House Regression Competition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "599c6448-2161-41ea-b6d1-f9ac146913c8",
   "metadata": {},
   "source": [
    "# House Prediction Competition (Regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c14625de-07e4-4830-9742-356a489ad217",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9098432-c90f-4ccb-b3d3-9b0d50742678",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30de3044-9a37-4b7b-a290-de936ad84e35",
   "metadata": {},
   "source": [
    "## Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56433473-015b-471b-b78a-4486ce04cda2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = pd.read_csv(r\"C:\\Users\\arnol\\Downloads\\Compressed\\house-prices-competition\\train.csv\")\n",
    "df_2 = pd.read_csv(r\"C:\\Users\\arnol\\Downloads\\Compressed\\house-prices-competition\\test.csv\")\n",
    "if 'SalePrice' not in df_2.columns:\n",
    "    df_2['SalePrice'] = 0\n",
    "df = pd.concat([df_1, df_2], axis = 0)\n",
    "df = df.set_index('Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0bb0eba6-8eee-46ec-9440-b1bfce76f02e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>LotConfig</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",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>Inside</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>2</th>\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>FR2</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>3</th>\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>Inside</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>4</th>\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>Corner</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>5</th>\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>FR2</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 × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "Id                                                                    \n",
       "1           60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "2           20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "3           60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "4           70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "5           60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "   LandContour Utilities LotConfig  ... PoolArea PoolQC Fence MiscFeature  \\\n",
       "Id                                  ...                                     \n",
       "1          Lvl    AllPub    Inside  ...        0    NaN   NaN         NaN   \n",
       "2          Lvl    AllPub       FR2  ...        0    NaN   NaN         NaN   \n",
       "3          Lvl    AllPub    Inside  ...        0    NaN   NaN         NaN   \n",
       "4          Lvl    AllPub    Corner  ...        0    NaN   NaN         NaN   \n",
       "5          Lvl    AllPub       FR2  ...        0    NaN   NaN         NaN   \n",
       "\n",
       "   MiscVal MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "Id                                                             \n",
       "1        0      2    2008        WD         Normal     208500  \n",
       "2        0      5    2007        WD         Normal     181500  \n",
       "3        0      9    2008        WD         Normal     223500  \n",
       "4        0      2    2006        WD        Abnorml     140000  \n",
       "5        0     12    2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e2a09dc8-3be0-4bff-88ae-a8a170f58392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass         0\n",
       "MSZoning           4\n",
       "LotFrontage      486\n",
       "LotArea            0\n",
       "Street             0\n",
       "                ... \n",
       "MoSold             0\n",
       "YrSold             0\n",
       "SaleType           1\n",
       "SaleCondition      0\n",
       "SalePrice          0\n",
       "Length: 80, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking for the amount of null values in each column.\n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2eda4a1-3845-4ff0-b01e-d8b2d12c1650",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets find the index of those null values:\n",
    "\n",
    "df_null = df[df.isnull().sum()[df.isnull().sum()>0].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79ac8ddb-26e7-43cb-9aad-894c981517d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>SaleType</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>...</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>...</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>...</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>...</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>...</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSZoning  LotFrontage Alley Utilities Exterior1st Exterior2nd MasVnrType  \\\n",
       "Id                                                                            \n",
       "1        RL         65.0   NaN    AllPub     VinylSd     VinylSd    BrkFace   \n",
       "2        RL         80.0   NaN    AllPub     MetalSd     MetalSd        NaN   \n",
       "3        RL         68.0   NaN    AllPub     VinylSd     VinylSd    BrkFace   \n",
       "4        RL         60.0   NaN    AllPub     Wd Sdng     Wd Shng        NaN   \n",
       "5        RL         84.0   NaN    AllPub     VinylSd     VinylSd    BrkFace   \n",
       "\n",
       "    MasVnrArea BsmtQual BsmtCond  ... GarageYrBlt GarageFinish  GarageCars  \\\n",
       "Id                                ...                                        \n",
       "1        196.0       Gd       TA  ...      2003.0          RFn         2.0   \n",
       "2          0.0       Gd       TA  ...      1976.0          RFn         2.0   \n",
       "3        162.0       Gd       TA  ...      2001.0          RFn         2.0   \n",
       "4          0.0       TA       Gd  ...      1998.0          Unf         3.0   \n",
       "5        350.0       Gd       TA  ...      2000.0          RFn         3.0   \n",
       "\n",
       "   GarageArea  GarageQual  GarageCond  PoolQC Fence  MiscFeature  SaleType  \n",
       "Id                                                                          \n",
       "1       548.0          TA          TA     NaN   NaN          NaN        WD  \n",
       "2       460.0          TA          TA     NaN   NaN          NaN        WD  \n",
       "3       608.0          TA          TA     NaN   NaN          NaN        WD  \n",
       "4       642.0          TA          TA     NaN   NaN          NaN        WD  \n",
       "5       836.0          TA          TA     NaN   NaN          NaN        WD  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_null.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8fb2e18-7da6-41b7-a45f-7401e7be8dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Id'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Expressing the null values in a heatmap\n",
    "sns.heatmap(df_null.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e00850e1-2b5e-4ad0-8952-79a45363fe55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a dataframe for all the object types\n",
    "df_objects = df[df.select_dtypes(include=['object']).columns]\n",
    "\n",
    "# dropping all columns in the objects were the null values is greater than 1100.\n",
    "df = df.drop(df[df_objects.isna().sum()[df_objects.isna().sum() > 1100].index], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ba4815c-54a8-4767-a65b-b574dab97748",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_objects = df_objects.drop(df_objects[df_objects.isna().sum()[df_objects.isna().sum() > 1100].index], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eb217966-cf59-4044-b401-5e0f5d43e098",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_objects = df_objects.fillna('null')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "021118db-b248-49f1-b87d-cf5f5c040160",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_objects_encoded = pd.get_dummies(df_objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "74f009f9-6af4-47a9-b568-46ed182720ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSZoning_C (all)</th>\n",
       "      <th>MSZoning_FV</th>\n",
       "      <th>MSZoning_RH</th>\n",
       "      <th>MSZoning_RL</th>\n",
       "      <th>MSZoning_RM</th>\n",
       "      <th>MSZoning_null</th>\n",
       "      <th>Street_Grvl</th>\n",
       "      <th>Street_Pave</th>\n",
       "      <th>LotShape_IR1</th>\n",
       "      <th>LotShape_IR2</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleType_null</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 247 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    MSZoning_C (all)  MSZoning_FV  MSZoning_RH  MSZoning_RL  MSZoning_RM  \\\n",
       "Id                                                                         \n",
       "1              False        False        False         True        False   \n",
       "2              False        False        False         True        False   \n",
       "3              False        False        False         True        False   \n",
       "4              False        False        False         True        False   \n",
       "5              False        False        False         True        False   \n",
       "\n",
       "    MSZoning_null  Street_Grvl  Street_Pave  LotShape_IR1  LotShape_IR2  ...  \\\n",
       "Id                                                                       ...   \n",
       "1           False        False         True         False         False  ...   \n",
       "2           False        False         True         False         False  ...   \n",
       "3           False        False         True          True         False  ...   \n",
       "4           False        False         True          True         False  ...   \n",
       "5           False        False         True          True         False  ...   \n",
       "\n",
       "    SaleType_New  SaleType_Oth  SaleType_WD  SaleType_null  \\\n",
       "Id                                                           \n",
       "1          False         False         True          False   \n",
       "2          False         False         True          False   \n",
       "3          False         False         True          False   \n",
       "4          False         False         True          False   \n",
       "5          False         False         True          False   \n",
       "\n",
       "    SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "Id                                                                       \n",
       "1                   False                  False                 False   \n",
       "2                   False                  False                 False   \n",
       "3                   False                  False                 False   \n",
       "4                    True                  False                 False   \n",
       "5                   False                  False                 False   \n",
       "\n",
       "    SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "Id                                                                     \n",
       "1                  False                  True                  False  \n",
       "2                  False                  True                  False  \n",
       "3                  False                  True                  False  \n",
       "4                  False                 False                  False  \n",
       "5                  False                  True                  False  \n",
       "\n",
       "[5 rows x 247 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_objects_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8ba5a084-a15a-4cf1-9d08-835567bdc5ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSZoning\n",
       "RL         2265\n",
       "RM          460\n",
       "FV          139\n",
       "RH           26\n",
       "C (all)      25\n",
       "null          4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_objects['MSZoning'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ba8c1d0-e955-49f7-ad3e-f101fd3f2fef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Utilities_null\n",
      "Exterior1st_null\n",
      "Exterior2nd_null\n",
      "BsmtQual_null\n",
      "BsmtCond_null\n",
      "BsmtExposure_null\n",
      "BsmtFinType1_null\n",
      "BsmtFinType2_null\n",
      "Electrical_null\n",
      "KitchenQual_null\n",
      "Functional_null\n",
      "GarageType_null\n",
      "GarageFinish_null\n",
      "GarageQual_null\n",
      "GarageCond_null\n",
      "SaleType_null\n"
     ]
    }
   ],
   "source": [
    "# Dropping all the null columns in the encoded datse:\n",
    "\n",
    "for i in df_objects_encoded.columns:\n",
    "    if 'null' in i:\n",
    "        df_objects_encoded = df_objects_encoded.drop(i, axis = 1)\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b286bdbe-2e75-43e2-9d3f-6639f8aec9cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74, 230)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns), len(df_objects_encoded.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ba97be33-14a1-46aa-88a6-33b3828f05b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.concat([df, df_objects_encoded], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ac6f6cd2-f910-4a26-a559-c8db6fc98ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "304"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6063aa7b-6e45-463b-a90f-4e066005a97f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage     486\n",
       "MasVnrArea       23\n",
       "BsmtFinSF1        1\n",
       "BsmtFinSF2        1\n",
       "BsmtUnfSF         1\n",
       "TotalBsmtSF       1\n",
       "BsmtFullBath      2\n",
       "BsmtHalfBath      2\n",
       "GarageYrBlt     159\n",
       "GarageCars        1\n",
       "GarageArea        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping all the object columns from the main df to get the others:\n",
    "\n",
    "new_df = new_df.drop(df.select_dtypes(include=['object']), axis = 1)\n",
    "\n",
    "new_df.isna().sum()[new_df.isna().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "27197648-8977-49b8-9bb0-0d3baf0b0069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mode_columns = ['GarageCars', 'GarageYrBlt', 'BsmtFullBath', 'BsmtHalfBath']\n",
    "Mean_columns = ['LotFrontage', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',\n",
    "                'TotalBsmtSF', 'GarageArea']\n",
    "\n",
    "for i in Mode_columns:\n",
    "    new_df[i] = new_df[i].fillna(new_df[i].mode()[0])\n",
    "\n",
    "for i in Mean_columns:\n",
    "    new_df[i] = new_df[i].fillna(np.round(new_df[i].mean()))\n",
    "\n",
    "new_df.isna().sum()[new_df.isna().sum() > 0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e0831bde-baf2-441b-9bc9-b5c6fb37ef80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the training and the testing data:\n",
    "\n",
    "training_data = new_df[0:len(df_1)]\n",
    "testing_data = new_df[len(df_1):]\n",
    "testing_data = testing_data.drop(columns='SalePrice')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8309d3a6-4e54-4ed5-a857-9f17bf8c4e67",
   "metadata": {},
   "source": [
    "# Training Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d3e6bef1-5004-419f-8429-1caeca69f2b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "49882300-130e-4da8-914a-2f507597f8ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1168, 266), (1168, 1))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = training_data.drop(columns='SalePrice')\n",
    "y = training_data['SalePrice']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size = 0.2)\n",
    "Y_train = np.reshape(Y_train, (-1, 1))\n",
    "Y_test = np.reshape(Y_test, (-1, 1))\n",
    "X_train.shape, Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "898a7020-8765-48b5-b4d9-921d0e9aaa2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "612530068.6607181"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1 = LinearRegression()\n",
    "model_1.fit(X, y)\n",
    "y_pred = model_1.predict(X_test)\n",
    "mean_squared_error(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fac65312-c778-437c-9bfd-805d03d8d570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3558.015597042972"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2 = XGBRegressor(n_estimators=1000, learning_rate=0.1, random_state=42)\n",
    "model_2.fit(X, y)\n",
    "y_pred = model_2.predict(X_test)\n",
    "mean_squared_error(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bf9cbd01-d62c-4fd9-959f-57ad41d45cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148869388.31414935"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3 = RandomForestRegressor(n_estimators=100)\n",
    "model_3.fit(X, y)\n",
    "y_pred = model_3.predict(X_test)\n",
    "mean_squared_error(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "966c7565-bb5d-49d2-a7a2-79d8eba67dd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model_2.predict(testing_data)\n",
    "\n",
    "final = pd.DataFrame()\n",
    "final['Id'] = testing_data.index\n",
    "final['SalePrice'] = pred\n",
    "\n",
    "# Write the DataFrame to a CSV file without index\n",
    "final.to_csv(r\"C:\\Users\\arnol\\Downloads\\Compressed\\house-prices-competition\\output.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fcd52eb-51a1-47c2-b18e-dad1c6e1c4f1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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