LinearRegression2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1535d151",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "dt = pd.read_csv('data.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "54b1fb6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = dt[dt['Make']=='Volkswagen']\n",
    "\n",
    "new_dt = dt[['Engine HP', 'MSRP']]\n",
    "new_dt = new_dt.dropna()\n",
    "X = new_dt.drop(['MSRP'], axis = 1)\n",
    "y = dt['MSRP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "92997bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21e46a13df0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.scatter(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1f1b43e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d4f823cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li = LinearRegression()\n",
    "li.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dc8f7a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6977723381984884"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a26f15c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = li.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "641259d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "31c16d9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21e46a7b3a0>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test,y_test)\n",
    "plt.plot(X_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cdbaa777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6977723381984884"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ce4179a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "259d73fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}