10) Water_Quality_Prediction.ipynb

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      "source": [
        "# **Water Quality Prediction**\n",
        "In this task, i will build, deploy and perform predictions to determine the quality of water. This project is for educational purposes. \n",
        "\n",
        "The dataset used in this task can be downloaded from [DATA](https://www.kaggle.com/datasets/mssmartypants/water-quality). The description is as follow:\n",
        "aluminium - dangerous if greater than 2.8\n",
        "\n",
        "ammonia - dangerous if greater than 32.5\n",
        "\n",
        "arsenic - dangerous if greater than 0.01\n",
        "\n",
        "barium - dangerous if greater than 2\n",
        "\n",
        "cadmium - dangerous if greater than 0.005\n",
        "\n",
        "chloramine - dangerous if greater than 4\n",
        "\n",
        "chromium - dangerous if greater than 0.1\n",
        "\n",
        "copper - dangerous if greater than 1.3\n",
        "\n",
        "flouride - dangerous if greater than 1.5\n",
        "\n",
        "bacteria - dangerous if greater than 0\n",
        "\n",
        "viruses - dangerous if greater than 0\n",
        "\n",
        "lead - dangerous if greater than 0.015\n",
        "\n",
        "nitrates - dangerous if greater than 10\n",
        "\n",
        "nitrites - dangerous if greater than 1\n",
        "\n",
        "mercury - dangerous if greater than 0.002\n",
        "\n",
        "perchlorate - dangerous if greater than 56\n",
        "\n",
        "radium - dangerous if greater than 5\n",
        "\n",
        "selenium - dangerous if greater than 0.5\n",
        "\n",
        "silver - dangerous if greater than 0.1\n",
        "\n",
        "uranium - dangerous if greater than 0.3\n",
        "\n",
        "is_safe - class attribute {0 - not safe, 1 - safe}\n",
        "\n",
        "[Reference](https://www.kaggle.com/datasets/mssmartypants/water-quality)"
      ],
      "metadata": {
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    },
    {
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      "source": [
        "import logging, sys\n",
        "logging.disable(sys.maxsize)"
      ],
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      },
      "execution_count": 1,
      "outputs": []
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      "execution_count": null,
      "metadata": {
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      "source": [
        "#Install necessary modules\n",
        "!pip install pycaret"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Install necessary modules\n",
        "!pip install streamlit"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load necessary modules\n",
        "from pycaret.classification import load_model, predict_model\n",
        "import streamlit as st\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "YMqO7kDeqcfW"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
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      "source": [
        "# Load data\n",
        "water_df = pd.read_csv('water_quality.csv')\n",
        "water_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "P_wIsaThqcmm",
        "outputId": "b74ffcae-c38c-4f38-f2f3-e6304cb1269b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   aluminium ammonia  arsenic  barium  cadmium  chloramine  chromium  copper  \\\n",
              "0       1.65    9.08     0.04    2.85    0.007        0.35      0.83    0.17   \n",
              "1       2.32   21.16     0.01    3.31    0.002        5.28      0.68    0.66   \n",
              "2       1.01   14.02     0.04    0.58    0.008        4.24      0.53    0.02   \n",
              "3       1.36   11.33     0.04    2.96    0.001        7.23      0.03    1.66   \n",
              "4       0.92   24.33     0.03    0.20    0.006        2.67      0.69    0.57   \n",
              "\n",
              "   flouride  bacteria  viruses   lead  nitrates  nitrites  mercury  \\\n",
              "0      0.05      0.20    0.000  0.054     16.08      1.13    0.007   \n",
              "1      0.90      0.65    0.650  0.100      2.01      1.93    0.003   \n",
              "2      0.99      0.05    0.003  0.078     14.16      1.11    0.006   \n",
              "3      1.08      0.71    0.710  0.016      1.41      1.29    0.004   \n",
              "4      0.61      0.13    0.001  0.117      6.74      1.11    0.003   \n",
              "\n",
              "   perchlorate  radium  selenium  silver  uranium is_safe  \n",
              "0        37.75    6.78      0.08    0.34     0.02       1  \n",
              "1        32.26    3.21      0.08    0.27     0.05       1  \n",
              "2        50.28    7.07      0.07    0.44     0.01       0  \n",
              "3         9.12    1.72      0.02    0.45     0.05       1  \n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>aluminium</th>\n",
              "      <th>ammonia</th>\n",
              "      <th>arsenic</th>\n",
              "      <th>barium</th>\n",
              "      <th>cadmium</th>\n",
              "      <th>chloramine</th>\n",
              "      <th>chromium</th>\n",
              "      <th>copper</th>\n",
              "      <th>flouride</th>\n",
              "      <th>bacteria</th>\n",
              "      <th>viruses</th>\n",
              "      <th>lead</th>\n",
              "      <th>nitrates</th>\n",
              "      <th>nitrites</th>\n",
              "      <th>mercury</th>\n",
              "      <th>perchlorate</th>\n",
              "      <th>radium</th>\n",
              "      <th>selenium</th>\n",
              "      <th>silver</th>\n",
              "      <th>uranium</th>\n",
              "      <th>is_safe</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.65</td>\n",
              "      <td>9.08</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.85</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.054</td>\n",
              "      <td>16.08</td>\n",
              "      <td>1.13</td>\n",
              "      <td>0.007</td>\n",
              "      <td>37.75</td>\n",
              "      <td>6.78</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.32</td>\n",
              "      <td>21.16</td>\n",
              "      <td>0.01</td>\n",
              "      <td>3.31</td>\n",
              "      <td>0.002</td>\n",
              "      <td>5.28</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.650</td>\n",
              "      <td>0.100</td>\n",
              "      <td>2.01</td>\n",
              "      <td>1.93</td>\n",
              "      <td>0.003</td>\n",
              "      <td>32.26</td>\n",
              "      <td>3.21</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.01</td>\n",
              "      <td>14.02</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.008</td>\n",
              "      <td>4.24</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.078</td>\n",
              "      <td>14.16</td>\n",
              "      <td>1.11</td>\n",
              "      <td>0.006</td>\n",
              "      <td>50.28</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.36</td>\n",
              "      <td>11.33</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.96</td>\n",
              "      <td>0.001</td>\n",
              "      <td>7.23</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.66</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.710</td>\n",
              "      <td>0.016</td>\n",
              "      <td>1.41</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.004</td>\n",
              "      <td>9.12</td>\n",
              "      <td>1.72</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.92</td>\n",
              "      <td>24.33</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.006</td>\n",
              "      <td>2.67</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.117</td>\n",
              "      <td>6.74</td>\n",
              "      <td>1.11</td>\n",
              "      <td>0.003</td>\n",
              "      <td>16.90</td>\n",
              "      <td>2.41</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26014595-fdea-4113-bcc8-5f314b9faf57')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-26014595-fdea-4113-bcc8-5f314b9faf57 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-26014595-fdea-4113-bcc8-5f314b9faf57');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "water_df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "PqOsWXMQz97w",
        "outputId": "4aa77808-48bc-4a68-ee10-4c10881fc98d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      aluminium ammonia  arsenic  barium  cadmium  chloramine  chromium  \\\n",
              "7994       0.05    7.78     0.00    1.95     0.04        0.10      0.03   \n",
              "7995       0.05   24.22     0.02    0.59     0.01        0.45      0.02   \n",
              "7996       0.09    6.85     0.00    0.61     0.03        0.05      0.05   \n",
              "7997       0.01      10     0.01    2.00     0.00        2.00      0.00   \n",
              "7998       0.04    6.85     0.01    0.70     0.03        0.05      0.01   \n",
              "\n",
              "      copper  flouride  bacteria  viruses   lead  nitrates  nitrites  mercury  \\\n",
              "7994    0.03      1.37       0.0      0.0  0.197     14.29       1.0    0.005   \n",
              "7995    0.02      1.48       0.0      0.0  0.031     10.27       1.0    0.001   \n",
              "7996    0.02      0.91       0.0      0.0  0.182     15.92       1.0    0.000   \n",
              "7997    0.09      0.00       0.0      0.0  0.000      0.00       0.0    0.000   \n",
              "7998    0.03      1.00       0.0      0.0  0.182     15.92       1.0    0.000   \n",
              "\n",
              "      perchlorate  radium  selenium  silver  uranium is_safe  \n",
              "7994         3.57    2.13      0.09    0.06     0.03       1  \n",
              "7995         1.48    1.11      0.09    0.10     0.08       1  \n",
              "7996         1.35    4.84      0.00    0.04     0.05       1  \n",
              "7997         0.00    0.00      0.00    0.00     0.00       1  \n",
              "7998         1.35    4.84      0.00    0.04     0.05       1  "
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              "    [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-2abd2747-213d-4904-9799-fff60b3f8993 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-2abd2747-213d-4904-9799-fff60b3f8993');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "water_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "g1L9NkdZqcpm",
        "outputId": "f56c6063-60c8-4bb4-9d38-df89f10c76a2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         aluminium      arsenic       barium      cadmium   chloramine  \\\n",
              "count  7999.000000  7999.000000  7999.000000  7999.000000  7999.000000   \n",
              "mean      0.666158     0.161445     1.567715     0.042806     2.176831   \n",
              "std       1.265145     0.252590     1.216091     0.036049     2.567027   \n",
              "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%       0.040000     0.030000     0.560000     0.008000     0.100000   \n",
              "50%       0.070000     0.050000     1.190000     0.040000     0.530000   \n",
              "75%       0.280000     0.100000     2.480000     0.070000     4.240000   \n",
              "max       5.050000     1.050000     4.940000     0.130000     8.680000   \n",
              "\n",
              "          chromium       copper     flouride     bacteria      viruses  \\\n",
              "count  7999.000000  7999.000000  7999.000000  7999.000000  7999.000000   \n",
              "mean      0.247226     0.805857     0.771565     0.319665     0.328583   \n",
              "std       0.270640     0.653539     0.435373     0.329485     0.378096   \n",
              "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%       0.050000     0.090000     0.405000     0.000000     0.002000   \n",
              "50%       0.090000     0.750000     0.770000     0.220000     0.008000   \n",
              "75%       0.440000     1.390000     1.160000     0.610000     0.700000   \n",
              "max       0.900000     2.000000     1.500000     1.000000     1.000000   \n",
              "\n",
              "              lead     nitrates     nitrites      mercury  perchlorate  \\\n",
              "count  7999.000000  7999.000000  7999.000000  7999.000000  7999.000000   \n",
              "mean      0.099450     9.818822     1.329961     0.005194    16.460299   \n",
              "std       0.058172     5.541331     0.573219     0.002967    17.687474   \n",
              "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%       0.048000     5.000000     1.000000     0.003000     2.170000   \n",
              "50%       0.102000     9.930000     1.420000     0.005000     7.740000   \n",
              "75%       0.151000    14.610000     1.760000     0.008000    29.480000   \n",
              "max       0.200000    19.830000     2.930000     0.010000    60.010000   \n",
              "\n",
              "            radium     selenium       silver      uranium  \n",
              "count  7999.000000  7999.000000  7999.000000  7999.000000  \n",
              "mean      2.920548     0.049685     0.147781     0.044673  \n",
              "std       2.323009     0.028770     0.143551     0.026904  \n",
              "min       0.000000     0.000000     0.000000     0.000000  \n",
              "25%       0.820000     0.020000     0.040000     0.020000  \n",
              "50%       2.410000     0.050000     0.080000     0.050000  \n",
              "75%       4.670000     0.070000     0.240000     0.070000  \n",
              "max       7.990000     0.100000     0.500000     0.090000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd8555b7-be90-40e5-9a62-e71b4c501c8f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>aluminium</th>\n",
              "      <th>arsenic</th>\n",
              "      <th>barium</th>\n",
              "      <th>cadmium</th>\n",
              "      <th>chloramine</th>\n",
              "      <th>chromium</th>\n",
              "      <th>copper</th>\n",
              "      <th>flouride</th>\n",
              "      <th>bacteria</th>\n",
              "      <th>viruses</th>\n",
              "      <th>lead</th>\n",
              "      <th>nitrates</th>\n",
              "      <th>nitrites</th>\n",
              "      <th>mercury</th>\n",
              "      <th>perchlorate</th>\n",
              "      <th>radium</th>\n",
              "      <th>selenium</th>\n",
              "      <th>silver</th>\n",
              "      <th>uranium</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "      <td>7999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.666158</td>\n",
              "      <td>0.161445</td>\n",
              "      <td>1.567715</td>\n",
              "      <td>0.042806</td>\n",
              "      <td>2.176831</td>\n",
              "      <td>0.247226</td>\n",
              "      <td>0.805857</td>\n",
              "      <td>0.771565</td>\n",
              "      <td>0.319665</td>\n",
              "      <td>0.328583</td>\n",
              "      <td>0.099450</td>\n",
              "      <td>9.818822</td>\n",
              "      <td>1.329961</td>\n",
              "      <td>0.005194</td>\n",
              "      <td>16.460299</td>\n",
              "      <td>2.920548</td>\n",
              "      <td>0.049685</td>\n",
              "      <td>0.147781</td>\n",
              "      <td>0.044673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.265145</td>\n",
              "      <td>0.252590</td>\n",
              "      <td>1.216091</td>\n",
              "      <td>0.036049</td>\n",
              "      <td>2.567027</td>\n",
              "      <td>0.270640</td>\n",
              "      <td>0.653539</td>\n",
              "      <td>0.435373</td>\n",
              "      <td>0.329485</td>\n",
              "      <td>0.378096</td>\n",
              "      <td>0.058172</td>\n",
              "      <td>5.541331</td>\n",
              "      <td>0.573219</td>\n",
              "      <td>0.002967</td>\n",
              "      <td>17.687474</td>\n",
              "      <td>2.323009</td>\n",
              "      <td>0.028770</td>\n",
              "      <td>0.143551</td>\n",
              "      <td>0.026904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.560000</td>\n",
              "      <td>0.008000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.405000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.002000</td>\n",
              "      <td>0.048000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.003000</td>\n",
              "      <td>2.170000</td>\n",
              "      <td>0.820000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.070000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>1.190000</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.530000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.770000</td>\n",
              "      <td>0.220000</td>\n",
              "      <td>0.008000</td>\n",
              "      <td>0.102000</td>\n",
              "      <td>9.930000</td>\n",
              "      <td>1.420000</td>\n",
              "      <td>0.005000</td>\n",
              "      <td>7.740000</td>\n",
              "      <td>2.410000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.280000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>2.480000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>4.240000</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>1.390000</td>\n",
              "      <td>1.160000</td>\n",
              "      <td>0.610000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.151000</td>\n",
              "      <td>14.610000</td>\n",
              "      <td>1.760000</td>\n",
              "      <td>0.008000</td>\n",
              "      <td>29.480000</td>\n",
              "      <td>4.670000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>0.240000</td>\n",
              "      <td>0.070000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.050000</td>\n",
              "      <td>1.050000</td>\n",
              "      <td>4.940000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>8.680000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>19.830000</td>\n",
              "      <td>2.930000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>60.010000</td>\n",
              "      <td>7.990000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.090000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd8555b7-be90-40e5-9a62-e71b4c501c8f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cd8555b7-be90-40e5-9a62-e71b4c501c8f 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-cd8555b7-be90-40e5-9a62-e71b4c501c8f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "water_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXcbhYqXr3Fe",
        "outputId": "3bcf7fdb-b386-462b-f53f-7dfb703d29db"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7999 entries, 0 to 7998\n",
            "Data columns (total 21 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   aluminium    7999 non-null   float64\n",
            " 1   ammonia      7999 non-null   object \n",
            " 2   arsenic      7999 non-null   float64\n",
            " 3   barium       7999 non-null   float64\n",
            " 4   cadmium      7999 non-null   float64\n",
            " 5   chloramine   7999 non-null   float64\n",
            " 6   chromium     7999 non-null   float64\n",
            " 7   copper       7999 non-null   float64\n",
            " 8   flouride     7999 non-null   float64\n",
            " 9   bacteria     7999 non-null   float64\n",
            " 10  viruses      7999 non-null   float64\n",
            " 11  lead         7999 non-null   float64\n",
            " 12  nitrates     7999 non-null   float64\n",
            " 13  nitrites     7999 non-null   float64\n",
            " 14  mercury      7999 non-null   float64\n",
            " 15  perchlorate  7999 non-null   float64\n",
            " 16  radium       7999 non-null   float64\n",
            " 17  selenium     7999 non-null   float64\n",
            " 18  silver       7999 non-null   float64\n",
            " 19  uranium      7999 non-null   float64\n",
            " 20  is_safe      7999 non-null   object \n",
            "dtypes: float64(19), object(2)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#water_df['is_safe'] = water_df['is_safe'].astype(int)\n",
        "water_df['is_safe'] = pd.to_numeric(water_df['is_safe'], errors='coerce')\n",
        "water_df['ammonia'] = pd.to_numeric(water_df['ammonia'], errors='coerce')"
      ],
      "metadata": {
        "id": "yhLiYoYfxZUZ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform water quality feature into binary (safe or not_safe)\n",
        "water_df.is_safe = np.where(water_df.is_safe >= 1,'Good', 'Bad')\n",
        "water_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "MmKQjx14vxTD",
        "outputId": "bffa0a20-fa16-461e-9f93-f19fa65c2a6c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   aluminium  ammonia  arsenic  barium  cadmium  chloramine  chromium  copper  \\\n",
              "0       1.65     9.08     0.04    2.85    0.007        0.35      0.83    0.17   \n",
              "1       2.32    21.16     0.01    3.31    0.002        5.28      0.68    0.66   \n",
              "2       1.01    14.02     0.04    0.58    0.008        4.24      0.53    0.02   \n",
              "3       1.36    11.33     0.04    2.96    0.001        7.23      0.03    1.66   \n",
              "4       0.92    24.33     0.03    0.20    0.006        2.67      0.69    0.57   \n",
              "\n",
              "   flouride  bacteria  viruses   lead  nitrates  nitrites  mercury  \\\n",
              "0      0.05      0.20    0.000  0.054     16.08      1.13    0.007   \n",
              "1      0.90      0.65    0.650  0.100      2.01      1.93    0.003   \n",
              "2      0.99      0.05    0.003  0.078     14.16      1.11    0.006   \n",
              "3      1.08      0.71    0.710  0.016      1.41      1.29    0.004   \n",
              "4      0.61      0.13    0.001  0.117      6.74      1.11    0.003   \n",
              "\n",
              "   perchlorate  radium  selenium  silver  uranium is_safe  \n",
              "0        37.75    6.78      0.08    0.34     0.02    Good  \n",
              "1        32.26    3.21      0.08    0.27     0.05    Good  \n",
              "2        50.28    7.07      0.07    0.44     0.01     Bad  \n",
              "3         9.12    1.72      0.02    0.45     0.05    Good  \n",
              "4        16.90    2.41      0.02    0.06     0.02    Good  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9c8cb8f2-303c-45b8-b65d-abeb7e1ec189\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>aluminium</th>\n",
              "      <th>ammonia</th>\n",
              "      <th>arsenic</th>\n",
              "      <th>barium</th>\n",
              "      <th>cadmium</th>\n",
              "      <th>chloramine</th>\n",
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              "      <th>bacteria</th>\n",
              "      <th>viruses</th>\n",
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              "      <th>nitrites</th>\n",
              "      <th>mercury</th>\n",
              "      <th>perchlorate</th>\n",
              "      <th>radium</th>\n",
              "      <th>selenium</th>\n",
              "      <th>silver</th>\n",
              "      <th>uranium</th>\n",
              "      <th>is_safe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.65</td>\n",
              "      <td>9.08</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.85</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.05</td>\n",
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              "      <td>16.08</td>\n",
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              "      <td>37.75</td>\n",
              "      <td>6.78</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.02</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.32</td>\n",
              "      <td>21.16</td>\n",
              "      <td>0.01</td>\n",
              "      <td>3.31</td>\n",
              "      <td>0.002</td>\n",
              "      <td>5.28</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.650</td>\n",
              "      <td>0.100</td>\n",
              "      <td>2.01</td>\n",
              "      <td>1.93</td>\n",
              "      <td>0.003</td>\n",
              "      <td>32.26</td>\n",
              "      <td>3.21</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.01</td>\n",
              "      <td>14.02</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.008</td>\n",
              "      <td>4.24</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.078</td>\n",
              "      <td>14.16</td>\n",
              "      <td>1.11</td>\n",
              "      <td>0.006</td>\n",
              "      <td>50.28</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.01</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.36</td>\n",
              "      <td>11.33</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.96</td>\n",
              "      <td>0.001</td>\n",
              "      <td>7.23</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.66</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.710</td>\n",
              "      <td>0.016</td>\n",
              "      <td>1.41</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.004</td>\n",
              "      <td>9.12</td>\n",
              "      <td>1.72</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.92</td>\n",
              "      <td>24.33</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.006</td>\n",
              "      <td>2.67</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.117</td>\n",
              "      <td>6.74</td>\n",
              "      <td>1.11</td>\n",
              "      <td>0.003</td>\n",
              "      <td>16.90</td>\n",
              "      <td>2.41</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.02</td>\n",
              "      <td>Good</td>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "water_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9RwMMwamvaa",
        "outputId": "a4f41bd7-c25c-4cad-9cb8-afa234271857"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7999 entries, 0 to 7998\n",
            "Data columns (total 21 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   aluminium    7999 non-null   float64\n",
            " 1   ammonia      7996 non-null   float64\n",
            " 2   arsenic      7999 non-null   float64\n",
            " 3   barium       7999 non-null   float64\n",
            " 4   cadmium      7999 non-null   float64\n",
            " 5   chloramine   7999 non-null   float64\n",
            " 6   chromium     7999 non-null   float64\n",
            " 7   copper       7999 non-null   float64\n",
            " 8   flouride     7999 non-null   float64\n",
            " 9   bacteria     7999 non-null   float64\n",
            " 10  viruses      7999 non-null   float64\n",
            " 11  lead         7999 non-null   float64\n",
            " 12  nitrates     7999 non-null   float64\n",
            " 13  nitrites     7999 non-null   float64\n",
            " 14  mercury      7999 non-null   float64\n",
            " 15  perchlorate  7999 non-null   float64\n",
            " 16  radium       7999 non-null   float64\n",
            " 17  selenium     7999 non-null   float64\n",
            " 18  silver       7999 non-null   float64\n",
            " 19  uranium      7999 non-null   float64\n",
            " 20  is_safe      7999 non-null   object \n",
            "dtypes: float64(20), object(1)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# find the minimum of each column\n",
        "minValues = print(water_df.min())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0wIRBjhfm_GK",
        "outputId": "eadcde49-689f-437d-b680-9f74527f25c6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "aluminium       0.0\n",
            "ammonia       -0.08\n",
            "arsenic         0.0\n",
            "barium          0.0\n",
            "cadmium         0.0\n",
            "chloramine      0.0\n",
            "chromium        0.0\n",
            "copper          0.0\n",
            "flouride        0.0\n",
            "bacteria        0.0\n",
            "viruses         0.0\n",
            "lead            0.0\n",
            "nitrates        0.0\n",
            "nitrites        0.0\n",
            "mercury         0.0\n",
            "perchlorate     0.0\n",
            "radium          0.0\n",
            "selenium        0.0\n",
            "silver          0.0\n",
            "uranium         0.0\n",
            "is_safe         Bad\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# find the maximum of each column\n",
        "maxValues = print(water_df.max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGDshTannHDI",
        "outputId": "995bfcf4-d6a9-471a-f898-d7bf658ae33b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "aluminium       5.05\n",
            "ammonia        29.84\n",
            "arsenic         1.05\n",
            "barium          4.94\n",
            "cadmium         0.13\n",
            "chloramine      8.68\n",
            "chromium         0.9\n",
            "copper           2.0\n",
            "flouride         1.5\n",
            "bacteria         1.0\n",
            "viruses          1.0\n",
            "lead             0.2\n",
            "nitrates       19.83\n",
            "nitrites        2.93\n",
            "mercury         0.01\n",
            "perchlorate    60.01\n",
            "radium          7.99\n",
            "selenium         0.1\n",
            "silver           0.5\n",
            "uranium         0.09\n",
            "is_safe         Good\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Setup PyCaret environment for model training\n",
        "from pycaret.classification import *\n",
        "exp_water = setup(data = water_df, target = 'is_safe',  use_gpu = True, session_id = 123)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "383dcabd63af47fe9b689ce8a8e03fb2",
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            "e8609cd0e8e740a7871ffb34565b1c1f"
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        "id": "A1AaUhtRukf0",
        "outputId": "3f0cf1b3-9f14-47d3-e0cd-a8c7c407d385"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id               123\n",
              "1                                   Target           is_safe\n",
              "2                              Target Type            Binary\n",
              "3                            Label Encoded   Bad: 0, Good: 1\n",
              "4                            Original Data        (7999, 21)\n",
              "5                           Missing Values              True\n",
              "6                         Numeric Features                20\n",
              "7                     Categorical Features                 0\n",
              "8                         Ordinal Features             False\n",
              "9                High Cardinality Features             False\n",
              "10                 High Cardinality Method              None\n",
              "11                   Transformed Train Set        (5599, 20)\n",
              "12                    Transformed Test Set        (2400, 20)\n",
              "13                      Shuffle Train-Test              True\n",
              "14                     Stratify Train-Test             False\n",
              "15                          Fold Generator   StratifiedKFold\n",
              "16                             Fold Number                10\n",
              "17                                CPU Jobs                -1\n",
              "18                                 Use GPU              True\n",
              "19                          Log Experiment             False\n",
              "20                         Experiment Name  clf-default-name\n",
              "21                                     USI              b9a4\n",
              "22                         Imputation Type            simple\n",
              "23          Iterative Imputation Iteration              None\n",
              "24                         Numeric Imputer              mean\n",
              "25      Iterative Imputation Numeric Model              None\n",
              "26                     Categorical Imputer          constant\n",
              "27  Iterative Imputation Categorical Model              None\n",
              "28           Unknown Categoricals Handling    least_frequent\n",
              "29                               Normalize             False\n",
              "30                        Normalize Method              None\n",
              "31                          Transformation             False\n",
              "32                   Transformation Method              None\n",
              "33                                     PCA             False\n",
              "34                              PCA Method              None\n",
              "35                          PCA Components              None\n",
              "36                     Ignore Low Variance             False\n",
              "37                     Combine Rare Levels             False\n",
              "38                    Rare Level Threshold              None\n",
              "39                         Numeric Binning             False\n",
              "40                         Remove Outliers             False\n",
              "41                      Outliers Threshold              None\n",
              "42                Remove Multicollinearity             False\n",
              "43             Multicollinearity Threshold              None\n",
              "44             Remove Perfect Collinearity              True\n",
              "45                              Clustering             False\n",
              "46                    Clustering Iteration              None\n",
              "47                     Polynomial Features             False\n",
              "48                       Polynomial Degree              None\n",
              "49                    Trignometry Features             False\n",
              "50                    Polynomial Threshold              None\n",
              "51                          Group Features             False\n",
              "52                       Feature Selection             False\n",
              "53                Feature Selection Method           classic\n",
              "54            Features Selection Threshold              None\n",
              "55                     Feature Interaction             False\n",
              "56                           Feature Ratio             False\n",
              "57                   Interaction Threshold              None\n",
              "58                           Fix Imbalance             False\n",
              "59                    Fix Imbalance Method             SMOTE"
            ],
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              "      <th></th>\n",
              "      <th>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>is_safe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Target Type</td>\n",
              "      <td>Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Label Encoded</td>\n",
              "      <td>Bad: 0, Good: 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(7999, 21)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(5599, 20)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(2400, 20)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>USI</td>\n",
              "      <td>b9a4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Remove Perfect Collinearity</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Fix Imbalance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Fix Imbalance Method</td>\n",
              "      <td>SMOTE</td>\n",
              "    </tr>\n",
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    {
      "cell_type": "code",
      "source": [
        "best = compare_models()"
      ],
      "metadata": {
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          "height": 488,
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        "outputId": "120a0da7-f504-4b38-e994-ead6a56f322d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "lightgbm  Light Gradient Boosting Machine    0.9668  0.9844  0.7973  0.8919   \n",
              "dt               Decision Tree Classifier    0.9552  0.8868  0.7989  0.7992   \n",
              "rf               Random Forest Classifier    0.9550  0.9741  0.6527  0.9188   \n",
              "gbc          Gradient Boosting Classifier    0.9550  0.9782  0.6977  0.8721   \n",
              "ada                  Ada Boost Classifier    0.9323  0.9344  0.5563  0.7718   \n",
              "et                 Extra Trees Classifier    0.9316  0.9498  0.4773  0.8402   \n",
              "lr                    Logistic Regression    0.9032  0.8485  0.2684  0.6541   \n",
              "lda          Linear Discriminant Analysis    0.8986  0.8549  0.3922  0.5624   \n",
              "ridge                    Ridge Classifier    0.8984  0.0000  0.1365  0.7285   \n",
              "dummy                    Dummy Classifier    0.8889  0.5000  0.0000  0.0000   \n",
              "svm                   SVM - Linear Kernel    0.8852  0.0000  0.2949  0.6495   \n",
              "knn                K Neighbors Classifier    0.8812  0.7277  0.1125  0.3803   \n",
              "qda       Quadratic Discriminant Analysis    0.8723  0.8922  0.6607  0.4512   \n",
              "nb                            Naive Bayes    0.8457  0.8153  0.6125  0.3809   \n",
              "\n",
              "              F1   Kappa     MCC  TT (Sec)  \n",
              "lightgbm  0.8411  0.8227  0.8248     0.324  \n",
              "dt        0.7974  0.7723  0.7733     0.040  \n",
              "rf        0.7622  0.7382  0.7520     1.221  \n",
              "gbc       0.7731  0.7486  0.7555     1.517  \n",
              "ada       0.6446  0.6085  0.6194     0.391  \n",
              "et        0.6032  0.5694  0.5994     1.154  \n",
              "lr        0.3792  0.3370  0.3771     1.568  \n",
              "lda       0.4608  0.4069  0.4157     0.058  \n",
              "ridge     0.2282  0.2005  0.2844     0.029  \n",
              "dummy     0.0000  0.0000  0.0000     0.008  \n",
              "svm       0.3360  0.2916  0.3504     0.066  \n",
              "knn       0.1722  0.1288  0.1572     0.190  \n",
              "qda       0.5348  0.4641  0.4763     0.021  \n",
              "nb        0.4687  0.3844  0.3999     0.013  "
            ],
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
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              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.9668</td>\n",
              "      <td>0.9844</td>\n",
              "      <td>0.7973</td>\n",
              "      <td>0.8919</td>\n",
              "      <td>0.8411</td>\n",
              "      <td>0.8227</td>\n",
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              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.9552</td>\n",
              "      <td>0.8868</td>\n",
              "      <td>0.7989</td>\n",
              "      <td>0.7992</td>\n",
              "      <td>0.7974</td>\n",
              "      <td>0.7723</td>\n",
              "      <td>0.7733</td>\n",
              "      <td>0.040</td>\n",
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              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.9550</td>\n",
              "      <td>0.9741</td>\n",
              "      <td>0.6527</td>\n",
              "      <td>0.9188</td>\n",
              "      <td>0.7622</td>\n",
              "      <td>0.7382</td>\n",
              "      <td>0.7520</td>\n",
              "      <td>1.221</td>\n",
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              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.9550</td>\n",
              "      <td>0.9782</td>\n",
              "      <td>0.6977</td>\n",
              "      <td>0.8721</td>\n",
              "      <td>0.7731</td>\n",
              "      <td>0.7486</td>\n",
              "      <td>0.7555</td>\n",
              "      <td>1.517</td>\n",
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              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.9323</td>\n",
              "      <td>0.9344</td>\n",
              "      <td>0.5563</td>\n",
              "      <td>0.7718</td>\n",
              "      <td>0.6446</td>\n",
              "      <td>0.6085</td>\n",
              "      <td>0.6194</td>\n",
              "      <td>0.391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>0.9316</td>\n",
              "      <td>0.9498</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.8402</td>\n",
              "      <td>0.6032</td>\n",
              "      <td>0.5694</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>1.154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.9032</td>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.2684</td>\n",
              "      <td>0.6541</td>\n",
              "      <td>0.3792</td>\n",
              "      <td>0.3370</td>\n",
              "      <td>0.3771</td>\n",
              "      <td>1.568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.8986</td>\n",
              "      <td>0.8549</td>\n",
              "      <td>0.3922</td>\n",
              "      <td>0.5624</td>\n",
              "      <td>0.4608</td>\n",
              "      <td>0.4069</td>\n",
              "      <td>0.4157</td>\n",
              "      <td>0.058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.8984</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.1365</td>\n",
              "      <td>0.7285</td>\n",
              "      <td>0.2282</td>\n",
              "      <td>0.2005</td>\n",
              "      <td>0.2844</td>\n",
              "      <td>0.029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dummy</th>\n",
              "      <td>Dummy Classifier</td>\n",
              "      <td>0.8889</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.8852</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2949</td>\n",
              "      <td>0.6495</td>\n",
              "      <td>0.3360</td>\n",
              "      <td>0.2916</td>\n",
              "      <td>0.3504</td>\n",
              "      <td>0.066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.8812</td>\n",
              "      <td>0.7277</td>\n",
              "      <td>0.1125</td>\n",
              "      <td>0.3803</td>\n",
              "      <td>0.1722</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>0.1572</td>\n",
              "      <td>0.190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.8723</td>\n",
              "      <td>0.8922</td>\n",
              "      <td>0.6607</td>\n",
              "      <td>0.4512</td>\n",
              "      <td>0.5348</td>\n",
              "      <td>0.4641</td>\n",
              "      <td>0.4763</td>\n",
              "      <td>0.021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.8457</td>\n",
              "      <td>0.8153</td>\n",
              "      <td>0.6125</td>\n",
              "      <td>0.3809</td>\n",
              "      <td>0.4687</td>\n",
              "      <td>0.3844</td>\n",
              "      <td>0.3999</td>\n",
              "      <td>0.013</td>\n",
              "    </tr>\n",
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    {
      "cell_type": "code",
      "source": [
        "#  Tuning the Setup model\n",
        "exp_water_1 = setup(data = water_df, target = 'is_safe', session_id=123,\n",
        "                  normalize = True, \n",
        "                  transformation = True,\n",
        "                  use_gpu = True)"
      ],
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      "execution_count": 16,
      "outputs": [
        {
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            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id               123\n",
              "1                                   Target           is_safe\n",
              "2                              Target Type            Binary\n",
              "3                            Label Encoded   Bad: 0, Good: 1\n",
              "4                            Original Data        (7999, 21)\n",
              "5                           Missing Values              True\n",
              "6                         Numeric Features                20\n",
              "7                     Categorical Features                 0\n",
              "8                         Ordinal Features             False\n",
              "9                High Cardinality Features             False\n",
              "10                 High Cardinality Method              None\n",
              "11                   Transformed Train Set        (5599, 20)\n",
              "12                    Transformed Test Set        (2400, 20)\n",
              "13                      Shuffle Train-Test              True\n",
              "14                     Stratify Train-Test             False\n",
              "15                          Fold Generator   StratifiedKFold\n",
              "16                             Fold Number                10\n",
              "17                                CPU Jobs                -1\n",
              "18                                 Use GPU              True\n",
              "19                          Log Experiment             False\n",
              "20                         Experiment Name  clf-default-name\n",
              "21                                     USI              ee43\n",
              "22                         Imputation Type            simple\n",
              "23          Iterative Imputation Iteration              None\n",
              "24                         Numeric Imputer              mean\n",
              "25      Iterative Imputation Numeric Model              None\n",
              "26                     Categorical Imputer          constant\n",
              "27  Iterative Imputation Categorical Model              None\n",
              "28           Unknown Categoricals Handling    least_frequent\n",
              "29                               Normalize              True\n",
              "30                        Normalize Method            zscore\n",
              "31                          Transformation              True\n",
              "32                   Transformation Method       yeo-johnson\n",
              "33                                     PCA             False\n",
              "34                              PCA Method              None\n",
              "35                          PCA Components              None\n",
              "36                     Ignore Low Variance             False\n",
              "37                     Combine Rare Levels             False\n",
              "38                    Rare Level Threshold              None\n",
              "39                         Numeric Binning             False\n",
              "40                         Remove Outliers             False\n",
              "41                      Outliers Threshold              None\n",
              "42                Remove Multicollinearity             False\n",
              "43             Multicollinearity Threshold              None\n",
              "44             Remove Perfect Collinearity              True\n",
              "45                              Clustering             False\n",
              "46                    Clustering Iteration              None\n",
              "47                     Polynomial Features             False\n",
              "48                       Polynomial Degree              None\n",
              "49                    Trignometry Features             False\n",
              "50                    Polynomial Threshold              None\n",
              "51                          Group Features             False\n",
              "52                       Feature Selection             False\n",
              "53                Feature Selection Method           classic\n",
              "54            Features Selection Threshold              None\n",
              "55                     Feature Interaction             False\n",
              "56                           Feature Ratio             False\n",
              "57                   Interaction Threshold              None\n",
              "58                           Fix Imbalance             False\n",
              "59                    Fix Imbalance Method             SMOTE"
            ],
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              "      <td>Transformed Train Set</td>\n",
              "      <td>(5599, 20)</td>\n",
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              "      <td>Transformed Test Set</td>\n",
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              "      <td>Shuffle Train-Test</td>\n",
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              "      <th>14</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
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              "      <th>15</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>StratifiedKFold</td>\n",
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              "      <th>16</th>\n",
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              "      <th>18</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>True</td>\n",
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              "      <th>19</th>\n",
              "      <td>Log Experiment</td>\n",
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              "      <td>Experiment Name</td>\n",
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              "      <th>21</th>\n",
              "      <td>USI</td>\n",
              "      <td>ee43</td>\n",
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              "      <th>22</th>\n",
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              "      <th>23</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
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              "      <th>29</th>\n",
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              "      <td>Normalize Method</td>\n",
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              "      <th>31</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>True</td>\n",
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              "      <th>32</th>\n",
              "      <td>Transformation Method</td>\n",
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              "      <th>33</th>\n",
              "      <td>PCA</td>\n",
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              "      <th>34</th>\n",
              "      <td>PCA Method</td>\n",
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              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>PCA Components</td>\n",
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              "      <th>36</th>\n",
              "      <td>Ignore Low Variance</td>\n",
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              "      <th>37</th>\n",
              "      <td>Combine Rare Levels</td>\n",
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              "      <td>Rare Level Threshold</td>\n",
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              "      <td>Outliers Threshold</td>\n",
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              "      <td>Remove Multicollinearity</td>\n",
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              "      <th>43</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
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              "      <th>44</th>\n",
              "      <td>Remove Perfect Collinearity</td>\n",
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              "      <th>45</th>\n",
              "      <td>Clustering</td>\n",
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              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Fix Imbalance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Fix Imbalance Method</td>\n",
              "      <td>SMOTE</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "best = compare_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488,
          "referenced_widgets": [
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        "id": "3u4TVqlVukpF",
        "outputId": "4f1b53a2-8643-48ba-b52a-2391f6bbce17"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "lightgbm  Light Gradient Boosting Machine    0.9661  0.9859  0.7877  0.8952   \n",
              "dt               Decision Tree Classifier    0.9548  0.8866  0.7989  0.7965   \n",
              "gbc          Gradient Boosting Classifier    0.9548  0.9781  0.6977  0.8703   \n",
              "rf               Random Forest Classifier    0.9546  0.9741  0.6527  0.9146   \n",
              "et                 Extra Trees Classifier    0.9475  0.9620  0.5916  0.9007   \n",
              "ada                  Ada Boost Classifier    0.9323  0.9344  0.5563  0.7718   \n",
              "lr                    Logistic Regression    0.9211  0.8892  0.4854  0.7200   \n",
              "knn                K Neighbors Classifier    0.9205  0.8822  0.4644  0.7221   \n",
              "lda          Linear Discriminant Analysis    0.9164  0.8823  0.5852  0.6343   \n",
              "svm                   SVM - Linear Kernel    0.9137  0.0000  0.4175  0.7011   \n",
              "qda       Quadratic Discriminant Analysis    0.9103  0.9321  0.7089  0.5788   \n",
              "ridge                    Ridge Classifier    0.9080  0.0000  0.2282  0.8110   \n",
              "dummy                    Dummy Classifier    0.8889  0.5000  0.0000  0.0000   \n",
              "nb                            Naive Bayes    0.8478  0.8345  0.7091  0.3980   \n",
              "\n",
              "              F1   Kappa     MCC  TT (Sec)  \n",
              "lightgbm  0.8363  0.8175  0.8206     0.233  \n",
              "dt        0.7961  0.7707  0.7717     0.040  \n",
              "gbc       0.7724  0.7478  0.7546     1.716  \n",
              "rf        0.7607  0.7365  0.7499     1.229  \n",
              "et        0.7118  0.6845  0.7040     1.114  \n",
              "ada       0.6446  0.6085  0.6194     0.395  \n",
              "lr        0.5762  0.5347  0.5492     0.035  \n",
              "knn       0.5631  0.5219  0.5383     0.352  \n",
              "lda       0.6080  0.5613  0.5623     0.061  \n",
              "svm       0.5050  0.4629  0.4893     0.041  \n",
              "qda       0.6370  0.5864  0.5906     0.020  \n",
              "ridge     0.3536  0.3207  0.3978     0.021  \n",
              "dummy     0.0000  0.0000  0.0000     0.008  \n",
              "nb        0.5091  0.4276  0.4531     0.013  "
            ],
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.9661</td>\n",
              "      <td>0.9859</td>\n",
              "      <td>0.7877</td>\n",
              "      <td>0.8952</td>\n",
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              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
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              "    <tr>\n",
              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.9548</td>\n",
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              "      <td>0.8703</td>\n",
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              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.9546</td>\n",
              "      <td>0.9741</td>\n",
              "      <td>0.6527</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>0.7607</td>\n",
              "      <td>0.7365</td>\n",
              "      <td>0.7499</td>\n",
              "      <td>1.229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>0.9475</td>\n",
              "      <td>0.9620</td>\n",
              "      <td>0.5916</td>\n",
              "      <td>0.9007</td>\n",
              "      <td>0.7118</td>\n",
              "      <td>0.6845</td>\n",
              "      <td>0.7040</td>\n",
              "      <td>1.114</td>\n",
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              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.9323</td>\n",
              "      <td>0.9344</td>\n",
              "      <td>0.5563</td>\n",
              "      <td>0.7718</td>\n",
              "      <td>0.6446</td>\n",
              "      <td>0.6085</td>\n",
              "      <td>0.6194</td>\n",
              "      <td>0.395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.9211</td>\n",
              "      <td>0.8892</td>\n",
              "      <td>0.4854</td>\n",
              "      <td>0.7200</td>\n",
              "      <td>0.5762</td>\n",
              "      <td>0.5347</td>\n",
              "      <td>0.5492</td>\n",
              "      <td>0.035</td>\n",
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              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.9205</td>\n",
              "      <td>0.8822</td>\n",
              "      <td>0.4644</td>\n",
              "      <td>0.7221</td>\n",
              "      <td>0.5631</td>\n",
              "      <td>0.5219</td>\n",
              "      <td>0.5383</td>\n",
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              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.9164</td>\n",
              "      <td>0.8823</td>\n",
              "      <td>0.5852</td>\n",
              "      <td>0.6343</td>\n",
              "      <td>0.6080</td>\n",
              "      <td>0.5613</td>\n",
              "      <td>0.5623</td>\n",
              "      <td>0.061</td>\n",
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              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.9137</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.4175</td>\n",
              "      <td>0.7011</td>\n",
              "      <td>0.5050</td>\n",
              "      <td>0.4629</td>\n",
              "      <td>0.4893</td>\n",
              "      <td>0.041</td>\n",
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              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.9103</td>\n",
              "      <td>0.9321</td>\n",
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              "      <td>0.5788</td>\n",
              "      <td>0.6370</td>\n",
              "      <td>0.5864</td>\n",
              "      <td>0.5906</td>\n",
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              "      <th>ridge</th>\n",
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              "      <td>0.9080</td>\n",
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              "      <td>0.2282</td>\n",
              "      <td>0.8110</td>\n",
              "      <td>0.3536</td>\n",
              "      <td>0.3207</td>\n",
              "      <td>0.3978</td>\n",
              "      <td>0.021</td>\n",
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              "      <th>dummy</th>\n",
              "      <td>Dummy Classifier</td>\n",
              "      <td>0.8889</td>\n",
              "      <td>0.5000</td>\n",
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              "      <td>0.0000</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>0.008</td>\n",
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              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
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              "            + ' 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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the Light Gradient Boosting Machine Classifier\n",
        "lightgbm_water_model = create_model('lightgbm')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "e3be4e23e64749c298867caa58b0186f",
            "8e5a69064b564942a689adc5e7901f34",
            "becf0a0833024b74970cc4f0e9d3f558"
          ]
        },
        "id": "ORYcgzKKukr9",
        "outputId": "e9440888-2d7d-4b15-f463-dd16921832a1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "Fold                                                          \n",
              "0       0.9732  0.9902  0.8548  0.8983  0.8760  0.8610  0.8614\n",
              "1       0.9554  0.9806  0.7419  0.8364  0.7863  0.7615  0.7632\n",
              "2       0.9571  0.9628  0.6613  0.9318  0.7736  0.7507  0.7641\n",
              "3       0.9679  0.9914  0.8226  0.8793  0.8500  0.8320  0.8326\n",
              "4       0.9625  0.9904  0.7581  0.8868  0.8174  0.7966  0.7997\n",
              "5       0.9571  0.9833  0.7097  0.8800  0.7857  0.7622  0.7677\n",
              "6       0.9714  0.9913  0.8226  0.9107  0.8644  0.8485  0.8499\n",
              "7       0.9643  0.9837  0.8095  0.8644  0.8361  0.8160  0.8166\n",
              "8       0.9750  0.9941  0.8095  0.9623  0.8793  0.8655  0.8695\n",
              "9       0.9767  0.9916  0.8871  0.9016  0.8943  0.8812  0.8813\n",
              "Mean    0.9661  0.9859  0.7877  0.8952  0.8363  0.8175  0.8206\n",
              "Std     0.0075  0.0088  0.0654  0.0333  0.0414  0.0453  0.0430"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.9732</td>\n",
              "      <td>0.9902</td>\n",
              "      <td>0.8548</td>\n",
              "      <td>0.8983</td>\n",
              "      <td>0.8760</td>\n",
              "      <td>0.8610</td>\n",
              "      <td>0.8614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.9554</td>\n",
              "      <td>0.9806</td>\n",
              "      <td>0.7419</td>\n",
              "      <td>0.8364</td>\n",
              "      <td>0.7863</td>\n",
              "      <td>0.7615</td>\n",
              "      <td>0.7632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.9571</td>\n",
              "      <td>0.9628</td>\n",
              "      <td>0.6613</td>\n",
              "      <td>0.9318</td>\n",
              "      <td>0.7736</td>\n",
              "      <td>0.7507</td>\n",
              "      <td>0.7641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.9679</td>\n",
              "      <td>0.9914</td>\n",
              "      <td>0.8226</td>\n",
              "      <td>0.8793</td>\n",
              "      <td>0.8500</td>\n",
              "      <td>0.8320</td>\n",
              "      <td>0.8326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.9625</td>\n",
              "      <td>0.9904</td>\n",
              "      <td>0.7581</td>\n",
              "      <td>0.8868</td>\n",
              "      <td>0.8174</td>\n",
              "      <td>0.7966</td>\n",
              "      <td>0.7997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.9571</td>\n",
              "      <td>0.9833</td>\n",
              "      <td>0.7097</td>\n",
              "      <td>0.8800</td>\n",
              "      <td>0.7857</td>\n",
              "      <td>0.7622</td>\n",
              "      <td>0.7677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.9714</td>\n",
              "      <td>0.9913</td>\n",
              "      <td>0.8226</td>\n",
              "      <td>0.9107</td>\n",
              "      <td>0.8644</td>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.8499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.9643</td>\n",
              "      <td>0.9837</td>\n",
              "      <td>0.8095</td>\n",
              "      <td>0.8644</td>\n",
              "      <td>0.8361</td>\n",
              "      <td>0.8160</td>\n",
              "      <td>0.8166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.9750</td>\n",
              "      <td>0.9941</td>\n",
              "      <td>0.8095</td>\n",
              "      <td>0.9623</td>\n",
              "      <td>0.8793</td>\n",
              "      <td>0.8655</td>\n",
              "      <td>0.8695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.9767</td>\n",
              "      <td>0.9916</td>\n",
              "      <td>0.8871</td>\n",
              "      <td>0.9016</td>\n",
              "      <td>0.8943</td>\n",
              "      <td>0.8812</td>\n",
              "      <td>0.8813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.9661</td>\n",
              "      <td>0.9859</td>\n",
              "      <td>0.7877</td>\n",
              "      <td>0.8952</td>\n",
              "      <td>0.8363</td>\n",
              "      <td>0.8175</td>\n",
              "      <td>0.8206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Std</th>\n",
              "      <td>0.0075</td>\n",
              "      <td>0.0088</td>\n",
              "      <td>0.0654</td>\n",
              "      <td>0.0333</td>\n",
              "      <td>0.0414</td>\n",
              "      <td>0.0453</td>\n",
              "      <td>0.0430</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-783b04e5-0e7f-40ba-bf68-f66fae9124f2 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-783b04e5-0e7f-40ba-bf68-f66fae9124f2');\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",
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              "  "
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        }
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    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate the model\n",
        "evaluate_model(lightgbm_water_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 792,
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        "outputId": "4e589572-c762-440b-c30d-2738f66bbc9e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
            ],
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        }
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    {
      "cell_type": "code",
      "source": [
        "predict_model(lightgbm_water_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "rnIjyzIwBVPU",
        "outputId": "fb807144-6c32-4fbd-e666-e50f0e43ed40"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                             Model  Accuracy     AUC  Recall  Prec.      F1  \\\n",
              "0  Light Gradient Boosting Machine    0.9733  0.9905  0.8345  0.938  0.8832   \n",
              "\n",
              "    Kappa     MCC  \n",
              "0  0.8682  0.8701  "
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              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
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              "      <th>0</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.9733</td>\n",
              "      <td>0.9905</td>\n",
              "      <td>0.8345</td>\n",
              "      <td>0.938</td>\n",
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              "                                                     [key], {});\n",
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              "  "
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      aluminium   ammonia   arsenic    barium   cadmium  chloramine  chromium  \\\n",
              "0     -0.850979 -1.296318 -0.869719  0.785364 -0.219413   -0.768039 -0.990150   \n",
              "1     -0.698416  1.552371 -0.486692 -1.103985  1.248661   -0.972439 -0.625500   \n",
              "2     -0.040529 -0.182472 -0.671864  1.365165 -1.176571    1.562961  1.626859   \n",
              "3      1.383837  0.090561  1.702120  0.437064  1.248661    1.648068 -0.625500   \n",
              "4     -0.649200 -1.508753  0.266516 -1.131205 -0.564349   -0.954170 -1.291731   \n",
              "...         ...       ...       ...       ...       ...         ...       ...   \n",
              "2395   1.699991 -0.268510  1.827127  1.722623  1.608791   -0.092498  1.424025   \n",
              "2396  -0.553168 -1.531742 -0.313693 -0.414687 -1.340339   -0.935996 -0.712708   \n",
              "2397  -0.649200 -1.359433 -0.002208  0.300888 -1.340339   -0.811416 -0.802539   \n",
              "2398  -0.850979  1.055304  0.137259 -1.616081  1.248661   -0.935996 -0.895013   \n",
              "2399   1.709082  0.586839  1.847849  1.106474 -0.219413    1.275703 -0.802539   \n",
              "\n",
              "        copper  flouride  bacteria  ...  nitrites   mercury  perchlorate  \\\n",
              "0    -1.138466  0.309171 -1.072046  ...  0.040630 -0.092332    -0.795107   \n",
              "1    -1.272977 -0.332992 -1.072046  ... -1.716512 -0.423554    -1.161251   \n",
              "2     0.397371 -1.437920  0.372574  ...  1.297258 -0.092332     0.331263   \n",
              "3    -0.323851  0.983399  0.941599  ...  0.525553  0.593155     1.426454   \n",
              "4     0.176282  0.101650 -1.072046  ... -1.354165  1.302212    -1.158608   \n",
              "...        ...       ...       ...  ...       ...       ...          ...   \n",
              "2395 -1.062473  0.610476 -1.072046  ...  0.420069 -1.068556    -0.459308   \n",
              "2396 -1.119408  1.240911  1.044676  ... -0.368028 -0.423554    -0.166942   \n",
              "2397  0.221502  1.569800  0.920291  ... -0.401077  0.593155    -0.652564   \n",
              "2398  0.874342  0.680237 -1.072046  ... -1.087736  0.945266    -0.819091   \n",
              "2399  0.454387  0.960032 -1.072046  ...  0.900083  0.945266     1.155512   \n",
              "\n",
              "        radium  selenium    silver   uranium  is_safe  Label   Score  \n",
              "0    -1.463592  1.404955 -0.844165 -0.899312      Bad    Bad  0.9989  \n",
              "1    -1.035612 -0.343642 -1.539189  0.579097      Bad    Bad  0.9997  \n",
              "2    -0.369448 -0.343642  1.183302 -0.531854     Good    Bad  0.9920  \n",
              "3     1.019591  0.354862 -0.402597 -0.899312      Bad    Bad  0.9726  \n",
              "4     0.618821 -0.692360 -0.542322 -1.631395      Bad    Bad  0.9999  \n",
              "...        ...       ...       ...       ...      ...    ...     ...  \n",
              "2395 -0.876891  1.404955 -0.270246 -1.631395      Bad    Bad  0.8720  \n",
              "2396  0.152314  0.704645 -0.402597 -0.531854      Bad    Bad  0.9948  \n",
              "2397 -1.313036 -0.343642 -0.844165 -1.265770      Bad    Bad  0.9943  \n",
              "2398  0.343596  1.404955 -1.353865 -0.531854      Bad    Bad  0.9998  \n",
              "2399  1.761900 -0.692360  1.398620 -1.631395      Bad    Bad  0.9995  \n",
              "\n",
              "[2400 rows x 23 columns]"
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              "      <th></th>\n",
              "      <th>aluminium</th>\n",
              "      <th>ammonia</th>\n",
              "      <th>arsenic</th>\n",
              "      <th>barium</th>\n",
              "      <th>cadmium</th>\n",
              "      <th>chloramine</th>\n",
              "      <th>chromium</th>\n",
              "      <th>copper</th>\n",
              "      <th>flouride</th>\n",
              "      <th>bacteria</th>\n",
              "      <th>...</th>\n",
              "      <th>nitrites</th>\n",
              "      <th>mercury</th>\n",
              "      <th>perchlorate</th>\n",
              "      <th>radium</th>\n",
              "      <th>selenium</th>\n",
              "      <th>silver</th>\n",
              "      <th>uranium</th>\n",
              "      <th>is_safe</th>\n",
              "      <th>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>-0.850979</td>\n",
              "      <td>-1.296318</td>\n",
              "      <td>-0.869719</td>\n",
              "      <td>0.785364</td>\n",
              "      <td>-0.219413</td>\n",
              "      <td>-0.768039</td>\n",
              "      <td>-0.990150</td>\n",
              "      <td>-1.138466</td>\n",
              "      <td>0.309171</td>\n",
              "      <td>-1.072046</td>\n",
              "      <td>...</td>\n",
              "      <td>0.040630</td>\n",
              "      <td>-0.092332</td>\n",
              "      <td>-0.795107</td>\n",
              "      <td>-1.463592</td>\n",
              "      <td>1.404955</td>\n",
              "      <td>-0.844165</td>\n",
              "      <td>-0.899312</td>\n",
              "      <td>Bad</td>\n",
              "      <td>Bad</td>\n",
              "      <td>0.9989</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.698416</td>\n",
              "      <td>1.552371</td>\n",
              "      <td>-0.486692</td>\n",
              "      <td>-1.103985</td>\n",
              "      <td>1.248661</td>\n",
              "      <td>-0.972439</td>\n",
              "      <td>-0.625500</td>\n",
              "      <td>-1.272977</td>\n",
              "      <td>-0.332992</td>\n",
              "      <td>-1.072046</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.716512</td>\n",
              "      <td>-0.423554</td>\n",
              "      <td>-1.161251</td>\n",
              "      <td>-1.035612</td>\n",
              "      <td>-0.343642</td>\n",
              "      <td>-1.539189</td>\n",
              "      <td>0.579097</td>\n",
              "      <td>Bad</td>\n",
              "      <td>Bad</td>\n",
              "      <td>0.9997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.040529</td>\n",
              "      <td>-0.182472</td>\n",
              "      <td>-0.671864</td>\n",
              "      <td>1.365165</td>\n",
              "      <td>-1.176571</td>\n",
              "      <td>1.562961</td>\n",
              "      <td>1.626859</td>\n",
              "      <td>0.397371</td>\n",
              "      <td>-1.437920</td>\n",
              "      <td>0.372574</td>\n",
              "      <td>...</td>\n",
              "      <td>1.297258</td>\n",
              "      <td>-0.092332</td>\n",
              "      <td>0.331263</td>\n",
              "      <td>-0.369448</td>\n",
              "      <td>-0.343642</td>\n",
              "      <td>1.183302</td>\n",
              "      <td>-0.531854</td>\n",
              "      <td>Good</td>\n",
              "      <td>Bad</td>\n",
              "      <td>0.9920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.383837</td>\n",
              "      <td>0.090561</td>\n",
              "      <td>1.702120</td>\n",
              "      <td>0.437064</td>\n",
              "      <td>1.248661</td>\n",
              "      <td>1.648068</td>\n",
              "      <td>-0.625500</td>\n",
              "      <td>-0.323851</td>\n",
              "      <td>0.983399</td>\n",
              "      <td>0.941599</td>\n",
              "      <td>...</td>\n",
              "      <td>0.525553</td>\n",
              "      <td>0.593155</td>\n",
              "      <td>1.426454</td>\n",
              "      <td>1.019591</td>\n",
              "      <td>0.354862</td>\n",
              "      <td>-0.402597</td>\n",
              "      <td>-0.899312</td>\n",
              "      <td>Bad</td>\n",
              "      <td>Bad</td>\n",
              "      <td>0.9726</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.649200</td>\n",
              "      <td>-1.508753</td>\n",
              "      <td>0.266516</td>\n",
              "      <td>-1.131205</td>\n",
              "      <td>-0.564349</td>\n",
              "      <td>-0.954170</td>\n",
              "      <td>-1.291731</td>\n",
              "      <td>0.176282</td>\n",
              "      <td>0.101650</td>\n",
              "      <td>-1.072046</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.354165</td>\n",
              "      <td>1.302212</td>\n",
              "      <td>-1.158608</td>\n",
              "      <td>0.618821</td>\n",
              "      <td>-0.692360</td>\n",
              "      <td>-0.542322</td>\n",
              "      <td>-1.631395</td>\n",
              "      <td>Bad</td>\n",
              "      <td>Bad</td>\n",
              "      <td>0.9999</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>2395</th>\n",
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              "      <td>1.608791</td>\n",
              "      <td>-0.092498</td>\n",
              "      <td>1.424025</td>\n",
              "      <td>-1.062473</td>\n",
              "      <td>0.610476</td>\n",
              "      <td>-1.072046</td>\n",
              "      <td>...</td>\n",
              "      <td>0.420069</td>\n",
              "      <td>-1.068556</td>\n",
              "      <td>-0.459308</td>\n",
              "      <td>-0.876891</td>\n",
              "      <td>1.404955</td>\n",
              "      <td>-0.270246</td>\n",
              "      <td>-1.631395</td>\n",
              "      <td>Bad</td>\n",
              "      <td>Bad</td>\n",
              "      <td>0.8720</td>\n",
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              "      <th>2396</th>\n",
              "      <td>-0.553168</td>\n",
              "      <td>-1.531742</td>\n",
              "      <td>-0.313693</td>\n",
              "      <td>-0.414687</td>\n",
              "      <td>-1.340339</td>\n",
              "      <td>-0.935996</td>\n",
              "      <td>-0.712708</td>\n",
              "      <td>-1.119408</td>\n",
              "      <td>1.240911</td>\n",
              "      <td>1.044676</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.368028</td>\n",
              "      <td>-0.423554</td>\n",
              "      <td>-0.166942</td>\n",
              "      <td>0.152314</td>\n",
              "      <td>0.704645</td>\n",
              "      <td>-0.402597</td>\n",
              "      <td>-0.531854</td>\n",
              "      <td>Bad</td>\n",
              "      <td>Bad</td>\n",
              "      <td>0.9948</td>\n",
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              "    <tr>\n",
              "      <th>2397</th>\n",
              "      <td>-0.649200</td>\n",
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              "      <td>-0.002208</td>\n",
              "      <td>0.300888</td>\n",
              "      <td>-1.340339</td>\n",
              "      <td>-0.811416</td>\n",
              "      <td>-0.802539</td>\n",
              "      <td>0.221502</td>\n",
              "      <td>1.569800</td>\n",
              "      <td>0.920291</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.401077</td>\n",
              "      <td>0.593155</td>\n",
              "      <td>-0.652564</td>\n",
              "      <td>-1.313036</td>\n",
              "      <td>-0.343642</td>\n",
              "      <td>-0.844165</td>\n",
              "      <td>-1.265770</td>\n",
              "      <td>Bad</td>\n",
              "      <td>Bad</td>\n",
              "      <td>0.9943</td>\n",
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              "    <tr>\n",
              "      <th>2398</th>\n",
              "      <td>-0.850979</td>\n",
              "      <td>1.055304</td>\n",
              "      <td>0.137259</td>\n",
              "      <td>-1.616081</td>\n",
              "      <td>1.248661</td>\n",
              "      <td>-0.935996</td>\n",
              "      <td>-0.895013</td>\n",
              "      <td>0.874342</td>\n",
              "      <td>0.680237</td>\n",
              "      <td>-1.072046</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.087736</td>\n",
              "      <td>0.945266</td>\n",
              "      <td>-0.819091</td>\n",
              "      <td>0.343596</td>\n",
              "      <td>1.404955</td>\n",
              "      <td>-1.353865</td>\n",
              "      <td>-0.531854</td>\n",
              "      <td>Bad</td>\n",
              "      <td>Bad</td>\n",
              "      <td>0.9998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2399</th>\n",
              "      <td>1.709082</td>\n",
              "      <td>0.586839</td>\n",
              "      <td>1.847849</td>\n",
              "      <td>1.106474</td>\n",
              "      <td>-0.219413</td>\n",
              "      <td>1.275703</td>\n",
              "      <td>-0.802539</td>\n",
              "      <td>0.454387</td>\n",
              "      <td>0.960032</td>\n",
              "      <td>-1.072046</td>\n",
              "      <td>...</td>\n",
              "      <td>0.900083</td>\n",
              "      <td>0.945266</td>\n",
              "      <td>1.155512</td>\n",
              "      <td>1.761900</td>\n",
              "      <td>-0.692360</td>\n",
              "      <td>1.398620</td>\n",
              "      <td>-1.631395</td>\n",
              "      <td>Bad</td>\n",
              "      <td>Bad</td>\n",
              "      <td>0.9995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>2400 rows × 23 columns</p>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "save_model(lightgbm_water_model, model_name = 'Trained_water_quality_model')"
      ],
      "metadata": {
        "id": "NuuDqIUQBVSP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3A1K3gHoC9k8"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating the web app with streamlit\n",
        "from pycaret.classification import load_model, predict_model\n",
        "import streamlit as st\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def predict_quality(model, df):\n",
        "    \n",
        "    predictions_data = predict_model(estimator = model, data = df)\n",
        "    \n",
        "    return predictions_data['Label'][0]\n",
        "    \n",
        "model = load_model('Trained_water_quality_model')\n",
        "\n",
        "\n",
        "st.title('Water Quality Estimation App')\n",
        "st.write('This is a web app to classify the quality of your water based on\\\n",
        "         several features shown on the sidebar. Please adjust the\\\n",
        "         value of each feature. Then, click on the Predict button at the bottom to\\\n",
        "         see the prediction of the classifier.')\n",
        "\n",
        "\n",
        "aluminium = st.sidebar.slider(label = 'Auminium', min_value = 0.00,\n",
        "                          max_value = 5.5,\n",
        "                          value = 2.5,\n",
        "                          step = 0.1)\n",
        "\n",
        "ammonia = st.sidebar.slider(label = 'Ammonia', min_value = 0.00,\n",
        "                          max_value = 30.0,\n",
        "                          value = 15.00,\n",
        "                          step = 0.01)\n",
        "                          \n",
        "arsenic = st.sidebar.slider(label = 'Arsenic ', min_value = 0.00,\n",
        "                          max_value = 1.10,\n",
        "                          value = 0.50,\n",
        "                          step = 0.01)  \n",
        " \n",
        "barium = st.sidebar.slider(label = 'Barium', min_value = 0.0,\n",
        "                          max_value = 5.0,\n",
        "                          value = 2.0,\n",
        "                          step = 0.2)                       \n",
        "\n",
        "cadmium = st.sidebar.slider(label = 'Cadmium', min_value = 0.0,\n",
        "                          max_value = 0.2 ,\n",
        "                          value = 0.0001,\n",
        "                          step = 0.001)\n",
        "\n",
        "chloramine = st.sidebar.slider(label = 'Chloramine', min_value = 0.0,\n",
        "                          max_value = 8.7,\n",
        "                          value = 1.0,\n",
        "                          step = 0.1)\n",
        "\n",
        "chromium = st.sidebar.slider(label = 'Chromium', min_value = 0.000,\n",
        "                          max_value = 1.000 ,\n",
        "                          value = 0.500,\n",
        "                          step = 0.001)\n",
        "   \n",
        "copper = st.sidebar.slider(label = 'Copper', min_value = 0.0,\n",
        "                          max_value = 2.0,\n",
        "                          value = 0.5,\n",
        "                          step = 0.02)\n",
        "\n",
        "flouride = st.sidebar.slider(label = 'Flouride', min_value = 0.0,\n",
        "                          max_value = 1.5,\n",
        "                          value = 0.5,\n",
        "                          step = 0.001)\n",
        "\n",
        "bacteria = st.sidebar.slider(label = 'Bacteria', min_value = 0.000,\n",
        "                          max_value = 1.0,\n",
        "                          value = 0.500,\n",
        "                          step = 0.001)\n",
        "\n",
        "viruses = st.sidebar.slider(label = 'Viruses', min_value = 0.000,\n",
        "                          max_value = 1.0,\n",
        "                          value = 0.500,\n",
        "                          step = 0.001)\n",
        "\n",
        "lead = st.sidebar.slider(label = 'Lead', min_value = 0.00,\n",
        "                          max_value = 0.2,\n",
        "                          value = 0.002,\n",
        "                          step = 0.0002)\n",
        "\n",
        "nitrates = st.sidebar.slider(label = 'Nitrates', min_value = 0.00,\n",
        "                          max_value = 20.00 ,\n",
        "                          value = 10.00,\n",
        "                          step = 0.5)\n",
        "                          \n",
        "nitrites = st.sidebar.slider(label = 'Nitrites', min_value = 0.00,\n",
        "                          max_value = 3.00,\n",
        "                          value = 0.50,\n",
        "                          step = 0.03)\n",
        "\n",
        "mercury = st.sidebar.slider(label = 'Mercury', min_value = 0.0,\n",
        "                          max_value = 0.01,\n",
        "                          value = 0.001,\n",
        "                          step = 0.001)\n",
        "\n",
        "perchlorate = st.sidebar.slider(label = 'Perchlorate', min_value = 0.00,\n",
        "                          max_value = 60.00 ,\n",
        "                          value = 20.00,\n",
        "                          step = 5.00)\n",
        "\n",
        "radium = st.sidebar.slider(label = 'Radium', min_value = 0.00,\n",
        "                          max_value = 8.00 ,\n",
        "                          value = 2.00,\n",
        "                          step = 1.00)\n",
        "\n",
        "selenium = st.sidebar.slider(label = 'Selenium', min_value = 0.000,\n",
        "                          max_value = 0.1,\n",
        "                          value = 0.0500,\n",
        "                          step = 0.001)\n",
        "\n",
        "silver = st.sidebar.slider(label = 'Silver', min_value = 0.000,\n",
        "                          max_value = 0.5,\n",
        "                          value = 0.0500,\n",
        "                          step = 0.001)\n",
        "\n",
        "\n",
        "uranium = st.sidebar.slider(label = 'Uranium', min_value = 0.000,\n",
        "                          max_value = 0.09,\n",
        "                          value = 0.000,\n",
        "                          step = 0.001)\n",
        "\n",
        "features = {'aluminium': aluminium, 'ammonia': ammonia,\n",
        "            'arsenic': arsenic, 'barium': barium,\n",
        "            'cadmium': cadmium, 'chloramine': chloramine,\n",
        "            'chromium': chromium, 'copper': copper,\n",
        "            'flouride': flouride, 'bacteria': bacteria, 'viruses': viruses,\n",
        "            'lead': lead, 'nitrates': nitrates, 'nitrites': nitrites, 'mercury': mercury,\n",
        "            'perchlorate': perchlorate, 'radium': radium, 'selenium': selenium, 'silver': silver, 'uranium': uranium\n",
        "            }\n",
        " \n",
        "\n",
        "features_df  = pd.DataFrame([features])\n",
        "\n",
        "st.table(features_df)  \n",
        "\n",
        "if st.button('Predict'):\n",
        "    \n",
        "    prediction = predict_quality(model, features_df)\n",
        "    \n",
        "    st.write('Based on feature values, your wine quality is '+ str(prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMhVdHzeDe-M",
        "outputId": "13954c41-99ae-468f-d393-40e82d36c953"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ]
        }
      ]
    }
  ]
}