Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing Library\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\jubair ahmed junjun\\\\Desktop\\\\ML Projects\\\\Linear_Regression\\\\dhaka homeprices.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>56500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>61000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000</td>\n",
       "      <td>71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2500</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2700</td>\n",
       "      <td>38000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1200</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area   price\n",
       "0  2600   55000\n",
       "1  3000   56500\n",
       "2  3200   61000\n",
       "3  3600   68000\n",
       "4  4000   72000\n",
       "5  5000   71000\n",
       "6  2500   40000\n",
       "7  2700   38000\n",
       "8  1200   17000\n",
       "9  5000  100000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</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>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>56500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>61000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  price\n",
       "0  2600  55000\n",
       "1  3000  56500\n",
       "2  3200  61000\n",
       "3  3600  68000\n",
       "4  4000  72000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>56500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>61000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  price\n",
       "0  2600  55000\n",
       "1  3000  56500\n",
       "2  3200  61000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area     False\n",
       "price    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area     0\n",
       "price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .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>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>56500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>61000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  price\n",
       "0  2600  55000\n",
       "1  3000  56500\n",
       "2  3200  61000\n",
       "3  3600  68000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area     0\n",
       "price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['area']]\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5000</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\n",
       "5  5000\n",
       "6  2500\n",
       "7  2700\n",
       "8  1200\n",
       "9  5000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     55000\n",
       "1     56500\n",
       "2     61000\n",
       "3     68000\n",
       "4     72000\n",
       "5     71000\n",
       "6     40000\n",
       "7     38000\n",
       "8     17000\n",
       "9    100000\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vizualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Home Prices in Dhaka')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEWCAYAAABbgYH9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAeSklEQVR4nO3df7xVdZ3v8ddbMKUURUVSILGkH8qY6dHsal0cfCRaiRXOA6sRG2a4mZPZTA/T6VbWXHukTWrW1Qm1QDOVUUvGO5Ze7KB1DTz4C5FMFBWUAEMFzUzwc/9Y353rbPfZZ8M53733Obyfj8d+7LU/a33X+qwlns/+rrX2dykiMDMz62/btToBMzMbnFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxizJpJ0i6TpTdrW+yU9nHH94ySFpKFb2T4k7dffeVn7cIGxtifpcUlHV8VOkfSrVuVUymO2pD9LekHSekm3SXpnT8tHxLERMacZuUXEnRHxjq1pm47v5rRfL0haIelHkt7e33na4OUCY9Z350fETsAYYC0wu3oBFQba/293pf3aBTgaeAlYLGlCa9OygWKg/YM3q0nSuyR1SnpO0lJJx5fmzZZ0STo99YKkX0t6s6SLJD0r6beS3lNafm9JN0hal765n95IDhHxR+AnwIS0nk5J50r6NfBH4K0p9velbf2DpGWSNkp6SNLBveUg6TBJXZI2SFoj6YIejslESatKnx+X9EVJD0h6XtJ1knZsYL82R8SjEfFZYAFwTtUin5T0pKRnJH25Ks+70n+T1ZK+L+kNPeR6pKSVko6SNELSzWnfn03TY3rL09qPC4wNeJK2B/4TuBXYE/gccLWk8umhvwH+J7AH8DJwF3BP+nw9cEFa13ZpXfcDo4FJwBmSjmkgj52ATwL3lsJ/C8wEdgaeqFr+RIo/1icDw4HjgT80kMN3ge9GxHDgbcDc3nIr+RtgMrAvcCBwyha0BbgReH9V7EjgHSnPr0p6V4pvBr5AcYzfl+Z/tnqFab+uAT4eEb+k+Lv0I2Af4C0UPafvb2Ge1gZcYGyg+Fn6JvycpOeAS0rzDgd2Ar4VEX+OiNuBm4GTSsv8NCIWR8SfgJ8Cf4qIKyNiM3AdUOnBHAqMjIhvpHU9BlwGTKuT2xdTTstTHqeU5s2OiKURsSkiXqlq9/cUp9fujsLyiHiigRxeAfaTtEdEvBARv+nl2JVdHBFPR8R6iiJ20Ba0BXga2K0q9vWIeCki7qcoiu8GSMf7N2nfHwd+APz3qrYnArOA4yJiUWr3h4i4ISL+GBEbgXNrtLMBwAXGBooTImLXyovu34T3BlZGxKul2BMU3/4r1pSmX6rxeac0vQ+wd1Ux+xdgVJ3c/i3l9eaIOD4iHi3NW1mn3Vjg0Rrx3nKYAbwd+K2kuyV9uM42qv2+NP1HXtvvRo0G1jeyTklvT6e3fi9pA/BNit5M2RnA3IhYUglIeqOkH0h6IrW7A9hV0pAtzNVabKtuLzRrM08DYyVtVyoybwF+txXrWgmsiIjx/ZRbveHKV1Kc4tqiHCLiEeCkdCrtY8D1knaPiBf7nG3vPgrc2eCyl1KcLjwpIjZKOgOYWrXMicAVkp6KiItS7J8pTrm9NyJ+L+mgtB71MXdrMvdgbDBYCLwInClpe0kTgY8A127FuhYBGyR9SdIwSUMkTZB0aP+l+xeXU5xeOyTdZbafpH16y0HSpySNTMX0ubSuzRnyI21viKR9JX0PmAh8vcGmOwMbgBdU3Lp9ao1lnqa4NnO6pM+W2r0EPCdpN+BrfcnfWscFxga8iPgzxQXyY4FnKK7PnBwRv92KdW2mKE4HASvS+i6nuFW3X0XEf1BcX/gJsBH4GbBbAzlMBpZKeoHigv+0dG2pv70vbWMD0ElxI8Kh5dNZvfgi8AmKfbuM4lrX60TEkxRF5kvpDruLgGEU+/0b4OdbvwvWSvIDx8zMLAf3YMzMLAsXGDMzy8IFxszMsnCBMTOzLPw7mGSPPfaIcePGtToNM7MBZfHixc9ExMha81xgknHjxtHV1dXqNMzMBhRJT/Q0z6fIzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCyLbAVG0g8lrZX0YCm2m6TbJD2S3keU5p0tabmkh8tPD0wjzS5J8y6WpBTfIT3ydbmkhZLGldpMT9t4RNL0XPtoZjbgTZxYvDLI2YOZTTHqa9lZwPz0nIv56TOS9qd4Wt8Bqc0lpYcLXUrxyNnx6VVZ5wzg2YjYD7gQOC+tqzK893uBw4CvlQuZmZk1R7YCExF38Pon300B5qTpOcAJpfi1EfFyRKygePTsYZL2AoZHxF1RDPt8ZVWbyrquByal3s0xwG0RsT4ingVu4/WFzsxs21bpuSxYULwy9GSafQ1mVESsBkjve6b4aLo/WnZVio1O09Xxbm0iYhPwPLB7nXW9jqSZkrokda1bt64Pu2VmZtXa5Zf8tR6FGnXiW9umezBiFjALoKOjww/GMbNtR2dn8V7ptVQ+96Nm92DWpNNepPe1Kb4KGFtabgzFo1RXpenqeLc2koZSPO1vfZ11mZlZEzW7wMwDKnd1TQduKsWnpTvD9qW4mL8onUbbKOnwdH3l5Ko2lXVNBW5P12l+AXxQ0oh0cf+DKWZmZtU6O7P0XiDjKTJJ1wATgT0kraK4s+tbwFxJM4AngRMBImKppLnAQ8Am4LT0XHKAUynuSBsG3JJeAFcAV0laTtFzmZbWtV7SvwJ3p+W+ERHVNxuYmVlmKr70W0dHR3g0ZTOzLSNpcUR01JrnX/KbmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXRkgIj6QuSlkp6UNI1knaUtJuk2yQ9kt5HlJY/W9JySQ9LOqYUP0TSkjTvYklK8R0kXZfiCyWNa8Fumplt05peYCSNBk4HOiJiAjAEmAacBcyPiPHA/PQZSfun+QcAk4FLJA1Jq7sUmAmMT6/JKT4DeDYi9gMuBM5rwq6ZmVlJq06RDQWGSRoKvBF4GpgCzEnz5wAnpOkpwLUR8XJErACWA4dJ2gsYHhF3RUQAV1a1qazremBSpXdjZmbN0fQCExFPAf8GPAmsBp6PiFuBURGxOi2zGtgzNRkNrCytYlWKjU7T1fFubSJiE/A8sHt1LpJmSuqS1LVu3br+2UEzMwNac4psBEUPY19gb+BNkj5Vr0mNWNSJ12vTPRAxKyI6IqJj5MiR9RM3M7Mt0opTZEcDKyJiXUS8AtwI/DdgTTrtRXpfm5ZfBYwttR9DcUptVZqujndrk07D7QKsz7I3ZmZWUysKzJPA4ZLemK6LTAKWAfOA6WmZ6cBNaXoeMC3dGbYvxcX8Rek02kZJh6f1nFzVprKuqcDt6TqNmZk1ydBmbzAiFkq6HrgH2ATcC8wCdgLmSppBUYROTMsvlTQXeCgtf1pEbE6rOxWYDQwDbkkvgCuAqyQtp+i5TGvCrpmZWYn8xb7Q0dERXV1drU7DzGxAkbQ4IjpqzfMv+c3MLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMBraJE4uXtR0XGDMzy6LpoymbmfWLSq9lwYLunzs7W5DMAJbxuLkHY2ZmWbgHY2YDU+Ubt3suW6cJPUD3YMzMLIuGejCSJgD7AztWYhFxZa6kzMwa5p7L1mlCD7DXAiPpa8BEigLzX8CxwK8AFxgzM+tRIz2YqcC7gXsj4tOSRgGX503LzMyaImMPsJFrMC9FxKvAJknDgbXAW7NlZGYDi3/oaD1opAfTJWlX4DJgMfACsChnUmZmNvD1WmAi4rNp8t8l/RwYDjycNSsza3/+oaP1otdTZJJ+WJmOiMeBxygu9puZmfWokVNkT0m6NCJOlTQC+D8Up8vMbFvmHzpaL3rtwUTEV4ANkv4duBX4TkT8KHtmZmY2oPXYg5H0sdLHRcBX0ntI+lhE3Jg7OTMbANxzsR7UO0X2karP9wLbp3gALjBmZtajHgtMRHy6mYmYmdng0shQMTsCM4AD6D4W2d9lzMvMzAa4Rn7JfxXwZuAYYAEwBtiYMykzMxv4Gikw+6U7yV6MiDnAh4C/ypuWmZkNdD0WGEmV02evpPfn0rD9uwDjMudlZmYDXL0eTGW8sVnpB5ZfAeYBDwHn507MzMwGtnoX+QUQEZWh+RfgUZTNzKxB9QrMSEn/1NPMiLggQz5mZjZI1CswQ4CdSD0ZMzOzLVGvwKyOiG80LRMz2zoebNLaVL2L/O65mJnZVqtXYCbl2qikXSVdL+m3kpZJep+k3STdJumR9D6itPzZkpZLeljSMaX4IZKWpHkXS1KK7yDpuhRfKGlcrn0xa5nKo4oXLChefnSxtZkeC0xErM+43e8CP4+IdwLvBpYBZwHzI2I8MD99RtL+wDSKoWomA5dIGpLWcykwExifXpNTfAbwbETsB1wInJdxX8zMrIZGHjjWryQNBz4AnAIQEX8G/ixpCjAxLTYH6AS+BEwBro2Il4EVkpYDh0l6HBgeEXel9V4JnADcktqck9Z1PfB9SYqIyLt3Zk3kB35Zm2tkqJj+9lZgHfAjSfdKulzSm4BREbEaIL3vmZYfDawstV+VYqPTdHW8W5uI2AQ8D+yeZ3fMzKyWXguMpI+l6yLPS9ogaaOkDX3Y5lDgYODSiHgP8CLpdFhPKdSIRZ14vTbdVyzNlNQlqWvdunX1szZrV52d7r1YW2qkB3M+cHxE7BIRwyNi54gY3odtrgJWRcTC9Pl6ioKzRtJeAOl9bWn5saX2Y4CnU3xMjXi3NmlMtV2A111TiohZEdERER0jR47swy6ZmVm1RgrMmohY1l8bjIjfAyslvSOFJlGMbzYPmJ5i04Gb0vQ8YFq6M2xfiov5i9JptI2SDk93j51c1aayrqnA7b7+YmbWXI1c5O+SdB3wM+DlSjAi+vLI5M8BV0t6A/AY8GmKYjdX0gzgSeDEtJ2lkuZSFKFNwGkRsTmt51RgNjCM4uL+LSl+BXBVuiFgPcVdaGZm1kTq7Yu9pB/VCMdge6JlR0dHdHV1tToNM7MBRdLiiOioNa/XHkxEfLr/UzKzv/BtxjZI9VhgJJ0ZEedL+h417sCKiNOzZmZmZgNavR5M5cK+zxuZ5VDpuSxY0P2zezI2SPRYYCLiP9P7nOalY9YC/sNulkXTh4oxs8RDvdgg5wJj2y6fojLLygXGrNVc0GyQ6rXASHo7xbD4oyJigqQDKYaO+V/ZszPLyaeozLJqZKiYy4CzgVcAIuIB/Mt4MzPrRSOnyN4YEYvSwyIrNmXKx6z53HMxy6KRHswzkt5G+rGlpKnA6qxZmZnZgNdID+Y0YBbwTklPASuAT2XNyszMBrxGxiJ7DDg6PXVyu4jYmD8tMzMb6Bp5ouU3Je0aES9GxEZJIyT5DjIzM6urkWswx0bEc5UPEfEscFy2jMzMbFBopMAMkbRD5YOkYcAOdZY3MzNr6CL/j4H56cFjAfwd4AEwzcysrkYu8p8vaQkwCRDwrxHxi+yZmZnZgNbQWGQRUX7evZmZWa/qPdHyVxFxpKSNdH+ipYCIiOHZszMzswGr3gPHjkzvOzcvHTMzGyzq3kUmaTtJDzYrGTMzGzzqFpiIeBW4X9JbmpSPmZkNEo1c5N8LWCppEfBiJRgRx2fLyszMBrxGCszXs2dhZmaDTr27yHYEPgPsBywBrogIPwfGzMwaUu8azBygg6K4HAt8pykZmZnZoFDvFNn+EfFXAJKuABY1JyUzMxsM6vVgXqlM+NSYmZltqXo9mHdL2pCmBQxLn/1LfjMz61W9X/IPaWYiZmY2uDTyPBgzM7Mt5gJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmlkXLCoykIZLulXRz+rybpNskPZLeR5SWPVvSckkPSzqmFD9E0pI072JJSvEdJF2X4gsljWv6Dlr7mDixeJlZU7WyB/N5YFnp81nA/IgYD8xPn5G0PzANOACYDFwiqTLKwKXATGB8ek1O8RnAsxGxH3AhcF7eXTEzs2otKTCSxgAfAi4vhadQPCKA9H5CKX5tRLwcESuA5cBhkvYChkfEXRERwJVVbSrruh6YVOnd2Dak0nNZsKB4uSdj1lSt6sFcBJwJvFqKjYqI1QDpfc8UHw2sLC23KsVGp+nqeLc2aSTo54Hdq5OQNFNSl6SudevW9XGXzMysrJFHJvcrSR8G1kbEYkkTG2lSIxZ14vXadA9EzAJmAXR0dLxuvg1wnZ3Fe6XXUvlsZk3R9AIDHAEcL+k4YEdguKQfA2sk7RURq9Ppr7Vp+VXA2FL7McDTKT6mRrzcZpWkocAuwPpcO2SDnAuU2VZp+imyiDg7IsZExDiKi/e3R8SngHnA9LTYdOCmND0PmJbuDNuX4mL+onQabaOkw9P1lZOr2lTWNTVtwz2UbVVnp4uDWQu0ogfTk28BcyXNAJ4ETgSIiKWS5gIPAZuA0yJic2pzKjAbGAbckl4AVwBXSVpO0XOZ1qydsEGk0nNZsKD7Zxcrs4a0tMBERCfQmab/AEzqYblzgXNrxLuACTXifyIVKDMza4126sGYtRffJGDWJx4qxszMsnAPxqw37rmYbRX3YMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMzLJwgTEzsyyaXmAkjZX0S0nLJC2V9PkU303SbZIeSe8jSm3OlrRc0sOSjinFD5G0JM27WJJSfAdJ16X4Qknjmr2fZmbbulb0YDYB/xwR7wIOB06TtD9wFjA/IsYD89Nn0rxpwAHAZOASSUPSui4FZgLj02tyis8Ano2I/YALgfOasWNmZvaapheYiFgdEfek6Y3AMmA0MAWYkxabA5yQpqcA10bEyxGxAlgOHCZpL2B4RNwVEQFcWdWmsq7rgUmV3o2ZmTVHS6/BpFNX7wEWAqMiYjUURQjYMy02GlhZarYqxUan6ep4tzYRsQl4Hti9xvZnSuqS1LVu3bp+2iszM4MWFhhJOwE3AGdExIZ6i9aIRZ14vTbdAxGzIqIjIjpGjhzZW8pmZrYFWlJgJG1PUVyujogbU3hNOu1Fel+b4quAsaXmY4CnU3xMjXi3NpKGArsA6/t/T8zMrCetuItMwBXAsoi4oDRrHjA9TU8HbirFp6U7w/aluJi/KJ1G2yjp8LTOk6vaVNY1Fbg9XacxM7MmGdqCbR4B/C2wRNJ9KfYvwLeAuZJmAE8CJwJExFJJc4GHKO5AOy0iNqd2pwKzgWHALekFRQG7StJyip7LtKx7NHFi8d7ZmXUzZmYDSdMLTET8itrXSAAm9dDmXODcGvEuYEKN+J9IBcrMzFqjFT2YwaPSc1mwoPtn92TMzDxUjJmZ5eEeTF9UeiruuZiZvY57MGZmloV7MP3BPRczs9dxD8bMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLORBhguS1gFPtDqPOvYAnml1EnU4v75xfn3j/PqmL/ntExE1H6jlAjNASOqKiI5W59ET59c3zq9vnF/f5MrPp8jMzCwLFxgzM8vCBWbgmNXqBHrh/PrG+fWN8+ubLPn5GoyZmWXhHoyZmWXhAmNmZlm4wLSIpB9KWivpwVLsHElPSbovvY4rzTtb0nJJD0s6phQ/RNKSNO9iSeqn/MZK+qWkZZKWSvp8iu8m6TZJj6T3Ea3IsU5+bXEMJe0oaZGk+1N+X0/xdjl+PeXXFsevtO4hku6VdHP63BbHr05+bXP8JD2e1nufpK4Ua+7xiwi/WvACPgAcDDxYip0DfLHGsvsD9wM7APsCjwJD0rxFwPsAAbcAx/ZTfnsBB6fpnYHfpTzOB85K8bOA81qRY5382uIYpnXtlKa3BxYCh7fR8espv7Y4fqXt/hPwE+Dm9Lktjl+d/Nrm+AGPA3tUxZp6/NyDaZGIuANY3+DiU4BrI+LliFgBLAcOk7QXMDwi7oriX8KVwAn9lN/qiLgnTW8ElgGjUy5z0mJzSttrao518utJs/OLiHghfdw+vYL2OX495deTpv8blDQG+BBweVUeLT9+dfLrSdPzq5NH046fC0z7+UdJD6g4hVbpvo4GVpaWWZVio9N0dbxfSRoHvIfiW+6oiFgNxR95YM9W51iVH7TJMUynT+4D1gK3RURbHb8e8oM2OX7ARcCZwKulWNscvx7yg/Y5fgHcKmmxpJkp1tTj5wLTXi4F3gYcBKwGvpPitc55Rp14v5G0E3ADcEZEbKi3aA+5ZM2xRn5tcwwjYnNEHASMofg2OKHO4u2SX1scP0kfBtZGxOJGm/SQR7Pza4vjlxwREQcDxwKnSfpAnWWz5OcC00YiYk36n/5V4DLgsDRrFTC2tOgY4OkUH1Mj3i8kbU/xx/vqiLgxhdekbjPpfW2rcqyVX7sdw5TTc0AnMJk2On618muj43cEcLykx4Frgb+W9GPa5/jVzK+Njh8R8XR6Xwv8NOXS3OPXHxeT/Nrqi3Dj6H6Rf6/S9BcozokCHED3C3CP8doFuLspLs5WLsAd10+5ieJ860VV8W/T/SLh+a3IsU5+bXEMgZHArml6GHAn8OE2On495dcWx68q14m8dhG9LY5fnfza4vgBbwJ2Lk3/P4ovOE09fv12kP3a4n8A11B0oV+h+JYwA7gKWAI8AMyr+sf6ZYo7Ox6mdBcH0AE8mOZ9nzQ6Qz/kdyRFV/gB4L70Og7YHZgPPJLed2tFjnXya4tjCBwI3JvyeBD4aoq3y/HrKb+2OH5VuU7ktT/gbXH86uTXFscPeCtFwbgfWAp8uRXHz0PFmJlZFr4GY2ZmWbjAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMDYNk/SRyWFpHdm3MZ/Sdo11/qbRdI1aRiUL0j6hqSjU/wMSW9sdX7WXnybsm3zJM2lGJ15fkScU2P+kIjY3PTEmkTS0IjY1MBybwYWRsQ+NeY9DnRExDMZUrQByj0Y26alscyOoPih67RSfKKK5838BFiSBob8tqS70zf4/1FpL2m+pHvSMzOm9LCdxyXtIWmcimfYXKbiOSy3ShpWY/kTJT2o4nktd6TYMEnXpu1fJ2mhpI4074VS26mSZqfpj6Tl7pX0fyWNSvFzJM2SdCtwpaSRkm5I+3e3pCNq7MatwJ4qni/yfkmz07ZOB/YGfinpl1v+X8EGKxcY29adAPw8In4HrJd0cGneYRS/gN6fogA9HxGHAocC/yBpX+BPwEejGFTwKOA7Uq8PZBoP/O+IOAB4Dvh4jWW+ChwTEe8Gjk+xU4E/RsSBwLnAIQ3s36+AwyPiPRRjZp1ZmncIMCUiPgF8F7gw7d/HqT0E/fHAoxFxUETcWQlGxMUU41MdFRFHNZCTbSOGtjoBsxY7iWLYdSj+AJ8E3JM+L4ri2RgAHwQOlDQ1fd6FolCsAr6ZRqp9lWIo81HA7+tsc0VE3JemF1OMSVft18DsdPquMtDoB4CLASLiAUkPNLB/Y4Dr0sCGbwBWlObNi4iX0vTRwP6l2jhc0s5RPGvHbKu4wNg2S9LuwF8DEyQFMAQISZVv+S+WFwc+FxG/qFrHKRQDRx4SEa+kaxE79rLpl0vTmykGm+wmIj4j6b0UD7S6T9JBlVk9rLMcL2//e8AFETFP0kSKJy5WlPdvO+B9pYJj1mc+RWbbsqnAlRGxT0SMi4ixFN/wj6yx7C+AU9MjApD0dklvoujJrE3F5SjgdRfAt4akt0XEwoj4KvAMxVDqdwCfTPMnUAxYWbFG0rskbQd8tBTfBXgqTU+vs8lbgX8sbf+gLUx5I8Wjq83+wgXGtmUnUTwno+wG4BM1lr0ceAi4R9KDwA8ozgBcDXRI6qL44//bfsrt2+mmgQcpCsv9FA+z2imdGjuT4lnpFWcBNwO3U4zSXXEO8B+S7qQoVD05Pe3HA5IeAj6zhfnOAm7xRX4r823KZgOUpE7gixHR1epczGpxD8bMzLJwD8bMzLJwD8bMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsvj/B1R9lAcnq88AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['area'],df['price'],marker='+',color='red')\n",
    "plt.xlabel('Area in square fit')\n",
    "plt.ylabel('Price in Taka')\n",
    "plt.title('Home Prices in Dhaka')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(x,y, test_size =.30,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>4</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "4  4000\n",
       "0  2600\n",
       "3  3600\n",
       "1  3000\n",
       "7  2700\n",
       "8  1200\n",
       "5  5000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>2</th>\n",
       "      <td>3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "2  3200\n",
       "9  5000\n",
       "6  2500"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    72000\n",
       "0    55000\n",
       "3    68000\n",
       "1    56500\n",
       "7    38000\n",
       "8    17000\n",
       "5    71000\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([54577.95521897, 81852.07441554, 43971.35330919])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25373057ee0>]"
      ]
     },
     "execution_count": 36,
     "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(df['area'],df['price'],marker='+',color='red')\n",
    "plt.xlabel('Area in square fit')\n",
    "plt.ylabel('Price in Taka')\n",
    "plt.title('Home Prices in Dhaka')\n",
    "plt.plot(df.area,reg.predict(df[['area']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = reg.predict([[35000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([536420.7276918])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y = mx + c\n",
    "m = reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.15228844])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6090.632202831752"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 15.15228844*35000+6090.632202831752\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "536420.7276028318"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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