linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>area</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area   price\n",
       "0  2600  550000\n",
       "1  3000  565000\n",
       "2  3200  610000\n",
       "3  3600  680000\n",
       "4  4000  725000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df=pd.read_csv('houseprices.csv')\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2071942e410>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('area')\n",
    "plt.ylabel('price')\n",
    "plt.scatter(df.area,df.price,color='red',marker='+')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "0  2600\n",
       "1  3000\n",
       "2  3200\n",
       "3  3600\n",
       "4  4000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.drop('price',axis='columns')\n",
    "new_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    550000\n",
       "1    565000\n",
       "2    610000\n",
       "3    680000\n",
       "4    725000\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price=df.price\n",
    "price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating linear regression model\n",
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(new_df,price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([628715.75342466])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "LinearRegression(copy_X=True,fit_intercept=True)\n",
    "reg.predict([[3300]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_\n",
    "reg.intercept_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([927448.63013699])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "135.78*3300+180616.438\n",
    "reg.predict([[5500]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>316404.109589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>452191.780822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3500</td>\n",
       "      <td>655873.287671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4000</td>\n",
       "      <td>723767.123288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area          price\n",
       "0  1000  316404.109589\n",
       "1  2000  452191.780822\n",
       "2  3500  655873.287671\n",
       "3  4000  723767.123288"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_df = pd.read_csv('Areas.csv')\n",
    "area_df\n",
    "p=reg.predict(area_df)\n",
    "p\n",
    "area_df['price']=p\n",
    "area_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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