Linear Regression_old.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('kc_house_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21603</th>\n",
       "      <td>7852140040</td>\n",
       "      <td>20140825T000000</td>\n",
       "      <td>507250.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2270</td>\n",
       "      <td>5536</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2270</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>98065</td>\n",
       "      <td>47.5389</td>\n",
       "      <td>-121.881</td>\n",
       "      <td>2270</td>\n",
       "      <td>5731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21604</th>\n",
       "      <td>9834201367</td>\n",
       "      <td>20150126T000000</td>\n",
       "      <td>429000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1490</td>\n",
       "      <td>1126</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1490</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5699</td>\n",
       "      <td>-122.288</td>\n",
       "      <td>1400</td>\n",
       "      <td>1230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21605</th>\n",
       "      <td>3448900210</td>\n",
       "      <td>20141014T000000</td>\n",
       "      <td>610685.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2520</td>\n",
       "      <td>6023</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>2520</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98056</td>\n",
       "      <td>47.5137</td>\n",
       "      <td>-122.167</td>\n",
       "      <td>2520</td>\n",
       "      <td>6023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21606</th>\n",
       "      <td>7936000429</td>\n",
       "      <td>20150326T000000</td>\n",
       "      <td>1007500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3510</td>\n",
       "      <td>7200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>2600</td>\n",
       "      <td>910</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5537</td>\n",
       "      <td>-122.398</td>\n",
       "      <td>2050</td>\n",
       "      <td>6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21607</th>\n",
       "      <td>2997800021</td>\n",
       "      <td>20150219T000000</td>\n",
       "      <td>475000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1310</td>\n",
       "      <td>1294</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1180</td>\n",
       "      <td>130</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98116</td>\n",
       "      <td>47.5773</td>\n",
       "      <td>-122.409</td>\n",
       "      <td>1330</td>\n",
       "      <td>1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>263000018</td>\n",
       "      <td>20140521T000000</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1530</td>\n",
       "      <td>1131</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1530</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.6993</td>\n",
       "      <td>-122.346</td>\n",
       "      <td>1530</td>\n",
       "      <td>1509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>6600060120</td>\n",
       "      <td>20150223T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2310</td>\n",
       "      <td>5813</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2310</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.5107</td>\n",
       "      <td>-122.362</td>\n",
       "      <td>1830</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>1523300141</td>\n",
       "      <td>20140623T000000</td>\n",
       "      <td>402101.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5944</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>291310100</td>\n",
       "      <td>20150116T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1600</td>\n",
       "      <td>2388</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.5345</td>\n",
       "      <td>-122.069</td>\n",
       "      <td>1410</td>\n",
       "      <td>1287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>1523300157</td>\n",
       "      <td>20141015T000000</td>\n",
       "      <td>325000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1076</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5941</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id             date      price  bedrooms  bathrooms  \\\n",
       "21603  7852140040  20140825T000000   507250.0         3       2.50   \n",
       "21604  9834201367  20150126T000000   429000.0         3       2.00   \n",
       "21605  3448900210  20141014T000000   610685.0         4       2.50   \n",
       "21606  7936000429  20150326T000000  1007500.0         4       3.50   \n",
       "21607  2997800021  20150219T000000   475000.0         3       2.50   \n",
       "21608   263000018  20140521T000000   360000.0         3       2.50   \n",
       "21609  6600060120  20150223T000000   400000.0         4       2.50   \n",
       "21610  1523300141  20140623T000000   402101.0         2       0.75   \n",
       "21611   291310100  20150116T000000   400000.0         3       2.50   \n",
       "21612  1523300157  20141015T000000   325000.0         2       0.75   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view     ...      grade  \\\n",
       "21603         2270      5536     2.0           0     0     ...          8   \n",
       "21604         1490      1126     3.0           0     0     ...          8   \n",
       "21605         2520      6023     2.0           0     0     ...          9   \n",
       "21606         3510      7200     2.0           0     0     ...          9   \n",
       "21607         1310      1294     2.0           0     0     ...          8   \n",
       "21608         1530      1131     3.0           0     0     ...          8   \n",
       "21609         2310      5813     2.0           0     0     ...          8   \n",
       "21610         1020      1350     2.0           0     0     ...          7   \n",
       "21611         1600      2388     2.0           0     0     ...          8   \n",
       "21612         1020      1076     2.0           0     0     ...          7   \n",
       "\n",
       "       sqft_above  sqft_basement  yr_built  yr_renovated  zipcode      lat  \\\n",
       "21603        2270              0      2003             0    98065  47.5389   \n",
       "21604        1490              0      2014             0    98144  47.5699   \n",
       "21605        2520              0      2014             0    98056  47.5137   \n",
       "21606        2600            910      2009             0    98136  47.5537   \n",
       "21607        1180            130      2008             0    98116  47.5773   \n",
       "21608        1530              0      2009             0    98103  47.6993   \n",
       "21609        2310              0      2014             0    98146  47.5107   \n",
       "21610        1020              0      2009             0    98144  47.5944   \n",
       "21611        1600              0      2004             0    98027  47.5345   \n",
       "21612        1020              0      2008             0    98144  47.5941   \n",
       "\n",
       "          long  sqft_living15  sqft_lot15  \n",
       "21603 -121.881           2270        5731  \n",
       "21604 -122.288           1400        1230  \n",
       "21605 -122.167           2520        6023  \n",
       "21606 -122.398           2050        6200  \n",
       "21607 -122.409           1330        1265  \n",
       "21608 -122.346           1530        1509  \n",
       "21609 -122.362           1830        7200  \n",
       "21610 -122.299           1020        2007  \n",
       "21611 -122.069           1410        1287  \n",
       "21612 -122.299           1020        1357  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21613, 21)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of row and columns in the data \n",
    "df.shape \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([dtype('int64'), dtype('O'), dtype('float64')], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data types in the data \n",
    "#df.dtypes\n",
    "df.dtypes.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['date']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select the data on bais of data type and getting the columns name\n",
    "#df.select_dtypes(include=['O',])\n",
    "#df.select_dtypes(include=['O',]).columns\n",
    "df.select_dtypes(include=['O',]).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0  /  21\n",
      "0  /  21613\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().any().sum(), ' / ', df.shape[1])\n",
    "print(df.isnull().any(axis=1).sum(), ' / ', df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21613 entries, 0 to 21612\n",
      "Data columns (total 21 columns):\n",
      "id               21613 non-null int64\n",
      "date             21613 non-null object\n",
      "price            21613 non-null float64\n",
      "bedrooms         21613 non-null int64\n",
      "bathrooms        21613 non-null float64\n",
      "sqft_living      21613 non-null int64\n",
      "sqft_lot         21613 non-null int64\n",
      "floors           21613 non-null float64\n",
      "waterfront       21613 non-null int64\n",
      "view             21613 non-null int64\n",
      "condition        21613 non-null int64\n",
      "grade            21613 non-null int64\n",
      "sqft_above       21613 non-null int64\n",
      "sqft_basement    21613 non-null int64\n",
      "yr_built         21613 non-null int64\n",
      "yr_renovated     21613 non-null int64\n",
      "zipcode          21613 non-null int64\n",
      "lat              21613 non-null float64\n",
      "long             21613 non-null float64\n",
      "sqft_living15    21613 non-null int64\n",
      "sqft_lot15       21613 non-null int64\n",
      "dtypes: float64(5), int64(15), object(1)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#features  = df.iloc[:,3:].columns.tolist()\n",
    "features  = df.iloc[:,3:].columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bedrooms',\n",
       " 'bathrooms',\n",
       " 'sqft_living',\n",
       " 'sqft_lot',\n",
       " 'floors',\n",
       " 'waterfront',\n",
       " 'view',\n",
       " 'condition',\n",
       " 'grade',\n",
       " 'sqft_above',\n",
       " 'sqft_basement',\n",
       " 'yr_built',\n",
       " 'yr_renovated',\n",
       " 'zipcode',\n",
       " 'lat',\n",
       " 'long',\n",
       " 'sqft_living15',\n",
       " 'sqft_lot15']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df.iloc[:,2].name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'price'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.3083495981456383, 0.0)\n",
      "(0.5251375054139619, 0.0)\n",
      "(0.7020350546118, 0.0)\n",
      "(0.08966086058710011, 7.972504510326147e-40)\n",
      "(0.2567938875507184, 1.6e-322)\n",
      "(0.2663694340306021, 0.0)\n",
      "(0.3972934882945043, 0.0)\n",
      "(0.036361789128997554, 8.935654062344094e-08)\n",
      "(0.6674342560202371, 0.0)\n",
      "(0.6055672983560783, 0.0)\n",
      "(0.32381602071198395, 0.0)\n",
      "(0.054011531494792715, 1.929872809374955e-15)\n",
      "(0.12643379344089295, 1.0213478858043326e-77)\n",
      "(-0.05320285429832561, 5.011050503318762e-15)\n",
      "(0.3070034799952186, 0.0)\n",
      "(0.021626241039305872, 0.0014750922063201787)\n",
      "(0.585378903579568, 0.0)\n",
      "(0.08244715251948598, 6.417559889335015e-34)\n"
     ]
    }
   ],
   "source": [
    "#calculate correlation between target and all features \n",
    "corr = {}\n",
    "\n",
    "for feature in features:\n",
    "    x = df[feature]\n",
    "    y = df[target]\n",
    "    key = feature \n",
    "    #correlation = pearsonr(x,y)\n",
    "    print(correlation)\n",
    "    corr[key]= correlation[0]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bedrooms': 0.3083495981456383,\n",
       " 'bathrooms': 0.5251375054139619,\n",
       " 'sqft_living': 0.7020350546118,\n",
       " 'sqft_lot': 0.08966086058710011,\n",
       " 'floors': 0.2567938875507184,\n",
       " 'waterfront': 0.2663694340306021,\n",
       " 'view': 0.3972934882945043,\n",
       " 'condition': 0.036361789128997554,\n",
       " 'grade': 0.6674342560202371,\n",
       " 'sqft_above': 0.6055672983560783,\n",
       " 'sqft_basement': 0.32381602071198395,\n",
       " 'yr_built': 0.054011531494792715,\n",
       " 'yr_renovated': 0.12643379344089295,\n",
       " 'zipcode': -0.05320285429832561,\n",
       " 'lat': 0.3070034799952186,\n",
       " 'long': 0.021626241039305872,\n",
       " 'sqft_living15': 0.585378903579568,\n",
       " 'sqft_lot15': 0.08244715251948598}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_correlations = pd.DataFrame(list(corr.items()), columns = ['feautes and target','corr'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>feautes and target</th>\n",
       "      <th>corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bedrooms</td>\n",
       "      <td>0.308350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>0.525138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sqft_living</td>\n",
       "      <td>0.702035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sqft_lot</td>\n",
       "      <td>0.089661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>floors</td>\n",
       "      <td>0.256794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>waterfront</td>\n",
       "      <td>0.266369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>view</td>\n",
       "      <td>0.397293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>condition</td>\n",
       "      <td>0.036362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>grade</td>\n",
       "      <td>0.667434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sqft_above</td>\n",
       "      <td>0.605567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sqft_basement</td>\n",
       "      <td>0.323816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>yr_built</td>\n",
       "      <td>0.054012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>yr_renovated</td>\n",
       "      <td>0.126434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>zipcode</td>\n",
       "      <td>-0.053203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>lat</td>\n",
       "      <td>0.307003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>long</td>\n",
       "      <td>0.021626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sqft_living15</td>\n",
       "      <td>0.585379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sqft_lot15</td>\n",
       "      <td>0.082447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feautes and target      corr\n",
       "0            bedrooms  0.308350\n",
       "1           bathrooms  0.525138\n",
       "2         sqft_living  0.702035\n",
       "3            sqft_lot  0.089661\n",
       "4              floors  0.256794\n",
       "5          waterfront  0.266369\n",
       "6                view  0.397293\n",
       "7           condition  0.036362\n",
       "8               grade  0.667434\n",
       "9          sqft_above  0.605567\n",
       "10      sqft_basement  0.323816\n",
       "11           yr_built  0.054012\n",
       "12       yr_renovated  0.126434\n",
       "13            zipcode -0.053203\n",
       "14                lat  0.307003\n",
       "15               long  0.021626\n",
       "16      sqft_living15  0.585379\n",
       "17         sqft_lot15  0.082447"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert all the negative correlation into positive \n",
    "data_correlations['corr'] = data_correlations['corr'].abs() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feautes and target</th>\n",
       "      <th>corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bedrooms</td>\n",
       "      <td>0.308350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>0.525138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sqft_living</td>\n",
       "      <td>0.702035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sqft_lot</td>\n",
       "      <td>0.089661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>floors</td>\n",
       "      <td>0.256794</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>waterfront</td>\n",
       "      <td>0.266369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>view</td>\n",
       "      <td>0.397293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>condition</td>\n",
       "      <td>0.036362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>grade</td>\n",
       "      <td>0.667434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sqft_above</td>\n",
       "      <td>0.605567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sqft_basement</td>\n",
       "      <td>0.323816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>yr_built</td>\n",
       "      <td>0.054012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>yr_renovated</td>\n",
       "      <td>0.126434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>zipcode</td>\n",
       "      <td>0.053203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>lat</td>\n",
       "      <td>0.307003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>long</td>\n",
       "      <td>0.021626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sqft_living15</td>\n",
       "      <td>0.585379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sqft_lot15</td>\n",
       "      <td>0.082447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feautes and target      corr\n",
       "0            bedrooms  0.308350\n",
       "1           bathrooms  0.525138\n",
       "2         sqft_living  0.702035\n",
       "3            sqft_lot  0.089661\n",
       "4              floors  0.256794\n",
       "5          waterfront  0.266369\n",
       "6                view  0.397293\n",
       "7           condition  0.036362\n",
       "8               grade  0.667434\n",
       "9          sqft_above  0.605567\n",
       "10      sqft_basement  0.323816\n",
       "11           yr_built  0.054012\n",
       "12       yr_renovated  0.126434\n",
       "13            zipcode  0.053203\n",
       "14                lat  0.307003\n",
       "15               long  0.021626\n",
       "16      sqft_living15  0.585379\n",
       "17         sqft_lot15  0.082447"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get n highest correlated feature\n",
    "data_correlations =data_correlations.sort_values('corr',ascending=False).iloc[:5,:].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>feautes and target</th>\n",
       "      <th>corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sqft_living</td>\n",
       "      <td>0.702035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grade</td>\n",
       "      <td>0.667434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sqft_above</td>\n",
       "      <td>0.605567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sqft_living15</td>\n",
       "      <td>0.585379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>0.525138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feautes and target      corr\n",
       "0        sqft_living  0.702035\n",
       "1              grade  0.667434\n",
       "2         sqft_above  0.605567\n",
       "3      sqft_living15  0.585379\n",
       "4          bathrooms  0.525138"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get all the features name \n",
    "top_5_features = data_correlations.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_5_features = list(top_5_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sqft_living', 'grade', 'sqft_above', 'sqft_living15', 'bathrooms']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_5_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = list(df.iloc[:,2].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       " 384000.0,\n",
       " 324000.0,\n",
       " 645000.0,\n",
       " 570000.0,\n",
       " 324000.0,\n",
       " 270000.0,\n",
       " 290000.0,\n",
       " 250000.0,\n",
       " 342000.0,\n",
       " 255000.0,\n",
       " 800000.0,\n",
       " 451000.0,\n",
       " 990000.0,\n",
       " 288400.0,\n",
       " 420000.0,\n",
       " 359000.0,\n",
       " 390000.0,\n",
       " 300000.0,\n",
       " 545000.0,\n",
       " 288349.0,\n",
       " 299900.0,\n",
       " 891000.0,\n",
       " 1200000.0,\n",
       " 378000.0,\n",
       " 299995.0,\n",
       " 253000.0,\n",
       " 350000.0,\n",
       " 565000.0,\n",
       " 315000.0,\n",
       " 570000.0,\n",
       " 971000.0,\n",
       " 915000.0,\n",
       " 419000.0,\n",
       " 260000.0,\n",
       " 1321500.0,\n",
       " 369950.0,\n",
       " 1029900.0,\n",
       " 435000.0,\n",
       " 601000.0,\n",
       " 460500.0,\n",
       " 720000.0,\n",
       " 851000.0,\n",
       " 550000.0,\n",
       " 700000.0,\n",
       " 323000.0,\n",
       " 345500.0,\n",
       " 378000.0,\n",
       " 650000.0,\n",
       " 385000.0,\n",
       " 425000.0,\n",
       " 210000.0,\n",
       " 630000.0,\n",
       " 335000.0,\n",
       " 196500.0,\n",
       " 582800.0,\n",
       " 440000.0,\n",
       " 752500.0,\n",
       " 532500.0,\n",
       " 265000.0,\n",
       " 397500.0,\n",
       " 524250.0,\n",
       " 765000.0,\n",
       " 648000.0,\n",
       " 469500.0,\n",
       " 237500.0,\n",
       " 216000.0,\n",
       " 427200.0,\n",
       " 280000.0,\n",
       " 718000.0,\n",
       " 420000.0,\n",
       " 379000.0,\n",
       " 326100.0,\n",
       " 535000.0,\n",
       " 283000.0,\n",
       " 260000.0,\n",
       " 399000.0,\n",
       " 484950.0,\n",
       " 640000.0,\n",
       " 585000.0,\n",
       " 645000.0,\n",
       " 253000.0,\n",
       " 178500.0,\n",
       " 291000.0,\n",
       " 199950.0,\n",
       " 553500.0,\n",
       " 189950.0,\n",
       " 289000.0,\n",
       " ...]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_features_target(top_5_features,target,df):\n",
    "    x = []\n",
    "    for feature in top_5_features:\n",
    "        x.append(list(df[feature]))\n",
    "    for i in range(len(x)):\n",
    "        plt.figure()\n",
    "        plt.scatter(x[i],target)\n",
    "        plt.xlabel(top_5_features[i])\n",
    "        plt.ylabel('Price')\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b4049aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b406679e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JKzOzje4+WG65tNlxd5hZf+z1TDP7eopV/xL4j8Dh8PodwIi7HwqvdxHVrgiPOwHC+/vD8hPlBesUK69mH4Xnu8LMhsxsaM+ePSlOU6TzKWlBGiFtc9x73X0k/8Ld9wELS61gZh8GXnP3jfHihEW9zHv1Ki+3/yMF7re5+6C7D86ePTthFZHuo6QFaYS0N6v2mNnMEHwws1kp1j0X+IiZXQQcDRxLVDPqN7NpoSYyB9gdlt8FnAjsCk1lxwF7Y+V58XWSyn9exT5EmqKeoxE0e2SD806dzTcf35FYLlKttDWhLwP/ZGZfMLMvAP8E/EWpFdz9anef4+7ziBILHnH3TwCPAh8Niy0HvhuerwuvCe8/Evpq1gGXh8y2+cDJwI+AJ4GTQybc9LCPdWGdSvch0nD17NjPIkng0eeTm6aLlYukkSoIufudwO8CrwKvAZe4+99Uuc8/BT5vZtuJ+mNuD+W3A+8I5Z/nSELCVuAe4FngB8Bn3H081HI+C6wnyr67Jyxb8T5EmqGeoxFkMbKB+oSkEUo2qZnZse7+i9D89s/At2LvzXL3VE1Z7v73wN+H5z8DzklY5k3g0iLrf5Eow66w/EHgwYTyivchnS+LgTnj6nkRzyIgnNDfl5gdpz4hqUW5fp1vAR8GNjK5A9/C65MadFwidVXvgTnzAW14ZJReM8bdGSgT2Op5Ec8iIKxcfMqkzxA074/UrmRznLt/2MwM+E13Pyn2N9/dFYCkbdSz+SreHwMwHroVy/XLlBuNoJL9v/HWoSnltQaEcqMhlLqRVaRaqW5WDTcdnd2E42k5ulm1M8xf9cDUXPxgoL+P806dzaPP70nVVFfsps349ordvFlrk2BhjS5v5owc1158ek2Zdkm1HAUZqVbam1XTpmg/bma/7u5P1nhcIpko1nwFUQ0mnnpcrqmuXL9LqfdrnbwtqUYHMGP6tLpvN19TVBCSRkqbon0eUSD6qZk9Y2ZbzOyZRh6YSD0lNYWVUqqprly/SyP7ZRqVkKDMN8lK2iB0IVESwvlE4799ODyKtIV4f0ZaxS7ApQJaozvqGzVqgUZDkKyUDEJmdrSZXQmsJBrBetjdX87/NeUIRepk2cIBHlt1fupAVOwCXBjQei0aDaoZHfX1Sm6Ia1Sig0gaJRMTzGwNMAb8A1Ft6GV3/1yTjq0lKDGhNVXSwV+47Hmnzua+jcOJfSt5+U55INN7i5LUe+ifRiQ6iKRNTCgXhLa4+xnh+TTgR+5+Vv0Os/UpCLWepAtn/sa1wnt1imV9/e7ZA5Oy4ZKy44COzxjT9AzSKPXKjhvLPwlz89R8YCJQ26/5pEyu/E+pwsy2Yllfjz6/p+xF9tzVjzQ8Y6yTRnEolPW5Za3bzz+tckHoTDP7RXhuQF94bYC7+7ENPTrpSLWOXlDuAhkPFLVcZBt1gY6PtpCvwUHtozhUo1EjL9R7hIp20+3nX4lyIyb0uvux4e/t7j4t9lwBSKpS6+gFaS6Q+UBRS9ZXIzLGCkdbKGwMb/QgpIUakegA2Qyw2kq6/fwrkfZmVZG6qaSGkdSkkTSGWaF8oCg33lmpJpNGjJVW7GbTuFKfQyVj1SWtX3ie+XXr3WzU7fcddfv5V0JBSGpWadt32iagYk0aN15yBjdeckZikxZMDhSlLrLlmkwacYFOcxEq9zkUjlUXP88kac6z3k1E3T7idreffyUUhKQm1bR9p61hlGrSeGzV+ZMy4EoFimIX2TRD1dT7Al1q+CCIOlsLZyotVXtKkyiRxZA83T7idreffyUUhKQmaS5wSUEiX5OJp0jftH4bV63ZPLFM2iaNagNFse2XChJ51WY+lWtKdOC+jcMMvmfWxPYqHauu8NhKjZl37upHGpK11ahmvnbR7edfiYYFITM7GvhfwFFhP/e6+7Vhiu67gVnAU8Dvu/tBMzsKuBM4G/gX4DJ3fyls62rgU8A48B/cfX0oXwJ8BegFvubuq0N5xfuQ6pQLFKWa1PIp0sWW6Z+RY9+BsSnbrleTRrELtIVjKnXza7WZT4UXp57QvxNXGMTL1Z7in0fSsRU2V8Y1MmurEc187aTbzz+ttGPHVeMt4Hx3PxNYACwxs0XAl4Cb3f1kYB9RcCE87nP3XwNuDsthZqcBlwOnEw0d9FUz6zWzXuCviEZyOA34eFiWSvch1SuXQZYmS6jYMu40JHMLoov1yIGDie95OKYk16zdwpVrNqfKfCo2P09++KAXV3+Iw0VuFo8H93Jj1Z136uyJ/fzxPU8n3kNV6g4/ZW1JlhoWhDzyy/AyF/6caBDUe0P5HcCy8HxpeE14/4NhQr2lwN3u/pa7vwhsJ5q6+xxgu7v/zN0PEtV8loZ1Kt2HVKlcim+aJrViy+wfHUs1iVqxi32p8pX3Ps0bB4tnqSUd0zVrt0ya8qHUOvFUbKf4hHfFgniP2cRxA4lj1fWaMTo2zl2P75jYT2GtKi8/mkSaY2+GchPoSfdoaJ9QqK1sBH6NqNbyU2DE3fOjJe4C8leUAWAnTIzOsB94Ryh/PLbZ+Do7C8rfF9apdB8/r/lku1S5tu80WUKllinXpFGsaWzo5b2TxoeLNzvdtH4bY+OlJ3NMCg7ffmJnwpLJ6xSr3V3/d1tTjWNXmAFXqvmy/LSUR4bhKTZMTzOztnQjp8Q1NAi5+ziwwMz6gb8F/vekxcJjUo2kWEuCk1yLK7V8qX1MYmYrgBUAc+fOTVhF4koFijRZQrVkEhW72H/riR0cLviXzTc7lfvVX7jvfEd/sVpGXj6rbe2m4aJ9OPsOjE30c+Un0+vL9TBzRo6RA2NF+4jiwStpmVJyPcaBg4eYv+oB+mfkyPUYY7EPp5FZW0kJHJpAT+Kakh3n7iNm9vfAIqDfzKaFmsocYHdYbBdwIrArDJZ6HLA3Vp4XXyep/OdV7KPweG8DboNoANMaTr3rpckSSptJlHRBKxZQCgNQXrmMsV6zSU1+xUaZTvLo83smlq/E6NhhwLj5sgVctWZz4jLx4FVJAJqR62HssE+su+/AGLleo78vx/7RsYZmbRWr8RT7LHUjZ3cqOYp2TRs2mw2MhQDUBzxElAiwHLjP3e82s78GnnH3r5rZZ4Az3P0Pzexy4BJ3/5iZnQ58i6gP6ATgYeBkolrNT4APAsPAk8DvuftWM/tOJfsodR4aRbs1FBsN++hcT2IGXTH5UQZW3vv0lCa5HoNjj558cc7fEJuGUT6TrdyxQboU8bR6i9SamjFKdrGmvyyPSZon7SjajcyOOx54NEwD/iSwwd2/B/wp8Hkz207UH3N7WP524B2h/PPAKgB33wrcAzwL/AD4jLuPh1rOZ4H1wHPAPWFZKt2HtL5SGXRp5Zudli0c4KaPnsnMGbmJ92bkeug1Y2R0bFIiQSUB4YT+vpK/5vv7ckXfg6gmUOk05OUUqzU1o9ZRbB/j7g3LepT207DmOHd/BliYUP4zolpNYfmbwKVFtvVF4IsJ5Q8CD9ZjH9LaSmXQ9fflGBmdWhvq78txzFHTEpv4Cvuxkn61j46NF/3VXnjvjcGkcd0K5WtgpZqjjuvLJTZNvvHWocTz6zXjsDsn9Pdx4OChxBphseNJk4hQ61QExWqFA7Fapm7kFI2YIG3huCKB5ri+HNd95PTESe5GRsd4/c1DqbLHSv1qL+zIB+jL9XBUrpd9B8YmBaSkC378V76VOJo3Dh6auEk2fkEu1hRZru8qP3lfYfZdmlpHPTLYSiWcdNqNnJo7qHoKQtIWit3NZTY5saFwhIByg33mLx7FQkN/X443Dh6aUn5g7DCOMbPIqA7xWsrkWVoPFz3HsXEvOtxR/4wcR03rKZpMUCq5Y/A9syq+QNYjg61bhq5RynltGpaY0CmUmNAa5q96IDFQGPDi6g9NvC7WGZ4X7/wul/lWTeJDtcdVuF6a2k8jpf28RVOkF1Ov6b1FqlJt80Sx9Yr1L/TPmNzZn2awz/jcPMX09+Uwo6oABFP7XNImOKQZ7qgZQUhTEaSnuYNqoyAkdVdt80TSeivvfZrr1m1N7A+CKEjMW/XARGd3uRTp4/pyqe77eevQ4VT3BiVJutm11CCi8fXy48AVO4dyF7Z69U1oKoL0FLBroyAkqVRycav2V3zSemPjXjQAxeUD1rSe4kMB5nqN/W+OlU3rtnC81brxkjOAqJkmP8JBuQA0UGIIn7hSF7Za+yYK/41/9+wBHn1+T0f359SDAnZtFISkrEovbtXO01Nr88XYuE+5ATVeAyk3XlxemqVKpWIDkz6vUiMcxEPmA8+8UjIA5Xqt5IWtWPD/43uenjRPU9K/WdK/8X0bh2vug+qGrLFuScBolEberCodIs10DHHFfq3n5+kpphHNF41IuzHgyx87s+gNl6VmQk06vvzNsWX7n8qcTKk081IjeUPl/8ZppB1JvBMsWzgw0Ry8e2SUm9Zv68jzbAQFISmr0v6JlYtPKTpSbKmLWr1HC6hWiRa98H60QLFpJhrVIT122Cd9foXTIRxXZkQGKB5YGtG53ojA1qq6KeDWm4KQlFRNzWXZwoGiP9pLXdSWLRyYdGE/Zno2AanY4Kd54+4TzZGPrTqfmy9bAMBVazanDgbVKpyxNn7Re+PgIXLlIijJ/wblJies5VjTlrezbgq49aYgJCVdt25r0feS+ifyv86LKXdRy886evNlC3izxI2djdBbwfyGo2PjXLduKwuuf4gr12yeFAzSJFJUq1QK99i487ajp00E8WLnk/RvUG5ywlqONW15O+umgFtvSkyQkkpdUJOmWiiV/pzrKd2xXridSqYsqIdK99fIYJMkzYy1IwfG2PSfLgCKD+WT9G/QiM71bsoa67Q07WYmlCgISdXmr3pg0he0XId8rte4af22splalXTs10uPlW+Gy0J+srtqZqytNLDUezy3Svbf7ll0nRRwmz0MkYbtKaPbh+1ZeMNDZbO2cr3GMdOnVVwzyA+wWXgvylVrNjckq63d9Jrx5Y+dmSqlGqKsvU8smsufLzujiUdZu6yHKKqXdg+kefUahijtsD0KQmV0exBau2k4cQK4RkozukCW2yx2j1Aj5C/GMLVGMfTyXu56fMek88r1GG87ehr7DoxNHOdAi18QNfZaa6nXuIEaO07qorBJpRmX3kbso17bHCgzcV29jY6Nc/3fbeXNscNTmkeOzvVMOa/4VN7lRhCH1vj1rk791tLs/i1lx0lZ+Yy1F1d/aGJEgDTy2VkD/X2TZjFtZ8PhYt1M+w6MJab/VjK4alK6cKvc29JNWXTtoBGZkqU0LAiZ2Ylm9qiZPWdmW83sc6F8lpltMLMXwuPMUG5mdouZbTezZ8zsrNi2loflXzCz5bHys81sS1jnFrPoqlfNPiSdSm4o/ZXjjuYvL1vAY6vO59qLT2+JG1FrZcB5p85uy3MprFm0yr0tzb7oSWmF9+vFb8RuhIb1CZnZ8cDx7v6Umb0d2AgsA/4A2Ovuq81sFTDT3f/UzC4C/gi4CHgf8BV3f5+ZzQKGgEGiVpWNwNnuvs/MfgR8DnicaJrvW9z9+2b2F5Xso9R5dGqfUC3NMIWTrf3yzUNTZh7Ny/UY06f18MbB6GJnBu7RF/vVX7zJoVZMSUthRq6H0bHDE81h03sNdyZ9Drke49Bhr6kpsFTSR1+uhzdjx1BOYR9LK80Z1ArNglJfafuEGlYTcvdX3P2p8Px14DlgAFgK3BEWu4MoMBHK7/TI40B/CGSLgQ3uvtfd9wEbgCXhvWPd/YceRdI7C7ZVyT66SlIzzJVrNrPg+ofKNsUUXiyuvfh0brr0zKLNdGOHfSIAQRSAcj3GeafOTh2A8tuO33ppwP+xaC4zctm0KB8ouPgfHHfGDvvEDLC9Zlx2zom178jhw2ceP6WmUCzAFbvdNqlm0UrNYPEm38dWnZ9JACocBklD7jRHU/4Hm9k8YCHwBPBud38FokAFvCssNgDsjK22K5SVKt+VUE4V++gqxe7DGRkdK9knUKwPYejlvRXtf+ywc9fjO1Ivv3LxKeR6J0+H4MA3H9/BgSaPqlBOvmFh3J37Ng7XPITP2GHn0ef3TGkeedvR0xIzFpPC+swZucTmlGY3g9VykW90gGiV/rFu1PDsODN7G3AfcKW7/8KBZelUAAATUklEQVSKD41SbMzLSstLHk6adcxsBbACYO7cuWU22X5KZR2VmvenWB/CNysIKHmVNFHdtH5bU1PE62V0bLwuN93uHhmdciPp/FUPpF5/5MAYV63ZzE3rt01q5mrmFAS13ADZjJsns57Jtps1tCZkZjmiAHSXu98fil/NN4GFx9dC+S4g3n4xB9hdpnxOQnk1+5jE3W9z90F3H5w9e3b6E24T5ZpbCoNU/ldo2imq6y2r/baKpH+vwmnNS4lPF3Hlms0svOFIs2uzmsFqSYJoRgKF0sSz08jsOANuB55z9/8ae2sdkM9wWw58N1Z+RchgWwTsD01p64ELzGxmyHK7AFgf3nvdzBaFfV1RsK1K9tFVymW4OUw0ecSbKVpZJYOPtpOk5rG1m4b55ZuHqt7mvgOlm10bodREhwtveIh5qx5g3qoHEvslq53qvBKt1D/WbRrZHHcu8PvAFjPbHMr+H2A1cI+ZfQrYAVwa3nuQKGttO3AA+CSAu+81sy8AT4blbnD3fCfEp4FvAH3A98Mfle6j2+R/7V7/d1uL3msSvyGy2eO4VaPZg502y++ePXU8t5vWb0vMRsz1QNousnhTUzMy04rdAAlM+g6OjI6x8jtPA0wcW7HRLuoZIDpp7Ld2o2F7yujUFO28/AWo1Ws63Spp/LhiqdUQzcEUz0YsxYCbL1vQlHHbio11V+w88unkxZqB88de72NUmnj9aOy4Oun0IJRX6sIm2evvy3HdR05n2cKBsv1zacfJy6e+J21r5owcM6ZPq+iCXO4iXvh+uXN4cfWHSn4vX2ryvUxSGY0dJxU5ri/X9PlxJL2R0TE+v2Yzn79nc9kpJ/Kpo04UTN4cG2e0oJ0u39R01ZrNSZtg34GxiWayNNloaTLYCjP8SgXTfFNbsWBVyfBR0to0dlwXKrzn4pq1W3jjYPUd3dIch0k/51E+AM2YPo03xw7T35dj5ozclGFY0varlMtGK5bBduWazcxb9cCkjLy8/P1fhfI3M+eDVOES6qvpLKoJdZlr1m6ZNPz/8MjolOkApDPEazMjo2NTmunWbhrmjbfS//iIZ6NV0rSWP5aV9x5JOIg/xhNk+vtyfPjM47lv4/BEUIvX7Fp9WgqpnIJQF1m7aTgx4CgAdYf4D4+V33kajCk3Ac+ckcM9eeryfK0pqektTT/U2LhPufkzaTbXc1c/MqVWlQ9Aml+o8ygIdbj4L9YeMwWcDpPPnivMPCun2ICzEI1VV/hjJd4EltT0lvZ7lebeHt042l3UJ9TBCsfD6tR7abrZopNmFh0LsBr7Doyx5smdk4KKMfl+pVqCQZo+KN042l0UhDpYPS9O0pp+9NK+ut/jVdhE58Cjz++ZeF1LMEiTUKD5hbqLglAHU/NF50s7sGthhlmup7JhjuLfpWJBotzsuX25nlQJBc2eVE2ypT6hDpYma0k630urP8Q1a7fw7Sd2Mu5Oj8G0XivZL1QoXvspNvo2ULRvKtdj3HjJe1PvLylhQTqTglAHSxoPS7rLMdN7WXD9Q5Oy3Q47U25eLSXXa1OawkoFifwwUL1mjLvTa1HAy99npOAicQpCHSzpF+veN96q6AIk7W304DhvUNuPkGOmT0sMHMWG6ckv24x5gKT9qU+oA8VHRMhPZPbi6g+xcvEpqafUlmz0WDRteb3U4+fG/oR7htLMRNqMeYCk/akm1KaSRr/uNWPRSTN5asf+Sb8+r1qzmaGX9/Lo83vacobSbnLYJ2eitYKkbLg0M5Hqfh9JQ0GoDSUNiw/RfUCP/XTvlOUdNDRPG2mlZJJiqdFpAkyxxBjd7yNxao5rQ9Xc/6MAJGlZ+CuVGp3mhtJ2uN+ncDDfZs42KxHVhNqQmjOk0V4sM1dPmplIi6Vyt0pSghInWkPDakJm9nUze83Mfhwrm2VmG8zshfA4M5Sbmd1iZtvN7BkzOyu2zvKw/AtmtjxWfraZbQnr3GJmVu0+2o2aM6SR0ny/0t5QumzhAI+tOp8XV3+Ix1ad31IXdyVOtIZGNsd9A1hSULYKeNjdTwYeDq8BLgRODn8rgFshCijAtcD7gHOAa/NBJSyzIrbekmr20U7yTQdJc6yIlNNr5b81lTSXtXKASUOJE62hYUHI3f8XUNhLvhS4Izy/A1gWK7/TI48D/WZ2PLAY2ODue919H7ABWBLeO9bdf+jR/OR3Fmyrkn20hXhKLKiPRyoz0N/Hlz92Ztlluml4HA2U2hqa3Sf0bnd/BcDdXzGzd4XyAWBnbLldoaxU+a6E8mr28UqtJ9UMGoxUSjGDUoOkHzh4iCuLTOUNpefqWbtpmOvWbZ0YdWHmjBzXXnx62werNP1a0nitkpiQ1E7gVZRXs4+pC5qtIGqyY+7c+t04WAs1EUgpJxwX/Xovlt6dn7m0mAMHD3HN2i08+vyeKePBrfzO05PGmUuaJbUdtXriRLdodhB61cyODzWU44HXQvku4MTYcnOA3aH8twrK/z6Uz0lYvpp9TOHutwG3AQwODjat5St+A2p+3K38dMb9M3JlLyTSvYZHRunvy5HrtUk3JKeZ8RSiwPLNx3dM2t7V92/hqGk9iQOdJs2S2o40UGr2mn2f0Dogn+G2HPhurPyKkMG2CNgfmtTWAxeY2cyQkHABsD6897qZLQpZcVcUbKuSfbSEwj6f/AR0wyOjXLlmswKQlDUyOgYeDVqaV8svqNGx8cRpvvNUO5d6aFhNyMy+TVSLeaeZ7SLKclsN3GNmnwJ2AJeGxR8ELgK2AweATwK4+14z+wLwZFjuBnfPJzt8migDrw/4fvij0n1kqXDqbc18KrUaO+wcOticvkN14Es9mOvCV9Lg4KAPDQ3VfbvFht4RSTJzRo4Z06exe2SU4/pymJXv56nXfn/55qEpTXK5XuOmj56ppiwpysw2uvtgueVaJTGh6yjbTSqx78AYm/7TBZPK8veMVaNYcInry/Vy7cWnA3Rkdpy0BgWhjKg9XSqRdKNpUopxuUSEeCp24XxA5506e0p2XD7QKOBIoygIZaTYCMPl7veQ7pTUX5iUYnzeqbO5b+NwYi07aWy3ZQsHJoLRXY/v4IT+Pm6+bIGCjjSNglBGkn7F9vYY45p0rqOlTZkuNHNGbuJ5sRlN8wbfM6toqn9hcNEgnpI1BaGMFP6KnTG9lzealNUk2RooUgsu5ZdvHpqYZqBc0Kjk3pc0k9OJNJKCUJMV/oqd944+do+MKgB1ieP6com14HLGDjt/fM/Tic1ytQQNDeIpWVMQaqKkpo9WmkVTGs/sSI2lWFApptSy1QYNzX4qWdPMqk2ktGzJ39uzbOEAX/7YmYkzj8b7f9KqNmi0w+yn0tlUE2oiNXFIPNW62ACaQEXNdbUEDQ3iKVlTEGqiYk0f0j0Km9RKJRGkGdKpWNZbJTSIp2RJQagJ1m4a5vq/26pBSIWBlM1m8cCQNMRTX663qyagk86lINRgn/jvP+SxnxZOMCud5pjpvRx2SjahVdtspiYz6WQKQg10zdotCkAdIl/zGHp576R5dyAazPOL//YMYOroBcWGwamUmsykUykINdC3nthRfiFpeb1mE01fyxYOTIxIoDHWRGqnINQA+RtSNQJP+0vqe1GtRKR+FITqTPMEtY8ewBLG65sepshW34tI4ykI1ZluSG0dPQa9BmOHo9fHTO8l19vD/tGxSffkqMNfJDtdF4TMbAnwFaAX+Jq7r67n9nVDamOZwQdOmsVL/zLK7pFR+mfkeGtsnAMh0vT35bjuI5VNuKagI5KdrgpCZtYL/BXwO8Au4EkzW+fuz9ZrH7ohNZ1cr3HZr59Yt+wxEWlPXRWEgHOA7e7+MwAzuxtYCtQtCFUzQnKn6TE47NGNmfk05TRz24hI9+m2IDQA7Iy93gW8r547SLqx8I23DjEy2r6jJZz7q7O46/96P1B+QjURkUp0WxCyhLIpidRmtgJYATB37tyKd1KYwpt1xlx+Ns/41OEzZ+S49uLK+k5A6ckiUl/dFoR2ASfGXs8Bdhcu5O63AbcBDA4O1ny3T2Ht6Li+HAcPHelMhyhQTOs5ksmVhpq1RKTddVsQehI42czmA8PA5cDvNWPHqkGIiEzVVUHI3Q+Z2WeB9UQp2l93960ZH5aISNfqqiAE4O4PAg9mfRwiIqLpvUVEJEMKQiIikhkFIRERyYx5kbnrJWJme4CXE956J/DzJh9OK9LncIQ+iyP0WRzRrZ/Fe9x9drmFFISqZGZD7j6Y9XFkTZ/DEfosjtBncYQ+i9LUHCciIplREBIRkcwoCFXvtqwPoEXoczhCn8UR+iyO0GdRgvqEREQkM6oJiYhIZhSEKmRmS8xsm5ltN7NVWR9PI5jZiWb2qJk9Z2ZbzexzoXyWmW0wsxfC48xQbmZ2S/hMnjGzs2LbWh6Wf8HMlmd1TrUws14z22Rm3wuv55vZE+Gc1pjZ9FB+VHi9Pbw/L7aNq0P5NjNbnM2Z1MbM+s3sXjN7Pnw33t/F34mrwv+NH5vZt83s6G79XtTM3fWX8o9o0NOfAicB04GngdOyPq4GnOfxwFnh+duBnwCnAX8BrArlq4AvhecXAd8nmpFiEfBEKJ8F/Cw8zgzPZ2Z9flV8Hp8HvgV8L7y+B7g8PP9r4NPh+b8H/jo8vxxYE56fFr4rRwHzw3eoN+vzquJzuAP4d+H5dKC/G78TRJNjvgj0xb4Pf9Ct34ta/1QTqszE9ODufhDITw/eUdz9FXd/Kjx/HXiO6D/eUqILEeFxWXi+FLjTI48D/WZ2PLAY2ODue919H7ABWNLEU6mZmc0BPgR8Lbw24Hzg3rBI4eeQ/3zuBT4Yll8K3O3ub7n7i8B2ou9S2zCzY4HfAG4HcPeD7j5CF34ngmlAn5lNA2YAr9CF34t6UBCqTNL04B09SVBoOlgIPAG8291fgShQAe8KixX7XDrh8/pL4D8C+ekG3wGMuPuh8Dp+ThPnG97fH5bvhM/hJGAP8D9C0+TXzOwYuvA74e7DwH8BdhAFn/3ARrrze1EzBaHKpJoevFOY2duA+4Ar3f0XpRZNKPMS5W3BzD4MvObuG+PFCYt6mffa+nMIpgFnAbe6+0LgDaLmt2I69rMI/V5LiZrQTgCOAS5MWLQbvhc1UxCqTKrpwTuBmeWIAtBd7n5/KH41NKkQHl8L5cU+l3b/vM4FPmJmLxE1vZ5PVDPqD80wMPmcJs43vH8csJf2/xwgOodd7v5EeH0vUVDqtu8EwG8DL7r7HncfA+4HPkB3fi9qpiBUmYnpwUPmy+XAuoyPqe5Ce/XtwHPu/l9jb60D8tlMy4HvxsqvCBlRi4D9oWlmPXCBmc0Mvx4vCGVtwd2vdvc57j6P6N/6EXf/BPAo8NGwWOHnkP98PhqW91B+eciSmg+cDPyoSadRF+7+z8BOMzslFH0QeJYu+04EO4BFZjYj/F/JfxZd972oi6wzI9rtjyjr5ydEmSx/lvXxNOgc/zVRs8AzwObwdxFRO/bDwAvhcVZY3oC/Cp/JFmAwtq3/k6jDdTvwyazPrYbP5Lc4kh13EtHFYjvwHeCoUH50eL09vH9SbP0/C5/PNuDCrM+nys9gATAUvhdribLbuvI7AVwPPA/8GPgbogy3rvxe1PqnERNERCQzao4TEZHMKAiJiEhmFIRERCQzCkIiIpIZBSEREcmMgpCIiGRGQUgkQ+FGxf9pZpvN7DIzu9LMZlS5rZfM7J31PkaRRppWfhERaaCFQM7dF0AUSIBvAgeyPCiRZlFNSKTOzOwYM3vAzJ4Ok55dZtFkiM+b2T+Gyd6+Z2bvIgo4C0JN6HNEA2I+amaPltj+rWY2FCZVu77g7ZVm9qPw92th+feY2cNhcrmHzWyumR0Xak49YZkZZrbTzHJm9qtm9gMz22hm/2BmpzbooxJREBJpgCXAbnc/093/FfAD4L8DFwP/BvgVAHd/Dfh3wD+4+wJ3/wrRAJbnuft5Jbb/Z+4+CLwX+E0ze2/svV+4+znAfyMabJXw/E53fy9wF3CLu+8nmlDtN8MyFwPrPRqQ8zbgj9z9bOBPgK/W8mGIlKIgJFJ/W4DfNrMvmdm/IRry/0V3f8GjcbK+WeP2P2ZmTwGbgNOJZujM+3bs8f3h+fuJZoaFaJyzfx2erwEuC88vB9aE6Ts+AHzHzDYD/x/RTLsiDaE+IZE6c/efmNnZRIO+3gg8RJ3miQmjLf8J8Ovuvs/MvkE0QObE7os8J6F8HXCjmc0CzgYeIZobZyTfRyXSaKoJidSZmZ0AHHD3bxLNwPkBYL6Z/WpY5OMlVn8deHuJ948lmlBuv5m9m6mTqV0We/xheP5PRDUdgE8A/wjg7r8kGtX5K0QjhI97NHnhi2Z2aTgXM7MzS52vSC1UExKpvzOAm8zsMDAGfBp4J/CAmf2cKAj8qyLr3gZ838xeSeoXcvenzWwTsBX4GfBYwSJHmdkTRD8w88HuPwBfN7OVRFN0fzK2/BqiaQZ+K1b2CeBWM7sGyBFN6Pd0mhMXqZSmchBpMjP7LeBP3P3DWR+LSNbUHCciIplRTUikRYVmtaMKin/f3bdkcTwijaAgJCIimVFznIiIZEZBSEREMqMgJCIimVEQEhGRzCgIiYhIZv5/ijc/uBuE8O8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b41014860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b4104efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b410282b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b4077d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b4119fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b4037f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b4123a7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b40cf1f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_features_target(top_5_features,target,df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[top_5_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>sqft_living</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>bathrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1180</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>1340</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2570</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>1690</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>770</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>2720</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1960</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>1360</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1680</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>1800</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5420</td>\n",
       "      <td>11</td>\n",
       "      <td>3890</td>\n",
       "      <td>4760</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1715</td>\n",
       "      <td>7</td>\n",
       "      <td>1715</td>\n",
       "      <td>2238</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1060</td>\n",
       "      <td>7</td>\n",
       "      <td>1060</td>\n",
       "      <td>1650</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1780</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>1780</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1890</td>\n",
       "      <td>7</td>\n",
       "      <td>1890</td>\n",
       "      <td>2390</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3560</td>\n",
       "      <td>8</td>\n",
       "      <td>1860</td>\n",
       "      <td>2210</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1160</td>\n",
       "      <td>7</td>\n",
       "      <td>860</td>\n",
       "      <td>1330</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1430</td>\n",
       "      <td>7</td>\n",
       "      <td>1430</td>\n",
       "      <td>1780</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1370</td>\n",
       "      <td>7</td>\n",
       "      <td>1370</td>\n",
       "      <td>1370</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1810</td>\n",
       "      <td>7</td>\n",
       "      <td>1810</td>\n",
       "      <td>1360</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2950</td>\n",
       "      <td>9</td>\n",
       "      <td>1980</td>\n",
       "      <td>2140</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1890</td>\n",
       "      <td>7</td>\n",
       "      <td>1890</td>\n",
       "      <td>1890</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1600</td>\n",
       "      <td>7</td>\n",
       "      <td>1600</td>\n",
       "      <td>1610</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1200</td>\n",
       "      <td>7</td>\n",
       "      <td>1200</td>\n",
       "      <td>1060</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1250</td>\n",
       "      <td>7</td>\n",
       "      <td>1250</td>\n",
       "      <td>1280</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1620</td>\n",
       "      <td>7</td>\n",
       "      <td>860</td>\n",
       "      <td>1400</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3050</td>\n",
       "      <td>9</td>\n",
       "      <td>2330</td>\n",
       "      <td>4110</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2270</td>\n",
       "      <td>8</td>\n",
       "      <td>2270</td>\n",
       "      <td>2240</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1070</td>\n",
       "      <td>7</td>\n",
       "      <td>1070</td>\n",
       "      <td>1220</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2450</td>\n",
       "      <td>8</td>\n",
       "      <td>2450</td>\n",
       "      <td>2200</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1710</td>\n",
       "      <td>6</td>\n",
       "      <td>1710</td>\n",
       "      <td>1030</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2450</td>\n",
       "      <td>8</td>\n",
       "      <td>1750</td>\n",
       "      <td>1760</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1400</td>\n",
       "      <td>8</td>\n",
       "      <td>1400</td>\n",
       "      <td>1860</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1520</td>\n",
       "      <td>7</td>\n",
       "      <td>790</td>\n",
       "      <td>1520</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2570</td>\n",
       "      <td>8</td>\n",
       "      <td>2570</td>\n",
       "      <td>2630</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21583</th>\n",
       "      <td>710</td>\n",
       "      <td>7</td>\n",
       "      <td>710</td>\n",
       "      <td>1370</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21584</th>\n",
       "      <td>1260</td>\n",
       "      <td>7</td>\n",
       "      <td>940</td>\n",
       "      <td>1310</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21585</th>\n",
       "      <td>1870</td>\n",
       "      <td>7</td>\n",
       "      <td>1870</td>\n",
       "      <td>2170</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21586</th>\n",
       "      <td>1430</td>\n",
       "      <td>8</td>\n",
       "      <td>1430</td>\n",
       "      <td>1430</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21587</th>\n",
       "      <td>1520</td>\n",
       "      <td>8</td>\n",
       "      <td>1520</td>\n",
       "      <td>1520</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21588</th>\n",
       "      <td>1210</td>\n",
       "      <td>8</td>\n",
       "      <td>1020</td>\n",
       "      <td>1210</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21589</th>\n",
       "      <td>2540</td>\n",
       "      <td>8</td>\n",
       "      <td>2540</td>\n",
       "      <td>2540</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21590</th>\n",
       "      <td>4910</td>\n",
       "      <td>11</td>\n",
       "      <td>3110</td>\n",
       "      <td>4560</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21591</th>\n",
       "      <td>2770</td>\n",
       "      <td>8</td>\n",
       "      <td>2770</td>\n",
       "      <td>1810</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21592</th>\n",
       "      <td>1190</td>\n",
       "      <td>8</td>\n",
       "      <td>1190</td>\n",
       "      <td>1180</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21593</th>\n",
       "      <td>4170</td>\n",
       "      <td>10</td>\n",
       "      <td>4170</td>\n",
       "      <td>3030</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21594</th>\n",
       "      <td>2500</td>\n",
       "      <td>8</td>\n",
       "      <td>2500</td>\n",
       "      <td>2530</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21595</th>\n",
       "      <td>1530</td>\n",
       "      <td>8</td>\n",
       "      <td>1480</td>\n",
       "      <td>1530</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21596</th>\n",
       "      <td>3600</td>\n",
       "      <td>9</td>\n",
       "      <td>3600</td>\n",
       "      <td>3550</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21597</th>\n",
       "      <td>3410</td>\n",
       "      <td>10</td>\n",
       "      <td>3410</td>\n",
       "      <td>2290</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21598</th>\n",
       "      <td>3118</td>\n",
       "      <td>9</td>\n",
       "      <td>3118</td>\n",
       "      <td>2673</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21599</th>\n",
       "      <td>3990</td>\n",
       "      <td>9</td>\n",
       "      <td>3990</td>\n",
       "      <td>3370</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21600</th>\n",
       "      <td>4470</td>\n",
       "      <td>11</td>\n",
       "      <td>4470</td>\n",
       "      <td>2780</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21601</th>\n",
       "      <td>1425</td>\n",
       "      <td>8</td>\n",
       "      <td>1425</td>\n",
       "      <td>1285</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21602</th>\n",
       "      <td>1500</td>\n",
       "      <td>6</td>\n",
       "      <td>1500</td>\n",
       "      <td>1320</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21603</th>\n",
       "      <td>2270</td>\n",
       "      <td>8</td>\n",
       "      <td>2270</td>\n",
       "      <td>2270</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21604</th>\n",
       "      <td>1490</td>\n",
       "      <td>8</td>\n",
       "      <td>1490</td>\n",
       "      <td>1400</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21605</th>\n",
       "      <td>2520</td>\n",
       "      <td>9</td>\n",
       "      <td>2520</td>\n",
       "      <td>2520</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21606</th>\n",
       "      <td>3510</td>\n",
       "      <td>9</td>\n",
       "      <td>2600</td>\n",
       "      <td>2050</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21607</th>\n",
       "      <td>1310</td>\n",
       "      <td>8</td>\n",
       "      <td>1180</td>\n",
       "      <td>1330</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>1530</td>\n",
       "      <td>8</td>\n",
       "      <td>1530</td>\n",
       "      <td>1530</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>2310</td>\n",
       "      <td>8</td>\n",
       "      <td>2310</td>\n",
       "      <td>1830</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>1020</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>1020</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>1600</td>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>1410</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>1020</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>1020</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21613 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sqft_living  grade  sqft_above  sqft_living15  bathrooms\n",
       "0             1180      7        1180           1340       1.00\n",
       "1             2570      7        2170           1690       2.25\n",
       "2              770      6         770           2720       1.00\n",
       "3             1960      7        1050           1360       3.00\n",
       "4             1680      8        1680           1800       2.00\n",
       "5             5420     11        3890           4760       4.50\n",
       "6             1715      7        1715           2238       2.25\n",
       "7             1060      7        1060           1650       1.50\n",
       "8             1780      7        1050           1780       1.00\n",
       "9             1890      7        1890           2390       2.50\n",
       "10            3560      8        1860           2210       2.50\n",
       "11            1160      7         860           1330       1.00\n",
       "12            1430      7        1430           1780       1.00\n",
       "13            1370      7        1370           1370       1.75\n",
       "14            1810      7        1810           1360       2.00\n",
       "15            2950      9        1980           2140       3.00\n",
       "16            1890      7        1890           1890       2.00\n",
       "17            1600      7        1600           1610       1.00\n",
       "18            1200      7        1200           1060       1.00\n",
       "19            1250      7        1250           1280       1.00\n",
       "20            1620      7         860           1400       1.75\n",
       "21            3050      9        2330           4110       2.75\n",
       "22            2270      8        2270           2240       2.50\n",
       "23            1070      7        1070           1220       1.50\n",
       "24            2450      8        2450           2200       2.25\n",
       "25            1710      6        1710           1030       2.00\n",
       "26            2450      8        1750           1760       1.75\n",
       "27            1400      8        1400           1860       1.00\n",
       "28            1520      7         790           1520       1.75\n",
       "29            2570      8        2570           2630       2.50\n",
       "...            ...    ...         ...            ...        ...\n",
       "21583          710      7         710           1370       1.00\n",
       "21584         1260      7         940           1310       2.50\n",
       "21585         1870      7        1870           2170       2.50\n",
       "21586         1430      8        1430           1430       2.50\n",
       "21587         1520      8        1520           1520       2.50\n",
       "21588         1210      8        1020           1210       1.50\n",
       "21589         2540      8        2540           2540       2.50\n",
       "21590         4910     11        3110           4560       3.50\n",
       "21591         2770      8        2770           1810       2.75\n",
       "21592         1190      8        1190           1180       2.25\n",
       "21593         4170     10        4170           3030       3.75\n",
       "21594         2500      8        2500           2530       2.75\n",
       "21595         1530      8        1480           1530       2.25\n",
       "21596         3600      9        3600           3550       2.75\n",
       "21597         3410     10        3410           2290       3.25\n",
       "21598         3118      9        3118           2673       2.50\n",
       "21599         3990      9        3990           3370       3.00\n",
       "21600         4470     11        4470           2780       3.75\n",
       "21601         1425      8        1425           1285       2.50\n",
       "21602         1500      6        1500           1320       1.75\n",
       "21603         2270      8        2270           2270       2.50\n",
       "21604         1490      8        1490           1400       2.00\n",
       "21605         2520      9        2520           2520       2.50\n",
       "21606         3510      9        2600           2050       3.50\n",
       "21607         1310      8        1180           1330       2.50\n",
       "21608         1530      8        1530           1530       2.50\n",
       "21609         2310      8        2310           1830       2.50\n",
       "21610         1020      7        1020           1020       0.75\n",
       "21611         1600      8        1600           1410       2.50\n",
       "21612         1020      7        1020           1020       0.75\n",
       "\n",
       "[21613 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df.iloc[:,2].name\n",
    "y = df[[target]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
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       "    <tr>\n",
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       "      <td>538000.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1225000.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>257500.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>291850.0</td>\n",
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       "      <th>8</th>\n",
       "      <td>229500.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>323000.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>662500.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>468000.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>310000.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>400000.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>530000.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>650000.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>395000.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>485000.0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>189000.0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>230000.0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>385000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>285000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>252700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>329000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>233000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>937000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>667000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>438000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>719000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>21583</th>\n",
       "      <td>399950.0</td>\n",
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       "    <tr>\n",
       "      <th>21584</th>\n",
       "      <td>380000.0</td>\n",
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       "    <tr>\n",
       "      <th>21585</th>\n",
       "      <td>270000.0</td>\n",
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       "    <tr>\n",
       "      <th>21586</th>\n",
       "      <td>505000.0</td>\n",
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       "    <tr>\n",
       "      <th>21587</th>\n",
       "      <td>385000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21588</th>\n",
       "      <td>414500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21589</th>\n",
       "      <td>347500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21590</th>\n",
       "      <td>1222500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21591</th>\n",
       "      <td>572000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21592</th>\n",
       "      <td>475000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21593</th>\n",
       "      <td>1088000.0</td>\n",
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       "    <tr>\n",
       "      <th>21594</th>\n",
       "      <td>350000.0</td>\n",
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       "    <tr>\n",
       "      <th>21595</th>\n",
       "      <td>520000.0</td>\n",
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       "    <tr>\n",
       "      <th>21596</th>\n",
       "      <td>679950.0</td>\n",
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       "    <tr>\n",
       "      <th>21597</th>\n",
       "      <td>1575000.0</td>\n",
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       "    <tr>\n",
       "      <th>21598</th>\n",
       "      <td>541800.0</td>\n",
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       "    <tr>\n",
       "      <th>21599</th>\n",
       "      <td>810000.0</td>\n",
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       "    <tr>\n",
       "      <th>21600</th>\n",
       "      <td>1537000.0</td>\n",
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       "    <tr>\n",
       "      <th>21601</th>\n",
       "      <td>467000.0</td>\n",
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       "    <tr>\n",
       "      <th>21602</th>\n",
       "      <td>224000.0</td>\n",
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       "    <tr>\n",
       "      <th>21603</th>\n",
       "      <td>507250.0</td>\n",
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       "    <tr>\n",
       "      <th>21604</th>\n",
       "      <td>429000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21605</th>\n",
       "      <td>610685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21606</th>\n",
       "      <td>1007500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21607</th>\n",
       "      <td>475000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>360000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>402101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>325000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21613 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           price\n",
       "0       221900.0\n",
       "1       538000.0\n",
       "2       180000.0\n",
       "3       604000.0\n",
       "4       510000.0\n",
       "5      1225000.0\n",
       "6       257500.0\n",
       "7       291850.0\n",
       "8       229500.0\n",
       "9       323000.0\n",
       "10      662500.0\n",
       "11      468000.0\n",
       "12      310000.0\n",
       "13      400000.0\n",
       "14      530000.0\n",
       "15      650000.0\n",
       "16      395000.0\n",
       "17      485000.0\n",
       "18      189000.0\n",
       "19      230000.0\n",
       "20      385000.0\n",
       "21     2000000.0\n",
       "22      285000.0\n",
       "23      252700.0\n",
       "24      329000.0\n",
       "25      233000.0\n",
       "26      937000.0\n",
       "27      667000.0\n",
       "28      438000.0\n",
       "29      719000.0\n",
       "...          ...\n",
       "21583   399950.0\n",
       "21584   380000.0\n",
       "21585   270000.0\n",
       "21586   505000.0\n",
       "21587   385000.0\n",
       "21588   414500.0\n",
       "21589   347500.0\n",
       "21590  1222500.0\n",
       "21591   572000.0\n",
       "21592   475000.0\n",
       "21593  1088000.0\n",
       "21594   350000.0\n",
       "21595   520000.0\n",
       "21596   679950.0\n",
       "21597  1575000.0\n",
       "21598   541800.0\n",
       "21599   810000.0\n",
       "21600  1537000.0\n",
       "21601   467000.0\n",
       "21602   224000.0\n",
       "21603   507250.0\n",
       "21604   429000.0\n",
       "21605   610685.0\n",
       "21606  1007500.0\n",
       "21607   475000.0\n",
       "21608   360000.0\n",
       "21609   400000.0\n",
       "21610   402101.0\n",
       "21611   400000.0\n",
       "21612   325000.0\n",
       "\n",
       "[21613 rows x 1 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pawan\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15129, 5)\n",
      "(6484, 5)\n",
      "(15129, 1)\n",
      "(6484, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 602046.42522605],\n",
       "       [ 567886.49256366],\n",
       "       [ 156073.4693375 ],\n",
       "       ...,\n",
       "       [ 302937.4942582 ],\n",
       "       [1032540.95139979],\n",
       "       [1024657.82908011]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5309903819211692"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg.score(x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "x  = df.iloc[:,3:]\n",
    "y = df.iloc[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lg_all = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_all.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.705010002417501"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_all.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "min_max_scaler = MinMaxScaler(feature_range = (0,1))\n",
    "\n",
    "x_min_max_scaler = min_max_scaler.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = min_max_scaler.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.09090909, 0.125     , 0.06716981, ..., 0.21760797, 0.16193426,\n",
       "        0.00574235],\n",
       "       [0.09090909, 0.28125   , 0.17207547, ..., 0.16611296, 0.22216486,\n",
       "        0.00802712],\n",
       "       [0.06060606, 0.125     , 0.03622642, ..., 0.23754153, 0.3994149 ,\n",
       "        0.00851302],\n",
       "       ...,\n",
       "       [0.06060606, 0.09375   , 0.05509434, ..., 0.18272425, 0.10686629,\n",
       "        0.00155764],\n",
       "       [0.09090909, 0.3125    , 0.09886792, ..., 0.37375415, 0.17398038,\n",
       "        0.00073057],\n",
       "       [0.06060606, 0.09375   , 0.05509434, ..., 0.18272425, 0.10686629,\n",
       "        0.00081098]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg_fs = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_fs.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6887136045441988"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_fs.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = [[5,1,1180,5650,1,0,0,3,17,1180,0,1955,0,98178,47.5112,-122.257,1340,5650]]\n",
    "\n",
    "\n",
    "test2 = [[3,1,1180,5650,1,0,0,3,7,1180,0,1955,0,98178,47.5112,-122.257,1340,5650]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1061106.8726237])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_fs.predict(test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([204410.7841398])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_fs.predict(test2)  #221900"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LinearRegression' object has no attribute 'summary'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-76-45a877bb8691>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlg_fs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'LinearRegression' object has no attribute 'summary'"
     ]
    }
   ],
   "source": [
    "lg_fs.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "list = lg_fs.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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