water_quality_data_collection-v2-2.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "eav8Z_I6Txhe"
      },
      "source": [
        "# Import Data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 695,
      "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",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/kittizz/Library/Python/3.11/lib/python/site-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /opt/homebrew/lib/python3.11/site-packages (from pandas) (2023.3)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /opt/homebrew/lib/python3.11/site-packages (from pandas) (2023.3)\n",
            "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",
            "Requirement already satisfied: cycler>=0.10 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib) (4.39.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: numpy>=1.20 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /Users/kittizz/Library/Python/3.11/lib/python/site-packages (from matplotlib) (23.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib) (10.0.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /Users/kittizz/Library/Python/3.11/lib/python/site-packages (from matplotlib) (2.8.2)\n",
            "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",
            "Requirement already satisfied: numpy>=1.17.3 in /opt/homebrew/lib/python3.11/site-packages (from scikit-learn) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /opt/homebrew/lib/python3.11/site-packages (from scikit-learn) (1.10.1)\n",
            "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",
            "Requirement already satisfied: cycler>=0.10 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.39.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
            "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": 3,
      "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",
        "from pandas import DataFrame\n",
        "import re\n",
        "import os\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define the directory to search for CSV and XLS files\n",
        "directory = Path(r'./dataset2/')\n",
        "# Find all CSV and XLS files in the directory and its subdirectories\n",
        "xlsx = list(directory.rglob('*.xlsx'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "sim_base = {}\n",
        "df_concat = pd.DataFrame()\n",
        "\n",
        "\n",
        "for xls in xlsx:\n",
        "    for sheet in list(pd.ExcelFile(xls).sheet_names):\n",
        "        df_concat = pd.concat(\n",
        "            [df_concat, pd.read_excel(xls, sheet_name=sheet)], axis=0)\n",
        "        # match = re.search(r'\\((\\D+\\.\\D+\\.)\\d+\\)', xls.name)\n",
        "\n",
        "        # if match:\n",
        "        #     month_abbreviation = match.group(1)\n",
        "        #     if month_abbreviation not in sim_base:\n",
        "        #         sim_base[month_abbreviation] = {min: 0, max: 0}\n",
        "        #     if sim_base[month_abbreviation][min] > df_concat.:\n",
        "        #         sim_base[month_abbreviation][min] = df_concat.min(skipna=True)\n",
        "        #     if sim_base[month_abbreviation][max] < df_concat.max(skipna=True):\n",
        "        #         sim_base[month_abbreviation][max] = df_concat.max(skipna=True)\n",
        "df_concat.columns.values[0] = 'date'\n",
        "df_concat.columns = df_concat.iloc[0]\n",
        "df_concat.columns = df_concat.columns.str.replace('\\n', '')\n",
        "df_concat = df_concat.drop(0)\n",
        "df_concat = df_concat.drop(columns=df_concat.columns[-2:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "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>ลำดับที่</th>\n",
              "      <th>จุดตรวจ</th>\n",
              "      <th>ค่าพิกัดUTM-X</th>\n",
              "      <th>ค่าพิกัดUTM-Y</th>\n",
              "      <th>วัน/เดือน/ปี</th>\n",
              "      <th>เวลา</th>\n",
              "      <th>Temp ไม่เกิน 40 ๐c</th>\n",
              "      <th>pH</th>\n",
              "      <th>EC</th>\n",
              "      <th>Sal</th>\n",
              "      <th>DO</th>\n",
              "      <th>TDS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(ระหว่าง</td>\n",
              "      <td>(ไม่เกิน 2,000</td>\n",
              "      <td>( ไม่เกิน</td>\n",
              "      <td>( ไม่น้อยกว่า</td>\n",
              "      <td>( ไม่เกิน 1,300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.5 - 8.5)</td>\n",
              "      <td>mmhos/cm.)</td>\n",
              "      <td>1.0 g/l )</td>\n",
              "      <td>2.0 mg/l )</td>\n",
              "      <td>mg/l )</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>โซนน้ำจืด (น้ำที่ใช้เพื่อการเกษตร)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>เหนือน้ำ ปตร.อุทกวิภาชประสิทธิ</td>\n",
              "      <td>632928</td>\n",
              "      <td>919603</td>\n",
              "      <td>1965-11-03 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>โรงสูบน้ำประปาปากพนัง</td>\n",
              "      <td>631753</td>\n",
              "      <td>918266</td>\n",
              "      <td>1965-11-03 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>21</td>\n",
              "      <td>ท้าย ปตร.เสือหึง</td>\n",
              "      <td>637859</td>\n",
              "      <td>906329</td>\n",
              "      <td>1965-07-20 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26.9</td>\n",
              "      <td>7.33</td>\n",
              "      <td>43976</td>\n",
              "      <td>27.2</td>\n",
              "      <td>1.7</td>\n",
              "      <td>27559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>22</td>\n",
              "      <td>ท้าย ปตร.ท่าพญา</td>\n",
              "      <td>637950</td>\n",
              "      <td>914923</td>\n",
              "      <td>1965-07-20 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.5</td>\n",
              "      <td>7.37</td>\n",
              "      <td>2482</td>\n",
              "      <td>1.21</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>23</td>\n",
              "      <td>ท้าย ปตร.ฉุกเฉิน</td>\n",
              "      <td>634278</td>\n",
              "      <td>919648</td>\n",
              "      <td>1965-07-20 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.2</td>\n",
              "      <td>7.49</td>\n",
              "      <td>45527</td>\n",
              "      <td>28.18</td>\n",
              "      <td>4.1</td>\n",
              "      <td>28440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>24</td>\n",
              "      <td>ท้าย ปตร.คลองลัด</td>\n",
              "      <td>634259</td>\n",
              "      <td>919880</td>\n",
              "      <td>1965-07-20 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.1</td>\n",
              "      <td>7.7</td>\n",
              "      <td>46090</td>\n",
              "      <td>28.56</td>\n",
              "      <td>3.8</td>\n",
              "      <td>28798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>*** อ้างอิง จากค่ามาตรฐานการระบายน้ำลงทางน้ำชล...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1199 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "0                                            ลำดับที่   \n",
              "1                                                 NaN  \\\n",
              "2                                                 NaN   \n",
              "3                  โซนน้ำจืด (น้ำที่ใช้เพื่อการเกษตร)   \n",
              "4                                                   1   \n",
              "5                                                   2   \n",
              "..                                                ...   \n",
              "25                                                 21   \n",
              "26                                                 22   \n",
              "27                                                 23   \n",
              "28                                                 24   \n",
              "29  *** อ้างอิง จากค่ามาตรฐานการระบายน้ำลงทางน้ำชล...   \n",
              "\n",
              "0                           จุดตรวจ ค่าพิกัดUTM-X ค่าพิกัดUTM-Y   \n",
              "1                               NaN           NaN           NaN  \\\n",
              "2                               NaN           NaN           NaN   \n",
              "3                               NaN           NaN           NaN   \n",
              "4    เหนือน้ำ ปตร.อุทกวิภาชประสิทธิ        632928        919603   \n",
              "5             โรงสูบน้ำประปาปากพนัง        631753        918266   \n",
              "..                              ...           ...           ...   \n",
              "25                 ท้าย ปตร.เสือหึง        637859        906329   \n",
              "26                  ท้าย ปตร.ท่าพญา        637950        914923   \n",
              "27                 ท้าย ปตร.ฉุกเฉิน        634278        919648   \n",
              "28                 ท้าย ปตร.คลองลัด        634259        919880   \n",
              "29                              NaN           NaN           NaN   \n",
              "\n",
              "0          วัน/เดือน/ปี เวลา Temp ไม่เกิน 40 ๐c         pH               EC    \n",
              "1                   NaN  NaN                NaN   (ระหว่าง   (ไม่เกิน 2,000   \\\n",
              "2                   NaN  NaN                NaN  6.5 - 8.5)       mmhos/cm.)   \n",
              "3                   NaN  NaN                NaN         NaN              NaN   \n",
              "4   1965-11-03 00:00:00  NaN                NaN         NaN              NaN   \n",
              "5   1965-11-03 00:00:00  NaN                NaN         NaN              NaN   \n",
              "..                  ...  ...                ...         ...              ...   \n",
              "25  1965-07-20 00:00:00  NaN               26.9        7.33            43976   \n",
              "26  1965-07-20 00:00:00  NaN               27.5        7.37             2482   \n",
              "27  1965-07-20 00:00:00  NaN               27.2        7.49            45527   \n",
              "28  1965-07-20 00:00:00  NaN               27.1         7.7            46090   \n",
              "29                  NaN  NaN                NaN         NaN              NaN   \n",
              "\n",
              "0          Sal              DO               TDS   \n",
              "1   ( ไม่เกิน    ( ไม่น้อยกว่า   ( ไม่เกิน 1,300   \n",
              "2     1.0 g/l )      2.0 mg/l )            mg/l )  \n",
              "3           NaN             NaN               NaN  \n",
              "4           NaN             NaN               NaN  \n",
              "5           NaN             NaN               NaN  \n",
              "..          ...             ...               ...  \n",
              "25         27.2             1.7             27559  \n",
              "26         1.21             3.5              1539  \n",
              "27        28.18             4.1             28440  \n",
              "28        28.56             3.8             28798  \n",
              "29          NaN             NaN               NaN  \n",
              "\n",
              "[1199 rows x 12 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_concat\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "HoBWH9Tj0GME"
      },
      "source": [
        "# Preprocessing - Data Cleaning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1199, 12)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_concat.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "df = df_concat.copy()\n",
        "df.rename(columns=lambda x: x.strip(), inplace=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['ลำดับที่', 'จุดตรวจ', 'ค่าพิกัดUTM-X', 'ค่าพิกัดUTM-Y', 'วัน/เดือน/ปี',\n",
              "       'เวลา', 'Temp ไม่เกิน 40 ๐c', 'pH', 'EC', 'Sal', 'DO', 'TDS'],\n",
              "      dtype='object', name=0)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "# เลือกเฉพาะ columns\n",
        "df = df[[\"วัน/เดือน/ปี\", \"Temp ไม่เกิน 40 ๐c\", \"pH\", \"EC\", \"Sal\",\t\"DO\",\t\"TDS\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "df.columns.values[0] = \"Date\"\n",
        "df.columns.values[1] = \"Temp\"\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "LXMIo2RE0qAt"
      },
      "source": [
        "## missing value\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hkenryba0gdD",
        "outputId": "076f426c-2035-485f-b7da-1df1190a2925"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0\n",
              "Date    196\n",
              "Temp    280\n",
              "pH      227\n",
              "EC      221\n",
              "Sal     232\n",
              "DO      236\n",
              "TDS     236\n",
              "dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ตรวจสอบ missing value\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "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": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "# df = df.fillna(df.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "# ลบข้อมูลที่มี missing value ออกไป\n",
        "df.dropna(subset=df.columns.to_list()[1:], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0\n",
              "Date    870\n",
              "Temp      0\n",
              "pH        0\n",
              "EC        0\n",
              "Sal       0\n",
              "DO        0\n",
              "TDS       0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "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": 21,
      "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>Date</th>\n",
              "      <th>Temp</th>\n",
              "      <th>pH</th>\n",
              "      <th>EC</th>\n",
              "      <th>Sal</th>\n",
              "      <th>DO</th>\n",
              "      <th>TDS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>0.0</td>\n",
              "      <td>870.000000</td>\n",
              "      <td>870.000000</td>\n",
              "      <td>870.000000</td>\n",
              "      <td>870.000000</td>\n",
              "      <td>870.000000</td>\n",
              "      <td>870.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>27.081793</td>\n",
              "      <td>7.056851</td>\n",
              "      <td>8439.196552</td>\n",
              "      <td>5.316368</td>\n",
              "      <td>4.581264</td>\n",
              "      <td>4230.288506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.744064</td>\n",
              "      <td>0.553029</td>\n",
              "      <td>14658.985206</td>\n",
              "      <td>10.791402</td>\n",
              "      <td>2.944436</td>\n",
              "      <td>8281.609516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>0.970000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>26.280000</td>\n",
              "      <td>6.700000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>2.670000</td>\n",
              "      <td>161.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>755.000000</td>\n",
              "      <td>0.350000</td>\n",
              "      <td>3.905000</td>\n",
              "      <td>495.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>27.700000</td>\n",
              "      <td>7.350000</td>\n",
              "      <td>5847.000000</td>\n",
              "      <td>3.142500</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>1986.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>8.730000</td>\n",
              "      <td>50237.000000</td>\n",
              "      <td>129.000000</td>\n",
              "      <td>16.790000</td>\n",
              "      <td>33366.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0      Date        Temp          pH            EC         Sal          DO   \n",
              "count   0.0  870.000000  870.000000    870.000000  870.000000  870.000000  \\\n",
              "mean    NaN   27.081793    7.056851   8439.196552    5.316368    4.581264   \n",
              "std     NaN    1.744064    0.553029  14658.985206   10.791402    2.944436   \n",
              "min     NaN    2.800000    0.970000     97.000000    0.040000    0.100000   \n",
              "25%     NaN   26.280000    6.700000    255.000000    0.120000    2.670000   \n",
              "50%     NaN   27.000000    7.000000    755.000000    0.350000    3.905000   \n",
              "75%     NaN   27.700000    7.350000   5847.000000    3.142500    5.600000   \n",
              "max     NaN   34.000000    8.730000  50237.000000  129.000000   16.790000   \n",
              "\n",
              "0               TDS  \n",
              "count    870.000000  \n",
              "mean    4230.288506  \n",
              "std     8281.609516  \n",
              "min        3.000000  \n",
              "25%      161.250000  \n",
              "50%      495.000000  \n",
              "75%     1986.500000  \n",
              "max    33366.000000  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# แสดงค่าสถิติพื้นฐาน\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Temp'}>, <Axes: title={'center': 'pH'}>],\n",
              "       [<Axes: title={'center': 'EC'}>, <Axes: title={'center': 'Sal'}>],\n",
              "       [<Axes: title={'center': 'DO'}>, <Axes: title={'center': 'TDS'}>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x1000 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.loc[:, df.columns != 'Date'].hist(\n",
        "    figsize=(10, 10), bins=100, alpha=0.5,)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 54,
          "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.loc[:, df.columns != 'Date'].boxplot()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "MvbgOZdP7H9X"
      },
      "source": [
        "จัดการ outlier data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "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": 25,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Temp>lower_bound:24.150000000000006 upper_bound:29.83\n",
            "pH>lower_bound:5.715000000000002 upper_bound:8.315\n",
            "EC>lower_bound:-14955.625 upper_bound:25603.375\n",
            "Sal>lower_bound:-0.66125 upper_bound:1.36875\n",
            "DO>lower_bound:-2.6687499999999997 upper_bound:10.56125\n",
            "TDS>lower_bound:-483.0 upper_bound:1157.0\n"
          ]
        }
      ],
      "source": [
        "df_out = df.copy()\n",
        "\n",
        "df_out = remove_outliers_iqr(\n",
        "    df_out, [\"Temp\", \"pH\", \"EC\", \"Sal\",\t\"DO\",\t\"TDS\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 26,
          "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_out.boxplot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Temp'}>, <Axes: title={'center': 'pH'}>],\n",
              "       [<Axes: title={'center': 'EC'}>, <Axes: title={'center': 'Sal'}>],\n",
              "       [<Axes: title={'center': 'DO'}>, <Axes: title={'center': 'TDS'}>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x1000 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_out.loc[:, df_out.columns != 'Date'].hist(\n",
        "    figsize=(10, 10), bins=100, alpha=0.5,)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "LsjeNPhQ7b1h"
      },
      "source": [
        "## Duplicate data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "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: 46\n"
          ]
        }
      ],
      "source": [
        "print('จำนวน Duplicate Data df:', df_out.duplicated().sum())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "b5A3CUAb78wd"
      },
      "outputs": [],
      "source": [
        "# ลบ Duplicate Data\n",
        "df_out = df_out.drop_duplicates()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_split_copy = df_out.copy()\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## แบ่งชุดข้อมูล\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [],
      "source": [
        "seed = 7777\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": 32,
      "metadata": {},
      "outputs": [],
      "source": [
        "# df_test = df_test.sort_values(by=['Sal'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "จำนวนชุดข้อมูลทั้งหมด : 458\n",
            "จำนวนชุดข้อมูล training : 412\n",
            "จำนวนชุดข้อมูล test : 46\n"
          ]
        }
      ],
      "source": [
        "# จำนวนชุดข้อมูล\n",
        "print('จำนวนชุดข้อมูลทั้งหมด :', len(df_out))\n",
        "print('จำนวนชุดข้อมูล training :', len(df_train))\n",
        "print('จำนวนชุดข้อมูล test :', len(df_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_columns = [\"Temp\", \"pH\", \"EC\", \t\"DO\",\t\"TDS\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "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>Date</th>\n",
              "      <th>Temp</th>\n",
              "      <th>pH</th>\n",
              "      <th>EC</th>\n",
              "      <th>Sal</th>\n",
              "      <th>DO</th>\n",
              "      <th>TDS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>NaN</td>\n",
              "      <td>26.30</td>\n",
              "      <td>7.12</td>\n",
              "      <td>670.0</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.67</td>\n",
              "      <td>415.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>NaN</td>\n",
              "      <td>28.37</td>\n",
              "      <td>6.45</td>\n",
              "      <td>228.0</td>\n",
              "      <td>0.10</td>\n",
              "      <td>8.12</td>\n",
              "      <td>139.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>27.22</td>\n",
              "      <td>6.58</td>\n",
              "      <td>295.0</td>\n",
              "      <td>0.13</td>\n",
              "      <td>9.59</td>\n",
              "      <td>184.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>26.20</td>\n",
              "      <td>6.99</td>\n",
              "      <td>1032.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>5.80</td>\n",
              "      <td>656.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>26.90</td>\n",
              "      <td>6.97</td>\n",
              "      <td>165.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.15</td>\n",
              "      <td>103.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>26.70</td>\n",
              "      <td>8.07</td>\n",
              "      <td>1601.0</td>\n",
              "      <td>0.78</td>\n",
              "      <td>5.20</td>\n",
              "      <td>1001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>NaN</td>\n",
              "      <td>25.40</td>\n",
              "      <td>6.89</td>\n",
              "      <td>344.0</td>\n",
              "      <td>0.17</td>\n",
              "      <td>1.42</td>\n",
              "      <td>222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>26.00</td>\n",
              "      <td>6.29</td>\n",
              "      <td>242.0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.18</td>\n",
              "      <td>154.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>25.80</td>\n",
              "      <td>6.93</td>\n",
              "      <td>316.0</td>\n",
              "      <td>0.15</td>\n",
              "      <td>1.96</td>\n",
              "      <td>202.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>26.35</td>\n",
              "      <td>6.38</td>\n",
              "      <td>561.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>8.50</td>\n",
              "      <td>456.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>412 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "0   Date   Temp    pH      EC   Sal    DO     TDS\n",
              "16   NaN  26.30  7.12   670.0  0.32  2.67   415.0\n",
              "14   NaN  28.37  6.45   228.0  0.10  8.12   139.0\n",
              "15   NaN  27.22  6.58   295.0  0.13  9.59   184.0\n",
              "6    NaN  26.20  6.99  1032.0  0.50  5.80   656.0\n",
              "15   NaN  26.90  6.97   165.0  0.08  2.15   103.0\n",
              "..   ...    ...   ...     ...   ...   ...     ...\n",
              "4    NaN  26.70  8.07  1601.0  0.78  5.20  1001.0\n",
              "22   NaN  25.40  6.89   344.0  0.17  1.42   222.0\n",
              "7    NaN  26.00  6.29   242.0  0.12  2.18   154.0\n",
              "11   NaN  25.80  6.93   316.0  0.15  1.96   202.0\n",
              "17   NaN  26.35  6.38   561.0  0.25  8.50   456.0\n",
              "\n",
              "[412 rows x 7 columns]"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train = df_train.loc[:, X_columns].values\n",
        "Y_train = df_train.loc[:, ['Sal']].values\n",
        "\n",
        "X_test = df_test.loc[:, X_columns].values\n",
        "Y_test = df_test.loc[:, ['Sal']].values\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# KNN\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_absolute_percentage_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [],
      "source": [
        "k = 5\n",
        "# สร้างโมเดล KNN Regressor\n",
        "model = KNeighborsRegressor(n_neighbors=k)\n",
        "\n",
        "# ฝึกโมเดลด้วยชุด Train\n",
        "model.fit(X_train,  Y_train)\n",
        "\n",
        "# ใช้โมเดลที่ฝึกแล้วทำนายคลาสของชุด test\n",
        "\n",
        "Y_pred = model.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mean_absolute_error:  0.012478260869565211\n",
            "mean_absolute_percentage_error:  0.04020623729155617\n",
            "mean_squared_error:  0.001858695652173912\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# คำนวณค่าความแม่นยำของโมเดล\n",
        "mae = mean_absolute_error(Y_test, Y_pred)\n",
        "mape = mean_absolute_percentage_error(Y_test, Y_pred)\n",
        "mse = mean_squared_error(Y_test, Y_pred)\n",
        "print(\"mean_absolute_error: \", mae)\n",
        "print(\"mean_absolute_percentage_error: \", mape)\n",
        "print(\"mean_squared_error: \", mse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "# font size\n",
        "plt.rcParams['font.size'] = 15\n",
        "plt.scatter(Y_test, Y_pred, color='blue')\n",
        "plt.plot([min(Y_test), max(Y_test)], [min(Y_test), max(Y_test)], color='red')\n",
        "plt.xlabel('Actual Sal')\n",
        "plt.ylabel('Predicted Sal')\n",
        "plt.title('KNN Regression')\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
}