Aqua365_modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "negative-queue",
   "metadata": {
    "id": "negative-queue"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "local-survivor",
   "metadata": {
    "id": "local-survivor"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"/content/Water_data_new.csv\", encoding= 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aware-living",
   "metadata": {
    "id": "aware-living"
   },
   "outputs": [],
   "source": [
    "df=df.rename(columns={df.columns[4]: \"CONDUCTIVITY\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adequate-module",
   "metadata": {
    "id": "adequate-module"
   },
   "outputs": [],
   "source": [
    "df['Temp']=pd.to_numeric(df['Temp'],errors='coerce')\n",
    "df['D.O. (mg/l)']=pd.to_numeric(df['D.O. (mg/l)'],errors='coerce')\n",
    "df['PH']=pd.to_numeric(df['PH'],errors='coerce')\n",
    "df['B.O.D. (mg/l)']=pd.to_numeric(df['B.O.D. (mg/l)'],errors='coerce')\n",
    "df['CONDUCTIVITY']=pd.to_numeric(df['CONDUCTIVITY'],errors='coerce')\n",
    "df['NITRATENAN N+ NITRITENANN (mg/l)']=pd.to_numeric(df['NITRATENAN N+ NITRITENANN (mg/l)'],errors='coerce')\n",
    "df['TOTAL COLIFORM (MPN/100ml)Mean']=pd.to_numeric(df['TOTAL COLIFORM (MPN/100ml)Mean'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cYhFtwbZuj-t",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "id": "cYhFtwbZuj-t",
    "outputId": "722afcdc-7118-4af6-e448-c9f153f477e3"
   },
   "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>STATION CODE</th>\n",
       "      <th>Temp</th>\n",
       "      <th>D.O. (mg/l)</th>\n",
       "      <th>PH</th>\n",
       "      <th>CONDUCTIVITY</th>\n",
       "      <th>B.O.D. (mg/l)</th>\n",
       "      <th>NITRATENAN N+ NITRITENANN (mg/l)</th>\n",
       "      <th>FECAL COLIFORM (MPN/100ml)</th>\n",
       "      <th>TOTAL COLIFORM (MPN/100ml)Mean</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1393</td>\n",
       "      <td>30.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>203.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.100</td>\n",
       "      <td>11</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1399</td>\n",
       "      <td>29.8</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.200</td>\n",
       "      <td>4953</td>\n",
       "      <td>8391.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1475</td>\n",
       "      <td>29.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>179.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.100</td>\n",
       "      <td>3243</td>\n",
       "      <td>5330.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3181</td>\n",
       "      <td>29.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.500</td>\n",
       "      <td>5382</td>\n",
       "      <td>8443.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3182</td>\n",
       "      <td>29.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.400</td>\n",
       "      <td>3428</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>2014</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>1330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "      <td>738.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.518</td>\n",
       "      <td>202.0</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1450</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>585.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.155</td>\n",
       "      <td>315.0</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>1403</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>98.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>570.0</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>1404</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>91.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>562.0</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>1726</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>546.0</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1991 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     STATION CODE  Temp  ...  TOTAL COLIFORM (MPN/100ml)Mean  year\n",
       "0            1393  30.6  ...                            27.0  2014\n",
       "1            1399  29.8  ...                          8391.0  2014\n",
       "2            1475  29.5  ...                          5330.0  2014\n",
       "3            3181  29.7  ...                          8443.0  2014\n",
       "4            3182  29.5  ...                          5500.0  2014\n",
       "...           ...   ...  ...                             ...   ...\n",
       "1986         1330   NaN  ...                           202.0  2003\n",
       "1987         1450  29.0  ...                           315.0  2003\n",
       "1988         1403  28.0  ...                           570.0  2003\n",
       "1989         1404  28.0  ...                           562.0  2003\n",
       "1990         1726  29.0  ...                           546.0  2003\n",
       "\n",
       "[1991 rows x 10 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "entitled-following",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "entitled-following",
    "outputId": "7157fa41-e1e1-4ed3-8b8d-27ec17f0a4ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start=2\n",
    "end=1779\n",
    "station=df.iloc [start:end ,0]\n",
    "# location=df.iloc [start:end ,1]\n",
    "#state=df.iloc [start:end ,2]\n",
    "do= df.iloc [start:end ,2].astype(np.float64)\n",
    "value=0\n",
    "ph = df.iloc[ start:end,3]  \n",
    "co = df.iloc [start:end ,4].astype(np.float64)   \n",
    "  \n",
    "year=df.iloc[start:end,9]\n",
    "tc=df.iloc [2:end ,8].astype(np.float64)\n",
    "\n",
    "\n",
    "bod = df.iloc [start:end ,5].astype(np.float64)\n",
    "na= df.iloc [start:end ,6].astype(np.float64)\n",
    "na.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "surprising-picnic",
   "metadata": {
    "id": "surprising-picnic"
   },
   "outputs": [],
   "source": [
    "df=pd.concat([station,do,ph,co,bod,na,tc,year],axis=1)\n",
    "df.columns = ['station','do','ph','co','bod','na','tc','year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "p4bAmm0nvIBW",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p4bAmm0nvIBW",
    "outputId": "4a9a7f2c-e5d2-4694-9b22-c0f2cb266526"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station      0\n",
       "do          27\n",
       "ph           5\n",
       "co          17\n",
       "bod         36\n",
       "na         171\n",
       "tc         119\n",
       "year         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hundred-stockholm",
   "metadata": {
    "id": "hundred-stockholm"
   },
   "outputs": [],
   "source": [
    "df['npH']=df.ph.apply(lambda x: (100 if (8.5>=x>=7)  \n",
    "                                 else(80 if  (8.6>=x>=8.5) or (6.9>=x>=6.8) \n",
    "                                      else(60 if (8.8>=x>=8.6) or (6.8>=x>=6.7) \n",
    "                                          else(40 if (9>=x>=8.8) or (6.7>=x>=6.5)\n",
    "                                              else 0)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abandoned-aerospace",
   "metadata": {
    "id": "abandoned-aerospace"
   },
   "outputs": [],
   "source": [
    "df['ndo']=df.do.apply(lambda x:(100 if (x>=6)  \n",
    "                                 else(80 if  (6>=x>=5.1) \n",
    "                                      else(60 if (5>=x>=4.1)\n",
    "                                          else(40 if (4>=x>=3) \n",
    "                                              else 0)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "rotary-monitor",
   "metadata": {
    "id": "rotary-monitor"
   },
   "outputs": [],
   "source": [
    "df['nco']=df.tc.apply(lambda x:(100 if (5>=x>=0)  \n",
    "                                 else(80 if  (50>=x>=5) \n",
    "                                      else(60 if (500>=x>=50)\n",
    "                                          else(40 if (10000>=x>=500) \n",
    "                                              else 0)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "floating-setting",
   "metadata": {
    "id": "floating-setting"
   },
   "outputs": [],
   "source": [
    "df['nbdo']=df.bod.apply(lambda x:(100 if (3>=x>=0)  \n",
    "                                 else(80 if  (6>=x>=3) \n",
    "                                      else(60 if (80>=x>=6)\n",
    "                                          else(40 if (125>=x>=80) \n",
    "                                              else 0)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "noble-amendment",
   "metadata": {
    "id": "noble-amendment"
   },
   "outputs": [],
   "source": [
    "df['nec']=df.co.apply(lambda x:(100 if (75>=x>=0)  \n",
    "                                 else(80 if  (150>=x>=75) \n",
    "                                      else(60 if (225>=x>=150)\n",
    "                                          else(40 if (300>=x>=225) \n",
    "                                              else 0)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "preliminary-casting",
   "metadata": {
    "id": "preliminary-casting"
   },
   "outputs": [],
   "source": [
    "df['nna']=df.na.apply(lambda x:(100 if (20>=x>=0)  \n",
    "                                 else(80 if  (50>=x>=20) \n",
    "                                      else(60 if (100>=x>=50)\n",
    "                                          else(40 if (200>=x>=100) \n",
    "                                              else 0)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "lesbian-sheet",
   "metadata": {
    "id": "lesbian-sheet"
   },
   "outputs": [],
   "source": [
    "df['wph']=df.npH * 0.165\n",
    "df['wdo']=df.ndo * 0.281\n",
    "df['wbdo']=df.nbdo * 0.234\n",
    "df['wec']=df.nec* 0.009\n",
    "df['wna']=df.nna * 0.028\n",
    "df['wco']=df.nco * 0.281\n",
    "df['year']=df['year']+6\n",
    "df['wqi']=df.wph+df.wdo+df.wbdo+df.wec+df.wna+df.wco "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "TcrCBmk0ygZK",
   "metadata": {
    "id": "TcrCBmk0ygZK"
   },
   "outputs": [],
   "source": [
    "df1 = df[['year','wph', 'wdo', 'wbdo','wec','wna','wco','wqi']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stone-tuition",
   "metadata": {
    "id": "stone-tuition"
   },
   "outputs": [],
   "source": [
    "ag=df.groupby('year')['wqi'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "grand-technical",
   "metadata": {
    "id": "grand-technical"
   },
   "outputs": [],
   "source": [
    "df=ag.reset_index(level=0,inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "yQgYRa5jxPzP",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 685
    },
    "id": "yQgYRa5jxPzP",
    "outputId": "e8b0bdbd-8ab7-49f6-a34e-4b230994709b"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year=df['year'].values\n",
    "AQI=df['wqi'].values\n",
    "df['wqi']=pd.to_numeric(df['wqi'],errors='coerce')\n",
    "df['year']=pd.to_numeric(df['year'],errors='coerce')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (20.0, 10.0)\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "ax.scatter(year,AQI, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "skilled-peter",
   "metadata": {
    "id": "skilled-peter"
   },
   "outputs": [],
   "source": [
    "df = df[np.isfinite(df['wqi'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eUn_DuCPwQ5g",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "id": "eUn_DuCPwQ5g",
    "outputId": "637b238a-69ed-4eaf-c14e-503ab0ca3eca"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DSVJVe5IsJnk4yVcl+YHufuWGTggAAAAzdOb8Yk6evpClK1eTJIuXl3Ly9IUkEX8YzrSnet2R5LHufnwjhgEAAICNdurswvujzzVLV67m1NmFLZoINs604eeeJA+u+Pobqup3qurHq+rDZjgXAAAAbIiLl5emWoedbM3hp6puSXJ3kocmSz+c5GOyfBrYW5J8/9M87r6qOldV5y5dunSjuwAAAMCmObh/bqp12MmmOeLnriSPdPcTSdLdT3T31e5+X5IfTfKiGz2oux/o7qPdffTAgQPrnxgAAADW4cSx+czt2/OUtbl9e3Li2PwWTQQbZ9WLO69wb1ac5lVVH93db5l8+cVJfneWgwEAAMBGuHYBZ+/qxW6wpvBTVbcmuTPJ165Y/r6qOpykk7zputsAAABg2zp+5JDQw66wpvDT3U8mue26ta/ckIkAAAAAmIlp39ULAAAAgB1C+AEAAAAYlPADAAAAMCjhBwAAAGBQwg8AAADAoIQfAAAAgEEJPwAAAACDEn4AAAAABiX8AAAAAAxK+AEAAAAYlPADAAAAMCjhBwAAAGBQwg8AAADAoIQfAAAAgEEJPwAAAACD2rvVAwAAbCdnzi/m1NmFXLy8lIP753Li2HyOHzm01WMBANwU4QcAYOLM+cWcPH0hS1euJkkWLy/l5OkLSSL+AAA7klO9AAAmTp1deH/0uWbpytWcOruwRRMBAKyP8AMAMHHx8tJU6wAA253wAwAwcXD/3FTrAADbnfADADBx4th85vbtecra3L49OXFsfosmAgBYHxd3BgCYuHYBZ+/qBQCMQvgBAFjh+JFDQg8AMAynegEAAAAMSvgBAAAAGJTwAwAAADAo4QcAAABgUMIPAAAAwKCEHwAAAIBBCT8AAAAAgxJ+AAAAAAYl/AAAAAAMSvgBAAAAGJTwAwAAADAo4QcAAABgUMIPAAAAwKCEHwAAAIBBCT8AAAAAgxJ+AAAAAAYl/AAAAAAMSvgBAAAAGJTwAwAAADAo4QcAAABgUMIPAAAAwKCEHwAAAIBBCT8AAAAAgxJ+AAAAAAYl/AAAAAAMSvgBAAAAGJTwAwAAADAo4QcAAABgUHu3egAA2I7OnF/MqbMLuXh5KQf3z+XEsfkcP3Joq8cCAICpCD8AcJ0z5xdz8vSFLF25miRZvLyUk6cvJIn4AwDAjuJULwC4zqmzC++PPtcsXbmaU2cXtmgiAAC4OcIPAFzn4uWlqdYBAGC7WjX8VNV8VT264uOdVXX/itv/eVV1VT1rY0cFgM1xcP/cVOsAALBdrRp+unuhuw939+EkL0zyriQPJ0lVPSfJ5yf5kw2dEgA20Ylj85nbt+cpa3P79uTEsfktmggAAG7OtKd63ZHkse5+fPL1DyT51iQ906kAYAsdP3Ior3jpC3Jo/1wqyaH9c3nFS1/gws4AAOw4076r1z1JHkySqnpJksXu/u2qmvlgALCVjh85JPQAALDjrTn8VNUtSe5OcrKqnpHkO7J8mtdqj7svyX1J8tznPvcmxwQAAABgWtOc6nVXkke6+4kkH5PkeUl+u6relOTZSR6pqo+6/kHd/UB3H+3uowcOHJjFzAAAAACswTSnet2byWle3X0hyUdcu2ESf45299tmOh0AAAAAN21NR/xU1a1J7kxyemPHAQAAAGBW1nTET3c/meS2v+L222c1EAAAAACzMe3buQMAAACwQwg/AAAAAIMSfgAAAAAGJfwAAAAADEr4AQAAABiU8AMAAAAwKOEHAAAAYFDCDwAAAMCghB8AAACAQQk/AAAAAIMSfgAAAAAGJfwAAAAADEr4AQAAABiU8AMAAAAwKOEHAAAAYFDCDwAAAMCghB8AAACAQQk/AAAAAIMSfgAAAAAGJfwAAAAADEr4AQAAABiU8AMAAAAwKOEHAAAAYFDCDwAAAMCghB8AAACAQQk/AAAAAIMSfgAAAAAGJfwAAAAADEr4AQAAABiU8AMAAAAwKOEHAAAAYFDCDwAAAMCghB8AAACAQQk/AAAAAIMSfgAAAAAGJfwAAAAADEr4AQAAABiU8AMAAAAwKOEHAAAAYFDCDwAAAMCghB8AAACAQQk/AAAAAIMSfgAAAAAGJfwAAAAADEr4AQAAABiU8AMAAAAwKOEHAAAAYFDCDwAAAMCghB8AAACAQQk/AAAAAIMSfgAAAAAGJfwAAAAADEr4AQAAABiU8AMAAAAwKOEHAAAAYFDCDwAAAMCg9q52h6qaT/LqFUvPT/JdSW5L8pIk70vy1iQv7+6LGzEkAAAAANNbNfx090KSw0lSVXuSLCZ5OMnbu/tfTtb/WZZj0Ndt3KgAAAAATGPV8HOdO5I81t2PX7d+a5KezUgAAAAAzMK04eeeJA9e+6KqvifJP0jyjiQvnuFcAAAAAKzTmi/uXFW3JLk7yUPX1rr7O7v7OUl+Ksk3PM3j7quqc1V17tKlS+udFwAAAIA1muZdve5K8kh3P3GD234qyZfc6EHd/UB3H+3uowcOHLiZGQEAAAC4CdOEn3vz1NO8PnbFbS9J8sZZDQUAAADA+q3pGj9VdWuSO5N87Yrl75281fv7kjwe7+gFAAAAsK2sKfx095NJbrtu7YandgEAAACwPUxzqhcAAAAAO4jwAwAAADAo4QcAAABgUMIPAAAAwKCEHwAAAIBBCT8AAAAAgxJ+AAAAAAYl/AAAAAAMSvgBAAAAGJTwAwAAADAo4QcAAABgUMIPAAAAwKCEHwAAAIBBCT8AAAAAgxJ+AAAAAAYl/AAAAAAMSvgBAAAAGJTwAwAAADAo4QcAAABgUMIPAAAAwKD2bvUAANvZmfOLOXV2IRcvL+Xg/rmcODaf40cObfVYAAAAayL8wBr44X93OnN+MSdPX8jSlatJksXLSzl5+kKS2H8AAGBHcKoXrOLaD/+Ll5fS+cAP/2fOL271aGywU2cX3h99rlm6cjWnzi5s0UQAAADTEX5gFX74370uXl6aah0AAGC7EX5gFX74370O7p+bah0AAGC7EX5gFX74371OHJvP3L49T1mb27cnJ47Nb9FEAAAA0xF+YBV++N+9jh85lFe89AU5tH8uleTQ/rm84qUvcGFnAABgx/CuXrCKaz/ke1ev3en4kUP2GgAA2LGEH1gDP/wDAACwEznVCwAAAGBQwg8AAADAoIQfAAAAgEEJPwAAAACDEn4AAAAABiX8AAAAAAxK+AEAAAAYlPADAAAAMCjhBwAAAGBQwg8AAADAoIQfAAAAgEEJPwAAAACDEn4AAAAABiX8AAAAAAxK+AEAAAAYlPADAAAAMCjhBwAAAGBQwg8AAADAoIQfAAAAgEEJPwAAAACDEn4AAAAABiX8AAAAAAxK+AEAAAAYlPADAAAAMCjhBwAAAGBQwg8AAADAoIQfAAAAgEHtXe0OVTWf5NUrlp6f5LuSHEry95K8J8ljSb6quy9vxJAAAAAATG/VI366e6G7D3f34SQvTPKuJA8neV2ST+juT0zyB0lObuikAAAAAExl2lO97kjyWHc/3t2v7e73TtZ/PcmzZzsaAAAAAOsxbfi5J8mDN1j/6iS/sP5xAAAAAJiVNYefqrolyd1JHrpu/TuTvDfJTz3N4+6rqnNVde7SpUvrmRUAAACAKUxzxM9dSR7p7ieuLVTVy5N8UZIv7+6+0YO6+4HuPtrdRw8cOLCuYQEAAABYu1Xf1WuFe7PiNK+q+oIk35rks7v7XbMeDAAAAID1WdMRP1V1a5I7k5xesfzvknxIktdV1aNV9SMbMB8AAAAAN2lNR/x095NJbrtu7W9syEQAAAAAzMS07+oFAAAAwA4h/AAAAAAMSvgBAAAAGJTwAwAAADAo4QcAAABgUMIPAAAAwKCEHwAAAIBBCT8AAAAAgxJ+AAAAAAYl/AAAAAAMSvgBAAAAGJTwAwAAADAo4QcAAABgUMIPAAAAwKCEHwAAAIBBCT8AAAAAgxJ+AAAAAAYl/AAAAAAMSvgBAAAAGJTwAwAAADAo4QcAAABgUMIPAAAAwKCEHwAAAIBBCT8AAAAAgxJ+AAAAAAYl/AAAAAAMSvgBAAAAGJTwAwAAADAo4QcAAABgUMIPAAAAwKCEHwAAAIBBCT8AAAAAgxJ+AAAAAAYl/AAAAAAMSvgBAAAAGJTwAwAAADCovVs9wE5z5vxiTp1dyMXLSzm4fy4njs3n+JFDWz0WAAAAwF8i/EzhzPnFnDx9IUtXriZJFi8v5eTpC0ki/gAAAADbjlO9pnDq7ML7o881S1eu5tTZhS2aCAAAAODpCT9TuHh5aap1AAAAgK0k/Ezh4P65qdYBAAAAtpLwM4UTx+Yzt2/PU9bm9u3JiWPzWzQRAAAAwNNzcecpXLuAs3f1AgAAAHYC4WdKx48cEnoAAACAHcGpXgAAAACDEn4AAAAABiX8AAAAAAxK+AEAAAAYlPADAAAAMCjhBwAAAGBQwg8AAADAoIQfAAAAgEEJPwAAAACDEn4AAAAABiX8AAAAAAxq1fBTVfNV9eiKj3dW1f1V9aVV9Yaqel9VHd2MYQEAAABYu72r3aG7F5IcTpKq2pNkMcnDSZ6R5KVJ/v1GDggAAADAzVk1/FznjiSPdffj1xaqarYTAQAAADAT017j554kD27EIAAAAADM1prDT1XdkuTuJA9N8wtU1X1Vda6qzl26dGna+QAAAAC4SdMc8XNXkke6+4lpfoHufqC7j3b30QMHDkw3HQAAAAA3bZrwc2+c5gUAAACwY6wp/FTVrUnuTHJ6xdoXV9Wbk3xakv9WVWc3ZkQAAAAAbsaa3tWru59Mctt1aw9n+W3dAQAAANiGqrs37xerupTk8VXvuDM8K8nbtnoINp19373s/e5l73cve7972fvdy97vTvZ99xpp7/96d9/wwsqbGn5GUlXnuvvoVs/B5rLvu5e9373s/e5l73cve7972fvdyb7vXrtl76e5uDMAAAAAO4jwAwAAADAo4efmPbDVA7Al7PvuZe93L3u/e9n73cve7172fney77vXrth71/gBAAAAGJQjfgAAAAAGJfxMVNVzquoXq+r3quoNVfWNk/UPr6rXVdUfTv77YZP1v1lVv1ZV766qb1ntedi+Zrj3H1xVv1lVvz15nu/eqtfE6ma17yueb09Vna+q/7rZr4XpzHLvq+pNVXWhqh6tqnNb8XpYuxnv/f6q+tmqemNV/X5VfdpWvCbWZoZ/1s9Pvt+vfbyzqu7fqtfF6mb8ff9Nk+f43ap6sKo+eCteE6ub8b5/42TP3+D7ffu7ib3/8qr6ncnf5361qj5pxXN9QVUtVNUfVdW3b9VrmgWnek1U1Ucn+ejufqSqPiTJ65McT/LyJH/e3d872ewP6+5vq6qPSPLXJ/d5e3e/8q96nu7+vS14WazBDPe+ktza3X9RVfuS/EqSb+zuX9+Cl8UqZrXvK57vm5McTfLM7v6izXwtTGeWe19Vb0pytLvfttmvg+nNeO9/Mskvd/erquqWJM/o7sub/ZpYm1n/nj95zj1JFpN8Snc/vlmvhenM8O95h7L8d7uP7+6lqnpNkp/v7v+w+a+K1cxw3z8hyc8keVGS9yT570m+rrv/aNNfFGtyE3v/6Ul+v7vfXlV3JflX3f0pk9/j/yDJnUnenOS3kty7U3+ud8TPRHe/pbsfmXz+f5P8fpJDSV6S5Ccnd/vJLP9Pk+5+a3f/VpIra3wetqkZ7n13919Mvtw3+VBWt6lZ7XuSVNWzk/zdJK/ahNFZp1nuPTvLrPa+qj40yWcl+bHJ/d4j+mxvG/R9f0eSx0Sf7W3Ge783yVxV7U3yjCQXN3h8btIM9/1vJfmN7n5Xd783yS8leekmvARu0k3s/a9299sn67+e5NmTz1+U5I+6+4+7+z1ZDoAv2ZxXMXvCzw1U1e1JjiT5jSQf2d1vmdz0Z0k+8iafhx1gvXtfy6f7PJrkrUle1932fgeYwff8Dyb51iTv24j52Dgz2PtO8tqqen1V3bchQ7Ih1rn3z0tyKclP1PIpnq+qqls3alZma1Z/z0tyT5IHZzocG2o9e9/di0lemeRPkrwlyTu6+7UbNiwzs87v+d9N8plVdVtVPSPJFyZ5zgaNyozdxN7/wyS/MPn8UJI/XXHbm7ODD+gQftbkwEAAAANPSURBVK5TVX8tyc8lub+737nytl4+L25NR3D8Vc/D9jSLve/uq919OMul+EWTw0PZxta771X1RUne2t2v37gp2Qgz+v3+M7r7k5PcleSfVNVnzX5SZm0Ge783yScn+eHuPpLkySQ7+tz/3WKGf8+7JcndSR6a+ZBsiBn8ef9hWf7X/uclOZjk1qr6ig0alxlZ77539+8n+TdJXpvl07weTXJ1Y6Zllqbd+6p6cZbDz7dt2pCbSPhZYXJdlp9L8lPdfXqy/MTkPMFr5wu+9Safh21sVnt/zeSQ/19M8gWznpXZmdG+/50kd0+u9fIzST63qv7jBo3MjMzqe37yL8Dp7rcmeTjLhwWzjc1o79+c5M0rjur82SyHILaxGf9Zf1eSR7r7idlPyqzNaO8/L8n/7u5L3X0lyekkn75RM7N+M/yz/se6+4Xd/VlJ3p7l676wjU2791X1iVm+ZMNLuvv/TJYX89Sju549WduRhJ+JyYV5fyzLF3b6tytu+s9JXjb5/GVJ/tNNPg/b1Az3/kBV7Z98PpflC4G9cfYTMwuz2vfuPtndz+7u27N82P//7G7/AriNzfB7/tbJRQMzOc3n87N8SDjb1Ay/7/8syZ9W1fxk6Y4kO/Jij7vFrPZ+hXvjNK8dYYZ7/ydJPrWqnjF5zjuyfO0QtqFZfs9PLvycqnpulq/v89OznZZZmnbvJ/t6OslXdvfKqPdbST62qp43Ocrznslz7Eje1Wuiqj4jyS8nuZAPXKfjO7J8PuBrkjw3yeNJvqy7/7yqPirJuSTPnNz/L5J8fJJPvNHzdPfPb9JLYUoz3Pvbs3yhsD1Zjqqv6e5/vXmvhGnMat9XHjpaVZ+T5Fvau3ptazP8nn9Wlo/ySZZP/fnp7v6ezXodTG+W3/dVdTjL/zp4S5I/TvJVKy4OyTYz472/NcsR4Pnd/Y7NfSVMa8Z7/91J/n6S9yY5n+Qfdfe7N/P1sDYz3vdfTnJbli/8/M3d/T829cUwlZvY+1cl+ZLJWpK8t7uPTp7rC7N8Lc89SX58J/89T/gBAAAAGJRTvQAAAAAGJfwAAAAADEr4AQAAABiU8AMAAAAwKOEHAAAAYFDCDwAAAMCghB8AAACAQQk/AAAAAIP6//3okoegw09JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols =['year']\n",
    "y = df['wqi']\n",
    "x=df[cols]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6NTPEjrbwWLG",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "id": "6NTPEjrbwWLG",
    "outputId": "d651291b-7393-479d-8acc-f49bd0cf4a6b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df=df.set_index('year')\n",
    "df.plot(figsize=(15,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "exceptional-sunrise",
   "metadata": {
    "id": "exceptional-sunrise"
   },
   "outputs": [],
   "source": [
    "from sklearn import neighbors,datasets\n",
    "df=df.reset_index(level=0,inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "specific-claim",
   "metadata": {
    "id": "specific-claim"
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "gross-colors",
   "metadata": {
    "id": "gross-colors"
   },
   "outputs": [],
   "source": [
    "cols =['year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "steady-covering",
   "metadata": {
    "id": "steady-covering"
   },
   "outputs": [],
   "source": [
    "y = df['wqi']\n",
    "x=df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "possible-october",
   "metadata": {
    "id": "possible-october"
   },
   "outputs": [],
   "source": [
    "reg=linear_model.LinearRegression()\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fantastic-crime",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fantastic-crime",
    "outputId": "bdeba3ee-f6ec-46be-df7e-a8c938c02412"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 98,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "seeing-peter",
   "metadata": {
    "id": "seeing-peter"
   },
   "outputs": [],
   "source": [
    "a=reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "touched-franklin",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "touched-franklin",
    "outputId": "3f64cc88-9d3c-4b1d-a54f-331b97039a72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse:0.03\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "print('mse:%.2f'%mean_squared_error(y_test,a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "buried-rider",
   "metadata": {
    "id": "buried-rider"
   },
   "outputs": [],
   "source": [
    "dt = pd.DataFrame({'Actual': y_test, 'Predicted': a}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "congressional-county",
   "metadata": {
    "id": "congressional-county"
   },
   "outputs": [],
   "source": [
    "x = (x - x.mean()) / x.std()\n",
    "x = np.c_[np.ones(x.shape[0]), x]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "educated-maple",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "educated-maple",
    "outputId": "32e722cc-14db-4c99-b5b8-cdc799cdd6b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Descent: 74.63, 2.01\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.1 #Step size\n",
    "iterations = 3000 #No. of iterations\n",
    "m = y.size #No. of data points\n",
    "np.random.seed(4) #Setting the seed\n",
    "theta = np.random.rand(2) #Picking some random values to start with\n",
    "\n",
    "def gradient_descent(x, y, theta, iterations, alpha):\n",
    "    past_costs = []\n",
    "    past_thetas = [theta]\n",
    "    for i in range(iterations):\n",
    "        prediction = np.dot(x, theta)\n",
    "        error = prediction - y\n",
    "        cost = 1/(2*m) * np.dot(error.T, error)\n",
    "        past_costs.append(cost)\n",
    "        theta = theta - (alpha * (1/m) * np.dot(x.T, error))\n",
    "        past_thetas.append(theta)\n",
    "        \n",
    "    return past_thetas, past_costs\n",
    "\n",
    "past_thetas, past_costs = gradient_descent(x, y, theta, iterations, alpha)\n",
    "theta = past_thetas[-1]\n",
    "\n",
    "#Print the results...\n",
    "print(\"Gradient Descent: {:.2f}, {:.2f}\".format(theta[0], theta[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "oTz-5Acyvcjz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 621
    },
    "id": "oTz-5Acyvcjz",
    "outputId": "c7975060-b575-4052-8021-6f04ed47ed7f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Cost Function J')\n",
    "plt.xlabel('No. of iterations')\n",
    "plt.ylabel('Cost')\n",
    "plt.plot(past_costs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "oriental-export",
   "metadata": {
    "id": "oriental-export"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "newB=[74.76, 2.13]\n",
    "\n",
    "def rmse(y,y_pred):\n",
    "    rmse= np.sqrt(sum(y-y_pred))\n",
    "    return rmse\n",
    "   \n",
    "\n",
    "y_pred=x.dot(newB)\n",
    "\n",
    "dt = pd.DataFrame({'Actual': y, 'Predicted': y_pred})  \n",
    "dt=pd.concat([df, dt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "chronic-tennessee",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "chronic-tennessee",
    "outputId": "66c7c865-9471-4a5c-ed85-e9aafe8f9f3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1987755149740886\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(np.sqrt(metrics.mean_squared_error(y,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "S7ezJ7ImvjH5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 607
    },
    "id": "S7ezJ7ImvjH5",
    "outputId": "51f9f0d3-7b28-46c6-836a-7a0a510a267b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis=dt.year\n",
    "y_axis=dt.Actual\n",
    "y1_axis=dt.Predicted\n",
    "plt.scatter(x_axis,y_axis)\n",
    "plt.plot(x_axis,y1_axis,color='r')\n",
    "plt.title(\"linear regression\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "proper-springfield",
   "metadata": {
    "id": "proper-springfield"
   },
   "outputs": [],
   "source": [
    "dfg= df1.groupby(\"year\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "entertaining-complexity",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "id": "entertaining-complexity",
    "outputId": "c763969f-ee61-4f25-8cdd-92371feb6593"
   },
   "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>year</th>\n",
       "      <th>wph</th>\n",
       "      <th>wdo</th>\n",
       "      <th>wbdo</th>\n",
       "      <th>wec</th>\n",
       "      <th>wna</th>\n",
       "      <th>wco</th>\n",
       "      <th>wqi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>13.458824</td>\n",
       "      <td>23.967647</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>2.657255</td>\n",
       "      <td>11.460392</td>\n",
       "      <td>71.308824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>24.634333</td>\n",
       "      <td>20.514000</td>\n",
       "      <td>0.405000</td>\n",
       "      <td>2.286667</td>\n",
       "      <td>10.959000</td>\n",
       "      <td>72.549000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>12.535849</td>\n",
       "      <td>24.105912</td>\n",
       "      <td>20.868679</td>\n",
       "      <td>0.457358</td>\n",
       "      <td>2.479497</td>\n",
       "      <td>12.123648</td>\n",
       "      <td>72.570943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>13.163536</td>\n",
       "      <td>24.622431</td>\n",
       "      <td>20.866077</td>\n",
       "      <td>0.305304</td>\n",
       "      <td>2.490608</td>\n",
       "      <td>12.637238</td>\n",
       "      <td>74.085193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>13.129787</td>\n",
       "      <td>25.439468</td>\n",
       "      <td>20.362979</td>\n",
       "      <td>0.423191</td>\n",
       "      <td>2.588511</td>\n",
       "      <td>12.704787</td>\n",
       "      <td>74.648723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>14.542857</td>\n",
       "      <td>24.791255</td>\n",
       "      <td>20.198961</td>\n",
       "      <td>0.417662</td>\n",
       "      <td>2.266667</td>\n",
       "      <td>13.429610</td>\n",
       "      <td>75.647013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>15.222945</td>\n",
       "      <td>25.482466</td>\n",
       "      <td>21.300411</td>\n",
       "      <td>0.486986</td>\n",
       "      <td>2.368493</td>\n",
       "      <td>14.107740</td>\n",
       "      <td>78.969041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>14.186207</td>\n",
       "      <td>24.288736</td>\n",
       "      <td>20.638621</td>\n",
       "      <td>0.466897</td>\n",
       "      <td>2.767816</td>\n",
       "      <td>12.661149</td>\n",
       "      <td>75.009425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>14.653086</td>\n",
       "      <td>24.468971</td>\n",
       "      <td>20.838519</td>\n",
       "      <td>0.464444</td>\n",
       "      <td>2.786173</td>\n",
       "      <td>13.668395</td>\n",
       "      <td>76.879588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year        wph        wdo  ...       wna        wco        wqi\n",
       "0  2012  13.458824  23.967647  ...  2.657255  11.460392  71.308824\n",
       "1  2013  13.750000  24.634333  ...  2.286667  10.959000  72.549000\n",
       "2  2014  12.535849  24.105912  ...  2.479497  12.123648  72.570943\n",
       "3  2015  13.163536  24.622431  ...  2.490608  12.637238  74.085193\n",
       "4  2016  13.129787  25.439468  ...  2.588511  12.704787  74.648723\n",
       "5  2017  14.542857  24.791255  ...  2.266667  13.429610  75.647013\n",
       "6  2018  15.222945  25.482466  ...  2.368493  14.107740  78.969041\n",
       "7  2019  14.186207  24.288736  ...  2.767816  12.661149  75.009425\n",
       "8  2020  14.653086  24.468971  ...  2.786173  13.668395  76.879588\n",
       "\n",
       "[9 rows x 8 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfg=dfg.reset_index(level=0,inplace=False)\n",
    "dfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "k68Y8IiM1vpt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "id": "k68Y8IiM1vpt",
    "outputId": "5ea2ac2b-688c-410e-b42c-b38ee3955d40"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>year</th>\n",
       "      <th>wph</th>\n",
       "      <th>wdo</th>\n",
       "      <th>wbdo</th>\n",
       "      <th>wec</th>\n",
       "      <th>wna</th>\n",
       "      <th>wco</th>\n",
       "      <th>wqi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>13.458824</td>\n",
       "      <td>23.967647</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>2.657255</td>\n",
       "      <td>11.460392</td>\n",
       "      <td>71.308824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>24.634333</td>\n",
       "      <td>20.514000</td>\n",
       "      <td>0.405000</td>\n",
       "      <td>2.286667</td>\n",
       "      <td>10.959000</td>\n",
       "      <td>72.549000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>12.535849</td>\n",
       "      <td>24.105912</td>\n",
       "      <td>20.868679</td>\n",
       "      <td>0.457358</td>\n",
       "      <td>2.479497</td>\n",
       "      <td>12.123648</td>\n",
       "      <td>72.570943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>13.163536</td>\n",
       "      <td>24.622431</td>\n",
       "      <td>20.866077</td>\n",
       "      <td>0.305304</td>\n",
       "      <td>2.490608</td>\n",
       "      <td>12.637238</td>\n",
       "      <td>74.085193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>13.129787</td>\n",
       "      <td>25.439468</td>\n",
       "      <td>20.362979</td>\n",
       "      <td>0.423191</td>\n",
       "      <td>2.588511</td>\n",
       "      <td>12.704787</td>\n",
       "      <td>74.648723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>14.542857</td>\n",
       "      <td>24.791255</td>\n",
       "      <td>20.198961</td>\n",
       "      <td>0.417662</td>\n",
       "      <td>2.266667</td>\n",
       "      <td>13.429610</td>\n",
       "      <td>75.647013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>15.222945</td>\n",
       "      <td>25.482466</td>\n",
       "      <td>21.300411</td>\n",
       "      <td>0.486986</td>\n",
       "      <td>2.368493</td>\n",
       "      <td>14.107740</td>\n",
       "      <td>78.969041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>14.186207</td>\n",
       "      <td>24.288736</td>\n",
       "      <td>20.638621</td>\n",
       "      <td>0.466897</td>\n",
       "      <td>2.767816</td>\n",
       "      <td>12.661149</td>\n",
       "      <td>75.009425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>14.653086</td>\n",
       "      <td>24.468971</td>\n",
       "      <td>20.838519</td>\n",
       "      <td>0.464444</td>\n",
       "      <td>2.786173</td>\n",
       "      <td>13.668395</td>\n",
       "      <td>76.879588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year        wph        wdo  ...       wna        wco        wqi\n",
       "0  2012  13.458824  23.967647  ...  2.657255  11.460392  71.308824\n",
       "1  2013  13.750000  24.634333  ...  2.286667  10.959000  72.549000\n",
       "2  2014  12.535849  24.105912  ...  2.479497  12.123648  72.570943\n",
       "3  2015  13.163536  24.622431  ...  2.490608  12.637238  74.085193\n",
       "4  2016  13.129787  25.439468  ...  2.588511  12.704787  74.648723\n",
       "5  2017  14.542857  24.791255  ...  2.266667  13.429610  75.647013\n",
       "6  2018  15.222945  25.482466  ...  2.368493  14.107740  78.969041\n",
       "7  2019  14.186207  24.288736  ...  2.767816  12.661149  75.009425\n",
       "8  2020  14.653086  24.468971  ...  2.786173  13.668395  76.879588\n",
       "\n",
       "[9 rows x 8 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4sN5HlVezVBW",
   "metadata": {
    "id": "4sN5HlVezVBW"
   },
   "outputs": [],
   "source": [
    "X=dfg.iloc[: , 1:-1]\n",
    "Y=dfg.iloc[ : ,-1 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "PbhpGjbFzpWt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PbhpGjbFzpWt",
    "outputId": "3585c163-3864-4d9e-b189-8cd24b6e82e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    71.308824\n",
       "1    72.549000\n",
       "2    72.570943\n",
       "3    74.085193\n",
       "4    74.648723\n",
       "5    75.647013\n",
       "6    78.969041\n",
       "7    75.009425\n",
       "8    76.879588\n",
       "Name: wqi, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e-QlNAroXwIX",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e-QlNAroXwIX",
    "outputId": "ecdd0c4f-3cbd-4356-dbe6-d29bce031df6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:26:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "RMSE :  0.504078\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xg\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(X, Y, \n",
    "                      test_size = 0.2, random_state = 50)\n",
    "  \n",
    "\n",
    "train_dmatrix = xg.DMatrix(data = x_train1, label = y_train1)\n",
    "test_dmatrix = xg.DMatrix(data = x_test1, label = y_test1)\n",
    "  \n",
    "\n",
    "param = {\"booster\":\"gblinear\", \"objective\":\"reg:linear\"}\n",
    "  \n",
    "xgb_r = xg.train(params = param, dtrain = train_dmatrix, num_boost_round = 10)\n",
    "pred1 = xgb_r.predict(test_dmatrix)\n",
    "  \n",
    "# RMSE Computation\n",
    "rmse1 = np.sqrt(MSE(y_test1, pred1))\n",
    "print(\"RMSE : % f\" %(rmse1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Sa5Z6mLU3pg2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 625
    },
    "id": "Sa5Z6mLU3pg2",
    "outputId": "6f1f63f2-83bc-4284-f9fb-3b0391e624bf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dmatrix=xg.DMatrix(data=X)\n",
    "y_pred1= xgb_r.predict(df_dmatrix)\n",
    "df1=df1.groupby([\"year\"]).mean()\n",
    "df1=df1.reset_index(level=0,inplace=False)\n",
    "df4 = pd.DataFrame({'Actual': y, 'Predicted':y_pred1 })\n",
    "df4['year']=df1[['year']]\n",
    "df4=df4.groupby([\"year\"]).mean()\n",
    "df4=df4.reset_index(level=0,inplace=False)\n",
    "\n",
    "x_axis=df4.year\n",
    "y_axis=df4.Actual\n",
    "y1_axis=df4.Predicted\n",
    "plt.scatter(x_axis,y_axis)\n",
    "plt.plot(x_axis,y1_axis,color='r')\n",
    "plt.title(\"xgboost\")\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pressing-pennsylvania",
   "metadata": {
    "id": "pressing-pennsylvania"
   },
   "outputs": [],
   "source": [
    "dt=dt.drop(columns=['wqi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sorted-chassis",
   "metadata": {
    "id": "sorted-chassis"
   },
   "outputs": [],
   "source": [
    "y_actual=dt['Actual'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "-hsDxvkcTRBJ",
   "metadata": {
    "id": "-hsDxvkcTRBJ"
   },
   "outputs": [],
   "source": [
    "import pmdarima as pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "talented-accordance",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "talented-accordance",
    "outputId": "1aa059c3-ec05-4ca1-b51b-ce29169efb6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,0,1)(0,0,0)[0]             : AIC=78.169, Time=0.06 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0]             : AIC=105.175, Time=0.01 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0]             : AIC=71.049, Time=0.02 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0]             : AIC=inf, Time=0.03 sec\n",
      " ARIMA(2,0,0)(0,0,0)[0]             : AIC=inf, Time=0.04 sec\n",
      " ARIMA(2,0,1)(0,0,0)[0]             : AIC=63.139, Time=0.04 sec\n",
      " ARIMA(3,0,1)(0,0,0)[0]             : AIC=96.499, Time=0.04 sec\n",
      " ARIMA(2,0,2)(0,0,0)[0]             : AIC=10066598.586, Time=0.08 sec\n",
      " ARIMA(1,0,2)(0,0,0)[0]             : AIC=10100669.321, Time=0.06 sec\n",
      " ARIMA(3,0,0)(0,0,0)[0]             : AIC=inf, Time=0.03 sec\n",
      " ARIMA(3,0,2)(0,0,0)[0]             : AIC=10042033.617, Time=0.10 sec\n",
      " ARIMA(2,0,1)(0,0,0)[0] intercept   : AIC=47.237, Time=0.06 sec\n",
      " ARIMA(1,0,1)(0,0,0)[0] intercept   : AIC=49.111, Time=0.04 sec\n",
      " ARIMA(2,0,0)(0,0,0)[0] intercept   : AIC=45.016, Time=0.04 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0] intercept   : AIC=43.507, Time=0.03 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0] intercept   : AIC=44.019, Time=0.01 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0] intercept   : AIC=44.744, Time=0.03 sec\n",
      "\n",
      "Best model:  ARIMA(1,0,0)(0,0,0)[0] intercept\n",
      "Total fit time: 0.740 seconds\n"
     ]
    }
   ],
   "source": [
    "pmarima = pm.auto_arima(y_actual, error_action='ignore', trace=True,\n",
    "                      suppress_warnings=True, maxiter=10,\n",
    "                      seasonal=False, m=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "g82faXYBTm9J",
   "metadata": {
    "id": "g82faXYBTm9J"
   },
   "outputs": [],
   "source": [
    "yhat = pmarima.predict( n_periods= 5,typ='levels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "YI5rXCiYzCrL",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "id": "YI5rXCiYzCrL",
    "outputId": "809a1e69-5fde-4cb4-d06e-d5f60691e2b7"
   },
   "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>year</th>\n",
       "      <th>wph</th>\n",
       "      <th>wdo</th>\n",
       "      <th>wbdo</th>\n",
       "      <th>wec</th>\n",
       "      <th>wna</th>\n",
       "      <th>wco</th>\n",
       "      <th>wqi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>13.458824</td>\n",
       "      <td>23.967647</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>2.657255</td>\n",
       "      <td>11.460392</td>\n",
       "      <td>71.308824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>24.634333</td>\n",
       "      <td>20.514000</td>\n",
       "      <td>0.405000</td>\n",
       "      <td>2.286667</td>\n",
       "      <td>10.959000</td>\n",
       "      <td>72.549000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>12.535849</td>\n",
       "      <td>24.105912</td>\n",
       "      <td>20.868679</td>\n",
       "      <td>0.457358</td>\n",
       "      <td>2.479497</td>\n",
       "      <td>12.123648</td>\n",
       "      <td>72.570943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>13.163536</td>\n",
       "      <td>24.622431</td>\n",
       "      <td>20.866077</td>\n",
       "      <td>0.305304</td>\n",
       "      <td>2.490608</td>\n",
       "      <td>12.637238</td>\n",
       "      <td>74.085193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>13.129787</td>\n",
       "      <td>25.439468</td>\n",
       "      <td>20.362979</td>\n",
       "      <td>0.423191</td>\n",
       "      <td>2.588511</td>\n",
       "      <td>12.704787</td>\n",
       "      <td>74.648723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>14.542857</td>\n",
       "      <td>24.791255</td>\n",
       "      <td>20.198961</td>\n",
       "      <td>0.417662</td>\n",
       "      <td>2.266667</td>\n",
       "      <td>13.429610</td>\n",
       "      <td>75.647013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>15.222945</td>\n",
       "      <td>25.482466</td>\n",
       "      <td>21.300411</td>\n",
       "      <td>0.486986</td>\n",
       "      <td>2.368493</td>\n",
       "      <td>14.107740</td>\n",
       "      <td>78.969041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>14.186207</td>\n",
       "      <td>24.288736</td>\n",
       "      <td>20.638621</td>\n",
       "      <td>0.466897</td>\n",
       "      <td>2.767816</td>\n",
       "      <td>12.661149</td>\n",
       "      <td>75.009425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>14.653086</td>\n",
       "      <td>24.468971</td>\n",
       "      <td>20.838519</td>\n",
       "      <td>0.464444</td>\n",
       "      <td>2.786173</td>\n",
       "      <td>13.668395</td>\n",
       "      <td>76.879588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year        wph        wdo  ...       wna        wco        wqi\n",
       "0  2012  13.458824  23.967647  ...  2.657255  11.460392  71.308824\n",
       "1  2013  13.750000  24.634333  ...  2.286667  10.959000  72.549000\n",
       "2  2014  12.535849  24.105912  ...  2.479497  12.123648  72.570943\n",
       "3  2015  13.163536  24.622431  ...  2.490608  12.637238  74.085193\n",
       "4  2016  13.129787  25.439468  ...  2.588511  12.704787  74.648723\n",
       "5  2017  14.542857  24.791255  ...  2.266667  13.429610  75.647013\n",
       "6  2018  15.222945  25.482466  ...  2.368493  14.107740  78.969041\n",
       "7  2019  14.186207  24.288736  ...  2.767816  12.661149  75.009425\n",
       "8  2020  14.653086  24.468971  ...  2.786173  13.668395  76.879588\n",
       "\n",
       "[9 rows x 8 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "level-footage",
   "metadata": {},
   "source": [
    "###### Examples of forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clbJphJ4IGGv",
   "metadata": {
    "id": "clbJphJ4IGGv"
   },
   "source": [
    "PH Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "NHs0jkOCHsW5",
   "metadata": {
    "id": "NHs0jkOCHsW5"
   },
   "outputs": [],
   "source": [
    "wph_np = df1['wph'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "WikzC8fYHlEu",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WikzC8fYHlEu",
    "outputId": "8a5e133e-2c5a-4627-efd9-086686b54e21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,0,1)(0,0,0)[0]             : AIC=41.312, Time=0.03 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0]             : AIC=74.880, Time=0.01 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0]             : AIC=39.367, Time=0.02 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0]             : AIC=67.653, Time=0.02 sec\n",
      " ARIMA(2,0,0)(0,0,0)[0]             : AIC=inf, Time=0.03 sec\n",
      " ARIMA(2,0,1)(0,0,0)[0]             : AIC=65.278, Time=0.03 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0] intercept   : AIC=25.694, Time=0.03 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0] intercept   : AIC=25.896, Time=0.02 sec\n",
      " ARIMA(2,0,0)(0,0,0)[0] intercept   : AIC=27.718, Time=0.04 sec\n",
      " ARIMA(1,0,1)(0,0,0)[0] intercept   : AIC=27.687, Time=0.04 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0] intercept   : AIC=26.291, Time=0.04 sec\n",
      " ARIMA(2,0,1)(0,0,0)[0] intercept   : AIC=29.843, Time=0.06 sec\n",
      "\n",
      "Best model:  ARIMA(1,0,0)(0,0,0)[0] intercept\n",
      "Total fit time: 0.366 seconds\n"
     ]
    }
   ],
   "source": [
    "pmarima = pm.auto_arima(wph_np, error_action='ignore', trace=True,\n",
    "                      suppress_warnings=True, maxiter=10,\n",
    "                      seasonal=False, m=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sGrYi3WJHx47",
   "metadata": {
    "id": "sGrYi3WJHx47"
   },
   "outputs": [],
   "source": [
    "forecast1 = pmarima.predict( n_periods= 5,typ='levels')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bxbPPaIIPoi",
   "metadata": {
    "id": "8bxbPPaIIPoi"
   },
   "source": [
    "DO Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "SQikXkkrIAne",
   "metadata": {
    "id": "SQikXkkrIAne"
   },
   "outputs": [],
   "source": [
    "wdo_np = df1['wdo'].to_numpy()"
   ]
  }
 ],
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