Petroleum.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0cbfc8d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b4f55e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Petrol_tax</th>\n",
       "      <th>Average_income</th>\n",
       "      <th>Paved_Highways</th>\n",
       "      <th>Population_Driver_licence(%)</th>\n",
       "      <th>Petrol_Consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3571</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.525</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4092</td>\n",
       "      <td>1250</td>\n",
       "      <td>0.572</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3865</td>\n",
       "      <td>1586</td>\n",
       "      <td>0.580</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.5</td>\n",
       "      <td>4870</td>\n",
       "      <td>2351</td>\n",
       "      <td>0.529</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>4399</td>\n",
       "      <td>431</td>\n",
       "      <td>0.544</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Petrol_tax  Average_income  Paved_Highways  Population_Driver_licence(%)  \\\n",
       "0         9.0            3571            1976                         0.525   \n",
       "1         9.0            4092            1250                         0.572   \n",
       "2         9.0            3865            1586                         0.580   \n",
       "3         7.5            4870            2351                         0.529   \n",
       "4         8.0            4399             431                         0.544   \n",
       "\n",
       "   Petrol_Consumption  \n",
       "0                 541  \n",
       "1                 524  \n",
       "2                 561  \n",
       "3                 414  \n",
       "4                 410  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('petrol_consumption.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20b0fd48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27ca6716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Petrol_tax                      0\n",
       "Average_income                  0\n",
       "Paved_Highways                  0\n",
       "Population_Driver_licence(%)    0\n",
       "Petrol_Consumption              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "202dc7f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Petrol_tax                      float64\n",
       "Average_income                    int64\n",
       "Paved_Highways                    int64\n",
       "Population_Driver_licence(%)    float64\n",
       "Petrol_Consumption                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1847e88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48, 4)\n",
      "(48,)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "x = df.iloc[:,:-1]\n",
    "y = df.iloc[:,-1]\n",
    "print(x.shape)\n",
    "print(y.shape)\n",
    "print(type(x))\n",
    "print(type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "afda77af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\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>Petrol_tax</th>\n",
       "      <th>Average_income</th>\n",
       "      <th>Paved_Highways</th>\n",
       "      <th>Population_Driver_licence(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3571</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4092</td>\n",
       "      <td>1250</td>\n",
       "      <td>0.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3865</td>\n",
       "      <td>1586</td>\n",
       "      <td>0.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.5</td>\n",
       "      <td>4870</td>\n",
       "      <td>2351</td>\n",
       "      <td>0.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>4399</td>\n",
       "      <td>431</td>\n",
       "      <td>0.544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Petrol_tax  Average_income  Paved_Highways  Population_Driver_licence(%)\n",
       "0         9.0            3571            1976                         0.525\n",
       "1         9.0            4092            1250                         0.572\n",
       "2         9.0            3865            1586                         0.580\n",
       "3         7.5            4870            2351                         0.529\n",
       "4         8.0            4399             431                         0.544"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc8e5e5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    541\n",
       "1    524\n",
       "2    561\n",
       "3    414\n",
       "4    410\n",
       "Name: Petrol_Consumption, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc18dff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2d25f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 4)\n",
      "(12, 4)\n",
      "(36,)\n",
      "(12,)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4994bae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "164dbe05",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_metrics(ytest,ypred):\n",
    "    mse = mean_squared_error(ytest,ypred)\n",
    "    rmse = np.sqrt(mean_squared_error(ytest,ypred))\n",
    "    mae = mean_absolute_error(ytest,ypred)\n",
    "    r2s = r2_score(ytest,ypred)\n",
    "    print('MSE',mse)\n",
    "    print('RMSE',rmse)\n",
    "    print('MAE',mae)\n",
    "    print('R2_Score',r2s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d83bcf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9fb0433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = LinearRegression()\n",
    "m1.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "47e77746",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score 0.6989276241929955\n",
      "Testing Score 0.40235094302310503\n"
     ]
    }
   ],
   "source": [
    "print('Training Score',m1.score(x_train,y_train))\n",
    "print('Testing Score',m1.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "620e1c9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[655.16105986 628.3831134  605.50437394 498.47598581 573.75867978\n",
      " 511.78133889 642.53227397 578.52760871 561.03672376 565.4261537\n",
      " 608.50501878 449.78299909]\n"
     ]
    }
   ],
   "source": [
    "ypred_m1 = m1.predict(x_test)\n",
    "print(ypred_m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5298b08a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 2311.818611584813\n",
      "RMSE 48.08137489282948\n",
      "MAE 41.578018529094855\n",
      "R2_Score 0.40235094302310503\n"
     ]
    }
   ],
   "source": [
    "gen_metrics(y_test,ypred_m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c93588e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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