water_quality_data_collection-v2.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "eav8Z_I6Txhe"
      },
      "source": [
        "# Import Data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 922,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numpy in /opt/homebrew/lib/python3.11/site-packages (1.25.2)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: pandas in /opt/homebrew/lib/python3.11/site-packages (2.0.0)\n",
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            "Requirement already satisfied: numpy>=1.21.0 in /opt/homebrew/lib/python3.11/site-packages (from pandas) (1.25.2)\n",
            "Requirement already satisfied: six>=1.5 in /opt/homebrew/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: matplotlib in /opt/homebrew/lib/python3.11/site-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib) (1.0.7)\n",
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            "Requirement already satisfied: numpy>=1.20 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib) (1.25.2)\n",
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            "Requirement already satisfied: six>=1.5 in /opt/homebrew/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: xlrd in /opt/homebrew/lib/python3.11/site-packages (2.0.1)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: scikit-learn in /opt/homebrew/lib/python3.11/site-packages (1.2.2)\n",
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            "Requirement already satisfied: joblib>=1.1.1 in /opt/homebrew/lib/python3.11/site-packages (from scikit-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/homebrew/lib/python3.11/site-packages (from scikit-learn) (3.1.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: seaborn in /opt/homebrew/lib/python3.11/site-packages (0.12.2)\n",
            "Requirement already satisfied: numpy!=1.24.0,>=1.17 in /opt/homebrew/lib/python3.11/site-packages (from seaborn) (1.25.2)\n",
            "Requirement already satisfied: pandas>=0.25 in /opt/homebrew/lib/python3.11/site-packages (from seaborn) (2.0.0)\n",
            "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /opt/homebrew/lib/python3.11/site-packages (from seaborn) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.7)\n",
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            "Requirement already satisfied: packaging>=20.0 in /Users/kittizz/Library/Python/3.11/lib/python/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (10.0.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /Users/kittizz/Library/Python/3.11/lib/python/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /opt/homebrew/lib/python3.11/site-packages (from pandas>=0.25->seaborn) (2023.3)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /opt/homebrew/lib/python3.11/site-packages (from pandas>=0.25->seaborn) (2023.3)\n",
            "Requirement already satisfied: six>=1.5 in /opt/homebrew/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: openpyxl in /opt/homebrew/lib/python3.11/site-packages (3.1.2)\n",
            "Requirement already satisfied: et-xmlfile in /opt/homebrew/lib/python3.11/site-packages (from openpyxl) (1.1.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install numpy\n",
        "%pip install pandas\n",
        "%pip install matplotlib\n",
        "%pip install xlrd\n",
        "%pip install scikit-learn\n",
        "%pip install seaborn\n",
        "%pip install openpyxl\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 923,
      "metadata": {
        "id": "3TEPV63DTwOO"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from pathlib import Path\n",
        "from sklearn.model_selection import train_test_split\n",
        "import seaborn as sns  # สร้างกราฟ Boxplot\n",
        "from scipy.stats import zscore\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "%matplotlib inline\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 924,
      "metadata": {
        "id": "sxLcLFDElkSa"
      },
      "outputs": [],
      "source": [
        "df1 = pd.read_excel(\"./dataset/ค่าเฉลี่ยคุณภาพน้ำคลอง (พ.ศ. 2561-2563).xlsx\")\n",
        "df2 = pd.read_excel(\n",
        "    \"./dataset/ค่าเฉลี่ยคุณภาพน้ำแม่น้ำเจ้าพระยา ในเขตกรุงเทพมหานคร (พ.ศ.2561-2563).xlsx\")\n",
        "\n",
        "\n",
        "df_concat = pd.concat([df1, df2], axis=0)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "HoBWH9Tj0GME"
      },
      "source": [
        "# Preprocessing - Data Cleaning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 925,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(938, 21)"
            ]
          },
          "execution_count": 925,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_concat.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 926,
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Canal_id</th>\n",
              "      <th>year</th>\n",
              "      <th>Canal_name</th>\n",
              "      <th>Sample_water_point</th>\n",
              "      <th>TEMP. (oC)</th>\n",
              "      <th>pH</th>\n",
              "      <th>DO (mg/l)</th>\n",
              "      <th>H2S (mg/l)</th>\n",
              "      <th>BOD (mg/l)</th>\n",
              "      <th>COD (mg/l)</th>\n",
              "      <th>...</th>\n",
              "      <th>TKN (mg/l)</th>\n",
              "      <th>NH3N (mg/l)</th>\n",
              "      <th>NO2 (mg/l)</th>\n",
              "      <th>NO3 (mg/l)</th>\n",
              "      <th>T-P (mg/l)</th>\n",
              "      <th>T.Coliform (col/100ml)</th>\n",
              "      <th>No_</th>\n",
              "      <th>year_</th>\n",
              "      <th>Conduct. (us/cm)</th>\n",
              "      <th>Salinity (ppt)</th>\n",
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              "      <td>2.70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.00</td>\n",
              "      <td>42.00</td>\n",
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              "      <td>2.20</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.20</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.0</td>\n",
              "      <td>2561.0</td>\n",
              "      <td>คลองคูเมืองเดิม</td>\n",
              "      <td>หน้ากรมที่ดิน</td>\n",
              "      <td>25.00</td>\n",
              "      <td>8.10</td>\n",
              "      <td>2.40</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.00</td>\n",
              "      <td>53.00</td>\n",
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              "      <th>2</th>\n",
              "      <td>14.0</td>\n",
              "      <td>2561.0</td>\n",
              "      <td>คลองคูเมืองเดิม</td>\n",
              "      <td>อนุสาวรีย์แม่ธรณีมวยผม สนามหลวง</td>\n",
              "      <td>25.00</td>\n",
              "      <td>8.00</td>\n",
              "      <td>1.70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.00</td>\n",
              "      <td>56.00</td>\n",
              "      <td>...</td>\n",
              "      <td>2.20</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.34</td>\n",
              "      <td>2.30</td>\n",
              "      <td>0.20</td>\n",
              "      <td>4600000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15.0</td>\n",
              "      <td>2561.0</td>\n",
              "      <td>คลองคูเมืองเดิม</td>\n",
              "      <td>ปตร. พระปิ่นเกล้า</td>\n",
              "      <td>25.00</td>\n",
              "      <td>7.90</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>47.00</td>\n",
              "      <td>...</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.90</td>\n",
              "      <td>0.10</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16.0</td>\n",
              "      <td>2561.0</td>\n",
              "      <td>คลองคูเมืองเดิม</td>\n",
              "      <td>สะพานหน้ากระทรวงมหาดไทย</td>\n",
              "      <td>25.00</td>\n",
              "      <td>7.30</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>36.00</td>\n",
              "      <td>...</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.14</td>\n",
              "      <td>1.10</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1500000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>สะพานพระพุทธยอดฟ้า</td>\n",
              "      <td>30.87</td>\n",
              "      <td>6.98</td>\n",
              "      <td>1.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.19</td>\n",
              "      <td>78.63</td>\n",
              "      <td>...</td>\n",
              "      <td>2.68</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.33</td>\n",
              "      <td>3.47</td>\n",
              "      <td>0.40</td>\n",
              "      <td>10300000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2563.0</td>\n",
              "      <td>5492.99</td>\n",
              "      <td>3.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>สะพานกรุงเทพ</td>\n",
              "      <td>30.87</td>\n",
              "      <td>6.96</td>\n",
              "      <td>2.23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.88</td>\n",
              "      <td>89.79</td>\n",
              "      <td>...</td>\n",
              "      <td>3.28</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.44</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.40</td>\n",
              "      <td>19900000.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2563.0</td>\n",
              "      <td>7305.01</td>\n",
              "      <td>4.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>สะพานพระราม 9</td>\n",
              "      <td>30.87</td>\n",
              "      <td>6.96</td>\n",
              "      <td>2.16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.67</td>\n",
              "      <td>98.00</td>\n",
              "      <td>...</td>\n",
              "      <td>2.75</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.44</td>\n",
              "      <td>3.13</td>\n",
              "      <td>0.38</td>\n",
              "      <td>432000.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2563.0</td>\n",
              "      <td>8578.67</td>\n",
              "      <td>5.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ท่าเรือวัดคลองภูมิ</td>\n",
              "      <td>30.58</td>\n",
              "      <td>7.02</td>\n",
              "      <td>3.41</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.18</td>\n",
              "      <td>126.11</td>\n",
              "      <td>...</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.55</td>\n",
              "      <td>2.73</td>\n",
              "      <td>0.53</td>\n",
              "      <td>25700000.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2563.0</td>\n",
              "      <td>11872.75</td>\n",
              "      <td>7.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>วัดบางนา</td>\n",
              "      <td>30.87</td>\n",
              "      <td>6.93</td>\n",
              "      <td>2.35</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.19</td>\n",
              "      <td>114.58</td>\n",
              "      <td>...</td>\n",
              "      <td>2.77</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2.93</td>\n",
              "      <td>0.58</td>\n",
              "      <td>874000.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2563.0</td>\n",
              "      <td>13328.97</td>\n",
              "      <td>8.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>938 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Canal_id    year       Canal_name               Sample_water_point   \n",
              "0       12.0  2561.0  คลองคูเมืองเดิม                       ปตร.ราชินี  \\\n",
              "1       13.0  2561.0  คลองคูเมืองเดิม                    หน้ากรมที่ดิน   \n",
              "2       14.0  2561.0  คลองคูเมืองเดิม  อนุสาวรีย์แม่ธรณีมวยผม สนามหลวง   \n",
              "3       15.0  2561.0  คลองคูเมืองเดิม                ปตร. พระปิ่นเกล้า   \n",
              "4       16.0  2561.0  คลองคูเมืองเดิม          สะพานหน้ากระทรวงมหาดไทย   \n",
              "..       ...     ...              ...                              ...   \n",
              "16       NaN     NaN              NaN               สะพานพระพุทธยอดฟ้า   \n",
              "17       NaN     NaN              NaN                     สะพานกรุงเทพ   \n",
              "18       NaN     NaN              NaN                   สะพานพระราม 9    \n",
              "19       NaN     NaN              NaN               ท่าเรือวัดคลองภูมิ   \n",
              "20       NaN     NaN              NaN                         วัดบางนา   \n",
              "\n",
              "    TEMP. (oC)    pH  DO (mg/l)  H2S (mg/l)  BOD (mg/l)  COD (mg/l)  ...   \n",
              "0        25.00  8.00       2.70         0.0        5.00       42.00  ...  \\\n",
              "1        25.00  8.10       2.40         0.0        5.00       53.00  ...   \n",
              "2        25.00  8.00       1.70         0.0        5.00       56.00  ...   \n",
              "3        25.00  7.90       3.00         0.0        4.00       47.00  ...   \n",
              "4        25.00  7.30       1.40         0.0        6.00       36.00  ...   \n",
              "..         ...   ...        ...         ...         ...         ...  ...   \n",
              "16       30.87  6.98       1.93         0.0        4.19       78.63  ...   \n",
              "17       30.87  6.96       2.23         0.0        4.88       89.79  ...   \n",
              "18       30.87  6.96       2.16         0.0        4.67       98.00  ...   \n",
              "19       30.58  7.02       3.41         0.0        5.18      126.11  ...   \n",
              "20       30.87  6.93       2.35         0.0        5.19      114.58  ...   \n",
              "\n",
              "    TKN (mg/l)  NH3N (mg/l)  NO2 (mg/l)  NO3 (mg/l)  T-P (mg/l)   \n",
              "0         2.20         0.90        0.00        2.20        0.10  \\\n",
              "1         1.70         0.60        0.00        2.20        0.10   \n",
              "2         2.20         0.60        0.34        2.30        0.20   \n",
              "3         2.80         0.60        0.00        1.90        0.10   \n",
              "4         2.80         0.60        0.14        1.10        0.30   \n",
              "..         ...          ...         ...         ...         ...   \n",
              "16        2.68         0.14        0.33        3.47        0.40   \n",
              "17        3.28         0.58        0.44        3.26        0.40   \n",
              "18        2.75         0.43        0.44        3.13        0.38   \n",
              "19        2.80         0.26        0.55        2.73        0.53   \n",
              "20        2.77         0.80        0.53        2.93        0.58   \n",
              "\n",
              "    T.Coliform (col/100ml)  No_   year_  Conduct. (us/cm)  Salinity (ppt)  \n",
              "0                 930000.0  NaN     NaN               NaN             NaN  \n",
              "1               11000000.0  NaN     NaN               NaN             NaN  \n",
              "2                4600000.0  NaN     NaN               NaN             NaN  \n",
              "3                  30000.0  NaN     NaN               NaN             NaN  \n",
              "4                1500000.0  NaN     NaN               NaN             NaN  \n",
              "..                     ...  ...     ...               ...             ...  \n",
              "16              10300000.0  3.0  2563.0           5492.99            3.09  \n",
              "17              19900000.0  4.0  2563.0           7305.01            4.21  \n",
              "18                432000.0  5.0  2563.0           8578.67            5.01  \n",
              "19              25700000.0  6.0  2563.0          11872.75            7.07  \n",
              "20                874000.0  7.0  2563.0          13328.97            8.11  \n",
              "\n",
              "[938 rows x 21 columns]"
            ]
          },
          "execution_count": 926,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_concat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 927,
      "metadata": {},
      "outputs": [],
      "source": [
        "df = df_concat.copy()\n",
        "df.rename(columns=lambda x: x.strip(), inplace=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 928,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Canal_id', 'year', 'Canal_name', 'Sample_water_point', 'TEMP. (oC)',\n",
              "       'pH', 'DO (mg/l)', 'H2S (mg/l)', 'BOD (mg/l)', 'COD (mg/l)',\n",
              "       'SS (mg/l)', 'TKN (mg/l)', 'NH3N (mg/l)', 'NO2 (mg/l)', 'NO3 (mg/l)',\n",
              "       'T-P (mg/l)', 'T.Coliform (col/100ml)', 'No_', 'year_',\n",
              "       'Conduct. (us/cm)', 'Salinity (ppt)'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 928,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 929,
      "metadata": {},
      "outputs": [],
      "source": [
        "weighting = {\n",
        "    \"DO (mg/l)\": 0.22,\n",
        "    \"BOD (mg/l)\": 0.19,\n",
        "    \"COD (mg/l)\": 0.16,\n",
        "    \"NH3N (mg/l)\": 0.15,\n",
        "    \"SS (mg/l)\": 0.16,\n",
        "    \"pH\": 0.12\n",
        "}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 930,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "# เลือกเฉพาะ columns\n",
        "df = df[[\"DO (mg/l)\", \"BOD (mg/l)\", \"COD (mg/l)\",\n",
        "         \"NH3N (mg/l)\", \"SS (mg/l)\", \"pH\"]]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 931,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DO (mg/l)</th>\n",
              "      <th>BOD (mg/l)</th>\n",
              "      <th>COD (mg/l)</th>\n",
              "      <th>NH3N (mg/l)</th>\n",
              "      <th>SS (mg/l)</th>\n",
              "      <th>pH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.70</td>\n",
              "      <td>5.00</td>\n",
              "      <td>42.00</td>\n",
              "      <td>0.90</td>\n",
              "      <td>26.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.40</td>\n",
              "      <td>5.00</td>\n",
              "      <td>53.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>25.00</td>\n",
              "      <td>8.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.70</td>\n",
              "      <td>5.00</td>\n",
              "      <td>56.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>24.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>47.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>17.00</td>\n",
              "      <td>7.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.40</td>\n",
              "      <td>6.00</td>\n",
              "      <td>36.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>7.00</td>\n",
              "      <td>7.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.93</td>\n",
              "      <td>4.19</td>\n",
              "      <td>78.63</td>\n",
              "      <td>0.14</td>\n",
              "      <td>57.83</td>\n",
              "      <td>6.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2.23</td>\n",
              "      <td>4.88</td>\n",
              "      <td>89.79</td>\n",
              "      <td>0.58</td>\n",
              "      <td>40.33</td>\n",
              "      <td>6.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2.16</td>\n",
              "      <td>4.67</td>\n",
              "      <td>98.00</td>\n",
              "      <td>0.43</td>\n",
              "      <td>48.29</td>\n",
              "      <td>6.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>3.41</td>\n",
              "      <td>5.18</td>\n",
              "      <td>126.11</td>\n",
              "      <td>0.26</td>\n",
              "      <td>54.50</td>\n",
              "      <td>7.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2.35</td>\n",
              "      <td>5.19</td>\n",
              "      <td>114.58</td>\n",
              "      <td>0.80</td>\n",
              "      <td>96.42</td>\n",
              "      <td>6.93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>938 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    DO (mg/l)  BOD (mg/l)  COD (mg/l)  NH3N (mg/l)  SS (mg/l)    pH\n",
              "0        2.70        5.00       42.00         0.90      26.00  8.00\n",
              "1        2.40        5.00       53.00         0.60      25.00  8.10\n",
              "2        1.70        5.00       56.00         0.60      24.00  8.00\n",
              "3        3.00        4.00       47.00         0.60      17.00  7.90\n",
              "4        1.40        6.00       36.00         0.60       7.00  7.30\n",
              "..        ...         ...         ...          ...        ...   ...\n",
              "16       1.93        4.19       78.63         0.14      57.83  6.98\n",
              "17       2.23        4.88       89.79         0.58      40.33  6.96\n",
              "18       2.16        4.67       98.00         0.43      48.29  6.96\n",
              "19       3.41        5.18      126.11         0.26      54.50  7.02\n",
              "20       2.35        5.19      114.58         0.80      96.42  6.93\n",
              "\n",
              "[938 rows x 6 columns]"
            ]
          },
          "execution_count": 931,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "LXMIo2RE0qAt"
      },
      "source": [
        "## missing value\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 932,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hkenryba0gdD",
        "outputId": "076f426c-2035-485f-b7da-1df1190a2925"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DO (mg/l)      0\n",
              "BOD (mg/l)     0\n",
              "COD (mg/l)     0\n",
              "NH3N (mg/l)    0\n",
              "SS (mg/l)      0\n",
              "pH             0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 932,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ตรวจสอบ missing value\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 933,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DO (mg/l)</th>\n",
              "      <th>BOD (mg/l)</th>\n",
              "      <th>COD (mg/l)</th>\n",
              "      <th>NH3N (mg/l)</th>\n",
              "      <th>SS (mg/l)</th>\n",
              "      <th>pH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.70</td>\n",
              "      <td>5.00</td>\n",
              "      <td>42.00</td>\n",
              "      <td>0.90</td>\n",
              "      <td>26.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.40</td>\n",
              "      <td>5.00</td>\n",
              "      <td>53.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>25.00</td>\n",
              "      <td>8.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.70</td>\n",
              "      <td>5.00</td>\n",
              "      <td>56.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>24.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>47.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>17.00</td>\n",
              "      <td>7.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.40</td>\n",
              "      <td>6.00</td>\n",
              "      <td>36.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>7.00</td>\n",
              "      <td>7.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.93</td>\n",
              "      <td>4.19</td>\n",
              "      <td>78.63</td>\n",
              "      <td>0.14</td>\n",
              "      <td>57.83</td>\n",
              "      <td>6.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2.23</td>\n",
              "      <td>4.88</td>\n",
              "      <td>89.79</td>\n",
              "      <td>0.58</td>\n",
              "      <td>40.33</td>\n",
              "      <td>6.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2.16</td>\n",
              "      <td>4.67</td>\n",
              "      <td>98.00</td>\n",
              "      <td>0.43</td>\n",
              "      <td>48.29</td>\n",
              "      <td>6.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>3.41</td>\n",
              "      <td>5.18</td>\n",
              "      <td>126.11</td>\n",
              "      <td>0.26</td>\n",
              "      <td>54.50</td>\n",
              "      <td>7.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2.35</td>\n",
              "      <td>5.19</td>\n",
              "      <td>114.58</td>\n",
              "      <td>0.80</td>\n",
              "      <td>96.42</td>\n",
              "      <td>6.93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>938 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    DO (mg/l)  BOD (mg/l)  COD (mg/l)  NH3N (mg/l)  SS (mg/l)    pH\n",
              "0        2.70        5.00       42.00         0.90      26.00  8.00\n",
              "1        2.40        5.00       53.00         0.60      25.00  8.10\n",
              "2        1.70        5.00       56.00         0.60      24.00  8.00\n",
              "3        3.00        4.00       47.00         0.60      17.00  7.90\n",
              "4        1.40        6.00       36.00         0.60       7.00  7.30\n",
              "..        ...         ...         ...          ...        ...   ...\n",
              "16       1.93        4.19       78.63         0.14      57.83  6.98\n",
              "17       2.23        4.88       89.79         0.58      40.33  6.96\n",
              "18       2.16        4.67       98.00         0.43      48.29  6.96\n",
              "19       3.41        5.18      126.11         0.26      54.50  7.02\n",
              "20       2.35        5.19      114.58         0.80      96.42  6.93\n",
              "\n",
              "[938 rows x 6 columns]"
            ]
          },
          "execution_count": 933,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 934,
      "metadata": {},
      "outputs": [],
      "source": [
        "# แปลงตัวเลขที่เป็น string ให้เป็นตัวเลข และ เปลี่ยนตัวอักษรที่ไม่ถูกต้องให้เป็น NaN\n",
        "for col in df.columns:\n",
        "    df[col] = pd.to_numeric(df[col], errors='coerce')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 935,
      "metadata": {},
      "outputs": [],
      "source": [
        "# ลบข้อมูลที่มี missing value ออกไป\n",
        "df.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 936,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DO (mg/l)      0\n",
              "BOD (mg/l)     0\n",
              "COD (mg/l)     0\n",
              "NH3N (mg/l)    0\n",
              "SS (mg/l)      0\n",
              "pH             0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 936,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 937,
      "metadata": {},
      "outputs": [],
      "source": [
        "# reset index\n",
        "df = df.reindex()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "LaGv8c0s07U5"
      },
      "source": [
        "## Outlier data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 938,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "AObeEgAt1jmz",
        "outputId": "feb327b3-aee5-4a99-84ca-a84bcf7d6bbe"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DO (mg/l)</th>\n",
              "      <th>BOD (mg/l)</th>\n",
              "      <th>COD (mg/l)</th>\n",
              "      <th>NH3N (mg/l)</th>\n",
              "      <th>SS (mg/l)</th>\n",
              "      <th>pH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>938.000000</td>\n",
              "      <td>938.000000</td>\n",
              "      <td>938.000000</td>\n",
              "      <td>938.000000</td>\n",
              "      <td>938.000000</td>\n",
              "      <td>938.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.681980</td>\n",
              "      <td>12.609649</td>\n",
              "      <td>64.169236</td>\n",
              "      <td>4.264895</td>\n",
              "      <td>22.539433</td>\n",
              "      <td>7.235315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.274289</td>\n",
              "      <td>9.374797</td>\n",
              "      <td>22.489426</td>\n",
              "      <td>3.612909</td>\n",
              "      <td>17.828665</td>\n",
              "      <td>0.458444</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.552083</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>1.360417</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>7.116667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.662500</td>\n",
              "      <td>9.583333</td>\n",
              "      <td>60.500000</td>\n",
              "      <td>3.375000</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>7.254167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.600000</td>\n",
              "      <td>15.666667</td>\n",
              "      <td>72.229167</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>26.083333</td>\n",
              "      <td>7.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.700000</td>\n",
              "      <td>84.500000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>23.333333</td>\n",
              "      <td>148.333333</td>\n",
              "      <td>9.100000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        DO (mg/l)  BOD (mg/l)  COD (mg/l)  NH3N (mg/l)   SS (mg/l)          pH\n",
              "count  938.000000  938.000000  938.000000   938.000000  938.000000  938.000000\n",
              "mean     1.681980   12.609649   64.169236     4.264895   22.539433    7.235315\n",
              "std      1.274289    9.374797   22.489426     3.612909   17.828665    0.458444\n",
              "min      0.000000    0.000000    0.000000     0.000000    0.000000    0.000000\n",
              "25%      0.552083    6.000000   51.000000     1.360417   12.000000    7.116667\n",
              "50%      1.662500    9.583333   60.500000     3.375000   17.166667    7.254167\n",
              "75%      2.600000   15.666667   72.229167     6.000000   26.083333    7.400000\n",
              "max      9.700000   84.500000  202.000000    23.333333  148.333333    9.100000"
            ]
          },
          "execution_count": 938,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# แสดงค่าสถิติพื้นฐาน\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 939,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'DO (mg/l)'}>,\n",
              "        <Axes: title={'center': 'BOD (mg/l)'}>],\n",
              "       [<Axes: title={'center': 'COD (mg/l)'}>,\n",
              "        <Axes: title={'center': 'NH3N (mg/l)'}>],\n",
              "       [<Axes: title={'center': 'SS (mg/l)'}>,\n",
              "        <Axes: title={'center': 'pH'}>]], dtype=object)"
            ]
          },
          "execution_count": 939,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 940,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 940,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.boxplot()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "MvbgOZdP7H9X"
      },
      "source": [
        "จัดการ outlier data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 941,
      "metadata": {
        "id": "aLW2u4iU5n66"
      },
      "outputs": [],
      "source": [
        "def remove_outliers_iqr(dataframe, columns):\n",
        "    for column in columns:\n",
        "        Q1 = dataframe[column].quantile(0.25)\n",
        "        Q3 = dataframe[column].quantile(0.75)\n",
        "        IQR = Q3 - Q1\n",
        "        threshold = 1.5\n",
        "        lower_bound = Q1 - threshold * IQR\n",
        "        upper_bound = Q3 + threshold * IQR\n",
        "        print(column+\">\"+\"lower_bound:\"+str(lower_bound) +\n",
        "              \" upper_bound:\"+str(upper_bound))\n",
        "        dataframe = dataframe[(dataframe[column] >= lower_bound) & (\n",
        "            dataframe[column] <= upper_bound)]\n",
        "    return dataframe\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 942,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DO (mg/l)>lower_bound:-2.5197916666666664 upper_bound:5.671875\n",
            "BOD (mg/l)>lower_bound:-8.5 upper_bound:30.166666666666664\n",
            "COD (mg/l)>lower_bound:19.979166666666664 upper_bound:100.14583333333334\n",
            "NH3N (mg/l)>lower_bound:-5.191666666666666 upper_bound:12.075\n",
            "SS (mg/l)>lower_bound:-8.59375 upper_bound:46.32291666666667\n",
            "pH>lower_bound:6.712499999999997 upper_bound:7.812500000000002\n"
          ]
        }
      ],
      "source": [
        "# DO\tEC\tTemp\tSalinity\tTurbidity\tBOD\tCOD\n",
        "df_out = df.copy()\n",
        "\n",
        "df_out = remove_outliers_iqr(\n",
        "    df_out, [\"DO (mg/l)\", \"BOD (mg/l)\", \"COD (mg/l)\", \"NH3N (mg/l)\", \"SS (mg/l)\", \"pH\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 943,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 943,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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pTfaAcvXqVaxbtw6zZs3Cu+++i7Nnz+Jf//oXzM3NMWbMGKSlpQEAnJyctO7n5OQkrXvc0qVLsWjRIp3lhw4dgpWVldxPQVaRkZH6LqHBeNrG8mY2AJji2LFjuG4tb99yjWVd1mgInrZtsi5xLOWj5LHMzc2tdlvZA0pJSQm6d++Ojz76CADQpUsXXL58GevXr8eYMWOeqM+QkBDMmjVLup2VlQU3NzcMGDAAtra2stQtN41Gg8jISAQEBMDMzEzf5Ri0p3Usf0/JwqeXTqF3797o6CrPdi73WNZFjYbgad0m6wLHUj6GMJale0CqQ/aA4uLigg4dOmgta9++PX744QcAgLOzMwAgPT0dLi4uUpv09HT4+PiU26dKpYJKpdJZbmZmpthfQilDqNFQPG1jWWxUBGOL27iZmwTzLHmmJ4qKipBSlILEh4kwNa39y/9mbjaMLW6j2KjoqfrdlHratsm6xLGUj5LHsiZ1yR5QevXqhfj4eK1lf/zxB1q0aAHg0QGzzs7OiIqKkgJJVlYWTp8+jcmTJ8tdDpHBSsm5DrXHarx7Rv6+10asla0vtQeQkuODbnCqujERUTXJHlBmzpyJZ599Fh999BFeffVVnDlzBl9++SW+/PJLAICRkRFmzJiBxYsXo02bNvDw8MCCBQvg6uqKYcOGyV0OkcFyVbdATvI0fDbSB56O8s2gHD92HL1695JlBiUpIxvTv7sA1/4tZKiOiOj/kz2gPPPMM9i9ezdCQkIQGhoKDw8PrFy5EsHBwVKbuXPnIicnBxMnTsSDBw/Qu3dvREREwMLCQu5yiAyWysQCJfnN4GHbDh0c7GTpU6PRINk0Ge3t28syBVySn4mS/DtQmfC1S0Tykj2gAMCQIUMwZMiQCtcbGRkhNDQUoaGhdfHwREREZOD4XTxERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDim+i6AiMqXpykGAFy+nSlbnzl5Bfj1DuB8/T7Ulqpa95eYkS1DVUREuhhQiBQq6a8//vN2XZK5Z1N8k3hW1h7VKr6VEJG8+K5CpFADOjoDADwdrWFpZiJLn/GpmZi98xJWjPBGOxc7WfpUq0zh0UQtS19ERKUYUIgUyl5tjlE93GXts6ioCADg2VSNTs3kCShERHWBB8kSERGR4jCgEBERkeIwoBAREZHiMKAQERGR4jCgEBERkeIwoBAREZHi8DRjIiLSi8LCQqxevRqHDx9GYmIipk2bBnNzc32XRQrBGRQiIqp3c+fOhVqtxpw5c3DgwAHMmTMHarUac+fO1XdppBCcQSEiono1d+5cfPLJJ3BycsKiRYugUqlQUFCAhQsX4pNPPgEALF++XM9Vkr5xBoWIiOpNYWEhwsPD4eTkhKtXryI7Oxs7duxAdnY2rl69CicnJ4SHh6OwsFDfpZKeMaAQEVG9Wbt2LYqKiuDj4wM7OzutXTx2dnb429/+hqKiIqxdu1bfpZKecRcPERHVm6SkJADATz/9VO4unsjISK129PTiDAoREdUbd/dHX4Bpa2uLW7duYdy4cWjcuDHGjRuHW7duwcbGRqsdPb04g0JERPVOCIHCwkKsW7dOOs148uTJ+i6LFIQBhYiI6s2NGzcAAA8fPoRarZaWlx6H8ng7enpxFw8REdUbT09PWdtRw8WAQkRE9eatt94CABgZGaFZs2Za65o1awYjIyOtdvT0YkAhIqJ68/XXXwN4dAzK7du3tdbdvn0bQgitdvT0YkAhIqJ6k5CQIGs7arh4kCwREdUbjUYj/dy0aVO4uLjgzz//hIODA1JTU3Hnzh2ddvR0YkAhIqJ6U3Zm5O7du1IguX37tnT8yePt6OnEXTxERFRvEhMTpZ9Ljzcp73bZdvR0YkAhIqJ6Y2trK2s7argYUIiIqN60bNlS+lmlUmmtK3u7bDt6OjGgEBFRvTExMZF+Ligo0FpX9nbZdvR0YkAhIqJ6w108VF0MKEREVG/+9re/ydqOGq46Dygff/wxjIyMMGPGDGlZfn4+pkyZAgcHB1hbWyMoKAjp6el1XQoREelZZmamrO2o4arTgHL27Fl88cUXOkl45syZ+PHHH7Fjxw5ER0cjJSUFw4cPr8tSiIhIAUpKSmRtRw1XnQWU7OxsBAcH46uvvkLjxo2l5ZmZmdiwYQPCwsLw/PPPo1u3bti4cSNOnDiBU6dO1VU5RESkAHfv3pV+dnd311rXokWLctvR06nOriQ7ZcoUDB48GP7+/li8eLG0PDY2FhqNBv7+/tIyLy8vuLu74+TJk+jZs6dOXwUFBVpHd2dlZQF4dClkpV4OubQupdZnSDiW8ikqKpL+53g+OW6TT+7EiRPSz506dcKMGTNw9epVtGrVCj///DOuX78uteP41owhbJc1qa1OAsq3336Lc+fO4ezZszrr0tLSYG5ujkaNGmktd3JyQlpaWrn9LV26FIsWLdJZfujQIVhZWclSc12JjIzUdwkNBsey9m5mA4ApTp06hduX9V2N4eM2WXMPHjyQfv75559x4MAB6XbZ66A8ePBAax1Vn5K3y9zc3Gq3lT2g3Lx5E9OnT0dkZCQsLCxk6TMkJASzZs2SbmdlZcHNzQ0DBgxQ7KloGo0GkZGRCAgIgJmZmb7LMWgcS/n8duMecOlX9OzZE53d7fVdjsHiNlm13NxcxMfH6yz39fXF7t27YWZmhkaNGiEjI0Na16hRI9y7dw8ajQa+vr5wcXHRuX+7du0U/8FUXwxhuyzdA1IdsgeU2NhYZGRkoGvXrtKy4uJi/PLLL/j888/x008/obCwEA8ePNCaRUlPT4ezs3O5fapUKp0rDgKAmZmZYn8JpQyhRkPBsaw9U1NT6X+OZe1xm6xYUlISfH19K1yv0Wi0wgkArbM5d+/ejd27d+vcLzY2VuvvC+lS8nZZk7pkDygvvPACLl26pLXszTffhJeXF9555x24ubnBzMwMUVFRCAoKAgDEx8fjxo0b8PPzk7scIiLSAy8vL8TGxpa7btasWYiOjq7wvn379kVYWFiF/dLTQfaAYmNjg06dOmktU6vVcHBwkJaPHz8es2bNgr29PWxtbTFt2jT4+fmVe4AsEREZHisrqwpnOo4ePYphw4Zh7969OuuGDh2KPXv21HF1ZAjq7CyeyoSHh8PY2BhBQUEoKChAYGAg1q5dq49SiIhID/bs2YO8vDyMnTQV+2LO4+U+XbDpi89haWmp79JIIeoloBw9elTrtoWFBdasWYM1a9bUx8MTEZECWVpaIuTD5Ti97hRCJvdkOCEt/C4eIiIiUhwGFCIiIlIcBhQiIiJSHAYUIiIiUhwGFCIiIlIcBhQiIiJSHAYUIiIiUhwGFCIiIlIcBhQiIiJSHAYUIiIiUhwGFCIiIlIcBhQiIiJSHAYUIiIiUhwGFCIiIlIcBhQiIiJSHAYUIiIiUhwGFCIiIlIcBhQiIiJSHAYUIiIiUhwGFCIiIlIcBhQiIiJSHAYUIiIiUhwGFFK04uJiREdH45dffkF0dDSKi4v1XRIREdUDBhRSrF27dsHT0xMBAQEICwtDQEAAPD09sWvXLn2XRkREdYwBhRRp165dCAoKwo0bN7SW37hxA0FBQQwpREQNnKm+CyB6XHFxMcaNGwcAaNKkCV5//XXk5ubCysoK//nPf3Dnzh2MGzcOQ4cOhYmJiZ6rJSKiusCAQopz+PBhZGZmQq1Ww9LSEuHh4dI6d3d3qNVqZGZm4vDhwwgICNBjpUREVFe4i4cU55tvvgEA5OTklLuLJycnR6sdERE1PAwopDhZWVmytiMiIsPDgEKK4+DgIP1sYWGhta7s7bLtiIioYWFAIcU5d+6c9HNhYaHWurK3y7YjIqKGhQGFFCc1NVX6uaSkRGtd2dtl2xERUcPCgEKKY2NjAwAwMjIqd33p8tJ2RETU8PA0Y1Kcbt26ITExEUIIODo6ok+fPrh//z4aN26MmJgYZGRkSO2IiKhhYkAhxWnVqpX0c0ZGBn744Ycq2xERUcPCXTykOE2aNJG1HRERGR4GFFIcJycn6WdLS0utdWVvl21HREQNC3fxkN7k5ubiypUrOstLrxQL6J7FI4TQalfeqcZeXl6wsrKSsVIiIqpvDCikN1euXKnyQNeCggKt2/n5+dLPkyZNKvc+sbGx6Nq1a+0LJCIivWFAIb3x8vJCbGxsuesOHz6MuXPnonfv3vDs6INdv93B8M5NkfT7BRw7dgzLly/H888/X2G/RERk2BhQSG+srKwqnOno2rUrWrVqhdmzZyMmJgYAsOkk4OHhgZ07d2L48OH1WSoREdUzHiRLijV8+HAkJibiq+170OSlt/HV9j1ISEhgOCEiegpwBoUUzcTEBN39ekN9wRTd/XrCxMRE3yUREVE94AwKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOAwoREREpDgMKERERKQ4DChERESkOL9RGRETVlnw3BzkFRbL1l3QnR/rf1FS+P0lqlSk8mqhl64/qHwMKERFVS/LdHPT/9Gid9D175yXZ+zwypx9DigFjQCEiomopnTlZOdIHrR2t5ekzrwD7j57EkH5+UFuqZOkzMSMbM767IOtMD9U/BhQiA5ebm4srV65Uq2186gMUpCUi7rIlSv5sVGV7Ly8vWFlZ1bJCamhaO1qjUzM7WfrSaDRIawp0bdEYZmZmsvRJDQMDCpGBu3LlCrp161aj+7y2uXrtYmNj0bVr1yeoioiodhhQiAycl5cXYmNjq9U2O68A/z1yEoP7+8G6GtPpXl5etS2PiOiJMKAQGTgrK6tqz3JoNBrcv5sBvx7dOZ1ORIom+3VQli5dimeeeQY2NjZwdHTEsGHDEB8fr9UmPz8fU6ZMgYODA6ytrREUFIT09HS5SyEiIiIDJXtAiY6OxpQpU3Dq1ClERkZCo9FgwIAByMnJkdrMnDkTP/74I3bs2IHo6GikpKRg+PDhcpdCREREBkr2XTwRERFatzdt2gRHR0fExsbiueeeQ2ZmJjZs2IBt27bh+eefBwBs3LgR7du3x6lTp9CzZ0+5SyIiIiIDU+fHoGRmZgIA7O3tATw6K0Cj0cDf319q4+XlBXd3d5w8ebLcgFJQUICCggLpdlZWFoBH+9M1Gk1dlv/ESutSan2GpKioSPqf41k73C7l8bSOY128FutiLJ/W9wxD2C5rUludBpSSkhLMmDEDvXr1QqdOnQAAaWlpMDc3R6NGjbTaOjk5IS0trdx+li5dikWLFuksP3TokOKv0RAZGanvEgzezWwAMMWpU6dw+7K+q2kYuF3K42kbx9LX4rFjx3Bdnuu0SeQcy7qs0xAoebvMzc2tdts6DShTpkzB5cuXcezYsVr1ExISglmzZkm3s7Ky4ObmhgEDBsDW1ra2ZdYJjUaDyMhIBAQE8GyJWvrtxj3g0q/o2bMnOrvb67scg8btUh5P6zj+npKFTy+dQu/evdHRVZ733roYy7qo0xAYwnZZugekOuosoEydOhX79+/HL7/8gubNm0vLnZ2dUVhYiAcPHmjNoqSnp8PZ2bncvlQqFVQq3Ws2mJmZKfaXUMoQalS60i8QMzU15VjKhNulPJ62cazL16KcY/m0v2coebusSV2yn8UjhMDUqVOxe/duHD58GB4eHlrru3XrBjMzM0RFRUnL4uPjcePGDfj5+cldDhERERkg2WdQpkyZgm3btmHv3r2wsbGRjiuxs7ODpaUl7OzsMH78eMyaNQv29vawtbXFtGnT4OfnxzN4iIiICEAdBJR169YBAPr166e1fOPGjRg7diwAIDw8HMbGxggKCkJBQQECAwOxdu1auUshIiIiAyV7QBFCVNnGwsICa9aswZo1a+R+eCIiImoAZD8GhYiIiKi2GFCIiIhIcRhQiIiISHEYUIiIiEhxGFCIiIhIcRhQiIiISHHq/NuMiYioYSgozoexxW0kZ8XD2EKeb+ErKipCSlEK4u7FSZeor63krGwYW9xGQXE+ADtZ+qT6x4BCRETVkpJzHWqP1Xj3jPx9r42Q92Kdag8gJccH3eAka79UfxhQiIioWlzVLZCTPA2fjfSBp6N8MyjHjx1Hr969ZJtBScrIxvTvLsC1fwtZ+iP9YEAhIqJqUZlYoCS/GTxs26GDgzy7TjQaDZJNk9Hevr1s38Bbkp+Jkvw7UJlYyNIf6QcPkiUiIiLF4QwKyS75bg5yCopk6y/pTo70v1xTwACgVpnCo4latv6IiEg+DCgkq+S7Oej/6dE66Xv2zkuy93lkTj+GFCIiBWJAIVmVzpysHOmD1jIdRJeTV4D9R09iSD8/qC1VsvSZmJGNGd9dkHWmh4iI5MOAQnWitaM1OjWT7yC6tKZA1xaNZTuIjoiIlI0HyRIR1UBxcTGio6Pxyy+/IDo6GsXFxfouiahBYkAhIqqmXbt2oXXr1ggICEBYWBgCAgLQunVr7Nq1S9+lETU4DChERNWwa9cujBgxAt7e3oiJicH27dsRExMDb29vjBgxgiGFSGYMKEREVSguLsbs2bMxZMgQ7NmzB76+vrC0tISvry/27NmDIUOGYM6cOdzdQyQjBhQioirExMTg2rVrePfdd2FsrP22aWxsjJCQECQnJyMmJkZPFRI1PDyLh4joL7m5ubhy5YrO8hMnTgB49L0x586dQ3ZeAU5cSkLjJr/C2lKFoqIiqZ2trW25fXt5ecHKyqruiq8HeZpHM0SXb2fK1mdOXgF+vQM4X78v62UEyPAxoBAR/eXKlSvo1q1bhev79OmjdXv5Y+vfe+89vPfee+XeNzY2Fl27dq1tiXqV9Ncf/nm75L5ooim+STwrc5+PrhZNhou/PSKiv3h5eSE2NlZneXFxMYYNG4bWrVtjxYoVSEjPwqwdlxD2ijfaONli9uzZSEpKwu7du2FiYlJh34ZuQEdnAICnozUszcp/njUVn5qJ2TsvYcUIb7RzkefaSQC/yqIhYEAhIvqLlZVVhbMcq1evxogRIxAaGoqgsf+EWWNXFBYUIjQ0FDExMdi5cyeeeeaZeq64ftmrzTGqh7usfZbuHvNsqpbt4o7UMDCgEBFVw/Dhw7Fz507Mnj0bPwYNBACMXQl4eHhg586dGD58uH4LJGpgeBYPEVE1DR8+HImJifhq+x40eeltfLV9DxISEhhOiOoAZ1CIiGrAxMQE3f16Q33BFN39elZ4zAkR1Q5nUIiIiEhxGFCIiIhIcbiLh2RVUJwPY4vbSM6Kh7GFtSx9FhUVIaUoBXH34mBqKs8mm5yVDWOL2ygozgfAMweIiJSGAYVklZJzHWqP1Xj3jPx9r41YK2t/ag8gJccH3eAka79ERPWtsLAQq1evxuHDh5GYmIhp06bB3Nxc32XVCgMKycpV3QI5ydPw2UgfeDrKN4Ny/Nhx9OrdS7YZlKSMbEz/7gJc+7eQpT8iIn2ZO3cuwsPDpWvKHDhwAPPmzcPMmTOxfPnj1zs2HAwoJCuViQVK8pvBw7YdOjjIs+tEo9Eg2TQZ7e3bw8zMTJY+S/IzUZJ/ByoTC1n6IyLSh7lz5+KTTz6Bk5MTFi1aBJVKhYKCAixcuBCffPIJABhsSOFBskRERAaosLAQ4eHhcHJywq1btzBu3Dg0btwY48aNw61bt+Dk5ITw8HAUFhbqu9QnwhkUIiIiharoG7YBYOvWrSgqKsKECRNw8eJFnW/Zfuutt7BkyRKEhIQgODhY5/5K/4ZtBhQiIiI9SL6bg5yCokrb/O/SBYwc2K/SNosXL8bixYul24/v0AkLC0NYWJjO/b47eBQdvH0q7VufX7rIgEJERFTPzt/MQNCGPVW2KykqQLMp88tdl3f1V+T+7yjUfxsAC/e/6azPv/4bci5FwqpDP1i26q6zfu6xizA+FV9lDQcmB8HLyaHKdnJjQCEiIqpnR69ehtpjda36aPwMALQGcPWvf495BsCI1gBu/fXvydzK7saAQkRE9DQY6dMNwGdws7eCyrTi81WuJv6BedMm1EkNH6/+Cq1at620jaW5CXq16FAnj18VBhQiIqJ65mpnh5l9n6+yXW6LTuizq/KA8Nlnn2Hr1q0oLi6WlpmYmCA4OBjTp0+v8H48SJaeKnmaRy+Qy7czZeszJ68Av94BnK/fh9pSJUufiRnZsvRDRFSXrKys0LVr10rbbN68GV999ZV0Jdnnn3+eV5IlelzSX3/45+26JHPPpvgm8azMfT46Qp2eDtU5Y6K6ku7kSP/LdXVjQL9nTJBhMzc3x7/+9S+0bt0agwYNku2ilvrEd+caquyc9LIePx+9OpQ+3VYdAzo6AwA8Ha1haWYiS5/xqZmYvfMSVozwRjsX+b7Yj38Mnh7Jd3PQ/9Ojsvc7e6fcQRw4Mqcft0siMKDU2JUrV9CtW7dqt6/JBYZjY2OrnMpTOnu1OUb1cJe1z9Lvl/BsqkanZvzmYaq50pmTlSN90FqG74jKySvA/qMnMaSfn6y7HWd8d0G2WR4iQ8eAUkNeXl6IjY2tsl186gPM2nEJYa94o51Lo2r3TUR1p7WjtSwhV6PRIK0p0LVF4wYxlU6kRAwoNVSdA5YAwPj6n1DF5KF9p87waVH/548TEREZMn5ZIBERESkOAwoREREpDnfxkN5U94yo+NQHKEhLRNxlS5T82ajK9g3hbCgioqcdAwrpTU3PiHptc/XaNYSzoYiInnYMKKQ31T0jKjuvAP89chKD+/tV65oyPBuKHldQnA9ji9tIzoqHsUXtTzMuKipCSlEK4u7FyXahtuSsbBhb3EZBcT4Ank5PxIBCelPdM6I0Gg3u382AX4/uPKWTnkhKznWoPVbj3TPy9rs2Yq2s/ak9gJQcH3SDk6z9EhkiBpQyeClsoobJVd0COcnT8NlIH3jKcKG2oqIiHD92HL1695Lt9Z2UkY3p312Aa/8WsvRHZOgYUP7CS2ETNVwqEwuU5DeDh207dHCQ50JtyabJaG/fXrZZvZL8TJTk34HKxEKW/ogMHQPKX3gpbCIi+fAsPaotBpS/lB5EZ2LRVJaD6CxNi+DaOAWWNmkwlmkK2MSCB9ERPYk8TTEA4PLtTFn6y8krwK93AOfr92X9ANKQ8Cw9qi0GlL/wIDqihivprz/+83bJucvVFN8knpWxv0fUqobxtsyz9Ki29PpKWLNmDT755BOkpaWhc+fOWL16NXr06KGXWngQHVHDNaCjMwDA09EalmYmte4vPjUTs3dewooR3mjnIt9sZkM6CJ5n6VFt6S2gfPfdd5g1axbWr18PX19frFy5EoGBgYiPj4ejo2O911NSYoaS/GbIeeiMEtvav+Hk5RUg5b4r8h46yzYFXJyfzYPoiJ6Avdoco3q4y9ZfUdGj48A8m6pl+XZkItKlt4ASFhaGCRMm4M033wQArF+/Hv/973/x73//G/Pmzav3ejgFTEREpBx6+UtXWFiI2NhYhISESMuMjY3h7++PkydP6rQvKChAQUGBdDsrKwvAo6lBjUYjS0392zpgydAOaNVUXekUcF5eLq4lJVTZ3/W72fjsSDKm9/dAiybV22XU0rMNLC0rPzpdrTJBcztz2Z63ISh9rk/Tc64rHMvK5ebmIj4+vsp2f6RmoiAtEZcvmKMwvXozKO3atePZJ+XgNikfQxjLmtRmJIQQdVhLuVJSUtCsWTOcOHECfn5+0vK5c+ciOjoap0+f1mr/wQcfYNGiRTr9bNu2rd5f8ElJSZg9e3ad9L1ixQp4enrWSd9EVDW+vonqVm5uLl577TVkZmbC1ta20rYGsa8gJCQEs2bNkm5nZWXBzc0NAwYMqPIJyi03Nxe9e/eusl12XgF+ijmLwD7PVOvIdICfsCqi0WgQGRmJgIAAHkRXSxzLyvH1Xf+4TcrHEMaydA9IdegloDRp0gQmJiZIT0/XWp6eng5nZ2ed9iqVCiqV7puAmZlZvf8S7OzsqnWmkUajwcMH99Dn2Z6K3VAMjT5+3w0Vx7J8fH3rD7dJ+Sh5LGtSl3Ed1lEhc3NzdOvWDVFRUdKykpISREVFae3yISIioqeT3nbxzJo1C2PGjEH37t3Ro0cPrFy5Ejk5OdJZPURERPT00ltAGTlyJO7cuYP3338faWlp8PHxQUREBJyceIVUIiKip51eD5KdOnUqpk6dqs8SiIiISIH0cgwKERERUWUYUIiIiEhxGFCIiIhIcRhQiIiISHEYUIiIiEhxGFCIiIhIcRhQiIiISHEYUIiIiEhxGFCIiIhIcfR6JdknJYQAULOvba5vGo0Gubm5yMrKUuy3ShoKjqV8OJby4DjKh2MpH0MYy9K/26V/xytjkAHl4cOHAAA3Nzc9V0JEREQ19fDhQ9jZ2VXaxkhUJ8YoTElJCVJSUmBjYwMjIyN9l1OurKwsuLm54ebNm7C1tdV3OQaNYykfjqU8OI7y4VjKxxDGUgiBhw8fwtXVFcbGlR9lYpAzKMbGxmjevLm+y6gWW1tbxW4ohoZjKR+OpTw4jvLhWMpH6WNZ1cxJKR4kS0RERIrDgEJERESKw4BSR1QqFRYuXAiVSqXvUgwex1I+HEt5cBzlw7GUT0MbS4M8SJaIiIgaNs6gEBERkeIwoBAREZHiMKAQERGR4jCgyOTPP/+Eo6Mjrl27Vu+PffToURgZGeHBgwcAgIiICPj4+KCkpKTea6krb7zxBj766CO9PHbLli2xcuVKAEBhYSFatmyJX3/9VS+11FZUVBTat2+P4uLien/sDz74AD4+PtLtefPmYdq0afVeR1157rnnsG3bNr08tpGREfbs2QMAuHv3LhwdHXHr1i291FKe+Ph4ODs7S1cBr0+bNm1Co0aNpNvr16/HSy+9VO91UM016IAyduxYGBkZwcjICGZmZnByckJAQAD+/e9/l/vH+8SJExg0aBAaN24MCwsLeHt7IywsrFpv5kuWLMHQoUPRsmXLOngm5evfvz++/vprneUvvvgizMzMsHXr1ifqt+y4GRkZwcHBAS+++CIuXryo1a64uBjh4eHw9vaGhYUFGjdujIEDB+L48eNa7TZt2iT1ZWJigsaNG8PX1xehoaHIzMyssp7ffvsNBw4cwL/+9a8nej5PYvPmzejdu7fOcnNzc8yZMwfvvPNOtfpJS0vDtGnT0KpVK6hUKri5ueGll15CVFSUVrvqbntlfy9qtRpt2rTB2LFjERsbW6165s6di/nz58PExKRa7WsrLy8ParUaiYmJOuvmzJmDzZs34+rVq1rLS7e/jz/+WGv5nj17tK4c/XgwL6tsqASASZMmwdPTE5aWlmjatCmGDh2KK1euSOuvXbsGIyMjODo66vwR9fHxwQcffFDp89y3bx/S09MxatSoStvJadGiRXj99dd1ljdp0gSjR4/GwoULa9TfnTt3MHnyZLi7u0OlUsHZ2RmBgYFar+fffvsNL7/8MhwdHWFhYYGWLVti5MiRyMjIqLTvkJAQTJs2DTY2NjWqqTY8PDzw888/6ywfN24czp07h5iYmHqrpb7069cPM2bM0Fn+eEgzFA06oACP/linpqbi2rVrOHjwIPr374/p06djyJAhKCoqktrt3r0bffv2RfPmzXHkyBFcuXIF06dPx+LFizFq1KhKv9goNzcXGzZswPjx4+vjKQEA7t27h+PHj1f4SWDs2LFYtWrVE/dfOm6pqamIioqCqakphgwZIq0XQmDUqFEIDQ3F9OnTERcXh6NHj8LNzQ39+vWTPs2VsrW1RWpqKm7duoUTJ05g4sSJ2LJlC3x8fJCSklJpLatXr8Yrr7wCa2vrJ34+NbV37168/PLL5a4LDg7GsWPH8Pvvv1fax7Vr19CtWzccPnwYn3zyCS5duoSIiAj0798fU6ZMkdrVdNvbuHEjUlNT8fvvv2PNmjXIzs6Gr68vtmzZUmk9x44dQ1JSEoKCgqo5CrUXGRmJFi1aoHXr1jrrmjRpgsDAQKxbt05nnYWFBZYtW4b79+/LUke3bt2wceNGxMXF4aeffoIQAgMGDNAJgA8fPsSnn35a4/5XrVqFN998s8pLd8upsm30zTffxNatW3Hv3r1q9xcUFITz589j8+bN+OOPP7Bv3z7069cPf/75J4BHAeaFF16Avb09fvrpJ8TFxWHjxo1wdXVFTk5Ohf3euHED+/fvx9ixY2v0/Grj4sWLuH//Pvr27auzztzcHK+99lqt3h+pnogGbMyYMWLo0KE6y6OiogQA8dVXXwkhhMjOzhYODg5i+PDhOm337dsnAIhvv/22wsfZsWOHaNq0qdayI0eOCAAiIiJC+Pj4CAsLC9G/f3+Rnp4uDhw4ILy8vISNjY34xz/+IXJycqT7ZWVliddee01YWVkJZ2dnERYWJvr27SumT5+u1f+WLVuEr6+v1mPdv39fWn/9+nUBQCQmJlY1TDrKG7eYmBgBQGRkZAghhPj2228FALFv3z6d+w8fPlw4ODiI7OxsIYQQGzduFHZ2djrt0tPTRZMmTURwcHCFtRQVFQk7Ozuxf/9+reUtWrQQH374oXjjjTeEWq0W7u7uYu/evSIjI0O8/PLLQq1WC29vb3H27Fmt+3355ZeiefPmwtLSUgwbNkysWLFCp7a8vDyhVqtFXFyc9Fjh4eFabfr37y/mz59fYd1CCDFw4EDRrFkzaRzKKv1d1XTbAyB2796t03b06NHCxsZG3Lt3r8J6pkyZIkaMGKG1bOHChaJz585iw4YNws3NTajVajF58mRRVFQkli1bJpycnETTpk3F4sWLte4XFxcnevXqJVQqlWjfvr2IjIwst7Zx48aJd955R+uxytq8ebNo3ry51rIxY8aIIUOGCC8vL/H2229Ly3fv3i3KvmWVt92XKu93VtZvv/2m9fpITk4WAMTbb78trK2tRXp6utS2c+fOYuHChRX2lZGRIYyMjMTly5e1lgMQ69evF4MHDxaWlpbCy8tLnDhxQiQkJIi+ffsKKysr4efnp/Ma/fDDD0XTpk2FtbW1GD9+vHjnnXd0xu3GjRvC3NxcZGZmSo/1+Nh7eHiIr7/+usK6y7p//74AII4ePVphm927dwtTU1Oh0Wiq1WepTz75RHTv3l1rWel7wo8//ijatm0rLC0tRVBQkMjJyRGbNm0SLVq0EI0aNRLTpk0TRUVF0v1SUlLEoEGDhIWFhWjZsqXYunVrub/r0NBQMXLkSK3HKis6OlqYm5uL3NzcGj0Xfevbt6+YMmWKmDJlirC1tRUODg5i/vz5oqSkRFr/+N8KISp+D1a6Bj+DUp7nn38enTt3xq5duwAAhw4dwp9//ok5c+botH3ppZfQtm1bbN++vcL+YmJi0K1bt3LXffDBB/j8889x4sQJ3Lx5E6+++ipWrlyJbdu24b///S8OHTqE1atXS+1nzZqF48ePY9++fYiMjERMTAzOnTun0+++ffswdOjQCmtyd3eHk5OTLNOY2dnZ+M9//oPWrVvDwcEBALBt2za0bdu23Bmc2bNn488//0RkZGSl/To6OiI4OBj79u2rcDfaxYsXkZmZie7du+usCw8PR69evXD+/HkMHjwYb7zxBkaPHo3XX38d586dg6enJ0aPHi3NQBw/fhz/93//h+nTp+PChQsICAjAkiVLdPqNiopCs2bN4OXlVWHtPXr0qHRs7927h4iICEyZMgVqtVpnfel0a223vVIzZ87Ew4cPKx3zmJiYcscxKSkJBw8eREREBLZv344NGzZg8ODBuHXrFqKjo7Fs2TLMnz8fp0+fBvBo196wYcNgZWWF06dP48svv8R7772n029JSQn2799f6Xbao0cP3Lp1S+fYLRMTE3z00UdYvXq17MdS5OTkYOPGjfDw8ND5RvR//OMfaN26NUJDQ6vd37Fjx2BlZYX27dvrrPvwww8xevRoXLhwAV5eXnjttdcwadIkhISE4Ndff4UQAlOnTpXab926FUuWLMGyZcsQGxsLd3f3cmeYSmc3Kvu+laq20bKsra1hbW2NPXv2oKCgoNw2zs7OKCoqwu7duyudUX5cRdtdbm4uVq1ahW+//RYRERE4evQo/v73v+PAgQM4cOAAvvnmG3zxxRfYuXOndJ/Ro0cjJSUFR48exQ8//IAvv/yy3N1LVb0/du/eHUVFRdI2bUg2b94MU1NTnDlzBp999hnCwsLK3dXfIOg5INWpimZQhBBi5MiRon379kIIIT7++OMKP4kJIcTLL78stS3P0KFDxbhx47SWlX66+/nnn6VlS5cuFQBEUlKStGzSpEkiMDBQCPFo9sTMzEzs2LFDWv/gwQNhZWWllYrz8/OFtbW19Imtok+SXbp0ER988EGFdVdkzJgxwsTERKjVaqFWqwUA4eLiImJjY6U2Xl5eFY7tvXv3BACxbNkyIUTl6X3dunUCgNYn1rJ2794tTExMpE8IpVq0aCFef/116XZqaqoAIBYsWCAtO3nypAAgUlNThRCPfueDBw/W6ic4OFintgkTJog5c+ZoPdbjn9A+++wz0bJly3JrFkKI06dPCwBi165dFbYRoubbHiqYQcnLy9Ma8/LY2dmJLVu2aC1buHChsLKyEllZWdKywMBA0bJlS1FcXCwta9eunVi6dKkQQoiDBw8KU1NTaVyFEOXOoBw/flw4OjpK/ZQ3g5KZmanzyb3s67Znz57Sa6uiGZTS7bTsPyMjI53f2Zo1a6TtuV27dlozF6UzKOfPnxcRERHCzMxMWl/VDEp4eLho1aqVznIAWrNspdvjhg0bpGXbt28XFhYW0m1fX18xZcoUrX569eqlM24BAQHi888/13qsx7eLmTNnin79+lVY9+N27twpGjduLCwsLMSzzz4rQkJCxG+//abV5t133xWmpqbC3t5evPjii2L58uUiLS2t0n47d+4sQkNDtZZt3LhRZ4Z30qRJwsrKSjx8+FBaFhgYKCZNmiSEeDRrB0BrVjQhIUEA0Ppd37p1S5ibm0uvqYrefxo3biw2bdpUae1K07dvX9G+fXut98N33nlHeo/o27evMDMz03k9qFQqzqAYEiGE1gF3pcueRF5eHiwsLMpd97e//U362cnJCVZWVmjVqpXWstJPAFevXoVGo0GPHj2k9XZ2dmjXrp1Wn4cPH4ajoyM6duxYaV2WlpbIzc2t8fMBHh2Ae+HCBVy4cAFnzpxBYGAgBg4ciOvXr0ttnnS8yirt4/HfRam8vDyoVKpy1z8+tgDg7e2ts6x0fOPj47XGFoDObSEEfvzxxwr37ZeqamxrOja1HcuqxhGoeDtt2bKl1sGLTk5O6NChg9bxFGW30/j4eLi5ucHZ2Vla//g4Ao+OkRgyZEilx2VYWloCQIVjuWzZMmzevBlxcXEV9hETEyNtq6X/XF1dddoFBwfj/PnziI6ORtu2bfHqq68iPz9fp11gYCB69+6NBQsWVPiYZdXk9Q/obqP5+fnIysoCUL1tNCsrC9HR0bXeRh8XFBSElJQU7Nu3Dy+++CKOHj2Krl27YtOmTVKbJUuWIC0tDevXr0fHjh2xfv16eHl54dKlSxX2W9H4WFlZwdPTU7rt5OSEli1bah1r9vh2Z2pqiq5du0rrW7dujcaNG2v1u2/fPvTu3bvKg0Jr8/6oTz179tR6nfv5+SEhIUGahQ4ODtZ5PdRkRlBJntqAEhcXBw8PDwBA27ZtpWUVtS1tU54mTZpUeDCfmZmZ9HPp2URlGRkZ1fh04H379lX55gQ82s3QtGnTGvVdSq1Wo3Xr1mjdujWeeeYZfP3118jJycFXX30F4NGYVTZepW2qEhcXB1tbW2nX0eOaNGmC3NxcFBYW6qx7fGwrWlaT8T1z5gyKiorw7LPPVtquqrFt06YNjIyMtM4UKU9tt72y7QBI23R5KtpOy9sm62s7LT2Is6KxfO655xAYGIiQkJAK+/Dw8JC21dJ/pqamOu3s7OzQpk0bPPfcc9i5cyeuXLmC3bt3l9vnxx9/jO+++w7nz5+vtH6gZq//ipbVZGwPHjyIDh066OyeetyTvP4tLCwQEBCABQsW4MSJExg7dqzO2UAODg545ZVX8OmnnyIuLg6urq6VHlisxO0OqN37o5LZ2dnpvB4cHR31XdYTeSoDyuHDh3Hp0iXpbIYBAwbA3t4eK1as0Gm7b98+JCQk4B//+EeF/XXp0gX/+9//al1Xq1atYGZmhrNnz0rLMjMz8ccff0i3Sz/hV7Z/FQDy8/ORlJSELl261Lou4NEbhbGxMfLy8gAAo0aNQkJCAn788UedtitWrICDgwMCAgIq7TMjIwPbtm3DsGHDKvyUXXrdDDnGt127dlpjC0Dn9t69ezF48OAqT8O9fPlypWNrb2+PwMBArFmzptwzHEpPja3ttldq5cqVsLW1hb+/f4Vt5NpO27Vrh5s3byI9PV1a9vg4JiQk4Pr161VuA5cvX4aZmVmls4Eff/wxfvzxR5w8ebJ2hZchhIAQosLjLXr06IHhw4dj3rx5VfbVpUsXpKWlyXLGUXW30ape/0DV22h1dOjQodIzdMzNzeHp6VlpGzm3u6KiIq3QmJiYqDXu2dnZOHLkSJXjk5SUhPz8fNneH+vT48fNnDp1Cm3atKm3SwfUpwYfUAoKCpCWlobbt2/j3Llz+OijjzB06FAMGTIEo0ePBvBotuCLL77A3r17MXHiRFy8eBHXrl3Dhg0bMHbsWIwYMQKvvvpqhY8RGBiI33//vdZvUDY2NhgzZgzefvttHDlyBL///jvGjx8PY2Nj6ZNWbGwscnNzy71GR1mnTp2CSqWCn5/fE9VSOm5paWmIi4vDtGnTkJ2dLR0UO2rUKPz973/HmDFjsGHDBly7dg0XL17EpEmTsG/fPnz99ddaB4cKIZCWlobU1FTExcXh3//+N5599lnY2dnpXO+irKZNm6Jr1644duzYEz2PsqZNm4YDBw4gLCwMCQkJ+OKLL3Dw4EGt6dLqfvqKiYnBgAEDKm2zZs0aFBcXo0ePHvjhhx+QkJCAuLg4rFq1Svq9PMm29+DBA6SlpeH69euIjIzEiBEjsG3bNqxbt67Sae3AwEBZxjEgIACenp4YM2YMLl68iOPHj2P+/PkA/v+MwN69e+Hv7w8rK6tK+4qJiUGfPn2kXT3l8fb2RnBw8BOfFnr16lUsXboUsbGxuHHjBk6cOIFXXnkFlpaWGDRoUIX3W7JkCQ4fPoz4+PhK++/SpQuaNGmic/2fJzFt2jRs2LABmzdvRkJCAhYvXoyLFy9K41pUVISDBw9WuY3m5uYiNja2ym201J9//onnn38e//nPf3Dx4kUkJydjx44dWL58ufTHfv/+/Xj99dexf/9+/PHHH4iPj8enn36KAwcOVBoIAgMDcfLkyVpfHNDLywv+/v6YOHEizpw5g/Pnz2PixImwtLSUxiciIgJt27at8npUMTExaNWqldYuJkNx48YNzJo1C/Hx8di+fTtWr16N6dOn67usOtHgA0pERARcXFzQsmVLvPjiizhy5AhWrVqFvXv3aiXOESNG4MiRI7hx4wb69OmDdu3aITw8HO+99x6+/fbbSvfte3t7o2vXrvj+++9rXW9YWBj8/PwwZMgQ+Pv7o1evXmjfvr20D3fv3r0YNGhQuVPYZW3fvh3BwcFV/oGoSOm4ubi4wNfXF2fPnsWOHTvQr18/AI/+EH3//fd49913ER4ejnbt2qFPnz64fv06jh49imHDhmn1l5WVBRcXFzRr1gx+fn744osvMGbMGJw/fx4uLi6V1vLWW2898UXnyurVqxfWr1+PsLAwdO7cGREREZg5c6Y0tklJSUhMTERgYGCl/Zw8eRKZmZkYMWJEpe1atWqFc+fOoX///pg9ezY6deqEgIAAREVFaZ2ZUdNt780334SLiwu8vLwwefJkWFtb48yZM3jttdcqrSc4OBi///57lX9wq2JiYoI9e/YgOzsbzzzzDN566y3pLJ6y22l1gt63336LCRMmVNkuNDT0ia+MbGFhgZiYGAwaNAitW7fGyJEjYWNjgxMnTlQ69d22bVuMGzeu3ONUyjIxMZGuO1JbwcHBCAkJwZw5c9C1a1ckJydj7Nix0rhGR0fD2tpa6ziM8uzduxfu7u7o06dPtR7X2toavr6+CA8Px3PPPYdOnTphwYIFmDBhAj7//HMAj2ZTrKysMHv2bPj4+KBnz574/vvv8fXXX+ONN96osO+BAwfC1NS03Ium1dSWLVvg5OSE5557Dn//+98xYcIE2NjY1Hi72759e7W2OyUaPXo08vLy0KNHD0yZMgXTp0/HxIkT9V1W3dDPsbkNz/79+0X79u21znyQQ3Z2trCzs5OuZ+Dt7S2+++67Su9z584dYW9vL65evSprLfqSm5sr3NzcxIkTJ2Tv+6233hK9e/cWQgixYsUKMXDgwCrv8+qrr4olS5bIXkt9mDNnjpg4caLs/R47dkw6K+POnTvC1NS0yrM7Dhw4INq3b1/j62ooUWpqqrC3txfXrl2TvW9/f3/pjLVp06aJyZMnV3kfX19fsXXrVtlreVKff/65GDBggOz93rx5UzpbUqPRCHt7e3H69OlK73P58mXh6OgoHjx4IHs9da2i65w0VJV/DKdqGzx4MBISEnD79u0qD16rzPnz53HlyhX06NEDmZmZ0tHXQ4cORWFhIYKCgjBw4MBK+7h27RrWrl1b6QGThsTS0hJbtmzB3bt3a93Xp59+ioCAAKjVahw8eBCbN2/G2rVrAQDNmzev9GBM4NF38Xh7e2PmzJm1rkUf3nvvPaxduxYlJSW1uurp7t27YW1tjTZt2iAxMRHTp09Hr1694OnpiT/++ANhYWHSWSsVKb0eSVWzgYbA2dkZGzZswI0bN9CiRYsn7ic3Nxfr169HYGAgTExMsH37dvz888/S9W06depU5W7bu3fvYvjw4dU6dqm+TJo0CQ8ePMDDhw9rdbn7w4cPIzs7G97e3khNTcXcuXPRsmVLPPfcc7h37x5mzpyJZ555ptI+UlNTsWXLFtjZ2T1xHVRP9J2QSNu5c+dE165dhVqtFo0bNxb+/v7i4sWL+i6rwXjllVdE06ZNhYWFhejQoYNYt26dvksySJs3bxZt2rQRKpVKNGvWTIwZM0bcvXtX32UZvNzcXPHCCy8Ie3t7YWVlJbp06SJ++OEHfZelGBEREaJjx47C0tJSODo6imHDhtXJrJVSPW0zKEZCyHAxCyIiIiIZNfiDZImIiMjwMKAQERGR4jCgEBERkeIwoBAREZHiMKAQERGR4jCgEBERkeIwoBAREZHiMKAQERGR4jCgEBERkeL8P2SMQ03nsPe0AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_out.boxplot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 944,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'DO (mg/l)'}>,\n",
              "        <Axes: title={'center': 'BOD (mg/l)'}>],\n",
              "       [<Axes: title={'center': 'COD (mg/l)'}>,\n",
              "        <Axes: title={'center': 'NH3N (mg/l)'}>],\n",
              "       [<Axes: title={'center': 'SS (mg/l)'}>,\n",
              "        <Axes: title={'center': 'pH'}>]], dtype=object)"
            ]
          },
          "execution_count": 944,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_out.hist()\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "LsjeNPhQ7b1h"
      },
      "source": [
        "## Duplicate data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 945,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2O3PQSQA7OQ0",
        "outputId": "20b91e56-bfd3-433d-9302-994768839ddb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "จำนวน Duplicate Data df: 3\n"
          ]
        }
      ],
      "source": [
        "print('จำนวน Duplicate Data df:', df_out.duplicated().sum())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 946,
      "metadata": {
        "id": "b5A3CUAb78wd"
      },
      "outputs": [],
      "source": [
        "# ลบ Duplicate Data\n",
        "df_out = df_out.drop_duplicates()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Jzq1yaJsAGy_"
      },
      "source": [
        "# Data Transformation: Normalization\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 947,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_columns = [\"DO (mg/l)\",\n",
        "             \"BOD (mg/l)\",\n",
        "             \"COD (mg/l)\",\n",
        "             \"NH3N (mg/l)\",\n",
        "             \"SS (mg/l)\",\n",
        "             \"pH\"]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 948,
      "metadata": {
        "id": "ihha_tsM_87e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "MinMaxScaler()"
            ]
          },
          "execution_count": 948,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(df[X_columns])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Water Quality Index\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 949,
      "metadata": {},
      "outputs": [],
      "source": [
        "# แยก X,Y\n",
        "df_wqi = df_out[X_columns].copy()\n",
        "# for x_train\n",
        "for index, row in df_wqi.iterrows():\n",
        "    wqi_score = 0\n",
        "    for x in X_columns:\n",
        "        wqi_score += row[x]*weighting[x]\n",
        "    df_wqi.loc[index, 'wqi_score'] = wqi_score\n",
        "\n",
        "    if wqi_score >= 90:\n",
        "        df_wqi.loc[index, 'Quality of Water'] = \"Excellent\"\n",
        "    elif wqi_score >= 70:\n",
        "        df_wqi.loc[index, 'Quality of Water'] = \"Good\"\n",
        "    elif wqi_score >= 50:\n",
        "        df_wqi.loc[index, 'Quality of Water'] = \"Medium\"\n",
        "    elif wqi_score >= 25:\n",
        "        df_wqi.loc[index, 'Quality of Water'] = \"Bad\"\n",
        "    else:\n",
        "        df_wqi.loc[index, 'Quality of Water'] = \"Very Bad\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 950,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DO (mg/l)</th>\n",
              "      <th>BOD (mg/l)</th>\n",
              "      <th>COD (mg/l)</th>\n",
              "      <th>NH3N (mg/l)</th>\n",
              "      <th>SS (mg/l)</th>\n",
              "      <th>pH</th>\n",
              "      <th>wqi_score</th>\n",
              "      <th>Quality of Water</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>2.100000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>7.477000</td>\n",
              "      <td>Very Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>1.400000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>7.963000</td>\n",
              "      <td>Very Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>1.400000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.400000</td>\n",
              "      <td>8.116000</td>\n",
              "      <td>Very Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>1.900000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.400000</td>\n",
              "      <td>8.241000</td>\n",
              "      <td>Very Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>7.300000</td>\n",
              "      <td>8.861000</td>\n",
              "      <td>Very Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>0.208333</td>\n",
              "      <td>19.333333</td>\n",
              "      <td>97.916667</td>\n",
              "      <td>6.816667</td>\n",
              "      <td>29.250000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>25.928333</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>7.600000</td>\n",
              "      <td>25.982000</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>7.400000</td>\n",
              "      <td>26.083000</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820</th>\n",
              "      <td>0.108333</td>\n",
              "      <td>30.083333</td>\n",
              "      <td>91.250000</td>\n",
              "      <td>11.445455</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>7.216667</td>\n",
              "      <td>26.282485</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>659</th>\n",
              "      <td>0.216667</td>\n",
              "      <td>28.333333</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>10.841667</td>\n",
              "      <td>30.083333</td>\n",
              "      <td>7.375000</td>\n",
              "      <td>28.275583</td>\n",
              "      <td>Bad</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>775 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     DO (mg/l)  BOD (mg/l)  COD (mg/l)  NH3N (mg/l)  SS (mg/l)        pH   \n",
              "166   2.100000    5.000000   22.000000     0.300000  10.000000  7.500000  \\\n",
              "167   1.400000    5.000000   25.000000     0.300000  11.000000  7.500000   \n",
              "253   1.400000    6.000000   25.000000     1.200000  10.000000  7.400000   \n",
              "265   1.900000    5.000000   26.000000     1.500000  10.000000  7.400000   \n",
              "252   1.000000    6.000000   27.000000     1.500000  13.000000  7.300000   \n",
              "..         ...         ...         ...          ...        ...       ...   \n",
              "354   0.208333   19.333333   97.916667     6.816667  29.250000  7.000000   \n",
              "225   0.000000   29.000000   98.000000     5.600000  19.000000  7.600000   \n",
              "178   0.000000   28.000000   94.000000     4.500000  26.000000  7.400000   \n",
              "820   0.108333   30.083333   91.250000    11.445455  21.000000  7.216667   \n",
              "659   0.216667   28.333333   97.000000    10.841667  30.083333  7.375000   \n",
              "\n",
              "     wqi_score Quality of Water  \n",
              "166   7.477000         Very Bad  \n",
              "167   7.963000         Very Bad  \n",
              "253   8.116000         Very Bad  \n",
              "265   8.241000         Very Bad  \n",
              "252   8.861000         Very Bad  \n",
              "..         ...              ...  \n",
              "354  25.928333              Bad  \n",
              "225  25.982000              Bad  \n",
              "178  26.083000              Bad  \n",
              "820  26.282485              Bad  \n",
              "659  28.275583              Bad  \n",
              "\n",
              "[775 rows x 8 columns]"
            ]
          },
          "execution_count": 950,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_wqi.sort_values(by=['wqi_score'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 951,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DO (mg/l)</th>\n",
              "      <th>BOD (mg/l)</th>\n",
              "      <th>COD (mg/l)</th>\n",
              "      <th>NH3N (mg/l)</th>\n",
              "      <th>SS (mg/l)</th>\n",
              "      <th>pH</th>\n",
              "      <th>wqi_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>775.000000</td>\n",
              "      <td>775.000000</td>\n",
              "      <td>775.000000</td>\n",
              "      <td>775.000000</td>\n",
              "      <td>775.000000</td>\n",
              "      <td>775.000000</td>\n",
              "      <td>775.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.708845</td>\n",
              "      <td>11.018244</td>\n",
              "      <td>59.926718</td>\n",
              "      <td>3.839725</td>\n",
              "      <td>18.231381</td>\n",
              "      <td>7.252369</td>\n",
              "      <td>16.429372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.142940</td>\n",
              "      <td>5.937266</td>\n",
              "      <td>13.702816</td>\n",
              "      <td>2.746289</td>\n",
              "      <td>8.987788</td>\n",
              "      <td>0.167597</td>\n",
              "      <td>3.393596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.520000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.791667</td>\n",
              "      <td>7.477000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.783333</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>50.166667</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>11.833333</td>\n",
              "      <td>7.125000</td>\n",
              "      <td>14.038458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.708333</td>\n",
              "      <td>9.166667</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>7.258333</td>\n",
              "      <td>16.352000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.600000</td>\n",
              "      <td>14.416667</td>\n",
              "      <td>69.083333</td>\n",
              "      <td>5.649242</td>\n",
              "      <td>22.916667</td>\n",
              "      <td>7.391667</td>\n",
              "      <td>18.559072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.300000</td>\n",
              "      <td>30.083333</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>12.018182</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>7.800000</td>\n",
              "      <td>28.275583</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        DO (mg/l)  BOD (mg/l)  COD (mg/l)  NH3N (mg/l)   SS (mg/l)   \n",
              "count  775.000000  775.000000  775.000000   775.000000  775.000000  \\\n",
              "mean     1.708845   11.018244   59.926718     3.839725   18.231381   \n",
              "std      1.142940    5.937266   13.702816     2.746289    8.987788   \n",
              "min      0.000000    3.520000   22.000000     0.000000    2.000000   \n",
              "25%      0.783333    6.000000   50.166667     1.500000   11.833333   \n",
              "50%      1.708333    9.166667   59.000000     3.200000   16.000000   \n",
              "75%      2.600000   14.416667   69.083333     5.649242   22.916667   \n",
              "max      5.300000   30.083333   98.000000    12.018182   46.000000   \n",
              "\n",
              "               pH   wqi_score  \n",
              "count  775.000000  775.000000  \n",
              "mean     7.252369   16.429372  \n",
              "std      0.167597    3.393596  \n",
              "min      6.791667    7.477000  \n",
              "25%      7.125000   14.038458  \n",
              "50%      7.258333   16.352000  \n",
              "75%      7.391667   18.559072  \n",
              "max      7.800000   28.275583  "
            ]
          },
          "execution_count": 951,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_wqi.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 952,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Quality of Water\n",
              "Very Bad    769\n",
              "Bad           6\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 952,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_wqi['Quality of Water'].value_counts()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 953,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Very Bad', 'Bad'], dtype=object)"
            ]
          },
          "execution_count": 953,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_wqi['Quality of Water'].unique()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 954,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_split_copy = df_wqi.copy()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## แบ่งชุดข้อมูล\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 955,
      "metadata": {},
      "outputs": [],
      "source": [
        "seed = 3012\n",
        "\n",
        "df_train, df_test = train_test_split(\n",
        "    df_split_copy, test_size=0.1, random_state=seed)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 956,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "จำนวนชุดข้อมูลทั้งหมด : 938\n",
            "จำนวนชุดข้อมูล training : 697\n",
            "จำนวนชุดข้อมูล test : 78\n"
          ]
        }
      ],
      "source": [
        "# จำนวนชุดข้อมูล\n",
        "print('จำนวนชุดข้อมูลทั้งหมด :', len(df))\n",
        "print('จำนวนชุดข้อมูล training :', len(df_train))\n",
        "print('จำนวนชุดข้อมูล test :', len(df_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 957,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train = df_train[X_columns]\n",
        "Y_train = df_train['Quality of Water'].values.ravel()\n",
        "\n",
        "X_test = df_test[X_columns]\n",
        "Y_test = df_test['Quality of Water'].values.ravel()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Transform\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 958,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train_transform = scaler.transform(X_train)\n",
        "\n",
        "X_test_transform = scaler.transform(X_test)\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# KNN\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 959,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# สร้างโมเดล KNNC\n",
        "model = KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "# ฝึกโมเดลด้วยชุด Train\n",
        "model.fit(X_train_transform,  Y_train)\n",
        "\n",
        "# ใช้โมเดลที่ฝึกแล้วทำนายคลาสของชุด test\n",
        "\n",
        "Y_pred = model.predict(X_test_transform)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 960,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "KNNC Accuracy: 0.9871794871794872\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# คำนวณค่าความแม่นยำของโมเดล\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "\n",
        "print(f\"KNNC Accuracy: {accuracy}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Evaluating Model KNNC\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 961,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "# คำนวณ Confusion Matrix\n",
        "cm = confusion_matrix(Y_test, Y_pred)\n",
        "\n",
        "# แสดงผล Confusion Matrix ด้วย Heatmap\n",
        "plt.figure()\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d')\n",
        "plt.title('Confusion Matrix : KNN')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "xJqnj1i7Q_ps"
      },
      "source": [
        "# Naive bayes\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 962,
      "metadata": {
        "id": "YHxec7RyQ_9g"
      },
      "outputs": [],
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 963,
      "metadata": {
        "id": "8ka8kni-RU-D"
      },
      "outputs": [],
      "source": [
        "# สร้างโมเดล Naive Bayes\n",
        "model = GaussianNB()\n",
        "\n",
        "# ฝึกโมเดลด้วยชุด Train\n",
        "model.fit(X_train_transform,  Y_train)\n",
        "\n",
        "# ใช้โมเดลที่ฝึกแล้วทำนายคลาสของชุด test\n",
        "\n",
        "Y_pred = model.predict(X_test_transform)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "pglw-m6iVFrN"
      },
      "source": [
        "## Evaluating Model Naive bayes\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 964,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqfBiJ9KVF6Y",
        "outputId": "70d0bc2d-77dd-4ee5-b4f8-b8f422d83849"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Naive Bayes Accuracy: 0.9871794871794872\n"
          ]
        }
      ],
      "source": [
        "# คำนวณค่าความแม่นยำของโมเดล\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "\n",
        "print(f\"Naive Bayes Accuracy: {accuracy}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 965,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# คำนวณ Confusion Matrix\n",
        "cm = confusion_matrix(Y_test, Y_pred)\n",
        "\n",
        "# แสดงผล Confusion Matrix ด้วย Heatmap\n",
        "plt.figure()\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d')\n",
        "plt.title('Confusion Matrix : Naive Bayes')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "k8Q8Q7AGTlD9"
      },
      "source": [
        "# Decision Tree\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 966,
      "metadata": {
        "id": "RquNdMN4ThkN"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n",
        "# สร้างโมเดล Decision Tree\n",
        "model = DecisionTreeClassifier()\n",
        "\n",
        "# ฝึกโมเดลด้วยชุด Train\n",
        "clf = model.fit(X_train_transform, Y_train)\n",
        "\n",
        "# ใช้โมเดลที่ฝึกแล้วทำนายคลาสของชุด Validation\n",
        "\n",
        "Y_pred = model.predict(X_test_transform)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 967,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 967,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from six import StringIO\n",
        "from IPython.display import Image\n",
        "from sklearn.tree import export_graphviz\n",
        "import pydotplus\n",
        "dot_data = StringIO()\n",
        "export_graphviz(clf, out_file=dot_data,\n",
        "                filled=True, rounded=True,\n",
        "                special_characters=True, feature_names=X_columns, class_names=df_wqi['Quality of Water'].unique())\n",
        "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
        "graph.write_png('diabetes.png')\n",
        "Image(graph.create_png())"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "N_r7HsgZU8_9"
      },
      "source": [
        "## Evaluating Model Decision Tree\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 968,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lgI2JCmKU-kW",
        "outputId": "c0a2a148-92fd-4a9a-9b54-dece5c66e0c8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Decision Tree Accuracy: 0.9743589743589743\n"
          ]
        }
      ],
      "source": [
        "# คำนวณค่าความแม่นยำของโมเดล\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "\n",
        "# แสดงผลลัพธ์\n",
        "print(f\"Decision Tree Accuracy: {accuracy}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 969,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# คำนวณ Confusion Matrix\n",
        "cm = confusion_matrix(Y_test, Y_pred)\n",
        "\n",
        "# แสดงผล Confusion Matrix ด้วย Heatmap\n",
        "plt.figure()\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d')\n",
        "plt.title('Confusion Matrix : Decision Tree')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}