "      <td>0.251741</td>\n",
              "      <td>1.058690</td>\n",
              "      <td>161.355315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DV</th>\n",
              "      <td>617.0</td>\n",
              "      <td>1.924830</td>\n",
              "      <td>1.484555</td>\n",
              "      <td>1.743070</td>\n",
              "      <td>1.743070</td>\n",
              "      <td>1.743070</td>\n",
              "      <td>1.743070</td>\n",
              "      <td>25.192930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DY</th>\n",
              "      <td>617.0</td>\n",
              "      <td>26.388989</td>\n",
              "      <td>18.116679</td>\n",
              "      <td>0.804068</td>\n",
              "      <td>14.715792</td>\n",
              "      <td>21.642456</td>\n",
              "      <td>34.058344</td>\n",
              "      <td>152.355164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EB</th>\n",
              "      <td>617.0</td>\n",
              "      <td>9.072700</td>\n",
              "      <td>6.200281</td>\n",
              "      <td>4.926396</td>\n",
              "      <td>5.965392</td>\n",
              "      <td>8.149404</td>\n",
              "      <td>10.503048</td>\n",
              "      <td>94.958580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EE</th>\n",
              "      <td>617.0</td>\n",
              "      <td>3.064778</td>\n",
              "      <td>2.058344</td>\n",
              "      <td>0.286201</td>\n",
              "      <td>1.648679</td>\n",
              "      <td>2.616119</td>\n",
              "      <td>3.910070</td>\n",
              "      <td>18.324926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EG</th>\n",
              "      <td>617.0</td>\n",
              "      <td>1731.248215</td>\n",
              "      <td>1790.227476</td>\n",
              "      <td>185.594100</td>\n",
              "      <td>1111.160625</td>\n",
              "      <td>1493.817413</td>\n",
              "      <td>1905.701475</td>\n",
              "      <td>30243.758780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EH</th>\n",
              "      <td>617.0</td>\n",
              "      <td>0.305107</td>\n",
              "      <td>1.847499</td>\n",
              "      <td>0.003042</td>\n",
              "      <td>0.003042</td>\n",
              "      <td>0.085176</td>\n",
              "      <td>0.237276</td>\n",
              "      <td>42.569748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EL</th>\n",
              "      <td>557.0</td>\n",
              "      <td>69.582596</td>\n",
              "      <td>38.555707</td>\n",
              "      <td>5.394675</td>\n",
              "      <td>30.927468</td>\n",
              "      <td>71.949306</td>\n",
              "      <td>109.125159</td>\n",
              "      <td>109.125159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EP</th>\n",
              "      <td>617.0</td>\n",
              "      <td>105.060712</td>\n",
              "      <td>68.445620</td>\n",
              "      <td>78.526968</td>\n",
              "      <td>78.526968</td>\n",
              "      <td>78.526968</td>\n",
              "      <td>112.766654</td>\n",
              "      <td>1063.594578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EU</th>\n",
              "      <td>617.0</td>\n",
              "      <td>69.117005</td>\n",
              "      <td>390.187057</td>\n",
              "      <td>3.828384</td>\n",
              "      <td>4.324656</td>\n",
              "      <td>22.641144</td>\n",
              "      <td>49.085352</td>\n",
              "      <td>6501.264480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FC</th>\n",
              "      <td>616.0</td>\n",
              "      <td>71.341526</td>\n",
              "      <td>165.551545</td>\n",
              "      <td>7.534128</td>\n",
              "      <td>25.815384</td>\n",
              "      <td>36.394008</td>\n",
              "      <td>56.714448</td>\n",
              "      <td>3030.655824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FD</th>\n",
              "      <td>617.0</td>\n",
              "      <td>6.930086</td>\n",
              "      <td>64.754262</td>\n",
              "      <td>0.296850</td>\n",
              "      <td>0.296850</td>\n",
              "      <td>1.870155</td>\n",
              "      <td>4.880214</td>\n",
              "      <td>1578.654237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FE</th>\n",
              "      <td>617.0</td>\n",
              "      <td>10306.810737</td>\n",
              "      <td>11331.294051</td>\n",
              "      <td>1563.136688</td>\n",
              "      <td>5164.666260</td>\n",
              "      <td>7345.143424</td>\n",
              "      <td>10647.951650</td>\n",
              "      <td>143224.682300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FI</th>\n",
              "      <td>617.0</td>\n",
              "      <td>10.111079</td>\n",
              "      <td>2.934025</td>\n",
              "      <td>3.583450</td>\n",
              "      <td>8.523098</td>\n",
              "      <td>9.945452</td>\n",
              "      <td>11.516657</td>\n",
              "      <td>35.851039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FL</th>\n",
              "      <td>616.0</td>\n",
              "      <td>5.433199</td>\n",
              "      <td>11.496257</td>\n",
              "      <td>0.173229</td>\n",
              "      <td>0.173229</td>\n",
              "      <td>3.028141</td>\n",
              "      <td>6.238814</td>\n",
              "      <td>137.932739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FR</th>\n",
              "      <td>617.0</td>\n",
              "      <td>3.533905</td>\n",
              "      <td>50.181948</td>\n",
              "      <td>0.497060</td>\n",
              "      <td>0.497060</td>\n",
              "      <td>1.131000</td>\n",
              "      <td>1.512060</td>\n",
              "      <td>1244.227020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FS</th>\n",
              "      <td>615.0</td>\n",
              "      <td>0.421501</td>\n",
              "      <td>1.305365</td>\n",
              "      <td>0.067730</td>\n",
              "      <td>0.067730</td>\n",
              "      <td>0.250601</td>\n",
              "      <td>0.535067</td>\n",
              "      <td>31.365763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GB</th>\n",
              "      <td>617.0</td>\n",
              "      <td>20.724856</td>\n",
              "      <td>9.991907</td>\n",
              "      <td>4.102182</td>\n",
              "      <td>14.036718</td>\n",
              "      <td>18.771436</td>\n",
              "      <td>25.608406</td>\n",
              "      <td>135.781294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GE</th>\n",
              "      <td>617.0</td>\n",
              "      <td>131.714987</td>\n",
              "      <td>144.181524</td>\n",
              "      <td>72.611063</td>\n",
              "      <td>72.611063</td>\n",
              "      <td>72.611063</td>\n",
              "      <td>127.591671</td>\n",
              "      <td>1497.351958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GF</th>\n",
              "      <td>617.0</td>\n",
              "      <td>14679.595398</td>\n",
              "      <td>19352.959387</td>\n",
              "      <td>13.038894</td>\n",
              "      <td>2798.992584</td>\n",
              "      <td>7838.273610</td>\n",
              "      <td>19035.709240</td>\n",
              "      <td>143790.071200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GH</th>\n",
              "      <td>617.0</td>\n",
              "      <td>31.489716</td>\n",
              "      <td>9.864239</td>\n",
              "      <td>9.432735</td>\n",
              "      <td>25.034888</td>\n",
              "      <td>30.608946</td>\n",
              "      <td>36.863947</td>\n",
              "      <td>81.210825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GI</th>\n",
              "      <td>617.0</td>\n",
              "      <td>50.584437</td>\n",
              "      <td>36.266251</td>\n",
              "      <td>0.897628</td>\n",
              "      <td>23.011684</td>\n",
              "      <td>41.007968</td>\n",
              "      <td>67.931664</td>\n",
              "      <td>191.194764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GL</th>\n",
              "      <td>616.0</td>\n",
              "      <td>8.530961</td>\n",
              "      <td>10.327010</td>\n",
              "      <td>0.001129</td>\n",
              "      <td>0.124392</td>\n",
              "      <td>0.337827</td>\n",
              "      <td>21.978000</td>\n",
              "      <td>21.978000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <td>617.0</td>\n",
              "      <td>0.175041</td>\n",
              "      <td>0.380310</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91cae0fc-a9d3-4982-8f4a-fc575686df82')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "\n",
              "\n",
              "\n",
              "    <div id=\"df-8d4dd6f9-7c29-4e94-9778-2afb6ae5aae5\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8d4dd6f9-7c29-4e94-9778-2afb6ae5aae5')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-8d4dd6f9-7c29-4e94-9778-2afb6ae5aae5 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91cae0fc-a9d3-4982-8f4a-fc575686df82 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-91cae0fc-a9d3-4982-8f4a-fc575686df82');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-o5tIWilEUT",
        "outputId": "4f9b7636-0982-4378-d3b6-17204eaeddb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'AB', 'AF', 'AH', 'AM', 'AR', 'AX', 'AY', 'AZ', 'BC', 'BD ', 'BN',\n",
              "       'BP', 'BQ', 'BR', 'BZ', 'CB', 'CC', 'CD ', 'CF', 'CH', 'CL', 'CR', 'CS',\n",
              "       'CU', 'CW ', 'DA', 'DE', 'DF', 'DH', 'DI', 'DL', 'DN', 'DU', 'DV', 'DY',\n",
              "       'EB', 'EE', 'EG', 'EH', 'EJ', 'EL', 'EP', 'EU', 'FC', 'FD ', 'FE', 'FI',\n",
              "       'FL', 'FR', 'FS', 'GB', 'GE', 'GF', 'GH', 'GI', 'GL', 'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Mxp2-t1lqyb",
        "outputId": "57d49cbf-a9ee-4a6f-971e-4adc1a5f4921"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(617, 58)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLickuaFlatl",
        "outputId": "e3564841-e274-4e15-848c-e2f9314afdd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id        0\n",
              "AB        0\n",
              "AF        0\n",
              "AH        0\n",
              "AM        0\n",
              "AR        0\n",
              "AX        0\n",
              "AY        0\n",
              "AZ        0\n",
              "BC        0\n",
              "BD        0\n",
              "BN        0\n",
              "BP        0\n",
              "BQ       60\n",
              "BR        0\n",
              "BZ        0\n",
              "CB        2\n",
              "CC        3\n",
              "CD        0\n",
              "CF        0\n",
              "CH        0\n",
              "CL        0\n",
              "CR        0\n",
              "CS        0\n",
              "CU        0\n",
              "CW        0\n",
              "DA        0\n",
              "DE        0\n",
              "DF        0\n",
              "DH        0\n",
              "DI        0\n",
              "DL        0\n",
              "DN        0\n",
              "DU        1\n",
              "DV        0\n",
              "DY        0\n",
              "EB        0\n",
              "EE        0\n",
              "EG        0\n",
              "EH        0\n",
              "EJ        0\n",
              "EL       60\n",
              "EP        0\n",
              "EU        0\n",
              "FC        1\n",
              "FD        0\n",
              "FE        0\n",
              "FI        0\n",
              "FL        1\n",
              "FR        0\n",
              "FS        2\n",
              "GB        0\n",
              "GE        0\n",
              "GF        0\n",
              "GH        0\n",
              "GI        0\n",
              "GL        1\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))  # Set the figure size to 10 inches by 8 inches\n",
        "sns.heatmap(train.isnull(), yticklabels=False, cbar=False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "ZFv0MSL_l0I7",
        "outputId": "72ba5aed-089f-478e-dc1e-a998fdc50018"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "BQ(60)  CB(2)  CC(3)  DU(1)  EL(60)  FC(1)  FL(1)\n",
        " FS(2)  GL(1)"
      ],
      "metadata": {
        "id": "-eZPvAKVmXUn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"BQ\"].plot.box(figsize=(8,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "sJqwrvwWmjFm",
        "outputId": "d6aab86a-7615-4b67-c195-a2f59a02a585"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"CB\"].plot.box(figsize=(8,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "NLCcFr1emmWQ",
        "outputId": "8642c4b1-d1b1-4cda-fad6-58c78e2ed5a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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ACohEIvL7/QoGg8rLy5PH42FlFUDK621eY4UVAFKAzWbTtGnTkt0GACQFe1gBAABgNAIrAAAAjEZgBQAAgNEIrAAAADAagRUAAABGI7ACAADAaARWAAAAGI3ACgAAAKMRWAEAAGA0AisAAACMRmAFAACA0QisAAAAMBqBFQAAAEYjsAIAAMBoBFYAAAAYjcAKAAAAoxFYAQAAYDQCKwAAAIw2KNkNAAC+XSQSkd/vVzAYVF5enjwej2w2W7LbAoABwQorABjO5/PJ5XKptLRUc+bMUWlpqVwul3w+X7JbA4ABQWAFAIP5fD5VVFTI7XYrEAiovb1dgUBAbrdbFRUVhFYAlwRLLBaLJbuJ/hAOh+V0OhUKheRwOJLdDgBcsEgkIpfLJbfbrdraWlmtX6wxRKNReb1eNTY26siRI2wPAJCSepvXWGEFAEP5/X41NzdrxYoVCWFVkqxWq6qqqtTU1CS/35+kDgFgYBBYAcBQwWBQklRUVNTjfPd4dx0ApCsCKwAYKi8vT5LU2NjY43z3eHcdAKQrAisAGMrj8WjMmDFavXq1otFowlw0GlV1dbUKCwvl8XiS1CEADAwCKwAYymazad26daqrq5PX6004JcDr9aqurk5r167lhisAaY8HBwCAwcrLy1VTU6PKykpNmTIlPl5YWKiamhqVl5cnsTsAGBgcawUAKYAnXQFIR73Na6ywAkAKsNlsmjZtWrLbAICkYA8rAAAAjEZgBQAAgNEIrAAAADAagRUAAABGI7ACAADAaARWAAAAGI3ACgAAAKMRWAEAAGA0AisAAACMRmAFAACA0QisAAAAMBqBFQAAAEYjsAIAAMBoBFYAAAAYjcAKAAAAoxFYAQAAYDQCKwAAAIx2wYF19+7duvfee5Wfny+LxaLa2tqE+VgsppUrVyovL09DhgzR9OnTdeTIkYSakydPau7cuXI4HBo+fLgWLFig06dPJ9QcOHBAHo9HgwcPVkFBgdasWXPhvw4AAAAp74ID65kzZ3TjjTdq/fr1Pc6vWbNGzzzzjDZs2KC33npLQ4cOVVlZmc6ePRuvmTt3rg4ePKjt27errq5Ou3fv1k9/+tP4fDgc1p133qnRo0dr//79euKJJ7Rq1Sq98MILF/ETAQAAkMossVgsdtFvtli0efNmeb1eSZ+vrubn56uyslKPPvqoJCkUCiknJ0cbN27U7NmzdejQIU2YMEFvv/22br75ZknStm3bdPfdd+ujjz5Sfn6+nn/+ef3d3/2dWlpaZLfbJUk///nPVVtbq/fee69XvYXDYTmdToVCITkcjov9iQAAAOgnvc1rfbqHtampSS0tLZo+fXp8zOl0avLkyQoEApKkQCCg4cOHx8OqJE2fPl1Wq1VvvfVWvOb222+Ph1VJKisr0+HDh/XZZ5/1+N2dnZ0Kh8MJFwAAAFJfnwbWlpYWSVJOTk7CeE5OTnyupaVF2dnZCfODBg3SiBEjEmp6+owvf8dXVVdXy+l0xq+CgoLv/oMAAACQdGlzSkBVVZVCoVD8OnbsWLJbAgAAQB/o08Cam5srSWptbU0Yb21tjc/l5uaqra0tYf78+fM6efJkQk1Pn/Hl7/iqjIwMORyOhAsAAACpr08Da2FhoXJzc7Vjx474WDgc1ltvvaWSkhJJUklJiU6dOqX9+/fHa3bu3KloNKrJkyfHa3bv3q1z587Fa7Zv366xY8fqiiuu6MuWAQAAYLgLDqynT59WQ0ODGhoaJH1+o1VDQ4OOHj0qi8Wihx9+WP/wD/+g//qv/9I777yjH//4x8rPz4+fJDB+/HjNmDFDDz74oPbt26c9e/Zo8eLFmj17tvLz8yVJc+bMkd1u14IFC3Tw4EG9/PLLevrpp7V06dI+++EAAABIDRd8rFV9fb1KS0u/Nj5//nxt3LhRsVhMjz32mF544QWdOnVKU6dO1XPPPafrr78+Xnvy5EktXrxYr732mqxWq2bNmqVnnnlGmZmZ8ZoDBw5o0aJFevvttzVy5EgtWbJEy5cv73WfHGsFAABgtt7mte90DqvJCKwAAABmS8o5rAAAAEBfI7ACAADAaARWAAAAGI3ACgAAAKMRWAEAAGA0AisAAACMRmAFAACA0QisAAAAMBqBFQAAAEYjsAIAAMBoBFYAAAAYjcAKAAAAoxFYAQAAYDQCKwAAAIxGYAUAAIDRCKwAAAAwGoEVAAAARhuU7AYAAN8uEonI7/crGAwqLy9PHo9HNpst2W0BwIBghRUADOfz+eRyuVRaWqo5c+aotLRULpdLPp8v2a0BwIBghRUADObz+VRRUaGZM2dq2bJlGjJkiDo6OrR161ZVVFSopqZG5eXlyW4TAPqVJRaLxZLdRH8Ih8NyOp0KhUJyOBzJbgcALlgkEpHL5dLIkSP1ySefqLm5OT43ZswYjRw5Up9++qmOHDnC9gAAKam3eY0tAQBgKL/fr+bmZu3fv19ut1uBQEDt7e0KBAJyu93av3+/mpqa5Pf7k90qAPQrAisAGOrjjz+WJM2YMUO1tbUqLi5WZmamiouLVVtbqxkzZiTUAUC6IrACgKFOnDghSSovL5fVmvjXtdVqldfrTagDgHRFYAUAQ2VlZUn6/MaraDSaMBeNRlVbW5tQBwDpisAKAIa66qqrJElbt26V1+tN2MPq9Xq1devWhDoASFecEgAAhvryKQEnTpzQhx9+GJ/jlAAA6aC3eY1zWAHAUDabTevWrevxHNZt27Zpy5YtqqmpIawCSHsEVgAwWHl5uWpqalRZWam6urr4eGFhIQ8NAHDJYEsAAKSArq4uPffcc/rggw907bXX6qGHHpLdbk92WwDwnbAlAADShM/nU2VlZcKTrp5++mmtW7eOFVYAlwROCQAAg/l8PlVUVPT4pKuKigr5fL5ktwgA/Y4tAQBgqO5TAtxut2praxMeHhCNRuX1etXY2MgpAQBSVm/zGiusAGAov9+v5uZmrVixoscnXVVVVampqUl+vz9JHQLAwCCwAoChgsGgJKmoqKjH+e7x7joASFcEVgAwVF5eniSpsbGxx/nu8e46AEhXBFYAMJTH49GYMWO0evVqRaPRhLloNKrq6moVFhbK4/EkqUMAGBgEVgAwVPeTrurq6uT1ehNOCfB6vaqrq9PatWu54QpA2uMcVgAw2JefdDVlypT4OE+6AnAp4VgrAEgBkUhEfr9fwWBQeXl58ng8rKwCSHk86QoA0ojNZtO0adOS3QYAJAV7WAEAAGA0AisAAACMRmAFAACA0djDCgApgJuuAFzKWGEFAMP5fD65XC6VlpZqzpw5Ki0tlcvlks/nS3ZrADAgCKwAYDCfz6eKigq1trYmjLe2tqqiooLQCuCSQGAFAENFIhEtXLhQsVhMd9xxR8KTru644w7FYjEtXLhQkUgk2a0CQL8isAKAoerr69XW1qapU6fq1VdfVXFxsTIzM1VcXKxXX31Vt912m9ra2lRfX5/sVgGgXxFYAcBQ3UH08ccfVywWU319vTZt2qT6+nrFYjGtWrUqoQ4A0hWnBACA4fx+vxYsWKDm5ub42JgxYzR//vzkNQUAA6jPV1hXrVoli8WScI0bNy4+f/bsWS1atEhXXnmlMjMzNWvWrK/dTHD06FHNnDlTl19+ubKzs7Vs2TKdP3++r1sFAKN1P4p11apVKioqStjDWlRUpMcffzyhDgDSVb+ssN5www36wx/+8MWXDPriax555BFt2bJFr7zyipxOpxYvXqzy8nLt2bNH0uc3GcycOVO5ubnau3evgsGgfvzjH+uyyy7T6tWr+6NdADCSx+OR1WpVNBqVJMVisfjVzWq1yuPxJKtFABgQ/RJYBw0apNzc3K+Nh0Ih/eu//qteeuklff/735ck/eY3v9H48eP15ptvqri4WG+88Ybeffdd/eEPf1BOTo4mTpyoX/3qV1q+fLlWrVolu93eHy0DgHH27t0bD6s7duxQXV1dfO7yyy+XJEWjUe3du5dVVgBprV9uujpy5Ijy8/N1zTXXaO7cuTp69Kgkaf/+/Tp37pymT58erx03bpyuvvpqBQIBSVIgEJDb7VZOTk68pqysTOFwWAcPHvzG7+zs7FQ4HE64ACCVBYNBSdLvfvc7ZWVlJcxlZWXpd7/7XUIdAKSrPg+skydP1saNG7Vt2zY9//zzampqksfjUXt7u1paWmS32zV8+PCE9+Tk5KilpUWS1NLSkhBWu+e7575JdXW1nE5n/CooKOjbHwYAAywvL0+SdOzYMVmtiX9dWyyW+GJAdx0ApKs+D6x33XWX/uZv/kbf+973VFZWptdff12nTp3Sf/7nf/b1VyWoqqpSKBSKX8eOHevX7wOA/ubxeJSVlaWqqqoeb7pasWKFsrOz2cMKIO31+zmsw4cP1/XXX6/3339fubm56urq0qlTpxJqWltb43tec3Nze3wEYffcN8nIyJDD4Ui4ACDVWSyW+J97uukKAC4F/R5YT58+rQ8++EB5eXmaNGmSLrvsMu3YsSM+f/jwYR09elQlJSWSpJKSEr3zzjtqa2uL12zfvl0Oh0MTJkzo73YBwBh+v19tbW2qrq5WY2OjpkyZIofDoSlTpujgwYNavXq12tra5Pf7k90qAPSrPj8l4NFHH9W9996r0aNH6/jx43rsscdks9l03333yel0asGCBVq6dKlGjBghh8OhJUuWqKSkRMXFxZKkO++8UxMmTNC8efO0Zs0atbS06O///u+1aNEiZWRk9HW7AGCs7pupFi9erGXLlsnv9ysYDCovL08ej0d//vOftWLFCm66ApD2+jywfvTRR7rvvvv06aefKisrS1OnTtWbb74Zv8P1n//5n2W1WjVr1ix1dnaqrKxMzz33XPz9NptNdXV1WrhwoUpKSjR06FDNnz9fv/zlL/u6VQAwWvfNVI2NjSouLv7a0VWNjY0JdQCQriyxNN0MFQ6H5XQ6FQqF2M8KICVFIhG5XC653W79/ve/1549e+IrrLfddptmzZqlxsZGHTlyRDabLdntAsAF621e65cHBwAAvjubzaZ169apoqJCTqdTHR0d8bkhQ4bo7NmzqqmpIawCSHv9ftMVAOC7icViOnv2bMLY2bNnOS0AwCWDLQEAYKhIJKL8/Hy1tbVp5syZuvvuuzVkyBB1dHTo9ddf15YtW5Sdna3jx4+zygogJfU2r7HCCgCGqq+vV1tbm6ZOnarNmzdrwoQJGjx4sCZMmKDNmzfrtttuU1tbm+rr65PdKgD0K/awAoChuoPo9OnTdd111+nDDz+Mz40ePVr333+/9uzZo/r6et1xxx1J6hIA+h+BFQAMt2rVKg0ePDhhrLW1VY8//niSOgKAgcWWAAAwlMfjif/5y49o/errL9cBQDoisAJACvjykVY9vQaAdEZgBQBD7dq1q0/rACBVEVgBwFBNTU2SpMGDB2v06NEJc2PGjFFGRkZCHQCkK266AgBDtbW1SZLy8vJ0+PDhrz2adezYsWpqaorXAUC6YoUVAAyVmZkp6fMV1B/+8Ic6ePCgOjo6dPDgQf3whz+Mr6x21wFAumKFFQAMdfvtt+vVV1+VJG3ZskVbtmz5xjoASGc8mhUADNXV1aXBgwcrFovJarUqGo3G57pfWywWnT17Vna7PYmdAsDF4dGsAJDibDbbt/4Ht8PhkM1mG6COACA5CKwAYCi/369QKKS5c+f2+OCAOXPmKBQKye/3J6lDABgYBFYAMFQwGJQkbdiwQW1tbSoqKtKIESNUVFSktrY2bdiwIaEOANIVN10BgKHy8vIkSbfeeqsOHToUHz958qSuvPJKjR8/PqEOANIVK6wAYCiPxyO73a5Dhw7JYrFo3rx5+p//+R/NmzdPFotFhw4dkt1ul8fjSXarANCvCKwAYKiOjg51dXVJkmbMmKHJkyfrT3/6kyZPnqwZM2ZI+vwkgY6OjmS2CQD9ji0BAGCoefPmSZImTJigN954Q1u3bo3P2Ww2jR8/XocOHdK8efO0efPmZLUJAP2OwAoAhvrggw8kSe++++7XTgmIRqPxfa3ddQCQrtgSAACGKiwsjP+5p2OteqoDgHREYAUAQz3wwAPxP3/5KVdfff3lOgBIRwRWADDUK6+80qd1AJCqCKwAYKj//d//7dM6AEhVBFYAMFRvj6viWCsA6Y7ACgCGIrACwOcIrABgqI8//rhP6wAgVRFYAcBQ58+f79M6AEhVBFYAMNSgQb17tktv6wAgVRFYAcBQl112WZ/WAUCqIrACgKFsNluf1gFAqiKwAoChrrrqqj6tA4BUxcYnAOhDHV0RfXDidJ98Vkams9d1jR+H+uQ7r83K1BA7K7YAzEJgBYA+9MGJ07rn2f/bJ5/V1trVq7p3Wrv67DvrlkxV0VW9C8oAMFAIrADQh67NylTdkql98lm/tc/U2l+9+a11i+6bqfn/p2++89qszD75HADoS5ZYLBZLdhP9IRwOy+l0KhQKyeFwJLsdALhgXV1dGjJkiKLRqDIyMtTZ2Rmf635ttVrV0dEhu92exE4B4OL0Nq9x0xUAGMput6uyslKSdO7cuYS57ocFVFZWElYBpD22BACAwdasWSNJevLJJxPGLRaLli1bFp8HgHTGlgAASAFdXV36xep1ev61vVp47xT9agUrqwBSX2/zGiusAJAC7Ha75j34kF4++z3Ne3AqYRXAJYU9rAAAADAagRUAAABGY0sAgEtO0ydndKbzfLLbuGDvt51O+GcqGZoxSIUjhya7DQApisAK4JLS9MkZla6tT3Yb38nDLzcku4WL8sdHpxFaAVwUAiuAS0r3yupTP5ooV3ZqPdXp7LmIPvqsQ6OuGKLBl9mS3U6vvd92Wg+/3JCSq9oAzEBgBXBJcmVnqugqZ7LbuGA3j0l2BwAw8LjpCgAAAEZjhRXAJaUzclbWwR+rKXxY1sGptSUgVTWFT8s6+GN1Rs5KSr1VbQDJR2AFcEk5fuZDDS18Viv2JbuTS8vQQun4mYmapJxktwIgBRkdWNevX68nnnhCLS0tuvHGG/Xss8/q1ltvTXZbAFLYFfZROtO0REtKXSl301Xn+ajawmeV7RisjEGps6Pr2Mk/a+32/6f80tHJbgVAijI2sL788staunSpNmzYoMmTJ+upp55SWVmZDh8+rOzs7GS3ByBFffTpeUXPXqWnt3ZI6kh2OxfpTLIbuAhXacTlqfUfCADMYYnFYrFkN9GTyZMn65ZbbtG//Mu/SJKi0agKCgq0ZMkS/fznP//W94fDYTmdToVCITkcjv5uF0CKOHmmS28cbNG12Zka0g9HQ3Uf4ZSq+uu4Lx4cAKAnvc1rRq6wdnV1af/+/aqqqoqPWa1WTZ8+XYFAoMf3dHZ2qrOzM/46HA73e58AUs+IoXbNvvXqfvv8a7MyVbdkar989kCcw3ptVqaG2FPnjFcAlwYjA+snn3yiSCSinJzEzfk5OTl67733enxPdXW1Hn/88YFoDwC+0RC7rV/Pd+UcVgCXotTZtf8tqqqqFAqF4texY8eS3RIAAAD6gJErrCNHjpTNZlNra2vCeGtrq3Jzc3t8T0ZGhjIyMgaiPQAAAAwgI1dY7Xa7Jk2apB07dsTHotGoduzYoZKSkiR2BgAAgIFm5AqrJC1dulTz58/XzTffrFtvvVVPPfWUzpw5o7/9279NdmsAAAAYQMYG1h/96Ec6ceKEVq5cqZaWFk2cOFHbtm372o1YAAAASG/GnsP6XXEOKwAAgNl6m9eM3MMKAAAAdCOwAgAAwGgEVgAAABiNwAoAAACjGXtKwHfVfS9ZOBxOcicAAADoSXdO+7YzANI2sLa3t0uSCgoKktwJAAAA/pL29nY5nc5vnE/bY62i0aiOHz+uYcOGyWKxJLsdAPjOwuGwCgoKdOzYMY7rA5AWYrGY2tvblZ+fL6v1m3eqpm1gBYB0w/nSAC5V3HQFAAAAoxFYAQAAYDQCKwCkiIyMDD322GPKyMhIdisAMKDYwwoAAACjscIKAAAAoxFYAQAAYDQCKwAAAIxGYAUAAIDRCKwAAAAwGoEVAAzT0tKiJUuW6JprrlFGRoYKCgp07733aseOHZKkMWPGyGKxyGKxyGazKT8/XwsWLNBnn32W5M4BoH8QWAHAIM3NzZo0aZJ27typJ554Qu+88462bdum0tJSLVq0KF73y1/+UsFgUEePHtWLL76o3bt362c/+1kSOweA/jMo2Q0AAL7w0EMPyWKxaN++fRo6dGh8/IYbbtADDzwQfz1s2DDl5uZKkq666irNnz9fmzZtGvB+AWAgsMIKAIY4efKktm3bpkWLFiWE1W7Dhw/v8X0ff/yxXnvtNU2ePLmfOwSA5CCwAoAh3n//fcViMY0bN+5ba5cvX67MzEwNGTJEo0aNksVi0ZNPPjkAXQLAwCOwAoAhLuRJ2cuWLVNDQ4MOHDgQvxlr5syZikQi/dUeACQNgRUADHHdddfJYrHovffe+9bakSNHyuVy6brrrtP3v/99PfXUU9q7d6/++Mc/DkCnADCwCKwAYIgRI0aorKxM69ev15kzZ742f+rUqW98r81mkyR1dHT0V3sAkDQEVgAwyPr16xWJRHTrrbfq97//vY4cOaJDhw7pmWeeUUlJSbyuvb1dLS0tCgaD2rdvn5YtW6asrCxNmTIlid0DQP+wxC5k0xQAoN8Fg0H9+te/Vl1dnYLBoLKysjRp0iQ98sgjmjZtmsaMGaMPP/wwXp+VlaVbbrlFv/71rzVx4sTkNQ4A/YTACgAAAKOxJQAAAABGI7ACAADAaARWAAAAGI3ACgAAAKMRWAEAAGA0AisAAACMRmAFAACA0QisAAAAMBqBFQAAAEYjsAIAAMBoBFYAAAAY7f8DxOtoGkMS3dIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"CC\"].plot.box(figsize=(8,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "-p4tcnbenc0p",
        "outputId": "64936bba-deb1-4273-fda2-35074b3b18ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"DU\"].plot.box(figsize=(8,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "krwOncsjnfY1",
        "outputId": "ecc23502-cb23-4de7-901a-9bffe5bdd5b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"EL\"].plot.box(figsize=(8,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "kT2mZlccniYJ",
        "outputId": "64c734e0-3f15-4e2a-d7b5-deac9e2e44dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqQAAAFfCAYAAACY+97uAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAWUUlEQVR4nO3df2yW9f3v8Veh2DaFlh8bLZ1FGyXDTTd/HkTNoq4ZOjUQyTYSTHQzsmzghubMyYmwyFQm8ascHMI0BnGRuZkc2TQZi8EzzCKi4lz2w6FuRJmkZd8oLbBRGfT8sbMmdcxNvcunhccjuTL7ua77c7/7j3vmuu/LVvX29vYGAAAKGVZ6AAAAjm6CFACAogQpAABFCVIAAIoSpAAAFCVIAQAoSpACAFBUdekB3o+DBw9mx44dGTVqVKqqqkqPAwDAO/T29mb37t1paWnJsGHvfg90SAbpjh070traWnoMAAD+je3bt+fYY49912uGZJCOGjUqyd9/wYaGhsLTAADwTt3d3Wltbe3rtnczJIP0Hx/TNzQ0CFIAgEHsP/l6pYeaAAAoSpACAFCUIAUAoChBCgBAUYIUAICiBCkAAEUJUgAAihKkAAAUJUgBAChKkAIAUNSQ/NOhAO9m23/vzd6ev1V83337D+RPb/214vseLseOqUvtiOEDsnd9TXXaPlQ/IHsDRz5BChxRtv333lxwx89Lj3FU+r//83xRCrwvghQ4ovzjzuiyL5yaE8ePrOje7pAe2qs792T+D18ckLvSwNFBkAJHpBPHj8zJH2ms+L5nHl/xLQGOeh5qAgCgKEEKAEBRghQAgKIEKQAARQlSAACKEqQAABQlSAEAKEqQAgBQlCAFAKAoQQoAQFGCFACAogQpAABFCVIAAIoSpAAAFCVIAQAoSpACAFCUIAUAoChBCgBAUe85SJ966qlcdtllaWlpSVVVVdatW9fvfG9vbxYtWpQJEyakrq4u7e3teeWVV/pd8+abb2b27NlpaGjI6NGjc/XVV2fPnj0f6BcBAGBoes9Bunfv3nzyk5/MihUrDnl+6dKlWb58eVatWpXNmzenvr4+06ZNy759+/qumT17dn7729/miSeeyOOPP56nnnoqc+bMef+/BQAAQ1b1e33BxRdfnIsvvviQ53p7e7Ns2bLcdNNNmT59epLkwQcfTFNTU9atW5dZs2blpZdeyvr16/Pcc8/lzDPPTJLcfffd+exnP5s77rgjLS0tH+DXAQBgqKnod0i3bduWjo6OtLe39601NjZmypQp2bRpU5Jk06ZNGT16dF+MJkl7e3uGDRuWzZs3H3Lfnp6edHd39zsAADgyVDRIOzo6kiRNTU391puamvrOdXR0ZPz48f3OV1dXZ+zYsX3XvNOSJUvS2NjYd7S2tlZybAAAChoST9kvWLAgXV1dfcf27dtLjwQAQIVUNEibm5uTJJ2dnf3WOzs7+841Nzdn586d/c7/7W9/y5tvvtl3zTvV1NSkoaGh3wEAwJGhokHa1taW5ubmbNiwoW+tu7s7mzdvztSpU5MkU6dOza5du7Jly5a+a5588skcPHgwU6ZMqeQ4AAAMAe/5Kfs9e/bk1Vdf7ft527ZtefHFFzN27NhMnDgx8+fPzy233JJJkyalra0tCxcuTEtLS2bMmJEkOemkk3LRRRflmmuuyapVq7J///7Mmzcvs2bN8oQ9AMBR6D0H6fPPP58LLrig7+frr78+SXLllVfmgQceyA033JC9e/dmzpw52bVrV84777ysX78+tbW1fa956KGHMm/evHz605/OsGHDMnPmzCxfvrwCvw4AAEPNew7S888/P729vf/yfFVVVRYvXpzFixf/y2vGjh2btWvXvte3BgDgCDQknrIHAODIJUgBAChKkAIAUJQgBQCgKEEKAEBRghQAgKIEKQAARQlSAACKEqQAABQlSAEAKEqQAgBQlCAFAKAoQQoAQFGCFACAogQpAABFCVIAAIoSpAAAFCVIAQAoSpACAFCUIAUAoChBCgBAUYIUAICiBCkAAEUJUgAAihKkAAAUJUgBAChKkAIAUJQgBQCgKEEKAEBRghQAgKIEKQAARQlSAACKEqQAABQlSAEAKEqQAgBQlCAFAKAoQQoAQFGCFACAogQpAABFCVIAAIoSpAAAFCVIAQAoSpACAFBUxYP0wIEDWbhwYdra2lJXV5cTTjgh3/72t9Pb29t3TW9vbxYtWpQJEyakrq4u7e3teeWVVyo9CgAAQ0DFg/T222/PypUr893vfjcvvfRSbr/99ixdujR333133zVLly7N8uXLs2rVqmzevDn19fWZNm1a9u3bV+lxAAAY5KorveHTTz+d6dOn55JLLkmSHH/88fnBD36QZ599Nsnf744uW7YsN910U6ZPn54kefDBB9PU1JR169Zl1qxZlR4JAIBBrOJ3SM8555xs2LAhL7/8cpLkV7/6VX7xi1/k4osvTpJs27YtHR0daW9v73tNY2NjpkyZkk2bNh1yz56ennR3d/c7AAA4MlT8DumNN96Y7u7uTJ48OcOHD8+BAwdy6623Zvbs2UmSjo6OJElTU1O/1zU1NfWde6clS5bk5ptvrvSoAAAMAhW/Q/qjH/0oDz30UNauXZsXXngha9asyR133JE1a9a87z0XLFiQrq6uvmP79u0VnBgAgJIqfof0G9/4Rm688ca+74Kecsopee2117JkyZJceeWVaW5uTpJ0dnZmwoQJfa/r7OzMqaeeesg9a2pqUlNTU+lRAQAYBCp+h/Qvf/lLhg3rv+3w4cNz8ODBJElbW1uam5uzYcOGvvPd3d3ZvHlzpk6dWulxAAAY5Cp+h/Syyy7LrbfemokTJ+bjH/94fvnLX+bOO+/Ml770pSRJVVVV5s+fn1tuuSWTJk1KW1tbFi5cmJaWlsyYMaPS4wAAMMhVPEjvvvvuLFy4MF/96lezc+fOtLS05Mtf/nIWLVrUd80NN9yQvXv3Zs6cOdm1a1fOO++8rF+/PrW1tZUeBwCAQa7iQTpq1KgsW7Ysy5Yt+5fXVFVVZfHixVm8eHGl3x4AgCHG37IHAKAoQQoAQFGCFACAogQpAABFCVIAAIoSpAAAFCVIAQAoSpACAFCUIAUAoChBCgBAUYIUAICiBCkAAEUJUgAAihKkAAAUVV16AIBK6jmwL8Nq38i27q0ZVjuy9DhHhW3dezKs9o30HNiXpLH0OMAQJEiBI8qOva+lvu3u/K9nS09ydKlvS3bsPTVnpKn0KMAQJEiBI0pL/XHZu+3a/O8vnJoTxrtDejj8YeeefP2HL6blguNKjwIMUYIUOKLUDK/NwX0fSVvDR/OxcT4+PhwO7uvKwX1/Ts3w2tKjAEOUh5oAAChKkAIAUJQgBQCgKEEKAEBRghQAgKIEKQAARQlSAACKEqQAABQlSAEAKEqQAgBQlCAFAKAoQQoAQFGCFACAogQpAABFCVIAAIoSpAAAFCVIAQAoSpACAFCUIAUAoChBCgBAUYIUAICiBCkAAEUJUgAAihKkAAAUJUgBAChqQIL0jTfeyBVXXJFx48alrq4up5xySp5//vm+8729vVm0aFEmTJiQurq6tLe355VXXhmIUQAAGOQqHqRvvfVWzj333IwYMSI//elP87vf/S7/9V//lTFjxvRds3Tp0ixfvjyrVq3K5s2bU19fn2nTpmXfvn2VHgcAgEGuutIb3n777Wltbc3q1av71tra2vr+ube3N8uWLctNN92U6dOnJ0kefPDBNDU1Zd26dZk1a1alRwIAYBCr+B3Sn/zkJznzzDPzuc99LuPHj89pp52W++67r+/8tm3b0tHRkfb29r61xsbGTJkyJZs2bTrknj09Penu7u53AABwZKh4kP7xj3/MypUrM2nSpPzsZz/LV77ylXzta1/LmjVrkiQdHR1Jkqampn6va2pq6jv3TkuWLEljY2Pf0draWumxAQAopOJBevDgwZx++um57bbbctppp2XOnDm55pprsmrVqve954IFC9LV1dV3bN++vYITAwBQUsWDdMKECfnYxz7Wb+2kk07K66+/niRpbm5OknR2dva7prOzs+/cO9XU1KShoaHfAQDAkaHiQXruuedm69at/dZefvnlHHfccUn+/oBTc3NzNmzY0He+u7s7mzdvztSpUys9DgAAg1zFn7K/7rrrcs455+S2227L5z//+Tz77LO59957c++99yZJqqqqMn/+/Nxyyy2ZNGlS2trasnDhwrS0tGTGjBmVHgcAgEGu4kF61lln5dFHH82CBQuyePHitLW1ZdmyZZk9e3bfNTfccEP27t2bOXPmZNeuXTnvvPOyfv361NbWVnocAAAGuYoHaZJceumlufTSS//l+aqqqixevDiLFy8eiLcHAGAI8bfsAQAoSpACAFCUIAUAoChBCgBAUYIUAICiBCkAAEUJUgAAihKkAAAUJUgBAChKkAIAUJQgBQCgKEEKAEBRghQAgKIEKQAARQlSAACKEqQAABQlSAEAKEqQAgBQlCAFAKAoQQoAQFGCFACAogQpAABFCVIAAIoSpAAAFCVIAQAoSpACAFCUIAUAoChBCgBAUYIUAICiBCkAAEUJUgAAihKkAAAUJUgBAChKkAIAUJQgBQCgKEEKAEBRghQAgKIEKQAARQlSAACKEqQAABQlSAEAKEqQAgBQlCAFAKCoAQ/S73znO6mqqsr8+fP71vbt25e5c+dm3LhxGTlyZGbOnJnOzs6BHgUAgEFoQIP0ueeey/e+97184hOf6Ld+3XXX5bHHHssjjzySjRs3ZseOHbn88ssHchQAAAapAQvSPXv2ZPbs2bnvvvsyZsyYvvWurq7cf//9ufPOO3PhhRfmjDPOyOrVq/P000/nmWeeGahxAAAYpAYsSOfOnZtLLrkk7e3t/da3bNmS/fv391ufPHlyJk6cmE2bNh1yr56ennR3d/c7AAA4MlQPxKYPP/xwXnjhhTz33HP/dK6joyPHHHNMRo8e3W+9qakpHR0dh9xvyZIlufnmmwdiVAAACqv4HdLt27fn61//eh566KHU1tZWZM8FCxakq6ur79i+fXtF9gUAoLyKB+mWLVuyc+fOnH766amurk51dXU2btyY5cuXp7q6Ok1NTXn77beza9eufq/r7OxMc3PzIfesqalJQ0NDvwMAgCNDxT+y//SnP51f//rX/da++MUvZvLkyfnmN7+Z1tbWjBgxIhs2bMjMmTOTJFu3bs3rr7+eqVOnVnocAAAGuYoH6ahRo3LyySf3W6uvr8+4ceP61q+++upcf/31GTt2bBoaGnLttddm6tSpOfvssys9DgAAg9yAPNT079x1110ZNmxYZs6cmZ6enkybNi333HNPiVEAACjssATpz3/+834/19bWZsWKFVmxYsXheHsAAAYxf8seAICiBCkAAEUJUgAAihKkAAAUJUgBAChKkAIAUJQgBQCgKEEKAEBRghQAgKIEKQAARQlSAACKEqQAABQlSAEAKEqQAgBQlCAFAKAoQQoAQFHVpQcAqKS/7j+QJPnNG12FJ3lv9u0/kD+99dccO6YutSOGlx7nPXl1557SIwBDnCAFjih/+P9xdOP/+XXhSY4+9TX+LwV4f/zbAziifObjzUmSE8aPTN0QutP46s49mf/DF7PsC6fmxPEjS4/zntXXVKftQ/WlxwCGKEEKHFHG1h+TWf9jYukx3rcTx4/MyR9pLD0GwGHloSYAAIoSpAAAFCVIAQAoSpACAFCUIAUAoChBCgBAUYIUAICiBCkAAEUJUgAAihKkAAAUJUgBAChKkAIAUJQgBQCgKEEKAEBRghQAgKIEKQAARQlSAACKEqQAABQlSAEAKEqQAgBQlCAFAKAoQQoAQFGCFACAoioepEuWLMlZZ52VUaNGZfz48ZkxY0a2bt3a75p9+/Zl7ty5GTduXEaOHJmZM2ems7Oz0qMAADAEVDxIN27cmLlz5+aZZ57JE088kf379+czn/lM9u7d23fNddddl8ceeyyPPPJINm7cmB07duTyyy+v9CgAAAwB1ZXecP369f1+fuCBBzJ+/Phs2bIln/rUp9LV1ZX7778/a9euzYUXXpgkWb16dU466aQ888wzOfvssys9EgAAg9iAf4e0q6srSTJ27NgkyZYtW7J///60t7f3XTN58uRMnDgxmzZtOuQePT096e7u7ncAAHBkGNAgPXjwYObPn59zzz03J598cpKko6MjxxxzTEaPHt3v2qampnR0dBxynyVLlqSxsbHvaG1tHcixAQA4jAY0SOfOnZvf/OY3efjhhz/QPgsWLEhXV1ffsX379gpNCABAaRX/Duk/zJs3L48//nieeuqpHHvssX3rzc3Nefvtt7Nr165+d0k7OzvT3Nx8yL1qampSU1MzUKMCAFBQxe+Q9vb2Zt68eXn00Ufz5JNPpq2trd/5M844IyNGjMiGDRv61rZu3ZrXX389U6dOrfQ4AAAMchW/Qzp37tysXbs2P/7xjzNq1Ki+74U2Njamrq4ujY2Nufrqq3P99ddn7NixaWhoyLXXXpupU6d6wh4A4ChU8SBduXJlkuT888/vt7569epcddVVSZK77rorw4YNy8yZM9PT05Np06blnnvuqfQoAAAMARUP0t7e3n97TW1tbVasWJEVK1ZU+u0BABhi/C17AACKEqQAABQlSAEAKEqQAgBQlCAFAKAoQQoAQFGCFACAogQpAABFCVIAAIoSpAAAFCVIAQAoSpACAFCUIAUAoChBCgBAUYIUAICiBCkAAEUJUgAAihKkAAAUJUgBAChKkAIAUJQgBQCgKEEKAEBRghQAgKIEKQAARQlSAACKEqQAABQlSAEAKEqQAgBQlCAFAKAoQQoAQFGCFACAoqpLDwAwVPz17QP5w5/3DMjer+7c0+9/B8IJHx6ZumOGD9j+AO+XIAX4D/3hz3ty6d2/GND3mP/DFwds78evPS8nf6RxwPYHeL8EKcB/6IQPj8zj1543IHvv238gf3rrrzl2TF1qRwzMXcwTPjxyQPYF+KAEKcB/qO6Y4QN6h/HM4wdsa4BBzUNNAAAUJUgBAChKkAIAUJQgBQCgKEEKAEBRghQAgKIEKQAARQlSAACKEqQAABQlSAEAKGpI/unQ3t7eJEl3d3fhSQAAOJR/dNo/uu3dDMkg3b17d5KktbW18CQAALyb3bt3p7Gx8V2vqer9T7J1kDl48GB27NiRUaNGpaqqqvQ4AB9Yd3d3Wltbs3379jQ0NJQeB+AD6+3tze7du9PS0pJhw979W6JDMkgBjjTd3d1pbGxMV1eXIAWOOh5qAgCgKEEKAEBRghRgEKipqcm3vvWt1NTUlB4F4LDzHVIAAIpyhxQAgKIEKQAARQlSAACKEqQAABQlSAEAKEqQAhxGV111Vaqqqv7puOiii5Ikxx9/fJYtW1Z2SIDDrLr0AABHm4suuiirV6/ut+a/PwoczQQpwGFWU1OT5ubm0mMADBo+sgcAoChBCnCYPf744xk5cmS/47bbbis9FkAxPrIHOMwuuOCCrFy5st/a2LFjC00DUJ4gBTjM6uvrc+KJJ5YeA2DQ8JE9AABFuUMKcJj19PSko6Oj31p1dXU+9KEPJUneeOONvPjii/3OH3fccRkzZszhGhHgsKrq7e3tLT0EwNHiqquuypo1a/5p/aMf/Wh+//vf5/jjj89rr732T+e///3v54orrjgcIwIcdoIUAICifIcUAICiBCkAAEUJUgAAihKkAAAUJUgBAChKkAIAUJQgBQCgKEEKAEBRghQAgKIEKQAARQlSAACK+n/T9uhmf5RR9QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"FC\"].plot.box(figsize=(8,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "awB-_D0iniqm",
        "outputId": "605aa952-93f0-4a21-f955-91cf897ae958"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"FL\"].plot.box(figsize=(8,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "Qt81eBFPnizG",
        "outputId": "407d9c71-e629-4da5-97a8-03b64cdc62f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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yMzNVXl6uQCCgzZs3x40DgGQ2Jg7GB4BkU1BQIEmaPn26Dh06JL/fH7fL/uWXX44bBwDJjDWkAGBAOByW3W6Xw+FQc3OzLJYPHlhFIhE5nU4FAgG1t7fLarUarBQALg1rSAFglLNarWpsbJTP55PT6YxbQ+p0OuXz+dTQ0EAYBTAu8MgeAAypra1VU1OT3G63ysvLY+02m01NTU28yx7AuMEjewAwLBwOX7CGlJlRAGPdUPIaM6QAYJjVauXwewDjGmtIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYlPJCGw2Hde++9stlsSk9P1/Tp0/XjH/9Y0Wg0NiYajWrNmjUqKChQenq6qqur1d7enuhSAAAAMAYkPJDef//92rBhgx599FG9/vrruv/++7Vu3To98sgjsTHr1q3T+vXrtXHjRu3du1cZGRmaO3euTp8+nehyAAAAMMqlRM+fukyAmpoa5eXl6cknn4y1LViwQOnp6dq8ebOi0agKCwvldru1evVqSVIwGFReXp42bdqkhQsXfuzvCIVCysrKUjAYVGZmZiLLBwAAQAIMJa8lfIa0vLxcO3bs0JtvvilJ+t3vfqff/OY3uvHGGyVJHR0d6u7uVnV1dew7WVlZmjNnjlpbWwe9Z39/v0KhUNwFAACA5JCa6Bvec889CoVCmjFjhqxWq8LhsH7yk59o8eLFkqTu7m5JUl5eXtz38vLyYn0fVl9frx/96EeJLhUAAACjQMJnSP/1X/9VW7Zs0datW7V//349/fTTamho0NNPP33J9/R4PAoGg7HryJEjCawYAAAAJiV8hvTOO+/UPffcE1sL6nA4dPjwYdXX1+u2225Tfn6+JKmnp0cFBQWx7/X09Oiaa64Z9J5paWlKS0tLdKkAAAAYBRI+Q/qnP/1JFkv8ba1WqyKRiCTJZrMpPz9fO3bsiPWHQiHt3btXZWVliS4HAEa9cDislpYWbdu2TS0tLQqHw6ZLAoARlfAZ0vnz5+snP/mJpk2bps997nP6r//6Lz3wwAP6+7//e0lSSkqKVq5cqfvuu09XXnmlbDab7r33XhUWFsrpdCa6HAAY1bxer9xutzo7O2NtxcXFamxsVG1trbnCAGAEJXyG9JFHHpHL5dJ3v/tdzZw5U6tXr9a3v/1t/fjHP46Nueuuu3T77bdr2bJluvbaa9XX16ft27dr4sSJiS4HAEYtr9crl8slh8Oh1tZWnThxQq2trXI4HHK5XPJ6vaZLBIARkfBzSEcC55ACGOvC4bDsdrscDoeam5vjljpFIhE5nU4FAgG1t7fLarUarBQALo3Rc0gBAB/P7/ers7NTdXV1F6y7t1gs8ng86ujokN/vN1QhAIwcAikAGNDV1SVJKikpGbR/oH1gHAAkMwIpABgwcOxdIBAYtH+g/fzj8QAgWRFIAcCAiooKFRcXa+3atbFj8QZEIhHV19fLZrOpoqLCUIUAMHIIpABggNVqVWNjo3w+n5xOZ9wue6fTKZ/Pp4aGBjY0ARgXEn4OKQDg4tTW1qqpqUlut1vl5eWxdpvNpqamJs4hBTBucOwTABgWDofl9/vV1dWlgoICVVRUMDMKYMwbSl5jhhQADLNaraqsrDRdBgAYwxpSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYxS57ADCMY58AjHfMkAKAQV6vV3a7XVVVVVq0aJGqqqpkt9vl9XpNlwYAI4ZACgCGeL1euVwuORyOuFeHOhwOuVwuQimAcYM3NQGAAeFwWHa7XQ6HQ83NzbJYPpgfiEQicjqdCgQCam9v5/E9gDFpKHmNGVIAMMDv96uzs1N1dXVxYVSSLBaLPB6POjo65Pf7DVUIACOHQAoABnR1dUmSSkpKBu0faB8YBwDJjEAKAAYUFBRIkgKBwKD9A+0D4wAgmRFIAcCAiooKFRcXa+3atYpEInF9kUhE9fX1stlsqqioMFQhAIwcAikAGGC1WtXY2Cifzyen0xm3y97pdMrn86mhoYENTQDGBQ7GBwBDamtr1dTUJLfbrfLy8li7zWZTU1OTamtrDVYHACOHY58AwDDe1AQgGQ0lrzFDCgCGWa1WVVZWmi4DAIxhDSkAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwalkD67rvv6u/+7u+Uk5Oj9PR0ORwOvfLKK7H+aDSqNWvWqKCgQOnp6aqurlZ7e/twlAIAAIBRLuGB9I9//KOuu+46XXbZZfrVr36l3//+92psbNSnPvWp2Jh169Zp/fr12rhxo/bu3auMjAzNnTtXp0+fTnQ5ADDqhcNhtbS0aNu2bWppaVE4HDZdEgCMqIQfjH/PPffoP//zP+X3+wftj0ajKiwslNvt1urVqyVJwWBQeXl52rRpkxYuXPixv4OD8QEkC6/XK7fbrc7OzlhbcXGxGhsbeVMTgDFtKHkt4TOkv/zlLzV79mx97WtfU25urmbNmqWf/exnsf6Ojg51d3eruro61paVlaU5c+aotbV10Hv29/crFArFXQAw1nm9XrlcLjkcjrh32TscDrlcLnm9XtMlAsCISHgg/Z//+R9t2LBBV155pV544QV95zvf0fe+9z09/fTTkqTu7m5JUl5eXtz38vLyYn0fVl9fr6ysrNhVVFSU6LIBYESFw2G53W7V1NSoublZpaWlmjRpkkpLS9Xc3KyamhqtXr2ax/cAxoWEB9JIJKLPf/7zWrt2rWbNmqVly5Zp6dKl2rhx4yXf0+PxKBgMxq4jR44ksGIAGHl+v1+dnZ2qq6uTxRL/T7HFYpHH41FHR8dfXP4EAMkk4YG0oKBAV111VVzbzJkz9fbbb0uS8vPzJUk9PT1xY3p6emJ9H5aWlqbMzMy4CwDGsq6uLklSSUnJoP0D7QPjACCZJTyQXnfddTp48GBc25tvvqkrrrhCkmSz2ZSfn68dO3bE+kOhkPbu3auysrJElwMAo1JBQYEkKRAIDNo/0D4wDgCSWcID6R133KE9e/Zo7dq1OnTokLZu3aonnnhCy5cvlySlpKRo5cqVuu+++/TLX/5Sr776qr7xjW+osLBQTqcz0eUAwKhUUVGh4uJirV27VpFIJK4vEomovr5eNptNFRUVhioEgJGTmugbXnvttXr22Wfl8Xj0j//4j7LZbHrooYe0ePHi2Ji77rpLJ0+e1LJly3T8+HFdf/312r59uyZOnJjocgBgVLJarWpsbJTL5dJXv/pVzZs3T+np6Tp16pS2b9+u559/Xk1NTbJaraZLBYBhl/BzSEcC55ACSBZ33XWXHnzwQZ07dy7WlpqaqjvuuEPr1q0zWBkAfDJDyWsJnyEFAFwcr9erhoYG3XzzzbrxxhtjM6S/+tWv1NDQoNLSUg7HBzAuMEMKAAaEw2HZ7XY5HA41NzfHHf0UiUTkdDoVCATU3t7OY3sAY5LRNzUBAD4e55ACwAcIpABgAOeQAsAHCKQAYADnkALABwikAGAA55ACwAcIpABgwMA5pD6fT06nU62trTpx4oRaW1vldDrl8/nU0NDAhiYA4wLHPgGAIbW1tWpqapLb7VZ5eXms3WazqampiSOfAIwbHPsEAIaFw2H5/X51dXWpoKBAFRUVzIwCGPM4GB8AxhCr1arKykrTZQCAMawhBQAAgFEEUgAAABhFIAUAAIBRrCEFAMPY1ARgvGOGFAAM8nq9stvtqqqq0qJFi1RVVSW73S6v12u6NAAYMQRSADDE6/XK5XLJ4XDEHYzvcDjkcrkIpQDGDc4hBQADwuGw7Ha7HA6HmpubZbF8MD8QiUTkdDoVCATU3t7O43sAY9JQ8hozpABggN/vV2dnp+rq6uLCqCRZLBZ5PB51dHTI7/cbqhAARg6bmgDAgK6uLklSSUnJoJuaSkpK4sYBQDIjkAKAAQUFBZKkRx99VI8//rg6OztjfcXFxVq2bFncOABIZjyyBwADKioqlJubK4/Ho5KSkrhNTSUlJaqrq1Nubq4qKipMlwoAw45ACgCGnL+nNBqNxi4AGG8IpABggN/v13vvvaf6+noFAgGVl5crMzNT5eXleu2117R27Vr19vayqQnAuEAgBQADBjYrrVixQocOHdKuXbu0detW7dq1S+3t7VqxYkXcOABIZmxqAgADBjYrBQIBlZaWqrKyMq4/EAjEjQOAZMYMKQAYUFFRoeLiYq1du1aRSCSuLxKJqL6+XjabjU1NAMYFAikAGGC1WtXY2Cifzyen0xm3y97pdMrn86mhoYG3NAEYF3hkDwCG1NbWqqmpSW63W+Xl5bF2m82mpqYm1dbWGqwOAEYO77IHAMMGe1MTM6MAxrqh5DVmSAHAMKvVesGmJgAYT4Z9Dek//dM/KSUlRStXroy1nT59WsuXL1dOTo4mTZqkBQsWqKenZ7hLAQAAwCg0rIF03759evzxx/VXf/VXce133HGHnnvuOf3iF7/QSy+9pKNHj7JWCgAAYJwatkf2fX19Wrx4sX72s5/pvvvui7UHg0E9+eST2rp1q7785S9Lkp566inNnDlTe/bsUWlp6QX36u/vV39/f+xzKBQarrIBYMSxhhTAeDdsM6TLly/XzTffrOrq6rj2trY2nT17Nq59xowZmjZtmlpbWwe9V319vbKysmJXUVHRcJUNACPK6/XKbrerqqpKixYtUlVVlex2u7xer+nSAGDEDEsg/fnPf679+/ervr7+gr7u7m5NmDBBU6ZMiWvPy8tTd3f3oPfzeDwKBoOx68iRI8NRNgCMKK/XK5fLJYfDEXcOqcPhkMvlIpQCGDcS/sj+yJEj+v73v68XX3xREydOTMg909LSlJaWlpB7AcBoEA6H5Xa7VVNTo+bmZlksf54fKC0tVXNzs5xOp1avXq2vfvWrPL4HkPQSPkPa1tam3t5eff7zn1dqaqpSU1P10ksvaf369UpNTVVeXp7OnDmj48ePx32vp6dH+fn5iS4HAEYlv9+vzs5O1dXVKRqNqqWlRdu2bVNLS4ui0ag8Ho86Ojrk9/tNlwoAwy7hM6Q33HCDXn311bi2b33rW5oxY4buvvtuFRUV6bLLLtOOHTu0YMECSdLBgwf19ttvq6ysLNHlAMCo1NXVJUl66623dMstt6izszPWV1xcHNsMOjAOAJJZwgPp5MmTVVJSEteWkZGhnJycWPuSJUu0atUqZWdnKzMzU7fffrvKysoG3WEPAMmooKBAknTrrbeqpqZG27ZtU0lJiQKBgNauXatbb701bhwAJDMjb2p68MEHZbFYtGDBAvX392vu3Ln66U9/aqIUADCivLxcqampysnJkdfrVWrqn/85Li0tldfr1eWXX673338/7h33AJCseJc9ABjQ0tKiqqoqSdL8+fPl8XhiM6T19fV67rnnJEm7du3itaIAxqSh5LVhf3UoAOBCA2tDN2/erFdffVXl5eXKzMxUeXm5AoGANm/eHDcOAJKZkUf2ADDeDawNnT59ug4dOnTBm5pefvnluHEAkMx4ZA8ABoTDYdntdjkcjrhzSCUpEonI6XQqEAiovb2dc0gBjEk8sgeAUc5qtaqxsVE+n09OpzPuTU1Op1M+n08NDQ2EUQDjAo/sAcCQ2tpaNTU1ye12x+2mt9lsampqUm1trcHqAGDk8MgeAAw7c+aMfvrTn+qtt97S9OnT9d3vflcTJkwwXRYAfCJDyWvMkAKAQV6vV263O+5NTQ8//LAaGxuZIQUwbrCGFAAM8Xq9crlccjgccWtIHQ6HXC6XvF6v6RIBYETwyB4ADGCXPYBkxy57ABjl/H6/Ojs7VVdXFxdGJcliscjj8aijo0N+v99QhQAwclhDCgAGDLyBqaSkROFw+IKD8UtKSuLGAUAyI5ACgAEDb2B69NFH9fjjj8dtaiouLtayZcvixgFAMuORPQAYUFFRoalTp8rj8aikpCRuU1NJSYnq6uqUm5uriooK06UCwLBjhhQADElJSZEkRaNRtbW16fe//71OnTqlMbjXFAA+EQIpABjg9/vV29urxYsX65lnntHzzz8f60tNTdWiRYu0detW+f1+VVZWmisUAEYAgRQADBjYrLRlyxbdfPPNstvtOnXqlNLT03Xo0CFt3bo1bhwAJDMCKQAYkJubK0maMWOGXnvttbgZ0uLiYs2YMUNvvPFGbBwAJDMCKQAY9MYbb+jmm2/WnXfeqfT0dJ06dUr//u//HhdQASDZEUgBwIDu7u7Yzzt37owLoOnp6YOOA4BkxbFPAGDAe++9l9BxADCWEUgBwICcnBxJUmZmpqZOnRrXN3Xq1Nh7nwfGAUAy45E9ABjw/vvvS5JCoZBCoVBc39tvv33BOABIZsyQAoABFzvzyQwpgPGAQAoABvT09CR0HACMZQRSADBg//79sZ8tlvh/is//fP44AEhWBFIAMODw4cOxnydMmBDXd/7n88cBQLIikAKAAadPnx7054/rA4BkRCAFAAMKCwtjP6ekpMT1nf/5/HEAkKwIpABgwOWXXx77ORqNxvWd//n8cQCQrBIeSOvr63Xttddq8uTJys3NldPp1MGDB+PGnD59WsuXL1dOTo4mTZqkBQsWsJMUwLgycPB9osYBwFiW8ED60ksvafny5dqzZ49efPFFnT17Vl/5yld08uTJ2Jg77rhDzz33nH7xi1/opZde0tGjR1VbW5voUgBg1Hr33XdjP3/ULvvzxwFAskr4m5q2b98e93nTpk3Kzc1VW1ubvvSlLykYDOrJJ5/U1q1b9eUvf1mS9NRTT2nmzJnas2ePSktLE10SAIxaEydOvGDjUiQSGbQdAJLVsK8hDQaDkqTs7GxJUltbm86ePavq6urYmBkzZmjatGlqbW0d9B79/f2x1+sN9po9ABhrrrjiCkl/eRf9QPvAOABIZsMaSCORiFauXKnrrrtOJSUlkqTu7m5NmDBBU6ZMiRubl5en7u7uQe9TX1+vrKys2FVUVDScZQPAsLv++usTOg4AxrJhDaTLly9XIBDQz3/+8090H4/Ho2AwGLuOHDmSoAoBwIwDBw4kdBwAjGXDFkhXrFghn8+nXbt2xR1bkp+frzNnzuj48eNx43t6epSfnz/ovdLS0pSZmRl3AcBYtmXLltjPH3UO6fnjACBZJTyQRqNRrVixQs8++6x27twpm80W1/+FL3xBl112mXbs2BFrO3jwoN5++22VlZUluhwAGJUG1tfn5ORcsAxp2rRpysnJiRsHAMks4bvsly9frq1bt+rf/u3fNHny5Ni60KysLKWnpysrK0tLlizRqlWrlJ2drczMTN1+++0qKytjhz2AcSM3N1fvvPOOgsGgJk2adEH/QBDNzc0d6dIAYMQlfIZ0w4YNCgaDqqysVEFBQex65plnYmMefPBB1dTUaMGCBfrSl76k/Px8eb3eRJcCAKPWX//1X0uSzp07p66uLt1999168803dffdd6urq0vnzp2LGwcAySwl+uF31o0BoVBIWVlZCgaDrCcFMCb9x3/8h+bOnfux41544QV95StfGYGKACCxhpLXeJc9ABhgtVoTOg4AxjICKQAY0NvbG/s5PT09ru/8z+ePA4BklfBNTQCQrE6dCeut9/oScq/TqZMlSd+/Z41+sfkpnXrng/OVP/XpqVq26DY9fP+PdTp1sgLvJman/fSpk5Q+gRlXAKMPgRQALtJb7/Wp5pHfJORe0UhE1qw8PdG0XZ9euF55776ucN8fZZ30KVk/M1NP/L96pWbl6Yf7IkppS8zv9N1+vUo+k5WQewFAIhFIAeAiTZ86Sb7bE/cqz19f+b+16tu3afrvHtfcRd/RT3/Xr+9enaYXtm7QO2/t0wOPP63qm/5Xwn7f9KkXHi8FAKMBu+wBwCCv1yu3263Ozs5Ym81mU0NDg2pra80VBgCf0FDyGoEUAAwLh8P6P95f6a7/u1vrbv2SvlF7I7vrAYx5Q8lrPLIHAMOsVquuLa9QRluKri2/njAKYNzh2CcAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRqaYLAIBE6/jDSZ3sP2e6jCE51NsX99+xJiMtVbZPZ5guA8AYRSAFkFQ6/nBSVQ0tpsu4ZCufOWC6hEu2a3UloRTAJSGQAkgqAzOjD339GtlzJxmu5uKdPhvWO388pcs/la6Jl1lNlzMkh3r7tPKZA2NuVhrA6EEgBZCU7LmTVPKZLNNlDMnsYtMVAIAZbGoCAACAUQRSAAAAGMUjewBJpT98WpaJ76ojdFCWiWNnDelY1hHqk2Xiu+oPn5Y0tpZJABgdCKQAksrRk4eVYXtEdS+brmR8ybBJR09eoy8oz3QpAMYgAimApFKYcYVOdtyuh79+jaaPoV32Y9lbvX36/jMHVFh1helSAIxRBFIASSXNOlGR05+RLfOzuiqHx8cjIXI6qMjp95RmnWi6FABjlLFNTY899piKi4s1ceJEzZkzRy+/zPM1AACA8chIIH3mmWe0atUq/eAHP9D+/ft19dVXa+7cuert7TVRDgAAAAwy8sj+gQce0NKlS/Wtb31LkrRx40Y9//zz+pd/+Rfdc889JkoCkCROnQ1LkgLvBhN+74G3KY1Vw/UWqEO9fQm/J4DxZcQD6ZkzZ9TW1iaPxxNrs1gsqq6uVmtr66Df6e/vV39/f+xzKBQa9joBjE1v/f/h6B7vq4YrGX8y0tiWAODSjPi/Hn/4wx8UDoeVlxd/NEheXp7eeOONQb9TX1+vH/3oRyNRHoAx7iufy5ckTc+dpPQEzwYyQ/qXZaSlyvbpjGG5N4DkNyb+d9bj8WjVqlWxz6FQSEVFRQYrAjBaZWdM0MIvThu2+/O+eQBIvBEPpJ/+9KdltVrV09MT197T06P8/PxBv5OWlqa0tLSRKA8AAAAjbMR32U+YMEFf+MIXtGPHjlhbJBLRjh07VFZWNtLlAAAAwDAjj+xXrVql2267TbNnz9YXv/hFPfTQQzp58mRs1z0AAADGDyOB9Otf/7ree+89rVmzRt3d3brmmmu0ffv2CzY6AQAAIPmlRKPRqOkihioUCikrK0vBYFCZmZmmywEAAMCHDCWvGXt1KAAAACARSAEAAGAYgRQAAABGEUgBAABgFIEUAAAARo2JV4d+2MDBAKFQyHAlAAAAGMxATruYA53GZCA9ceKEJPE+ewAAgFHuxIkTysrK+sgxY/Ic0kgkoqNHj2ry5MlKSUkxXQ4AfGKhUEhFRUU6cuQI5ysDSArRaFQnTpxQYWGhLJaPXiU6JgMpACQbXvgBYDxjUxMAAACMIpACAADAKAIpAIwCaWlp+sEPfqC0tDTTpQDAiGMNKQAAAIxihhQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAFghH3zm99USkrKBdehQ4f0zW9+U06n03SJADCiUk0XAADj0bx58/TUU0/FtU2dOtVQNQBgFoEUAAxIS0tTfn6+6TIAYFTgkT0AAACMIpACgAE+n0+TJk2KXV/72tdMlwQAxvDIHgAMqKqq0oYNG2KfMzIyDFYDAGYRSAHAgIyMDNntdtNlAMCowCN7AAAAGMUMKQCMMsFgUAcOHIhry8nJUVFRkZmCAGCYEUgBYJRpaWnRrFmz4tqWLFmif/7nfzZUEQAMr5RoNBo1XQQAAADGL9aQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAqP8PrzUic+SQ0+AAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"FS\"].plot.box(figsize=(8,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "_XXHnMBdnqkc",
        "outputId": "954cb7b1-f08e-43ef-864b-15ebc67650e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"GL\"].plot.box(figsize=(8,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "XzqP-j1hnv-u",
        "outputId": "fd801624-4fdd-4aba-ebdd-9f34f590f1f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.fillna(np.nan, inplace=True)\n",
        "\n",
        "# Calculate the correlation matrix using Pearson correlation\n",
        "corr_matrix = train.corr()\n",
        "\n",
        "# Print the correlation matrix\n",
        "print(\"Correlation Matrix:\")\n",
        "print(corr_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGWNsJjpn2lJ",
        "outputId": "ea69ba95-c43c-4ac5-9d08-08255e0d8569"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation Matrix:\n",
            "             AB        AF        AH        AM        AR        AX        AY  \\\n",
            "AB     1.000000  0.350231  0.249246  0.530687  0.157712  0.471912  0.011004   \n",
            "AF     0.350231  1.000000  0.044140  0.183961  0.044917  0.248439  0.039417   \n",
            "AH     0.249246  0.044140  1.000000  0.128268  0.749794  0.039324  0.055385   \n",
            "AM     0.530687  0.183961  0.128268  1.000000  0.175212  0.225382  0.081666   \n",
            "AR     0.157712  0.044917  0.749794  0.175212  1.000000  0.011715  0.099947   \n",
            "AX     0.471912  0.248439  0.039324  0.225382  0.011715  1.000000  0.016315   \n",
            "AY     0.011004  0.039417  0.055385  0.081666  0.099947  0.016315  1.000000   \n",
            "AZ     0.158569  0.219172  0.095627  0.131885  0.099282  0.269728  0.036790   \n",
            "BC     0.331736  0.058831  0.366910  0.329658  0.073730  0.079799  0.132495   \n",
            "BD     0.278920  0.045430  0.209508  0.277908 -0.002509  0.124581  0.506917   \n",
            "BN     0.246410  0.131428  0.055154  0.158404  0.048671  0.143351  0.042365   \n",
            "BP     0.412869  0.241027  0.223916  0.178260  0.177579  0.428020  0.027618   \n",
            "BQ     0.237270  0.309300  0.131165  0.260038  0.184149  0.183762  0.116206   \n",
            "BR     0.148966  0.018409  0.011948  0.011272  0.007044  0.038888  0.000098   \n",
            "BZ     0.375898  0.067106  0.440221  0.333209  0.137466  0.117972  0.118981   \n",
            "CB     0.081112  0.013128  0.077926  0.004800  0.009110  0.016100 -0.008312   \n",
            "CC     0.233556  0.091546  0.162371  0.160961 -0.021310  0.180710  0.008158   \n",
            "CD     0.437661  0.181395  0.131419  0.249581  0.020578  0.247612  0.028359   \n",
            "CF     0.299386  0.066975  0.164696  0.370031 -0.002361  0.197018  0.024272   \n",
            "CH     0.426383  0.126804  0.677612  0.250566  0.660409  0.176463  0.150066   \n",
            "CL     0.040467 -0.030457  0.693637 -0.029723  0.745504  0.054424  0.055311   \n",
            "CR    -0.005741 -0.035850 -0.018642 -0.026143 -0.153166 -0.063454 -0.063460   \n",
            "CS     0.158294  0.054071  0.632552  0.032254  0.720853  0.092060  0.030026   \n",
            "CU    -0.066341  0.132456  0.070736  0.001013  0.108454  0.080091  0.099241   \n",
            "CW    -0.109078 -0.118106  0.062493 -0.031086  0.076620 -0.086019  0.068402   \n",
            "DA    -0.183127 -0.127525  0.260513 -0.255359  0.281715 -0.032053 -0.054766   \n",
            "DE    -0.050492 -0.113353  0.143512 -0.062401  0.105616  0.043023 -0.019275   \n",
            "DF     0.299234  0.080841  0.105094  0.321732  0.301627  0.024006  0.001266   \n",
            "DH    -0.204446 -0.027899  0.109871 -0.202972  0.174468 -0.038901 -0.001491   \n",
            "DI     0.343071  0.330857  0.148272  0.347550  0.318558  0.183382  0.138382   \n",
            "DL    -0.137980  0.057947 -0.050009 -0.091830 -0.010439 -0.015662  0.013437   \n",
            "DN     0.195190  0.255259 -0.026802  0.059895 -0.028360  0.176643 -0.022305   \n",
            "DU     0.059821  0.110287 -0.032162  0.038487 -0.006020 -0.030058 -0.012687   \n",
            "DV     0.036950 -0.018922  0.746715 -0.036929  0.823012  0.024087  0.064777   \n",
            "DY     0.016847 -0.006084  0.002736 -0.040123 -0.047274  0.028683  0.001053   \n",
            "EB     0.279021  0.141196  0.705110  0.082645  0.741000  0.110120  0.083718   \n",
            "EE     0.058040 -0.045524  0.175891 -0.020696  0.175292  0.032320 -0.030944   \n",
            "EG     0.124917  0.096498  0.027994  0.043258  0.027764  0.158917 -0.007844   \n",
            "EH     0.072192  0.116330 -0.023344  0.010132 -0.009067 -0.030023 -0.010550   \n",
            "EL    -0.023105  0.114897 -0.067320  0.055216 -0.051864  0.112591  0.041698   \n",
            "EP     0.033086 -0.052378  0.678742 -0.042153  0.754615 -0.028936  0.077790   \n",
            "EU    -0.034322 -0.001646 -0.010162 -0.019131  0.014996 -0.014398  0.004263   \n",
            "FC     0.290276  0.060819  0.217062  0.027564 -0.006601  0.173032 -0.015492   \n",
            "FD     0.067027  0.088511 -0.010942  0.002447  0.004419 -0.031413 -0.006392   \n",
            "FE     0.041684  0.080458 -0.006100  0.077170  0.028447  0.012873 -0.010587   \n",
            "FI     0.004967  0.021914  0.035902 -0.038974  0.018049  0.114727  0.357537   \n",
            "FL     0.169934  0.148289 -0.022822  0.187974  0.080164  0.020747 -0.027435   \n",
            "FR     0.017990  0.021009 -0.009112 -0.009183 -0.007933 -0.014095 -0.002789   \n",
            "FS     0.036913 -0.003108  0.018328  0.040846  0.068271  0.045893 -0.010552   \n",
            "GB     0.318741  0.079993  0.159666  0.217443  0.002816  0.233188  0.025665   \n",
            "GE     0.003327  0.076449  0.011332 -0.012176  0.034682  0.099148  0.002803   \n",
            "GF    -0.017192 -0.159796 -0.029422 -0.019639  0.027927  0.075500 -0.006869   \n",
            "GH     0.148456  0.187105 -0.037542  0.089008 -0.120285  0.119736  0.196392   \n",
            "GI     0.002903  0.140935 -0.073142  0.100873  0.074428  0.046596  0.022473   \n",
            "GL    -0.061601 -0.127870  0.029175 -0.079717  0.028150 -0.004384  0.067403   \n",
            "Class  0.280612  0.302638  0.044645  0.239170  0.064380  0.097616  0.082420   \n",
            "\n",
            "             AZ        BC       BD   ...        FL        FR        FS  \\\n",
            "AB     0.158569  0.331736  0.278920  ...  0.169934  0.017990  0.036913   \n",
            "AF     0.219172  0.058831  0.045430  ...  0.148289  0.021009 -0.003108   \n",
            "AH     0.095627  0.366910  0.209508  ... -0.022822 -0.009112  0.018328   \n",
            "AM     0.131885  0.329658  0.277908  ...  0.187974 -0.009183  0.040846   \n",
            "AR     0.099282  0.073730 -0.002509  ...  0.080164 -0.007933  0.068271   \n",
            "AX     0.269728  0.079799  0.124581  ...  0.020747 -0.014095  0.045893   \n",
            "AY     0.036790  0.132495  0.506917  ... -0.027435 -0.002789 -0.010552   \n",
            "AZ     1.000000  0.036424  0.135976  ... -0.050647 -0.010384  0.020465   \n",
            "BC     0.036424  1.000000  0.753051  ... -0.018865  0.034514  0.018158   \n",
            "BD     0.135976  0.753051  1.000000  ... -0.008830 -0.010248  0.021651   \n",
            "BN     0.066121  0.079007  0.152439  ...  0.124006  0.038793 -0.048361   \n",
            "BP     0.140070  0.150575  0.194293  ...  0.044527 -0.008156  0.043394   \n",
            "BQ     0.090152  0.110724  0.101895  ...  0.248188 -0.014247  0.036793   \n",
            "BR     0.023675  0.015795 -0.013374  ... -0.021150 -0.000429 -0.007740   \n",
            "BZ     0.036973  0.907981  0.679898  ... -0.012513  0.014934  0.026446   \n",
            "CB     0.007457  0.000202 -0.025233  ... -0.000887 -0.008540  0.008125   \n",
            "CC     0.094239  0.498859  0.400274  ... -0.059844 -0.078934  0.022341   \n",
            "CD    -0.002935  0.089896  0.045723  ...  0.048568 -0.008192  0.054237   \n",
            "CF     0.095292  0.552240  0.435546  ...  0.003072  0.007277  0.076237   \n",
            "CH     0.165505  0.097916  0.097453  ...  0.107383 -0.005506  0.004580   \n",
            "CL     0.115379  0.054335 -0.013055  ... -0.028766 -0.006472  0.009943   \n",
            "CR    -0.032964 -0.033249 -0.034969  ... -0.011756 -0.054496  0.036660   \n",
            "CS     0.157605  0.032730  0.046533  ... -0.002028 -0.009993  0.014397   \n",
            "CU     0.038382  0.081194  0.104431  ... -0.005895 -0.026354  0.049280   \n",
            "CW     0.086353 -0.024555  0.041040  ... -0.080981 -0.012614  0.032585   \n",
            "DA     0.100645 -0.069050 -0.051047  ... -0.068561 -0.033915  0.079428   \n",
            "DE     0.041028  0.026901  0.077612  ... -0.020251 -0.035766 -0.003609   \n",
            "DF     0.127565  0.029582  0.013856  ...  0.300925 -0.009182  0.023980   \n",
            "DH     0.078346 -0.118913 -0.042052  ... -0.082410 -0.052127  0.007349   \n",
            "DI     0.154908  0.076304  0.072287  ...  0.133871 -0.024729  0.092804   \n",
            "DL     0.022836  0.004083  0.014654  ... -0.009942  0.011896  0.027859   \n",
            "DN     0.181068  0.018282  0.042445  ... -0.028111 -0.023176  0.083186   \n",
            "DU    -0.031124 -0.008833 -0.001424  ...  0.607507  0.002201  0.016484   \n",
            "DV     0.090403  0.034925 -0.035599  ... -0.015305 -0.003447  0.011997   \n",
            "DY     0.046389 -0.023469 -0.006652  ... -0.056317 -0.033031 -0.070887   \n",
            "EB     0.115690  0.182379  0.101378  ...  0.029030 -0.009889  0.035006   \n",
            "EE     0.060968 -0.058307 -0.000885  ...  0.135657 -0.045808 -0.002680   \n",
            "EG     0.121449  0.010885  0.038375  ... -0.016033 -0.012368  0.012484   \n",
            "EH     0.026389 -0.008026 -0.003555  ...  0.540120  0.000370  0.011617   \n",
            "EL     0.118017  0.022380  0.030702  ... -0.019265 -0.020134 -0.053684   \n",
            "EP     0.000732  0.067516 -0.033334  ... -0.062359  0.005486  0.002440   \n",
            "EU    -0.033378 -0.004551 -0.026805  ... -0.018127 -0.003568 -0.000838   \n",
            "FC     0.127710  0.031910  0.045149  ... -0.043322 -0.008228  0.013952   \n",
            "FD     0.029792 -0.004416 -0.013903  ...  0.531682 -0.002082  0.001169   \n",
            "FE    -0.050120  0.056867 -0.001578  ...  0.001135 -0.017628 -0.012488   \n",
            "FI     0.098576  0.027701  0.264924  ...  0.007525 -0.027593  0.001107   \n",
            "FL    -0.050647 -0.018865 -0.008830  ...  1.000000 -0.004991  0.035711   \n",
            "FR    -0.010384  0.034514 -0.010248  ... -0.004991  1.000000 -0.011521   \n",
            "FS     0.020465  0.018158  0.021651  ...  0.035711 -0.011521  1.000000   \n",
            "GB     0.223358  0.419790  0.439534  ...  0.028677 -0.011292  0.056143   \n",
            "GE     0.016947  0.052096 -0.003691  ... -0.022796 -0.014754 -0.025035   \n",
            "GF     0.218551 -0.043482  0.005017  ... -0.181186 -0.032820  0.071674   \n",
            "GH     0.111962  0.128950  0.212123  ... -0.021168 -0.013582 -0.040239   \n",
            "GI     0.067379 -0.020755 -0.009943  ...  0.042032 -0.031238  0.069710   \n",
            "GL     0.112823  0.041742  0.020937  ... -0.370082 -0.032971 -0.088700   \n",
            "Class  0.013516  0.155882  0.112184  ...  0.244185  0.104099  0.001139   \n",
            "\n",
            "             GB        GE        GF        GH        GI        GL     Class  \n",
            "AB     0.318741  0.003327 -0.017192  0.148456  0.002903 -0.061601  0.280612  \n",
            "AF     0.079993  0.076449 -0.159796  0.187105  0.140935 -0.127870  0.302638  \n",
            "AH     0.159666  0.011332 -0.029422 -0.037542 -0.073142  0.029175  0.044645  \n",
            "AM     0.217443 -0.012176 -0.019639  0.089008  0.100873 -0.079717  0.239170  \n",
            "AR     0.002816  0.034682  0.027927 -0.120285  0.074428  0.028150  0.064380  \n",
            "AX     0.233188  0.099148  0.075500  0.119736  0.046596 -0.004384  0.097616  \n",
            "AY     0.025665  0.002803 -0.006869  0.196392  0.022473  0.067403  0.082420  \n",
            "AZ     0.223358  0.016947  0.218551  0.111962  0.067379  0.112823  0.013516  \n",
            "BC     0.419790  0.052096 -0.043482  0.128950 -0.020755  0.041742  0.155882  \n",
            "BD     0.439534 -0.003691  0.005017  0.212123 -0.009943  0.020937  0.112184  \n",
            "BN     0.210520  0.088247  0.003322  0.095943  0.058388 -0.124164  0.198283  \n",
            "BP     0.184630  0.045260 -0.029768  0.088173  0.056383 -0.043425  0.157286  \n",
            "BQ     0.051109  0.016935 -0.134377  0.093087  0.113406 -0.279813  0.281257  \n",
            "BR    -0.002348 -0.026204 -0.020926  0.056285 -0.012663  0.044893  0.088554  \n",
            "BZ     0.448241  0.051946 -0.025348  0.095890 -0.028377  0.060099  0.112423  \n",
            "CB    -0.074274  0.123333  0.063854 -0.074014  0.052242  0.054348 -0.014777  \n",
            "CC     0.259422  0.189037 -0.031450  0.103308  0.069542  0.009534 -0.054723  \n",
            "CD     0.056199  0.026863 -0.044747  0.018543  0.057225 -0.061499  0.171304  \n",
            "CF     0.274068  0.031161  0.053388  0.074107 -0.020963  0.084074  0.109748  \n",
            "CH     0.096325 -0.048895  0.048191  0.068250 -0.050052  0.033245  0.008144  \n",
            "CL    -0.017242  0.032376  0.000071 -0.104205  0.033261  0.038527  0.016852  \n",
            "CR    -0.000765 -0.019614 -0.051226  0.172213 -0.038766 -0.084741 -0.227547  \n",
            "CS     0.095215 -0.020782  0.088530 -0.032896 -0.075706  0.038999 -0.047438  \n",
            "CU     0.036698  0.168842  0.010708  0.060664  0.214857  0.115898 -0.083185  \n",
            "CW    -0.009936 -0.021208  0.133426 -0.036216  0.059269  0.119215 -0.062639  \n",
            "DA    -0.008850 -0.062447  0.182491 -0.103302 -0.109944  0.068470 -0.204612  \n",
            "DE     0.084139 -0.058212  0.016970  0.083498 -0.050215  0.005033 -0.124977  \n",
            "DF     0.069156 -0.010456  0.051124 -0.001612  0.037505 -0.013676  0.064272  \n",
            "DH    -0.074615  0.061531  0.025363  0.035097  0.065047  0.068240 -0.206839  \n",
            "DI     0.078180  0.134715  0.019143  0.001852  0.397015  0.065299  0.260760  \n",
            "DL    -0.006402  0.148944 -0.105027  0.009459  0.227716 -0.035938 -0.147716  \n",
            "DN     0.030795  0.074528  0.092981  0.207625  0.112036  0.009165 -0.008478  \n",
            "DU     0.018976 -0.010265 -0.084878  0.004884  0.045450 -0.162444  0.261000  \n",
            "DV    -0.040340  0.047320 -0.020982 -0.113503  0.001464  0.030442  0.015477  \n",
            "DY     0.100108 -0.107150 -0.057390  0.130540 -0.131162  0.154608  0.062734  \n",
            "EB     0.086227  0.140563 -0.006705 -0.103297  0.051895  0.024306  0.088005  \n",
            "EE     0.101033  0.006330 -0.041695  0.041576 -0.061852 -0.141878 -0.135324  \n",
            "EG     0.094714 -0.003156  0.032343 -0.051496  0.045038 -0.069013 -0.024610  \n",
            "EH     0.013758  0.015400 -0.071362  0.010236  0.063649 -0.129900  0.179855  \n",
            "EL     0.032288  0.186231  0.063886  0.043105  0.126628  0.314225  0.067947  \n",
            "EP    -0.049774 -0.005969  0.008102 -0.097312 -0.115044  0.102423 -0.068383  \n",
            "EU    -0.001562  0.478345 -0.021343 -0.051835  0.226188  0.077732 -0.039739  \n",
            "FC     0.120617 -0.037421  0.094127  0.066510 -0.050541  0.023108  0.030689  \n",
            "FD     0.013286  0.027931 -0.049801  0.015354  0.073765 -0.080241  0.131100  \n",
            "FE     0.037392  0.041686 -0.010534 -0.015863  0.064296  0.087683  0.216359  \n",
            "FI     0.087318  0.045553 -0.029979  0.209544  0.046059 -0.023895 -0.094327  \n",
            "FL     0.028677 -0.022796 -0.181186 -0.021168  0.042032 -0.370082  0.244185  \n",
            "FR    -0.011292 -0.014754 -0.032820 -0.013582 -0.031238 -0.032971  0.104099  \n",
            "FS     0.056143 -0.025035  0.071674 -0.040239  0.069710 -0.088700  0.001139  \n",
            "GB     1.000000  0.020053  0.119499  0.071095  0.024033 -0.038006  0.083343  \n",
            "GE     0.020053  1.000000 -0.047636 -0.045717  0.252729  0.128076 -0.070766  \n",
            "GF     0.119499 -0.047636  1.000000 -0.021858  0.003062  0.426114 -0.128208  \n",
            "GH     0.071095 -0.045717 -0.021858  1.000000 -0.039864  0.035004  0.033540  \n",
            "GI     0.024033  0.252729  0.003062 -0.039864  1.000000  0.073352  0.076894  \n",
            "GL    -0.038006  0.128076  0.426114  0.035004  0.073352  1.000000 -0.119202  \n",
            "Class  0.083343 -0.070766 -0.128208  0.033540  0.076894 -0.119202  1.000000  \n",
            "\n",
            "[56 rows x 56 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming 'train' is your dataset DataFrame\n",
        "\n",
        "# Bar Plot\n",
        "sns.barplot(x='BQ', y='EL', data=train)\n",
        "plt.title('Bar Plot: Missing vs. Other Feature')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "1cELg4TfplcX",
        "outputId": "d686322d-4bb6-4ae4-f0cb-3b3b7fc90a15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'df' is your dataset DataFrame\n",
        "sns.relplot(x='BQ', y='Class', data=train, kind='scatter')\n",
        "plt.title('Relational Plot: Missing vs. Other Feature')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "w0Ap68QvpllG",
        "outputId": "e366eb2c-7784-4c1b-d0d1-85112f43d9c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dropping the Id coloumn"
      ],
      "metadata": {
        "id": "XPyHQulJkp4C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Assuming your dataset is stored in a pandas DataFrame called 'train'\n",
        "column_name = 'Id'\n",
        "train = train.drop(column_name, axis=1)  # axis=1 indicates dropping a column\n",
        "\n",
        "# The modified DataFrame 'train' will no longer contain the dropped column"
      ],
      "metadata": {
        "id": "PPTi3Nirjj3Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Filling the missing values"
      ],
      "metadata": {
        "id": "xi519BBtlf4p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"FS\"].fillna(train[\"FS\"].mean(),inplace=True)\n",
        "train[\"GL\"].fillna(train[\"GL\"].mean(),inplace=True)\n",
        "train[\"FL\"].fillna(train[\"FL\"].median(),inplace=True)\n",
        "train[\"FC\"].fillna(train[\"FC\"].median(),inplace=True)\n",
        "train[\"DU\"].fillna(train[\"DU\"].median(),inplace=True)\n",
        "train[\"CC\"].fillna(train[\"CC\"].median(),inplace=True)\n",
        "train[\"CB\"].fillna(train[\"CB\"].median(),inplace=True)"
      ],
      "metadata": {
        "id": "0PGV5nUSklFB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fancyimpute\n",
        "\n",
        "import pandas as pd\n",
        "from fancyimpute import IterativeImputer\n",
        "\n",
        "# Assuming 'train' is your dataset DataFrame\n",
        "\n",
        "# Select the features with missing values\n",
        "features_with_missing = ['BQ', 'EL']\n",
        "\n",
        "# Create the iterative imputer\n",
        "imputer = IterativeImputer()\n",
        "\n",
        "# Perform multiple imputations\n",
        "imputed_data = imputer.fit_transform(train[features_with_missing])\n",
        "\n",
        "# Create a DataFrame with imputed values\n",
        "imputed_df = pd.DataFrame(imputed_data, columns=features_with_missing)\n",
        "\n",
        "# Replace the missing values in the original dataset\n",
        "train[features_with_missing] = imputed_df"
      ],
      "metadata": {
        "id": "N5nEYBdCmM43",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb33d823-62da-422c-fbf3-6bfed7cf45f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting fancyimpute\n",
            "  Downloading fancyimpute-0.7.0.tar.gz (25 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting knnimpute>=0.1.0 (from fancyimpute)\n",
            "  Downloading knnimpute-0.1.0.tar.gz (8.3 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scikit-learn>=0.24.2 in /usr/local/lib/python3.10/dist-packages (from fancyimpute) (1.2.2)\n",
            "Requirement already satisfied: cvxpy in /usr/local/lib/python3.10/dist-packages (from fancyimpute) (1.3.2)\n",
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            "Collecting nose (from fancyimpute)\n",
            "  Downloading nose-1.3.7-py3-none-any.whl (154 kB)\n",
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            "Building wheels for collected packages: fancyimpute, knnimpute\n",
            "  Building wheel for fancyimpute (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fancyimpute: filename=fancyimpute-0.7.0-py3-none-any.whl size=29880 sha256=2bf575a65a9bc7957db1da3b3d4c8b1bfe22aeef63c90a9bc995b60f4edddf61\n",
            "  Stored in directory: /root/.cache/pip/wheels/7b/0c/d3/ee82d1fbdcc0858d96434af108608d01703505d453720c84ed\n",
            "  Building wheel for knnimpute (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for knnimpute: filename=knnimpute-0.1.0-py3-none-any.whl size=11335 sha256=1d2f416cd9e11fb77996189e703690d3b672db90f3b5c6154eb8e9dc0e456307\n",
            "  Stored in directory: /root/.cache/pip/wheels/46/06/a5/45a724630562413c374e29c08732411d496092408b3a7bf754\n",
            "Successfully built fancyimpute knnimpute\n",
            "Installing collected packages: nose, knnimpute, fancyimpute\n",
            "Successfully installed fancyimpute-0.7.0 knnimpute-0.1.0 nose-1.3.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##One hot encoding"
      ],
      "metadata": {
        "id": "9D0jEynZn5Et"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "train = pd.get_dummies(train, columns=['EJ'])"
      ],
      "metadata": {
        "id": "PS5WdKxkn-LA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "column_name = 'EJ_A'\n",
        "train = train.drop(column_name, axis=1)"
      ],
      "metadata": {
        "id": "atVOggPvoXLa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0eO0LEF9odzP",
        "outputId": "a6b8b14b-dcad-4fd6-8ad8-ee375ff32340"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 0             1             2             3             4\n",
              "AB        0.209377      0.145282      0.470030      0.252107      0.380297\n",
              "AF     3109.033290    978.764160   2635.106540   3819.651770   3733.048440\n",
              "AH       85.200147     85.200147     85.200147    120.201618     85.200147\n",
              "AM       22.394407     36.968889     32.360553     77.112203     14.103738\n",
              "AR        8.138688      8.138688      8.138688      8.138688      8.138688\n",
              "AX        0.699861      3.632190      6.732840      3.685344      3.942255\n",
              "AY        0.025578      0.025578      0.025578      0.025578      0.054810\n",
              "AZ        9.812214     13.517790     12.824570     11.053708      3.396778\n",
              "BC        5.555634      1.229900      1.229900      1.229900    102.151980\n",
              "BD     4126.587310   5496.928240   5135.780240   4169.677380   5728.734120\n",
              "BN       22.598400     19.420500     26.482500     23.657700     24.010800\n",
              "BP      175.638726    155.868030    128.988531    237.282264    324.546318\n",
              "BQ      152.707705     14.754720    219.320160     11.050410    149.717165\n",
              "BR      823.928241     51.216883    482.141594    661.518640   6074.859475\n",
              "BZ      257.432377    257.432377    257.432377    257.432377    257.432377\n",
              "CB       47.223358     30.284345     32.563713     15.201914     82.213495\n",
              "CC        0.563481      0.484710      0.495852      0.717882      0.536467\n",
              "CD       23.387600     50.628208     85.955376     88.159360     72.644264\n",
              "CF        4.851915      6.085041      5.376488      2.347652     30.537722\n",
              "CH        0.023482      0.031442      0.036218      0.029054      0.025472\n",
              "CL        1.050225      1.113875      1.050225      1.400300      1.050225\n",
              "CR        0.069225      1.117800      0.700350      0.636075      0.693150\n",
              "CS       13.784111     28.310953     39.364743     41.116960     31.724726\n",
              "CU        1.302012      1.357182      1.009611      0.722727      0.827550\n",
              "CW       36.205956     37.476568     21.459644     21.530392     34.415360\n",
              "DA       69.083400     70.798360     70.819700     47.275860     74.065320\n",
              "DE      295.570575    178.553100    321.426625    196.607985    200.178160\n",
              "DF        0.238680      0.238680      0.238680      0.238680      0.238680\n",
              "DH        0.284232      0.363489      0.210441      0.292431      0.207708\n",
              "DI       89.245560    110.581815    120.056438    139.824570     97.920120\n",
              "DL       84.316640     75.745480     65.469840     71.571200     52.838880\n",
              "DN       29.657104     37.532000     28.053464     24.354856     26.019912\n",
              "DU        5.310690      0.005518      1.289739      2.655345      1.144902\n",
              "DV        1.743070      1.743070      1.743070      1.743070      1.743070\n",
              "DY       23.187704     17.222328     36.861352     52.003884      9.064856\n",
              "EB        7.294176      4.926396      7.813674      7.386060      7.350720\n",
              "EE        1.987283      0.858603      8.146651      3.813326      3.490846\n",
              "EG     1433.166750   1111.287150   1494.076488  15691.552180   1403.656300\n",
              "EH        0.949104      0.003042      0.377208      0.614484      0.164268\n",
              "EL       30.879420    109.125159    109.125159     31.674357    109.125159\n",
              "EP       78.526968     95.415086     78.526968     78.526968     91.994825\n",
              "EU        3.828384     52.260480      5.390628     31.323372     51.141336\n",
              "FC       13.394640     17.175984    224.207424     59.301984     29.102640\n",
              "FD       10.265073      0.296850      8.745201      7.884336      4.274640\n",
              "FE     9028.291921   6785.003474   8338.906181  10965.766040  16198.049590\n",
              "FI        3.583450     10.358927     11.626917     14.852022     13.666727\n",
              "FL        7.298162      0.173229      7.709560      6.122162      8.153058\n",
              "FR        1.738550      0.497060      0.975560      0.497060     48.501340\n",
              "FS        0.094822      0.568932      1.198821      0.284466      0.121914\n",
              "GB       11.339138      9.292698     37.077772     18.529584     16.408728\n",
              "GE       72.611063     72.611063     88.609437     82.416803    146.109943\n",
              "GF     2003.810319  27981.562750  13676.957810   2094.262452   8524.370502\n",
              "GH       22.136229     29.135430     28.022851     39.948656     45.381316\n",
              "GI       69.834944     32.131996     35.192676     90.493248     36.262628\n",
              "GL        0.120343     21.978000      0.196941      0.155829      0.096614\n",
              "Class     1.000000      0.000000      0.000000      0.000000      1.000000\n",
              "EJ_B      1.000000      0.000000      1.000000      1.000000      1.000000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-1d6f660a-02fb-4159-94a7-d954dced8f86\">\n",
              "    <div 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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AB</th>\n",
              "      <td>0.209377</td>\n",
              "      <td>0.145282</td>\n",
              "      <td>0.470030</td>\n",
              "      <td>0.252107</td>\n",
              "      <td>0.380297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AF</th>\n",
              "      <td>3109.033290</td>\n",
              "      <td>978.764160</td>\n",
              "      <td>2635.106540</td>\n",
              "      <td>3819.651770</td>\n",
              "      <td>3733.048440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AH</th>\n",
              "      <td>85.200147</td>\n",
              "      <td>85.200147</td>\n",
              "      <td>85.200147</td>\n",
              "      <td>120.201618</td>\n",
              "      <td>85.200147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AM</th>\n",
              "      <td>22.394407</td>\n",
              "      <td>36.968889</td>\n",
              "      <td>32.360553</td>\n",
              "      <td>77.112203</td>\n",
              "      <td>14.103738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR</th>\n",
              "      <td>8.138688</td>\n",
              "      <td>8.138688</td>\n",
              "      <td>8.138688</td>\n",
              "      <td>8.138688</td>\n",
              "      <td>8.138688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AX</th>\n",
              "      <td>0.699861</td>\n",
              "      <td>3.632190</td>\n",
              "      <td>6.732840</td>\n",
              "      <td>3.685344</td>\n",
              "      <td>3.942255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AY</th>\n",
              "      <td>0.025578</td>\n",
              "      <td>0.025578</td>\n",
              "      <td>0.025578</td>\n",
              "      <td>0.025578</td>\n",
              "      <td>0.054810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AZ</th>\n",
              "      <td>9.812214</td>\n",
              "      <td>13.517790</td>\n",
              "      <td>12.824570</td>\n",
              "      <td>11.053708</td>\n",
              "      <td>3.396778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BC</th>\n",
              "      <td>5.555634</td>\n",
              "      <td>1.229900</td>\n",
              "      <td>1.229900</td>\n",
              "      <td>1.229900</td>\n",
              "      <td>102.151980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BD</th>\n",
              "      <td>4126.587310</td>\n",
              "      <td>5496.928240</td>\n",
              "      <td>5135.780240</td>\n",
              "      <td>4169.677380</td>\n",
              "      <td>5728.734120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BN</th>\n",
              "      <td>22.598400</td>\n",
              "      <td>19.420500</td>\n",
              "      <td>26.482500</td>\n",
              "      <td>23.657700</td>\n",
              "      <td>24.010800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BP</th>\n",
              "      <td>175.638726</td>\n",
              "      <td>155.868030</td>\n",
              "      <td>128.988531</td>\n",
              "      <td>237.282264</td>\n",
              "      <td>324.546318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BQ</th>\n",
              "      <td>152.707705</td>\n",
              "      <td>14.754720</td>\n",
              "      <td>219.320160</td>\n",
              "      <td>11.050410</td>\n",
              "      <td>149.717165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BR</th>\n",
              "      <td>823.928241</td>\n",
              "      <td>51.216883</td>\n",
              "      <td>482.141594</td>\n",
              "      <td>661.518640</td>\n",
              "      <td>6074.859475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BZ</th>\n",
              "      <td>257.432377</td>\n",
              "      <td>257.432377</td>\n",
              "      <td>257.432377</td>\n",
              "      <td>257.432377</td>\n",
              "      <td>257.432377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CB</th>\n",
              "      <td>47.223358</td>\n",
              "      <td>30.284345</td>\n",
              "      <td>32.563713</td>\n",
              "      <td>15.201914</td>\n",
              "      <td>82.213495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CC</th>\n",
              "      <td>0.563481</td>\n",
              "      <td>0.484710</td>\n",
              "      <td>0.495852</td>\n",
              "      <td>0.717882</td>\n",
              "      <td>0.536467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CD</th>\n",
              "      <td>23.387600</td>\n",
              "      <td>50.628208</td>\n",
              "      <td>85.955376</td>\n",
              "      <td>88.159360</td>\n",
              "      <td>72.644264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CF</th>\n",
              "      <td>4.851915</td>\n",
              "      <td>6.085041</td>\n",
              "      <td>5.376488</td>\n",
              "      <td>2.347652</td>\n",
              "      <td>30.537722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CH</th>\n",
              "      <td>0.023482</td>\n",
              "      <td>0.031442</td>\n",
              "      <td>0.036218</td>\n",
              "      <td>0.029054</td>\n",
              "      <td>0.025472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CL</th>\n",
              "      <td>1.050225</td>\n",
              "      <td>1.113875</td>\n",
              "      <td>1.050225</td>\n",
              "      <td>1.400300</td>\n",
              "      <td>1.050225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CR</th>\n",
              "      <td>0.069225</td>\n",
              "      <td>1.117800</td>\n",
              "      <td>0.700350</td>\n",
              "      <td>0.636075</td>\n",
              "      <td>0.693150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CS</th>\n",
              "      <td>13.784111</td>\n",
              "      <td>28.310953</td>\n",
              "      <td>39.364743</td>\n",
              "      <td>41.116960</td>\n",
              "      <td>31.724726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CU</th>\n",
              "      <td>1.302012</td>\n",
              "      <td>1.357182</td>\n",
              "      <td>1.009611</td>\n",
              "      <td>0.722727</td>\n",
              "      <td>0.827550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CW</th>\n",
              "      <td>36.205956</td>\n",
              "      <td>37.476568</td>\n",
              "      <td>21.459644</td>\n",
              "      <td>21.530392</td>\n",
              "      <td>34.415360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DA</th>\n",
              "      <td>69.083400</td>\n",
              "      <td>70.798360</td>\n",
              "      <td>70.819700</td>\n",
              "      <td>47.275860</td>\n",
              "      <td>74.065320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DE</th>\n",
              "      <td>295.570575</td>\n",
              "      <td>178.553100</td>\n",
              "      <td>321.426625</td>\n",
              "      <td>196.607985</td>\n",
              "      <td>200.178160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DF</th>\n",
              "      <td>0.238680</td>\n",
              "      <td>0.238680</td>\n",
              "      <td>0.238680</td>\n",
              "      <td>0.238680</td>\n",
              "      <td>0.238680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DH</th>\n",
              "      <td>0.284232</td>\n",
              "      <td>0.363489</td>\n",
              "      <td>0.210441</td>\n",
              "      <td>0.292431</td>\n",
              "      <td>0.207708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DI</th>\n",
              "      <td>89.245560</td>\n",
              "      <td>110.581815</td>\n",
              "      <td>120.056438</td>\n",
              "      <td>139.824570</td>\n",
              "      <td>97.920120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DL</th>\n",
              "      <td>84.316640</td>\n",
              "      <td>75.745480</td>\n",
              "      <td>65.469840</td>\n",
              "      <td>71.571200</td>\n",
              "      <td>52.838880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DN</th>\n",
              "      <td>29.657104</td>\n",
              "      <td>37.532000</td>\n",
              "      <td>28.053464</td>\n",
              "      <td>24.354856</td>\n",
              "      <td>26.019912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DU</th>\n",
              "      <td>5.310690</td>\n",
              "      <td>0.005518</td>\n",
              "      <td>1.289739</td>\n",
              "      <td>2.655345</td>\n",
              "      <td>1.144902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DV</th>\n",
              "      <td>1.743070</td>\n",
              "      <td>1.743070</td>\n",
              "      <td>1.743070</td>\n",
              "      <td>1.743070</td>\n",
              "      <td>1.743070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DY</th>\n",
              "      <td>23.187704</td>\n",
              "      <td>17.222328</td>\n",
              "      <td>36.861352</td>\n",
              "      <td>52.003884</td>\n",
              "      <td>9.064856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EB</th>\n",
              "      <td>7.294176</td>\n",
              "      <td>4.926396</td>\n",
              "      <td>7.813674</td>\n",
              "      <td>7.386060</td>\n",
              "      <td>7.350720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EE</th>\n",
              "      <td>1.987283</td>\n",
              "      <td>0.858603</td>\n",
              "      <td>8.146651</td>\n",
              "      <td>3.813326</td>\n",
              "      <td>3.490846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EG</th>\n",
              "      <td>1433.166750</td>\n",
              "      <td>1111.287150</td>\n",
              "      <td>1494.076488</td>\n",
              "      <td>15691.552180</td>\n",
              "      <td>1403.656300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EH</th>\n",
              "      <td>0.949104</td>\n",
              "      <td>0.003042</td>\n",
              "      <td>0.377208</td>\n",
              "      <td>0.614484</td>\n",
              "      <td>0.164268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EL</th>\n",
              "      <td>30.879420</td>\n",
              "      <td>109.125159</td>\n",
              "      <td>109.125159</td>\n",
              "      <td>31.674357</td>\n",
              "      <td>109.125159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EP</th>\n",
              "      <td>78.526968</td>\n",
              "      <td>95.415086</td>\n",
              "      <td>78.526968</td>\n",
              "      <td>78.526968</td>\n",
              "      <td>91.994825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EU</th>\n",
              "      <td>3.828384</td>\n",
              "      <td>52.260480</td>\n",
              "      <td>5.390628</td>\n",
              "      <td>31.323372</td>\n",
              "      <td>51.141336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FC</th>\n",
              "      <td>13.394640</td>\n",
              "      <td>17.175984</td>\n",
              "      <td>224.207424</td>\n",
              "      <td>59.301984</td>\n",
              "      <td>29.102640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FD</th>\n",
              "      <td>10.265073</td>\n",
              "      <td>0.296850</td>\n",
              "      <td>8.745201</td>\n",
              "      <td>7.884336</td>\n",
              "      <td>4.274640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FE</th>\n",
              "      <td>9028.291921</td>\n",
              "      <td>6785.003474</td>\n",
              "      <td>8338.906181</td>\n",
              "      <td>10965.766040</td>\n",
              "      <td>16198.049590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FI</th>\n",
              "      <td>3.583450</td>\n",
              "      <td>10.358927</td>\n",
              "      <td>11.626917</td>\n",
              "      <td>14.852022</td>\n",
              "      <td>13.666727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FL</th>\n",
              "      <td>7.298162</td>\n",
              "      <td>0.173229</td>\n",
              "      <td>7.709560</td>\n",
              "      <td>6.122162</td>\n",
              "      <td>8.153058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FR</th>\n",
              "      <td>1.738550</td>\n",
              "      <td>0.497060</td>\n",
              "      <td>0.975560</td>\n",
              "      <td>0.497060</td>\n",
              "      <td>48.501340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FS</th>\n",
              "      <td>0.094822</td>\n",
              "      <td>0.568932</td>\n",
              "      <td>1.198821</td>\n",
              "      <td>0.284466</td>\n",
              "      <td>0.121914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GB</th>\n",
              "      <td>11.339138</td>\n",
              "      <td>9.292698</td>\n",
              "      <td>37.077772</td>\n",
              "      <td>18.529584</td>\n",
              "      <td>16.408728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GE</th>\n",
              "      <td>72.611063</td>\n",
              "      <td>72.611063</td>\n",
              "      <td>88.609437</td>\n",
              "      <td>82.416803</td>\n",
              "      <td>146.109943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GF</th>\n",
              "      <td>2003.810319</td>\n",
              "      <td>27981.562750</td>\n",
              "      <td>13676.957810</td>\n",
              "      <td>2094.262452</td>\n",
              "      <td>8524.370502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GH</th>\n",
              "      <td>22.136229</td>\n",
              "      <td>29.135430</td>\n",
              "      <td>28.022851</td>\n",
              "      <td>39.948656</td>\n",
              "      <td>45.381316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GI</th>\n",
              "      <td>69.834944</td>\n",
              "      <td>32.131996</td>\n",
              "      <td>35.192676</td>\n",
              "      <td>90.493248</td>\n",
              "      <td>36.262628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GL</th>\n",
              "      <td>0.120343</td>\n",
              "      <td>21.978000</td>\n",
              "      <td>0.196941</td>\n",
              "      <td>0.155829</td>\n",
              "      <td>0.096614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EJ_B</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d6f660a-02fb-4159-94a7-d954dced8f86')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uaWKRVyVohzp",
        "outputId": "a2ef9e25-0173-4292-b2e9-0a2b5d6b2986"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 617 entries, 0 to 616\n",
            "Data columns (total 57 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   AB      617 non-null    float64\n",
            " 1   AF      617 non-null    float64\n",
            " 2   AH      617 non-null    float64\n",
            " 3   AM      617 non-null    float64\n",
            " 4   AR      617 non-null    float64\n",
            " 5   AX      617 non-null    float64\n",
            " 6   AY      617 non-null    float64\n",
            " 7   AZ      617 non-null    float64\n",
            " 8   BC      617 non-null    float64\n",
            " 9   BD      617 non-null    float64\n",
            " 10  BN      617 non-null    float64\n",
            " 11  BP      617 non-null    float64\n",
            " 12  BQ      617 non-null    float64\n",
            " 13  BR      617 non-null    float64\n",
            " 14  BZ      617 non-null    float64\n",
            " 15  CB      617 non-null    float64\n",
            " 16  CC      617 non-null    float64\n",
            " 17  CD      617 non-null    float64\n",
            " 18  CF      617 non-null    float64\n",
            " 19  CH      617 non-null    float64\n",
            " 20  CL      617 non-null    float64\n",
            " 21  CR      617 non-null    float64\n",
            " 22  CS      617 non-null    float64\n",
            " 23  CU      617 non-null    float64\n",
            " 24  CW      617 non-null    float64\n",
            " 25  DA      617 non-null    float64\n",
            " 26  DE      617 non-null    float64\n",
            " 27  DF      617 non-null    float64\n",
            " 28  DH      617 non-null    float64\n",
            " 29  DI      617 non-null    float64\n",
            " 30  DL      617 non-null    float64\n",
            " 31  DN      617 non-null    float64\n",
            " 32  DU      617 non-null    float64\n",
            " 33  DV      617 non-null    float64\n",
            " 34  DY      617 non-null    float64\n",
            " 35  EB      617 non-null    float64\n",
            " 36  EE      617 non-null    float64\n",
            " 37  EG      617 non-null    float64\n",
            " 38  EH      617 non-null    float64\n",
            " 39  EL      617 non-null    float64\n",
            " 40  EP      617 non-null    float64\n",
            " 41  EU      617 non-null    float64\n",
            " 42  FC      617 non-null    float64\n",
            " 43  FD      617 non-null    float64\n",
            " 44  FE      617 non-null    float64\n",
            " 45  FI      617 non-null    float64\n",
            " 46  FL      617 non-null    float64\n",
            " 47  FR      617 non-null    float64\n",
            " 48  FS      617 non-null    float64\n",
            " 49  GB      617 non-null    float64\n",
            " 50  GE      617 non-null    float64\n",
            " 51  GF      617 non-null    float64\n",
            " 52  GH      617 non-null    float64\n",
            " 53  GI      617 non-null    float64\n",
            " 54  GL      617 non-null    float64\n",
            " 55  Class   617 non-null    int64  \n",
            " 56  EJ_B    617 non-null    uint8  \n",
            "dtypes: float64(55), int64(1), uint8(1)\n",
            "memory usage: 270.7 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Rename the 'EJ_B' column to 'EJ'\n",
        "train.rename(columns={'EJ_B': 'EJ'}, inplace=True)\n",
        "\n",
        "# Convert the data type of 'EJ' column from uint8 to float64\n",
        "train['EJ'] = train['EJ'].astype('float64')"
      ],
      "metadata": {
        "id": "dAhYML7wpkjt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ZGn1JR9qioI",
        "outputId": "767183f1-7d65-472b-9b41-dcb562a8e29c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 617 entries, 0 to 616\n",
            "Data columns (total 57 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   AB      617 non-null    float64\n",
            " 1   AF      617 non-null    float64\n",
            " 2   AH      617 non-null    float64\n",
            " 3   AM      617 non-null    float64\n",
            " 4   AR      617 non-null    float64\n",
            " 5   AX      617 non-null    float64\n",
            " 6   AY      617 non-null    float64\n",
            " 7   AZ      617 non-null    float64\n",
            " 8   BC      617 non-null    float64\n",
            " 9   BD      617 non-null    float64\n",
            " 10  BN      617 non-null    float64\n",
            " 11  BP      617 non-null    float64\n",
            " 12  BQ      617 non-null    float64\n",
            " 13  BR      617 non-null    float64\n",
            " 14  BZ      617 non-null    float64\n",
            " 15  CB      617 non-null    float64\n",
            " 16  CC      617 non-null    float64\n",
            " 17  CD      617 non-null    float64\n",
            " 18  CF      617 non-null    float64\n",
            " 19  CH      617 non-null    float64\n",
            " 20  CL      617 non-null    float64\n",
            " 21  CR      617 non-null    float64\n",
            " 22  CS      617 non-null    float64\n",
            " 23  CU      617 non-null    float64\n",
            " 24  CW      617 non-null    float64\n",
            " 25  DA      617 non-null    float64\n",
            " 26  DE      617 non-null    float64\n",
            " 27  DF      617 non-null    float64\n",
            " 28  DH      617 non-null    float64\n",
            " 29  DI      617 non-null    float64\n",
            " 30  DL      617 non-null    float64\n",
            " 31  DN      617 non-null    float64\n",
            " 32  DU      617 non-null    float64\n",
            " 33  DV      617 non-null    float64\n",
            " 34  DY      617 non-null    float64\n",
            " 35  EB      617 non-null    float64\n",
            " 36  EE      617 non-null    float64\n",
            " 37  EG      617 non-null    float64\n",
            " 38  EH      617 non-null    float64\n",
            " 39  EL      617 non-null    float64\n",
            " 40  EP      617 non-null    float64\n",
            " 41  EU      617 non-null    float64\n",
            " 42  FC      617 non-null    float64\n",
            " 43  FD      617 non-null    float64\n",
            " 44  FE      617 non-null    float64\n",
            " 45  FI      617 non-null    float64\n",
            " 46  FL      617 non-null    float64\n",
            " 47  FR      617 non-null    float64\n",
            " 48  FS      617 non-null    float64\n",
            " 49  GB      617 non-null    float64\n",
            " 50  GE      617 non-null    float64\n",
            " 51  GF      617 non-null    float64\n",
            " 52  GH      617 non-null    float64\n",
            " 53  GI      617 non-null    float64\n",
            " 54  GL      617 non-null    float64\n",
            " 55  Class   617 non-null    int64  \n",
            " 56  EJ      617 non-null    float64\n",
            "dtypes: float64(56), int64(1)\n",
            "memory usage: 274.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Splitting of data"
      ],
      "metadata": {
        "id": "Hy_-Nfjg7YOL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "_oWhUxXK7cK3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "col_name = \"Class\"\n",
        "\n",
        "# find the index no\n",
        "index_no = train.columns.get_loc(col_name)\n",
        "print(index_no)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4y5aITjo7pJO",
        "outputId": "edef2908-4e6d-4646-d857-f3ca331649f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "55\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdShvpf47zpf",
        "outputId": "6a993641-fb9b-4a74-8cef-7e26d8ee71cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(617, 57)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = train.iloc[:, :55].values  # Independent variables\n",
        "extra_feature = train.iloc[:, 56].values  # Additional feature\n",
        "\n",
        "# Update 'x' to include 'extra_feature'\n",
        "x = np.column_stack((x,extra_feature))"
      ],
      "metadata": {
        "id": "K4OXzBU88IF3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-Uggf_z9Mpt",
        "outputId": "a6ae4705-92b1-406e-f4f2-a10663262910"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34552"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "34552/617"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-sj5OILp9Mv_",
        "outputId": "86a45632-636f-4458-96d7-5e1b02d90785"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56.0"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=train.iloc[:,55].values #dependent variable"
      ],
      "metadata": {
        "id": "LnQMbVm98BYp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets\n",
        "x_train,x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)"
      ],
      "metadata": {
        "id": "4vfSy5xk7pO-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ucn_Hpq77pa6",
        "outputId": "0a56db22-e03f-48d0-a635-dd5ee6444d36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25872"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "34552*3/4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zG9_fDBA9pfb",
        "outputId": "8027727f-aab0-476f-8dd7-3e5c64c32d37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25914.0"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Feature Scaling"
      ],
      "metadata": {
        "id": "MUfGjIvh-Kcc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "1BFUI8HM-Oh8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stndrd=StandardScaler()\n",
        "x_train=stndrd.fit_transform(x_train)\n",
        "x_test=stndrd.transform(x_test)"
      ],
      "metadata": {
        "id": "LBJ-Mo1E-Rye"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Imbalanced Dataset\n"
      ],
      "metadata": {
        "id": "nNtfGBjP_xe7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Create the combined sampling pipeline\n",
        "combined_sampler = [('under', RandomUnderSampler()), ('over', SMOTE())]\n",
        "\n",
        "# Perform combined undersampling and oversampling on the training data\n",
        "x_train_resampled, y_train_resampled = SMOTE(sampling_strategy=0.8).fit_resample(x_train, y_train)\n",
        "x_train_resampled, y_train_resampled = RandomUnderSampler(sampling_strategy=0.8).fit_resample(x_train_resampled, y_train_resampled)"
      ],
      "metadata": {
        "id": "GRtV8LXz_xqF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_resampled.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6eOT68UDZRA",
        "outputId": "40e4d568-d1cb-4a50-f79c-0dbb573bd400"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38640"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "38640/56"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bls5zqGwDZT-",
        "outputId": "caa81af0-1ac6-4a50-f215-78d080832d69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "690.0"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_resampled.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNgP6vgIDZXG",
        "outputId": "a9946668-4972-4c33-d3bd-f0a0383a664e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "690"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training of Model"
      ],
      "metadata": {
        "id": "98yZE7lg-he9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normal"
      ],
      "metadata": {
        "id": "sPUl0j_0SY_b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "import xgboost as xgb\n",
        "\n",
        "# Define the models\n",
        "models = [\n",
        "    ('SVM', SVC()),\n",
        "    ('Logistic Regression', LogisticRegression()),\n",
        "    ('Decision Tree', DecisionTreeClassifier()),\n",
        "    ('Random Forest (Entropy)', RandomForestClassifier(criterion='entropy')),\n",
        "    ('Random Forest (Gini)', RandomForestClassifier(criterion='gini')),\n",
        "    ('Naive Bayes', GaussianNB()),\n",
        "    ('XGBoost', xgb.XGBClassifier()),\n",
        "    ('Gradient Boosting', GradientBoostingClassifier())\n",
        "]\n",
        "\n",
        "# Train and evaluate each model with cross-validation\n",
        "for name, model in models:\n",
        "    print(f\"Training model: {name}\")\n",
        "\n",
        "    # Perform cross-validation\n",
        "    scores = cross_val_score(model, x_train, y_train, cv=5)\n",
        "\n",
        "    # Fit the model on the training data\n",
        "    model.fit(x_train, y_train)\n",
        "\n",
        "    # Make predictions on the test data\n",
        "    y_pred = model.predict(x_test)\n",
        "\n",
        "    # Evaluate the model performance\n",
        "    report = classification_report(y_test, y_pred)\n",
        "    print(\"Cross-Validation Scores:\", scores)\n",
        "    print(\"Average Cross-Validation Score:\", scores.mean())\n",
        "    print(report)\n",
        "    print(\"---------------------------------------\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOAV2GsR_Bdq",
        "outputId": "5ad34658-e49f-43ce-db53-826c77e0503f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model: SVM\n",
            "Cross-Validation Scores: [0.89247312 0.88172043 0.86956522 0.88043478 0.83695652]\n",
            "Average Cross-Validation Score: 0.8722300140252456\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.98      0.93       125\n",
            "           1       0.88      0.47      0.61        30\n",
            "\n",
            "    accuracy                           0.88       155\n",
            "   macro avg       0.88      0.73      0.77       155\n",
            "weighted avg       0.88      0.88      0.87       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Logistic Regression\n",
            "Cross-Validation Scores: [0.87096774 0.83870968 0.85869565 0.86956522 0.90217391]\n",
            "Average Cross-Validation Score: 0.8680224403927068\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.96      0.95       125\n",
            "           1       0.81      0.73      0.77        30\n",
            "\n",
            "    accuracy                           0.92       155\n",
            "   macro avg       0.88      0.85      0.86       155\n",
            "weighted avg       0.91      0.92      0.91       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Decision Tree\n",
            "Cross-Validation Scores: [0.91397849 0.86021505 0.81521739 0.92391304 0.88043478]\n",
            "Average Cross-Validation Score: 0.8787517531556801\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.88      0.91       125\n",
            "           1       0.59      0.73      0.66        30\n",
            "\n",
            "    accuracy                           0.85       155\n",
            "   macro avg       0.76      0.81      0.78       155\n",
            "weighted avg       0.87      0.85      0.86       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Random Forest (Entropy)\n",
            "Cross-Validation Scores: [0.91397849 0.89247312 0.85869565 0.90217391 0.88043478]\n",
            "Average Cross-Validation Score: 0.8895511921458625\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      1.00      0.96       125\n",
            "           1       1.00      0.63      0.78        30\n",
            "\n",
            "    accuracy                           0.93       155\n",
            "   macro avg       0.96      0.82      0.87       155\n",
            "weighted avg       0.93      0.93      0.92       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Random Forest (Gini)\n",
            "Cross-Validation Scores: [0.90322581 0.91397849 0.88043478 0.91304348 0.91304348]\n",
            "Average Cross-Validation Score: 0.9047452080411407\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      1.00      0.97       125\n",
            "           1       1.00      0.73      0.85        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.97      0.87      0.91       155\n",
            "weighted avg       0.95      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Naive Bayes\n",
            "Cross-Validation Scores: [0.87096774 0.82795699 0.82608696 0.88043478 0.86956522]\n",
            "Average Cross-Validation Score: 0.855002337540907\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.95      0.91       125\n",
            "           1       0.67      0.40      0.50        30\n",
            "\n",
            "    accuracy                           0.85       155\n",
            "   macro avg       0.77      0.68      0.70       155\n",
            "weighted avg       0.83      0.85      0.83       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: XGBoost\n",
            "Cross-Validation Scores: [0.93548387 0.93548387 0.90217391 0.95652174 0.89130435]\n",
            "Average Cross-Validation Score: 0.9241935483870968\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      1.00      0.97       125\n",
            "           1       1.00      0.77      0.87        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.97      0.88      0.92       155\n",
            "weighted avg       0.96      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Gradient Boosting\n",
            "Cross-Validation Scores: [0.93548387 0.91397849 0.86956522 0.91304348 0.90217391]\n",
            "Average Cross-Validation Score: 0.9068489948574101\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97       125\n",
            "           1       0.96      0.80      0.87        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.96      0.90      0.92       155\n",
            "weighted avg       0.96      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Weights and Sampling"
      ],
      "metadata": {
        "id": "RLbnzs-6SctX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "import xgboost as xgb\n",
        "\n",
        "# Define the models\n",
        "models = [\n",
        "    ('SVM', SVC(class_weight='balanced')),\n",
        "    ('Logistic Regression', LogisticRegression(class_weight='balanced')),\n",
        "    ('Decision Tree', DecisionTreeClassifier(class_weight='balanced')),\n",
        "    ('Random Forest', RandomForestClassifier(class_weight='balanced')),\n",
        "    ('Gradient Boosting', GradientBoostingClassifier()),\n",
        "    ('Naive Bayes', GaussianNB()),\n",
        "    ('XGBoost', xgb.XGBClassifier())\n",
        "]\n",
        "\n",
        "# Train and evaluate each model\n",
        "for name, model in models:\n",
        "    print(f\"Training model: {name}\")\n",
        "    model.fit(x_train_resampled, y_train_resampled)\n",
        "    y_pred = model.predict(x_test)\n",
        "    report = classification_report(y_test, y_pred)\n",
        "    print(report)\n",
        "    print(\"---------------------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YDtdaj6WPQee",
        "outputId": "39a1fe6e-9acf-4162-9d79-e3bf90e57b96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model: SVM\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.94      0.94       125\n",
            "           1       0.76      0.73      0.75        30\n",
            "\n",
            "    accuracy                           0.90       155\n",
            "   macro avg       0.85      0.84      0.84       155\n",
            "weighted avg       0.90      0.90      0.90       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Logistic Regression\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.93      0.94       125\n",
            "           1       0.74      0.83      0.78        30\n",
            "\n",
            "    accuracy                           0.91       155\n",
            "   macro avg       0.85      0.88      0.86       155\n",
            "weighted avg       0.92      0.91      0.91       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Decision Tree\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.91      0.92       125\n",
            "           1       0.65      0.67      0.66        30\n",
            "\n",
            "    accuracy                           0.86       155\n",
            "   macro avg       0.78      0.79      0.79       155\n",
            "weighted avg       0.87      0.86      0.87       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Random Forest\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97       125\n",
            "           1       0.87      0.87      0.87        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.92      0.92      0.92       155\n",
            "weighted avg       0.95      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Gradient Boosting\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97       125\n",
            "           1       0.87      0.87      0.87        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.92      0.92      0.92       155\n",
            "weighted avg       0.95      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Naive Bayes\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.94      0.90       125\n",
            "           1       0.62      0.43      0.51        30\n",
            "\n",
            "    accuracy                           0.84       155\n",
            "   macro avg       0.75      0.68      0.71       155\n",
            "weighted avg       0.82      0.84      0.83       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: XGBoost\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.98      0.97       125\n",
            "           1       0.89      0.83      0.86        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.93      0.90      0.92       155\n",
            "weighted avg       0.95      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sampling"
      ],
      "metadata": {
        "id": "HWDxSA9DSjGZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "import xgboost as xgb\n",
        "\n",
        "# Define the models\n",
        "models = [\n",
        "    ('SVM', SVC()),\n",
        "    ('Logistic Regression', LogisticRegression()),\n",
        "    ('Decision Tree', DecisionTreeClassifier()),\n",
        "    ('Random Forest (Entropy)', RandomForestClassifier(criterion='entropy')),\n",
        "    ('Random Forest (Gini)', RandomForestClassifier(criterion='gini')),\n",
        "    ('Naive Bayes', GaussianNB()),\n",
        "    ('XGBoost', xgb.XGBClassifier()),\n",
        "    ('Gradient Boosting', GradientBoostingClassifier())\n",
        "]\n",
        "\n",
        "# Train and evaluate each model with cross-validation\n",
        "for name, model in models:\n",
        "    print(f\"Training model: {name}\")\n",
        "\n",
        "    # Perform cross-validation\n",
        "    scores = cross_val_score(model, x_train_resampled, y_train_resampled, cv=5)\n",
        "\n",
        "    # Fit the model on the training data\n",
        "    model.fit(x_train_resampled, y_train_resampled)\n",
        "\n",
        "    # Make predictions on the test data\n",
        "    y_pred = model.predict(x_test)\n",
        "\n",
        "    # Evaluate the model performance\n",
        "    report = classification_report(y_test, y_pred)\n",
        "    print(\"Cross-Validation Scores:\", scores)\n",
        "    print(\"Average Cross-Validation Score:\", scores.mean())\n",
        "    print(report)\n",
        "    print(\"---------------------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQMj6c2OPb4u",
        "outputId": "4aae0700-350a-44b5-9c23-a5e32f50e89c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model: SVM\n",
            "Cross-Validation Scores: [0.95652174 0.93478261 0.92753623 0.94202899 0.92028986]\n",
            "Average Cross-Validation Score: 0.9362318840579711\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.96      0.95       125\n",
            "           1       0.81      0.73      0.77        30\n",
            "\n",
            "    accuracy                           0.92       155\n",
            "   macro avg       0.88      0.85      0.86       155\n",
            "weighted avg       0.91      0.92      0.91       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Logistic Regression\n",
            "Cross-Validation Scores: [0.91304348 0.86231884 0.9057971  0.88405797 0.87681159]\n",
            "Average Cross-Validation Score: 0.8884057971014492\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.94      0.95       125\n",
            "           1       0.78      0.83      0.81        30\n",
            "\n",
            "    accuracy                           0.92       155\n",
            "   macro avg       0.87      0.89      0.88       155\n",
            "weighted avg       0.92      0.92      0.92       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Decision Tree\n",
            "Cross-Validation Scores: [0.82608696 0.84057971 0.9057971  0.9057971  0.87681159]\n",
            "Average Cross-Validation Score: 0.8710144927536232\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.89      0.90       125\n",
            "           1       0.59      0.67      0.62        30\n",
            "\n",
            "    accuracy                           0.85       155\n",
            "   macro avg       0.75      0.78      0.76       155\n",
            "weighted avg       0.85      0.85      0.85       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Random Forest (Entropy)\n",
            "Cross-Validation Scores: [0.91304348 0.98550725 0.92753623 0.95652174 0.96376812]\n",
            "Average Cross-Validation Score: 0.9492753623188406\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.96      0.97       125\n",
            "           1       0.84      0.90      0.87        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.91      0.93      0.92       155\n",
            "weighted avg       0.95      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Random Forest (Gini)\n",
            "Cross-Validation Scores: [0.93478261 0.97826087 0.93478261 0.97101449 0.95652174]\n",
            "Average Cross-Validation Score: 0.955072463768116\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.97      0.97       125\n",
            "           1       0.87      0.90      0.89        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.92      0.93      0.93       155\n",
            "weighted avg       0.96      0.95      0.96       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Naive Bayes\n",
            "Cross-Validation Scores: [0.7173913  0.73188406 0.73188406 0.65217391 0.69565217]\n",
            "Average Cross-Validation Score: 0.7057971014492753\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.94      0.90       125\n",
            "           1       0.62      0.43      0.51        30\n",
            "\n",
            "    accuracy                           0.84       155\n",
            "   macro avg       0.75      0.68      0.71       155\n",
            "weighted avg       0.82      0.84      0.83       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: XGBoost\n",
            "Cross-Validation Scores: [0.92028986 0.96376812 0.92028986 0.97826087 0.92753623]\n",
            "Average Cross-Validation Score: 0.9420289855072463\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.98      0.97       125\n",
            "           1       0.89      0.83      0.86        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.93      0.90      0.92       155\n",
            "weighted avg       0.95      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Gradient Boosting\n",
            "Cross-Validation Scores: [0.9057971  0.95652174 0.92028986 0.96376812 0.94202899]\n",
            "Average Cross-Validation Score: 0.9376811594202898\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97       125\n",
            "           1       0.87      0.87      0.87        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.92      0.92      0.92       155\n",
            "weighted avg       0.95      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Weight"
      ],
      "metadata": {
        "id": "q6RyTaLCSnGi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "import xgboost as xgb\n",
        "\n",
        "# Define the models\n",
        "models = [\n",
        "    ('SVM', SVC(class_weight='balanced')),\n",
        "    ('Logistic Regression', LogisticRegression(class_weight='balanced')),\n",
        "    ('Decision Tree', DecisionTreeClassifier(class_weight='balanced')),\n",
        "    ('Random Forest', RandomForestClassifier(class_weight='balanced')),\n",
        "    ('Gradient Boosting', GradientBoostingClassifier()),\n",
        "    ('Naive Bayes', GaussianNB()),\n",
        "    ('XGBoost', xgb.XGBClassifier())\n",
        "]\n",
        "\n",
        "# Train and evaluate each model\n",
        "for name, model in models:\n",
        "    print(f\"Training model: {name}\")\n",
        "    model.fit(x_train, y_train)\n",
        "    y_pred = model.predict(x_test)\n",
        "    report = classification_report(y_test, y_pred)\n",
        "    print(report)\n",
        "    print(\"---------------------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4j9PEe2QgxN",
        "outputId": "abcb77b2-94ae-4a31-fd14-662aecdfbc29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model: SVM\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.95      0.94       125\n",
            "           1       0.79      0.73      0.76        30\n",
            "\n",
            "    accuracy                           0.91       155\n",
            "   macro avg       0.86      0.84      0.85       155\n",
            "weighted avg       0.91      0.91      0.91       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Logistic Regression\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.92      0.94       125\n",
            "           1       0.71      0.83      0.77        30\n",
            "\n",
            "    accuracy                           0.90       155\n",
            "   macro avg       0.84      0.88      0.85       155\n",
            "weighted avg       0.91      0.90      0.91       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Decision Tree\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.95      0.94       125\n",
            "           1       0.77      0.67      0.71        30\n",
            "\n",
            "    accuracy                           0.90       155\n",
            "   macro avg       0.85      0.81      0.83       155\n",
            "weighted avg       0.89      0.90      0.89       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Random Forest\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      1.00      0.97       125\n",
            "           1       1.00      0.73      0.85        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.97      0.87      0.91       155\n",
            "weighted avg       0.95      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Gradient Boosting\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      1.00      0.98       125\n",
            "           1       1.00      0.80      0.89        30\n",
            "\n",
            "    accuracy                           0.96       155\n",
            "   macro avg       0.98      0.90      0.93       155\n",
            "weighted avg       0.96      0.96      0.96       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: Naive Bayes\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.95      0.91       125\n",
            "           1       0.67      0.40      0.50        30\n",
            "\n",
            "    accuracy                           0.85       155\n",
            "   macro avg       0.77      0.68      0.70       155\n",
            "weighted avg       0.83      0.85      0.83       155\n",
            "\n",
            "---------------------------------------\n",
            "Training model: XGBoost\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      1.00      0.97       125\n",
            "           1       1.00      0.77      0.87        30\n",
            "\n",
            "    accuracy                           0.95       155\n",
            "   macro avg       0.97      0.88      0.92       155\n",
            "weighted avg       0.96      0.95      0.95       155\n",
            "\n",
            "---------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
        "\n",
        "# Define the models with their respective parameter grids\n",
        "models = [\n",
        "    {\n",
        "        'name': 'SVM',\n",
        "        'model': SVC(),\n",
        "        'param_grid': {\n",
        "            'C': [0.1, 1, 10],\n",
        "            'kernel': ['linear', 'rbf']\n",
        "        }\n",
        "    },\n",
        "    {\n",
        "        'name': 'Logistic Regression',\n",
        "        'model': LogisticRegression(),\n",
        "        'param_grid': {\n",
        "            'C': [0.1, 1, 10],\n",
        "            'penalty': ['l1', 'l2']\n",
        "        }\n",
        "    },\n",
        "    {\n",
        "        'name': 'Decision Tree',\n",
        "        'model': DecisionTreeClassifier(),\n",
        "        'param_grid': {\n",
        "            'max_depth': [3, 5, 7, 20],\n",
        "            'min_samples_split': [2, 5, 10, 5],\n",
        "            'min_samples_leaf': [1, 2, 4, 2]\n",
        "        }\n",
        "    },\n",
        "    {\n",
        "        'name': 'Naive Bayes',\n",
        "        'model': GaussianNB(),\n",
        "        'param_grid': {}\n",
        "    },\n",
        "    {\n",
        "        'name': 'XGBoost',\n",
        "        'model': xgb.XGBClassifier(),\n",
        "        'param_grid': {\n",
        "            'n_estimators': [100, 200, 300, 270],\n",
        "            'max_depth': [3, 5, 7, 20],\n",
        "            'learning_rate': [0.01, 0.1, 1.0]\n",
        "        }\n",
        "    },\n",
        "    {\n",
        "        'name': 'Gradient Boosting',\n",
        "        'model': GradientBoostingClassifier(),\n",
        "        'param_grid': {\n",
        "            'n_estimators': [100, 200, 300],\n",
        "            'max_depth': [3, 5, 7]\n",
        "        }\n",
        "    }\n",
        "]\n",
        "\n",
        "# Train and evaluate each model with hyperparameter tuning\n",
        "for model_info in models:\n",
        "    print(f\"Training model: {model_info['name']}\")\n",
        "\n",
        "    # Perform randomized search to find the best parameters\n",
        "    randomized_search = RandomizedSearchCV(model_info['model'], model_info['param_grid'], cv=5, n_iter=10)\n",
        "    randomized_search.fit(x_train_resampled, y_train_resampled)\n",
        "    # Print the best parameters and score\n",
        "    print(\"Best Parameters:\", randomized_search.best_params_)\n",
        "    print(\"Best Score:\", randomized_search.best_score_)\n",
        "\n",
        "    # Make predictions on the test data using the best model\n",
        "    best_model = randomized_search.best_estimator_\n",
        "    y_pred = best_model.predict(x_test)\n",
        "\n",
        "    # Evaluate the model performance\n",
        "    print(classification_report(y_test, y_pred))\n",
        "    print(\"----------------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jAcv_D6GKAag",
        "outputId": "91c31f6c-c3e5-4813-a8a3-cbcd20072080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model: SVM\n",
            "Best Parameters: {'kernel': 'rbf', 'C': 10}\n",
            "Best Score: 0.9536231884057971\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.98      0.95       125\n",
            "           1       0.90      0.60      0.72        30\n",
            "\n",
            "    accuracy                           0.91       155\n",
            "   macro avg       0.91      0.79      0.83       155\n",
            "weighted avg       0.91      0.91      0.90       155\n",
            "\n",
            "----------------------------------\n",
            "Training model: Logistic Regression\n",
            "Best Parameters: {'penalty': 'l2', 'C': 10}\n",
            "Best Score: 0.9\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.94      0.95       125\n",
            "           1       0.77      0.80      0.79        30\n",
            "\n",
            "    accuracy                           0.92       155\n",
            "   macro avg       0.86      0.87      0.87       155\n",
            "weighted avg       0.92      0.92      0.92       155\n",
            "\n",
            "----------------------------------\n",
            "Training model: Decision Tree\n",
            "Best Parameters: {'min_samples_split': 5, 'min_samples_leaf': 2, 'max_depth': 7}\n",
            "Best Score: 0.8869565217391304\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.90      0.91       125\n",
            "           1       0.61      0.67      0.63        30\n",
            "\n",
            "    accuracy                           0.85       155\n",
            "   macro avg       0.76      0.78      0.77       155\n",
            "weighted avg       0.86      0.85      0.85       155\n",
            "\n",
            "----------------------------------\n",
            "Training model: Naive Bayes\n",
            "Best Parameters: {}\n",
            "Best Score: 0.7057971014492753\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.94      0.90       125\n",
            "           1       0.62      0.43      0.51        30\n",
            "\n",
            "    accuracy                           0.84       155\n",
            "   macro avg       0.75      0.68      0.71       155\n",
            "weighted avg       0.82      0.84      0.83       155\n",
            "\n",
            "----------------------------------\n",
            "Training model: XGBoost\n",
            "Best Parameters: {'n_estimators': 270, 'max_depth': 3, 'learning_rate': 1.0}\n",
            "Best Score: 0.9478260869565217\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.94      0.95       125\n",
            "           1       0.78      0.83      0.81        30\n",
            "\n",
            "    accuracy                           0.92       155\n",
            "   macro avg       0.87      0.89      0.88       155\n",
            "weighted avg       0.92      0.92      0.92       155\n",
            "\n",
            "----------------------------------\n",
            "Training model: Gradient Boosting\n",
            "Best Parameters: {'n_estimators': 100, 'max_depth': 5}\n",
            "Best Score: 0.9492753623188406\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.94      0.95       125\n",
            "           1       0.76      0.83      0.79        30\n",
            "\n",
            "    accuracy                           0.92       155\n",
            "   macro avg       0.86      0.88      0.87       155\n",
            "weighted avg       0.92      0.92      0.92       155\n",
            "\n",
            "----------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Define the models with their respective parameter grids\n",
        "models = [\n",
        "    {\n",
        "        'name': 'Random Forest (Entropy)',\n",
        "        'model': RandomForestClassifier(criterion='entropy'),\n",
        "        'param_grid': {\n",
        "            'n_estimators': [100, 200, 300, 60, 80],\n",
        "            'max_depth': [None, 5, 10, 12],\n",
        "            'min_samples_split': [2, 5, 10],\n",
        "            'min_samples_leaf': [1, 2, 4]\n",
        "        }\n",
        "    },\n",
        "    {\n",
        "        'name': 'Random Forest (Gini)',\n",
        "        'model': RandomForestClassifier(criterion='gini'),\n",
        "        'param_grid': {\n",
        "            'n_estimators': [100, 200, 300, 80],\n",
        "            'max_depth': [None, 5, 10, 10],\n",
        "            'min_samples_split': [2, 5, 10],\n",
        "            'min_samples_leaf': [1, 2, 4]\n",
        "        }\n",
        "    }\n",
        "]\n",
        "\n",
        "# Train and evaluate each model with hyperparameter tuning\n",
        "for model_info in models:\n",
        "    print(f\"Training model: {model_info['name']}\")\n",
        "\n",
        "    # Perform randomized search to find the best parameters\n",
        "    randomized_search = RandomizedSearchCV(model_info['model'], model_info['param_grid'], cv=5, n_iter=10)\n",
        "    randomized_search.fit(x_train_resampled, y_train_resampled)\n",
        "    # Print the best parameters and score\n",
        "    print(\"Best Parameters:\", randomized_search.best_params_)\n",
        "    print(\"Best Score:\", randomized_search.best_score_)\n",
        "\n",
        "    # Make predictions on the test data using the best model\n",
        "    best_model = randomized_search.best_estimator_\n",
        "    y_pred = best_model.predict(x_test)\n",
        "\n",
        "    # Evaluate the model performance\n",
        "    print(classification_report(y_test, y_pred))\n",
        "    print(\"----------------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gR6-UZBFK7Q6",
        "outputId": "656d58b6-7a1b-4c74-d16a-b3f9feffa541"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model: Random Forest (Entropy)\n",
            "Best Parameters: {'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_depth': None}\n",
            "Best Score: 0.9478260869565217\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.96      0.96       125\n",
            "           1       0.84      0.87      0.85        30\n",
            "\n",
            "    accuracy                           0.94       155\n",
            "   macro avg       0.90      0.91      0.91       155\n",
            "weighted avg       0.94      0.94      0.94       155\n",
            "\n",
            "----------------------------------\n",
            "Training model: Random Forest (Gini)\n",
            "Best Parameters: {'n_estimators': 300, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_depth': 10}\n",
            "Best Score: 0.944927536231884\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98       125\n",
            "           1       0.90      0.90      0.90        30\n",
            "\n",
            "    accuracy                           0.96       155\n",
            "   macro avg       0.94      0.94      0.94       155\n",
            "weighted avg       0.96      0.96      0.96       155\n",
            "\n",
            "----------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Submission"
      ],
      "metadata": {
        "id": "DBDpMTA_QdGH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_decision_forests"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJ4vLWkRUhoe",
        "outputId": "aaecc764-d09c-49d5-fc11-e8a209c2eb5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow_decision_forests\n",
            "  Downloading tensorflow_decision_forests-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (1.22.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (1.5.3)\n",
            "Collecting tensorflow~=2.13.0 (from tensorflow_decision_forests)\n",
            "  Downloading tensorflow-2.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (524.1 MB)\n",
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            "Collecting wurlitzer (from tensorflow_decision_forests)\n",
            "  Downloading wurlitzer-3.0.3-py3-none-any.whl (7.3 kB)\n",
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            "Collecting keras<2.14,>=2.13.1 (from tensorflow~=2.13.0->tensorflow_decision_forests)\n",
            "  Downloading keras-2.13.1-py3-none-any.whl (1.7 MB)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow~=2.13.0->tensorflow_decision_forests) (67.7.2)\n",
            "Collecting tensorboard<2.14,>=2.13 (from tensorflow~=2.13.0->tensorflow_decision_forests)\n",
            "  Downloading tensorboard-2.13.0-py3-none-any.whl (5.6 MB)\n",
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            "\u001b[?25hCollecting tensorflow-estimator<2.14,>=2.13.0 (from tensorflow~=2.13.0->tensorflow_decision_forests)\n",
            "  Downloading tensorflow_estimator-2.13.0-py2.py3-none-any.whl (440 kB)\n",
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            "Collecting typing-extensions<4.6.0,>=3.6.6 (from tensorflow~=2.13.0->tensorflow_decision_forests)\n",
            "  Downloading typing_extensions-4.5.0-py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow~=2.13.0->tensorflow_decision_forests) (1.14.1)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow_decision_forests) (2.8.2)\n",
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            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.14,>=2.13->tensorflow~=2.13.0->tensorflow_decision_forests) (2.17.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.14,>=2.13->tensorflow~=2.13.0->tensorflow_decision_forests) (1.0.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.14,>=2.13->tensorflow~=2.13.0->tensorflow_decision_forests) (3.4.3)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow~=2.13.0->tensorflow_decision_forests) (3.2.2)\n",
            "Installing collected packages: wurlitzer, typing-extensions, tensorflow-estimator, keras, tensorboard, tensorflow, tensorflow_decision_forests\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.7.1\n",
            "    Uninstalling typing_extensions-4.7.1:\n",
            "      Successfully uninstalled typing_extensions-4.7.1\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.12.0\n",
            "    Uninstalling tensorflow-estimator-2.12.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.12.0\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.12.0\n",
            "    Uninstalling keras-2.12.0:\n",
            "      Successfully uninstalled keras-2.12.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.12.3\n",
            "    Uninstalling tensorboard-2.12.3:\n",
            "      Successfully uninstalled tensorboard-2.12.3\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.12.0\n",
            "    Uninstalling tensorflow-2.12.0:\n",
            "      Successfully uninstalled tensorflow-2.12.0\n",
            "Successfully installed keras-2.13.1 tensorboard-2.13.0 tensorflow-2.13.0 tensorflow-estimator-2.13.0 tensorflow_decision_forests-1.5.0 typing-extensions-4.5.0 wurlitzer-3.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_decision_forests as tfdf\n",
        "import pandas as pd\n",
        "\n",
        "# Read the test dataset\n",
        "test_df = pd.read_csv('/content/test.csv')\n",
        "FEATURE_COLUMNS =['Id' ,'AB', 'AF', 'AH', 'AM', 'AR', 'AX', 'AY', 'AZ', 'BC', 'BD ', 'BN ',\n",
        "       'BP', 'BQ', 'BR', 'BZ', 'CB', 'CC', 'CD ', 'CF', 'CH', 'CL', 'CR', 'CS',\n",
        "       'CU', 'CW ', 'DA', 'DE', 'DF', 'DH', 'DI', 'DL', 'DN', 'DU', 'DV', 'DY',\n",
        "       'EB', 'EE', 'EG', 'EH', 'EJ', 'EL', 'EP', 'EU', 'FC', 'FD ', 'FE', 'FI',\n",
        "       'FL', 'FR', 'FS', 'GB', 'GE', 'GF', 'GH', 'GI', 'GL', 'Class']\n",
        "# Select the desired feature columns\n",
        "TEST_FEATURE_COLUMNS = [col for col in test_df.columns if col in FEATURE_COLUMNS and col != \"Class\"]\n",
        "test_ds_pd = test_df[TEST_FEATURE_COLUMNS]\n",
        "test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd)\n",
        "\n",
        "# # Make predictions using the 'fold_1' model\n",
        "# predictions = models['fold_1'].predict(test_ds)\n",
        "\n",
        "# # Create a DataFrame for submission\n",
        "# submission_df = pd.DataFrame({\n",
        "#  'Id': test_df['Id'],\n",
        "#  'class_0': predictions[:, 0],\n",
        "#  'class_1': predictions[:, 1]\n",
        "# })\n",
        "\n",
        "# # Save the submission file\n",
        "# submission_df.to_csv('/content/submission.csv', index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2a7WyGdUQdRb",
        "outputId": "890ea45c-4371-4cae-8e36-d736081b0d6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Some of the feature names have been changed automatically to be compatible with SavedModels because fix_feature_names=True.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Some of the feature names have been changed automatically to be compatible with SavedModels because fix_feature_names=True.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = best_model.predict(test_ds_pd)\n",
        "n_predictions= [[round(abs(i-1), 8), i] for i in predictions.ravel()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "HdlB8owc3KLV",
        "outputId": "581bc98f-3df4-4a59-949a-f4f599043841"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-62-7e1c9b7746db>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbest_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_ds_pd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mn_predictions\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    818\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    819\u001b[0m         \"\"\"\n\u001b[0;32m--> 820\u001b[0;31m         \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    821\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    822\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    860\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m         \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    864\u001b[0m         \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    600\u001b[0m         Validate X whenever one tries to predict, apply, predict_proba.\"\"\"\n\u001b[1;32m    601\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    603\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindptr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No support for np.int64 index based sparse matrices\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Validation should be done on X, y or both.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 565\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"X\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    566\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    567\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    877\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    878\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 879\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    880\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    881\u001b[0m                 raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_array_api.py\u001b[0m in \u001b[0;36m_asarray_with_order\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m    183\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"numpy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"numpy.array_api\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;31m# Use NumPy API to support order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   2068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2069\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2070\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2072\u001b[0m     def __array_wrap__(\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '00eed32682bb'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Predict probabilities on the test set\n",
        "# test_pred_prob = models.predict_proba(test)[:, 1]\n",
        "\n",
        "# # Create a submission DataFrame\n",
        "# submission = pd.DataFrame({'Id': test_ids, 'class_0': 1 - test_pred_prob, 'class_1': test_pred_prob})\n",
        "\n",
        "# # Save the submission to a CSV file\n",
        "# submission.to_csv('submission.csv', index=False)"
      ],
      "metadata": {
        "id": "OgagwWDEQjQg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_submission = pd.read_csv(\"/content/sample_submission.csv\")\n",
        "sample_submission[['class_0', 'class_1']] = n_predictions\n",
        "sample_submission.to_csv('/content/submission.csv',index=False)"
      ],
      "metadata": {
        "id": "TkQpL7ZqID_V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Weights and Sampling -> Gradient Boost"
      ],
      "metadata": {
        "id": "zsue5vReV35c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sampling -> Random forest(entropy)"
      ],
      "metadata": {
        "id": "uB7iEpDhV8oX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " test.head()"
      ],
      "metadata": {
        "id": "gXWB5JarfImE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}