Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"C:\\\\Users\\\\Admin\\\\Desktop\\\\simple regression\\\\homeprices.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 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",
       "        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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21dd0189670>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEGCAYAAABYV4NmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAdhElEQVR4nO3df5BV5Z3n8fcndCTECQraui5g0BG1NBWJ3EGtRENCAiSbFZNiYlvZko3WEl3jjJPK7uiaGrL4z5hMygllBZfERHSMYpgY2SlROzqwqVlBbkfiLzA0BocORDs2MUZdHMh3/zjPtU83t5sWeW7fi59X1al7zvc8z3O/p/Xy7eec0/coIjAzMzvU3jXaCZiZ2eHJBcbMzLJwgTEzsyxcYMzMLAsXGDMzy6JttBNoFscee2xMnTp1tNMwM2spXV1dv42I9nr7XGCSqVOnUq1WRzsNM7OWIun5ofb5FJmZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZZGtwEg6TdKm0vJ7SddI+qakLZKekHSvpKNT+6mSXi+1v6U01gxJT0rqlrRUklJ8rKSVKb5B0tRSn4WStqZlYa7jNDNrabNmFUsG2QpMRDwbEdMjYjowA3gNuBfoBD4QER8EfglcV+q2rdYnIq4oxZcBi4BpaZmX4pcDuyPiFOAm4EYASROBxcA5wExgsaQJeY7UzMzqadTfwcymKB7PA+V7ptcDC4brKOkEYHxEPJq2bwcuAtYA84Gvp6argJvT7GYu0BkRfalPJ0VRuusQHY+ZWWurzVrWrRu4vXbtIXuLRl2D6aD+P+6XURSKmpMkPS5pnaTzU2wS0FNq05NitX07ACJiL/AycEw5XqfPmyQtklSVVO3t7X3rR2VmZkPKPoORdARwIQNPhSHpemAvcGcK7QJOjIiXJM0AfiLpTEB1hq09JW2ofcP16Q9ELAeWA1QqFT95zczeOWozlQwzl5pGzGA+Bfw8Il6oBdJF988AX4j0SM2I2BMRL6X1LmAbcCrF7GNyabzJwM603gNMSWO2AUcBfeV4nT5mZtYAjSgwl1A6PSZpHvDXwIUR8Vop3i5pTFo/meJi/nMRsQt4RdK56frKpcB9qdtqoHaH2ALgkVSwHgTmSJqQLu7PSTEzMytbuzbL7AUynyKT9F7gk8CXSuGbgbFAZ7rbeH26Y+wCYImkvcA+4IraRXrgSuA2YBzFNZvadZtbgTskdVPMXDoAIqJP0g3AxtRuSWksMzNrAKUzVO94lUol/G3KZmZvjaSuiKjU2+e/5DczsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLIluBkXSapE2l5feSrpE0UVKnpK3pdUKpz3WSuiU9K2luKT5D0pNp31JJSvGxklam+AZJU0t9Fqb32CppYa7jNDOz+rIVmIh4NiKmR8R0YAbwGnAvcC3wcERMAx5O20g6A+gAzgTmAd+RNCYNtwxYBExLy7wUvxzYHRGnADcBN6axJgKLgXOAmcDiciEzM7P8GnWKbDawLSKeB+YDK1J8BXBRWp8P3B0ReyLiV0A3MFPSCcD4iHg0IgK4fVCf2lirgNlpdjMX6IyIvojYDXTSX5TMzKwBGlVgOoC70vrxEbELIL0el+KTgB2lPj0pNimtD44P6BMRe4GXgWOGGWsASYskVSVVe3t7D/rgzMxsf9kLjKQjgAuBHx2oaZ1YDBM/2D79gYjlEVGJiEp7e/sB0jMzs7eiETOYTwE/j4gX0vYL6bQX6fXFFO8BppT6TQZ2pvjkOvEBfSS1AUcBfcOMZWZmDdKIAnMJ/afHAFYDtbu6FgL3leId6c6wkygu5j+WTqO9IuncdH3l0kF9amMtAB5J12keBOZImpAu7s9JMTMza5C2nINLei/wSeBLpfDfAvdIuhz4V+DPASLiaUn3AM8Ae4GrImJf6nMlcBswDliTFoBbgTskdVPMXDrSWH2SbgA2pnZLIqIvy0GamVldKn7ht0qlEtVqdbTTMDNrKZK6IqJSb5//kt/MzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMzLJwgTEzsyxcYMys+c2aVSzWUlxgzMwsi6xPtDQze1tqs5Z16wZur107CsnYW+UZjJmZZeEZjJk1r9pMxTOXluQZjJmZZZG1wEg6WtIqSVskbZZ0nqSVkjalZbukTantVEmvl/bdUhpnhqQnJXVLWipJKT42jdctaYOkqaU+CyVtTcvCnMdpZpmtXevZSwvKfYrs28ADEbFA0hHAeyPi4tpOSd8CXi613xYR0+uMswxYBKwH7gfmAWuAy4HdEXGKpA7gRuBiSROBxUAFCKBL0uqI2H3Ij9DMzOrKNoORNB64ALgVICLeiIjflfYL+Dxw1wHGOQEYHxGPRkQAtwMXpd3zgRVpfRUwO407F+iMiL5UVDopipKZmTVIzlNkJwO9wA8kPS7pe5KOLO0/H3ghIraWYieltusknZ9ik4CeUpueFKvt2wEQEXspZkPHlON1+rxJ0iJJVUnV3t7egz5QMzPbX84C0wacDSyLiA8BrwLXlvZfwsDZyy7gxNT2K8AP0yxIdcaO9DrUvuH69AcilkdEJSIq7e3tBzoeMzN7C3IWmB6gJyI2pO1VFAUHSW3A54CVtcYRsSciXkrrXcA24NQ0zuTSuJOBnaX3mFIa8yigrxyv08fMzBogW4GJiN8AOySdlkKzgWfS+ieALRHx5qkvSe2SxqT1k4FpwHMRsQt4RdK56frKpcB9qdtqoHaH2ALgkXSd5kFgjqQJkiYAc1LMzMwaJPddZFcDd6Y7yJ4DvpjiHex/cf8CYImkvcA+4IqI6Ev7rgRuA8ZR3D22JsVvBe6Q1E0xc+kAiIg+STcAG1O7JaWxzMysAVT8wm+VSiWq1epop2Fm1lIkdUVEpd4+/yW/mZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRZZC4ykoyWtkrRF0mZJ50n6uqRfS9qUlk+X2l8nqVvSs5LmluIzJD2Z9i2VpBQfK2llim+QNLXUZ6GkrWlZmPM4zcxsf7lnMN8GHoiI04GzgM0pflNETE/L/QCSzgA6gDOBecB3JI1J7ZcBi4BpaZmX4pcDuyPiFOAm4MY01kRgMXAOMBNYLGlC1iM1M7MBshUYSeOBC4BbASLijYj43TBd5gN3R8SeiPgV0A3MlHQCMD4iHo2IAG4HLir1WZHWVwGz0+xmLtAZEX0RsRvopL8omZlZA+ScwZwM9AI/kPS4pO9JOjLt+7KkJyR9vzSzmATsKPXvSbFJaX1wfECfiNgLvAwcM8xYA0haJKkqqdrb2/s2DtXMzAbLWWDagLOBZRHxIeBV4FqK011/CkwHdgHfSu1VZ4wYJn6wffoDEcsjohIRlfb29qGPxMzM3rKcBaYH6ImIDWl7FXB2RLwQEfsi4o/AdymukdTaTyn1nwzsTPHJdeID+khqA44C+oYZy8zMGiRbgYmI3wA7JJ2WQrOBZ9I1lZrPAk+l9dVAR7oz7CSKi/mPRcQu4BVJ56brK5cC95X61O4QWwA8kq7TPAjMkTQhnYKbk2JmZtYgbSNtKOn9wLSI+KmkcUBbRLxygG5XA3dKOgJ4DvgisFTSdIpTVtuBLwFExNOS7gGeAfYCV0XEvjTOlcBtwDhgTVqguIHgDkndFDOXjjRWn6QbgI2p3ZKI6BvpsZqZ2dun4hf+AzSS/gvFbcITI+JPJU0DbomI2bkTbJRKpRLVanW00zAzaymSuiKiUm/fSE+RXQV8GPg9QERsBY47NOmZmdnhaKQFZk9EvFHbSBfUDzz1MTOzd6yRFph1kv4HME7SJ4EfAf87X1pmZtbqRlpgrqX4o8knKS7K3w98LVdSZmbW+kZ6F9k44PsR8V2A9B1h44DXciVmZmatbaQzmIcpCkrNOOCnhz4dMzM7XIy0wLwnIv5Q20jr782TkpmZHQ5GWmBelXR2bUPSDOD1PCmZmdnhYKTXYK4BfiSp9n1eJwAXZ8nIzMwOCyMqMBGxUdLpwGkU31S8JSL+LWtmZmbW0oYtMJI+HhGPSPrcoF3TJBERP86Ym5mZtbADzWA+CjwC/Mc6+wJwgTEzs7qGLTARsVjSu4A1EXFPg3IyM7PDwAHvIksPBvtyA3IxM7PDyEhvU+6U9FVJUyRNrC1ZMzMzs5Y20tuUL6O45vJfB8VPPrTpmJnZ4WKkBeYMiuLyEYpC8zPgllxJmZlZ6xtpgVlB8bCxpWn7khT7fI6kzMys9Y20wJwWEWeVtv9Z0i9yJGRmZoeHkV7kf1zSubUNSecA/3KgTpKOlrRK0hZJmyWdJ+mbafsJSfdKOjq1nSrpdUmb0nJLaZwZkp6U1C1pqSSl+FhJK1N8g6SppT4LJW1Ny8IRHqeZmR0iIy0w5wD/V9J2SduBR4GPpn/0nxim37eBByLidOAsYDPQCXwgIj4I/BK4rtR+W0RMT8sVpfgyYBEwLS3zUvxyYHdEnALcBNwIkO5wW5zyngksljRhhMdqZmaHwEhPkc07cJOBJI0HLgD+M0BEvAG8ATxUarYeWHCAcU4AxkfEo2n7duAiYA0wH/h6aroKuDnNbuYCnRHRl/p0pmO4660eh5mZHZyRftnl8wcx9skUj1n+gaSzgC7gLyPi1VKby4CVpe2TJD1OcUPB1yLiZ8AkoKfUpifFSK87Uo57Jb0MHFOO1+nzJkmLKGZGnHjiiQdxiGZmNpSRniI7GG3A2cCyiPgQ8CpwbW2npOuBvcCdKbQLODG1/QrwwzQLUp2xozbMEPuG69MfiFgeEZWIqLS3t4/sqMzMbERyFpgeoCciNqTtVRQFh3TR/TPAFyIiACJiT0S8lNa7gG3AqWmcyaVxJwO159L0AFPSmG3AUUBfOV6nj5mZNUC2AhMRvwF2SDothWYDz0iaB/w1cGFEvFZrL6ld0pi0fjLFxfznImIX8Iqkc9P1lUuB+1K31UDtDrEFwCOpYD0IzJE0IV3cn5NiZmbWICO9yH+wrgbulHQE8BzwRWAjMJbi+80A1qc7xi4AlkjaC+wDrqhdpAeuBG4DxlFc3F+T4rcCd0jqppi5dABERJ+kG9J7ASwpjWVmZg2gdIbqHa9SqUS1Wh3tNMzMWoqkroio1NuX8xqMmZm9g7nAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmN2qMyaVSxmBrjAmJlZJrm/i8zs8FebtaxbN3B77dpRSMaseXgGY2ZmWXgGY/Z21WYqnrmYDeAZjJmZZeEZjNmh4pmL2QCewZiZWRYuMGZmloULjJmZZeECY2ZmWWQtMJKOlrRK0hZJmyWdJ2mipE5JW9PrhFL76yR1S3pW0txSfIakJ9O+pZKU4mMlrUzxDZKmlvosTO+xVdLCnMdpZmb7yz2D+TbwQEScDpwFbAauBR6OiGnAw2kbSWcAHcCZwDzgO5LGpHGWAYuAaWmZl+KXA7sj4hTgJuDGNNZEYDFwDjATWFwuZGZmll+2AiNpPHABcCtARLwREb8D5gMrUrMVwEVpfT5wd0TsiYhfAd3ATEknAOMj4tGICOD2QX1qY60CZqfZzVygMyL6ImI30El/UTIzswbIOYM5GegFfiDpcUnfk3QkcHxE7AJIr8el9pOAHaX+PSk2Ka0Pjg/oExF7gZeBY4YZawBJiyRVJVV7e3vfzrGamdkgOQtMG3A2sCwiPgS8SjodNgTVicUw8YPt0x+IWB4RlYiotLe3D5OamZm9VTkLTA/QExEb0vYqioLzQjrtRXp9sdR+Sqn/ZGBnik+uEx/QR1IbcBTQN8xYZmbWINkKTET8Btgh6bQUmg08A6wGand1LQTuS+urgY50Z9hJFBfzH0un0V6RdG66vnLpoD61sRYAj6TrNA8CcyRNSBf356SYmZk1SO7vIrsauFPSEcBzwBcpito9ki4H/hX4c4CIeFrSPRRFaC9wVUTsS+NcCdwGjAPWpAWKGwjukNRNMXPpSGP1SboB2JjaLYmIvpwHamZmA6n4hd8qlUpUq9XRTsPMrKVI6oqISr19/kt+MzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMzLJwgTEzsyxcYMzMLIusBUbSdklPStokqZpiK9P2prR/U4pPlfR6ad8tpXFmpHG6JS2VpBQfm8brlrRB0tRSn4WStqZlYc7jNDOz/bU14D0+FhG/rW1ExMW1dUnfAl4utd0WEdPrjLEMWASsB+4H5gFrgMuB3RFxiqQO4EbgYkkTgcVABQigS9LqiNh9SI/MzMyGNGqnyNIs5PPAXQdodwIwPiIejYgAbgcuSrvnAyvS+ipgdhp3LtAZEX2pqHRSFCUzM2uQ3AUmgIckdUlaNGjf+cALEbG1FDtJ0uOS1kk6P8UmAT2lNj0pVtu3AyAi9lLMho4px+v0eZOkRZKqkqq9vb0Hd4RmZlZX7lNkH46InZKOAzolbYmI/5P2XcLA2csu4MSIeEnSDOAnks4EVGfcSK9D7RuuT38gYjmwHKBSqey338zMDl7WGUxE7EyvLwL3AjMBJLUBnwNWltruiYiX0noXsA04lWL2Mbk07GRgZ1rvAaaUxjwK6CvH6/QxM7MGyFZgJB0p6X21dWAO8FTa/QlgS0T0lNq3SxqT1k8GpgHPRcQu4BVJ56brK5cC96Vuq4HaHWILgEfSdZoHgTmSJkiakN77wVzHamZm+8t5iux44N50R3Eb8MOIeCDt62D/i/sXAEsk7QX2AVdERF/adyVwGzCO4u6xNSl+K3CHpG6KmUsHQET0SboB2JjaLSmNZWZmDaDiF36rVCpRrVZHOw0zs5YiqSsiKvX2+S/5zcwsCxcYMzPLwgXGmtusWcViZi3HBcbMzLJoxHeRmb11tVnLunUDt9euHYVkzOxgeAZjZmZZeAZjzak2U/HMxaxleQZjZmZZeAZjzc0zF7OW5RmMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWWQtMJK2S3pS0iZJ1RT7uqRfp9gmSZ8utb9OUrekZyXNLcVnpHG6JS2VpBQfK2llim+QNLXUZ6GkrWlZmPM4/VAsM7P9NeK7yD4WEb8dFLspIv6uHJB0BtABnAn8e+Cnkk6NiH3AMmARsB64H5gHrAEuB3ZHxCmSOoAbgYslTQQWAxUggC5JqyNid7ajNDOzAZrpyy7nA3dHxB7gV5K6gZmStgPjI+JRAEm3AxdRFJj5wNdT/1XAzWl2MxfojIi+1KeToijddUgz9kOxzMyGlPsaTAAPSeqStKgU/7KkJyR9X9KEFJsE7Ci16UmxSWl9cHxAn4jYC7wMHDPMWANIWiSpKqna29t7sMdoZmZ15J7BfDgidko6DuiUtIXidNcNFMXnBuBbwGWA6vSPYeIcZJ/+QMRyYDlApVLZb/8B+aFYZmZDyjqDiYid6fVF4F5gZkS8EBH7IuKPwHeBmal5DzCl1H0ysDPFJ9eJD+gjqQ04CugbZiwzM2uQbAVG0pGS3ldbB+YAT0k6odTss8BTaX010JHuDDsJmAY8FhG7gFcknZuur1wK3FfqU7tDbAHwSEQE8CAwR9KEdApuTorlsXatZy9mZoPkPEV2PHBvuqO4DfhhRDwg6Q5J0ylOWW0HvgQQEU9Lugd4BtgLXJXuIAO4ErgNGEdxcX9Nit8K3JFuCOijuAuNiOiTdAOwMbVbUrvgb2ZmjaHiF36rVCpRrVZHOw0zs5YiqSsiKvX2+S/5zcwsCxcYMzPLwgXGzMyycIExM7MsfJE/kdQLPP82hjgWGPyda82qlXKF1sq3lXKF1sq3lXKF1sr37eT6/ohor7fDBeYQkVQd6k6KZtNKuUJr5dtKuUJr5dtKuUJr5ZsrV58iMzOzLFxgzMwsCxeYQ2f5aCfwFrRSrtBa+bZSrtBa+bZSrtBa+WbJ1ddgzMwsC89gzMwsCxcYMzPLwgVmCJKmSPpnSZslPS3pL0v7rpb0bIp/oxS/TlJ32je3FJ8h6cm0b2l67ED2XCVNl7Re0qb05M6ZpT6jkmt6j/dIekzSL1K+/zPFJ0rqlLQ1vU4o9Rmtn+1QuX5T0pb0ZNZ7JR092rkOl29p/1clhaRjRzvf4XJtts/YcPk26+csvc8YSY9L+qe03djPWER4qbMAJwBnp/X3Ab8EzgA+BvwUGJv2HZdezwB+AYwFTgK2AWPSvseA8yietLkG+FSDcn2o9l7Ap4G1o51reg8Bf5LW3w1sAM4FvgFcm+LXAjeOdr7D5DoHaEvxG5sh1+HyTdtTKJ6L9Dxw7GjnO8zPtuk+YwfItyk/Z+l9vgL8EPintN3Qz5hnMEOIiF0R8fO0/gqwGZhE8Wyav42IPWnfi6nLfODuiNgTEb8CuoGZKh6wNj4iHo3iv9btwEUNyjWA8anZUfQ/1XPUck05RkT8IW2+Oy2R8lqR4itK7z2aP9u6uUbEQxGxN8XX0//U1Wb92QLcBPx3Bj4+vOl+tjThZ+wA+Tbl50zSZOA/AN8rhRv6GXOBGQFJU4EPUfzGcipwvqQNktZJ+rPUbBKwo9StJ8UmpfXB8Ubkeg3wTUk7gL8DrmuWXNPUfRPwItAZERuA46N4ginp9bhmyHeIXMsuo/8heE35s5V0IfDriPjFoObN+LNt2s/YEPleQ3N+zv6e4heKP5ZiDf2MucAcgKQ/Af4RuCYifk/xdM4JFFPj/wbck85J1jsvGcPEG5HrlcBfRcQU4K8ongBKM+QaEfsiYjrFb/4zJX1gmOajmu9wuUq6nuIJrHfWQkPkNJo/2w8C1wN/U6d5M/5sm/YzNkS+Tfc5k/QZ4MWI6Bpplzqxt52rC8wwJL2b4h/sOyPixyncA/w4TZcfo/jt4NgUn1LqPpliqtxD/+mTcrwRuS4Eaus/AmoXH0c117KI+B2wFpgHvJCm5KTX2qmRpsh3UK5IWgh8BvhCOn3QNLkOync+xXn1X0jant7755L+XbPkO+hn25SfsWHybcbP2YeBC9N/77uBj0v6Bxr9GRvq4sw7faGo3LcDfz8ofgWwJK2fSjGtFHAmAy+SPUf/RbKNFL+N1S6SfbpBuW4GZqX12UBXWh+1XNN7tANHp/VxwM8o/qH+JgMvQH5jtPMdJtd5wDNA+6D2TfmzHdRmO/0X+ZvxZ9t0n7ED5NuUn7NS3rPov8jf0M9YlgM6HBbgIxRTwSeATWn5NHAE8A/AU8DPgY+X+lxPcffFs5TutAAqqf024GbSNyg0INePAF3pf5wNwIzRzjW9xweBx1O+TwF/k+LHAA8DW9PrxNHOd5hcuyn+4av9vG8Z7VyHy3dQm+2kAtOkP9um+4wdIN+m/JyV3msW/QWmoZ8xf1WMmZll4WswZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmPWxCSNGe0czA6WC4zZKJL0E0ld6fkii1LsD5KWSNoAnCfpP6XnkGyS9L9qRUfSsvT8kf2e+2LWDFxgzEbXZRExg+Kvpf9C0jHAkcBTEXEO8BJwMfDhKL5kcR/whdT3+oioUPyF+UfTl1qaNY220U7A7B3uLyR9Nq1PAaZRFJF/TLHZwAxgY3qQ4Dj6v6Dw82nW00bx0LkzKL7GxKwpuMCYjRJJs4BPAOdFxGuS1gLvAf5fROyrNQNWRMR1g/qeBHwV+LOI2C3pttTXrGn4FJnZ6DkK2J2Ky+kU31g72MPAAknHwZvPVH8/xRMUXwVelnQ88KlGJW02Up7BmI2eB4ArJD1B8Q226wc3iIhnJH0NeEjSu4B/A66KiPWSHgeepvhq9X9pYN5mI+JvUzYzsyx8iszMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsi/8PHmcHtx/npqEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel('area')\n",
    "plt.ylabel('price')\n",
    "plt.scatter(df.area,df.price,color=\"red\",marker=\"+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(df[['area']],df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([628715.75342466])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[3300]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([135.78767123])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "628715.7534151643"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3300*135.78767123+180616.43835616432"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([859554.79452055])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[5000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([135.78767123])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "859554.7945061643"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5000*135.78767123+180616.43835616432"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=pd.read_csv(\"C:\\\\Users\\\\Admin\\\\Desktop\\\\simple regression\\\\areas.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area\n",
       "0   1000\n",
       "1   1500\n",
       "2   2300\n",
       "3   3540\n",
       "4   4120\n",
       "5   4560\n",
       "6   5490\n",
       "7   3460\n",
       "8   4750\n",
       "9   2300\n",
       "10  9000\n",
       "11  8600\n",
       "12  7100"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "0  1000\n",
       "1  1500\n",
       "2  2300"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=reg.predict(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([316404.10958904, 384297.94520548, 492928.08219178])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(d.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "d[\"prices\"]=p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>3.164041e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "      <td>3.842979e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "      <td>4.929281e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3540</td>\n",
       "      <td>6.613048e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4120</td>\n",
       "      <td>7.400616e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4560</td>\n",
       "      <td>7.998082e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5490</td>\n",
       "      <td>9.260908e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3460</td>\n",
       "      <td>6.504418e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4750</td>\n",
       "      <td>8.256079e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2300</td>\n",
       "      <td>4.929281e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9000</td>\n",
       "      <td>1.402705e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8600</td>\n",
       "      <td>1.348390e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7100</td>\n",
       "      <td>1.144709e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area        prices\n",
       "0   1000  3.164041e+05\n",
       "1   1500  3.842979e+05\n",
       "2   2300  4.929281e+05\n",
       "3   3540  6.613048e+05\n",
       "4   4120  7.400616e+05\n",
       "5   4560  7.998082e+05\n",
       "6   5490  9.260908e+05\n",
       "7   3460  6.504418e+05\n",
       "8   4750  8.256079e+05\n",
       "9   2300  4.929281e+05\n",
       "10  9000  1.402705e+06\n",
       "11  8600  1.348390e+06\n",
       "12  7100  1.144709e+06"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>prices</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>1500</td>\n",
       "      <td>384297.945205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "      <td>492928.082192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area         prices\n",
       "0  1000  316404.109589\n",
       "1  1500  384297.945205\n",
       "2  2300  492928.082192"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.to_csv(\"prediction.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21dd08e7b80>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEGCAYAAABYV4NmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAu5klEQVR4nO3de5yVVdn/8c83eSQzQTQsAw191BJNESbUJ0+FAZaJ9dLExx4pSQQtNTvpYz0Y1i9JzcQSBDHEQ2ooSCoigeCRw4yCoGiMpyBQ0cETKgpcvz/WGtngMIzInr1n5vt+vfZr9l73ve597ZHtNde97nstRQRmZmZb2sdKHYCZmTVPTjBmZlYUTjBmZlYUTjBmZlYUTjBmZlYUrUodQLn41Kc+FZ06dSp1GGZmTUpVVdXLEdG+rm1OMFmnTp2orKwsdRhmZk2KpOc3ts2nyMzMrCicYMzMrCicYMzMrCicYMzMrCicYMzMrCiKlmAkfV7S3ILH65LOlnSxpCclPSZpvKTt8/6dJL1dsP+IgmN1kzRfUrWkYZKU21tLujm3z5LUqaBPP0mL8qNfsT6nmVmTdsQR6VEERUswEfFURHSJiC5AN+AtYDwwBdg3IvYD/gmcV9Dt6do+ETGwoH04MADYMz965/b+wIqI2AO4DBgKIGkHYDBwINAdGCypXXE+qZmZ1aWx7oPpQUoezwOF10zPBI6rr6OknYE2EfFwfj0WOBaYBPQBLsi7jgP+lKubXsCUiKjJfaaQktJft9DnMTNr2mqrlhkz1n89ffoWe4vGGoPpS93/cz+FlChq7SbpUUkzJB2a2zoASwr2WZLbarctBoiI1cBrwI6F7XX0eZ+kAZIqJVUuX778w38qM7Mmbik7U0XXohy76BWMpK2BY1j/VBiSzgdWAzfkpmXArhHxiqRuwARJ+wCq47C1q6RtbFt9fdY1RIwERgJUVFR45TUzazHi3umMHg0/feBNPtv6ZRZM68THtnDJ0RgVzFHAIxHxYm1DHnQ/Gjgp8pKaEbEqIl7Jz6uAp4G9SNVHx4LjdQSW5udLgF3yMVsBbYGawvY6+piZtWjV1dCjB5x6KnT5ZDW373P+Fk8u0DgJ5kQKTo9J6g38AjgmIt4qaG8vaav8fHfSYP4zEbEMeEPSQXl85WTg9txtIlB7hdhxwLScsCYDPSW1y4P7PXObmVmLtXo1XHwxfPGLUFUFV10F02q6sOfsGzbdeTMU9RSZpE8AXwNOK2j+E9AamJKvNp6Zrxg7DBgiaTWwBhhYO0gPDALGANuQxmxqx21GA9dJqiZVLn0BIqJG0oXAnLzfkIJjmZm1OHPnwg9+kBLLMcfAlVdChw+MTG9ZymeoWryKiorwbMpm1ty88w5ceCEMHQo77ghXXAHHHw+qa6R6M0iqioiKurZ5un4zs2bq/vvTOMtTT0G/fnDppSnJNBZPFWNm1sy8/jqcfjocdhisWgWTJ8OYMY2bXMAJxsysWbnzTthnHxgxAs46C+bPh549SxOLE4yZWTOwfDn893/D0UdD27bw0EPwxz/CJz9ZupicYMzMmrAIuP562HtvGDcOLrgAHnkEDjqo1JF5kN/MrMl6/nkYNAgmTUoJ5eqr0+mxcuEKxsysiVm7Fv70p5RMZsyAyy+HBx4or+QCrmDMzJqUhQvTDZMPPZQG76+6Cjp1KnVUdXMFY2bWBLz7brphsksXePJJuPZauPvu8k0u4ArGzKzszZkD/funS45POCGdEvv0p0sd1aa5gjEzK1MrV8JPfpIG8F95BW6/HW66qWkkF3AFY2ZWlqZOTdO8PPssnHZamkusbdtSR/XhuIIxMysjK1bAKafAkUdCq1ZpBeMRI5pecgEnGDOzsnHrrdC5M4wdC+eeC/PmweGHlzqqzedTZGZmJbZsGZxxBowfDwccAHfdlX42da5gzMxKJCLdfb/33ulu/Isugtmzm0dyAVcwZmYlUV0NAwbAvfem02CjRsGee5Y6qi2raBWMpM9LmlvweF3S2ZJ2kDRF0qL8s11Bn/MkVUt6SlKvgvZukubnbcOU11qW1FrSzbl9lqROBX365fdYJKlfsT6nmdmHsXo1XHIJ7LdfWr74qqtg2rTml1ygiAkmIp6KiC4R0QXoBrwFjAfOBaZGxJ7A1PwaSZ2BvsA+QG/gSklb5cMNBwYAe+ZH79zeH1gREXsAlwFD87F2AAYDBwLdgcGFiczMrBTmzYODD4af/Qy+9jV44olUxXysmQ5WNNbH6gE8HRHPA32Aa3P7tcCx+Xkf4KaIWBURzwLVQHdJOwNtIuLhiAhg7AZ9ao81DuiRq5tewJSIqImIFcAU1iUlM7NG9c47cP75UFEB//oX3HwzTJgAHTqUOrLiaqwxmL7AX/PzT0fEMoCIWCZpp9zeAZhZ0GdJbnsvP9+wvbbP4nys1ZJeA3YsbK+jz/skDSBVRuy6666b+9nMzDbqgQfS5JRPPQUnnwx/+EPjL11cKkWvYCRtDRwD/G1Tu9bRFvW0b26fdQ0RIyOiIiIq2rdvv4nwzMwa7vXX06XHhx6aKpi7704TVLaU5AKNc4rsKOCRiHgxv34xn/Yi/3wpty8Bdino1xFYmts71tG+Xh9JrYC2QE09xzIzK7o770xrswwfDmedBQsWQK9em+7X3DRGgjmRdafHACYCtVd19QNuL2jvm68M2400mD87n057Q9JBeXzl5A361B7rOGBaHqeZDPSU1C4P7vfMbWZmRbN8OZx0Ehx9NLRpk9Zs+eMf4ZOfLHVkpVHUMRhJnwC+BpxW0HwRcIuk/sC/gOMBIuJxSbcATwCrgTMiYk3uMwgYA2wDTMoPgNHAdZKqSZVL33ysGkkXAnPyfkMioqYoH9LMWrwIuPHGVK28/joMHgznnQetW5c6stJS+oPfKioqorKystRhmFkT869/waBBaXqXAw+E0aPLb+niYpJUFREVdW1rpldfm5kV19q18Oc/p2QyfXo6Ffbggy0ruWyKp4oxM/uQnnwyXXr84IPQs2e6G7+cly4uFVcwZmYN9N578JvfwP77p7vwx4xJlx87udTNFYyZWQNUVkL//vDYY/Cd78CwYU1n6eJScQVjZlaPt96Cn/40DeC//DLcfnua6sXJZdNcwZiZbcS0aXDqqfDMM2lSyt//vmkuXVwqrmDMzDawYkUaxO/RI810PH16Gsh3cvlwnGDMzArcdht07pwG8H/xizTmcvjhpY6qafIpMjMzYNky+OEPU4Lp0iXNJ9a1a6mjatpcwZhZixYB11yTqpY774Tf/Q5mz3Zy2RJcwZhZi1U7eD91Khx2GIwaBXvtVeqomg9XMGbW4qxeDZdeCvvuC3PmwIgRcO+9Ti5bmisYMyt/RxyRfk6f/pEP9dhj6YbJykr45jfhyiuhY8dN97MPzxWMmbUIq1bBr34F3brB88/DTTelmyadXIrHFYyZla/aymXGjPVff8hK5sEH030tTz4JJ58Mf/hDy1q6uFRcwZhZs/XGG+nS40MPTVO+3H03XHutk0tjcQVjZuWrtlLZjMrlrrtg4EBYsgR+9CP47W9b7tLFpVLUCkbS9pLGSXpS0kJJB0u6WdLc/HhO0ty8bydJbxdsG1FwnG6S5kuqljRMknJ763y8akmzJHUq6NNP0qL86FfMz2lm5ePll+G734VvfAO22y6dHrv8cieXUih2BXM5cHdEHCdpa+ATEXFC7UZJlwKvFez/dER0qeM4w4EBwEzgLqA3MAnoD6yIiD0k9QWGAidI2gEYDFQAAVRJmhgRK7b4JzSz4mtA5RIBf/0rnHUWvPYaDB4M550HrVsXPzyrW9EqGEltgMOA0QAR8W5EvFqwXcB3gL9u4jg7A20i4uGICGAscGze3Ae4Nj8fB/TIx+0FTImImpxUppCSkpk1Q4sXw9FHw0knwe67wyOPwAUXOLmUWjFPke0OLAf+IulRSVdL2rZg+6HAixGxqKBtt7zvDEmH5rYOwJKCfZbkttptiwEiYjWpGtqxsL2OPu+TNEBSpaTK5cuXb/YHNbPSWLs23cfSuXMqci67DB56KN1AaaVXzATTCugKDI+IA4CVwLkF209k/eplGbBr3vcc4MZcBamOY0f+ubFt9fVZ1xAxMiIqIqKiffv2m/o8ZlZGnnwyzXJ8xhlw8MGwYAGcfTZstVWpI7NaxUwwS4AlETErvx5HSjhIagV8G7i5dueIWBURr+TnVcDTwF75OIW3QnUElha8xy4Fx2wL1BS219HHzJqw995LV4Ttvz88/niaVn/yZNhtt1JHZhsqWoKJiBeAxZI+n5t6AE/k50cCT0bE+6e+JLWXtFV+vjuwJ/BMRCwD3pB0UB5fORm4PXebCNReIXYcMC2P00wGekpqJ6kd0DO3mVkTVlkJFRXwy19Cnz6wcCH06weq65yFlVyxryL7EXBDvoLsGeD7ub0vHxzcPwwYImk1sAYYGBE1edsgYAywDenqsUm5fTRwnaRqUuXSFyAiaiRdCMzJ+w0pOJaZNTFvvZWuCvvDH+Azn4EJE1KCsfKm9Ae/VVRURGVlZanDMLMNTJuWptR/+un0c+hQ2H77UkdltSRVRURFXds8VYyZlaVXX03zh/XokU6B3XsvXHWVk0tT4gRjZmVn/Ph06fGYMfDzn6cp9mtni7Gmw3ORmVnZeOGFNDnlrbdCly5wxx1eurgpcwVjZiUXAddcA3vvnZLK//t/MHu2k0tT5wrGzErqmWfS4P3UqWla/VGj4POf33Q/K3+uYMysJNasSZcd77tvqlaGD0/TvTi5NB+uYMys0c2fD/37w5w5aZLK4cO9dHFz5ArGzBrNqlXwq1+lsZXnnkvT60+c6OTSXLmCMbNG8dBD6b6WhQvhf/4nzXzspYubN1cwZlZUb7yRliw+5BBYuRImTYKxY51cWgInGDMrmkmT0iD+n/+c7m9ZsAB6e+m/FsMJxsy2uJdfTqfBvv512HZbePBBGDYMttuu1JFZY3KCMbMtJiIN3O+9N9x0E/zf/8Gjj6YFwazl8SC/mW0RixfD6aenO/G7d4err4YvfrHUUVkpuYIxs49k7dp0H8s++6Sp9f/wh3TFmJOLuYIxs8321FNw6qlw//1w5JEwcqSXLrZ1XMGY2Yf23ntpQsr990935V9zDdxzj5OLra+oCUbS9pLGSXpS0kJJB0u6QNK/Jc3Nj68X7H+epGpJT0nqVdDeTdL8vG2YlFbgltRa0s25fZakTgV9+klalB/9ivk5zVqSqir40pfg/PPhm99MN05+//tpUTCzQsWuYC4H7o6ILwD7Awtz+2UR0SU/7gKQ1BnoC+wD9AaulLRV3n84MADYMz9qr6TvD6yIiD2Ay4Ch+Vg7AIOBA4HuwGBJ7Yr6Sc2aubfeSot/de8OL72UFgX729/gM58pdWRWroqWYCS1AQ4DRgNExLsR8Wo9XfoAN0XEqoh4FqgGukvaGWgTEQ9HRABjgWML+lybn48DeuTqphcwJSJqImIFMIV1ScnMPqR774X99oOLL06TVD7xBBx7bKmjsnJXzApmd2A58BdJj0q6WtK2edsPJT0m6ZqCyqIDsLig/5Lc1iE/37B9vT4RsRp4DdixnmOtR9IASZWSKpcvX/4RPqpZ8/Tqq2mtlq9+Nb2eNi0N5G+/fSmjsqaimAmmFdAVGB4RBwArgXNJp7v+E+gCLAMuzfvXdQY36mnf3D7rGiJGRkRFRFS0b99+45/ErAWaMAE6d4bRo+FnP4PHHoOvfKXUUVlTUswEswRYEhGz8utxQNeIeDEi1kTEWmAUaYykdv9dCvp3BJbm9o51tK/XR1IroC1QU8+xzGwTXngBjj8evvUt2GmntBjY738Pn/hEqSOzpqZoCSYiXgAWS6pdn64H8EQeU6n1LWBBfj4R6JuvDNuNNJg/OyKWAW9IOiiPr5wM3F7Qp/YKseOAaXmcZjLQU1K7fAquZ24zs42IgDFjUtXy97+ny5DnzIFu3UodmTVVxb7R8kfADZK2Bp4Bvg8Mk9SFdMrqOeA0gIh4XNItwBPAauCMiFiTjzMIGANsA0zKD0gXEFwnqZpUufTNx6qRdCEwJ+83JCJqivcxzZq2Z5+F006DKVPStPpXX+2li+2jU/qD3yoqKqKysrLUYZg1qjVr0izHv/wlbLUVDB2aEs3HfAu2NZCkqoioqGubp4oxa6Hmz08rTM6eDUcfDVdeCbvssul+Zg3lv1PMWphVq2DwYOjaFZ55Jk2vP3Gik4ttea5gzFqQhx9ON0ouXAjf/S5cdhl86lOljsqaK1cwZi3Am2/CmWfCl78MK1fCXXfBddc5uVhxuYIxa+buvjsN3C9eDGeckS4/9tLF1hgaXMFI+pykI/PzbST5n6hZGXvlFTj5ZDjqqHST5AMPwBVXOLlY42lQgpF0KulO/KtyU0dgQpFiMrOPIAJuugn23jsN4P/qVzB3LvzXf5U6MmtpGnqK7AzSlC6zACJikaSdihaVmW2WJUtg0CC44460ZsvUqV662EqnoafIVkXEu7Uv8rxfvkPTrEysXQvDh6dpXqZOhUsvTVeMOblYKTW0gpkh6X+BbSR9DTgd+HvxwjKzhvrnP9MNk/ffDz16pOn0d9+91FGZNbyCOZe0tst80txhdwG/LFZQZrZp770Hv/tdWghs/ny45po0l5iTi5WLhlYw2wDXRMQogLyU8TbAW8UKzMw2rqoqVS1z58Jxx6Wrw7x0sZWbhlYwU0kJpdY2wD+2fDhmVp+33oKf/xwOPDCt23LbbfC3vzm5WHlqaAXz8Yh4s/ZFRLwpycsPmTWi6dPh1FOhujpVLxdf7KWLrbw1tIJZKalr7QtJ3YC3ixOSmRV69VUYMCAtV7x2bbpKbNQoJxcrfw2tYM4G/iapdtnhnYETihKRmb1vwgQ4/XR48UX46U/h17/20sXWdDSogomIOcAXSCtLng7sHRFVm+onaXtJ4yQ9KWmhpIMlXZxfPyZpvKTt876dJL0taW5+jCg4TjdJ8yVVSxqWl04mL698c26fJalTQZ9+khblR78NYzMrZy+8AMcfD9/6FrRvD7NmpVNiTi7WlNSbYCR9Nf/8NvBNYC9gT+CbuW1TLgfujogvAPsDC4EpwL4RsR/wT+C8gv2fjogu+TGwoH04MCC/955A79zeH1gREXsAlwFDc7w7AIOBA0kzEAyW1K4B8ZqVVASMGZNumJw4EX77W6ishIo61ws0K2+bOkV2ODCNlFw2FMBtG+soqQ1wGPA9gDwTwLvAPQW7zQSOqy8ASTsDbSLi4fx6LHAsMAnoA1yQdx0H/ClXN72AKRFRk/tMISWlv9b3Xmal9OyzadbjKVPgkEPSOMsXvlDqqMw2X70JJiIGS/oYMCkibvmQx96ddHPmXyTtD1QBZ0XEyoJ9TgFuLni9m6RHgdeBX0bE/UAHYEnBPktyG/nn4hzrakmvATsWttfRx6ysrFkDw4bBL38JH/sY/PnPMHBgem7WlG3yn3BErAV+uBnHbgV0BYZHxAHAStKMAABIOh9YDdyQm5YBu+Z9zwFuzFWQ6gqr9jAb2VZfn/dJGiCpUlLl8uXLG/apzLagBQvSImDnnANHHAGPP54G9Z1crDlo6D/jKZJ+KmkXSTvUPjbRZwmwJCJm5dfjSAmHPOh+NHBSRARARKyKiFfy8yrgadKYzxLS8gC1OgK1V7MtAXbJx2wFtAVqCtvr6PO+iBgZERURUdG+ffuG/B7MtohVq2DwYOjaFZ5+Gm64Ic2AvOuupY7MbMtpaII5hXT12AygsuCxURHxArBY0udzUw/gCUm9gV8Ax0TE+1PNSGqfp6BB0u6kwfxnImIZ8Iakg/L4ysnA7bnbRKD2CrHjgGk5YU0Gekpqlwf3e+Y2s5J7+OGUWIYMge98BxYuhP/+b1BddbdZE9bQ+2A6kxLMIaRTTfcDI+rtkfwIuEHS1sAzwPeBOUBrUlUEMDNfMXYYMETSamANMLB2kJ50efQY0hQ1k/IDYDRwnaRqUuXSFyAiaiRdmN8LYEjBscxK4s034fzz07xhHTvCXXel1SbNmivlM1T17yTdQhp4rx0vORHYPiK+U8TYGlVFRUVUVtZblJlttsmT0xVi//pXGmP53e+8dLE1D5KqIqLOC+kbWsF8PiL2L3h9r6R5Hz00s+btlVfgxz+G665Llxzff38a1DdrCRo6BvOopINqX0g6EHiwOCGZNX0RcPPNsPfe8Ne/pkuQH33UycValoZWMAcCJ0v6V369K7BQ0nwg8l35ZgYsWZJOg/397+kO/H/8Iy0KZtbSNDTB9N70LmYt29q1abnin/8cVq+GSy6Bs86CVg39lpk1Mw36px8Rzxc7ELOm7J//TGu13HcffPWrKdH853+WOiqz0vL9wmYfwXvvwUUXpVNg8+bB1VenU2JOLmYNP0VmZht45BHo3x/mzoVvfxv+9CfYeedSR2VWPlzBmH1Ib78Nv/gFdO+e1m259db02PnEI9KEYmYGuIIx+1BmzEhjLYsWperl4ouhnVcaMquTE4xZA7z2Wro6bORI2H33NM7So0feWFu1zJix/uvp0xs3SLMy41NkZpswcWJaYfLqq+EnP4H58wuSi5ltlCsYs4148UU480y45ZZ0ldiECfClL9WxY22l4srFbD2uYMw2EAHXXpumeZkwAX7zG6is3EhyMbONcgVjVuC559Ksx/fck+YNGzUqJZoGceVith5XMGbAmjVw+eWwzz7w0EPpnpb77vsQycXMPsAVjLV4jz+eLjmeNSstADZihJcuNtsSXMFYi7VqFVxwARxwAFRXw/XXw513OrmYbSlFTTCStpc0TtKTkhZKOljSDpKmSFqUf7Yr2P88SdWSnpLUq6C9m6T5edsw5bWWJbWWdHNunyWpU0Gffvk9FknqV8zPaU3PzJnQtSv8+tdw/PGwcCGcdBKkf1lmtiUUu4K5HLg7Ir4A7A8sBM4FpkbEnsDU/BpJnYG+wD6k5QGulLRVPs5wYACwZ37ULh/QH1gREXsAlwFD87F2AAaT1rHpDgwuTGTWcr35Jpx9NvzXf8Ebb8Add8ANN0D79qWOzKz5KVqCkdQGOAwYDRAR70bEq0Af4Nq827XAsfl5H+CmiFgVEc8C1UB3STsDbSLi4YgIYOwGfWqPNQ7okaubXsCUiKiJiBXAFLymTYt3zz2w775pMP/009PYyze+UeqozJqvYlYwuwPLgb9IelTS1ZK2BT4dEcsA8s+d8v4dgMUF/Zfktg75+Ybt6/WJiNXAa8CO9RxrPZIGSKqUVLl8+fKP8lmtjL3yCvTrB716wcc/Dvffn64S2267Ukdm1rwVM8G0AroCwyPiAGAl+XTYRtR19jvqad/cPusaIkZGREVEVLT3OZJmJyLdhd+5M9x4I5x/fppa/5BDSh2ZWctQzASzBFgSEbPy63GkhPNiPu1F/vlSwf67FPTvCCzN7R3raF+vj6RWQFugpp5jWQvx73/DscfCCSfALrukO/F/85tUwZhZ4yhagomIF4DFkj6fm3oATwATgdqruvoBt+fnE4G++cqw3UiD+bPzabQ3JB2Ux1dO3qBP7bGOA6blcZrJQE9J7fLgfs/cZs3c2rVw1VWpapkyJU2nP3Mm7L9/qSMza3mKfaPlj4AbJG0NPAN8n5TUbpHUH/gXcDxARDwu6RZSEloNnBERa/JxBgFjgG2ASfkB6QKC6yRVkyqXvvlYNZIuBObk/YZERE0xP6iV3qJFaa2WGTPgK19J07x46WKz0lH6g98qKiqisrKy1GHYZli9Gi69NN002bp1en7KKb6nxawxSKqKiIq6tnmqGGvSHn00TfPy6KPwrW+lq8M++9lSR2Vm4KlirIl6+20477w0hf7SpTBuHNx2m5OLWTlxBWNNzn33wQ9+kMZcTjkFLrkE2nmeBrOy4wrGmozXXoOBA+Hww9O4y5QpMHq0k4tZuXKCsSZh4sS0VsuoUXDOOTB/Phx5ZKmjMrP6OMFYWXvpJejbF/r0gR12gIcfTleJbbttqSMzs01xgrGyFAFjx6YVJcePhyFD0t343buXOjIzaygP8lvZee65NNYyeXKaVn/UqHRnvpk1La5grGysWZOm0t93X3jwQbjiijTzsZOLWdPkCsbKwhNPpBsmZ86Eo46CESO8dLFZU+cKxkrq3XfTssVduqT7Wq67Du6808nFrDlwBWMlM2tWqloefxxOPBH++EfYaadNdjOzJsIVjDW6lSvhxz+Ggw9ON0/+/e9pQTAnF7PmxRWMNaopU2DAgHSl2KBBcNFF0KZNqaMys2JwBWONoqYGvvc96NkTtt46zSd25ZVOLmbNmROMFVUE/O1v6YbJ66+H//1fmDcPDj201JGZWbH5FJkVzdKlcPrpcPvt0LVrunGyS5dSR2VmjaWoFYyk5yTNlzRXUmVuuzm/npu3z83tnSS9XbBtRMFxuuXjVEsaJqW1CiW1zserljRLUqeCPv0kLcqPfsX8nLa+tWth5MhUtUyeDL//fbpizMnFrGVpjArmKxHxcu2LiDih9rmkS4HXCvZ9OiK61HGM4cAAYCZwF9AbmAT0B1ZExB6S+gJDgRMk7QAMBiqAAKokTYyIFVv0k9kHVFfDqafC9OlwxBFpmpc99ih1VGZWCiUbg8lVyHeAv25iv52BNhHxcEQEMBY4Nm/uA1ybn48DeuTj9gKmRERNTipTSEnJimT16lSpfPGLafnikSNh2jQnF7OWrNgJJoB7JFVJGrDBtkOBFyNiUUHbbpIelTRDUu0wcAdgScE+S3Jb7bbFABGxmlQN7VjYXkef90kaIKlSUuXy5cs37xMac+fCgQfCL34BvXunaV9OPRXSiUwza6mKnWC+HBFdgaOAMyQdVrDtRNavXpYBu0bEAcA5wI2S2gB1/W8q8s+Nbauvz7qGiJERURERFe3bt9/0p7H1vPNOuiqsogL+/e90tdhtt8FnP1vqyMysHBQ1wUTE0vzzJWA80B1AUivg28DNBfuuiohX8vMq4GlgL1L10bHgsB2Bpfn5EmCXgmO2BWoK2+voY1vAfffB/vvD734H//M/qWo57jhXLWa2TtESjKRtJW1X+xzoCSzIm48EnoyIJQX7t5e0VX6+O7An8ExELAPekHRQHl85Gbg9d5sI1F4hdhwwLY/TTAZ6SmonqV1+78nF+qwtyeuvpzvwDz88TVR5zz3wl7+k1SbNzAoV8yqyTwPj8xXFrYAbI+LuvK0vHxzcPwwYImk1sAYYGBE1edsgYAywDenqsUm5fTRwnaRqUuXSFyAiaiRdCMzJ+w0pOJZtpjvuSMll6dI0l9iFF3rpYjPbOKU/+K2ioiIqKytLHUZZeuklOOssuOmmtBjY6NFeutjMEklVEVFR1zZPFWMbFZHWZ9l7b7j11rRuS1WVk4uZNYynirE6Pf88nHZauhP/4IPh6qu9dLGZfTiuYGw9a9bAFVfAPvvAAw/AsGFw//0lTC5HHJEeZtbkuIKx9z3xBPzgB/Dww+mGyREj4HOfK3VUZtZUOcEY776bFv767W/hk5+EsWPhu98t8T0ttVXLjBnrv54+vQTBmNnmcIJp4WbPhv79YcEC6NsXLr/cSxeb2ZbhBNNCrVwJv/pVSig77wwTJ8I3v1nqqArUViquXMyaLCeYFugf/4ABA+DZZ2HgwHR6rG3bUkdlZs2NE0wLsmIF/OQnaWqXvfZKwxuHHbbpfiXlysWsyfJlyi1ABIwbl26YHDsWzjsP5s1rAsnFzJo0VzDN3NKlcMYZMGECdO0Kd9/tpYvNrHG4gmmmItJyxZ07p6QydCjMmuXkYmaNxxVMM1RdnQbx7703XYQ1ciTsuWepozKzlsYVTDOyejVcfDF88YtpUsqRI2HqVCcXMysNVzDNxNy5aZqXqiro0wf+/Gfo0KHUUZlZS+YKpol75x343/+FigpYvBhuuQXGj3dyMbPScwXThN1/f6pa/vlP+N734NJLvXSxmZWPolYwkp6TNF/SXEmVue0CSf/ObXMlfb1g//MkVUt6SlKvgvZu+TjVkoYpr8MsqbWkm3P7LEmdCvr0k7QoP/oV83M29pTyr78Op5+e7mN59920Zstf/uLkYmblpTEqmK9ExMsbtF0WEZcUNkjqDPQF9gE+C/xD0l4RsQYYDgwAZgJ3Ab2BSUB/YEVE7CGpLzAUOEHSDsBgoAIIoErSxIhYUbRP2UjuuAMGDYJ//xvOPhsuvDDNgGxmVm7KaQymD3BTRKyKiGeBaqC7pJ2BNhHxcEQEMBY4tqDPtfn5OKBHrm56AVMioiYnlSmkpLRl1VYuM2akRxErmZdeghNPTBNStm2b1my57DInFzMrX8VOMAHcI6lK0oCC9h9KekzSNZLa5bYOwOKCfZbktg75+Ybt6/WJiNXAa8CO9RxrPZIGSKqUVLl8+fLN/YxFFQHXX59umLz1Vvj1r+GRR+DAA0sdmZlZ/Yp9iuzLEbFU0k7AFElPkk53XUhKPhcClwKnAHUtbxX1tLOZfdY1RIwERgJUVFR8YPsmFXlK+eefT6fDJk2Cgw6Cq69OSxmbmTUFRa1gImJp/vkSMB7oHhEvRsSaiFgLjAK6592XALsUdO8ILM3tHetoX6+PpFZAW6CmnmM1CWvXwhVXpGRy331pzZYHHnByMbOmpWgJRtK2krarfQ70BBbkMZVa3wIW5OcTgb75yrDdgD2B2RGxDHhD0kF5fOVk4PaCPrVXiB0HTMvjNJOBnpLa5VNwPXNbcUyfvsWql4UL4dBD4cwz4ZBD0kqTZ54JW221RQ5vZtZoinmK7NPA+HxFcSvgxoi4W9J1krqQTlk9B5wGEBGPS7oFeAJYDZyRryADGASMAbYhXT02KbePBq6TVE2qXPrmY9VIuhCYk/cbEhE1xfuoH92776YJKX/zmzRwP3YsfPe7oLpO9pmZNQFKf/BbRUVFVFZWluS9Z89ON0zOnw8nnADDhsFOO5UkFDOzD0VSVURU1LWtnC5TbnFWroRzzoGDD4aaGrj9drjpJicXM2sePFVMiUydCqeeCs8+CwMHwkUXpftbzMyaC1cwjWzFCjjlFDjySGjVKl0bMHy4k4uZNT9OMI3o1lth773TAP6558K8eXD44aWOysysOHyKrBEsXQo//GGaRv+AA9KNkwccUOqozMyKyxVMEUWku+87d05J5aKL0hVjTi5m1hK4gimS6moYMADuvTedBhs1yksXm1nL4gpmC1u9Gi65BPbbLy1ffNVVMG2ak4uZtTyuYLagefOgf/+UWI45Bq680ksXm1nL5QpmC3jnHTj/fKiogMWL4eabYcIEJxcza9lcwXxEzz4LRx0FTz0F/frBpZfCjjuWOiozs9JzgvmIOnSAPfZIU+r36lXqaMzMyocTzEe09dZwxx2ljsLMrPx4DMbMzIrCCcbMzIrCCcbMzIrCCcbMzIqiqAlG0nOS5kuaK6kyt10s6UlJj0kaL2n73N5J0tt537mSRhQcp1s+TrWkYcrrMEtqLenm3D5LUqeCPv0kLcqPfsX8nGZm9kGNUcF8JSK6FCypOQXYNyL2A/4JnFew79N53y4RMbCgfTgwANgzP3rn9v7AiojYA7gMGAogaQdgMHAg0B0YLKldcT6emZnVpdFPkUXEPRGxOr+cCXSsb39JOwNtIuLhiAhgLHBs3twHuDY/Hwf0yNVNL2BKRNRExApSUuuNmZk1mmInmADukVQlaUAd208BJhW83k3So5JmSDo0t3UAlhTssyS31W5bDJCT1mvAjoXtdfR5n6QBkiolVS5fvvzDfzozM9uoYt9o+eWIWCppJ2CKpCcj4j4ASecDq4Eb8r7LgF0j4hVJ3YAJkvYBVMdxI//c2Lb6+qxriBgJjMzxLJf0/If4bBv6FPDyR+jfmJpSrNC04m1KsULTircpxQpNK96PEuvnNrahqAkmIpbmny9JGk8aD7kvD7ofDfTIp72IiFXAqvy8StLTwF6k6qPwNFpHYGl+vgTYBVgiqRXQFqjJ7Uds0Gf6JmJtv9kfFJBUWTDOVNaaUqzQtOJtSrFC04q3KcUKTSveYsVatFNkkraVtF3tc6AnsEBSb+AXwDER8VbB/u0lbZWf704azH8mIpYBb0g6KI+vnAzcnrtNBGqvEDsOmJYT1mSgp6R2eXC/Z24zM7NGUswK5tPA+HxFcSvgxoi4W1I10Jp0ygxgZr5i7DBgiKTVwBpgYETU5GMNAsYA25DGbGrHbUYD1+Vj1gB9ASKiRtKFwJy835CCY5mZWSMoWoKJiGeA/eto32Mj+98K3LqRbZXAvnW0vwMcv5E+1wDXfIiQP6qRjfheH1VTihWaVrxNKVZoWvE2pVihacVblFiVh0DMzMy2KE8VY2ZmReEEY2ZmReEEsxGSdpF0r6SFkh6XdFbBth9Jeiq3/76g/bw8L9pTknoVtNc5l1qxY5XURdLM2rngJHUvdaz5PT4uabakeTneX+f2HSRNyfPHTSmc3qeEv9uNxVrnnHqljLW+eAu2/1RSSPpUqeOtL9Zy+47VF2+5fs/y+2yldPP6Hfl1437HIsKPOh7AzkDX/Hw70rxpnYGvAP8AWudtO+WfnYF5pCvkdgOeBrbK22YDB5NuAJ0EHNVIsd5T+17A14HppY41v4eAT+bn/wHMAg4Cfg+cm9vPBYaWOt56Yu0JtMrtQ8sh1vriza93IV2u/zzwqVLHW8/vtuy+Y5uItyy/Z/l9zgFuBO7Irxv1O+YKZiMiYllEPJKfvwEsJE03Mwi4KNKNoUTES7lLH+CmiFgVEc8C1UB31T+XWrFjDaBN3q0t625QLVmsOcaIiDfzy//Ij2D9ueWuZf0550r1u60z1tj4nHrl+ruFNCHsz1l/Vouy+91Sht+xTcRblt8zSR2BbwBXFzQ36nfMCaYBlJYBOID0F8tewKFKywPMkPSlvNvG5j+rby61Ysd6NnCxpMXAJaybubrksebSfS7wEmli0lnApyPdWEv+uVM5xLuRWAsVzqlXlr9bSccA/46IeRvsXo6/27L9jm0k3rMpz+/ZH0l/UKwtaGvU75gTzCZI+iTp/pyzI+J10r1D7Uil8c+AW/I5yY80L1qRYh0E/DgidgF+TLoxlXKINSLWREQX0l/+3SV94D6nAiWNt75Y9cE59crxd7sfcD7wf3XsXo6/27L9jm0k3rL7nkk6GngpIqoa2qWOto8cqxNMPST9B+l/2DdExG25eQlwWy6XZ5P+OvgU6+ZFq1U7Z1p9c6kVO9Z+QO3zv5Hmgqv9DCWLtVBEvEqaJ6438GIuyWuXaag9NVIW8W4QK1o3p95J+fRB2cS6Qbx9SOfV50l6Lr/3I5I+Uy7xbvC7LcvvWD3xluP37MvAMfm/903AVyVdT2N/xzY2ONPSH6TMPRb44wbtA0lTz0Aq5Rfnffdh/UGyZ1g3SDaH9NdY7SDZ1xsp1oXAEfl5D6AqPy9ZrPk92gPb5+fbAPeT/kd9MesPQP6+1PHWE2tv4Amg/Qb7l+XvdoN9nmPdIH85/m7L7ju2iXjL8ntWEPcRrBvkb9TvWFE+UHN4AIeQSsHHgLn58XVga+B6YAHwCPDVgj7nk66+eIqCKy2Airz/08CfyDMoNEKshwBV+R/OLKBbqWPN77Ef8GiOdwHwf7l9R2AqsCj/3KHU8dYTazXpf3y1v+8RpY61vng32Oc5coIp099t2X3HNhFvWX7PCt7rCNYlmEb9jnmqGDMzKwqPwZiZWVE4wZiZWVE4wZiZWVE4wZiZWVE4wZiZWVE4wZiZWVE4wZiVMUlblToGs83lBGNWQpImSKrK64sMyG1vShoiaRZwsKTv5nVI5kq6qjbpSBqe1x/5wLovZuXACcastE6JiG6ku6XPlLQjsC2wICIOBF4BTgC+HGmSxTXASbnv+RFRQbrD/PA8qaVZ2WhV6gDMWrgzJX0rP98F2JOURG7NbT2AbsCcvJDgNqyboPA7ueppRVp0rjNpGhOzsuAEY1Yiko4AjgQOjoi3JE0HPg68ExFrancDro2I8zbouxvwU+BLEbFC0pjc16xs+BSZWem0BVbk5PIF0oy1G5oKHCdpJ3h/TfXPkVZQXAm8JunTwFGNFbRZQ7mCMSudu4GBkh4jzWA7c8MdIuIJSb8E7pH0MeA94IyImCnpUeBx0tTqDzZi3GYN4tmUzcysKHyKzMzMisIJxszMisIJxszMisIJxszMisIJxszMisIJxszMisIJxszMiuL/A+OpHYI7IvAnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel(\"area\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.scatter(df.area,df.price,color=\"red\",marker=\"+\")\n",
    "plt.plot(df.area,reg.predict(df[[\"area\"]]),color=\"blue\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}