house-price-prediction-pv.ipynb

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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "from pandas.plotting import scatter_matrix\n",
    "import math\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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    "tags": []
   },
   "source": [
    "# Explore data"
   ]
  },
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "train_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')\n",
    "test_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')"
   ]
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   "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>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",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
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   ],
   "source": [
    "train_data.head()"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     588 non-null    object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
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    {
     "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": 4,
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   ],
   "source": [
    "train_data.columns"
   ]
  },
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   },
   "source": [
    "## Select Attributes\n",
    "- For simply, consider only number-attributes"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual',\n",
       "       'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1',\n",
       "       'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd',\n",
       "       'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF',\n",
       "       'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',\n",
       "       'MiscVal', 'MoSold', 'YrSold', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_num =  train_data.copy()\n",
    "train_num = train_num.select_dtypes(include=['float64', 'int64'])\n",
    "train_num.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9522814",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:05.198784Z",
     "iopub.status.busy": "2024-07-27T11:05:05.198384Z",
     "iopub.status.idle": "2024-07-27T11:05:05.206085Z",
     "shell.execute_reply": "2024-07-27T11:05:05.204858Z"
    },
    "papermill": {
     "duration": 0.028856,
     "end_time": "2024-07-27T11:05:05.208554",
     "exception": false,
     "start_time": "2024-07-27T11:05:05.179698",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "attribute = ['LotArea', 'OverallQual',\n",
    "       'OverallCond', 'YearBuilt', 'YearRemodAdd','TotalBsmtSF',\n",
    "       'LowQualFinSF', 'GrLivArea', 'FullBath',\n",
    "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', \n",
    "       'GarageCars',  'MiscVal', 'YrSold', 'LotFrontage','Fireplaces','WoodDeckSF',\n",
    "       'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea','SalePrice']\n",
    "train_num = train_num[attribute]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4af7ee81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:05.245129Z",
     "iopub.status.busy": "2024-07-27T11:05:05.244698Z",
     "iopub.status.idle": "2024-07-27T11:05:05.258158Z",
     "shell.execute_reply": "2024-07-27T11:05:05.256717Z"
    },
    "papermill": {
     "duration": 0.035065,
     "end_time": "2024-07-27T11:05:05.260934",
     "exception": false,
     "start_time": "2024-07-27T11:05:05.225869",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SalePrice        1.000000\n",
      "OverallQual      0.790982\n",
      "GrLivArea        0.708624\n",
      "GarageCars       0.640409\n",
      "TotalBsmtSF      0.613581\n",
      "FullBath         0.560664\n",
      "YearBuilt        0.522897\n",
      "YearRemodAdd     0.507101\n",
      "Fireplaces       0.466929\n",
      "LotFrontage      0.351799\n",
      "WoodDeckSF       0.324413\n",
      "OpenPorchSF      0.315856\n",
      "HalfBath         0.284108\n",
      "LotArea          0.263843\n",
      "BedroomAbvGr     0.168213\n",
      "ScreenPorch      0.111447\n",
      "PoolArea         0.092404\n",
      "3SsnPorch        0.044584\n",
      "MiscVal         -0.021190\n",
      "LowQualFinSF    -0.025606\n",
      "YrSold          -0.028923\n",
      "OverallCond     -0.077856\n",
      "EnclosedPorch   -0.128578\n",
      "KitchenAbvGr    -0.135907\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "corr_candidate = train_num.corr()['SalePrice'].sort_values(ascending=False)\n",
    "print(corr_candidate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5c34272",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:05.299163Z",
     "iopub.status.busy": "2024-07-27T11:05:05.298690Z",
     "iopub.status.idle": "2024-07-27T11:05:05.308001Z",
     "shell.execute_reply": "2024-07-27T11:05:05.306686Z"
    },
    "papermill": {
     "duration": 0.031803,
     "end_time": "2024-07-27T11:05:05.311101",
     "exception": false,
     "start_time": "2024-07-27T11:05:05.279298",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF',\n",
       "       'FullBath', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', 'LotFrontage',\n",
       "       'WoodDeckSF', 'OpenPorchSF'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_attr_num = corr_candidate[abs(corr_candidate.values) > 0.3].index\n",
    "select_attr_num"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5bd043",
   "metadata": {
    "papermill": {
     "duration": 0.01723,
     "end_time": "2024-07-27T11:05:05.346609",
     "exception": false,
     "start_time": "2024-07-27T11:05:05.329379",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e915acc3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:05.383369Z",
     "iopub.status.busy": "2024-07-27T11:05:05.382941Z",
     "iopub.status.idle": "2024-07-27T11:05:05.400348Z",
     "shell.execute_reply": "2024-07-27T11:05:05.399244Z"
    },
    "papermill": {
     "duration": 0.038609,
     "end_time": "2024-07-27T11:05:05.402754",
     "exception": false,
     "start_time": "2024-07-27T11:05:05.364145",
     "status": "completed"
    },
    "tags": []
   },
   "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>SalePrice</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208500</td>\n",
       "      <td>7</td>\n",
       "      <td>1710</td>\n",
       "      <td>2</td>\n",
       "      <td>856</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>181500</td>\n",
       "      <td>6</td>\n",
       "      <td>1262</td>\n",
       "      <td>2</td>\n",
       "      <td>1262</td>\n",
       "      <td>2</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>223500</td>\n",
       "      <td>7</td>\n",
       "      <td>1786</td>\n",
       "      <td>2</td>\n",
       "      <td>920</td>\n",
       "      <td>2</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140000</td>\n",
       "      <td>7</td>\n",
       "      <td>1717</td>\n",
       "      <td>3</td>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>250000</td>\n",
       "      <td>8</td>\n",
       "      <td>2198</td>\n",
       "      <td>3</td>\n",
       "      <td>1145</td>\n",
       "      <td>2</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>84.0</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalePrice  OverallQual  GrLivArea  GarageCars  TotalBsmtSF  FullBath  \\\n",
       "0     208500            7       1710           2          856         2   \n",
       "1     181500            6       1262           2         1262         2   \n",
       "2     223500            7       1786           2          920         2   \n",
       "3     140000            7       1717           3          756         1   \n",
       "4     250000            8       2198           3         1145         2   \n",
       "\n",
       "   YearBuilt  YearRemodAdd  Fireplaces  LotFrontage  WoodDeckSF  OpenPorchSF  \n",
       "0       2003          2003           0         65.0           0           61  \n",
       "1       1976          1976           1         80.0         298            0  \n",
       "2       2001          2002           1         68.0           0           42  \n",
       "3       1915          1970           1         60.0           0           35  \n",
       "4       2000          2000           1         84.0         192           84  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_attr_num = train_num[select_attr_num]\n",
    "train_attr_num.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa5a606",
   "metadata": {
    "papermill": {
     "duration": 0.017682,
     "end_time": "2024-07-27T11:05:05.438387",
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     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6c70f2fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:05.476086Z",
     "iopub.status.busy": "2024-07-27T11:05:05.475577Z",
     "iopub.status.idle": "2024-07-27T11:05:09.181469Z",
     "shell.execute_reply": "2024-07-27T11:05:09.180262Z"
    },
    "papermill": {
     "duration": 3.733434,
     "end_time": "2024-07-27T11:05:09.189623",
     "exception": false,
     "start_time": "2024-07-27T11:05:05.456189",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'SalePrice'}>,\n",
       "        <Axes: title={'center': 'OverallQual'}>,\n",
       "        <Axes: title={'center': 'GrLivArea'}>],\n",
       "       [<Axes: title={'center': 'GarageCars'}>,\n",
       "        <Axes: title={'center': 'TotalBsmtSF'}>,\n",
       "        <Axes: title={'center': 'FullBath'}>],\n",
       "       [<Axes: title={'center': 'YearBuilt'}>,\n",
       "        <Axes: title={'center': 'YearRemodAdd'}>,\n",
       "        <Axes: title={'center': 'Fireplaces'}>],\n",
       "       [<Axes: title={'center': 'LotFrontage'}>,\n",
       "        <Axes: title={'center': 'WoodDeckSF'}>,\n",
       "        <Axes: title={'center': 'OpenPorchSF'}>]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_attr_num.hist(bins=50, figsize=(14,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0d5c90",
   "metadata": {
    "papermill": {
     "duration": 0.020492,
     "end_time": "2024-07-27T11:05:09.238940",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.218448",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    " - Don't need to convert them to normal distribution.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5767600b",
   "metadata": {
    "papermill": {
     "duration": 0.023025,
     "end_time": "2024-07-27T11:05:09.281753",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.258728",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bdbcc5aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:09.330883Z",
     "iopub.status.busy": "2024-07-27T11:05:09.330353Z",
     "iopub.status.idle": "2024-07-27T11:05:09.341685Z",
     "shell.execute_reply": "2024-07-27T11:05:09.340366Z"
    },
    "papermill": {
     "duration": 0.037291,
     "end_time": "2024-07-27T11:05:09.345684",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.308393",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice         0\n",
       "OverallQual       0\n",
       "GrLivArea         0\n",
       "GarageCars        0\n",
       "TotalBsmtSF       0\n",
       "FullBath          0\n",
       "YearBuilt         0\n",
       "YearRemodAdd      0\n",
       "Fireplaces        0\n",
       "LotFrontage     259\n",
       "WoodDeckSF        0\n",
       "OpenPorchSF       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_attr_num.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "811afc09",
   "metadata": {
    "papermill": {
     "duration": 0.019583,
     "end_time": "2024-07-27T11:05:09.387188",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.367605",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Fill null with median value to 'LotFrontage' attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7449c66f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:09.429260Z",
     "iopub.status.busy": "2024-07-27T11:05:09.428267Z",
     "iopub.status.idle": "2024-07-27T11:05:09.438140Z",
     "shell.execute_reply": "2024-07-27T11:05:09.436896Z"
    },
    "papermill": {
     "duration": 0.034191,
     "end_time": "2024-07-27T11:05:09.441141",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.406950",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19/1899149328.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  train_attr_num['LotFrontage'].fillna(train_attr_num['LotFrontage'].median(), inplace=True)\n",
      "/tmp/ipykernel_19/1899149328.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_attr_num['LotFrontage'].fillna(train_attr_num['LotFrontage'].median(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "train_attr_num['LotFrontage'].fillna(train_attr_num['LotFrontage'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6c8345c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:09.483131Z",
     "iopub.status.busy": "2024-07-27T11:05:09.482667Z",
     "iopub.status.idle": "2024-07-27T11:05:09.492359Z",
     "shell.execute_reply": "2024-07-27T11:05:09.491135Z"
    },
    "papermill": {
     "duration": 0.033956,
     "end_time": "2024-07-27T11:05:09.495191",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.461235",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice       0\n",
       "OverallQual     0\n",
       "GrLivArea       0\n",
       "GarageCars      0\n",
       "TotalBsmtSF     0\n",
       "FullBath        0\n",
       "YearBuilt       0\n",
       "YearRemodAdd    0\n",
       "Fireplaces      0\n",
       "LotFrontage     0\n",
       "WoodDeckSF      0\n",
       "OpenPorchSF     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_attr_num.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b276431a",
   "metadata": {
    "papermill": {
     "duration": 0.020746,
     "end_time": "2024-07-27T11:05:09.536321",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.515575",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Check duplicate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4305a68e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:09.641052Z",
     "iopub.status.busy": "2024-07-27T11:05:09.640635Z",
     "iopub.status.idle": "2024-07-27T11:05:09.652337Z",
     "shell.execute_reply": "2024-07-27T11:05:09.651033Z"
    },
    "papermill": {
     "duration": 0.036504,
     "end_time": "2024-07-27T11:05:09.655187",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.618683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_attr_num.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21807ebd",
   "metadata": {
    "papermill": {
     "duration": 0.020509,
     "end_time": "2024-07-27T11:05:09.695976",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.675467",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "None of duplicated row"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34da2dc9",
   "metadata": {
    "papermill": {
     "duration": 0.019857,
     "end_time": "2024-07-27T11:05:09.736107",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.716250",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Quick train machine learning model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c4e016",
   "metadata": {
    "papermill": {
     "duration": 0.020054,
     "end_time": "2024-07-27T11:05:09.776353",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.756299",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a6a8523a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:09.819304Z",
     "iopub.status.busy": "2024-07-27T11:05:09.818732Z",
     "iopub.status.idle": "2024-07-27T11:05:11.330962Z",
     "shell.execute_reply": "2024-07-27T11:05:11.329912Z"
    },
    "papermill": {
     "duration": 1.53713,
     "end_time": "2024-07-27T11:05:11.333700",
     "exception": false,
     "start_time": "2024-07-27T11:05:09.796570",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = train_attr_num[['OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF',\n",
    "       'FullBath', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', 'LotFrontage',\n",
    "       'WoodDeckSF', 'OpenPorchSF']]\n",
    "y = train_attr_num['SalePrice']\n",
    "# train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aad48045",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:11.376411Z",
     "iopub.status.busy": "2024-07-27T11:05:11.375990Z",
     "iopub.status.idle": "2024-07-27T11:05:11.418662Z",
     "shell.execute_reply": "2024-07-27T11:05:11.416907Z"
    },
    "papermill": {
     "duration": 0.075977,
     "end_time": "2024-07-27T11:05:11.430186",
     "exception": false,
     "start_time": "2024-07-27T11:05:11.354209",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lin_model = LinearRegression().fit(X_train, y_train)\n",
    "y_pred = lin_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4b7844d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:11.549332Z",
     "iopub.status.busy": "2024-07-27T11:05:11.548905Z",
     "iopub.status.idle": "2024-07-27T11:05:11.556153Z",
     "shell.execute_reply": "2024-07-27T11:05:11.555055Z"
    },
    "papermill": {
     "duration": 0.035505,
     "end_time": "2024-07-27T11:05:11.558965",
     "exception": false,
     "start_time": "2024-07-27T11:05:11.523460",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE =  36934.27623504811\n"
     ]
    }
   ],
   "source": [
    "# Evaluation\n",
    "from sklearn.metrics import mean_squared_error\n",
    "RMSE = mean_squared_error(y_train, y_pred, squared=False)\n",
    "print('RMSE = ', RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad0bc0b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:11.601502Z",
     "iopub.status.busy": "2024-07-27T11:05:11.601083Z",
     "iopub.status.idle": "2024-07-27T11:05:11.647294Z",
     "shell.execute_reply": "2024-07-27T11:05:11.646076Z"
    },
    "papermill": {
     "duration": 0.070706,
     "end_time": "2024-07-27T11:05:11.650138",
     "exception": false,
     "start_time": "2024-07-27T11:05:11.579432",
     "status": "completed"
    },
    "tags": []
   },
   "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>SalePrice</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>180921.195890</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>1515.463699</td>\n",
       "      <td>1.767123</td>\n",
       "      <td>1057.429452</td>\n",
       "      <td>1.565068</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>0.613014</td>\n",
       "      <td>69.863699</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>79442.502883</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>525.480383</td>\n",
       "      <td>0.747315</td>\n",
       "      <td>438.705324</td>\n",
       "      <td>0.550916</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>0.644666</td>\n",
       "      <td>22.027677</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>34900.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>129975.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1129.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>795.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>163000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1464.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>991.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>214000.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1776.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1298.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>755000.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5642.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6110.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SalePrice  OverallQual    GrLivArea   GarageCars  TotalBsmtSF  \\\n",
       "count    1460.000000  1460.000000  1460.000000  1460.000000  1460.000000   \n",
       "mean   180921.195890     6.099315  1515.463699     1.767123  1057.429452   \n",
       "std     79442.502883     1.382997   525.480383     0.747315   438.705324   \n",
       "min     34900.000000     1.000000   334.000000     0.000000     0.000000   \n",
       "25%    129975.000000     5.000000  1129.500000     1.000000   795.750000   \n",
       "50%    163000.000000     6.000000  1464.000000     2.000000   991.500000   \n",
       "75%    214000.000000     7.000000  1776.750000     2.000000  1298.250000   \n",
       "max    755000.000000    10.000000  5642.000000     4.000000  6110.000000   \n",
       "\n",
       "          FullBath    YearBuilt  YearRemodAdd   Fireplaces  LotFrontage  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1460.000000  1460.000000   \n",
       "mean      1.565068  1971.267808   1984.865753     0.613014    69.863699   \n",
       "std       0.550916    30.202904     20.645407     0.644666    22.027677   \n",
       "min       0.000000  1872.000000   1950.000000     0.000000    21.000000   \n",
       "25%       1.000000  1954.000000   1967.000000     0.000000    60.000000   \n",
       "50%       2.000000  1973.000000   1994.000000     1.000000    69.000000   \n",
       "75%       2.000000  2000.000000   2004.000000     1.000000    79.000000   \n",
       "max       3.000000  2010.000000   2010.000000     3.000000   313.000000   \n",
       "\n",
       "        WoodDeckSF  OpenPorchSF  \n",
       "count  1460.000000  1460.000000  \n",
       "mean     94.244521    46.660274  \n",
       "std     125.338794    66.256028  \n",
       "min       0.000000     0.000000  \n",
       "25%       0.000000     0.000000  \n",
       "50%       0.000000    25.000000  \n",
       "75%     168.000000    68.000000  \n",
       "max     857.000000   547.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compared to sd.\n",
    "train_attr_num.describe() # sd. of SalePrice is 79442"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4e497c15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:11.694617Z",
     "iopub.status.busy": "2024-07-27T11:05:11.694211Z",
     "iopub.status.idle": "2024-07-27T11:05:11.705129Z",
     "shell.execute_reply": "2024-07-27T11:05:11.703541Z"
    },
    "papermill": {
     "duration": 0.041695,
     "end_time": "2024-07-27T11:05:11.713198",
     "exception": false,
     "start_time": "2024-07-27T11:05:11.671503",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score =  0.7646752851929541\n"
     ]
    }
   ],
   "source": [
    "print('Score = ', lin_model.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ccde5f4",
   "metadata": {
    "papermill": {
     "duration": 0.044642,
     "end_time": "2024-07-27T11:05:11.801899",
     "exception": false,
     "start_time": "2024-07-27T11:05:11.757257",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Improve Model - Clear Outliners, Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fc6c9906",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:11.856981Z",
     "iopub.status.busy": "2024-07-27T11:05:11.856531Z",
     "iopub.status.idle": "2024-07-27T11:05:13.556708Z",
     "shell.execute_reply": "2024-07-27T11:05:13.555299Z"
    },
    "papermill": {
     "duration": 1.72591,
     "end_time": "2024-07-27T11:05:13.559266",
     "exception": false,
     "start_time": "2024-07-27T11:05:11.833356",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize box plot - Outliners \n",
    "import matplotlib.pyplot as plt\n",
    "for column in train_attr_num:\n",
    "    plt.figure(figsize=(2,2))\n",
    "    train_attr_num.boxplot([column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bbc44c3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:13.609329Z",
     "iopub.status.busy": "2024-07-27T11:05:13.608897Z",
     "iopub.status.idle": "2024-07-27T11:05:13.617223Z",
     "shell.execute_reply": "2024-07-27T11:05:13.615917Z"
    },
    "papermill": {
     "duration": 0.036707,
     "end_time": "2024-07-27T11:05:13.620125",
     "exception": false,
     "start_time": "2024-07-27T11:05:13.583418",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def filterOutliners(data, col):\n",
    "    Q1 = np.quantile(data[col], 0.25)\n",
    "    Q3 = np.quantile(data[col], 0.75)\n",
    "    IQR = Q3-Q1\n",
    "    lower_range = Q1-1.5*IQR\n",
    "    upper_range = Q3+1.5*IQR\n",
    "    #print('Q1 = ', Q1, ' Q3 = ', Q3)\n",
    "    #print('lower_range = ', lower_range, ' upper_range = ', upper_range)\n",
    "    outliner_free_list = [] #list\n",
    "\n",
    "    outliner_free_list = [x for x in data[col] if ((x >= lower_range) & (x <= upper_range))]\n",
    "    #print(len(outliner_free_list))\n",
    "\n",
    "    #print(outliner_free_list)\n",
    "    filtered_data = data[data[col].isin(outliner_free_list)] # True if this data is not an outliner\n",
    "    return filtered_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4ef8e774",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:13.672971Z",
     "iopub.status.busy": "2024-07-27T11:05:13.672477Z",
     "iopub.status.idle": "2024-07-27T11:05:13.769552Z",
     "shell.execute_reply": "2024-07-27T11:05:13.768358Z"
    },
    "papermill": {
     "duration": 0.127788,
     "end_time": "2024-07-27T11:05:13.773471",
     "exception": false,
     "start_time": "2024-07-27T11:05:13.645683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop @column =  SalePrice\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1399 entries, 0 to 1459\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1399 non-null   int64  \n",
      " 1   OverallQual   1399 non-null   int64  \n",
      " 2   GrLivArea     1399 non-null   int64  \n",
      " 3   GarageCars    1399 non-null   int64  \n",
      " 4   TotalBsmtSF   1399 non-null   int64  \n",
      " 5   FullBath      1399 non-null   int64  \n",
      " 6   YearBuilt     1399 non-null   int64  \n",
      " 7   YearRemodAdd  1399 non-null   int64  \n",
      " 8   Fireplaces    1399 non-null   int64  \n",
      " 9   LotFrontage   1399 non-null   float64\n",
      " 10  WoodDeckSF    1399 non-null   int64  \n",
      " 11  OpenPorchSF   1399 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 142.1 KB\n",
      "None\n",
      "loop @column =  OverallQual\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1397 entries, 0 to 1459\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1397 non-null   int64  \n",
      " 1   OverallQual   1397 non-null   int64  \n",
      " 2   GrLivArea     1397 non-null   int64  \n",
      " 3   GarageCars    1397 non-null   int64  \n",
      " 4   TotalBsmtSF   1397 non-null   int64  \n",
      " 5   FullBath      1397 non-null   int64  \n",
      " 6   YearBuilt     1397 non-null   int64  \n",
      " 7   YearRemodAdd  1397 non-null   int64  \n",
      " 8   Fireplaces    1397 non-null   int64  \n",
      " 9   LotFrontage   1397 non-null   float64\n",
      " 10  WoodDeckSF    1397 non-null   int64  \n",
      " 11  OpenPorchSF   1397 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 141.9 KB\n",
      "None\n",
      "loop @column =  GrLivArea\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1376 entries, 0 to 1459\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1376 non-null   int64  \n",
      " 1   OverallQual   1376 non-null   int64  \n",
      " 2   GrLivArea     1376 non-null   int64  \n",
      " 3   GarageCars    1376 non-null   int64  \n",
      " 4   TotalBsmtSF   1376 non-null   int64  \n",
      " 5   FullBath      1376 non-null   int64  \n",
      " 6   YearBuilt     1376 non-null   int64  \n",
      " 7   YearRemodAdd  1376 non-null   int64  \n",
      " 8   Fireplaces    1376 non-null   int64  \n",
      " 9   LotFrontage   1376 non-null   float64\n",
      " 10  WoodDeckSF    1376 non-null   int64  \n",
      " 11  OpenPorchSF   1376 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 139.8 KB\n",
      "None\n",
      "loop @column =  GarageCars\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1371 entries, 0 to 1459\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1371 non-null   int64  \n",
      " 1   OverallQual   1371 non-null   int64  \n",
      " 2   GrLivArea     1371 non-null   int64  \n",
      " 3   GarageCars    1371 non-null   int64  \n",
      " 4   TotalBsmtSF   1371 non-null   int64  \n",
      " 5   FullBath      1371 non-null   int64  \n",
      " 6   YearBuilt     1371 non-null   int64  \n",
      " 7   YearRemodAdd  1371 non-null   int64  \n",
      " 8   Fireplaces    1371 non-null   int64  \n",
      " 9   LotFrontage   1371 non-null   float64\n",
      " 10  WoodDeckSF    1371 non-null   int64  \n",
      " 11  OpenPorchSF   1371 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 139.2 KB\n",
      "None\n",
      "loop @column =  TotalBsmtSF\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1322 entries, 0 to 1459\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1322 non-null   int64  \n",
      " 1   OverallQual   1322 non-null   int64  \n",
      " 2   GrLivArea     1322 non-null   int64  \n",
      " 3   GarageCars    1322 non-null   int64  \n",
      " 4   TotalBsmtSF   1322 non-null   int64  \n",
      " 5   FullBath      1322 non-null   int64  \n",
      " 6   YearBuilt     1322 non-null   int64  \n",
      " 7   YearRemodAdd  1322 non-null   int64  \n",
      " 8   Fireplaces    1322 non-null   int64  \n",
      " 9   LotFrontage   1322 non-null   float64\n",
      " 10  WoodDeckSF    1322 non-null   int64  \n",
      " 11  OpenPorchSF   1322 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 134.3 KB\n",
      "None\n",
      "loop @column =  FullBath\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1322 entries, 0 to 1459\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1322 non-null   int64  \n",
      " 1   OverallQual   1322 non-null   int64  \n",
      " 2   GrLivArea     1322 non-null   int64  \n",
      " 3   GarageCars    1322 non-null   int64  \n",
      " 4   TotalBsmtSF   1322 non-null   int64  \n",
      " 5   FullBath      1322 non-null   int64  \n",
      " 6   YearBuilt     1322 non-null   int64  \n",
      " 7   YearRemodAdd  1322 non-null   int64  \n",
      " 8   Fireplaces    1322 non-null   int64  \n",
      " 9   LotFrontage   1322 non-null   float64\n",
      " 10  WoodDeckSF    1322 non-null   int64  \n",
      " 11  OpenPorchSF   1322 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 134.3 KB\n",
      "None\n",
      "loop @column =  YearBuilt\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1317 entries, 0 to 1459\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1317 non-null   int64  \n",
      " 1   OverallQual   1317 non-null   int64  \n",
      " 2   GrLivArea     1317 non-null   int64  \n",
      " 3   GarageCars    1317 non-null   int64  \n",
      " 4   TotalBsmtSF   1317 non-null   int64  \n",
      " 5   FullBath      1317 non-null   int64  \n",
      " 6   YearBuilt     1317 non-null   int64  \n",
      " 7   YearRemodAdd  1317 non-null   int64  \n",
      " 8   Fireplaces    1317 non-null   int64  \n",
      " 9   LotFrontage   1317 non-null   float64\n",
      " 10  WoodDeckSF    1317 non-null   int64  \n",
      " 11  OpenPorchSF   1317 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 133.8 KB\n",
      "None\n",
      "loop @column =  YearRemodAdd\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1317 entries, 0 to 1459\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1317 non-null   int64  \n",
      " 1   OverallQual   1317 non-null   int64  \n",
      " 2   GrLivArea     1317 non-null   int64  \n",
      " 3   GarageCars    1317 non-null   int64  \n",
      " 4   TotalBsmtSF   1317 non-null   int64  \n",
      " 5   FullBath      1317 non-null   int64  \n",
      " 6   YearBuilt     1317 non-null   int64  \n",
      " 7   YearRemodAdd  1317 non-null   int64  \n",
      " 8   Fireplaces    1317 non-null   int64  \n",
      " 9   LotFrontage   1317 non-null   float64\n",
      " 10  WoodDeckSF    1317 non-null   int64  \n",
      " 11  OpenPorchSF   1317 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 133.8 KB\n",
      "None\n",
      "loop @column =  Fireplaces\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1315 entries, 0 to 1459\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1315 non-null   int64  \n",
      " 1   OverallQual   1315 non-null   int64  \n",
      " 2   GrLivArea     1315 non-null   int64  \n",
      " 3   GarageCars    1315 non-null   int64  \n",
      " 4   TotalBsmtSF   1315 non-null   int64  \n",
      " 5   FullBath      1315 non-null   int64  \n",
      " 6   YearBuilt     1315 non-null   int64  \n",
      " 7   YearRemodAdd  1315 non-null   int64  \n",
      " 8   Fireplaces    1315 non-null   int64  \n",
      " 9   LotFrontage   1315 non-null   float64\n",
      " 10  WoodDeckSF    1315 non-null   int64  \n",
      " 11  OpenPorchSF   1315 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 133.6 KB\n",
      "None\n",
      "loop @column =  LotFrontage\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1197 entries, 0 to 1459\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1197 non-null   int64  \n",
      " 1   OverallQual   1197 non-null   int64  \n",
      " 2   GrLivArea     1197 non-null   int64  \n",
      " 3   GarageCars    1197 non-null   int64  \n",
      " 4   TotalBsmtSF   1197 non-null   int64  \n",
      " 5   FullBath      1197 non-null   int64  \n",
      " 6   YearBuilt     1197 non-null   int64  \n",
      " 7   YearRemodAdd  1197 non-null   int64  \n",
      " 8   Fireplaces    1197 non-null   int64  \n",
      " 9   LotFrontage   1197 non-null   float64\n",
      " 10  WoodDeckSF    1197 non-null   int64  \n",
      " 11  OpenPorchSF   1197 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 121.6 KB\n",
      "None\n",
      "loop @column =  WoodDeckSF\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1175 entries, 0 to 1458\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1175 non-null   int64  \n",
      " 1   OverallQual   1175 non-null   int64  \n",
      " 2   GrLivArea     1175 non-null   int64  \n",
      " 3   GarageCars    1175 non-null   int64  \n",
      " 4   TotalBsmtSF   1175 non-null   int64  \n",
      " 5   FullBath      1175 non-null   int64  \n",
      " 6   YearBuilt     1175 non-null   int64  \n",
      " 7   YearRemodAdd  1175 non-null   int64  \n",
      " 8   Fireplaces    1175 non-null   int64  \n",
      " 9   LotFrontage   1175 non-null   float64\n",
      " 10  WoodDeckSF    1175 non-null   int64  \n",
      " 11  OpenPorchSF   1175 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 119.3 KB\n",
      "None\n",
      "loop @column =  OpenPorchSF\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1109 entries, 0 to 1458\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   SalePrice     1109 non-null   int64  \n",
      " 1   OverallQual   1109 non-null   int64  \n",
      " 2   GrLivArea     1109 non-null   int64  \n",
      " 3   GarageCars    1109 non-null   int64  \n",
      " 4   TotalBsmtSF   1109 non-null   int64  \n",
      " 5   FullBath      1109 non-null   int64  \n",
      " 6   YearBuilt     1109 non-null   int64  \n",
      " 7   YearRemodAdd  1109 non-null   int64  \n",
      " 8   Fireplaces    1109 non-null   int64  \n",
      " 9   LotFrontage   1109 non-null   float64\n",
      " 10  WoodDeckSF    1109 non-null   int64  \n",
      " 11  OpenPorchSF   1109 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 112.6 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "global filtered_train\n",
    "i = 0\n",
    "for col in train_attr_num.columns:\n",
    "    if i == 0 :\n",
    "        filtered_train = filterOutliners(train_attr_num, col)\n",
    "        i += 1\n",
    "    else :\n",
    "        filtered_train = filterOutliners(filtered_train, col)\n",
    "    print('loop @column = ', col)\n",
    "    print(filtered_train.info())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ae0cc41a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:13.824615Z",
     "iopub.status.busy": "2024-07-27T11:05:13.824128Z",
     "iopub.status.idle": "2024-07-27T11:05:13.844407Z",
     "shell.execute_reply": "2024-07-27T11:05:13.843357Z"
    },
    "papermill": {
     "duration": 0.048743,
     "end_time": "2024-07-27T11:05:13.846850",
     "exception": false,
     "start_time": "2024-07-27T11:05:13.798107",
     "status": "completed"
    },
    "tags": []
   },
   "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>SalePrice</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208500</td>\n",
       "      <td>7</td>\n",
       "      <td>1710</td>\n",
       "      <td>2</td>\n",
       "      <td>856</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>181500</td>\n",
       "      <td>6</td>\n",
       "      <td>1262</td>\n",
       "      <td>2</td>\n",
       "      <td>1262</td>\n",
       "      <td>2</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>223500</td>\n",
       "      <td>7</td>\n",
       "      <td>1786</td>\n",
       "      <td>2</td>\n",
       "      <td>920</td>\n",
       "      <td>2</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140000</td>\n",
       "      <td>7</td>\n",
       "      <td>1717</td>\n",
       "      <td>3</td>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>250000</td>\n",
       "      <td>8</td>\n",
       "      <td>2198</td>\n",
       "      <td>3</td>\n",
       "      <td>1145</td>\n",
       "      <td>2</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>84.0</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>185000</td>\n",
       "      <td>7</td>\n",
       "      <td>1221</td>\n",
       "      <td>2</td>\n",
       "      <td>1221</td>\n",
       "      <td>2</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>175000</td>\n",
       "      <td>6</td>\n",
       "      <td>1647</td>\n",
       "      <td>2</td>\n",
       "      <td>953</td>\n",
       "      <td>2</td>\n",
       "      <td>1999</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>210000</td>\n",
       "      <td>6</td>\n",
       "      <td>2073</td>\n",
       "      <td>2</td>\n",
       "      <td>1542</td>\n",
       "      <td>2</td>\n",
       "      <td>1978</td>\n",
       "      <td>1988</td>\n",
       "      <td>2</td>\n",
       "      <td>85.0</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>266500</td>\n",
       "      <td>7</td>\n",
       "      <td>2340</td>\n",
       "      <td>1</td>\n",
       "      <td>1152</td>\n",
       "      <td>2</td>\n",
       "      <td>1941</td>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>142125</td>\n",
       "      <td>5</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>1950</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>366</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1109 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SalePrice  OverallQual  GrLivArea  GarageCars  TotalBsmtSF  FullBath  \\\n",
       "0        208500            7       1710           2          856         2   \n",
       "1        181500            6       1262           2         1262         2   \n",
       "2        223500            7       1786           2          920         2   \n",
       "3        140000            7       1717           3          756         1   \n",
       "4        250000            8       2198           3         1145         2   \n",
       "...         ...          ...        ...         ...          ...       ...   \n",
       "1454     185000            7       1221           2         1221         2   \n",
       "1455     175000            6       1647           2          953         2   \n",
       "1456     210000            6       2073           2         1542         2   \n",
       "1457     266500            7       2340           1         1152         2   \n",
       "1458     142125            5       1078           1         1078         1   \n",
       "\n",
       "      YearBuilt  YearRemodAdd  Fireplaces  LotFrontage  WoodDeckSF  \\\n",
       "0          2003          2003           0         65.0           0   \n",
       "1          1976          1976           1         80.0         298   \n",
       "2          2001          2002           1         68.0           0   \n",
       "3          1915          1970           1         60.0           0   \n",
       "4          2000          2000           1         84.0         192   \n",
       "...         ...           ...         ...          ...         ...   \n",
       "1454       2004          2005           0         62.0           0   \n",
       "1455       1999          2000           1         62.0           0   \n",
       "1456       1978          1988           2         85.0         349   \n",
       "1457       1941          2006           2         66.0           0   \n",
       "1458       1950          1996           0         68.0         366   \n",
       "\n",
       "      OpenPorchSF  \n",
       "0              61  \n",
       "1               0  \n",
       "2              42  \n",
       "3              35  \n",
       "4              84  \n",
       "...           ...  \n",
       "1454          113  \n",
       "1455           40  \n",
       "1456            0  \n",
       "1457           60  \n",
       "1458            0  \n",
       "\n",
       "[1109 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "08e85d5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:13.899727Z",
     "iopub.status.busy": "2024-07-27T11:05:13.899311Z",
     "iopub.status.idle": "2024-07-27T11:05:15.788957Z",
     "shell.execute_reply": "2024-07-27T11:05:15.787773Z"
    },
    "papermill": {
     "duration": 1.91912,
     "end_time": "2024-07-27T11:05:15.791764",
     "exception": false,
     "start_time": "2024-07-27T11:05:13.872644",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize box plot - Outliners \n",
    "import matplotlib.pyplot as plt\n",
    "for column in filtered_train.columns:\n",
    "    plt.figure(figsize=(2,2))\n",
    "    filtered_train.boxplot([column])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3b418ed",
   "metadata": {
    "papermill": {
     "duration": 0.029008,
     "end_time": "2024-07-27T11:05:15.849946",
     "exception": false,
     "start_time": "2024-07-27T11:05:15.820938",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Scaler (Normalization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4f51a73e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:15.908976Z",
     "iopub.status.busy": "2024-07-27T11:05:15.908510Z",
     "iopub.status.idle": "2024-07-27T11:05:15.921051Z",
     "shell.execute_reply": "2024-07-27T11:05:15.919872Z"
    },
    "papermill": {
     "duration": 0.045003,
     "end_time": "2024-07-27T11:05:15.923759",
     "exception": false,
     "start_time": "2024-07-27T11:05:15.878756",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler(feature_range=(-1,1))\n",
    "X = filtered_train.iloc[:,1:]\n",
    "y = filtered_train.iloc[:, 0]\n",
    "\n",
    "min_max_scaler.fit(X)\n",
    "#print(min_max_scaler.transform(X))\n",
    "X_scaled = pd.DataFrame(min_max_scaler.transform(X), columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9034a288",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:15.981842Z",
     "iopub.status.busy": "2024-07-27T11:05:15.981404Z",
     "iopub.status.idle": "2024-07-27T11:05:16.002360Z",
     "shell.execute_reply": "2024-07-27T11:05:16.001151Z"
    },
    "papermill": {
     "duration": 0.05273,
     "end_time": "2024-07-27T11:05:16.004618",
     "exception": false,
     "start_time": "2024-07-27T11:05:15.951888",
     "status": "completed"
    },
    "tags": []
   },
   "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>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.160584</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.166482</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.104478</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.227848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.248175</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.284129</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.467742</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.343284</td>\n",
       "      <td>0.425837</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.229927</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.095450</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.014925</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.468354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.166971</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.277469</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.516129</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.253731</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.556962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.605839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154273</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.854839</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.462687</td>\n",
       "      <td>-0.081340</td>\n",
       "      <td>0.063291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OverallQual  GrLivArea  GarageCars  TotalBsmtSF  FullBath  YearBuilt  \\\n",
       "0         0.25   0.160584    0.333333    -0.166482  0.333333   0.903226   \n",
       "1         0.00  -0.248175    0.333333     0.284129  0.333333   0.467742   \n",
       "2         0.25   0.229927    0.333333    -0.095450  0.333333   0.870968   \n",
       "3         0.25   0.166971    1.000000    -0.277469 -0.333333  -0.516129   \n",
       "4         0.50   0.605839    1.000000     0.154273  0.333333   0.854839   \n",
       "\n",
       "   YearRemodAdd  Fireplaces  LotFrontage  WoodDeckSF  OpenPorchSF  \n",
       "0      0.766667        -1.0    -0.104478   -1.000000    -0.227848  \n",
       "1     -0.133333         0.0     0.343284    0.425837    -1.000000  \n",
       "2      0.733333         0.0    -0.014925   -1.000000    -0.468354  \n",
       "3     -0.333333         0.0    -0.253731   -1.000000    -0.556962  \n",
       "4      0.666667         0.0     0.462687   -0.081340     0.063291  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e1dbbc7",
   "metadata": {
    "papermill": {
     "duration": 0.027799,
     "end_time": "2024-07-27T11:05:16.060811",
     "exception": false,
     "start_time": "2024-07-27T11:05:16.033012",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Train & Evaluate (After Removing Outliners, Normalizing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "280fe646",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:16.119574Z",
     "iopub.status.busy": "2024-07-27T11:05:16.119124Z",
     "iopub.status.idle": "2024-07-27T11:05:16.139570Z",
     "shell.execute_reply": "2024-07-27T11:05:16.137888Z"
    },
    "papermill": {
     "duration": 0.05293,
     "end_time": "2024-07-27T11:05:16.142414",
     "exception": false,
     "start_time": "2024-07-27T11:05:16.089484",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score =  0.8368490653012441\n",
      "RMSE =  23058.18267606375\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.33, random_state=42)\n",
    "lin_model2 = LinearRegression().fit(X_train, y_train)\n",
    "y_pred2 = lin_model2.predict(X_train)\n",
    "print('Score = ', lin_model2.score(X_train,y_train))\n",
    "print('RMSE = ', mean_squared_error(y_train, y_pred2, squared=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "677adb43",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:16.201080Z",
     "iopub.status.busy": "2024-07-27T11:05:16.200649Z",
     "iopub.status.idle": "2024-07-27T11:05:16.213052Z",
     "shell.execute_reply": "2024-07-27T11:05:16.211804Z"
    },
    "papermill": {
     "duration": 0.044524,
     "end_time": "2024-07-27T11:05:16.215460",
     "exception": false,
     "start_time": "2024-07-27T11:05:16.170936",
     "status": "completed"
    },
    "tags": []
   },
   "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>y_train</th>\n",
       "      <th>y_pred2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>159500</td>\n",
       "      <td>205463.598226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>146500</td>\n",
       "      <td>162603.074315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>175000</td>\n",
       "      <td>137733.503531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>143000</td>\n",
       "      <td>159526.596436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>126000</td>\n",
       "      <td>101102.135960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>236500</td>\n",
       "      <td>242867.088474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>171000</td>\n",
       "      <td>233784.831886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>226000</td>\n",
       "      <td>223026.244435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>239900</td>\n",
       "      <td>241628.737862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>227000</td>\n",
       "      <td>209674.314585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      y_train        y_pred2\n",
       "716    159500  205463.598226\n",
       "467    146500  162603.074315\n",
       "925    175000  137733.503531\n",
       "5      143000  159526.596436\n",
       "418    126000  101102.135960\n",
       "730    236500  242867.088474\n",
       "137    171000  233784.831886\n",
       "130    226000  223026.244435\n",
       "737    239900  241628.737862\n",
       "1015   227000  209674.314585"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = pd.DataFrame({'y_train':y_train, 'y_pred2':y_pred2})\n",
    "Y_pred.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61cc4dd",
   "metadata": {
    "papermill": {
     "duration": 0.028483,
     "end_time": "2024-07-27T11:05:16.272613",
     "exception": false,
     "start_time": "2024-07-27T11:05:16.244130",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Results from 2 models \n",
    "* model 1 -> fill na value with median : Score =  0.7647 RMSE =  36934.28\n",
    "\n",
    "* model 2 -> remove outliners and normalize data with -1 to 1 range : Score = 0.8368 RMSE = 23058.18\n",
    "\n",
    "* **So, we choose the model 2 for testing.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35234553",
   "metadata": {
    "papermill": {
     "duration": 0.029323,
     "end_time": "2024-07-27T11:05:16.330484",
     "exception": false,
     "start_time": "2024-07-27T11:05:16.301161",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ce62fa3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:16.390091Z",
     "iopub.status.busy": "2024-07-27T11:05:16.389636Z",
     "iopub.status.idle": "2024-07-27T11:05:16.401177Z",
     "shell.execute_reply": "2024-07-27T11:05:16.400074Z"
    },
    "papermill": {
     "duration": 0.044044,
     "end_time": "2024-07-27T11:05:16.403506",
     "exception": false,
     "start_time": "2024-07-27T11:05:16.359462",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score = 0.8334189291014072\n",
      "RMSE =  21874.0632799743\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = lin_model2.predict(X_test)\n",
    "print('Score =', lin_model2.score(X_test, y_test))\n",
    "print('RMSE = ', mean_squared_error(y_test, y_pred_test, squared=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2897541",
   "metadata": {
    "papermill": {
     "duration": 0.028705,
     "end_time": "2024-07-27T11:05:16.460906",
     "exception": false,
     "start_time": "2024-07-27T11:05:16.432201",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## For Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "57cdfcbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:16.520517Z",
     "iopub.status.busy": "2024-07-27T11:05:16.520075Z",
     "iopub.status.idle": "2024-07-27T11:05:16.545795Z",
     "shell.execute_reply": "2024-07-27T11:05:16.544512Z"
    },
    "papermill": {
     "duration": 0.058833,
     "end_time": "2024-07-27T11:05:16.548522",
     "exception": false,
     "start_time": "2024-07-27T11:05:16.489689",
     "status": "completed"
    },
    "tags": []
   },
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID = test_data['Id']\n",
    "test_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a5a24d99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:16.609803Z",
     "iopub.status.busy": "2024-07-27T11:05:16.609392Z",
     "iopub.status.idle": "2024-07-27T11:05:16.618162Z",
     "shell.execute_reply": "2024-07-27T11:05:16.616887Z"
    },
    "papermill": {
     "duration": 0.04246,
     "end_time": "2024-07-27T11:05:16.620864",
     "exception": false,
     "start_time": "2024-07-27T11:05:16.578404",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Choosing only attributes that we will use\n",
    "X_test_actual = test_data[['OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath',\n",
    "       'YearBuilt', 'YearRemodAdd', 'Fireplaces', 'LotFrontage', 'WoodDeckSF',\n",
    "       'OpenPorchSF']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "30430f38",
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>896</td>\n",
       "      <td>1.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1329.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1629</td>\n",
       "      <td>2.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
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       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
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       "      <td>1992</td>\n",
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       "      <td>0</td>\n",
       "      <td>82</td>\n",
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       "</table>\n",
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      "text/plain": [
       "   OverallQual  GrLivArea  GarageCars  TotalBsmtSF  FullBath  YearBuilt  \\\n",
       "0            5        896         1.0        882.0         1       1961   \n",
       "1            6       1329         1.0       1329.0         1       1958   \n",
       "2            5       1629         2.0        928.0         2       1997   \n",
       "3            6       1604         2.0        926.0         2       1998   \n",
       "4            8       1280         2.0       1280.0         2       1992   \n",
       "\n",
       "   YearRemodAdd  Fireplaces  LotFrontage  WoodDeckSF  OpenPorchSF  \n",
       "0          1961           0         80.0         140            0  \n",
       "1          1958           0         81.0         393           36  \n",
       "2          1998           1         74.0         212           34  \n",
       "3          1998           1         78.0         360           36  \n",
       "4          1992           0         43.0           0           82  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_actual.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "344961c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:16.763441Z",
     "iopub.status.busy": "2024-07-27T11:05:16.762200Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#scaling test data\n",
    "scaler_test = preprocessing.MinMaxScaler(feature_range=(-1,1))\n",
    "scaler_test.fit(X_test_actual)\n",
    "X_test_actual_scaled = pd.DataFrame(scaler_test.transform(X_test_actual), columns=X_test_actual.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d1e8ccce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:16.838338Z",
     "iopub.status.busy": "2024-07-27T11:05:16.837927Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "OverallQual       0\n",
       "GrLivArea         0\n",
       "GarageCars        1\n",
       "TotalBsmtSF       1\n",
       "FullBath          0\n",
       "YearBuilt         0\n",
       "YearRemodAdd      0\n",
       "Fireplaces        0\n",
       "LotFrontage     227\n",
       "WoodDeckSF        0\n",
       "OpenPorchSF       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_actual_scaled.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f05defbf",
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  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c673145b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:16.974979Z",
     "iopub.status.busy": "2024-07-27T11:05:16.974483Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19/3723279220.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  X_test_actual_scaled['LotFrontage'].fillna(X_test_actual_scaled['LotFrontage'].median(), inplace=True)\n",
      "/tmp/ipykernel_19/3723279220.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  X_test_actual_scaled['GarageCars'].fillna(X_test_actual_scaled['GarageCars'].median(), inplace=True)\n",
      "/tmp/ipykernel_19/3723279220.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  X_test_actual_scaled['TotalBsmtSF'].fillna(X_test_actual_scaled['TotalBsmtSF'].median(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "X_test_actual_scaled['LotFrontage'].fillna(X_test_actual_scaled['LotFrontage'].median(), inplace=True)\n",
    "X_test_actual_scaled['GarageCars'].fillna(X_test_actual_scaled['GarageCars'].median(), inplace=True)\n",
    "X_test_actual_scaled['TotalBsmtSF'].fillna(X_test_actual_scaled['TotalBsmtSF'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a3a58fb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:17.050759Z",
     "iopub.status.busy": "2024-07-27T11:05:17.050353Z",
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     "shell.execute_reply": "2024-07-27T11:05:17.058735Z"
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "OverallQual     0\n",
       "GrLivArea       0\n",
       "GarageCars      0\n",
       "TotalBsmtSF     0\n",
       "FullBath        0\n",
       "YearBuilt       0\n",
       "YearRemodAdd    0\n",
       "Fireplaces      0\n",
       "LotFrontage     0\n",
       "WoodDeckSF      0\n",
       "OpenPorchSF     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_actual_scaled.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b159c64e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:17.125642Z",
     "iopub.status.busy": "2024-07-27T11:05:17.125247Z",
     "iopub.status.idle": "2024-07-27T11:05:17.136756Z",
     "shell.execute_reply": "2024-07-27T11:05:17.135062Z"
    },
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     "duration": 0.049181,
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     "exception": false,
     "start_time": "2024-07-27T11:05:17.092104",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_actual_pred = lin_model2.predict(X_test_actual_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "76189c70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:17.261027Z",
     "iopub.status.busy": "2024-07-27T11:05:17.260611Z",
     "iopub.status.idle": "2024-07-27T11:05:17.274082Z",
     "shell.execute_reply": "2024-07-27T11:05:17.272793Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>86587.886467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>114217.677075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>128731.172399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>142395.765218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>152078.488825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>70768.335274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>75836.133107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>124553.021398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>101830.145590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>165157.115213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id      SalePrice\n",
       "0     1461   86587.886467\n",
       "1     1462  114217.677075\n",
       "2     1463  128731.172399\n",
       "3     1464  142395.765218\n",
       "4     1465  152078.488825\n",
       "...    ...            ...\n",
       "1454  2915   70768.335274\n",
       "1455  2916   75836.133107\n",
       "1456  2917  124553.021398\n",
       "1457  2918  101830.145590\n",
       "1458  2919  165157.115213\n",
       "\n",
       "[1459 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame({'Id':ID, 'SalePrice':y_actual_pred})\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4aa03cdf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T11:05:17.339220Z",
     "iopub.status.busy": "2024-07-27T11:05:17.338795Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "result.to_csv('/kaggle/working/result.csv', index=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "4c66d01d",
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