p2m_interp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import the dependencies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Collection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_6936\\3978362536.py:1: DtypeWarning: Columns (8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data = pd.read_csv('UNFCCC_v24.csv', encoding= 'unicode_escape')\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('UNFCCC_v24.csv', encoding= 'unicode_escape')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing the unnamed Column\n",
    "data.drop(columns='Notation' , axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Country_code</th>\n",
       "      <th>Country</th>\n",
       "      <th>Format_name</th>\n",
       "      <th>Pollutant_name</th>\n",
       "      <th>Sector_code</th>\n",
       "      <th>Sector_name</th>\n",
       "      <th>Parent_sector_code</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Year</th>\n",
       "      <th>emissions</th>\n",
       "      <th>PublicationDate</th>\n",
       "      <th>DataSource</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.2</td>\n",
       "      <td>2.E.2 - TFT Flat Panel Display</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>2008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.3</td>\n",
       "      <td>2.E.3 - Photovoltaics</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.3</td>\n",
       "      <td>2.E.3 - Photovoltaics</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.3</td>\n",
       "      <td>2.E.3 - Photovoltaics</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.5</td>\n",
       "      <td>2.E.5 - Other electronics industry</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>1993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country_code      Country                   Format_name  \\\n",
       "0           NL  Netherlands  IPCC Common Reporting Format   \n",
       "1           NL  Netherlands  IPCC Common Reporting Format   \n",
       "2           NL  Netherlands  IPCC Common Reporting Format   \n",
       "3           NL  Netherlands  IPCC Common Reporting Format   \n",
       "4           NL  Netherlands  IPCC Common Reporting Format   \n",
       "\n",
       "           Pollutant_name Sector_code                         Sector_name  \\\n",
       "0  SF6 - (CO2 equivalent)       2.E.2      2.E.2 - TFT Flat Panel Display   \n",
       "1  SF6 - (CO2 equivalent)       2.E.3               2.E.3 - Photovoltaics   \n",
       "2  SF6 - (CO2 equivalent)       2.E.3               2.E.3 - Photovoltaics   \n",
       "3  SF6 - (CO2 equivalent)       2.E.3               2.E.3 - Photovoltaics   \n",
       "4  SF6 - (CO2 equivalent)       2.E.5  2.E.5 - Other electronics industry   \n",
       "\n",
       "  Parent_sector_code               Unit  Year  emissions  PublicationDate  \\\n",
       "0                2.E  Gg CO2 equivalent  2008        NaN         20210616   \n",
       "1                2.E  Gg CO2 equivalent  1992        NaN         20210616   \n",
       "2                2.E  Gg CO2 equivalent  2010        NaN         20210616   \n",
       "3                2.E  Gg CO2 equivalent  2017        NaN         20210616   \n",
       "4                2.E  Gg CO2 equivalent  1993        NaN         20210616   \n",
       "\n",
       "  DataSource  \n",
       "0        EEA  \n",
       "1        EEA  \n",
       "2        EEA  \n",
       "3        EEA  \n",
       "4        EEA  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(686348, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 686348 entries, 0 to 686347\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   Country_code        686348 non-null  object \n",
      " 1   Country             686348 non-null  object \n",
      " 2   Format_name         686348 non-null  object \n",
      " 3   Pollutant_name      686348 non-null  object \n",
      " 4   Sector_code         686348 non-null  object \n",
      " 5   Sector_name         686348 non-null  object \n",
      " 6   Parent_sector_code  628766 non-null  object \n",
      " 7   Unit                686348 non-null  object \n",
      " 8   Year                686348 non-null  object \n",
      " 9   emissions           426498 non-null  float64\n",
      " 10  PublicationDate     686348 non-null  int64  \n",
      " 11  DataSource          686348 non-null  object \n",
      "dtypes: float64(1), int64(1), object(10)\n",
      "memory usage: 62.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country_code               0\n",
       "Country                    0\n",
       "Format_name                0\n",
       "Pollutant_name             0\n",
       "Sector_code                0\n",
       "Sector_name                0\n",
       "Parent_sector_code     57582\n",
       "Unit                       0\n",
       "Year                       0\n",
       "emissions             259850\n",
       "PublicationDate            0\n",
       "DataSource                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a 37,85% de données manquantes pour les quatitées d'émission\n",
    "et 8,38% de données manquantes pour les Parent_sector_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.00000        955\n",
       " 0.00001        717\n",
       " 0.00002        601\n",
       " 0.00005        410\n",
       " 24.96794       404\n",
       "               ... \n",
       "-20474.65619      1\n",
       "-21510.69465      1\n",
       " 12773.20649      1\n",
       " 1203.99933       1\n",
       " 0.23575          1\n",
       "Name: emissions, Length: 281411, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['emissions'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hungary                        24408\n",
       "Slovenia                       21896\n",
       "Romania                        21801\n",
       "Spain                          20610\n",
       "Ireland                        20610\n",
       "Lithuania                      20606\n",
       "Poland                         20564\n",
       "Denmark                        20460\n",
       "Slovakia                       20430\n",
       "Finland                        20222\n",
       "Estonia                        20052\n",
       "United Kingdom (Convention)    20010\n",
       "Bulgaria                       19831\n",
       "EU (KP)                        19590\n",
       "EU-27                          19590\n",
       "EU-27+UK                       19590\n",
       "Netherlands                    19470\n",
       "Germany                        19440\n",
       "France                         19401\n",
       "Czechia                        19369\n",
       "Portugal                       19290\n",
       "Belgium                        19110\n",
       "Malta                          19095\n",
       "Cyprus                         19077\n",
       "Italy                          18990\n",
       "Latvia                         18990\n",
       "Croatia                        18900\n",
       "Greece                         18876\n",
       "Iceland                        18603\n",
       "Switzerland                    18360\n",
       "Luxembourg                     18300\n",
       "Norway                         18240\n",
       "Austria                        18190\n",
       "Turkey                         17277\n",
       "Sweden                         17100\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IPCC Common Reporting Format    686348\n",
       "Name: Format_name, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Format_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "All greenhouse gases - (CO2 equivalent)                180335\n",
       "CH4                                                    136493\n",
       "CO2                                                    128506\n",
       "N2O                                                    126180\n",
       "HFCs - (CO2 equivalent)                                 25821\n",
       "PFCs - (CO2 equivalent)                                 24390\n",
       "SF6 - (CO2 equivalent)                                  22987\n",
       "Unspecified mix of HFCs and PFCs - (CO2 equivalent)     21536\n",
       "NF3 - (CO2 equivalent)                                  20100\n",
       "Name: Pollutant_name, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Pollutant_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gg                   391179\n",
       "Gg CO2 equivalent    295169\n",
       "Name: Unit, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Unit'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dealing with missing values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['emissions']=data['emissions'].interpolate(method=\"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing the missing values of the column \"Parent_sector_code\" with the most frequent value\n",
    "data = data.fillna(data['Parent_sector_code'].value_counts().index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country_code          0\n",
       "Country               0\n",
       "Format_name           0\n",
       "Pollutant_name        0\n",
       "Sector_code           0\n",
       "Sector_name           0\n",
       "Parent_sector_code    0\n",
       "Unit                  0\n",
       "Year                  0\n",
       "emissions             0\n",
       "PublicationDate       0\n",
       "DataSource            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=> Il n'y a pas encore de valeurs manquantes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country_code</th>\n",
       "      <th>Country</th>\n",
       "      <th>Format_name</th>\n",
       "      <th>Pollutant_name</th>\n",
       "      <th>Sector_code</th>\n",
       "      <th>Sector_name</th>\n",
       "      <th>Parent_sector_code</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Year</th>\n",
       "      <th>emissions</th>\n",
       "      <th>PublicationDate</th>\n",
       "      <th>DataSource</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.2</td>\n",
       "      <td>2.E.2 - TFT Flat Panel Display</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.3</td>\n",
       "      <td>2.E.3 - Photovoltaics</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>1992</td>\n",
       "      <td>2</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.3</td>\n",
       "      <td>2.E.3 - Photovoltaics</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.3</td>\n",
       "      <td>2.E.3 - Photovoltaics</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.5</td>\n",
       "      <td>2.E.5 - Other electronics industry</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>1993</td>\n",
       "      <td>2</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>SF6 - (CO2 equivalent)</td>\n",
       "      <td>2.E.5</td>\n",
       "      <td>2.E.5 - Other electronics industry</td>\n",
       "      <td>2.E</td>\n",
       "      <td>Gg CO2 equivalent</td>\n",
       "      <td>2001</td>\n",
       "      <td>2</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LT</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>IPCC Common Reporting Format</td>\n",
       "      <td>N2O</td>\n",
       "      <td>1.A.3.b</td>\n",
       "      <td>1.A.3.b - Road Transportation</td>\n",
       "      <td>1.A.3</td>\n",
       "      <td>Gg</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.10775</td>\n",
       "      <td>20210616</td>\n",
       "      <td>EEA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country_code      Country                   Format_name  \\\n",
       "0           NL  Netherlands  IPCC Common Reporting Format   \n",
       "1           NL  Netherlands  IPCC Common Reporting Format   \n",
       "2           NL  Netherlands  IPCC Common Reporting Format   \n",
       "3           NL  Netherlands  IPCC Common Reporting Format   \n",
       "4           NL  Netherlands  IPCC Common Reporting Format   \n",
       "5           NL  Netherlands  IPCC Common Reporting Format   \n",
       "6           LT    Lithuania  IPCC Common Reporting Format   \n",
       "\n",
       "           Pollutant_name Sector_code                         Sector_name  \\\n",
       "0  SF6 - (CO2 equivalent)       2.E.2      2.E.2 - TFT Flat Panel Display   \n",
       "1  SF6 - (CO2 equivalent)       2.E.3               2.E.3 - Photovoltaics   \n",
       "2  SF6 - (CO2 equivalent)       2.E.3               2.E.3 - Photovoltaics   \n",
       "3  SF6 - (CO2 equivalent)       2.E.3               2.E.3 - Photovoltaics   \n",
       "4  SF6 - (CO2 equivalent)       2.E.5  2.E.5 - Other electronics industry   \n",
       "5  SF6 - (CO2 equivalent)       2.E.5  2.E.5 - Other electronics industry   \n",
       "6                     N2O     1.A.3.b       1.A.3.b - Road Transportation   \n",
       "\n",
       "  Parent_sector_code               Unit  Year emissions  PublicationDate  \\\n",
       "0                2.E  Gg CO2 equivalent  2008         2         20210616   \n",
       "1                2.E  Gg CO2 equivalent  1992         2         20210616   \n",
       "2                2.E  Gg CO2 equivalent  2010         2         20210616   \n",
       "3                2.E  Gg CO2 equivalent  2017         2         20210616   \n",
       "4                2.E  Gg CO2 equivalent  1993         2         20210616   \n",
       "5                2.E  Gg CO2 equivalent  2001         2         20210616   \n",
       "6              1.A.3                 Gg  2009   0.10775         20210616   \n",
       "\n",
       "  DataSource  \n",
       "0        EEA  \n",
       "1        EEA  \n",
       "2        EEA  \n",
       "3        EEA  \n",
       "4        EEA  \n",
       "5        EEA  \n",
       "6        EEA  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00000         1302\n",
       "0.00001          735\n",
       "24.96794         670\n",
       "0.00002          651\n",
       "0.00005          413\n",
       "                ... \n",
       "21071.27530        1\n",
       "318294.20803       1\n",
       "331160.53641       1\n",
       "311968.87272       1\n",
       "137.60288          1\n",
       "Name: emissions, Length: 533108, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['emissions'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Encoding the features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "data['Country_code']=le.fit_transform(data['Country_code'])\n",
    "data['Country']=le.fit_transform(data['Country'])\n",
    "data['Sector_code']=le.fit_transform(data['Sector_code'])\n",
    "data['Sector_name']=le.fit_transform(data['Sector_name'])\n",
    "data['Parent_sector_code']=le.fit_transform(data['Parent_sector_code'])\n",
    "data['Pollutant_name']=le.fit_transform(data['Pollutant_name'])\n",
    "data['Unit']=le.fit_transform(data['Unit'])\n",
    "data['DataSource']=le.fit_transform(data['DataSource'])\n",
    "data['Format_name']=le.fit_transform(data['Format_name'])\n",
    "data.replace({'PublicationDate':{20210616:0}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Country_code</th>\n",
       "      <th>Country</th>\n",
       "      <th>Format_name</th>\n",
       "      <th>Pollutant_name</th>\n",
       "      <th>Sector_code</th>\n",
       "      <th>Sector_name</th>\n",
       "      <th>Parent_sector_code</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Year</th>\n",
       "      <th>emissions</th>\n",
       "      <th>PublicationDate</th>\n",
       "      <th>DataSource</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1992</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1993</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2001</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.10775</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.08124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.07069</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.10371</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.03074</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.02634</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0.00029</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.00019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.014259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.028327</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0.042396</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.056464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.070533</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.084601</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country_code  Country  Format_name  Pollutant_name  Sector_code  \\\n",
       "0             25       23            0               7           73   \n",
       "1             25       23            0               7           74   \n",
       "2             25       23            0               7           74   \n",
       "3             25       23            0               7           74   \n",
       "4             25       23            0               7           76   \n",
       "5             25       23            0               7           76   \n",
       "6             21       20            0               4           16   \n",
       "7             21       20            0               4           17   \n",
       "8             21       20            0               4           17   \n",
       "9             21       20            0               4           17   \n",
       "10            21       20            0               4           19   \n",
       "11            21       20            0               4           19   \n",
       "12            21       20            0               4           20   \n",
       "13            21       20            0               4           20   \n",
       "14            21       20            0               4           21   \n",
       "15            21       20            0               4           21   \n",
       "16            21       20            0               4           21   \n",
       "17            21       20            0               4           21   \n",
       "18            21       20            0               4           21   \n",
       "19            21       20            0               4           21   \n",
       "\n",
       "    Sector_name  Parent_sector_code  Unit  Year emissions  PublicationDate  \\\n",
       "0            73                  16     1  2008         2                0   \n",
       "1            74                  16     1  1992         2                0   \n",
       "2            74                  16     1  2010         2                0   \n",
       "3            74                  16     1  2017         2                0   \n",
       "4            76                  16     1  1993         2                0   \n",
       "5            76                  16     1  2001         2                0   \n",
       "6            16                   3     0  2009   0.10775                0   \n",
       "7            17                   4     0  2009   0.08124                0   \n",
       "8            17                   4     0  2012   0.07069                0   \n",
       "9            17                   4     0  2019   0.10371                0   \n",
       "10           19                   4     0  2008   0.03074                0   \n",
       "11           19                   4     0  2011   0.02634                0   \n",
       "12           20                   4     0  1997   0.00029                0   \n",
       "13           20                   4     0  2001   0.00019                0   \n",
       "14           21                   4     0  1991  0.014259                0   \n",
       "15           21                   4     0  1996  0.028327                0   \n",
       "16           21                   4     0  1997  0.042396                0   \n",
       "17           21                   4     0  2003  0.056464                0   \n",
       "18           21                   4     0  2004  0.070533                0   \n",
       "19           21                   4     0  2005  0.084601                0   \n",
       "\n",
       "    DataSource  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "5            0  \n",
       "6            0  \n",
       "7            0  \n",
       "8            0  \n",
       "9            0  \n",
       "10           0  \n",
       "11           0  \n",
       "12           0  \n",
       "13           0  \n",
       "14           0  \n",
       "15           0  \n",
       "16           0  \n",
       "17           0  \n",
       "18           0  \n",
       "19           0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Separating the data and labels**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X =data.drop(['emissions'],axis=1) \n",
    "Y =data['emissions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.apply(pd.to_numeric, errors='coerce')\n",
    "Y = Y.apply(pd.to_numeric, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.fillna(0, inplace=True)\n",
    "Y.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Country_code  Country  Format_name  Pollutant_name  Sector_code  \\\n",
      "0                 25       23            0               7           73   \n",
      "1                 25       23            0               7           74   \n",
      "2                 25       23            0               7           74   \n",
      "3                 25       23            0               7           74   \n",
      "4                 25       23            0               7           76   \n",
      "...              ...      ...          ...             ...          ...   \n",
      "686343            25       23            0               7           71   \n",
      "686344            25       23            0               7           72   \n",
      "686345            25       23            0               7           72   \n",
      "686346            25       23            0               7           73   \n",
      "686347            25       23            0               7           73   \n",
      "\n",
      "        Sector_name  Parent_sector_code  Unit    Year  PublicationDate  \\\n",
      "0                73                  16     1  2008.0                0   \n",
      "1                74                  16     1  1992.0                0   \n",
      "2                74                  16     1  2010.0                0   \n",
      "3                74                  16     1  2017.0                0   \n",
      "4                76                  16     1  1993.0                0   \n",
      "...             ...                 ...   ...     ...              ...   \n",
      "686343           71                  11     1  1994.0                0   \n",
      "686344           72                  16     1  1990.0                0   \n",
      "686345           72                  16     1  2016.0                0   \n",
      "686346           73                  16     1  2003.0                0   \n",
      "686347           73                  16     1  2007.0                0   \n",
      "\n",
      "        DataSource  \n",
      "0                0  \n",
      "1                0  \n",
      "2                0  \n",
      "3                0  \n",
      "4                0  \n",
      "...            ...  \n",
      "686343           0  \n",
      "686344           0  \n",
      "686345           0  \n",
      "686346           0  \n",
      "686347           0  \n",
      "\n",
      "[686348 rows x 11 columns]\n",
      "0           2.00\n",
      "1           2.00\n",
      "2           2.00\n",
      "3           2.00\n",
      "4           2.00\n",
      "           ...  \n",
      "686343    276.45\n",
      "686344    276.45\n",
      "686345    276.45\n",
      "686346    276.45\n",
      "686347    276.45\n",
      "Name: emissions, Length: 686348, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Splitting Training and Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2) #80% will be trained data and 20% will be testing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(686348, 11) (549078, 11) (137270, 11)\n",
      "(686348,) (549078,) (137270,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, X_train.shape, X_test.shape)\n",
    "print(Y.shape, Y_train.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model Training**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading lineair regression model\n",
    "lin_reg_model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction on Training data\n",
    "training_data_prediction = lin_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error: 0.013896776978032599\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_train, training_data_prediction) \n",
    "print('R squared Error:' , error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[579.50443907]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[21, 20,0, 4,19,19,4,0,2011,0,0]])\n",
    "print(lin_reg_model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1357.12947385]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[21, 20,0, 4,16,16,3,0,2009,0,0]])\n",
    "print(lin_reg_model.predict(features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "model = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "model.score(X_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.10775]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[21, 20,0, 4,16,16,3,0,2009,0,0]])\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02634]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[21, 20,0, 4,19,19,4,0,2011,0,0]])\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0004]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[21, 20,0, 4,23,23,3,0,2014,0,0]])\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.51773]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[21, 20,0, 4,32,32,0,0,2008,0,0]])\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.2699535]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[22, 21,0, 1,137,137,30,0,1992,0,0]])\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2535.61199]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[25, 23,0, 6,167,167,11,1,1996,0,0]])\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00414526]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[23, 21,0, 6,120,126,3,0,2009,0,0]]) #aléatoire\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[276.45]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[25, 23,0, 7,42,42,38,1,1998,0,0]])\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[117.31745]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[25, 23,0, 6,170,170,11,1,2019,0,0]])\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[251.608989]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features = np.array([[25, 23,0, 7,73,73,16,1,2008,0,0]])\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr = SVR()\n",
    "svr.fit(X_train, Y_train)"
   ]
  }
 ],
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