task1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0         Lvl    AllPub  ...         120        0    NaN  MnPrv         NaN   \n",
       "1         Lvl    AllPub  ...           0        0    NaN    NaN        Gar2   \n",
       "2         Lvl    AllPub  ...           0        0    NaN  MnPrv         NaN   \n",
       "3         Lvl    AllPub  ...           0        0    NaN    NaN         NaN   \n",
       "4         HLS    AllPub  ...         144        0    NaN    NaN         NaN   \n",
       "\n",
       "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0       0      6    2010        WD         Normal  \n",
       "1   12500      6    2010        WD         Normal  \n",
       "2       0      3    2010        WD         Normal  \n",
       "3       0      6    2010        WD         Normal  \n",
       "4       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns\n",
    "df1 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SalePrice'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_df = pd.read_csv('sample_submission.csv')\n",
    "price_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=None, indicator=False, validate=None)[source]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=None, indicator=False, validate=None)[source]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df.merge(price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.merge(price_df, how='left', on='Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1461\n",
       "1       1462\n",
       "2       1463\n",
       "3       1464\n",
       "4       1465\n",
       "        ... \n",
       "1454    2915\n",
       "1455    2916\n",
       "1456    2917\n",
       "1457    2918\n",
       "1458    2919\n",
       "Name: Id, Length: 1459, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n",
    "df['Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.drop([ 'MSSubClass', 'MSZoning', 'LotFrontage',  'Street',\n",
    "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
    "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
    "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
    "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
    "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
    "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
    "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
    "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
    "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath',  'KitchenAbvGr', 'KitchenQual',\n",
    "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
    "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
    "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
    "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
    "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
    "       'SaleCondition'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>LotArea</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>11622</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>169277.052498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>14267</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>187758.393989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>13830</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>183583.683570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>9978</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>179317.477511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>5005</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>150730.079977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  LotArea  FullBath  HalfBath  BedroomAbvGr      SalePrice\n",
       "0  1461    11622         1         0             2  169277.052498\n",
       "1  1462    14267         1         1             3  187758.393989\n",
       "2  1463    13830         2         1             3  183583.683570\n",
       "3  1464     9978         2         1             3  179317.477511\n",
       "4  1465     5005         2         0             2  150730.079977"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id              0\n",
       "LotArea         0\n",
       "FullBath        0\n",
       "HalfBath        0\n",
       "BedroomAbvGr    0\n",
       "SalePrice       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11622, 14267, 13830, ...,  1894, 20000, 10441], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['LotArea'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Id            1459 non-null   int64  \n",
      " 1   LotArea       1459 non-null   int64  \n",
      " 2   FullBath      1459 non-null   int64  \n",
      " 3   HalfBath      1459 non-null   int64  \n",
      " 4   BedroomAbvGr  1459 non-null   int64  \n",
      " 5   SalePrice     1459 non-null   float64\n",
      "dtypes: float64(1), int64(5)\n",
      "memory usage: 68.5 KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_float(x):\n",
    "    try:\n",
    "        float(x)\n",
    "    except:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>LotArea</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>11622</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>169277.052498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>14267</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>187758.393989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>13830</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>183583.683570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>9978</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>179317.477511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>5005</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>150730.079977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1466</td>\n",
       "      <td>10000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>177150.989247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1467</td>\n",
       "      <td>7980</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>172070.659229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1468</td>\n",
       "      <td>8402</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>175110.956520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1469</td>\n",
       "      <td>10176</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>162011.698832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1470</td>\n",
       "      <td>8400</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160726.247831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  LotArea  FullBath  HalfBath  BedroomAbvGr      SalePrice\n",
       "0  1461    11622         1         0             2  169277.052498\n",
       "1  1462    14267         1         1             3  187758.393989\n",
       "2  1463    13830         2         1             3  183583.683570\n",
       "3  1464     9978         2         1             3  179317.477511\n",
       "4  1465     5005         2         0             2  150730.079977\n",
       "5  1466    10000         2         1             3  177150.989247\n",
       "6  1467     7980         2         0             3  172070.659229\n",
       "7  1468     8402         2         1             3  175110.956520\n",
       "8  1469    10176         1         1             2  162011.698832\n",
       "9  1470     8400         1         0             2  160726.247831"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['LotArea'].apply(is_float)].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'LotArea', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'SalePrice'], dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='SalePrice'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HH3yQ7f7UqTV1Rc6ePVumTZsmsbGxyvLII4/I7Nmz2RVJHqNTtPsDaHFZv369Ki4tLU169+4tgYGBYjKZZPfu3arxf/3rXzJ9+nTp1q2b6PV6eeyxx+T8+fOqmG+//VbuvPNO0el0cvPNN8srr7zSLJ9NmzbJrbfeKlqtVgYPHiw7duxQjTudTlm6dKmEhYWJTqeTCRMmSGlpqcufl4WNPE1hYaHExsZKSUmJNDY2SmFhofz1r3+VwsJCaWxslJKSEomNjZXCwkJ3p0rk8jHUR0TEXWeLNxq73Q6DwQCbzQa9Xu/udIiQk5ODF198EVlZWQgMDGw2funSJUyaNAlLly7FhAkT3JAh0b+5egz1mHZ/Iup4l3dFtoRdkdQZsbAR3cCauiI//PBD5faaJk6nEx9++CG7IqnTYWEjuoFpNBrMmzcPFosFS5YsweHDh3Hp0iUcPnwYS5YsgcViwdy5c9kVSZ0Kr7F1IF5jI0+Vn5+P9PR01X1sRqMR8+bNw/jx492YGdG/8RobEbWKj4+Pu1MgahcsbEQ3uPz8fKSmpqJfv35IT09HVlYW0tPT0a9fP6SmpvKZbNTp8KfIDsSfIsnTOBwOJCQkoF+/fnjppZdUM/w7nU4sWbIEZWVl2LBhA6+zkdvxp0giuiY+toa8EQsb0Q2Mj60hb8TCRnQD4w3a5I1Y2IhuYJffoN3Q0ICDBw8iJycHBw8eRENDA2/Qpk7JI57HRkTu0XSD9rJly3D//fejrq5OGdPpdKirq8MLL7zAxhHqVHjGRkRXvYeN97ZRZ8QzNqIbmMPhwOrVq2EymfD888+jpKQEVVVVCA4ORnR0NFJTU7FmzRqMGzeOZ23UafCMjegGdnm7v5+fH0aMGIEJEyZgxIgR8PPzY7s/dUosbEQ3MLb7kzdiYSO6gbHdn7wRCxvRDYzPYyNvxMJGdAPj89jIG3ES5A7ESZDJU+Xn52P16tWwWq3Ktl69emHu3Ll8Hht5jE4xCXJaWhpGjx6N7t27IzQ0FJMnT0ZpaWmLsSKC++67Dz4+PtiyZYtqrLy8HPHx8QgMDERoaCgWLVqExsZGVcyuXbswcuRI6HQ69O/fHxkZGc3eIz09HX379oW/vz9iYmKwf/9+1XhtbS0SExNx0003oVu3bpg6darqwYxEndX48ePx4YcfYsWKFVi6dClWrFiBDRs2sKhRp+TWwpaXl4fExETs3bsX2dnZaGhowMSJE3Hx4sVmsStXrmzxZlGHw4H4+HjU19djz549eO+995CRkYFly5YpMWVlZYiPj8c999yDoqIizJ8/H7NmzcIXX3yhxHzyySdITk5GamoqCgsLMWzYMJjNZlRWVioxCxYswLZt27B582bk5eXh9OnTmDJlSjt/K0TuodFoVO3+/PmROi3xIJWVlQJA8vLyVNsPHjwoN998s5w5c0YASGZmpjKWlZUlvr6+YrValW1r1qwRvV4vdXV1IiKyePFiGTx4sGqf06ZNE7PZrKyPGTNGEhMTlXWHwyHh4eGSlpYmIiLV1dXi5+cnmzdvVmKOHj0qAMRisbj0+Ww2mwAQm83mUjwREf2bq8dQj2oesdlsANStxZcuXcKvfvUrpKenw2g0NnuNxWLBkCFDEBYWpmwzm82w2+04fPiwEhMXF6d6ndlshsViAQDU19ejoKBAFePr64u4uDglpqCgAA0NDaqYgQMHIjIyUom5Ul1dHex2u2ohIqLry2MKm9PpxPz58zFu3DhER0cr2xcsWIA77rgDDz74YIuvs1qtqqIGQFlvuhB+tRi73Y6amhqcPXsWDoejxZjL96HVahEUFHTVmCulpaXBYDAoS0RExDW+BSIi+rk8Zq7IxMRElJSU4Ouvv1a2bd26Fbm5uTh48KAbM2u7lJQUJCcnK+t2u53FjYjoOvOIwpaUlITt27cjPz8fvXv3Vrbn5ubi73//e7OzpKlTp+Kuu+7Crl27YDQam3UvNnUqNv10aTQam3UvVlRUQK/XIyAgABqNBhqNpsWYy/dRX1+P6upqVT6Xx1xJp9NBp9O5/kUQuZHD4UBxcbEyCfLQoUPZQEKdklsLm4jgqaeeQmZmJnbt2tVsvrpnn30Ws2bNUm0bMmQIVqxYgQceeAAAYDKZ8PLLL6OyshKhoaEAgOzsbOj1egwaNEiJycrKUu0nOzsbJpMJAKDVajFq1Cjk5ORg8uTJAH76aTQnJwdJSUkAgFGjRsHPzw85OTmYOnUqAKC0tBTl5eXKfog6q/z8fKSnp6v+chcWFobExES2/FPn0zG9LC2bO3euGAwG2bVrl5w5c0ZZLl26dNXX4IquyMbGRomOjpaJEydKUVGR7Ny5U3r27CkpKSlKzPfffy+BgYGyaNEiOXr0qKSnp4tGo5GdO3cqMRs3bhSdTicZGRly5MgRmT17tgQFBam6LefMmSORkZGSm5srBw4cEJPJJCaTyeXPy65I8kR5eXkSGxt71eXKLmUid3H1GOrWwgagxWX9+vX/8TWXFzYRkZMnT8p9990nAQEBEhISIgsXLpSGhgZVzFdffSXDhw8XrVYr/fr1a/E93nzzTYmMjBStVitjxoyRvXv3qsZrampk3rx50qNHDwkMDJSHHnpIzpw54/LnZWEjT9PY2CiTJk2S2NhYefDBB2Xbtm1y9uxZ2bZtmzz44IMSGxsrkyZNksbGRnenSuTyMZRTanUgTqlFnuabb77BokWL0L17d3z66ac4cuSIco1t0KBB+OUvf4nz58/jtddew+jRo92dLt3gXD2GekTzCBG5x5dffgkAuOuuuzBjxgzVrStGoxF33nknPv/8c3z55ZcsbNRpsLAR3cBqamoAAFlZWbjjjjuwdOlSREVFoaysDB9++CE+//xzVRxRZ+AxN2gTUccbPHgwACAwMBCpqamor6+HxWJBfX09UlNTERgYqIoj6gx4xkZ0A+vfvz+An6aue+CBB1BfX6+MabVaZb0pjqgz4Bkb0Q3s8vlLLy9qV65znlPqTFjYiG5gTbPohISENHsslK+vL0JCQlRxRJ0BCxsRtYh3AlFnxWtsRDew6upqAMDZs2ebjYmIsr0pjqgz4Bkb0Q3M1YkCOKEAdSY8YyO6gZ04cQLAT9fTduzYgWPHjikzjwwcOBDx8fFwOp04ceIEb9CmToNnbEQ3sD179gD46WkWL7zwAk6ePIm6ujqcPHkSL7zwApxOpyqOqDPgGRsRYdiwYdi/fz8sFouyTaPRYOjQoSguLnZjZkStx8JGdAMbN24cSkpK8O2332L06NEICAjA+fPn0b17d9TU1OCbb75R4og6izbP7v/BBx9g7dq1KCsrg8ViQZ8+fbBy5UpERUXhwQcfbO88vQJn9ydPU1NTg/vuu++acZ9//jkCAgI6ICOiq3P1GNqma2xr1qxBcnIyJk2ahOrqajgcDgA/3cS5cuXKNiVMRB3v2LFj7RpH5AnaVNjefPNNvP3223juueeg0WiU7bfffjsOHTrUbskR0fVVWVnZrnFEnqBNha2srAwjRoxotl2n0+HixYs/Oyki6hglJSXtGkfkCdpU2KKiolBUVNRs+86dO3Hbbbf93JyIqIN8//337RpH5Ana1BWZnJyMxMRE1NbWQkSwf/9+fPzxx0hLS8M777zT3jkS0XVy4cKFdo0j8gRtKmyzZs1CQEAAlixZgkuXLuFXv/oVwsPDsWrVKjzyyCPtnSMRXSddurh2CHA1jsgTtHnmkYSEBJw4cQIXLlyA1WrFP/7xD8ycObNV+0hLS8Po0aPRvXt3hIaGYvLkySgtLVXGq6qq8NRTT2HAgAEICAhAZGQkfvOb38Bms6n2U15ejvj4eAQGBiI0NBSLFi1CY2OjKmbXrl0YOXIkdDod+vfvj4yMjGb5pKeno2/fvvD390dMTAz279+vGq+trUViYiJuuukmdOvWDVOnTkVFRUWrPjORJ2loaGjXOCJP0ObmkaY55pqKCfDTvHMnT550eT95eXlITEzE3r17kZ2djYaGBkycOFFpQDl9+jROnz6N119/HSUlJcjIyMDOnTtVBdThcCA+Ph719fXYs2cP3nvvPWRkZGDZsmWqfOPj43HPPfegqKgI8+fPx6xZs/DFF18oMZ988gmSk5ORmpqKwsJCDBs2DGazWdUNtmDBAmzbtg2bN29GXl4eTp8+jSlTprTlKyQioutF2mD8+PGSkZHRbPsHH3wgsbGxbdmliIhUVlYKAMnLy7tqzKZNm0Sr1UpDQ4OIiGRlZYmvr69YrVYlZs2aNaLX66Wurk5ERBYvXiyDBw9W7WfatGliNpuV9TFjxkhiYqKy7nA4JDw8XNLS0kREpLq6Wvz8/GTz5s1KzNGjRwWAWCwWlz6fzWYTAGKz2VyKJ7repk+fLrGxsddcpk+f7u5UiVw+hrbpjO3gwYMtTrEzduzYFrslXdX0E2NwcPB/jNHr9cpv/haLBUOGDEFYWJgSYzabYbfbcfjwYSUmLi5OtR+z2azMi1dfX4+CggJVjK+vL+Li4pSYgoICNDQ0qGIGDhyIyMhI1fx6l6urq4PdblctRER0fbWpsPn4+OD8+fPNtttsNmUWktZyOp2YP38+xo0bh+jo6BZjzp49ixdffBGzZ89WtlmtVlVRA6CsW63W/xhjt9tRU1ODs2fPwuFwtBhz+T60Wi2CgoKuGnOltLQ0GAwGZYmIiLjGt0DUsVydJovTaVFn0qbCNn78eKSlpamKmMPhQFpaGu688842JZKYmIiSkhJs3LixxXG73Y74+HgMGjQIv/vd79r0Hh0tJSUFNptNWU6dOuXulIhUunXr1q5xRJ6gTT28r776KsaPH48BAwbgrrvuAgDs3r0bdrsdubm5rd5fUlIStm/fjvz8fPTu3bvZ+Pnz53Hvvfeie/fuyMzMhJ+fnzJmNBqbdS82dSoajUbln1d2L1ZUVECv1yMgIAAajQYajabFmMv3UV9fj+rqatVZ2+UxV9LpdNDpdC5+C0Qd78oO458bR+QJ2nTGNmjQIBQXF+Phhx9GZWUlzp8/j1//+tc4duzYVX9GbImIICkpCZmZmcjNzUVUVFSzGLvdjokTJ0Kr1WLr1q3w9/dXjZtMJhw6dEjVvZidnQ29Xo9BgwYpMTk5OarXZWdnw2QyAQC0Wi1GjRqlinE6ncjJyVFiRo0aBT8/P1VMaWkpysvLlRiizqZr167tGkfkCdp812V4eDj+7//+72e9eWJiIj766CN89tln6N69u3KtymAwICAgQClqly5dwoYNG1QNGD179oRGo8HEiRMxaNAgPProo1i+fDmsViuWLFmCxMRE5Wxpzpw5eOutt7B48WI8/vjjyM3NxaZNm7Bjxw4ll+TkZMyYMQO33347xowZg5UrV+LixYt47LHHlJxmzpyJ5ORkBAcHQ6/X46mnnoLJZMLYsWN/1vdA5C59+vRRmqyuFUfUWbhc2IqLixEdHQ1fX99rPlF36NChLu1zzZo1AIC7775btX39+vX43//9XxQWFmLfvn0AgP79+6tiysrK0LdvX2g0Gmzfvh1z586FyWRC165dMWPGDLzwwgtKbFRUFHbs2IEFCxZg1apV6N27N9555x2YzWYlZtq0afjnP/+JZcuWwWq1Yvjw4di5c6eqoWTFihXw9fXF1KlTUVdXB7PZjNWrV7v0WYk8UU1NTbvGEXkClx806uvrC6vVitDQUPj6+sLHxwctvdTHx6fNnZHejg8aJU+TlJTk0sz90dHReOuttzogI6Krc/UY6vIZW1lZGXr27Kn8OxF1fmFhYSgpKUGXLl3gdDrhdDqVMY1GAx8fHzQ2Nja7FYbIk7lc2Jp+Y29oaMDzzz+PpUuXttjsQUSdR//+/ZGTk4PGxkaMGTMGERERqKurg06nw6lTp5SO4ysvBRB5slY3j/j5+eHPf/4zli5dej3yIepQtbW1KC8vd3cabnP55MbffPON6tYZHx8fVdzx48c7NDdPERkZ2awbmzyby9fYLjdjxgwMHz4cCxYsuB45eS1eY/M8x48fV81kQ3SldevW4dZbb3V3GoTrcI3tcrfccgteeOEF/O1vf8OoUaOa3ePym9/8pi27JepwkZGRWLdunbvTcBun04nnnnsO3bp1g91ux7lz55Sx4OBgdO/eHRcvXsRLL70EX982P+WqU4uMjHR3CtRKbTpj+0/X1nx8fPgY+avgGRt5ovz8fKSmpmLs2LHo27cvPv74Y0yfPh0nT57E3r178fzzz2P8+PHuTpPI5WNomwobtQ0LG3mq/Px8rF69WjWhd69evTB37lwWNfIY162w7d27F9u2bUN9fT0mTJiAe++992cne6NgYSNP5nA4kJWVhd///vdYuHAhJk2aBI1G4+60iBTX5Rrbp59+imnTpiEgIAB+fn74wx/+gFdffRVPP/30z06YiNxLo9FgwIABAIABAwawqFGn1aqrwWlpaXjiiSdgs9lw7tw5vPTSSz97vkgiIqL21KrCVlpaiqefflr5m9zChQtx/vx51cz6RERE7tSqwnbp0iXV75parRb+/v64cOFCuydGRETUFq2+j+2dd95RPU23sbERGRkZCAkJUbbxPjYiInKXVhW2yMhIvP3226ptRqMRH3zwgbLu4+PDwkZERG7TqsJ28uTJ65QGERFR+/jZc+TU1ta2Rx5ERETtok2FzeFw4MUXX8TNN9+Mbt26KVNoLV26FH/605/aNUEiIqLWaFNhe/nll5GRkYHly5dDq9Uq26Ojo/HOO++0W3JERESt1abC9v7772PdunVISEhQzU4wbNgwHDt2rN2SIyIiaq02FbYff/yxxSfqOp1O1YMLiYiIOlqbCtugQYOwe/fuZts//fRTjBgx4mcnRURE1FZtKmzLli1DUlISXn31VTidTvzlL3/BE088gZdffhnLli1zeT9paWkYPXo0unfvjtDQUEyePBmlpaWqmNraWiQmJuKmm25Ct27dMHXqVFRUVKhiysvLER8fj8DAQISGhmLRokVobGxUxezatQsjR46ETqdD//79kZGR0Syf9PR09O3bF/7+/oiJicH+/ftbnQsREbmZtFF+fr7ExcVJz549JSAgQMaNGydffPFFq/ZhNptl/fr1UlJSIkVFRTJp0iSJjIyUCxcuKDFz5syRiIgIycnJkQMHDsjYsWPljjvuUMYbGxslOjpa4uLi5ODBg5KVlSUhISGSkpKixHz//fcSGBgoycnJcuTIEXnzzTdFo9HIzp07lZiNGzeKVquVd999Vw4fPixPPPGEBAUFSUVFhcu5XIvNZhMAYrPZWvU9EXWU0tJSiY2NldLSUnenQtSMq8fQNhe266GyslIASF5enoiIVFdXi5+fn2zevFmJOXr0qAAQi8UiIiJZWVni6+srVqtViVmzZo3o9Xqpq6sTEZHFixfL4MGDVe81bdo0MZvNyvqYMWMkMTFRWXc4HBIeHi5paWku53Kl2tpasdlsynLq1CkWNvJoLGzkyVwtbD/7Bu32ZLPZAADBwcEAgIKCAjQ0NCAuLk6JGThwICIjI2GxWAAAFosFQ4YMQVhYmBJjNptht9tx+PBhJebyfTTFNO2jvr4eBQUFqhhfX1/ExcUpMa7kcqW0tDQYDAZliYiIaNsXQ0RELnN5Sq0ePXrAx8fHpdiqqqpWJ+J0OjF//nyMGzcO0dHRAACr1QqtVougoCBVbFhYmPIIe6vVqipqTeNNY/8pxm63o6amBufOnYPD4Wgxpun2BVdyuVJKSgqSk5OVdbvdzuJGRHSduVzYVq5ceR3TABITE1FSUoKvv/76ur5PR9LpdNDpdO5Og4johuJyYZsxY8Z1SyIpKQnbt29Hfn4+evfurWw3Go2or69HdXW16kypoqICRqNRibmye7GpU/HymCu7FysqKqDX6xEQEACNRgONRtNizOX7uFYuRETkfu0yCbLdblctrhIRJCUlITMzE7m5uYiKilKNjxo1Cn5+fsjJyVG2lZaWory8HCaTCQBgMplw6NAh1VO8s7OzodfrMWjQICXm8n00xTTtQ6vVYtSoUaoYp9OJnJwcJcaVXIiIyAO0pTPlwoULkpiYKD179hRfX99mi6vmzp0rBoNBdu3aJWfOnFGWS5cuKTFz5syRyMhIyc3NlQMHDojJZBKTyaSMN7X7T5w4UYqKimTnzp3Ss2fPFtv9Fy1aJEePHpX09PQW2/11Op1kZGTIkSNHZPbs2RIUFKTqtrxWLtfCdn/ydOyKJE92Xdv9582bJ7fddpt8+umnEhAQIO+++668+OKL0rt3b9mwYYPL+wHQ4rJ+/XolpqamRubNmyc9evSQwMBAeeihh+TMmTOq/Zw8eVLuu+8+CQgIkJCQEFm4cKE0NDSoYr766isZPny4aLVa6devn+o9mrz55psSGRkpWq1WxowZI3v37lWNu5LLf8LCRp6OhY08mavHUB8Rkdae5UVGRuL999/H3XffDb1ej8LCQvTv3x8ffPABPv74Y2RlZbXfKaUXsdvtMBgMsNls0Ov17k6HqJnjx49j9uzZWLduHW699VZ3p0Ok4uoxtE3X2KqqqtCvXz8AgF6vV9r777zzTuTn57dll0RERO2iTYWtX79+KCsrA/DTTcqbNm0CAGzbtq3ZfV5EREQdqU2F7bHHHsO3334LAHj22WeRnp4Of39/LFiwAIsWLWrXBImIiFrD5fvYLrdgwQLl3+Pi4nDs2DEUFBSgf//+GDp0aLslR0RE1FqtOmOzWCzYvn27altTE8mcOXPw1ltvoa6url0TJCIiao1WFbYXXnhBmVgYAA4dOoSZM2ciLi4OKSkp2LZtG9LS0to9SSIiIle1qrAVFRVhwoQJyvrGjRsRExODt99+GwsWLMAbb7yhNJIQERG5Q6sK27lz51Qz4Ofl5eG+++5T1kePHo1Tp061X3ZERESt1KrCFhYWprT519fXo7CwEGPHjlXGz58/Dz8/v/bNkIiIqBVaVdgmTZqEZ599Frt370ZKSgoCAwNx1113KePFxcX4r//6r3ZPkoiIyFWtavd/8cUXMWXKFMTGxqJbt2547733oNVqlfF3330XEydObPckiYiIXNWqwhYSEoL8/HzYbDZ069YNGo1GNb5582Z069atXRMkIiJqjTbdoG0wGFrcHhwc/LOSoY5TUVEBm83m7jTIw/zwww+qfxI1MRgMquZBT9am2f2pbTxldv+Kigr8z6O/RkM9b6YnItf4aXXY8MH7bi1urh5D23TGRp2bzWZDQ30davrFwunf8tk3EVET31ob8H0ebDZbpzhrY2G7gTn9DXB2DXF3GkRE7apNs/sTERF5KhY2IiLyKixsRETkVVjYiIjIq7i1sOXn5+OBBx5AeHg4fHx8sGXLFtX4hQsXkJSUhN69eyMgIACDBg3C2rVrVTG1tbVITEzETTfdhG7dumHq1KmoqKhQxZSXlyM+Ph6BgYEIDQ3FokWL0NjYqIrZtWsXRo4cCZ1Oh/79+yMjI6NZvunp6ejbty/8/f0RExOD/fv3t8v3QERE7cethe3ixYsYNmwY0tPTWxxPTk7Gzp07sWHDBhw9ehTz589HUlIStm7dqsQsWLAA27Ztw+bNm5GXl4fTp09jypQpyrjD4UB8fDzq6+uxZ88evPfee8jIyMCyZcuUmLKyMsTHx+Oee+5BUVER5s+fj1mzZuGLL75QYj755BMkJycjNTUVhYWFGDZsGMxmMyorK6/DN0NERG3lMTdo+/j4IDMzE5MnT1a2RUdHY9q0aVi6dKmybdSoUbjvvvvw0ksvwWazoWfPnvjoo4/wy1/+EgBw7Ngx3HbbbbBYLBg7diw+//xz3H///Th9+rRy/8XatWvxzDPP4J///Ce0Wi2eeeYZ7NixAyUlJcr7PPLII6iursbOnTsBADExMRg9ejTeeustAIDT6URERASeeuopPPvssy1+prq6OtUTxe12OyIiItx+g/bx48cxe/ZsXBz0C7b7E9E1+V48i65HtmLdunW49dZb3ZaHqzdoe/Q1tjvuuANbt27Fjz/+CBHBV199hePHjysTLRcUFKChoQFxcXHKawYOHIjIyEhYLBYAgMViwZAhQ1Q3FZrNZtjtduVp4BaLRbWPppimfdTX16OgoEAV4+vri7i4OCWmJWlpaTAYDMoSERHxM78RIiK6Fo++QfvNN9/E7Nmz0bt3b3Tp0gW+vr54++23MX78eACA1WqFVqtFUFCQ6nVhYWGwWq1KzJV3yjetXyvGbrejpqYG586dg8PhaDHm2LFjV80/JSUFycnJynrTGZun8K2pdncKRNQJdLZjhccXtr1792Lr1q3o06cP8vPzkZiYiPDw8GZnWJ5Ip9NBp9O5O42rCijLd3cKRETtzmMLW01NDX77298iMzMT8fHxAIChQ4eiqKgIr7/+OuLi4mA0GlFfX4/q6mrVWVtFRQWMRiMAwGg0NutebOqavDzmyk7KiooK6PV6BAQEQKPRQKPRtBjTtI/OqCZqPJwBQe5Og4g8nG9Ndaf6i7DHFraGhgY0NDTA11d9GVCj0cDpdAL4qZHEz88POTk5mDp1KgCgtLQU5eXlMJlMAACTyYSXX34ZlZWVCA0NBQBkZ2dDr9dj0KBBSkxWVpbqfbKzs5V9aLVajBo1Cjk5OUpzi9PpRE5ODpKSkq7PF9ABnAFBbB4hIq/j1sJ24cIFfPfdd8p6WVkZioqKEBwcjMjISMTGxmLRokUICAhAnz59kJeXh/fffx9/+MMfAPz0fKCZM2ciOTkZwcHB0Ov1eOqpp2AymTB27FgAwMSJEzFo0CA8+uijWL58OaxWK5YsWYLExETlZ8I5c+bgrbfewuLFi/H4448jNzcXmzZtwo4dO5TckpOTMWPGDNx+++0YM2YMVq5ciYsXL+Kxxx7rwG+MiIiuxa2F7cCBA7jnnnuU9aZGixkzZiAjIwMbN25ESkoKEhISUFVVhT59+uDll1/GnDlzlNesWLECvr6+mDp1Kurq6mA2m7F69WplXKPRYPv27Zg7dy5MJhO6du2KGTNm4IUXXlBioqKisGPHDixYsACrVq1C79698c4778BsNisx06ZNwz//+U8sW7YMVqsVw4cPx86dOzvFIxyIiG4kHnMf243AUx40yvvYiKg1eB8bERGRG7GwERGRV2FhIyIir8LCRkREXoWFjYiIvAoLGxEReRUWNiIi8iosbERE5FVY2IiIyKt47CTIdP351trcnQIRdQKd7VjBwnYDMhgM8NPqgO/z3J0KEXUSflodDAaDu9NwCQvbDSgsLAwbPngfNlvn+lsYXX8//PADXn75ZTz33HPo06ePu9MhD2IwGDrNpO8sbDeosLCwTvM/KXW8Pn36uHWyW6Kfg80jRETkVVjYiIjIq7CwERGRV2FhIyIir8LCRkREXoWFjYiIvAoLGxEReRW3Frb8/Hw88MADCA8Ph4+PD7Zs2dIs5ujRo/jFL34Bg8GArl27YvTo0SgvL1fGa2trkZiYiJtuugndunXD1KlTUVFRodpHeXk54uPjERgYiNDQUCxatAiNjY2qmF27dmHkyJHQ6XTo378/MjIymuWSnp6Ovn37wt/fHzExMdi/f3+7fA9ERNR+3FrYLl68iGHDhiE9Pb3F8b///e+48847MXDgQOzatQvFxcVYunQp/P39lZgFCxZg27Zt2Lx5M/Ly8nD69GlMmTJFGXc4HIiPj0d9fT327NmD9957DxkZGVi2bJkSU1ZWhvj4eNxzzz0oKirC/PnzMWvWLHzxxRdKzCeffILk5GSkpqaisLAQw4YNg9lsRmVl5XX4ZoiIqM3EQwCQzMxM1bZp06bJ//zP/1z1NdXV1eLn5yebN29Wth09elQAiMViERGRrKws8fX1FavVqsSsWbNG9Hq91NXViYjI4sWLZfDgwc3e22w2K+tjxoyRxMREZd3hcEh4eLikpaW5/BltNpsAEJvN5vJriDpSaWmpxMbGSmlpqbtTIWrG1WOox15jczqd2LFjB2699VaYzWaEhoYiJiZG9XNlQUEBGhoaEBcXp2wbOHAgIiMjYbFYAAAWiwVDhgxRTR9lNptht9tx+PBhJebyfTTFNO2jvr4eBQUFqhhfX1/ExcUpMS2pq6uD3W5XLUREdH15bGGrrKzEhQsX8Morr+Dee+/Fl19+iYceeghTpkxBXt5Ps9JbrVZotVoEBQWpXhsWFgar1arEXDknYtP6tWLsdjtqampw9uxZOByOFmOa9tGStLQ0GAwGZYmIiGj9F0FERK3isYXN6XQCAB588EEsWLAAw4cPx7PPPov7778fa9eudXN2rklJSYHNZlOWU6dOuTslIiKv57GFLSQkBF26dMGgQYNU22+77TalK9JoNKK+vh7V1dWqmIqKChiNRiXmyi7JpvVrxej1egQEBCAkJAQajabFmKZ9tESn00Gv16sWIiK6vjy2sGm1WowePRqlpaWq7cePH1eeEzVq1Cj4+fkhJydHGS8tLUV5eTlMJhMAwGQy4dChQ6ruxezsbOj1eqVomkwm1T6aYpr2odVqMWrUKFWM0+lETk6OEkNERB6ig5pZWnT+/Hk5ePCgHDx4UADIH/7wBzl48KD88MMPIiLyl7/8Rfz8/GTdunVy4sQJefPNN0Wj0cju3buVfcyZM0ciIyMlNzdXDhw4ICaTSUwmkzLe2Ngo0dHRMnHiRCkqKpKdO3dKz549JSUlRYn5/vvvJTAwUBYtWiRHjx6V9PR00Wg0snPnTiVm48aNotPpJCMjQ44cOSKzZ8+WoKAgVbfltbArkjwduyLJk7l6DHVrYfvqq68EQLNlxowZSsyf/vQn6d+/v/j7+8uwYcNky5Ytqn3U1NTIvHnzpEePHhIYGCgPPfSQnDlzRhVz8uRJue+++yQgIEBCQkJk4cKF0tDQ0CyX4cOHi1arlX79+sn69eub5fvmm29KZGSkaLVaGTNmjOzdu7dVn5eFjTwdCxt5MlePoT4iIu46W7zR2O12GAwG2Gw2Xm8jj3T8+HHMnj0b69at4xO0yeO4egz12GtsREREbcHCRkREXoWFjYiIvAoLGxEReRUWNiIi8iosbERE5FVY2IiIyKuwsBERkVdhYSMiIq/CwkZERF6FhY2IiLwKCxsREXkVFjYiIvIqLGxERORVWNiIiMirsLAREZFXYWEjIiKvwsJGRERehYWNiAAADocDpaWlAIDS0lI4HA43Z0TUNj4iIu5O4kZht9thMBhgs9mg1+vdnQ6RIj8/H+np6aioqFC2hYWFITExEePHj3djZkT/5uoxlIWtA7GweZ7a2lqUl5e7Ow23KiwsxNq1a+Hn54eGhgZle9P6nDlzMHLkSDdm6F6RkZHw9/d3dxoE14+hXTowp2by8/Px2muvoaCgAGfOnEFmZiYmT57cYuycOXPwxz/+EStWrMD8+fOV7VVVVXjqqaewbds2+Pr6YurUqVi1ahW6deumxBQXFyMxMRHffPMNevbsiaeeegqLFy9W7X/z5s1YunQpTp48iVtuuQWvvvoqJk2apIyLCFJTU/H222+juroa48aNw5o1a3DLLbe063dCHau8vByzZ892dxoe4fKidvn62rVr3ZGOx1i3bh1uvfVWd6dBreDWwnbx4kUMGzYMjz/+OKZMmXLVuMzMTOzduxfh4eHNxhISEnDmzBlkZ2ejoaEBjz32GGbPno2PPvoIwE8VfuLEiYiLi8PatWtx6NAhPP744wgKClIOaHv27MH06dORlpaG+++/Hx999BEmT56MwsJCREdHAwCWL1+ON954A++99x6ioqKwdOlSmM1mHDlyhH+b68QiIyOxbt06d6fhNkePHsWKFSuuGbdgwQLcdtttHZCR54mMjHR3CtRa4iEASGZmZrPt//jHP+Tmm2+WkpIS6dOnj6xYsUIZO3LkiACQb775Rtn2+eefi4+Pj/z4448iIrJ69Wrp0aOH1NXVKTHPPPOMDBgwQFl/+OGHJT4+XvW+MTEx8uSTT4qIiNPpFKPRKK+99poyXl1dLTqdTj7++OOrfqba2lqx2WzKcurUKQEgNpvNtS+F6Dpbu3atxMbGSmxsrDzwwAOSnp4umZmZkp6eLg888IAytnbtWnenSiQ2m82lY6hHd0U6nU48+uijWLRoEQYPHtxs3GKxICgoCLfffruyLS4uDr6+vti3b58SM378eGi1WiXGbDajtLQU586dU2Li4uJU+zabzbBYLACAsrIyWK1WVYzBYEBMTIwS05K0tDQYDAZliYiIaMO3QHT9HDhwAADg6+uLwMBAbNq0CStXrsSmTZsQGBgIX19fVRxRZ+DRhe3VV19Fly5d8Jvf/KbFcavVitDQUNW2Ll26IDg4GFarVYkJCwtTxTStXyvm8vHLX9dSTEtSUlJgs9mU5dSpU//x8xJ1tKa/3DmdTkRFRSE9PR1ZWVlIT09HVFQUnE6nKo6oM3DrNbb/pKCgAKtWrUJhYSF8fHzcnU6b6HQ66HQ6d6dBdFWBgYHKvzudThw/fhw//PAD6urqlKJ2ZRyRp/PYwrZ7925UVlaqLtw6HA4sXLgQK1euxMmTJ2E0GlFZWal6XWNjI6qqqmA0GgEARqNRdW8OAGX9WjGXjzdt69Wrlypm+PDh7fBpidxjyJAhyu0O+/btU37CbymOqLPw2J8iH330URQXF6OoqEhZwsPDsWjRInzxxRcAAJPJhOrqahQUFCivy83NhdPpRExMjBKTn5+vamXOzs7GgAED0KNHDyUmJydH9f7Z2dkwmUwAgKioKBiNRlWM3W7Hvn37lBiizmjo0KHtGkfkCdx6xnbhwgV89913ynpZWRmKiooQHByMyMhI3HTTTap4Pz8/GI1GDBgwAABw22234d5778UTTzyBtWvXoqGhAUlJSXjkkUeUWwN+9atf4fnnn8fMmTPxzDPPoKSkBKtWrVK1OP+///f/EBsbi9///veIj4/Hxo0bceDAAaUN3MfHB/Pnz8dLL72EW265RWn3Dw8Pv+p9d0SdQUhISLvGEXkCtxa2AwcO4J577lHWk5OTAQAzZsxARkaGS/v48MMPkZSUhAkTJig3aL/xxhvKuMFgwJdffonExESMGjUKISEhWLZsmeqm3DvuuAMfffQRlixZgt/+9re45ZZbsGXLFuUeNgBYvHgxLl68iNmzZ6O6uhp33nkndu7cyXvYyCuEhITg7NmzLm8n8mScUqsDcUot8jQ5OTl48cUX4ePjg5iYGNx8882oq6uDTqfDjz/+iH379kFEsHTpUkyYMMHd6dINrlNMqUVE7hUcHAwAmDVrFrZt24a9e/cqY7169cKsWbPw9ttvK3FEnQELG9ENbOjQoTAajTh8+DA++OADlJSUoKqqCsHBwYiOjkZqaip69erF5hHqVDy2K5KIrj+NRoN58+bBYrEgNTUVWq0WJpMJWq0WqampsFgsmDt3LjQajbtTJXIZr7F1IF5jI0+Vn5+P1atXq2bS6dWrF+bOncvnsZHH4PPYPBALG3kyh8OB4uJi5afIoUOH8kyNPAqbR4ioVTQaDUaMGOHuNIh+Nl5jIyIir8LCRkREXoWFjYiIvAqvsXWgpj4du93u5kyIiDqfpmPntXoeWdg60Pnz5wGAT9ImIvoZzp8/D4PBcNVxtvt3IKfTidOnT6N79+6d9uGp5N3sdjsiIiJw6tQp3pJCHkdEcP78eYSHh8PX9+pX0ljYiEjBey3JG7B5hIiIvAoLGxEReRUWNiJS6HQ6pKamQqfTuTsVojbjNTYiIvIqPGMjIiKvwsJGRERehYWNiIi8CgsbERF5FRY2IiLyKixsRETkVVjYiIjIq7CwERGRV/n/YbrIr1+prhoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,1)\n",
    "sns.boxplot(df2['LotArea'])\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,2)\n",
    "sns.boxplot(df2['FullBath'])\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,4)\n",
    "sns.boxplot(df2['BedroomAbvGr'])\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,5)\n",
    "sns.boxplot(df2['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'LotArea', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'SalePrice'], dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.copy()\n",
    "df3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['LotArea'] = df3['LotArea'].clip(lower=df3['LotArea'].quantile(0.05), upper=df3['LotArea'].quantile(0.95))\n",
    "df3['SalePrice'] = df3['SalePrice'].clip(lower=df3['SalePrice'].quantile(0.05), upper=df3['SalePrice'].quantile(0.95))\n",
    "df3['FullBath'] = df3['FullBath'].clip(lower=df3['FullBath'].quantile(0.05), upper=df3['FullBath'].quantile(0.95))\n",
    "df3['BedroomAbvGr'] = df3['BedroomAbvGr'].clip(lower=df3['BedroomAbvGr'].quantile(0.05), upper=df3['BedroomAbvGr'].quantile(0.95))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='SalePrice'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,1)\n",
    "sns.boxplot(df3['LotArea'])\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,2)\n",
    "sns.boxplot(df3['FullBath'])\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,3)\n",
    "sns.boxplot(df3['BedroomAbvGr'])\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,4)\n",
    "sns.boxplot(df3['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['LotArea'] = df2['LotArea'].clip(lower=df2['LotArea'].quantile(0.05), upper=df2['LotArea'].quantile(0.95))\n",
    "df2['SalePrice'] = df2['SalePrice'].clip(lower=df2['SalePrice'].quantile(0.05), upper=df2['SalePrice'].quantile(0.95))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='SalePrice'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7d6+8jW+//VZMmzZN9O7dWxgMBvH444+Ls2fPKvZz9OhRkZqaKvR6vRgwYIBYuXJlp142b94sEhIShE6nE0OGDBE7duzw6Vh8mcpJFAicDk/BzJdzqEYIIQKSmDcYj8cDo9EISZIU9+ECob6+HpIkBbQHCj41NTVYsWIFFi9ejLi4uEC3Q0HGaDR2ml19PflyDmVwdZNgCa76+no8Nn0G2s63BqwHIlKfUJ0ef/nzuwELL1/OoUE/q5B8I0kS2s634tytafCGGQPdDhGpgLZFAr7cD0mSAnrVdbUYXDcob5gR3oh+gW6DiKjbXVNwnThxArW1tfIsvw4//CJcIiKi7uJXcH355ZeYOHEijh07Bo1Gg47bZBqNBgDQ3t7efR0SERFdxK/Pcc2ePRvx8fFoaGhAr169UFlZiZKSEiQnJ2Pfvn3d3CIREdH/59cVl8PhQHFxMfr16wetVgutVovU1FTk5eXhueeew5EjR7q7TyIiIgB+XnG1t7ejT58+AIB+/frJv0ocFxcHp9PZfd0RERH9gF9XXEOHDsXRo0cRHx+PlJQU5OfnQ6fToaCgALfeemt390hERCTzK7iWLFmC5uZmAMCLL76IBx98EKNGjULfvn3xwQcfdGuDREREF/MruOx2u/zv22+/HZ9//jkaGxsRFRUlzywkIiLqCdf07fBffPEFdu/ejXPnziE6Orq7eiIiIrokv4Lr22+/xZgxY5CQkIAHHngA33zzDQAgOzsbzz//fLc2SEREdDG/gmvu3LkIDQ1FbW0tevXqJa+fMmUKCgsLu605IiKiH/LrHtfHH3+M3bt34+abb1asHzhwIGpqarqlMSIioq74dcXV3NysuNLq0NjYCL1ef81NERERXYpfwTVq1Ci8++678mONRgOv14v8/Hzcf//93dYcERHRD/n1VmF+fj7GjBmDw4cP4/z581iwYAEqKyvR2NiITz/9tLt7JCIikvl1xTV06FBUVVUhNTUVEyZMQHNzMyZNmoQjR47gtttu6+4eiYiIZD5fcbW1tWHcuHFYv349Fi9e3BM9ERERXZLPV1yhoaGoqKjoiV6IiIiuyK+3Ch977DFs2LChu3shIiK6Ir+C68KFC1i3bh2Sk5Px5JNPYt68eYrlapWUlGD8+PGwWCzQaDTYtm2bYlwIgWXLlqF///4IDw9Heno6Tp06pahpbGxEVlYWDAYDIiMjkZ2djaamJkVNRUUFRo0ahbCwMFitVuTn53fqZcuWLUhKSkJYWBiGDRuGnTt3Xv0LQkRE141fwXX8+HGMHDkSffr0QVVVFY4cOaJYrlZzczOGDx+ON998s8vx/Px8rFmzBuvXr0dpaSkiIiJgt9vR0tIi12RlZaGyshJFRUXYvn07SkpKkJOTI497PB6MHTsWcXFxKCsrw6pVq7B8+XIUFBTINQcOHMC0adOQnZ2NI0eOIDMzE5mZmTh+/Lgfrw4REfUkjRBCBLoJ4PvPgm3duhWZmZkAvr/aslgseP755/HLX/4SACBJEkwmEzZu3IipU6fi5MmTGDx4MA4dOoTk5GQAQGFhIR544AF89dVXsFgsWLduHRYvXgyXywWdTgcAeOGFF7Bt2zZ8/vnnAL7/qqrm5mZs375d7ueee+7BiBEjsH79+i77bW1tRWtrq/zY4/HAarVCkiQYDIZuf32uVlVVFXJyctA8+CF4I/oFrA8iUg9t8/8i4sSHKCgoQEJCQkB68Hg8MBqNV3UOvaZvh7+YEAK7du3Cww8/3C3bq66uhsvlQnp6urzOaDQiJSUFDocDAOBwOBAZGSmHFgCkp6dDq9WitLRUrhk9erQcWsD3P8vidDpx5swZuebi/XTUdOynK3l5eTAajfJitVqv/aCJiOiKrjm4qqursXTpUsTGxmLixImKt/GuhcvlAgCYTCbFepPJJI+5XC7ExMQoxkNCQhAdHa2o6WobF+/jUjUd411ZtGgRJEmSl7q6Ol8PkYiI/ODXN2e0trbib3/7GzZs2IBPPvkE7e3teOWVV5CdnR3Qt8muJ71ez+9lJCIKAJ+uuMrKyvDMM8/AbDbj9ddfR2ZmJurq6qDVamG327s1tMxmMwCgvr5esb6+vl4eM5vNaGhoUIxfuHABjY2NipqutnHxPi5V0zFORETBw6fgSklJgV6vx7///W8cOnQIzz33XKe32LpLfHw8zGYz9uzZI6/zeDwoLS2FzWYDANhsNrjdbpSVlck1xcXF8Hq9SElJkWtKSkrQ1tYm1xQVFSExMRFRUVFyzcX76ajp2A8REQUPn4JrzJgx2LBhA1588UUUFhbiWickNjU1oby8HOXl5QC+v19WXl6O2tpaaDQazJkzBy+99BI+/PBDHDt2DDNmzIDFYpFnHg4aNAjjxo3DE088gYMHD+LTTz9Fbm4upk6dCovFAgB49NFHodPpkJ2djcrKSnzwwQdYvXq14vNms2fPRmFhIV599VV8/vnnWL58OQ4fPozc3NxrOj4iIup+Pt3j2r17N+rq6vD222/j6aefxrlz5zBlyhQA309n99Xhw4cVP4PSESYzZ87Exo0bsWDBAjQ3NyMnJwdutxupqakoLCxEWFiY/JxNmzYhNzcXY8aMgVarxeTJk7FmzRp53Gg04uOPP8asWbNw1113oV+/fli2bJnis1733nsv3nvvPSxZsgS/+tWvMHDgQGzbtg1Dhw71+ZiIiKhnXdPnuIqKivD2229j69atsFqtePjhh/Hwww9j5MiR3dmjKvjyGYSexM9xEZGvflSf4/rJT36C9957D6dPn8azzz6LXbt24e67776WTRIREV2WX9Pha2trYbVa5bcHo6Ki8OyzzyI3N9enr3yinqM95w50C0SkEmo7X/gVXPHx8fjmm286ffi3sbERd999N9rb27ulOfJfeHVJoFsgIuoRfgWXEKLLyRhNTU2KiRMUOOfiR8MbHhnoNohIBbTn3Kr6Y9en4OqY9afRaLB06VL06tVLHmtvb0dpaSlGjBjRrQ2Sf7zhkZycQUQ3JJ+Cq+P+lRACx44dU3xxrU6nw/Dhw+VvciciIuoJPgXX3r17AQCPP/44Vq9e/aP5XkIiIgoeft3jevvtt+V/f/XVVwCAm2++uXs6IiIiugy/Psfl9Xrx4osvwmg0Ii4uDnFxcYiMjMRvfvMbeL3e7u6RiIhI5tcV1+LFi7FhwwasXLkS9913HwDgk08+wfLly9HS0oIVK1Z0a5NEREQd/Aqud955B3/84x/x0EMPyevuuOMODBgwAM888wyDi4iIeoxfbxU2NjYiKSmp0/qkpCQ0NjZec1NERESX4ldwDR8+HGvXru20fu3atbjjjjuuuSkiIqJL8eutwvz8fGRkZOCf//yn/GOLDocDdXV12LlzZ7c2SEREdDG/rrjS0tJQVVWFiRMnwu12w+12Y9KkSaisrMSf//zn7u6RiIhI5tcVFwBYLJZOkzCOHj2KDRs2oKCg4JobIyIi6so1/R4XERHR9cbgIiIiVWFwERGRqvh0j2vSpEmXHXe73dfSCxER0RX5FFxGo/GK4zNmzLimhoiIiC7Hp+C6+FvhiYiIAiGo73G1t7dj6dKliI+PR3h4OG677Tb85je/gRBCrhFCYNmyZejfvz/Cw8ORnp6OU6dOKbbT2NiIrKwsGAwGREZGIjs7G01NTYqaiooKjBo1CmFhYbBarcjPz78ux0hERL4J6uB6+eWXsW7dOqxduxYnT57Eyy+/jPz8fLzxxhtyTX5+PtasWYP169ejtLQUERERsNvtaGlpkWuysrJQWVmJoqIibN++HSUlJcjJyZHHPR4Pxo4di7i4OJSVlWHVqlVYvnw5P49GRBSE/P4A8vVw4MABTJgwARkZGQCAW265BX/9619x8OBBAN9fbb3++utYsmQJJkyYAAB49913YTKZsG3bNkydOhUnT55EYWEhDh06hOTkZADAG2+8gQceeACvvPIKLBYLNm3ahPPnz+NPf/oTdDodhgwZgvLycvzud79TBNzFWltb0draKj/2eDw9+VIQEdH/E9RXXPfeey/27NmDqqoqAN9/M8cnn3yCn/70pwCA6upquFwupKeny88xGo1ISUmBw+EA8P13KEZGRsqhBQDp6enQarUoLS2Va0aPHg2dTifX2O12OJ1OnDlzpsve8vLyYDQa5cVqtXbvwRMRUZeC+orrhRdegMfjQVJSEm666Sa0t7djxYoVyMrKAgC4XC4AgMlkUjzPZDLJYy6XCzExMYrxkJAQREdHK2ri4+M7baNjLCoqqlNvixYtwrx58+THHo+H4UVEdB0EdXBt3rwZmzZtwnvvvSe/fTdnzhxYLBbMnDkzoL3p9Xro9fqA9kBE9GMU1ME1f/58vPDCC5g6dSoAYNiwYaipqUFeXh5mzpwJs9kMAKivr0f//v3l59XX12PEiBEAALPZjIaGBsV2L1y4gMbGRvn5ZrMZ9fX1ipqOxx01REQUHIL6Htd3330HrVbZ4k033QSv1wsAiI+Ph9lsxp49e+Rxj8eD0tJS+XfCbDYb3G43ysrK5Jri4mJ4vV6kpKTINSUlJWhra5NrioqKkJiY2OXbhEREFDhBHVzjx4/HihUrsGPHDvz3v//F1q1b8bvf/Q4TJ04EAGg0GsyZMwcvvfQSPvzwQxw7dgwzZsyAxWJBZmYmAGDQoEEYN24cnnjiCRw8eBCffvopcnNzMXXqVFgsFgDAo48+Cp1Oh+zsbFRWVuKDDz7A6tWrFfewiIgoOAT1W4VvvPEGli5dimeeeQYNDQ2wWCx48sknsWzZMrlmwYIFaG5uRk5ODtxuN1JTU1FYWIiwsDC5ZtOmTcjNzcWYMWOg1WoxefJkrFmzRh43Go34+OOPMWvWLNx1113o168fli1bdsmp8EREFDgacfHXUJDfPB4PjEYjJEmCwWAIWB9VVVXIyclB8+CH4I3oF7A+iEg9tM3/i4gTH6KgoAAJCQkB6cGXc2hQv1VIRET0Q0H9ViH5T9siBboFIlIJtZ0vGFw3GKPRiFCdHvhyf6BbISIVCdXpr/jTVcGCwXWDMZlM+Muf34UkqesvKOp5NTU1WLFiBRYvXoy4uLhAt0NBxmg0dvoWomDF4LoBmUwm1fwfkK6/uLi4gN2AJ+oOnJxBRESqwuAiIiJVYXAREZGqMLiIiEhVGFxERKQqDC4iIlIVBhcREakKg4uIiFSFwUVERKrC4CIiIlVhcBERkaowuIiISFUYXEREpCoMLiIiUhUGFxERqQqDi4iIVIXBRUREqhL0wfX111/jscceQ9++fREeHo5hw4bh8OHD8rgQAsuWLUP//v0RHh6O9PR0nDp1SrGNxsZGZGVlwWAwIDIyEtnZ2WhqalLUVFRUYNSoUQgLC4PVakV+fv51OT4iIvJNUAfXmTNncN999yE0NBS7du3CiRMn8OqrryIqKkquyc/Px5o1a7B+/XqUlpYiIiICdrsdLS0tck1WVhYqKytRVFSE7du3o6SkBDk5OfK4x+PB2LFjERcXh7KyMqxatQrLly9HQUHBdT1eIiK6CiKILVy4UKSmpl5y3Ov1CrPZLFatWiWvc7vdQq/Xi7/+9a9CCCFOnDghAIhDhw7JNbt27RIajUZ8/fXXQggh3nrrLREVFSVaW1sV+05MTLzkvltaWoQkSfJSV1cnAAhJkvw+XqKe5HQ6RVpamnA6nYFuhagTSZKu+hwa1FdcH374IZKTk/Gzn/0MMTExuPPOO/GHP/xBHq+urobL5UJ6erq8zmg0IiUlBQ6HAwDgcDgQGRmJ5ORkuSY9PR1arRalpaVyzejRo6HT6eQau90Op9OJM2fOdNlbXl4ejEajvFit1m49diIi6lpQB9eXX36JdevWYeDAgdi9ezeefvppPPfcc3jnnXcAAC6XCwBgMpkUzzOZTPKYy+VCTEyMYjwkJATR0dGKmq62cfE+fmjRokWQJEle6urqrvFoiYjoaoQEuoHL8Xq9SE5Oxm9/+1sAwJ133onjx49j/fr1mDlzZkB70+v10Ov1Ae2BiOjHKKivuPr374/Bgwcr1g0aNAi1tbUAALPZDACor69X1NTX18tjZrMZDQ0NivELFy6gsbFRUdPVNi7eBxERBYegDq777rsPTqdTsa6qqgpxcXEAgPj4eJjNZuzZs0ce93g8KC0thc1mAwDYbDa43W6UlZXJNcXFxfB6vUhJSZFrSkpK0NbWJtcUFRUhMTFRMYORiIiCwHWYLOK3gwcPipCQELFixQpx6tQpsWnTJtGrVy/xl7/8Ra5ZuXKliIyMFP/4xz9ERUWFmDBhgoiPjxfnzp2Ta8aNGyfuvPNOUVpaKj755BMxcOBAMW3aNHnc7XYLk8kkpk+fLo4fPy7ef/990atXL/H73//+qnv1ZUYMUSBwViEFM1/OoUEdXEII8dFHH4mhQ4cKvV4vkpKSREFBgWLc6/WKpUuXCpPJJPR6vRgzZkyn/zC//fZbMW3aNNG7d29hMBjE448/Ls6ePauoOXr0qEhNTRV6vV4MGDBArFy50qc+GVwU7BhcFMx8OYdqhBAisNd8NwaPxwOj0QhJkmAwGALdDlEnVVVVyMnJQUFBARISEgLdDpGCL+fQoL7HRURE9EMMLiIiUhUGFxERqQqDi4iIVIXBRUREqsLgIiIiVWFwERGRqjC4iIhIVRhcRESkKgwuIiJSFQYXERGpCoOLiIhUhcFFRESqwuAiIiJVYXAREZGqMLiIiEhVGFxERKQqDC4iIlIVBhcREakKg4uIiFSFwUVERKrC4CIiIlVRVXCtXLkSGo0Gc+bMkde1tLRg1qxZ6Nu3L3r37o3Jkyejvr5e8bza2lpkZGSgV69eiImJwfz583HhwgVFzb59+zBy5Ejo9Xrcfvvt2Lhx43U4IiIi8pVqguvQoUP4/e9/jzvuuEOxfu7cufjoo4+wZcsW7N+/H6dPn8akSZPk8fb2dmRkZOD8+fM4cOAA3nnnHWzcuBHLli2Ta6qrq5GRkYH7778f5eXlmDNnDn7xi19g9+7d1+34iIjoKgkVOHv2rBg4cKAoKioSaWlpYvbs2UIIIdxutwgNDRVbtmyRa0+ePCkACIfDIYQQYufOnUKr1QqXyyXXrFu3ThgMBtHa2iqEEGLBggViyJAhin1OmTJF2O32q+5RkiQBQEiS5O9hEvUop9Mp0tLShNPpDHQrRJ34cg5VxRXXrFmzkJGRgfT0dMX6srIytLW1KdYnJSUhNjYWDocDAOBwODBs2DCYTCa5xm63w+PxoLKyUq754bbtdru8ja60trbC4/EoFiIi6nkhgW7gSt5//3189tlnOHToUKcxl8sFnU6HyMhIxXqTyQSXyyXXXBxaHeMdY5er8Xg8OHfuHMLDwzvtOy8vD7/+9a/9Pi4iIvJPUF9x1dXVYfbs2di0aRPCwsIC3Y7CokWLIEmSvNTV1QW6JSKiH4WgDq6ysjI0NDRg5MiRCAkJQUhICPbv3481a9YgJCQEJpMJ58+fh9vtVjyvvr4eZrMZAGA2mzvNMux4fKUag8HQ5dUWAOj1ehgMBsVCREQ9L6iDa8yYMTh27BjKy8vlJTk5GVlZWfK/Q0NDsWfPHvk5TqcTtbW1sNlsAACbzYZjx46hoaFBrikqKoLBYMDgwYPlmou30VHTsQ0iIgoeQX2Pq0+fPhg6dKhiXUREBPr27Suvz87Oxrx58xAdHQ2DwYBnn30WNpsN99xzDwBg7NixGDx4MKZPn478/Hy4XC4sWbIEs2bNgl6vBwA89dRTWLt2LRYsWICf//znKC4uxubNm7Fjx47re8BERHRFQR1cV+O1116DVqvF5MmT0draCrvdjrfeeksev+mmm7B9+3Y8/fTTsNlsiIiIwMyZM/Hiiy/KNfHx8dixYwfmzp2L1atX4+abb8Yf//hH2O32QBwSERFdhkYIIQLdxI3A4/HAaDRCkiTe76KgVFVVhZycHBQUFCAhISHQ7RAp+HIODep7XERERD/E4CIiIlVhcBERkaowuIiISFUYXEREpCoMLiIiUhUGFxERqYrqP4BMdCktLS2ora0NdBtBo6amRvG/BMTGxgbdF3jTlTG46IZVW1uLnJycQLcRdFasWBHoFoIGP4ytTgwuumHFxsaioKAg0G1QEIuNjQ10C+QHBhfdsMLCwvjXNNENiJMziIhIVRhcRESkKgwuIiJSFQYXERGpCoOLiIhUhcFFRESqwuAiIiJV4ee4uokQAsD3Pz9NRES+6Th3dpxLL4fB1U3Onj0LALBarQHuhIhIvc6ePQuj0XjZGo24mnijK/J6vTh9+jT69OkDjUYT6HaIOvF4PLBarairq4PBYAh0O0QKQgicPXsWFosFWu3l72IxuIh+JDweD4xGIyRJYnCRqnFyBhERqQqDi4iIVIXBRfQjodfr8T//8z/Q6/WBboXomvAeFxERqQqvuIiISFUYXEREpCoMLiIiUhUGFxERqQqDi4iIVIXBRUREqsLgIiIiVWFwERGRqvwfBm1MdG8AdG8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,1)\n",
    "sns.boxplot(df2['LotArea'])\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,2)\n",
    "sns.boxplot(df2['FullBath'])\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,3)\n",
    "sns.boxplot(df2['BedroomAbvGr'])\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(4,4,4)\n",
    "sns.boxplot(df2['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       2\n",
       "3       2\n",
       "4       2\n",
       "       ..\n",
       "1454    1\n",
       "1455    1\n",
       "1456    1\n",
       "1457    1\n",
       "1458    2\n",
       "Name: FullBath, Length: 1459, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['FullBath']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'LotArea', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'SalePrice',\n",
       "       'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 3)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df3[['LotArea','FullBath','BedroomAbvGr']]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459,)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df3['SalePrice']\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8933432701342473"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr_clf = LinearRegression()\n",
    "lr_clf.fit(X_train,y_train)\n",
    "lr_clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88415045, 0.88609024, 0.88020194, 0.89505678, 0.89915801])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
    "\n",
    "cross_val_score(LinearRegression(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Manas\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "10 fits failed out of a total of 20.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "10 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Manas\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\Manas\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py\", line 1145, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"C:\\Users\\Manas\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py\", line 638, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"C:\\Users\\Manas\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\utils\\_param_validation.py\", line 96, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'criterion' parameter of DecisionTreeRegressor must be a str among {'friedman_mse', 'squared_error', 'absolute_error', 'poisson'}. Got 'mse' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\Manas\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [       nan        nan 0.82715792 0.82605086]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>linear_regression</td>\n",
       "      <td>0.888931</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'posit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>0.888941</td>\n",
       "      <td>{'alpha': 2, 'selection': 'random'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>0.827158</td>\n",
       "      <td>{'criterion': 'friedman_mse', 'splitter': 'best'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  best_score  \\\n",
       "0  linear_regression    0.888931   \n",
       "1              lasso    0.888941   \n",
       "2      decision_tree    0.827158   \n",
       "\n",
       "                                         best_params  \n",
       "0  {'copy_X': True, 'fit_intercept': True, 'posit...  \n",
       "1                {'alpha': 2, 'selection': 'random'}  \n",
       "2  {'criterion': 'friedman_mse', 'splitter': 'best'}  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def find_best_model_using_gridsearchcv(X,y):\n",
    "    algos = {\n",
    "        'linear_regression' : {\n",
    "            'model': LinearRegression(),\n",
    "            'params': {\n",
    "                 'fit_intercept':[True,False],\n",
    "    \n",
    "    'positive':[True,False], \n",
    "    'copy_X':[True,False],\n",
    "            }\n",
    "        },\n",
    "        'lasso': {\n",
    "            'model': Lasso(),\n",
    "            'params': {\n",
    "                'alpha': [1,2],\n",
    "                'selection': ['random', 'cyclic']\n",
    "            }\n",
    "        },\n",
    "        'decision_tree': {\n",
    "            'model': DecisionTreeRegressor(),\n",
    "            'params': {\n",
    "                'criterion' : ['mse','friedman_mse'],\n",
    "                'splitter': ['best','random']\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    scores = []\n",
    "    cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
    "    for algo_name, config in algos.items():\n",
    "        gs =  GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
    "        gs.fit(X,y)\n",
    "        scores.append({\n",
    "            'model': algo_name,\n",
    "            'best_score': gs.best_score_,\n",
    "            'best_params': gs.best_params_\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
    "\n",
    "find_best_model_using_gridsearchcv(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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