Ryhan's project - Ames Housing Price Predictor(4)(1).ipynb

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  {
   "cell_type": "markdown",
   "id": "58b866ef",
   "metadata": {},
   "source": [
    "# Comprehensive Analysis and Prediction of Housing Prices: A Machine Learning Approach with XGBoost in AWS SageMaker"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f09ee64",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "1. [The Dataset](#the-dataset)\n",
    "2. [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)\n",
    "   1. [Understanding the Data](#1-understanding-the-data---overview)\n",
    "   2. [Statistical Information of Numerical Columns](#2-statistical-information-of-numerical-columns)\n",
    "   3. [Statistical Information of Non-Numerical Columns](#3statistical-information-of-non-numerical-columns)\n",
    "   4. [Missing Values](#4missing-values)\n",
    "   5. [Distribution Analysis](#5-distribution-analysis)\n",
    "   6. [Correlation Analysis](#6correlation-analysis)\n",
    "3. [Data Cleaning](#data-cleaning)\n",
    "   1. [Dealing with Missing Value](#1-dealinf-with-missing-value)\n",
    "   2. [Outlier Removal](#2-outlier-removal)\n",
    "   3. [Dropping Unimportant Columns](#dropping-unimportant-columns)\n",
    "4. [Feature Engineering](#feature-engineering)\n",
    "   1. [Derived Feature Creation](#1-derived-feature-creation)\n",
    "   2. [Ordinal Variables Mapping](#2-ordinal-variables-mapping)\n",
    "   3. [One-Hot Encoding (Categorical Features)](#3-one-hot-encoding--categorical-features)\n",
    "5. [Modeling](#modeling)\n",
    "   1. [Setup](#setup)\n",
    "   2. [Prediction Type](#prediction-type)\n",
    "   3. [Feature Scaling with StandardScaler](#feature-scaling-with-standanrdscaler)\n",
    "   4. [Splitting Data (Train-Test-Split)](#splitting-data-train-test-split)\n",
    "   5. [Saving Train Data to S3](#saving-train-data-to-s3)\n",
    "6. [XGBoost Model](#xgboost-model)\n",
    "   1. [Container Image and Training Job](#xgboost-container-image-and-training-job)\n",
    "   2. [Training](#training)\n",
    "   3. [Hyperparameter Tuning](#hyperparameter-tuning)\n",
    "7. [Inferencing](#inferencing)\n",
    "   1. [Hosting the Model](#hosting-the-model)\n",
    "   2. [Endpoint Config](#endpoint-config)\n",
    "   3. [Creating Endpoint](#creating-endpoint)\n",
    "   4. [Prediction](#prediction)\n",
    "   5. [MAE Comparison](#mae-comparison)\n",
    "   6. [Prediction Accuracy Test: MAE, RMSE and r²](#prediction-accuracy-test-mae-rmse-and-r2)\n",
    "8. [Report](#report)\n"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "<a id=\"the-dataset\"></a>\n",
    "# THE DATASET"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34296d33",
   "metadata": {},
   "source": [
    "### I will analyze a residential property dataset originally compiled by De Cock in 2011, detailing property sales in Ames, Iowa, spanning from 2006 to 2010. The dataset contains `2930` records (rows) and `82` features (columns).\n",
    "\n",
    "DATA SOURCE: https://www.kaggle.com/datasets/prevek18/ames-housing-dataset/\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298c30eb",
   "metadata": {},
   "source": [
    "## Reading the Dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2bc7086",
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   "outputs": [
    {
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     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.0' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "dataset = pd.read_csv(\"AmesHousing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45c71866",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2930 entries, 0 to 2929\n",
      "Data columns (total 82 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Order            2930 non-null   int64  \n",
      " 1   PID              2930 non-null   int64  \n",
      " 2   MS SubClass      2930 non-null   int64  \n",
      " 3   MS Zoning        2930 non-null   object \n",
      " 4   Lot Frontage     2440 non-null   float64\n",
      " 5   Lot Area         2930 non-null   int64  \n",
      " 6   Street           2930 non-null   object \n",
      " 7   Alley            198 non-null    object \n",
      " 8   Lot Shape        2930 non-null   object \n",
      " 9   Land Contour     2930 non-null   object \n",
      " 10  Utilities        2930 non-null   object \n",
      " 11  Lot Config       2930 non-null   object \n",
      " 12  Land Slope       2930 non-null   object \n",
      " 13  Neighborhood     2930 non-null   object \n",
      " 14  Condition 1      2930 non-null   object \n",
      " 15  Condition 2      2930 non-null   object \n",
      " 16  Bldg Type        2930 non-null   object \n",
      " 17  House Style      2930 non-null   object \n",
      " 18  Overall Qual     2930 non-null   int64  \n",
      " 19  Overall Cond     2930 non-null   int64  \n",
      " 20  Year Built       2930 non-null   int64  \n",
      " 21  Year Remod/Add   2930 non-null   int64  \n",
      " 22  Roof Style       2930 non-null   object \n",
      " 23  Roof Matl        2930 non-null   object \n",
      " 24  Exterior 1st     2930 non-null   object \n",
      " 25  Exterior 2nd     2930 non-null   object \n",
      " 26  Mas Vnr Type     1155 non-null   object \n",
      " 27  Mas Vnr Area     2907 non-null   float64\n",
      " 28  Exter Qual       2930 non-null   object \n",
      " 29  Exter Cond       2930 non-null   object \n",
      " 30  Foundation       2930 non-null   object \n",
      " 31  Bsmt Qual        2850 non-null   object \n",
      " 32  Bsmt Cond        2850 non-null   object \n",
      " 33  Bsmt Exposure    2847 non-null   object \n",
      " 34  BsmtFin Type 1   2850 non-null   object \n",
      " 35  BsmtFin SF 1     2929 non-null   float64\n",
      " 36  BsmtFin Type 2   2849 non-null   object \n",
      " 37  BsmtFin SF 2     2929 non-null   float64\n",
      " 38  Bsmt Unf SF      2929 non-null   float64\n",
      " 39  Total Bsmt SF    2929 non-null   float64\n",
      " 40  Heating          2930 non-null   object \n",
      " 41  Heating QC       2930 non-null   object \n",
      " 42  Central Air      2930 non-null   object \n",
      " 43  Electrical       2929 non-null   object \n",
      " 44  1st Flr SF       2930 non-null   int64  \n",
      " 45  2nd Flr SF       2930 non-null   int64  \n",
      " 46  Low Qual Fin SF  2930 non-null   int64  \n",
      " 47  Gr Liv Area      2930 non-null   int64  \n",
      " 48  Bsmt Full Bath   2928 non-null   float64\n",
      " 49  Bsmt Half Bath   2928 non-null   float64\n",
      " 50  Full Bath        2930 non-null   int64  \n",
      " 51  Half Bath        2930 non-null   int64  \n",
      " 52  Bedroom AbvGr    2930 non-null   int64  \n",
      " 53  Kitchen AbvGr    2930 non-null   int64  \n",
      " 54  Kitchen Qual     2930 non-null   object \n",
      " 55  TotRms AbvGrd    2930 non-null   int64  \n",
      " 56  Functional       2930 non-null   object \n",
      " 57  Fireplaces       2930 non-null   int64  \n",
      " 58  Fireplace Qu     1508 non-null   object \n",
      " 59  Garage Type      2773 non-null   object \n",
      " 60  Garage Yr Blt    2771 non-null   float64\n",
      " 61  Garage Finish    2771 non-null   object \n",
      " 62  Garage Cars      2929 non-null   float64\n",
      " 63  Garage Area      2929 non-null   float64\n",
      " 64  Garage Qual      2771 non-null   object \n",
      " 65  Garage Cond      2771 non-null   object \n",
      " 66  Paved Drive      2930 non-null   object \n",
      " 67  Wood Deck SF     2930 non-null   int64  \n",
      " 68  Open Porch SF    2930 non-null   int64  \n",
      " 69  Enclosed Porch   2930 non-null   int64  \n",
      " 70  3Ssn Porch       2930 non-null   int64  \n",
      " 71  Screen Porch     2930 non-null   int64  \n",
      " 72  Pool Area        2930 non-null   int64  \n",
      " 73  Pool QC          13 non-null     object \n",
      " 74  Fence            572 non-null    object \n",
      " 75  Misc Feature     106 non-null    object \n",
      " 76  Misc Val         2930 non-null   int64  \n",
      " 77  Mo Sold          2930 non-null   int64  \n",
      " 78  Yr Sold          2930 non-null   int64  \n",
      " 79  Sale Type        2930 non-null   object \n",
      " 80  Sale Condition   2930 non-null   object \n",
      " 81  SalePrice        2930 non-null   int64  \n",
      "dtypes: float64(11), int64(28), object(43)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Display basic information about the dataset\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab0d483c",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order</th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
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       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
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       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.00</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</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",
       "      <td>105000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.00</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</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",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.00</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</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>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>527105010</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.00</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</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",
       "      <td>189900</td>\n",
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       "<p>5 rows × 82 columns</p>\n",
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      "text/plain": [
       "   Order        PID  MS SubClass MS Zoning         Lot Frontage  Lot Area  \\\n",
       "0      1  526301100           20        RL               141.00     31770   \n",
       "1      2  526350040           20        RH                80.00     11622   \n",
       "2      3  526351010           20        RL                81.00     14267   \n",
       "3      4  526353030           20        RL                93.00     11160   \n",
       "4      5  527105010           60        RL                74.00     13830   \n",
       "\n",
       "  Street Alley Lot Shape Land Contour  ... Pool Area Pool QC  Fence  \\\n",
       "0   Pave   NaN       IR1          Lvl  ...         0     NaN    NaN   \n",
       "1   Pave   NaN       Reg          Lvl  ...         0     NaN  MnPrv   \n",
       "2   Pave   NaN       IR1          Lvl  ...         0     NaN    NaN   \n",
       "3   Pave   NaN       Reg          Lvl  ...         0     NaN    NaN   \n",
       "4   Pave   NaN       IR1          Lvl  ...         0     NaN  MnPrv   \n",
       "\n",
       "  Misc Feature Misc Val Mo Sold Yr Sold Sale Type  Sale Condition  SalePrice  \n",
       "0          NaN        0       5    2010       WD           Normal     215000  \n",
       "1          NaN        0       6    2010       WD           Normal     105000  \n",
       "2         Gar2    12500       6    2010       WD           Normal     172000  \n",
       "3          NaN        0       4    2010       WD           Normal     244000  \n",
       "4          NaN        0       3    2010       WD           Normal     189900  \n",
       "\n",
       "[5 rows x 82 columns]"
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Configuring float numbers format\n",
    "pd.options.display.float_format = '{:20.2f}'.format\n",
    "dataset.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827448d3",
   "metadata": {},
   "source": [
    "<a id=\"exploratory-data-analysis-eda\"></a>\n",
    "# EXPLORATORY DATA ANALYSIS (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea9e2587",
   "metadata": {},
   "source": [
    "<a id=\"1-understanding-the-data---overview\"></a>\n",
    "## 1. Understanding the Data - Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44588451",
   "metadata": {},
   "source": [
    " Numerical data: These include columns like 'Lot Frontage', 'Lot Area', 'Year Built', 'SalePrice', etc.\n",
    " \n",
    " Categorical data: These are columns like 'MS Zoning', 'Street', 'Alley', 'Lot Shape', 'Neighborhood', etc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe1ad87",
   "metadata": {},
   "source": [
    "For the exploratory data analysis (EDA), I'll focus on several aspects:\n",
    "\n",
    "      Summary Statistics: Basic statistical details like count, mean, standard deviation, etc.\n",
    "  \n",
    "      Missing Values: Identification of columns with missing values and their extent.\n",
    "  \n",
    "      Distribution of Key Features: Analyzing the distribution of certain important features like 'SalePrice'.\n",
    "  \n",
    "      Correlation Analysis: Understanding how different numerical features are related to each other, \n",
    "      especially with respect to 'SalePrice'.\n",
    "\n",
    "Let's start with the summary statistics and information about missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e644f80f",
   "metadata": {},
   "source": [
    "<a id=\"2-statistical-information-of-numerical-columns\"></a>\n",
    "## 2. Statistical Information of Numerical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3c37609",
   "metadata": {
    "scrolled": true
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       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>50%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>Order</th>\n",
       "      <td>1465.50</td>\n",
       "      <td>845.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1465.50</td>\n",
       "      <td>2930.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PID</th>\n",
       "      <td>714464496.99</td>\n",
       "      <td>188730844.65</td>\n",
       "      <td>526301100.00</td>\n",
       "      <td>535453620.00</td>\n",
       "      <td>1007100110.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SubClass</th>\n",
       "      <td>57.39</td>\n",
       "      <td>42.64</td>\n",
       "      <td>20.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>190.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot Frontage</th>\n",
       "      <td>69.22</td>\n",
       "      <td>23.37</td>\n",
       "      <td>21.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>313.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot Area</th>\n",
       "      <td>10147.92</td>\n",
       "      <td>7880.02</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>9436.50</td>\n",
       "      <td>215245.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall Qual</th>\n",
       "      <td>6.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall Cond</th>\n",
       "      <td>5.56</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year Built</th>\n",
       "      <td>1971.36</td>\n",
       "      <td>30.25</td>\n",
       "      <td>1872.00</td>\n",
       "      <td>1973.00</td>\n",
       "      <td>2010.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <td>1984.27</td>\n",
       "      <td>20.86</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>1993.00</td>\n",
       "      <td>2010.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <td>101.90</td>\n",
       "      <td>179.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <td>442.63</td>\n",
       "      <td>455.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>370.00</td>\n",
       "      <td>5644.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <td>49.72</td>\n",
       "      <td>169.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1526.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <td>559.26</td>\n",
       "      <td>439.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>466.00</td>\n",
       "      <td>2336.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <td>1051.61</td>\n",
       "      <td>440.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>990.00</td>\n",
       "      <td>6110.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1st Flr SF</th>\n",
       "      <td>1159.56</td>\n",
       "      <td>391.89</td>\n",
       "      <td>334.00</td>\n",
       "      <td>1084.00</td>\n",
       "      <td>5095.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <td>335.46</td>\n",
       "      <td>428.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2065.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Qual Fin SF</th>\n",
       "      <td>4.68</td>\n",
       "      <td>46.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1064.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gr Liv Area</th>\n",
       "      <td>1499.69</td>\n",
       "      <td>505.51</td>\n",
       "      <td>334.00</td>\n",
       "      <td>1442.00</td>\n",
       "      <td>5642.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Full Bath</th>\n",
       "      <td>1.57</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Half Bath</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedroom AbvGr</th>\n",
       "      <td>2.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kitchen AbvGr</th>\n",
       "      <td>1.04</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRms AbvGrd</th>\n",
       "      <td>6.44</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Yr Blt</th>\n",
       "      <td>1978.13</td>\n",
       "      <td>25.53</td>\n",
       "      <td>1895.00</td>\n",
       "      <td>1979.00</td>\n",
       "      <td>2207.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Cars</th>\n",
       "      <td>1.77</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Area</th>\n",
       "      <td>472.82</td>\n",
       "      <td>215.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>480.00</td>\n",
       "      <td>1488.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <td>93.75</td>\n",
       "      <td>126.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1424.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open Porch SF</th>\n",
       "      <td>47.53</td>\n",
       "      <td>67.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>742.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <td>23.01</td>\n",
       "      <td>64.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1012.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <td>2.59</td>\n",
       "      <td>25.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>508.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Screen Porch</th>\n",
       "      <td>16.00</td>\n",
       "      <td>56.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>576.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pool Area</th>\n",
       "      <td>2.24</td>\n",
       "      <td>35.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misc Val</th>\n",
       "      <td>50.64</td>\n",
       "      <td>566.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mo Sold</th>\n",
       "      <td>6.22</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr Sold</th>\n",
       "      <td>2007.79</td>\n",
       "      <td>1.32</td>\n",
       "      <td>2006.00</td>\n",
       "      <td>2008.00</td>\n",
       "      <td>2010.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>180796.06</td>\n",
       "      <td>79886.69</td>\n",
       "      <td>12789.00</td>\n",
       "      <td>160000.00</td>\n",
       "      <td>755000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                mean                  std  \\\n",
       "Order                        1465.50               845.96   \n",
       "PID                     714464496.99         188730844.65   \n",
       "MS SubClass                    57.39                42.64   \n",
       "Lot Frontage                   69.22                23.37   \n",
       "Lot Area                    10147.92              7880.02   \n",
       "Overall Qual                    6.09                 1.41   \n",
       "Overall Cond                    5.56                 1.11   \n",
       "Year Built                   1971.36                30.25   \n",
       "Year Remod/Add               1984.27                20.86   \n",
       "Mas Vnr Area                  101.90               179.11   \n",
       "BsmtFin SF 1                  442.63               455.59   \n",
       "BsmtFin SF 2                   49.72               169.17   \n",
       "Bsmt Unf SF                   559.26               439.49   \n",
       "Total Bsmt SF                1051.61               440.62   \n",
       "1st Flr SF                   1159.56               391.89   \n",
       "2nd Flr SF                    335.46               428.40   \n",
       "Low Qual Fin SF                 4.68                46.31   \n",
       "Gr Liv Area                  1499.69               505.51   \n",
       "Bsmt Full Bath                  0.43                 0.52   \n",
       "Bsmt Half Bath                  0.06                 0.25   \n",
       "Full Bath                       1.57                 0.55   \n",
       "Half Bath                       0.38                 0.50   \n",
       "Bedroom AbvGr                   2.85                 0.83   \n",
       "Kitchen AbvGr                   1.04                 0.21   \n",
       "TotRms AbvGrd                   6.44                 1.57   \n",
       "Fireplaces                      0.60                 0.65   \n",
       "Garage Yr Blt                1978.13                25.53   \n",
       "Garage Cars                     1.77                 0.76   \n",
       "Garage Area                   472.82               215.05   \n",
       "Wood Deck SF                   93.75               126.36   \n",
       "Open Porch SF                  47.53                67.48   \n",
       "Enclosed Porch                 23.01                64.14   \n",
       "3Ssn Porch                      2.59                25.14   \n",
       "Screen Porch                   16.00                56.09   \n",
       "Pool Area                       2.24                35.60   \n",
       "Misc Val                       50.64               566.34   \n",
       "Mo Sold                         6.22                 2.71   \n",
       "Yr Sold                      2007.79                 1.32   \n",
       "SalePrice                  180796.06             79886.69   \n",
       "\n",
       "                                 min                  50%                  max  \n",
       "Order                           1.00              1465.50              2930.00  \n",
       "PID                     526301100.00         535453620.00        1007100110.00  \n",
       "MS SubClass                    20.00                50.00               190.00  \n",
       "Lot Frontage                   21.00                68.00               313.00  \n",
       "Lot Area                     1300.00              9436.50            215245.00  \n",
       "Overall Qual                    1.00                 6.00                10.00  \n",
       "Overall Cond                    1.00                 5.00                 9.00  \n",
       "Year Built                   1872.00              1973.00              2010.00  \n",
       "Year Remod/Add               1950.00              1993.00              2010.00  \n",
       "Mas Vnr Area                    0.00                 0.00              1600.00  \n",
       "BsmtFin SF 1                    0.00               370.00              5644.00  \n",
       "BsmtFin SF 2                    0.00                 0.00              1526.00  \n",
       "Bsmt Unf SF                     0.00               466.00              2336.00  \n",
       "Total Bsmt SF                   0.00               990.00              6110.00  \n",
       "1st Flr SF                    334.00              1084.00              5095.00  \n",
       "2nd Flr SF                      0.00                 0.00              2065.00  \n",
       "Low Qual Fin SF                 0.00                 0.00              1064.00  \n",
       "Gr Liv Area                   334.00              1442.00              5642.00  \n",
       "Bsmt Full Bath                  0.00                 0.00                 3.00  \n",
       "Bsmt Half Bath                  0.00                 0.00                 2.00  \n",
       "Full Bath                       0.00                 2.00                 4.00  \n",
       "Half Bath                       0.00                 0.00                 2.00  \n",
       "Bedroom AbvGr                   0.00                 3.00                 8.00  \n",
       "Kitchen AbvGr                   0.00                 1.00                 3.00  \n",
       "TotRms AbvGrd                   2.00                 6.00                15.00  \n",
       "Fireplaces                      0.00                 1.00                 4.00  \n",
       "Garage Yr Blt                1895.00              1979.00              2207.00  \n",
       "Garage Cars                     0.00                 2.00                 5.00  \n",
       "Garage Area                     0.00               480.00              1488.00  \n",
       "Wood Deck SF                    0.00                 0.00              1424.00  \n",
       "Open Porch SF                   0.00                27.00               742.00  \n",
       "Enclosed Porch                  0.00                 0.00              1012.00  \n",
       "3Ssn Porch                      0.00                 0.00               508.00  \n",
       "Screen Porch                    0.00                 0.00               576.00  \n",
       "Pool Area                       0.00                 0.00               800.00  \n",
       "Misc Val                        0.00                 0.00             17000.00  \n",
       "Mo Sold                         1.00                 6.00                12.00  \n",
       "Yr Sold                      2006.00              2008.00              2010.00  \n",
       "SalePrice                   12789.00            160000.00            755000.00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe(include=[np.number], percentiles=[.5]) \\\n",
    "    .transpose().drop(\"count\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3217da91",
   "metadata": {},
   "source": [
    "   =The SalePrice (target variable) has a mean of approximately \\\\$180,796 and a standard deviation of \\\\$79,886.\n",
    "    \n",
    "   =The range of Year Built for the houses spans from 1872 to 2010.\n",
    "    \n",
    "   -Features like Lot Area, Overall Qual, and Overall Cond show a wide range of values, indicating diverse housing characteristics."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e22873c",
   "metadata": {},
   "source": [
    "<a id=\"3-statistical-information-of-non-numerical-columns\"></a>\n",
    "## 3.Statistical information of NON-NUMERICAL COLUMNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7eae90ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10134/811848876.py:1: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dataset.describe(include=[np.object]).transpose() \\\n"
     ]
    },
    {
     "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>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MS Zoning</th>\n",
       "      <td>7</td>\n",
       "      <td>RL</td>\n",
       "      <td>2273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Street</th>\n",
       "      <td>2</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>2</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot Shape</th>\n",
       "      <td>4</td>\n",
       "      <td>Reg</td>\n",
       "      <td>1859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Land Contour</th>\n",
       "      <td>4</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>2633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>3</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot Config</th>\n",
       "      <td>5</td>\n",
       "      <td>Inside</td>\n",
       "      <td>2140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Land Slope</th>\n",
       "      <td>3</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>2789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <td>28</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition 1</th>\n",
       "      <td>9</td>\n",
       "      <td>Norm</td>\n",
       "      <td>2522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition 2</th>\n",
       "      <td>8</td>\n",
       "      <td>Norm</td>\n",
       "      <td>2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bldg Type</th>\n",
       "      <td>5</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House Style</th>\n",
       "      <td>8</td>\n",
       "      <td>1Story</td>\n",
       "      <td>1481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roof Style</th>\n",
       "      <td>6</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roof Matl</th>\n",
       "      <td>8</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>2887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior 1st</th>\n",
       "      <td>16</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>1026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior 2nd</th>\n",
       "      <td>17</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mas Vnr Type</th>\n",
       "      <td>4</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exter Qual</th>\n",
       "      <td>4</td>\n",
       "      <td>TA</td>\n",
       "      <td>1799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exter Cond</th>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation</th>\n",
       "      <td>6</td>\n",
       "      <td>PConc</td>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Qual</th>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>1283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Cond</th>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Exposure</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin Type 1</th>\n",
       "      <td>6</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin Type 2</th>\n",
       "      <td>6</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating</th>\n",
       "      <td>6</td>\n",
       "      <td>GasA</td>\n",
       "      <td>2885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating QC</th>\n",
       "      <td>5</td>\n",
       "      <td>Ex</td>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central Air</th>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>2734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>5</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>2682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kitchen Qual</th>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>1494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional</th>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplace Qu</th>\n",
       "      <td>5</td>\n",
       "      <td>Gd</td>\n",
       "      <td>744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Type</th>\n",
       "      <td>6</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Finish</th>\n",
       "      <td>3</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Qual</th>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Cond</th>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paved Drive</th>\n",
       "      <td>3</td>\n",
       "      <td>Y</td>\n",
       "      <td>2652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pool QC</th>\n",
       "      <td>4</td>\n",
       "      <td>Ex</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>4</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misc Feature</th>\n",
       "      <td>5</td>\n",
       "      <td>Shed</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sale Type</th>\n",
       "      <td>10</td>\n",
       "      <td>WD</td>\n",
       "      <td>2536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sale Condition</th>\n",
       "      <td>6</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               unique      top  freq\n",
       "MS Zoning           7       RL  2273\n",
       "Street              2     Pave  2918\n",
       "Alley               2     Grvl   120\n",
       "Lot Shape           4      Reg  1859\n",
       "Land Contour        4      Lvl  2633\n",
       "Utilities           3   AllPub  2927\n",
       "Lot Config          5   Inside  2140\n",
       "Land Slope          3      Gtl  2789\n",
       "Neighborhood       28    NAmes   443\n",
       "Condition 1         9     Norm  2522\n",
       "Condition 2         8     Norm  2900\n",
       "Bldg Type           5     1Fam  2425\n",
       "House Style         8   1Story  1481\n",
       "Roof Style          6    Gable  2321\n",
       "Roof Matl           8  CompShg  2887\n",
       "Exterior 1st       16  VinylSd  1026\n",
       "Exterior 2nd       17  VinylSd  1015\n",
       "Mas Vnr Type        4  BrkFace   880\n",
       "Exter Qual          4       TA  1799\n",
       "Exter Cond          5       TA  2549\n",
       "Foundation          6    PConc  1310\n",
       "Bsmt Qual           5       TA  1283\n",
       "Bsmt Cond           5       TA  2616\n",
       "Bsmt Exposure       4       No  1906\n",
       "BsmtFin Type 1      6      GLQ   859\n",
       "BsmtFin Type 2      6      Unf  2499\n",
       "Heating             6     GasA  2885\n",
       "Heating QC          5       Ex  1495\n",
       "Central Air         2        Y  2734\n",
       "Electrical          5    SBrkr  2682\n",
       "Kitchen Qual        5       TA  1494\n",
       "Functional          8      Typ  2728\n",
       "Fireplace Qu        5       Gd   744\n",
       "Garage Type         6   Attchd  1731\n",
       "Garage Finish       3      Unf  1231\n",
       "Garage Qual         5       TA  2615\n",
       "Garage Cond         5       TA  2665\n",
       "Paved Drive         3        Y  2652\n",
       "Pool QC             4       Ex     4\n",
       "Fence               4    MnPrv   330\n",
       "Misc Feature        5     Shed    95\n",
       "Sale Type          10      WD   2536\n",
       "Sale Condition      6   Normal  2413"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe(include=[np.object]).transpose() \\\n",
    "    .drop(\"count\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9809ece1",
   "metadata": {},
   "source": [
    "<a id=\"4-missing-values\"></a>\n",
    "##  4.Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9dc69ff2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pool QC           2917\n",
       "Misc Feature      2824\n",
       "Alley             2732\n",
       "Fence             2358\n",
       "Mas Vnr Type      1775\n",
       "Fireplace Qu      1422\n",
       "Lot Frontage       490\n",
       "Garage Cond        159\n",
       "Garage Qual        159\n",
       "Garage Finish      159\n",
       "Garage Yr Blt      159\n",
       "Garage Type        157\n",
       "Bsmt Exposure       83\n",
       "BsmtFin Type 2      81\n",
       "Bsmt Cond           80\n",
       "Bsmt Qual           80\n",
       "BsmtFin Type 1      80\n",
       "Mas Vnr Area        23\n",
       "Bsmt Half Bath       2\n",
       "Bsmt Full Bath       2\n",
       "BsmtFin SF 1         1\n",
       "Garage Cars          1\n",
       "Garage Area          1\n",
       "Total Bsmt SF        1\n",
       "Bsmt Unf SF          1\n",
       "BsmtFin SF 2         1\n",
       "Electrical           1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Information about missing values\n",
    "missing_values = dataset.isnull().sum()\n",
    "missing_values = missing_values[missing_values > 0].sort_values(ascending=False)\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b2641d2",
   "metadata": {},
   "source": [
    "### Several columns have missing values, some with a significant number:\n",
    "\n",
    "   -Pool QC, Misc Feature, Alley, and Fence have the highest number of missing values, indicating that many houses do not have these features.\n",
    "    \n",
    "   -Fireplace Qu and Lot Frontage also have a substantial number of missing values.\n",
    "    \n",
    "   -Other features related to the garage, basement, and masonry veneer also show missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76140c6c",
   "metadata": {},
   "source": [
    "<a id=\"5-distribution-analysis\"></a>\n",
    "## 5. Distribution Analysis: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "14c9e6b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UqFEaP368EhMTVVRUpD/84Q9KS0s7ZnV+AEDoINQDAELWj370I0mS3W6Xy+VSZmamFi5cqGuuueaEgV5qWSjv/fff12OPPaaSkhJFRUVp9OjReuqpp3TOOee0H3f//ffrV7/6lRYtWqTm5mZdccUVeuihhwKqd+zYsbryyiv12GOPKT8/X8nJyfrRj36km266qf0Yq9Wqp59+Wr/4xS/005/+VA6HQ3PnztVPfvKTDkPKJemGG27Q7t27tXTpUtXU1Mjv9x/Tw98mIyNDf/rTn7RkyRLde++9amxsVGZmph588MGAtmo7nilTpuj3v/+9HnvsMf3oRz+Sz+fTmDFj9NRTT+ncc88N6DUvu+wySdK//vUvLV++XDU1NYqLi9O4ceO0bNkyzZo1q/3YX//617r//vv18MMPy+PxaNKkSXruuef0rW9966TXmTZtmlauXKmnn35aDzzwgKqrqxUfH6/MzExdcsklAdV+qmbMmKE//vGPeuKJJ3TfffepqalJqampHRYyHDlypF5++WU9+eST+r//+z+Vl5crNjZWw4YN6/C1mTp1ql599VWtXLlStbW1SkpK0llnnaXbb79ddrvdjLcHAOgBhv9kywMDAAAAAICgxJx6AAAAAABCFKEeAAAAAIAQRagHAAAAACBEEeoBAAAAAAhRhHoAAAAAAEIUoR4AAAAAgBDFPvUnsWXLFvn9fvZvBQAAAAD0CbfbLcMwNHHixJMeS0/9Sfj9fvn9/j69XnNzc59eEz2PdgwPtGPoow3DA+0YHmjH8EA7hgfaMfh1J4fSU38SbT30OTk5fXK9+vp67dixQyNHjlRUVFSfXBM9j3YMD7Rj6KMNwwPtGB5ox/BAO4YH2jH4bd26tcvH0lMPAAAAAECIItQDAAAAABCiCPUAAAAAAIQoQj0AAAAAACGKUA8AAAAAQIgi1AMAAAAAEKII9QAAAAAAhChCPQAAAAAAIYpQDwAAAABAiCLUAwAAAAAQogj1AAAAAACEKEI9AAAAAAAhilAPAAAAAECIItQDAAAAABCiCPUAAAAAAIQoQj0AAAAAACGKUA8AAAAAQIgi1AMAAAAAEKII9QAAAAAAhChCPQAAAAAAIYpQD4Qpn9/fp+cBAAAA6Hs2swsA0DsshqG3Nxeqtt7d5XNiouyaPWloL1YFAAAAoCcR6oEwVlvvVnVds9llAAAAAOglDL8HAAAAACBEEeoBAAAAAAhRhHoAAAAAAEIUoR4AAAAAgBBFqAcAAAAAIEQR6gEAAAAACFGEegAAAAAAQhShHgAAAACAEEWoBwAAAAAgRBHqAQAAAAAIUYR6AAAAAABCFKEeAAAAAIAQRagHAAAAACBEEeoBAAAAAAhRhHoAAAAAAEIUoR4AAAAAgBBleqh/9913dd1112natGkaP368zjvvPD344IOqqanpcNy6det0+eWXKycnRxdccIFWrFjR6estX75cc+bMUU5Ojq666ipt2LChL94GAAAAAAB9zvRQX1VVpYkTJ+oXv/iFli9frm9+85tatWqV7rzzzvZjtmzZottvv13Z2dl65plndMUVV+i+++7TypUrO7zW8uXLtXTpUn3jG9/QsmXLNGzYMC1cuFC7du3q67cFAAAAAECvs5ldwLx58zRv3rz2z6dOnSqHw6Ef//jHKi4uVkpKip544gllZ2frgQcekCRNmzZNBw8e1KOPPqqrrrpKFotFzc3Neuqpp3TDDTfolltukSRNmTJF8+fP19NPP62lS5ea8v4AAAAAAOgtpvfUdyY+Pl6S5PF41NzcrPXr12vu3Lkdjpk/f75KSkq0fft2SdLmzZtVU1PT4QaB1WrVpZdeqnXr1snv9/dZ/QAAAAAA9IWgCfVer1dNTU3atm2bnnjiCZ177rlKS0tTQUGB3G63MjIyOhw/cuRISVJubm6Hj0cfl5mZqbq6OhUXF/fBuwAAAAAAoO+YPvy+zbnnntsevGfMmKElS5ZIaplzL0kul6vD8W2ftz1fXV0th8Mhp9PZ4bi4uDhJUmVlpVJTUwOqze/3q76+PqBzu6uhoaHDR4Qms9vRMAxFRkbK4/HI7XZ3+TyPp+U+X0NDA6NbZH474tTRhuGBdgwPtGN4oB3DA+0Y/Px+vwzD6NKxQRPqly1bpvr6eu3Zs0dPPvmkbrvtNj333HPtzx/vDR35eGfHtAWTrn5BOuN2u7Vjx46Azw9Efn5+n14PvcOsdnQ6nRo3bpzKy8tVXtX1G1Le5ihJUl5enhobG3urvJDD92Poow3DA+0YHmjH8EA7hgfaMbg5HI4uHRc0oX7MmDGSpEmTJik7O1tXXXWVXnvttfZh9m098m2qq6slfdlj73K51NTUpKamJkVERBxzXFuPfSDsdnt7Hb2toaFB+fn5Gj58uCIjI/vkmuh5Zrdj202sxMREWR3RXT4vLqbleycjI4Oeepnfjjh1tGF4oB3DA+0YHmjH8EA7Br89e/Z0+digCfVHGjt2rKxWqwoKCjRnzhzZ7Xbl5eVp5syZ7ce0vcnMzMwOH3Nzc5Wdnd1+XG5urqKjo5WSkhJwPYZhKCoqKuDzAxEZGdnn10TPM7sdbTab7HZft46XxA/3o5jdjjh1tGF4oB3DA+0YHmjH8EA7Bq/ujDQPmoXyjrRlyxZ5vV4NGTJEDodD06ZN09q1azscs3r1aiUlJbUH+EmTJik2NlZr1qxpP8br9Wrt2rWaNWvWKQ2/BwAAAAAgGJneU3/HHXdo/PjxysrKktPp1M6dO/Xss88qKytL559/viRp8eLFuu6663TPPfdo/vz52rx5s1auXKl7771XFkvLfQmHw6FFixZp6dKlSkxMVHZ2tlauXKnCwsL2RfcAAAAAAAgnpof60047TWvWrNGyZcvk9/uVlpam//qv/9Itt9zSvjDAxIkT9eSTT2rJkiVatWqVUlNTdc899+iaa67p8Fo333yz/H6/XnjhBZWWlmr06NFatmyZsrKyzHhrAAAAAAD0KtND/a233qpbb731pMfNmjVLs2bNOuExhmFowYIFWrBgQU+VBwAAAABA0ArKOfUAAAAAAODkCPUAAAAAAIQoQj0AAAAAACGKUA8AAAAAQIgi1AMAAAAAEKII9QAAAAAAhChCPQAAAAAAIYpQDwAAAABAiCLUAwAAAAAQogj1AAAAAACEKEI9AAAAAAAhilAPAAAAAECIItQDAAAAABCiCPUAAAAAAIQoQj0AAAAAACGKUA8AAAAAQIgi1AMAAAAAEKII9QAAAAAAhChCPQAAAAAAIYpQDwAAAABAiCLUAwAAAAAQogj1AAAAAACEKEI9AAAAAAAhilAPAAAAAECIItQDAAAAABCiCPUAAAAAAIQoQj0AAAAAACGKUA8AAAAAQIgi1AMAAAAAEKII9QAAAAAAhChCPQAAAAAAIYpQDwAAAABAiCLUAwAAAAAQogj1AAAAAACEKEI9AAAAAAAhilAPAAAAAECIItQDAAAAABCiCPUAAAAAAIQoQj0AAAAAACGKUA8AAAAAQIgi1AMAAAAAEKII9QAAAAAAhChCPQAAAAAAIYpQDwAAAABAiCLUAwAAAAAQogj1AAAAAACEKEI9AAAAAAAhilAPAAAAAECIItQDAAAAABCiCPUAAAAAAIQoQj0AAAAAACGKUA8AAAAAQIgi1AMAAAAAEKJsZhcAwFy1DW7t2leugkM1GhAXqdHpCRqdnmB2WQAAAAC6gFAP9FPl1Y3akV+uwuIa+f0tjxWV1Or7j76jsycM1g2XjNXgpBhziwQAAABwQoR6oB/ata9Cm3cdbv88dUCURg6JV1lVo3buK9f7nx7Qh1sP6opZmbpxbrYMwzjh6/n8fllOcgwAAACAnmd6qF+7dq3++c9/atu2baqqqtLQoUP1ta99Tddee60slpYp/3fffbf+/ve/H3PuM888o5kzZ3Z4bPny5VqxYoVKSko0evRo3XXXXZo6dWqfvBcgFFTXNemT3SWSpPTUWGWPSFRCrFOSNHVcqm6/eoKWvrRJeUXV+ttbe1RS0aCxIxKP+3oxUXbNnjS0T2oHAAAA0JHpof65557T4MGDddddd2nAgAHasGGD7r//fhUWFuqHP/xh+3FDhw7VI4880uHczMzMDp8vX75cS5cu1Xe/+11lZ2dr5cqVWrhwoVauXKmsrKw+eT9AMPP5/dqw7ZB8Pr8GDYjWWTmDjumFHz7IpQumDNP6zw/q092lev+zA4qJsisuJsKkqgEAAAAcj+mh/umnn1Zi4pe9gNOmTVN9fb1WrFih7373u3I4HJIkp9Op008//biv09zcrKeeeko33HCDbrnlFknSlClTNH/+fD399NNaunRpr74PIBTsLqhUaWWjbFaLzsxOOeGw+rHDE1VcXq9DZfX6YOtBXTglXVYrG2YAAAAAwcT039CPDPRtxo4dq6amJlVWVnb5dTZv3qyamhrNmzev/TGr1apLL71U69atk79tJTCgn6qtb9anrcPuTx+dpOhI+wmPNwxD08YPUoTdqsqaL4fsAwAAAAgepof6zmzatEnx8fEaMGBA+2MFBQWaPHmyxo8fryuvvFKvv/56h3Nyc3MlSRkZGR0ez8zMVF1dnYqLi3u/cCBI+f1+fbS9WF6fX8kJkRo5JK5L50VG2DR1fKok6YuCShWV1PZmmQAAAAC6yfTh90fbunWrXn75ZS1evFhWq1VSS899Tk6ORo4cqZqaGr300ktavHixHn30UV188cWSpOrqajkcDjmdzg6vFxfXEl4qKyuVmpoaUE1+v1/19fWn8K66rqGhocNHhKbeaMeTrUB/9LFOp1Mej0dut1t7D1SruLxeVouhSaMHyuPxdHqex+tt+dh6niQlx0do5BCX9uyv1vrPD+qiKUMV4bB+eY6n5d5gQ0ND2I2I4fsx9NGG4YF2DA+0Y3igHcMD7Rj8/H5/l3//D6pQX1JSou985zvKycnRwoUL2x+/8cYbOxw3Z84cXXvttfrNb37THuqlzkNPW8joTiA6mtvt1o4dOwI+PxD5+fl9ej30jp5oR5vNpnHjxstms5784KNUVlaqpLxGW3PrJEnDkh1qqKtUQ13nx8dEtIT6qqoqlZTXtD8+yOXXwQiL6pp82rLrgDJTv7x55m2OkiTl5eWpsbGx2zWGAr4fQx9tGB5ox/BAO4YH2jE80I7BrW19uZMJmlBfU1OjhQsXyul06qmnnpLdfvz5vhaLRRdeeKEefvhhNTY2yul0yuVyqampSU1NTYqI+HKV7urqaklf9tgHwm63a+TIkQGf3x0NDQ3Kz8/X8OHDFRkZ2SfXRM/ryXY0DEM2m1VvfJSvmvrmLp2Tkhit6aelKT4+Xoeq/Gr21Mpht+j0rMGyWI5/gysuLrb1Y5xk7Tjq5XRrvd7fekgHyz2amJXY3lvftip+RkZGWPbU8/0Y2mjD8EA7hgfaMTzQjuGBdgx+e/bs6fKxQRHqm5qatGjRIpWWlurPf/6zEhISTnrO0eGhbXu73NxcZWdntz+em5ur6OhopaSkBFyfYRiKiooK+PxAREZG9vk10fN6sh0bmn2qb/J16dgmd8txNptN+w61zIMfMShOEREnvttna53yYrPZZLd3vNbQVJcS8itUUdOkPQdqdPqopPZjJYX1fwh8P4Y+2jA80I7hgXYMD7RjeKAdg1d3RpqbvlCex+PRnXfeqZ07d+rZZ59VWlraSc/x+Xx69dVXNWrUqPY59JMmTVJsbKzWrFnTfpzX69XatWs1a9asUxp+D4SqhiZP++J2GWmBj1aRWn6wjM9sWbxyd0GFmpq9p1wfAAAAgFNjek/9vffeq7feekv/8z//o8bGRn3yySftz40cOVJVVVW6++67NW/ePKWnp6uqqkovvfSSPv/8cz322GPtxzocDi1atEhLly5VYmKisrOztXLlShUWFmrJkiUmvDPAfLsLKuT3S4kup+JjI05+wkmkJcUoPjZClTVN2rWvXKe19tYDAAAAMIfpof69996TJD388MPHPPf8888rKytLMTExeuKJJ1ReXi673a7x48frmWee0YwZMzocf/PNN8vv9+uFF15QaWmpRo8erWXLlikrK6tP3gsQTPx+v3buq5AkZZ5iL30bwzA0PmOA3vv0gHYVVGrM8MQeeV0AAAAAgTE91L/55psnPeapp57q0msZhqEFCxZowYIFp1oWEPK+KGiZ/261GEpPje2x1x2SHKO4GIeqapu1a1+FBsaH71x6AAAAINiZPqceQO947aMCSdLQlFg57N3fDu94WnrrB0qSdjG3HgAAADAVoR4IQ26PV+9sKZLUc0PvjzQ0JUauaIfcHp+27y3r8dcHAAAA0DWEeiAMfVFQqYYmj1zRDiUl9PzweMMwNLZ1Pv32veXy+sJrf3oAAAAgVBDqgTC0Nbel9zxrWEKvbeeYnhorh82i2ga3Nu0s7pVrAAAAADgxQj0QZuob3SoqqZXFkLLSE3rtOjarRSNah/av/SC/164DAAAA4PgI9UCYOVhaJ0kalZ6g6Eh7r15r5JB4SdKmncUqLq/v1WsBAAAAOBahHggzB8taQv0ZWcm9fi1XtENpSdHy+6VX1+f3+vUAAAAAdESoB8KIz+fXobKWHvNJY3o/1EtS9ogBkqTXNhTI7fH1yTUBAAAAtCDUA2GkrLpRbo9PTodVI4f23nz6Iw0b5FKiK0KVtU1av/Vgn1wTAAAAQAtCPRBG2ubTD0t1yWrpnVXvj2a1GLpg6jBJ0toP8/vkmgAAAABaEOqBMNIW6ocPdvXpdS+aOlwWQ9qaW6rC4po+vTYAAADQnxHqgTDR1OxReXWjJGn4oL4N9UkJkTozO1USvfUAAABAXyLUA2HiYOsCefExEYrp5a3sOnPx9OGSpLc3Fcrt8fb59QEAAID+iFAPhIm2ofeDBkabcv2JWclKdDlVU+/Wx9uLTakBAAAA6G8I9UAY8Pv9OlRmbqi3Wgyde8YQSdKbGwtNqQEAAADobwj1QBiorGlSY7NXNquhgfGRptUxZ/JQSdLGHcWqqm0yrQ4AAACgvyDUA2HgYGsvfUpiVJ9tZdeZ9FSXRg6Nl9fn17rN+02rAwAAAOgvCPVAGGifTz/AnKH3Rzqvtbf+DYbgAwAAAL2OUA+EOLfHq5LKBknmzac/0syJQ2SzGsorqlL+wWqzywEAAADCGqEeCHGHyxvk90sxUXbFRDnMLkeuaEf7nvVvfFxgcjUAAABAeCPUAyGurZc+JSHK5Eq+1LZg3tub98vr9ZlcDQAAABC+CPVAiCttDfVmrnp/tDPGpMgV7VBlTZO2fFFidjkAAABA2CLUAyHM6/OprLpRUnCFervNolmTWvasZwg+AAAA0HsI9UAIq6huks/nV4Tdqtgou9nldNA2BH/DtkOqrW82uRoAAAAgPBHqgRBWcsTQe8Mwb3/6zmSmxWlYaqzcHp/e/fSA2eUAAAAAYYlQD4SwL+fTO02u5FiGYWjO5HRJ0psMwQcAAAB6BaEeCFF+v7891CcF0Xz6I80+Y4gshrRzX4WKSmrNLgcAAAAIO4R6IETVNrjV2OyVxTCU6Aq+nnpJSnQ5NTErWZL05sZCk6sBAAAAwg+hHghRbb30ia4IWa3B+618XusQ/Lc2Fcrn85tcDQAAABBegjcJADihYNmfPsJulc9//LA+dXyqop02lVQ0aGtuafvjJzoHAAAAQNfYzC4AQGBKgiTU220WWQxDb28uVG29u9Nj0lNd2pFfruf/tV3nTh6qmCi7Zk8a2seVAgAAAOGHUA+EoGa3V1W1LXu/mx3q29TWu1Vd1/l+9GnJMdqRX668A1U6rSqpjysDAAAAwhfD74EQVFrVKEmKibQrMiL4780NjHMqNsouj9evwuIas8sBAAAAwgahHghBwTKfvqsMw9DwwXGSpL0Hqk2uBgAAAAgfhHogBAX7/vSdGTHIJUk6XFGvmvrOh+kDAAAA6B5CPRBifD6/yqpCq6dekqIj7UpJjJIk7S6oNLcYAAAAIEwQ6oEQU1nbJI/XL7vNorgYh9nldMuIwS299bsKKuRnSzsAAADglBHqgRDTNvR+QJxThmGYXE33DEmOlc1qqLquWTvyy80uBwAAAAh5hHogxJRWhd58+jZ2m0VDU2IlSW9uLDS5GgAAACD0EeqBEFNR3SRJSnQ5Ta4kMCNaV8F/95MiNbm9JlcDAAAAhDZCPRBC3B6vqutaVo5PCNFQn5wQqZgou+obPVq/9aDZ5QAAAAAhjVAPhJC2Xvoop02RETaTqwmMYRganZ4giSH4AAAAwKki1AMhpLy6UVLoDr1vM3povCTpky8Ot2/PBwAAAKD7CPVACAmXUB8XE6FxGQPk80tvbdpvdjkAAABAyCLUAyEkXEK9JM2ZPFSS9ObGAvasBwAAAAJEqAdCRJPbq5p6tyQp0RVhcjWn7pwJg+WwW1VYXKvdhZVmlwMAAACEJEI9ECJKK1vmnkc7bYpwhOYieUeKctp1Vs4gSdLrHxWYXA0AAAAQmgj1QIgoqWgJ9eEw9L7N+VPSJUnrtuxXY7PH5GoAAACA0EOoB0JEW099qO5P35mczIFKSYxSfaNHH3zGnvUAAABAdxHqgRBR0hrqE+PCJ9RbLIYuaO2t/8+GfSZXAwAAAIQeQj0QAmrrm1Vd1ywpvIbfS9J5Z6bLYkjb8sp0oKTW7HIAAACAkEKoB0JA7v4qSVJ0pF0RdqvJ1fSsgfGRmjQmRZL0GgvmAQAAAN1CqAdCwO79lZLCr5e+zYVTW4bgv/Fxgbxen8nVAAAAAKGDUA+EgD2t+7iHw/70nTkzO1XxMRGqqGnSxh3FZpcDAAAAhAxCPRAC9oR5T73NatG5k4dKYgg+AAAA0B2EeiDIVdc1q7i8XlL4hnpJ7avgf7yjWOXVjSZXAwAAAIQGQj0Q5HJbe+ld0Q45wmyRvCMNTYnV2OGJ8vn8euNjeusBAACArjA91K9du1a33367Zs2apdNPP13z58/Xiy++KJ+v42JZ69at0+WXX66cnBxdcMEFWrFiRaevt3z5cs2ZM0c5OTm66qqrtGHDhr54G0CvaRt6nxQfaW4hfaBtwbzXPiqQ3+83uRoAAAAg+Jke6p977jk5HA7dddddevrpp3X++efr/vvv18MPP9x+zJYtW3T77bcrOztbzzzzjK644grdd999WrlyZYfXWr58uZYuXapvfOMbWrZsmYYNG6aFCxdq165dff22gB6zu3WRvKSE8A/1Z09IU2SETQdL6/R5XpnZ5QAAAABBz2Z2AU8//bQSExPbP582bZrq6+u1YsUKffe735XD4dATTzyh7OxsPfDAA+3HHDx4UI8++qiuuuoqWSwWNTc366mnntINN9ygW265RZI0ZcoUzZ8/X08//bSWLl1qyvsDTlVuUcse9QP7QU99ZIRNMyem6dX1+/Tahn3KyRxodkkAAABAUDO9p/7IQN9m7NixampqUmVlpZqbm7V+/XrNnTu3wzHz589XSUmJtm/fLknavHmzampqNG/evPZjrFarLr30Uq1bt46hvAhJtQ1uHW5dJG9gXPiHeunLBfPe//SAahvcJlcDAAAABDfTQ31nNm3apPj4eA0YMEAFBQVyu93KyMjocMzIkSMlSbm5uR0+Hn1cZmam6urqVFzM3tcIPXsPtPTSJyVEKsIRvovkHWl0eoKGpcaq2ePTO1v2m10OAAAAENRMH35/tK1bt+rll1/W4sWLZbVaVVXVEmpcLleH49o+b3u+urpaDodDTmfHLb/i4uIkSZWVlUpNTQ2oJr/fr/r6+oDO7a6GhoYOHxGaeqodv8gvlSRlDI6Tx+OR2921nmuP19vysRvnBHpeQOd4Wu4nNjQ0dDqKZtbEQXp+bY1e/XCvZp+e0qXX7A18P4Y+2jA80I7hgXYMD7RjeKAdg5/f75dhGF06NqhCfUlJib7zne8oJydHCxcu7PDc8d7QkY93dkxbYOjqF6QzbrdbO3bsCPj8QOTn5/fp9dA7TrUdP9lZLkkaMThO5eXlKq/q2s2lmIiWoF1VVaWS8pouXy+Q8wI5x9scJUnKy8tTY+Oxe9KnRHplsUh5B2r05gefalCCo0uv21v4fgx9tGF4oB3DA+0YHmjH8EA7BjeHo2u/AwdNqK+pqdHChQvldDr11FNPyW63S/qyp72tR75NdXW1pC977F0ul5qamtTU1KSIiIhjjmt7nUDY7fb24f69raGhQfn5+Ro+fLgiI/vHHOpw1FPt+Pu31kuSMtJcKi6zyOqI7tJ5cXGxrR/jJKvzJEef2nkBnRPT8j2akZFx3PUupn7h04efFyu/PEJzzhrTpdftaXw/hj7aMDzQjuGBdgwPtGN4oB2D3549e7p8bFCE+qamJi1atEilpaX685//rISEhPbn0tPTZbfblZeXp5kzZ7Y/3vYmMzMzO3zMzc1VdnZ2+3G5ubmKjo5WSkrgQ3gNw1BUVFTA5wciMjKyz6+Jnncq7ej2+LT/cJ2klp76sqpG2e2+Lp1rs7bMv7fZbF0+J9DzAjrH1vKj50T/iVx8VoY+/LxY7312SAuvmKAIu3lrCvD9GPpow/BAO4YH2jE80I7hgXYMXt0ZaW76Qnkej0d33nmndu7cqWeffVZpaWkdnnc4HJo2bZrWrl3b4fHVq1crKSmpPcBPmjRJsbGxWrNmTfsxXq9Xa9eu1axZs05p+D1ghv2Ha+Tx+hTltCklsf/9sD19VJKSEiJV1+DWh1sPml0OAAAAEJRM76m/99579dZbb+l//ud/1NjYqE8++aT9uZEjRyomJkaLFy/Wddddp3vuuUfz58/X5s2btXLlSt17772yWFruSzgcDi1atEhLly5VYmKisrOztXLlShUWFmrJkiUmvTsgcG0r348YHNcvb0pZLIYuODNdL/5nl17bsE+zJw0xuyQAAAAg6AQc6ktKSpSUlHTKBbz33nuSpIcffviY555//nlNnTpVEydO1JNPPqklS5Zo1apVSk1N1T333KNrrrmmw/E333yz/H6/XnjhBZWWlmr06NFatmyZsrKyTrlOoK/tPdCyHsSIwa6THBm+zpuSrpde26XP9pTqYGmdBg3s2poCAAAAQH8RcKg/99xzdeGFF+ob3/iGzjjjjIALePPNN7t03KxZszRr1qwTHmMYhhYsWKAFCxYEXA8QLPKKWnrqMwYHvshjsIqwW+Xz+2U5yQiE5IQoTRydrM27Duu1j/bpukvGnvQcAAAAoD8JONTfdttt+stf/qK1a9dq9OjRuu666zR//vxj9okH0H1+v//L4fdp4Rfq7TaLLIahtzcXqrb+xHvbD4hr+Zmy5oN8feOiMZKVUA8AAAC0CXihvDvuuENvvfWWfv3rXysmJkY//vGPNWvWLP3yl79UQUFBT9YI9DullY2qqXfLajGUnhJrdjm9prbereq65hP+SXA5FWG3qq7BrY07is0uGQAAAAgqp7T6vdVq1aWXXqoVK1Zo1apVuvDCC/WnP/1JF198sb71rW/p3Xff7ak6gX6lrZd+aEqsHCZu5RYMrBajfV2B/2zghiEAAABwpB7b0i4rK0szZ87UqFGj5PP59OGHH+rWW2/VlVdeqb179/bUZYB+Ia811A/vx4vkHSlzSLwkaeOOQyqrajC3GAAAACCInHKoLy8v129/+1udd955+s53viOr1aqlS5dq06ZNeuKJJ1RXV6cf/ehHPVEr0G+09dSH4yJ5gXBFO5Q6IEo+v/T6x/TWAwAAAG0CXijv008/1YoVK/Tvf/9bfr9fl156qW644QaNGzeu/Zg5c+bIarVq8eLFPVIs0F/sLWrZzo5Q/6WczIE6VFag1zYU6Jo5o2WxdG3BvK6ssg8AAACEqoBD/Ve/+lUNHDhQt956q772ta9pwIABnR43ZMgQTZw4MeACgf6mvtGtg2V1khh+f6SxwxP1wWcHVFxer2WrtmpIcsxJz4mJsmv2pKF9UB0AAABgjoBD/S9/+UtdeumlstvtJzwuMzNTL7zwQqCXAfqdvQdaeukHxjkVFxNhcjXBw26zaNakIVrzQb625pbKFe0wuyQAAADAdAHPqb/ssstOGugBdF84709/qi6cOkyStL+4Vk3NHpOrAQAAAMwXcKhftmyZfvGLX3T63C9+8QstX7484KKA/iyvqDXUM5/+GJlD4jUw3imf36+9B6vNLgcAAAAwXcChftWqVRo1alSnz40ZM0arVq0K9KWBfq0trLJIXufGDk+UJOXur5Lf7ze5GgAAAMBcAYf6AwcOaPjw4Z0+l56erv379wf60kC/5fX6tK811I9IY5G8zmQOiZfVYqi6rlllVY1mlwMAAACYKuBQb7PZVF5e3ulzZWVlMthCCui2/SW1cnt8ioywKjUx2uxyglKE3ar01FhJUm7rVAUAAACgvwo41I8fP15/+ctfOn3uL3/5i8aPHx9wUUB/tbc1pA4fFNflfdj7o8zWRQT3HayW2+M1uRoAAADAPAGH+ptvvlkbNmzQ9ddfrzVr1mjTpk1as2aNrr/+em3YsEELFy7syTqBfiGvdTu7DFa+P6GB8ZFyRTvk9fm171CN2eUAAAAApgl4n/qZM2fq3nvv1S9/+Ut973vfk2EY8vv9io2N1S9+8QvNmDGjJ+sE+oW9rHzfJYZhKCMtTp98UaLc/VUaOSTe7JIAAAAAUwQc6iXpmmuu0dy5c7VlyxaVl5crMTFREydOVFRUVE/VB/Qbfr9few+2hXoWyTuZEYNd+mx3icqrG1VR06iEWKfZJQEAAAB97pRCvSRFRUXp7LPP7olagH6tvLpRVbXNshjSsEGE+pNxOmxKS45VYXGNcvdXafJYQj0AAAD6n1MK9X6/X1u3blVRUZGampqOef7yyy8/lZcH+pW9rfPp05JjFWG3mlxNaMhMi1NhcY3yD1br9NFJslkDXiYEAAAACEkBh/q9e/dq0aJF2rdvn/x+/zHPG4ZBqAe6Ia91Pn0G8+m7LHVAlKKdNtU1elRYXMNaBAAAAOh3Ag719957r5qbm7V06VJlZWXJ4XD0ZF1Av5N3oDXUpzH0vqvaFszbmlumvQeqCfUAAADodwIO9Z999pl+8Ytf6OKLL+7JeoB+i5XvAzN8cEuoLy6vV12DW9GRdrNLAgAAAPpMwBNQo6KiFBMT05O1AP1WQ5NHB8vqJBHquysm0q7khJYdN/IPVptcDQAAANC3Ag71V155pVavXt2TtQD91r6D1fL7pURXhOJjI8wuJ+S0bQGYd6Cq0zU+AAAAgHAV8PD70aNH61//+pduu+02zZkzR/Hx8cccc+GFF55KbUC/0Tafnl76wAxNidWmncWqrXertLJBSa099wAAAEC4CzjUf//735ck7d+/X2+//fYxzxuGoR07dgRcGNCftK98n0aoD4TdZtHQlFjtPVCtvQeqCfUAAADoNwIO9c8//3xP1gH0a3vpqT9lIwbHae+BahUU12jSmGT2rAcAAEC/EHConzJlSk/WAfRbXp9f+QdrJNFTfyqSEyIV7bSrrtGt/YdrNXwQWwMCAAAg/J1yV1ZNTY3effddvfLKK6qqquqJmoB+5UBJrZrdXkU4rEodEG12OSHLMIz2BfPaRj4AAAAA4e6UQv0TTzyhGTNmaOHChfrhD3+o/fv3S5JuvPFGLVu2rEcKBMJdWwAdPsglq8UwuZrQNrw11B8qq1d9o9vkagAAAIDeF3CoX7FihZ544gldffXV+u1vf9thG6lzzz2308XzAByrfZE85tOfstgoh5ISIiWxZz0AAAD6h4Dn1K9YsUI33XST7rrrLnm93g7PDRs2TPv27Tvl4oD+YO+BlvA5gvn0PWLE4DiVVDQor6haU8elml0OAAAA0KsC7qkvLCzUjBkzOn0uOjpa1dX0kgFd0bZHfcZgFnbrCekpsbJaDNXUN+twRYPZ5QAAAAC9KuBQHxsbq9LS0k6fKyoq0oABAwIuCugvKqobVVnTJIshDWO19h7Rtme9JH1RUGFyNQAAAEDvCjjUT58+Xc8++6zq6+vbHzMMQx6PRy+99JLOOeecHikQCGdtQ+8HJ8XI6Qh4NgyO0rYK/p79lWp2e09yNAAAABC6Ag713/nOd3TgwAHNnTtXDz30kAzD0B//+Eddc8012rdvn26//faerBMIS21D70ewSF6PSkmMUpTTpma3Txu2HTK7HAAAAKDXBBzqhw0bppdeekkZGRl66aWX5Pf79Y9//EMJCQl68cUXNXjw4J6sEwhLe4vaQj1D73uSYRga0Tqd4Y2PC0yuBgAAAOg9pzTed+TIkVq+fLmam5tVUVGhuLg4OZ3OnqoNCHvti+Sx8n2PGzE4Ttv2lmvLrsMqq2rQgLhIs0sCAAAAelzAPfVHcjgcSklJIdAD3dDY5FFRSa0k9qjvDbHRDqUmRsnnl9Zt3m92OQAAAECvCLin/vHHHz/h84ZhaPHixYG+PBD29h2qlt8vxcdGKMHFDbHeMDo9QYfK6/X6x4W6YvZIGYZhdkkAAABAjyLUA33M5/fLYhjKa135nl763pMxJE7rPz+owuIa7dlfqVFDE8wuCQAAAOhRAYf6nTt3HvNYZWWlXn/9df3hD3/QsmXLTqkwIFxZDENvby7U25sKJUlen0+r38s77vHJiZGakj2or8oLKxF2q6bnDNa6Lfv1xseFhHoAAACEnR6ZU98mPj5eV199tebPn6/77ruvJ18aCCu19W4dLq+XJEU57aquaz7un/oGj8nVhrY5Zw6V1DKv3u1hz3oAAACElx4N9W1ycnL04Ycf9sZLA2HB5/ersrZJkpQQG2FyNeFtwqgkJbqcqm1wa+OOYrPLAQAAAHpUr4T6Xbt2KSoqqjdeGggL1bXN8nj9sloMxUY7zC4nrFkths49Y4gk6c2NhSZXAwAAAPSsgOfUr1q16pjHmpubtWvXLv3tb3/TV77ylVOpCwhrZVUNkqT4mAhZWJG91517xlD97a092rijWNV1zXJxIwUAAABhIuBQf/fdd3f6eEREhL7yla/orrvuCrgoINyVVjVKkuJdDL3vC8MGuZSRFqe8oiq9+0mR5p49wuySAAAAgB4RcKh/4403jnksIiJCAwcOPKWCgP6gvDXUJ8QQ6vvKnMlDlVdUpbc2FhLqAQAAEDYCDvVpaWk9WQfQr5S2Db93OU2upP+YOTFNv/vnNu0qqFBRSa3SkmLMLgkAAAA4Zb2yUB6A46usaVJ9Y8s2dfH01PeZhFinJmUlS5LeYsE8AAAAhImAe+rHjBkjo4sLfBmGoe3btwd6KSCs7D1QJUmKjbLLbuO+Wl8694wh2rijWG9tKtTXLxoji4VFCgEAABDaAg71ixcv1t///nfV1dVpzpw5GjhwoEpKSvTWW28pOjpaV155ZU/WCYSNtlAfH8vQ+742dfwgRTltOlzRoO17yzQ+kzVAAAAAENoCDvXR0dEaOHCg/vnPfyo6Orr98draWn3zm9+U0+nUggULeqRIIJzkFVVLkhJiGXrf1yLsVp192mC99lGB3txYSKgHAABAyAt47O+LL76oBQsWdAj0khQTE6MFCxboxRdfPOXigHC092BLTz2h3hznTh4qSXr/swNqcntNrgYAAAA4NQGH+uLiYlmt1k6fs1qtKi0tDbgoIFw1ub3af7hWkhRPqDfFuBEDlJwQqfpGjz76/JDZ5QAAAACnJOBQn5mZqd///vdyu90dHm9ubtZzzz2njIyMUy4OCDcFh6rl8/nldFgVGRHw7BecAovF0OwzWnrr39zEKvgAAAAIbQGniv/+7//W4sWLdf755+uCCy5QUlKSSkpK9Nprr6m0tFRPPPFEl15n3759Wr58uT799FPt3r1bGRkZWr16dYdj7r77bv39738/5txnnnlGM2fO7PDY8uXLtWLFCpWUlGj06NG66667NHXq1EDfJtCj2ubTD4iL7PLuEeh5554xRH95/Qtt3nVYFTWNSmDRQgAAAISogEP97Nmz9eyzz2rp0qV68cUX5fP5ZBiGTjvtND344IM666yzuvQ6u3fv1rp16zRhwgT5fD75/f5Ojxs6dKgeeeSRDo9lZmZ2+Hz58uVaunSpvvvd7yo7O1srV67UwoULtXLlSmVlZQX2RoEe1Lby/YA4QqSZhiTHKis9QbsKKvTOliJdNjPz5CcBAAAAQeiUxv9Onz5d06dPV0NDg6qrq+VyuRQZGdmt15gzZ47OP/98SS098p9//nmnxzmdTp1++unHfZ3m5mY99dRTuuGGG3TLLbdIkqZMmaL58+fr6aef1tKlS7tVF9Ab9uyvlCQlxXfv+wQ979wzhmhXQYXe3FhIqAcAAEDICnhO/ZHahhHb7fbuF2DpkRK0efNm1dTUaN68ee2PWa1WXXrppVq3bt1xRwAAfcXr9WnvgZbh9wMJ9aY75/Q02ayG8oqqtO9gtdnlAAAAAAE5pUS9fv16ffWrX9WkSZN07rnnateuXZKkn//85/rPf/7TIwW2KSgo0OTJkzV+/HhdeeWVev311zs8n5ubK0nHLNCXmZmpuro6FRcX92g9QHftL6lVs9uryAir4mIcZpfT78XFRGjy2BRJ0lssmAcAAIAQFfDw+w8//FALFizQqFGjdPPNN+vZZ59tfy4hIUEvv/yyLrzwwh4pcuzYscrJydHIkSNVU1Ojl156SYsXL9ajjz6qiy++WJJUXV0th8Mhp7PjXOW4uDhJUmVlpVJTUwO6vt/vV319/am9iS5qaGjo8BGhqbN23JF3WJI0YnCcvF7vMTtHHI/H27KXusfj6dVz+vJafXaOp+W+ZUNDQ6ejdc4an6z1nx/SW5sKdfXs4bJYOi5eyPdj6KMNwwPtGB5ox/BAO4YH2jH4+f3+Li+sHXCo/81vfqOZM2fqqaeeksfj6RDqx4wZo5dffjnQlz7GjTfe2OHzOXPm6Nprr9VvfvOb9lAvqdM33faL/KmsNO52u7Vjx46Azw9Efn5+n14PvePIdtz4eaUkaeSQeJWXl6u8qms3imIiWsJsVVWVSspreu2cvrxWX53jbY6SJO3fv1/Nzc3HPJ8Y4Vekw6Ly6ia9/sF2jUqLan/O4/HI4/FI4vsxHNCG4YF2DA+0Y3igHcMD7RjcHI6uje4NONTv2LFDjz76qKRjA3NiYqLKysoCfemTslgsuvDCC/Xwww+rsbFRTqdTLpdLTU1NampqUkRERPux1dUtc2XbeuwDYbfbNXLkyFOuuysaGhqUn5+v4cOHd3vRQQSPztrxT+9/LEnKHBKn6toIWR3RXXqtuLjY1o9xkrVrq+YHck5fXquvzklOjJLP79eoUaOOe8zsL7xa+2G+8kotuvzCce2P+3w+VVZWau/evXw/hjB+poYH2jE80I7hgXYMD7Rj8NuzZ0+Xjw041Fut1uMOgS0rK1N0dNcCS6COHkrbtr1dbm6usrOz2x/Pzc1VdHS0UlJSAr6WYRiKioo6+YE9KDIyss+viZ7X1o4+n1/7DrX0LmemxWtrbqnsdl+XXsNmtbZ8tNl69Zy+vFZfnRMZ4ZDFMPT25kLV1nf+88phbxmi/+4nBzRisEt2m1UxUXbNnjS0/T85vh9DH20YHmjH8EA7hgfaMTzQjsGrOyPNA14oLycnR6+88kqnz7366qsn3H7uVPl8Pr366qsaNWpU+xz6SZMmKTY2VmvWrGk/zuv1au3atZo1a9YpDb8HTtWB0lo1NHnlsFs1JDnG7HL6ndp6t6rrmjv9ExlhU0yUXR6vT9v3lqu6rvm4NwAAAACAYBNwT/2tt96qW265RYsXL9bll18uwzD06aef6m9/+5teffVV/eEPf+jS6zQ0NGjdunWSpKKiItXW1urf//63pJZ95hsaGnT33Xdr3rx5Sk9PV1VVlV566SV9/vnneuyxx9pfx+FwaNGiRVq6dKkSExOVnZ2tlStXqrCwUEuWLAn0bQI9Ind/lSRpxGCXrNae2cYRPcMwDI0Y5NLW3DLlFVVrxODAp+oAAAAAfS3gUH/WWWfpoYce0gMPPKA33nhDknTvvffK5XLpwQcf1OTJk7v0OmVlZbrzzjs7PNb2+fPPP6+srCzFxMToiSeeUHl5uex2u8aPH69nnnlGM2bM6HDezTffLL/frxdeeEGlpaUaPXq0li1bpqysrEDfJtAjcotaQn1mGoExGI0YHKetuWU6XFGv2vpmuaLZchAAAAChIaBQ7/V6VVBQoHPPPVcXXXSRtmzZotLSUiUkJGjSpEndmpcxZMiQ9v3tj+epp57q0msZhqEFCxZowYIFXb4+0Bdy91dKkjKHxJtaBzoXHWlXSmKUisvrtfdAtQYnMUUCAAAAoSGgccB+v19z587Vli1b5HQ6NX36dM2fP1/nnHMOCy0AR/H7/e099SMJ9UEro3UUxd4DVZ3uaQ8AAAAEo4BCvc1m08CBA/nFF+iC4vJ61TW4ZbNaNDQl1uxycBxDkmNkt1lU1+hRUUmd2eUAAAAAXRLwil1z587VqlWrerAUIDy1LZI3fFCs7DYWyQtWNqtFw1JdkqRd+8pNrgYAAADomoAXyhszZozWrFmjG264QRdeeKGSkpKO2TbuwgsvPOUCgVC3h/n0ISMjzaU9+yu190C1ahvcsppdEAAAAHASAYf6H/7wh5Kk4uJiffTRR8c8bxiGduzYEXhlQJhgkbzQkehyKi7GoaraZr27Zb9mT0w1uyQAAADghLoV6n/1q1/phhtuUGpqqp5//nlJLSvhW630ZwGdOXKRPLazC36GYShjcJy2fFGi1z8uINQDAAAg6HUr1D/33HO6+OKLlZqaqilTpsjr9Wr8+PH661//qnHjxvVWjUDIKqtqVHVds6wWQ8MHucwuB10wfJBLn+4u0RcFlSosrjW7HAAAAOCEurVqV2er3bMCPnB8ew/USJLSU2PlsDOiJRQ4I2xKb10w763NRSZXAwAAAJwYS3EDvWjvwWpJUmZavLmFoFuyhiVIktZtOSCPlxuXAAAACF6EeqAXtfXUZw5hPn0oSU+J1cA4p2rq3dpR2GB2OQAAAMBxdXv1+7y8vPaF8bxeb/tjnWGePfqzlkXyWnrqR7LyfUixWAxdOHWYXvzPLm3cU6v/MrsgAAAA4Di6Hep/9KMfHfPYXXfd1eFzv9/Plnbo96rqvapqXSRvBCvfh5wLpw3Tn17/QvsON2v/4VqNHh5ldkkAAADAMboV6h988MHeqgMIO0VlzZKk4YNdimCRvJAzIC5Sk7IGauOOEr2xsUijhyebXRIAAABwjG6F+iuuuKK36gDCTluoHz00weRKEKgLzhyijTtKtG7LAd182WncnAEAAEDQYaE8oJe0h/r0eHMLQcAmjByo+Gir6ho9ev9TtrcDAABA8CHUA73A5/PrQLlbkjQqnZ76UGWxGJo0MlqStPaDfHOLAQAAADpBqAd6wf6SOrk9fkU4rBqSHGt2OTgFEzOiZbUY2rmvQnsPVJldDgAAANABoR7oBbn7W8Jf5mCXrBbD5GpwKmIjrZo8NkmS9O8P880tBgAAADgKoR7oBW3702cOcZlcCXrCBWcOkSS9tWm/6hvdJlcDAAAAfIlQD/SCPUUtPfUj2Z8+LIwbkaghyTFqaPLo9Y8LzC4HAAAAaEeoB3pYk9urgkO1kqSR9NSHBYvF0FdmZEiSVr+7V16f3+SKAAAAgBaEeqCH7S2qktfnV7TTogFxTrPLQQ8594yhiom062BZnTZuP2R2OQAAAIAkQj3Q474oqJAkpSU6ZBgskhcunBE2XTRtmCTplXfzTK4GAAAAaEGoB3rYFwWVkqS0AQ5zC0GPm3t2hiwWQ5/tKWV7OwAAAAQFQj3Qw74obO2pJ9SHnaSESJ1z2mBJ0ivv0FsPAAAA8xHqgR5UU9+sg6V1kqTBhPqw9JWZLQvmvb15vyprmkyuBgAAAP0doR7oQbsLKyVJqYmRiorg2yscZQ1LVNawBHm8Pq39MN/scgAAANDPkTqAHrS7dZG8zCHsTx/OLpuRKUla88FeuT1ek6sBAABAf0aoB3pQ2yJ5Iwn1YW36aYM0MM6pypomvbmx0OxyAAAA0I8R6oEe4vf72xfJy0xzmVwNepPNatHls0dKkv765m55vT6TKwIAAEB/RagHesjhigZV1jTJajE0YlCs2eWgl100dZhc0Q4dKqvXu58UmV0OAAAA+ilCPdBDduSXS2qZT++wW02uBr3NGWHT5bNa5tb/5Y3d8vn8JlcEAACA/ohQD/SQHXvLJEljhieaXAn6yqVnjVC006bC4hqt//yg2eUAAACgHyLUAz2krac+e/gAkytBX4mOtGveOS371v/ljS/k99NbDwAAgL5FqAd6QH2jW/sOVkuSxgxPMLka9KX5MzIU4bAqd3+VNu86bHY5AAAA6GcI9UAP2LWvQj6/lJwYpQFxkWaXgz4UFxOhS6YPlyT95fUvzC0GAAAA/Q6hHugBXw69Zz59f3T5rEzZrBZt31uurbml3TrXx5B9AAAAnAKb2QUA4aAt1LNIXv80IC5SF0xN19oP8vXEyk809+wRMgzjpOfFRNk1e9LQPqgQAAAA4YpQD5wir8+vXfsqJEnZIwj1/dVXzx+tNz4qUFFJnXbtq9DgpBizSwIAAEA/wPB74BQVHKpWQ5NHkRE2pae6zC4HJhkQF6m5rSvhf7qnlJXwAQAA0CcI9cAp2r63Zeh91rAEWS0nH3KN8HX1nFFy2CyqrGnSvkM1ZpcDAACAfoBQD5yiHXtZJA8tXNEOTRiVJEnauqdUPh+99QAAAOhdhHrgFO3YxyJ5+FLOyAGKcFhV2+BWblGV2eUAAAAgzBHqgVNQVtWgw+X1shgtw+8Bu82q8RkDJEnb8krl8fpMrggAAADhjFAPnIK2reyGDXIpymk3uRr0NMMw5HQ6u7Q93ZEyh8Qp2mlTQ5NXXxRU9FJ1AAAAAKEeOCVtoX4sQ+/DSoTdKp/fL6fTqXHjxikyMrJb51stFuWMHCipZSHFhiZPb5QJAAAAsE89cCraFskbO2KAyZWgJ9ltFlkMQ298lK99+w8pMTFRNtuJf1wmJ0ZqSvag9s+HD3Lpi4IKlVc3aeueUk0Zl9rbZQMAAKAfItQDAWps9iivdSE0eurDU019s8qr6mV1RMtuP/Hc+JjIjtMvDMPQpKxkvf5xoXKLqjRyaLwSXc7eLBcAAAD9EMPvgQDtLqyU1+dXosup5ITuDc9G/5CUEKVhqbGSpM07D8vvZ4s7AAAA9CxCPRCgz3PLJEnZIxK7vZAa+o8Jo5NktRgqqWxQQXGN2eUAAAAgzBDqgQB9nlsqSTqtdUE0oDPRTruyR7RMz/jkixK2uAMAAECPItQDAWh2e7WzdeX78ZmEepzYmOGJinLaVN/oad8xAQAAAOgJhHogALsKKtTs8Sk+NkJDkmPMLgdBzma1aOLoJEktOybU1jebXBEAAADCBaEeCMDne1qG3udkDmQ+PbpkaEqskhOi5PX59fGOYhbNAwAAQI8g1AMB2Nq6SF4O8+nRRYZh6MzsFFkMQ4fK6lk0DwAAAD2CUA90U7Pbq537WuZF52QO6PQYwzDkdDrpxUcHrmhH+6J5m3ceVlOz1+SKAAAAEOpMD/X79u3TT37yE1122WXKzs7WvHnzOj1u3bp1uvzyy5WTk6MLLrhAK1as6PS45cuXa86cOcrJydFVV12lDRs29Gb56Id2FVTI7fEpITZCgwZGd3pMZGSkxo0bp8hI9q9HR9kjEhUbZVdjs1cfbT9kdjkAAAAIcTazC9i9e7fWrVunCRMmyOfzdTrPdMuWLbr99tt12WWX6e6779bmzZt13333yeFw6Jprrmk/bvny5Vq6dKm++93vKjs7WytXrtTChQu1cuVKZWVl9eXbQhg7cj691WLR25sLVVvv7nCMx+NReXm5EhMTZbN9+W2WnBipKdmD+rReBBer1aIzs1P15sZCbd9brl37ypU1LNHssgAAABCiTO+pnzNnjtatW6ff/OY3GjduXKfHPPHEE8rOztYDDzygadOm6fbbb9fVV1+tRx99VD5fy57Pzc3Neuqpp3TDDTfolltu0fTp0/Xwww9ryJAhevrpp/vyLSHMfda6P/341vn0tfVuVdc1d/hTVduk8qp6VdU2dXi8vsFjZukIEimJURo+yCVJenzlp+xdDwAAgICZHuotlhOX0NzcrPXr12vu3LkdHp8/f75KSkq0fft2SdLmzZtVU1PTYfi+1WrVpZdeqnXr1rHSNHpEs9urXfsqJB1/Pj3QFROzkhRhtyr/YLVefmuP2eUAAAAgRJke6k+moKBAbrdbGRkZHR4fOXKkJCk3N7fDx6OPy8zMVF1dnYqLi/ugWoS7Xfta5tMnuiKUlsT+9Aic02HTWae1TMV46T+7tO9QtckVAQAAIBSZPqf+ZKqqqiRJLperw+Ntn7c9X11dLYfDIafT2eG4uLg4SVJlZaVSU1MDqsHv96u+vj6gc7uroaGhw0cEl807D0qSxg5LUGNjoyIjI+XxeOR2d5xT3/b50Y97vC2rnXd2zvH01TnBXp8ZXwdv68eunBvItUYMilHN2BRt3FGspS9u0i8WnimrNejvtYYUfqaGB9oxPNCO4YF2DA+0Y/Dz+/1d3kkr6EN9m+O9oSMf7+yYtmH3p7K1mNvt1o4dOwI+PxD5+fl9ej10zcfbDkuSEiIblZeXp3Hjxqm8vFzlVZ3f9KmsrOzweUxES/CrqqpSSXnX9invq3OCvT4zvg61tbWSjm3HnrqWtzlKd1wzQbc99Lpyi6r1u1UbdU52bJfrRNfxMzU80I7hgXYMD7RjeKAdg5vD4ejScUEf6tt62tt65NtUV7cMVW3rsXe5XGpqalJTU5MiIiKOOa7tdQJht9vbh/v3toaGBuXn52v48OFshxZkmt1eHfjLAUnSedOz24ffJyYmyurouLWd2+1WZWWl4uPjZbfb2x+Pi4tt/RgnWTuOKjmevjon2Osz4+sQExOjkvKaY9qxx+qLidCAuEh9c+5YPfX3bVr3eY0unTlOaUmdb5WI7uNnanigHcMD7RgeaMfwQDsGvz17ur7mUtCH+vT0dNntduXl5WnmzJntj7e9yczMzA4fc3NzlZ2d3X5cbm6uoqOjlZKSEnANhmEoKioq4PMDERkZ2efXxInl7iltn0+fOXRg++gPm80mu73z1cvtdnuHMGizWk96ztH66pxgr8+Mr4O19ePR7dhj9bVud3jJ2ZnasL1Em3cd1m//sUO/vGOGrJbARxfhWPxMDQ+0Y3igHcMD7RgeaMfg1Z2R5kE/edPhcGjatGlau3Zth8dXr16tpKSk9gA/adIkxcbGas2aNe3HeL1erV27VrNmzTql4feAJH3Wvj99Ev+e0KMMw9Ad15yuyAibdu2r0D/W5ZpdEgAAAEKE6T31DQ0NWrdunSSpqKhItbW1+ve//y1JmjJlihITE7V48WJdd911uueeezR//nxt3rxZK1eu1L333tu+JZ7D4dCiRYu0dOlSJSYmKjs7WytXrlRhYaGWLFli2vtD+NjyRct8+pzW/emBnpSUEKlbvjJej6/8RC+s3aFJY5Lb97IHAAAAjsf0UF9WVqY777yzw2Ntnz///POaOnWqJk6cqCeffFJLlizRqlWrlJqaqnvuuUfXXHNNh/Nuvvlm+f1+vfDCCyotLdXo0aO1bNkyZWVl9dn7QXiqrW/W7oKW/eknZSWbXA3C1YVT07Vh20F9vL1Yv16xSUv+e6bsNqvZZQEAACCImR7qhwwZol27dp30uFmzZmnWrFknPMYwDC1YsEALFizoqfIASdKne0rl80tDU2KUlMBiIugdhmHo2/91uu54+C3lH6zWin/v1E3zxpldFgAAAIJY0M+pB4LBll0tQ+8njqaXHr0rIdapO645XZL08tt7tC2vzNyCAAAAENQI9cBJ+P3+L0M9Q+/RB6bnDNL5Z6bL75eWvLRZ9Y1us0sCAABAkCLUAydxoLROhysaZLNaND5jgNnloJ9YePl4JSdG6XB5vZat2mp2OQAAAAhShHrgJNp66bNHJMoZYfoyFOgnopx2fe9rk2QxpDc+LtS7W4rMLgkAAABBiFAPnMRmht7DJOMyBuia80dLkh7/6yc6VFZnckUAAAAINoR64ATcHp+27imVxFZ26HkRdqt8fv8Jj/naBVkaOzxR9Y0e/XrFJnm9vpOeAwAAgP6DscTACezcV67GZq/iYyI0fJDL7HIQZuw2iyyGobc3F6q2/viL4U0cnaTcokrt3Fehh57/WP/7zal9WCUAAACCGT31wAm0zac/fXSSLBbD5GoQrmrr3aquaz7uH7+kyWNTJEnrPz/UPnoEAAAAINQDJ/DlVnZJJleC/m5YqksZaXGSpF+/uElVtU0mVwQAAIBgQKgHjqOqtkm5RVWSpNNHM58e5jsjK1nxMREqq2rUkpc2y+djbj0AAEB/R6gHjuOTL0rk90vDB7mU6HKaXQ4gm82i86eky2G3avPOw/rbW7vNLgkAAAAmI9QDx7HlC7ayQ/AZEOfUbVfkSJL+uHaHPs9lfj0AAEB/RqgHWh25TZjP59fmnS2hfhLz6RFkzp+SrjmTh8rnlx7+40ZV1jC/HgAAoL9iSzug1ZFbix2uqFdFTZPsNov2HarW/sO1xxyfnBipKdmDTKgU/Z1hGFp05WnaXVipwuIa/frFTfrZwumyskMDAABAv0NPPXCEtq3FviiolCSlJkaprsHT6TZj9Q0ec4tFv+aMsOnuGyYrwmHVJ1+U6KX/7DS7JAAAAJiAUA90oqikpWd+cFKMyZUAx5ee6tIdV0+QJP35tS/00bZDJlcEAACAvkaoB45S1+hun6M8OCna5GqAE5t9xlDNO2eEJGnJi5t0oOTYqSIAAAAIX4R64CgHSuokSQPjnXI6WHYCwe/m+eM1dnii6ho9euD3H6mxiakhAAAA/QWhHjhKW0/n4IEMvUdosNssuvvGM5UQG6F9h2r02F8+kf+I3RwAAAAQvgj1wBHcHp8OlddLktKYT48Qkuhy6u4bz5TVYuidT4q0al2u2SUBAACgDxDqgSMUHa6Vz+dXtNOuuBiH2eUA3ZI9YoAWXDZekvT71du0cUexyRUBAACgtxHqgSPsO1QtSUpLjpZhsOc3Qs/cs0fowqnD5PNLD/9xowqLa8wuCQAAAL2IUA+08vn8KjjUEoCYT49QZRiGbrvyNGWPSFR9o0f3/W6DauubzS4LAAAAvYRQD7Tas79S9U0e2ayGkhMjzS4H6FSE3SrfSRbBs9ss+tGNU5SUEKkDpXX65Qsb5fZ4+6hCAAAA9CX26wJafby9Zf7xoAHRslq434XgZLdZZDEMvb25ULX17hMeO2timlaty9UnX5Tod//cpm9dcVofVQkAAIC+QqgHWn20/ZAkaTCr3iME1Na7VV134mH1dptV08YP0nufHtDq9/Zq0MBofWVGZh9VCAAAgL5AdyQg6XBFvfKKqiRJg5OiTa4G6DlDU2I1dVyqJOnZf3yuDZ8fNLkiAAAA9CRCPSDpg89ags6gAdFyOhjAgvAyYdRAXTRtmPx+6eEVm7SnsNLskgAAANBDCPWApA8+OyBJGpHmMrkSoOe1rYg/cXSSmpq9unf5eh2uqDe7LAAAAPQAQj36vbKqBu3cVy5JGjE4zuRqgN5hs1p0941navgglypqmvTzZ9ez1R0AAEAYINSj31u/9aD8filrWIJiIu1mlwP0uLZt8KKcdv3klmlKdEWo4FCN7nvuIzW5T7zV3cm2zwMAAIC5mDyMfu+DrS3z6c/KGWxyJUDvOHobvDmTh+qVd/O0La9MP3j0HV0wJV0Wi3HMeTFRds2eNNSEigEAANBV9NSjX6uqbdLnuaWSpLNOG2RyNUDvatsGz26zasaENFkshvIPVuvNTYWqqm1SdV1zhz+19W6zSwYAAMBJEOrRr63//KB8filzSJxSB7CVHfqP5MQonZXTciMrd3+VPs8tM7kiAAAABIJQj36tbSs7ht6jPxqaEqvJY5MlSZ/nlemLggqTKwIAAEB3EerRb9XWN+vT3SWSpLMnEOrRP40amqDxGQMkSZt2HlZeUZXJFQEAAKA7CPXotzZsOySvz69hqbFKS4oxuxzANOMzBygrPUGS9NG2QyosrjG5IgAAAHQVoR791vufHZAknXUavfTo3wzD0MSsJGWkxckv6YPPDuhgaZ3ZZQEAAKAL2NIO/VJ9o1tbdrUOvSfUAzIMQ2dmp8jt8amwuEbvflKk2GiH2WUBAADgJOipR7+0Ydshebw+pSVFKz011uxygKBgMQxNzxmkwQOj5fX5tfaDfG3LY1V8AACAYEaoR7+0bvN+SdKM04fIMAyTqwGCh9Vi6OwJg5U6IEoer08/e+ZDgj0AAEAQI9Sj36mqbdKWL1qG3s+alGZyNUDwsVktmnF6moYkx6ix2aufP/uhtu8l2AMAAAQjQj36nfc+KZLP59fIIXEakszQe6AzNqtFF00bptNHJamhyaufPUOwBwAACEaEevQ767YUSZJmTRpiciVAcLNZLfrfm6fotJED24P9jr3lZpcFAACAIxDq0a8cKqvTjvxyGYY043SG3gMn43TY9ONbprYH+58+86F25hPsAQAAggWhHv3KO6299DmZAzUgLtLkaoDQ4HTY9OObpyonc6Aamjz6ybIPtXMfwR4AACAYEOrRb/j9fr3duuo9Q++B7nFG2PSTW74M9j9d9qF2EewBAABMR6hHv5F/sFqFxTWyWS0667TBZpcDhJy2YD8uY4DqGz368W/Z7g4AAMBshHr0G21705+ZnaKYSLvJ1QChyRlh008XTNP4zAHtQ/E37zpsdlkAAAD9FqEe/YLP52fVe6CHRLYG+zPGJKvZ7dUvlm/Qh1sPml0WAABAv0SoR7+wfW+ZSisbFOW06cyxKWaXA4Q8p8Om//3mVJ112iB5vD499PzH7WtWAAAAoO8Q6tEvtIWN6TmD5LBbTa4GCA92m0V3XTdZcyYPlc/n15IXN+nfH+abXRYAAEC/QqhH2Gts8rRvZXfemekmVwOEF6vVoju/OlGXnDVcfr/0xF8/1ap1uWaXBQAA0G8Q6hH23v/sgBqaPBo0IFrjMwaYXQ4QdiwWQ4uuPE1XnTtSkrT8lc/1p9d2ye/3m1wZAABA+CPUI+y99lGBJOm8KUNlGIbJ1QDhyTAM3Tg3W9ddPEaStOLfO/X71dsJ9gAAAL2MUI+wdqCkVtvyymQxpPMmM/Qe6I4Iu1W+boRywzD01QuydMtXxkmSXn57jx5f+am8Xt9Jz+3OdQAAAPAlm9kFdMXLL7+sH/3oR8c8vnDhQv3gBz9o/3zdunVaunSpcnNzlZqaqptuuknf+MY3+rJUBJnXP27ppZ+YlayB8ZEmVwOEFrvNIoth6O3Nhaqtd3fpnOTESF0+a6QKimv0+oYC/WfDPn1RUKHzzhwqm7Xz+8gxUXbNnjS0J0sHAADoN0Ii1Ld59tlnFRsb2/55SsqXW5Nt2bJFt99+uy677DLdfffd2rx5s+677z45HA5dc801ZpQLk/j8flkMQ16vT298XChJumDKMJOrAkJXbb1b1XXNXTo2JtIuScoYHKezJwzWB1sPKv9gtV55N08zT09j9wkAAIAeFlKhfty4cUpMTOz0uSeeeELZ2dl64IEHJEnTpk3TwYMH9eijj+qqq66SxcJMg/6irWdxe16Zyqsb5XRYVVpZr9Xv5R33nOTESE3JHtSHVQLhb2hKrM61W/XOJ0UqqWjQGx8XavYZQxQZEVL/9QAAAAS1sEi6zc3NWr9+vebOndvh8fnz56ukpETbt283qTKYpbberc9zyyRJw1Jdqmv0qLqu+bh/6hs8JlcMhKfkxCidd+ZQOR1WVdY26bUNBarpYq8/AAAATi6kQv28efM0duxYnXfeefrtb38rr9crSSooKJDb7VZGRkaH40eObNleKTeXPZP7m4Ymj4pKaiVJGWlxJlcD9G8JsU5dMCVdMVF21TW69dpHBSqvbjS7LAAAgLAQEmMgk5KS9O1vf1sTJkyQYRh688039X//938qLi7WT37yE1VVVUmSXC5Xh/PaPm97PlB+v1/19fWn9Bpd1dDQ0OEjuscwDEVGRmpXfpl8fikhNkLRTovc7hMv8uVpvUHk8XhOemxXzmn7/OjHe/o6PXlOsNdnxteh7cZhV84N5vcUDP8eIuyGZk8cpPc+PaTK2ma98XGBpo9PUUpilDyelvvLDQ0NPb4FHj9TwwPtGB5ox/BAO4YH2jH4+f3+Lm/HHRKhfsaMGZoxY0b75+ecc44iIiL0hz/8Qbfddlv748d706e6N7nb7daOHTtO6TW6Kz8/v0+vFy6cTqeys7O1fW/L0PuBMVJJSclJz4uJaAkiVVVVKimv6dK1unJOZWVln1ynJ84J9vrM+DrU1raM9ji6Hc2sL9T/PYwb6tC2Aq8q67x677NDGjPEqTHpLaNp8vLy1NjYOz34/EwND7RjeKAdwwPtGB5ox+DmcDi6dFxIhPrOXHLJJfrd736nHTt2KC0tTdKxPfLV1dWSju3B7y673d4+lL+3NTQ0KD8/X8OHD1dkJFuwdZdhGNqaW6qaBq+sVkPZIwfJbjv5LJO4uNjWj3GS1dmla53oHLfbrcrKSsXHx8tut/fadXrynGCvz4yvQ0xMjErKa45pRzPrC4d/D8nJfn2847D2H67TjsJGxca65Pf7lZGR0Ss99fxMDX20Y3igHcMD7RgeaMfgt2fPni4fG7Kh/kjp6emy2+3Ky8vTzJkz2x9v+0JkZmae0usbhqGoqKhTeo3uioyM7PNrhovV722VJI0YFKeoyIgunWOztmyzZbPZZLf7euwcu93eIQz21nV64pxgr8+Mr4O19ePR7WhmfeHw78Eu6ewJadqy67C+KKjUR9sP6+mXP9Otl+fIepy97E8VP1PDA+0YHmjH8EA7hgfaMXh1Z7R5SC2Ud6Q1a9bIarUqOztbDodD06ZN09q1azscs3r1aiUlJSk7O9ukKtHXSioatOHzg5Kk0enx5hYD4LgshqEzxqRoYlaSJGnNB/m6//cfqbGp6ztR+Hq4Vx8AACAUhURP/S233KJp06Zp9OjRkqQ33nhDf/nLX3TDDTcoKanlF8LFixfruuuu0z333KP58+dr8+bNWrlype699172qO9H/r0+Xz6/NHhgtOJiutZLD8A8Y4YlakhSjF5dv08fby/W7b96UxdNH6Zo54lHRsRE2TV70tA+qhIAACB4hUSoHzFihP7617/q0KFD8vl8Gj58uP7f//t/uv7669uPmThxop588kktWbJEq1atUmpqqu655x5dc801JlaOvuT2ePXq+nxJ0riMAeYWA6DLRqcnaObEIfrxbz9QSWWD/vbmHs2cmKZEV9fn/wMAAPRXIRHq77nnni4dN2vWLM2aNauXq0Gweu/TA6qqbdaAOKeGD3KptqHrW3cBMNeY4Ym6fFam1nyQr+q6Zr3+UYGm5wzS0JRYs0sDAAAIaoxLR9j41/t7JUmXTB8ui+XUtjEE0PfiYiJ0wZR0pQ6Iktfn13ufHtC2vLIeXxE/EIHM32fOPwAA6Ash0VMPnMyewkrt2lchm9XQhdOG6f1PD5hdEoAAOOxWzZo4RFu+aFkZ/7M9paqsbdLU7FTZurA9ZW+xGIbe3lyo2vqujQBizj8AAOgrhHqEhbZe+rNPS1NCLPNwgVBmsbSsjO+KdmjTzsMqOFSjqtomzTg9TbFRDtPqqq13q7qu2bTrAwAAdIbh9wh5lTVNemfLfknSvHNGmFwNgJ4yamiCzps8VE6HVVW1zXp1/T4VldSaXRYAAEBQIdQj5P3zvTw1e3wanR6vrGEJZpcDoAclJUTpomnDNTDeKbfHp3e2FOmz3SXy+ZivDgAAIBHqEeLqG93tQ++vOneUDIMF8oBwE+W0ac7kdI0aGi9J2ra3XK+8m6fi8npzCwMAAAgChHqEtFfX71Ndg1tpSdGaOn6Q2eUA6CVWi6HJY1N01mmDZLdZVFxerzt//Zbe3VJkdmkAAACmItQjZLk9Pv3jnVxJ0hWzR8nKNnZA2BuW6tLF04crJTFKdY0e/eqPG7X0pc2qrWcBOwAA0D8R6hGy1m0uVFlVoxJdEZozeYjZ5QDoIzGRdn1lRoa+ev5oGYb05sZC3f6rN/XBZ2xlCQAA+h9CPUKSz+fXX9/cI0m6bGam7DaryRUB6EsWi6HrLhmrhxafo7SkGFXUNOnBP3ysB37/kcqrG80uDwAAoM8Q6hGSNmw7pKKSWkU7bbp4+nCzywFgkuwRA/Sb78/Wf50/WlaLoQ+3HtTtv3xDf397j9wer9nlAQAA9DpCPUKO3+/X397cLUm65KwRinLaTa4IgJkcdquuv2Ssln53lkYOiVNdo0e/++c23f6rN7VhW7H8fra/AwAA4YtQj5CzNbdUuwoqZLdZ9JUZGWaXAyBIjBgcp0funKU7v3q6El0ROlRWryV/+kzPvV6iz/PKCfcAACAsEeoRUvx+v15Ys0OSdMGUdCW4nCZXBCCYWC2Gzp8yTE/ffb6uvSBLDrtFBSXN+sVzm3TXY+/q4+2HCPcAACCsEOoRUj7eXqyd+yrksFv11QuyzC4HQJCKjLDpGxeP0f/debbOHBUtu82infsqdO/yDfrvJev09qZC5twDAICwQKhHyPD5/HphbUsv/fxzRiiRXnoAJzEgzqm5Zybose+doytmj5TTYVXegSr9+sXN+uYv/qPn12zX4Yp6s8sEAAAImM3sAoCueveTIuUfrFaU06ar5owyuxwAISQhNkI3zx+nq+eM0poP9urfH+arrKpRK9/Yrb+9uVtnjE3R+Wem68zsVNlt3O8GAAChg1CPkODx+rTi1Z2SpCtnj1RslMPkigCEIle0Q9dekKVr5ozShm2H9K/39+qzPaX6eHuxPt5eLFe0Q7MnDdH5U9I1YnCc2eUCAACcFKEeIeGNjwt0sLROcTEOzWfFewBdZBiGnE6nDMPo8LjVatFZpw3WWacNVmFxjd74uEBvbixURU2TXnk3T6+8m6eMwXE6b8pQzZo4RHExESa9AwAAgBMj1CPoNbu9euk/uyRJ15w3mn3pASjCbpXP75flqLB+tMjISI0bN67DY0efNzQlVjfNG6frLxmrLV+U6PWPCrRh2yHlHahS3qoqPffPbTozO1Vx0Q7FxUTIYjnxNQEAAPoSoR5Bb/V7eSqratTA+EhdMn242eUACAJ2m0UWw9DbmwtVW+8+7nEej0fl5eVKTEyUzWZTTJRdsycN7fRYq9WiyWNTNHlsiqrrmvXOlv16/eMC5e6v0odbD0qSnA6rhg9yKSMtjt57AAAQFAj1CGplVQ3602stvfTfuChLDrvV5IoABJPaereq65qP+7zb7VZ5Vb2sjmjZ7b4uv64r2qF552Ro3jkZ2nugSm98XKhX1+ersdmrnfsqtHNfhQbGR2p0eryGJsfSew8AAExDqEdQe+6f29XQ5FVWeoLmTE43uxwA/dCIwXFacFmckhKc2rWvQnlF1TpQWqvSygaVVjYoMqJEI4fEKXNIvCIj+G8VAAD0LX77QNDauqdU67bsl2FIt115Gj1hAExltVg0JDlWQ5JjVd/oUe7+Su3ZX6mGJo+25pZp295yZQyO09jhCXJFs0MHAADoG4R6BCWP16en//6ZJOniacM1cmi8uQUBwBGinDbljByo7IwBKiyu0Rf7KlRW3ag9+yuVW1SpzLQ4jcsYwLZ4AACg1xHqEZRWv7dXBYdqFBvl0Ig0l1a/l9el85ITIzUle1AvVwcALawWQ8MHuTQsNVaHKxq0fW+ZDpXVa8/+Kn3n12/rnAmD9Y2Lx2hIcqzZpQIAgDBFqEfQKa9u1Iuv7pQk3Th3rNwe3wkXwjpSTCTb3QHoe4ZhKCUxSimJUSqvbtTuwkrlFVXpvU8P6IPPDmjO5HR97cIsJSdGmV0qAAAIMxazCwCOtvyVz9XQ5NGoofG6YMows8sBgG5JdDl1wZR0/eb7szV1XKp8fun1jwv0rYde129f/kwV1Y1mlwgAAMIIoR5B5d1PivTOliJZWBwPQIgbMThO99w8VQ9/Z4YmjBooj9ev1e/v1YIHXtfvV29TTX3XRiABAACcCKEeQaOsqkFP/vVTSdLV543W6PQEkysCgFM3Zlii7rvtbN1321nKGpagZrdXf3trjxbc/5pe+s8u1Te6zS4RAACEMObUIyj4/X795s+fqLbBrcwhcbr2giyzSwKAHjVhVJJOGzlQH+8o1gtrdij/YLVefHWn/vlunq6eM0pzzxmhCLvV7DIBAECIIdQjKKz9MF+bdx2W3WbR9742SXYbg0gAhB/DMDQlO1WTx6To/U8PaMWrO1RUUqfnVm/TP97Zo/86P0sXTh3Gz0AAANBlhHqYrqikVstf2SZJumluttJTXSZXBAC9y2IxNGNims46bZDe2lSol/6zS4crGvT0y5/p5bd262sXZuncM4bKaiXcAwCAE+O3BZjK7fFqyYub1Oz2asKogZp3TobZJQHAKYuwW+Xz+096nNVq0flThunpu8/XbVeepgRXhA5XNOjRP3+ixQ+/pXe27JfXd/LXAQAA/Rc99TCN3+/X0y9v1RcFlYqOtOvOr05itXsAYcFus8hiGHp7c6Fq67u2EF5MlF3LfnS+1ryfr7++uVtFJbV6+I+b9MLaHbp8ZqbOm5Iup4P/tgEAQEf8dgDTrPkgX//ZsE8WQ/qf685QUkKk2SUBQI+qrXeruq7rW9c5HTZdee5IXTx9mF55N0+vvJOnQ2X1evrvW7Xi1V269OzhunjacA2MN+fnpc/vl8Xo3s3XQM4BAABdR6iHKbbuKdUzq7ZKkm6cm60zxqSYXBGA/qBtWHywh8wop13XXpCly2dl6o2PCrTqnVwdKqvXn1/7Qitf/0KTxqTowqnDdGZ2imx9OO8+kNEHsycN7eWqAADo3wj16HPF5fV66PmP5fX5NWviEF0xe6TZJQHoJwIZFp+cGKkp2YN6ubLOOR02zT0nQxefNUIfbj2g1e/t1ba8Mm3cUayNO4oVHxuhcyYM1rTxgzQuY0CfBPzujj4AAAC9i1CPPtXQ5NH9z21QdV2zMofE6dtfPV1GkPeYAQg/3QmmMZH2Xq7m5KwWQ+dMSNM5E9K0/3CNXv+oQG98XKjKmiatfm+vVr+3VzGRdp2ZnaJJY1KUPSJRyQlRZpcNAAD6AKEefaax2aN7l6/X3gPVio+J0P/eNFURdqvZZQFASBmSHKub5o3TdZeM1eZdh7V+60F9tP2Qqmqb9dam/Xpr035J0sA4p7JHDNDoYQkamhyrISkxSoqP5EYqAABhhlCPPuH2ePXAcx/p89wyRTlt+vEtU1kYDwBOgc1q0ZTsVE0emyK/X9qZX671nx/U53llyiuqUmlVo975pEjvfFLUfo7TYdXgpBgNiHMq0dXyJ8HlVGyUXVFOu6KdNkVH2hXttCsq0i6HzcJNAAAAghyhHr3O4/Xpl89v1JYvSuR0WPXTBdM0Oj3B7LIAICxYDENvb2lZIyAlMUopiVGaeXqaDlfU61BZnUorG1VZ26Tq2iY1NnuVV1SlvKKqLr22zWq0hn27oiNtinLaVV3XLMOQ7DarHDaL7HaLHDarnA6rIp02RUXY5LBbZRhGwAsThsJihgAABAtCPXqV1+vTIys2acO2Q3LYLPrxLVOVPWKA2WUBQFjpbI2A2CiHYqMcGtW6+LzP51dtg1s19c3y+6WhyTEqq25URXWT6hrdqmtwq67RrfoGt+qbPPL7JY/Xr+q65m4vjGexGIp22pWcEKnCw7WqbWiW02FTfIxDkRG2E/b+s2I+AADdQ6hHr2ls8rQHepvV0P/75hSdNjLJ7LIAICj19nZ7FoshV7Sj/c+8czKOe6zP51djs0d1DR7VN7aG/UaPahvc2vD5QdXUN6vZ45Pb7ZPb41Wzx6eGJo8aGj1qcnvl8/lVU9+smvpm5R41KsBusyg2qqWG+NgIJbT+iXDwKwkAAIHgf1D0ioqaRv1i+QbtLqyU3WbRXddP1sSsZLPLAoCgFUzb7VksLcPuo5x2SR3XP6mtP3HPvdfnU0PrDQDDMBQdadeWXYdVUdOkuga33B6fyqsbVV7dKB388ryoCJviYyOUOiBKCS6nMgbHKSUxShYLw/ABADgRQj16XMGhav18+QYdLq9XbJRD99w8pX3IfTD8sgoAwSzUtts7mtViUUyUQzFRDg0eGK3ZZwzV6vfyVF3XLK/Xp9qGlvdXVdesyppGVdQ0qba+Zch/fZNHB0rrtHlXiSQpMsKmEYNdGpocrQjVKSKuWlnDnbLbLCa/SwAAggehHj3qky8O66E/fKy6Ro8GDYzWzxZM0+CkmPbnQ/2XVQBA4KxWi+JiIhQXE6EjZ827PV5V1jS19+Z7vD7tO1SjhiaPtu8t1/a95ZKkVzZskM1qaNggl0YOiVfmkHiNHBKn4YNcstvYIhUA0D8R6tEj3B6fVvx7h15+e4/8fmns8ET97zenKC4mwuzSAABBzm6zKikhSkkJUe3z/T1en4oO1yrvQJW+2FembXsO6XC1V3UNHuXur1Lu/ipJ+yRJVouh9NRYjRwSr5FD4zVySLyGD3LJYT950GelfQBAqCPU45QVldTqkT9u1J79LYshXTRtmG69PKdLv0wBANAZm9WiYYNcGjbIpaljB2jHDq/GjBmjmkYpd3+V9uyv1J79lcrdX6maerf2HqjW3gPVeu2jAkmSYUgJsU4lxUdqYEKkkuIjNSDOKZv1y6H7rLQPAAgHhHoEzOfz6z8b9unZVz5XU7NXsVF2ffu/Ttf0nMFmlwYACEOGYSh1QJRSB0Tr7Akt/9f4/X6VVDRoz/5K/fvDfB0sq1NFdZOa3N72Bfl2FVS0ni+5oh1KdDmV6HJqSHKMpo0fJCcr7wMAQhj/iyEgu/aV67d/36rdhZWSpNNGDtT3vj5JA+IiT3wiAMB0vb19Xk8zDENOp7PT/e0Nw1ByYpSSE6NUXt2o6rpm+f1+1Td5VFHdqPLqptaPjWps9qqqtllVtc3ae6Bam3Ye1j/fzdPQlNjW+fktf0akuQj6AICQwf9Y6JaK6kb9/l/b9ebGQkktKxN//aIx+sqMDLYdAoAQEUzb5x2tsxsOkZGRGjduXJdfwzAMRTvtinbaNSQ5VlJLj35Dk6dDyK+saVJ9k0f7DtVo36Ga9v/bLIY0JCW2dTG+OI0YHKf0lFjWiQEABCVCPbqkrKpB/3gnT//+cK8amrySpPPOHKobL81WgstpcnUAgEAE444knd1w8Hg8Ki8vV2Jiomy2Y3916coNB8MwFOW0K8pp15Dkll1ZXNEOTc8ZpD2FldrTOk8/d3+lKmqaVHCoRgVHBH1Jio1yaGhKjIamxLb8SW75ODC+81EEAAD0BUI9TuhAaa1efmuP3vi4UB6vT5I0Oj1et16eo6xhiSZXBwAIV0fecHC73SqvqpfVES273XfMsYHecIiwW5Xgcmrq+EGaOv7LmwJlVQ0dFuMrOFSjwxX1qqlv7rDFXhunw9oyBSAhSkkJkUpOiFJCbETr9n0OxUVHKC42QhEsIAsA6AWEehzD7fFqw7ZDev2jAm3ZdVg+f8vjY4cn6przRmny2BT5zS0RAIBTdrJpCK5ohyZlJWtSVrLcHp+qapvU0ORRTJRDhcU1Kiyu0YHSOjU2e9t79k/E6bC2B/2YKIciHTY5I6ytH7/8e4TDpsgIq5wRtvZjnA6bIiNsrY9ZZT1iFX8AQP9GqIckyevza2d+ud79pEjrNu9XbcOXv9ycMSZZ15w3WuMyBrQ/ZkhBORcTAIDu6uo0BIfdqoHxkZp3Tkb7Y26PT8XldTpc0aDD5fU6XFGvksoGVdU0tSzKV9fy0eP1qbHZq8byehWX159yzQ67VZER1pag3xr4I502xUTa5YpyKDrKrtgoh2Kj7IqJdCim/XOHYqMdsh61Dk4oLZwIAOgo7EL93r17dd9992nTpk2KjIzU3Llz9YMf/EBOJ/O+j1Zb36wtu0r00Y5D2rSjWDVHBPQBcU7NmTxU552ZrrSkmOOcH3xzMQEA6Et2m0VDkmPbF+TrjN/vV32jR//5KF9llY1qbPKqye2V2+uTx+OT2+OV2+Nr+dP6mGFIdptVZVWNanZ71ezxyuPxtY+ea3Z71exuWc0/EE5HS++/M8KqmCi7MtPiW0YRRDvkiomQK9qhuGhH+8gCu42pAwAQrMIq1FdXV+vGG2/U4MGD9Zvf/Ebl5eV68MEHVVlZqUceecTs8kzl8/lVXF6vHfnl2pFfru17y44ZJhgTadfksSk694yhmjA66Zi7+AAAoPsMw1B0pF12q7V9sb6TGTwwWrPPGKrV7+W130D3+/3y+f2tNwJ88nj97TcBPF5fy2r/KTHavOuwaurcavZ4W8O/T83u1hsJnpY1CRqbvWps9kq1ksqkPYVVJ6wnMsLWEvRjHHJFH7FWQIxDrtYbAW03AVzRDkVG2DpdPNBm6/xxAEDgwirU/+lPf1J1dbVWrVqlxMSWRdysVqt+8IMfaNGiRcrMzDS5wt7n9fpUUtmgQ2V1Kiqp076D1dp7oEr7DlW3r1p/pCHJMZqSnaozs1M0dngic/QAADiBzrbc6yuGYchqGLI6LIpwHPt8240Am9Vy3JF0Pp9fTW6vmpq97R8NQ0pPiVVVXbOqaptUXdes6iP+7vW1bAfY0OTp8tQBu83S2tsfIVfrDYCoCEMJrigdqC/ucAPAFd0yLeB4W+P25dc7kGv5fP5ub+sb9O8piM8BcKywCvXvvPOOpk+f3h7oJemiiy7S//t//0/r1q0L61D/7idF+uO/d+hweb083s6XsbNZLcpMi9PYEYnKHpGoMcMTlRDr1NubC5V/sFr5B6u7dC3mxwMA+quTLa7XmWD6f9NiMVrm30d8+StgUnykzpuS3mm48vv9qmtwq6quWdVHrBFQXdekytom1dQ1tz7X1HpToFnNrSMCyqoaVVbV2KW6DEkRDmv7YoBOh1U2q0VRTptGDI5TRNt0AYdVTodVEXabrFZDhmHIYrTc8DCO+ihJnvYpDi2jGdo/9/rapzy0TG/wtU9pKCqpVWOzV16vX16vTx5fy0evzy+P1ye/v+Xr4ve3FG4xJLfH/+VjkiwWyWIYslgMWQxDhqWlTovFkM3acsPDajFktVpks1pkbX3cZrXIajVkb/145GNH//14WdjQsU/sPVClJrdX8kt+fVmnv6WR5W/5IKnl3/jQ5Fj5Wh/w+f2S/8uPzW63yisq9N7u7bJabR2+HgfLauV2+9oXVPb7/ZJhtH4tOn5NnBFWZQyOl81mKMJuVYTDpgh7a/u2tnfL40d87rDKYbMw2gMdcEMpzEJ9bm6urrrqqg6PORwOpaenKzc316Sq+sbGHcU6UFInqeU/DFeUQ64YhxJdTg1wOZUY51RcTET7kPqyqkbtLqzQlOxB3ZobLzE/HgCAcFpXJpAbFUkJkbrmvNGdPtfY7DnqBkDLTYCyynrVNXq1a1+56ps8amqdAuD2tITA9ikBNU0dXu/T3aWn+hb7nM8r6QR7BXX1ZodZPt5e3IWj6k75Oh9t68p1OjKMlhEzbTd/vrzZY2u/MRRxxJoRTkfLjhERR3zeduOg7UaJ3WZp/7vNZpGt9caKxWJwA+EU+P1++Xwt04a8vpa/t90c83pb/u5tvdl25OPtH31tx7VMN2o5tvPP9+yvVGOTR77Wm0xHMmToyHtdzgirbrw0+4RroYQaw3/0uw5h48aN05133qlbb721w+Nf+9rXNGDAAD3++OPdfs3NmzfL7/fLbu+b/5D9fr88Hk+355z5/S0r2Ls9xw6xPx6rxaIIh1UNTZ5j/vH39HnBfE5vXMvvl3w+nyyWjnfS+9vXIRTPOfo8j8d7TDuaWR//Hrp3ztHfi8FWnxnnBHt9nZ1zvJ+pp3Ids99TsJwT6HkWiyGnI7C+oaOv09ZL3Nbb62/tOW7LxFarpfW51ueP6Glu6Xk+npau9LZ/M4YkGa392K2/47f9rmW0fW4x5Gm9ydD2T639GKlDMDAkWVq/do3NHvl9Rzzf9m/36Ir8LT37Dru1w/vs0LPddp6/Y0/6ES/brfY1DEMer69bx9usJ/5Pr/37seWM9q+n2+vrtEHa39WRzxlGe2fTke1+9L+DI0dAmOXLfzOdfF2MTv96zDHGCY/wd/Jc1950p0d18evV2VW7fL320R5HfC+HUKqMcFgVF93JPKYg4na7ZRiGJk2adNJjw6qn/nj8fn/Ad9m+/GHfN3fpDMOQw9H9f2BG65Auu637c+KPHILX2+cF8zl9ea1gPqcvrxXM57Sf181zg/k98e+h768VzOf05bWC+Zy+vFYwn3Mq5wXrdfpSdBcWPwSA7miZTtS1DBpWP1VdLpeqq4+dF15TUxPwfPqJEyeealkAAAAAAPSKsFrqPDMz85i5883NzSooKAjrRfIAAAAAAP1TWIX6mTNnav369aqoqGh/7LXXXlNzc7NmzZplYmUAAAAAAPS8sFoor7q6WvPmzVNaWppuv/12lZWV6aGHHtI555yjRx55xOzyAAAAAADoUWEV6iVp7969uu+++7Rp0yY5nU7NmzdPP/jBD+R0Os0uDQAAAACAHhV2oR4AAAAAgP4irObU4/+3d+9BUdVvGMAfRfCWCBpmRqZS3FcuDajITRk1AkRM0xJ1lFRMRUMtm1BzygsOGSpo6GqZZZaFU6A0OhlqwdJMkKlJuVChlhc0LgICi+/vj99wcrnojpXsweczswP7Pe+efeFxwffs2YWIiIiIiIjuJxzqiYiIiIiIiFSKQz0RERERERGRSnGoJyIiIiIiIlIpDvVEREREREREKsWhnoiIiIiIiEilONQTERERERERqRSHejPx66+/IiYmBp6enhg2bBjefPNN3Lhxo63bald+//13rFixApGRkXB1dUV4eHiLdUePHsW4ceOg0WgwatQofPjhhy3W7dixAyNHjoRGo8EzzzyDvLy8ZjXXr1/HihUrMGTIEHh5eSE2NhYXLlxoVmdq/qb21l5lZWXhxRdfRFBQEDw9PREREYE9e/bg5s2bRnXM0LwdP34c0dHRGDp0KNzd3RESEoK1a9eisrLSqI45qktVVRUCAwPh5OSEkydPGm1jluYrPT0dTk5OzS5JSUlGdcxQHfbt24exY8dCo9Fg2LBhiI2NNdrOHM3b1KlTW3w8Ojk54cCBA0odc6RmhNpceXm5BAQEyKRJk+To0aOyf/9+8fX1lcWLF7d1a+3K4cOHJTAwUBYsWCDh4eESFhbWrCY/P19cXV3l1VdfldzcXElNTRVnZ2f55JNPjOq0Wq24ubmJVquVnJwceemll0Sj0UhhYaFR3ezZs2X48OGSkZEhX3/9tURFRcmoUaOkpqZGqTE1f1N7a88mTpwoCxculMzMTMnNzZXk5GRxdXWVdevWKTXM0PxlZGRIUlKSHDp0SHQ6nezevVt8fX1lxowZSg1zVJ/169eLn5+fODo6yo8//qisM0vz9tlnn4mjo6McO3ZMCgoKlMsff/yh1DBDddi0aZN4e3vLtm3bJC8vTw4dOiTLly9XtjNH83f27Fmjx2FBQYEsWrRIXF1d5erVqyLCHKllHOrNQFpamnh4eCgPVhGRL774QhwdHUWv17dhZ+1LQ0OD8vkrr7zS4lAfExMjEyZMMFpLSEiQ4cOHK7evra2VJ598UhITE5Uag8EgoaGhsmjRImXthx9+EEdHR8nOzlbWLly4IK6urrJnzx5lzdT8Temtvbv1e9RozZo1otFopLa2VkSYoVp9/PHH4ujoKBcvXhQR5qg2er1ePD095aOPPmo21DNL89Y41Lf087URMzR/er1eXFxc5Pjx463WMEd1GjlypMyaNUu5zhypJTz93gwcO3YMw4YNQ69evZS1MWPGwMrKCkePHm3DztqXjh1v/8+9rq4OOp0OYWFhRusRERG4cuUKfvrpJwBAfn4+KisrjU7ft7CwwNNPP42jR49CRAD8//Qja2trBAYGKnX9+vWDt7e3Ua6m5G9qb+3drd+jRi4uLqitrUVZWRkzVDEbGxsAgMFgYI4qtHr1akyePBkDBw40WmeW6scM1SE9PR2PPvoo/P39W9zOHNUpPz8f58+fR0REBADmSK3jUG8GioqK4ODgYLRmZWWF/v37o6ioqI26uv+UlJSgvr4egwYNMlp//PHHAUDJovFj0zoHBwdUVVXh0qVLSt3AgQPRoUOHZvu7NVdT8je1t/vR999/DxsbG/Tu3ZsZqkxDQwNqa2tx+vRppKamYsSIEXjkkUeYo8p8+eWXKCwsxLx585ptY5bqER4eDhcXF4SEhCAtLQ0NDQ0AmKFanDhxAo6OjkhNTcWwYcPg7u6O6OhonDlzBgBzVKvMzEx07doVISEhAJgjta5TWzdAQEVFBaytrZutW1tbo7y8vA06uj81fq+bZtF4vXF7RUUFrKys0KVLF6O6nj17AgDKysrQt29fVFRUoEePHs3up2mupuRvam/3m5MnTyI9PR3z5s2DhYUFM1SZESNGKP+xCAgIwIYNGwDwsagmNTU1WLduHeLj4/HAAw80284szZ+dnR0WLFgADw8PdOjQAUeOHEFycjIuXbqEFStWMEOVuHLlCk6fPo2zZ89i1apVsLS0REpKCmbMmIFDhw4xRxUyGAz48ssvERISgm7dugHgz1RqHYd6MyYizY6c0X+vte/5rest1TSeynSnututN91f07p/sr/25sqVK4iLi4NGo8GsWbOMtjFDddi2bRuqq6uh1+uxZcsWxMbG4t1331W2M0fzt3XrVvTu3Rvjx4+/bR2zNF8BAQEICAhQrvv7+6Nz587YtWuX0TunM0PzJiKorq7G5s2b8cQTTwAA3NzcEBISgo8//hje3t4AmKOafPvtt7h69WqLf62JOVJTPP3eDFhbW6OioqLZemVlZYtHxei/0Xj0sumRxMZsGrOwtrZGbW0tamtrW6xr3E9ruTY92mlK/qb2dr+orKzErFmz0KVLF2zduhWWlpYAmKHaODs7w9vbG88++yxSUlKQl5eHw4cPM0eVuHDhAnbu3Im4uDhcv34dFRUVqK6uBgBUV1ejqqqKWapUaGgoGhoacObMGWaoEj179sSDDz6oDPQA0KdPHwwaNAh6vZ45qlBmZiZsbGyM3ieBOVJrONSbAQcHh2avM6mrq0NJSUmz16/Qf6d///6wtLREcXGx0bperwcAJYvGj00zKyoqQvfu3fHQQw8pdb/++qtyVPTW/d2aqyn5m9rb/aC2thZz585FaWkptFotbG1tlW3MUL1cXFxgYWGBkpIS5qgS58+fR319PWbPng0fHx/4+Pgoz+xOmzYNM2bMYJbtADNUh9a+VhFBx44dmaPK3LhxA1999RWeeuop5YkLgI9Hah2HejMQGBgInU6Hv/76S1k7fPgw6urqEBQU1Iad3V+srKwwdOhQZGVlGa1nZmbCzs4Orq6uAABvb2/06NEDBw8eVGoaGhqQlZWFoKAg5fSioKAgVFRU4Pjx40rdn3/+ifz8fKNcTcnf1N7aO4PBgIULF6KwsBBarRaPPPKI0XZmqF4FBQVoaGiAvb09c1QJFxcXvP/++0aXV199FQCwatUqrFy5klmq1MGDB2FhYQFXV1dmqBLBwcEoLS3FL7/8oqxdunQJxcXFcHJyYo4qc+TIEVRVVSnvet+IOVKr7sGfzaM7KC8vl4CAAJk8ebIcO3ZM9u/fL0OGDJHFixe3dWvtSnV1tWRlZUlWVpZER0dLUFCQcr3x727m5+eLq6urvPbaa6LT6WTLli3i7Owsn3zyidG+tFqtuLm5yY4dOyQ3N1fi4+NFo9FIYWGhUd3s2bPF399fMjMzJTs7W6KiomTUqFFSU1Oj1Jiav6m9tWfLly8XR0dH2b59uxQUFBhdKisrRYQZqsG8efNk69atcuTIEcnJyZGdO3eKn5+fRERESG1trYgwR7XS6XTN/k49szRvM2fOlG3btkl2drZkZ2fL8uXLxcnJSVavXq3UMEPzZzAYJCoqSkaPHi0HDhyQw4cPy7hx4yQgIECqqqpEhDmqSWxsrAQHB8vNmzebbWOO1BIO9WaiuLhYZs6cKR4eHjJkyBB54403jB5M9M+dO3dOHB0dW7zodDqlLjs7W8aOHStubm4SEhIiH3zwQbN93bx5U7Zv3y7BwcHi7u4u48ePl9zc3GZ1lZWVkpCQID4+PuLp6Slz5syR8+fPN6szNX9TemvPRowYwQzbgbS0NImMjBQvLy/x9PSUsLAwSU5OVg7MNGKO6tPSUC/CLM3ZG2+8IaNHj5bBgweLu7u7hIeHy65du5oNE8zQ/JWWlkp8fLw8+eST4uHhIS+88IIUFRUZ1TBH81dWViZubm6yfv36VmuYIzXVQaTJiyiIiIiIiIiISBX4mnoiIiIiIiIileJQT0RERERERKRSHOqJiIiIiIiIVIpDPREREREREZFKcagnIiIiIiIiUikO9UREREREREQqxaGeiIiIiIiISKU41BMREanciRMnMG/ePAQHB8Pd3R1+fn6YNGkS1q1bd1f727x5M5ycnP61/vLy8uDk5KRcXFxc4Ofnh7i4OBQVFbVJT0RERO1Fp7ZugIiIiO5ednY25s6dC19fXyxduhR2dna4cuUKTp06hQMHDmDZsmVt3aIiPj4eQ4YMQX19PU6dOoXU1FTodDpkZGTgoYceuu1tJ06ciICAgHvUKRERkXpwqCciIlIxrVYLe3t77NixA506/f1rPSwsDEuXLm3Dzpp77LHH4OnpCQDw8fFBjx498NprryE9PR1z585t8TY1NTXo2rUr+vbti759+97DbomIiNSBp98TERGpWFlZGWxtbY0G+kYdOxr/mj948CBmzpwJf39/DB48GKGhoUhKSkJ1dbVJ93Xw4EFMmjQJnp6e8PLyQkxMDH766ae77r1xwP/jjz8A/H2K/enTpxEXFwcfHx+MGjXKaFtTGRkZmDRpEry8vODl5YXIyEjs27fPqCYnJwfTp0+Ht7c3PDw8MHnyZOTm5t5130REROaEQz0REZGKeXp64sSJE3jzzTdx4sQJ1NfXt1r722+/ITAwEKtXr4ZWq8X06dORlZWF2NjYO97PO++8g/j4eDg4OCA5ORnr169HVVUVpkyZAr1ef1e9//777wCAXr16Ga0vWLAA/fv3x8aNG/H666+3evuNGzdiyZIl6NOnD9auXYuUlBRERUUpBwkA4PPPP8fMmTPxwAMPIDExEcnJybCxsUFMTAwHeyIiahd4+j0REZGKLV68GMXFxdi9ezd2794NS0tLuLu7Y+TIkZgyZQq6d++u1L744ovK5yICb29vODg4IDo6GoWFhXB2dm7xPv78809s3rwZ0dHRSEhIUNb9/PwwZswYpKSkIDk5+Y693rx5EwaDAQaDAadOnUJiYiIsLCwQGhpqVDdu3DjExcXddl/nzp1DWloaIiIikJSUpKwPHz5c+bympgZr1qxBcHAwUlNTlfWgoCBERUVhw4YNzZ7VJyIiUhsO9URERCpma2uLPXv24OTJk8jNzcWpU6fw3Xff4a233sLevXvx6aefKs+Enzt3DsnJydDpdLh69SpERNlPcXFxq0P9N998A4PBgMjISBgMBmW9c+fO8PHxQV5enkm9vvTSS0bX7e3tsWnTpmb3O3r06DvuKycnBw0NDZgyZUqrNQUFBSgrK0NUVJRR3wAQEBAArVaL6upqdOvWzaT+iYiIzBGHeiIionZAo9FAo9EAAOrr65GUlIT33nsPWq0WL7/8MqqqqvD888+jc+fOWLRoEQYMGIAuXbrg4sWLmD9/Pm7cuNHqvktLSwEAEyZMaHF709fut2bJkiUYOnQoLCwsYGtri4cffrjFuj59+txxX9euXQOA2755XmPft3vWv7y8nEM9ERGpGod6IiKidsbS0hLz58/He++9h7NnzwIAdDodLl++jN27d8PX11epraysvOP+bG1tAQCbNm1Cv3797rqvRx99VDnw8E81nn1w8eLFVg8ONPa9fPlyeHh4tFjTu3fvf6UfIiKitsKhnoiISMUuX77c4jPbRUVFAP5+1rtDhw4AACsrK6O6vXv33vE+/P390alTJ5SUlGDMmDH/tOV/xfDhw2FhYYGPPvoIXl5eLdZ4e3vD2toaer0e0dHR97hDIiKie4NDPRERkYrFxMSgb9++GDFiBAYNGgQRwZkzZ7Bz505069YN06ZNAwB4eXmhZ8+eWLlyJebPn49OnTohIyMDP//88x3vw97eHnFxcUhOTsa5c+cQGBgIa2trlJaW4uTJk+jatesd39ju32Zvb485c+Zgy5YtuHHjBsLDw9GjRw/o9Xr89ddfiIuLQ/fu3ZGQkIBly5ahvLwcY8aMQe/evXHt2jUUFhbi2rVrWLVq1T3tm4iI6N/GoZ6IiEjF5s6di6+++gq7du3C5cuXUV9fDzs7O/j5+WHOnDlwcHAA8P9T0dPS0pCYmIilS5eia9euCAkJwdtvv42oqKg73k/jvt5//30cOHAAdXV1sLOzg7u7O5577rn/+sts0cKFC/HYY4/hgw8+wJIlS2BhYYEBAwZg6tSpSk1kZCT69esHrVaLlStXoqqqCr169YKLi4tJXzcREZG56yC3vvUtEREREREREamGaW9XS0RERERERERmh0M9ERERERERkUpxqCciIiIiIiJSKQ71RERERERERCrFoZ6IiIiIiIhIpTjUExEREREREakUh3oiIiIiIiIileJQT0RERERERKRSHOqJiIiIiIiIVIpDPREREREREZFKcagnIiIiIiIiUikO9UREREREREQq9T+dyNgws7MT7AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Setting up the plotting style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Plotting the distribution of SalePrice\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(dataset['SalePrice'], kde=True)\n",
    "plt.title('Distribution of Sale Prices')\n",
    "plt.xlabel('Sale Price')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Plotting the distribution of a few other key features\n",
    "key_features = ['Overall Qual', 'Gr Liv Area', 'Total Bsmt SF', 'Year Built']\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
    "\n",
    "for i, feature in enumerate(key_features):\n",
    "    sns.histplot(dataset[feature], kde=True, ax=axes[i//2, i%2])\n",
    "    axes[i//2, i%2].set_title(f'Distribution of {feature}')\n",
    "    axes[i//2, i%2].set_xlabel(feature)\n",
    "    axes[i//2, i%2].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d792c698",
   "metadata": {},
   "source": [
    "### 1. Sale Price Distribution:\n",
    "   - The distribution of Sale Prices is right-skewed, indicating that most houses are priced below the mean sale price, and a few houses have much higher prices.\n",
    "        \n",
    "   - There is a clear concentration of sales around the \\\\$100,000 to $200,000 range.\n",
    "\n",
    "### 2.Key Features Distribution:\n",
    "   - Overall Quality: This is a categorical feature with a clear mode at a score of 5. Most houses have a quality rating between 5 and 7.\n",
    "    \n",
    "   - Above Ground Living Area (Gr Liv Area): The distribution is right-skewed, similar to Sale Price, with most living areas concentrated around 1000 to 2000 square feet.\n",
    "        \n",
    "   - Total Basement Area (Total Bsmt SF): This feature also shows a right-skewed distribution. A notable number of houses have no basement (0 square feet).\n",
    "   \n",
    "   - Year Built: The distribution shows that more houses were built in recent years. There's a noticeable increase in houses built after the 1950s."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e8ead23",
   "metadata": {},
   "source": [
    "<a id=\"6-correlation-analysis\"></a>\n",
    "##  6.Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0083b095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Selecting only numeric columns for correlation\n",
    "numeric_dataset = dataset.select_dtypes(include=[np.number])\n",
    "\n",
    "# Recalculating the correlation matrix\n",
    "numeric_correlation_matrix = numeric_dataset.corr()\n",
    "\n",
    "# Plotting the correlation matrix with respect to SalePrice\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(numeric_correlation_matrix[['SalePrice']].sort_values(by='SalePrice', ascending=False), \n",
    "            annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "plt.title('Correlation of Numeric Features with Sale Price')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655d690f",
   "metadata": {},
   "source": [
    "### 1. Strong Positive Correlations:\n",
    "   \n",
    "   - Overall Qual (Overall material and finish quality): This has a strong positive correlation with SalePrice, suggesting that higher quality homes tend to sell for more.\n",
    "   \n",
    "   - Gr Liv Area (Above grade (ground) living area): Larger living areas are associated with higher sale prices.\n",
    "        \n",
    "   - Garage Cars and Garage Area: Both these garage-related features show a strong positive correlation, indicating that properties with larger and/or more garage space tend to have higher prices.\n",
    "\n",
    "### 2.Moderate Positive Correlations:\n",
    "        \n",
    "   - Total Bsmt SF (Total square feet of basement area) and 1st Flr SF (First Floor square feet) are moderately positively correlated with SalePrice.\n",
    "        \n",
    "  - Year Built and Year Remod/Add (Year of remodeling/addition) also show a moderate positive correlation, suggesting newer or more recently updated homes fetch higher prices.\n",
    "\n",
    "### 3. Negative or Low Correlations:\n",
    "        \n",
    "  - Some features have very low or negative correlations, indicating a weak relationship with sale price. For example, PID (Parcel identification number) and Order show negligible correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ed1056",
   "metadata": {},
   "source": [
    "<a id=\"data-cleaning\"></a>\n",
    "# DATA CLEANING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7545dfa",
   "metadata": {},
   "source": [
    "<a id=\"1-dealing-with-missing-value\"></a>\n",
    "## 1. Dealinf with Missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02b5b010",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "05937a9b",
   "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>Missing Values</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pool QC</th>\n",
       "      <td>2917</td>\n",
       "      <td>99.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misc Feature</th>\n",
       "      <td>2824</td>\n",
       "      <td>96.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>2732</td>\n",
       "      <td>93.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>2358</td>\n",
       "      <td>80.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mas Vnr Type</th>\n",
       "      <td>1775</td>\n",
       "      <td>60.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplace Qu</th>\n",
       "      <td>1422</td>\n",
       "      <td>48.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot Frontage</th>\n",
       "      <td>490</td>\n",
       "      <td>16.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Cond</th>\n",
       "      <td>159</td>\n",
       "      <td>5.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Qual</th>\n",
       "      <td>159</td>\n",
       "      <td>5.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Finish</th>\n",
       "      <td>159</td>\n",
       "      <td>5.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Yr Blt</th>\n",
       "      <td>159</td>\n",
       "      <td>5.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Type</th>\n",
       "      <td>157</td>\n",
       "      <td>5.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Exposure</th>\n",
       "      <td>83</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin Type 2</th>\n",
       "      <td>81</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Cond</th>\n",
       "      <td>80</td>\n",
       "      <td>2.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Qual</th>\n",
       "      <td>80</td>\n",
       "      <td>2.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin Type 1</th>\n",
       "      <td>80</td>\n",
       "      <td>2.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <td>23</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "      <td>2</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <td>2</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Cars</th>\n",
       "      <td>1</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garage Area</th>\n",
       "      <td>1</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <td>1</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <td>1</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Missing Values           Percentage\n",
       "Pool QC                   2917                99.56\n",
       "Misc Feature              2824                96.38\n",
       "Alley                     2732                93.24\n",
       "Fence                     2358                80.48\n",
       "Mas Vnr Type              1775                60.58\n",
       "Fireplace Qu              1422                48.53\n",
       "Lot Frontage               490                16.72\n",
       "Garage Cond                159                 5.43\n",
       "Garage Qual                159                 5.43\n",
       "Garage Finish              159                 5.43\n",
       "Garage Yr Blt              159                 5.43\n",
       "Garage Type                157                 5.36\n",
       "Bsmt Exposure               83                 2.83\n",
       "BsmtFin Type 2              81                 2.76\n",
       "Bsmt Cond                   80                 2.73\n",
       "Bsmt Qual                   80                 2.73\n",
       "BsmtFin Type 1              80                 2.73\n",
       "Mas Vnr Area                23                 0.78\n",
       "Bsmt Half Bath               2                 0.07\n",
       "Bsmt Full Bath               2                 0.07\n",
       "BsmtFin SF 1                 1                 0.03\n",
       "Garage Cars                  1                 0.03\n",
       "Garage Area                  1                 0.03\n",
       "Total Bsmt SF                1                 0.03\n",
       "Bsmt Unf SF                  1                 0.03\n",
       "BsmtFin SF 2                 1                 0.03\n",
       "Electrical                   1                 0.03"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the number of missing values in each column\n",
    "num_missing = dataset.isna().sum()\n",
    "# Excluding columns that contains 0 missing values\n",
    "num_missing = num_missing[num_missing > 0]\n",
    "# Getting the percentages of missing values\n",
    "percent_missing = num_missing * 100 / dataset.shape[0]\n",
    "# Concatenating the number and perecentage of missing values \n",
    "# into one dataframe and sorting it\n",
    "pd.concat([num_missing, percent_missing], axis=1, \n",
    "          keys=['Missing Values', 'Percentage']).\\\n",
    "          sort_values(by=\"Missing Values\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0648a720",
   "metadata": {},
   "source": [
    "### \"Pool QC\"\n",
    "\n",
    "The percentage of missing values in Pool QC column is 99.56% which is very high. We think that a missing value in this column denotes that the corresponding house doesn't have a pool. To verify this, let's take a look at the values of Pool Area column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fe328943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pool Area\n",
       "0      2917\n",
       "144       1\n",
       "480       1\n",
       "576       1\n",
       "555       1\n",
       "368       1\n",
       "444       1\n",
       "228       1\n",
       "561       1\n",
       "519       1\n",
       "648       1\n",
       "800       1\n",
       "512       1\n",
       "738       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Pool Area\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f32c590",
   "metadata": {},
   "source": [
    "We can see that there are 2917 entries in Pool Area column that have a value of 0. This verfies our hypothesis that each house without a pool has a missing value in Pool QC column and a value of 0 in Pool Area column. So let's fill the missing values in Pool QC column with \"No Pool\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52074286",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"Pool QC\"].fillna(\"No Pool\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d67624",
   "metadata": {},
   "source": [
    "### \"Misc Feature\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b254c237",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Misc Val\n",
       "0        2827\n",
       "400        18\n",
       "500        13\n",
       "450         9\n",
       "600         8\n",
       "700         7\n",
       "2000        7\n",
       "1500        3\n",
       "1200        3\n",
       "650         3\n",
       "480         2\n",
       "3000        2\n",
       "2500        2\n",
       "4500        2\n",
       "455         1\n",
       "1512        1\n",
       "17000       1\n",
       "1000        1\n",
       "15500       1\n",
       "460         1\n",
       "8300        1\n",
       "1300        1\n",
       "560         1\n",
       "620         1\n",
       "900         1\n",
       "1150        1\n",
       "6500        1\n",
       "1400        1\n",
       "750         1\n",
       "800         1\n",
       "12500       1\n",
       "350         1\n",
       "490         1\n",
       "80          1\n",
       "54          1\n",
       "3500        1\n",
       "300         1\n",
       "420         1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Misc Val\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "210952a4",
   "metadata": {},
   "source": [
    "Misc Feature has 2824 missing values. Then, as with Pool QC, we can say that each house without a \"miscellaneous feature\" has a missing value in Misc Feature column and a value of 0 in Misc Val column. So let's fill the missing values in Misc Feature column with \"No Feature\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aacddc73",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Misc Feature'].fillna('No feature', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e836ce9c",
   "metadata": {},
   "source": [
    "### \"Alley\",  \"Fence\", and \"Fireplace Qu\"\n",
    "According to the dataset documentation, `NA` in `Alley`, `Fence`, and `Fireplace Qu` columns denotes that the house doesn't have an alley, fence, or fireplace. So we fill in the missing values in these columns with `\"No Alley\"`, `\"No Fence\"`, and `\"No Fireplace\"` accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "358fc7be",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Alley'].fillna('No Alley', inplace=True)\n",
    "dataset['Fence'].fillna('No Fence', inplace=True)\n",
    "dataset['Fireplace Qu'].fillna('No Fireplace', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed4b395c",
   "metadata": {},
   "source": [
    "### \"Lot Frontage\"\n",
    "\n",
    "As we saw previously, Lot Frontage represents the linear feet of street connected to the house. So we assume that the missing values in this column indicates that the house is not connected to any street, and we fill in the missing values with 0:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ef3cc673",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Lot Frontage'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe97b2a",
   "metadata": {},
   "source": [
    "### \"Garage Cond\", \"Garage Qual\", \"Garage Finish\", \"Garage Yr Blt\", \"Garage Type\", \"Garage Cars\", and \"Garage Area\"\n",
    "\n",
    "According to the dataset documentation, NA in Garage Cond, Garage Qual, Garage Finish, and Garage Type indicates that there is no garage in the house. So we fill in the missing values in these columns with \"No Garage\". We notice that Garage Cond, Garage Qual, Garage Finish, Garage Yr Blt columns have 159 missing values, but Garage Type has 157 and both Garage Cars and Garage Area have one missing value. Let's take a look at the row that contains the missing value in Garage Cars:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9114dc1c",
   "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>Garage Type</th>\n",
       "      <th>Garage Yr Blt</th>\n",
       "      <th>Garage Finish</th>\n",
       "      <th>Garage Cars</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Garage Qual</th>\n",
       "      <th>Garage Cond</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>Detchd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Garage Type  Garage Yr Blt Garage Finish  Garage Cars  Garage Area  \\\n",
       "2236      Detchd            NaN           NaN          NaN          NaN   \n",
       "\n",
       "     Garage Qual Garage Cond  \n",
       "2236         NaN         NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "garage_columns = [col for col in dataset.columns if col.startswith(\"Garage\")]\n",
    "dataset[dataset['Garage Cars'].isna()][garage_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f8f7c19",
   "metadata": {},
   "source": [
    "We can see that this is the same row that contains the missing value in `Garage Area`, and that all garage columns except `Garage Type` are null in this row, so we will fill the missing values in `Garage Cars` and `Garage Area` with 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2265b79",
   "metadata": {},
   "source": [
    "We saw that there are 2 rows where `Garage Type` is not null while `Garage Cond`, `Garage Qual`, `Garage Finish`, and `Garage Yr Blt` columns are null. Let's take a look at these two rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5ea7c567",
   "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>Garage Type</th>\n",
       "      <th>Garage Yr Blt</th>\n",
       "      <th>Garage Finish</th>\n",
       "      <th>Garage Cars</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Garage Qual</th>\n",
       "      <th>Garage Cond</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>Detchd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>Detchd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Garage Type  Garage Yr Blt Garage Finish          Garage Cars  \\\n",
       "1356      Detchd            NaN           NaN                 1.00   \n",
       "2236      Detchd            NaN           NaN                  NaN   \n",
       "\n",
       "              Garage Area Garage Qual Garage Cond  \n",
       "1356               360.00         NaN         NaN  \n",
       "2236                  NaN         NaN         NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[~pd.isna(dataset['Garage Type']) & \n",
    "        pd.isna(dataset['Garage Qual'])][garage_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43ee11c",
   "metadata": {},
   "source": [
    "We will replace the values of `Garage Type` with `\"No Garage\"` in these two rows also.\n",
    "\n",
    "For `Garage Yr Blt`, we will fill in missing values with 0 since this is a numerical column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "375bba1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Garage Cars'].fillna(0, inplace=True)\n",
    "dataset['Garage Area'].fillna(0, inplace=True)\n",
    "\n",
    "dataset.loc[~pd.isna(dataset['Garage Type']) & \n",
    "            pd.isna(dataset['Garage Qual']), \"Garage Type\"] = \"No Garage\"\n",
    "\n",
    "for col in ['Garage Type', 'Garage Finish', 'Garage Qual', 'Garage Cond']:\n",
    "    dataset[col].fillna('No Garage', inplace=True)\n",
    "    \n",
    "dataset['Garage Yr Blt'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74293a1",
   "metadata": {},
   "source": [
    "### \"Bsmt Exposure\", \"BsmtFin Type 2\", \"BsmtFin Type 1\", \"Bsmt Qual\", \"Bsmt Cond\", \"Bsmt Half Bath\", \"Bsmt Full Bath\", \"Total Bsmt SF\", \"Bsmt Unf SF\", \"BsmtFin SF 2\", and \"BsmtFin SF 1\"\n",
    "\n",
    "\n",
    "In the dataset, `NA` in the first five basement columns means there's no basement, so we replace these missing values with \"No Basement.\" Notably, while these columns typically have 80 missing values, `BsmtFin Type 2` and `Bsmt Exposure` show 81 and 83 missing values, respectively. In contrast, `Bsmt Half Bath` and `Bsmt Full Bath` have only 2 missing values each. Let's inspect the rows with missing `Bsmt Half Bath` values for further clarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "977d52a3",
   "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>Bsmt Qual</th>\n",
       "      <th>Bsmt Cond</th>\n",
       "      <th>Bsmt Exposure</th>\n",
       "      <th>BsmtFin Type 1</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>BsmtFin Type 2</th>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1         BsmtFin SF 1  \\\n",
       "1341       NaN       NaN           NaN            NaN                  NaN   \n",
       "1497       NaN       NaN           NaN            NaN                 0.00   \n",
       "\n",
       "     BsmtFin Type 2         BsmtFin SF 2          Bsmt Unf SF  \\\n",
       "1341            NaN                  NaN                  NaN   \n",
       "1497            NaN                 0.00                 0.00   \n",
       "\n",
       "            Total Bsmt SF  Bsmt Full Bath  Bsmt Half Bath  \n",
       "1341                  NaN             NaN             NaN  \n",
       "1497                 0.00             NaN             NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bsmt_columns = [col for col in dataset.columns if \"Bsmt\" in col]\n",
    "dataset[dataset['Bsmt Half Bath'].isna()][bsmt_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a769dd",
   "metadata": {},
   "source": [
    "The missing values in `Bsmt Half Bath` coincide with those in `Bsmt Full Bath`. Additionally, one of these rows also has missing values in `Total Bsmt SF`, `Bsmt Unf SF`, `BsmtFin SF 2`, and `BsmtFin SF 1`. These rows lack data in `Bsmt Exposure`, `BsmtFin Type 2`, `BsmtFin Type 1`, `Bsmt Qual`, and `Bsmt Cond`, suggesting we fill these missing basement values with 0. Separately, there are 3 rows where `Bsmt Exposure` is null but `BsmtFin Type 1`, `Bsmt Qual`, and `Bsmt Cond` are not, meriting a closer look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b79935f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7ebd7f03",
   "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>Bsmt Qual</th>\n",
       "      <th>Bsmt Cond</th>\n",
       "      <th>Bsmt Exposure</th>\n",
       "      <th>BsmtFin Type 1</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>BsmtFin Type 2</th>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1595.00</td>\n",
       "      <td>1595.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>725.00</td>\n",
       "      <td>725.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2779</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>936.00</td>\n",
       "      <td>936.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1         BsmtFin SF 1  \\\n",
       "66          Gd        TA           NaN            Unf                 0.00   \n",
       "1796        Gd        TA           NaN            Unf                 0.00   \n",
       "2779        Gd        TA           NaN            Unf                 0.00   \n",
       "\n",
       "     BsmtFin Type 2         BsmtFin SF 2          Bsmt Unf SF  \\\n",
       "66              Unf                 0.00              1595.00   \n",
       "1796            Unf                 0.00               725.00   \n",
       "2779            Unf                 0.00               936.00   \n",
       "\n",
       "            Total Bsmt SF       Bsmt Full Bath       Bsmt Half Bath  \n",
       "66                1595.00                 0.00                 0.00  \n",
       "1796               725.00                 0.00                 0.00  \n",
       "2779               936.00                 0.00                 0.00  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[~pd.isna(dataset['Bsmt Cond']) & \n",
    "        pd.isna(dataset['Bsmt Exposure'])][bsmt_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b43c0b90",
   "metadata": {},
   "source": [
    "We will fill in the missing values in `Bsmt Exposure` for these three rows with `\"No\"`. According to the dataset documentation, `\"No\"` for `Bsmt Exposure` means \"No Exposure\":"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e3e1028",
   "metadata": {},
   "source": [
    "Let's now take a look at the row where `BsmtFin Type 2` is null while `BsmtFin Type 1`, `Bsmt Qual`, and `Bsmt Cond` are not null:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef2f0644",
   "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>Bsmt Qual</th>\n",
       "      <th>Bsmt Cond</th>\n",
       "      <th>Bsmt Exposure</th>\n",
       "      <th>BsmtFin Type 1</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>BsmtFin Type 2</th>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>1124.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>479.00</td>\n",
       "      <td>1603.00</td>\n",
       "      <td>3206.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1         BsmtFin SF 1  \\\n",
       "444        Gd        TA            No            GLQ              1124.00   \n",
       "\n",
       "    BsmtFin Type 2         BsmtFin SF 2          Bsmt Unf SF  \\\n",
       "444            NaN               479.00              1603.00   \n",
       "\n",
       "           Total Bsmt SF       Bsmt Full Bath       Bsmt Half Bath  \n",
       "444              3206.00                 1.00                 0.00  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[~pd.isna(dataset['Bsmt Cond']) & \n",
    "        pd.isna(dataset['BsmtFin Type 2'])][bsmt_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51bd1d6",
   "metadata": {},
   "source": [
    "We will fill in the missing value in `BsmtFin Type 2` for this row with `\"Unf\"`. According to the dataset documentation, `\"Unf\"` for `BsmtFin Type 2` means \"Unfinished\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7b7d19ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in [\"Bsmt Half Bath\", \"Bsmt Full Bath\", \"Total Bsmt SF\", \n",
    "            \"Bsmt Unf SF\", \"BsmtFin SF 2\", \"BsmtFin SF 1\"]:\n",
    "    dataset[col].fillna(0, inplace=True)\n",
    "\n",
    "dataset.loc[~pd.isna(dataset['Bsmt Cond']) & \n",
    "            pd.isna(dataset['Bsmt Exposure']), \"Bsmt Exposure\"] = \"No\"\n",
    "dataset.loc[~pd.isna(dataset['Bsmt Cond']) & \n",
    "            pd.isna(dataset['BsmtFin Type 2']), \"BsmtFin Type 2\"] = \"Unf\"\n",
    "\n",
    "for col in [\"Bsmt Exposure\", \"BsmtFin Type 2\", \n",
    "            \"BsmtFin Type 1\", \"Bsmt Qual\", \"Bsmt Cond\"]:\n",
    "    dataset[col].fillna(\"No Basement\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30586ce2",
   "metadata": {},
   "source": [
    "### Mas Vnr Area and Mas Vnr Type\n",
    "\n",
    "Each of these two columns have 23 missing values. We will fill in these missing values with \"None\" for Mas Vnr Type and with 0 for Mas Vnr Area. We use \"None\" for Mas Vnr Type because in the dataset documentation, \"None\" for Mas Vnr Type means \"None\" (i.e. no masonry veneer):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1d9e0ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Mas Vnr Area'].fillna(0, inplace=True)\n",
    "dataset['Mas Vnr Type'].fillna(\"None\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73441e4c",
   "metadata": {},
   "source": [
    "### Electrical\n",
    "\n",
    "This column has one missing value. We will fill in this value with the mode of this column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "02602886",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Electrical'].fillna(dataset['Electrical'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8f2a21",
   "metadata": {},
   "source": [
    "#### Now let's check if there is any remaining missing value in our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "834428e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a2e087",
   "metadata": {},
   "source": [
    "<a id=\"2-outlier-removal\"></a>\n",
    "## 2. Outlier Removal\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a1e4865",
   "metadata": {},
   "source": [
    "### plotting `SalePrice` against `Gr Liv Area` to spot the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "72827ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.scatter(x=dataset['Gr Liv Area'], y=dataset['SalePrice'], \n",
    "            color=\"orange\", edgecolors=\"#000000\", linewidths=0.5);\n",
    "plt.xlabel(\"Gr Liv Area\"); plt.ylabel(\"SalePrice\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1beb614e",
   "metadata": {},
   "source": [
    "### We can see five outlier values in the plot above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "05d8042d",
   "metadata": {
    "scrolled": true
   },
   "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>Gr Liv Area</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>5642</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>4476</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>745000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>4316</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>755000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>5095</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>183850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>4676</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>184750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gr Liv Area Sale Type Sale Condition  SalePrice\n",
       "1498         5642       New        Partial     160000\n",
       "1760         4476       WD         Abnorml     745000\n",
       "1767         4316       WD          Normal     755000\n",
       "2180         5095       New        Partial     183850\n",
       "2181         4676       New        Partial     184750"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlirt_columns = [\"Gr Liv Area\"] + \\\n",
    "                  [col for col in dataset.columns if \"Sale\" in col]\n",
    "dataset[dataset[\"Gr Liv Area\"] > 4000][outlirt_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0be14276",
   "metadata": {},
   "outputs": [],
   "source": [
    "# REMOVING Outliers\n",
    "dataset = dataset[dataset[\"Gr Liv Area\"] < 4000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ec0a968c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=dataset['Gr Liv Area'], y=dataset['SalePrice'], \n",
    "            color=\"orange\", edgecolors=\"#000000\", linewidths=0.5);\n",
    "plt.xlabel(\"Gr Liv Area\"); plt.ylabel(\"SalePrice\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169ab75d",
   "metadata": {},
   "source": [
    "To avoid problems in modeling later, we will reset our dataset index after removing the outlier rows, so no gaps remain in our dataset index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0dee8d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7852201f",
   "metadata": {},
   "source": [
    "<a id=\"3-dropping-unimportant-columns\"></a>\n",
    "## 3. Dropping Unimportant Columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0d7283a",
   "metadata": {},
   "source": [
    "The columns to be deleted are `Order` and `PID`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bdf8fcdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10134/4030495044.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",
      "  dataset.drop(['Order', 'PID'], axis=1, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "dataset.drop(['Order', 'PID'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0476fc9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1c0fe973",
   "metadata": {},
   "source": [
    "<a id=\"feature-engineering\"></a>\n",
    "# FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79d59ce",
   "metadata": {},
   "source": [
    "<a id=\"1-derived-feature-creation\"></a>\n",
    "## 1. Derived Feature Creation\n",
    "\n",
    "Recognizing the strong positive correlation between SalePrice, Overall Qual, and Gr Liv Area, we'll enhance our predictive model by generating polynomial features. Specifically, we will create new features by squaring and cubing the values of Overall Qual and Gr Liv Area. Additionally, we'll introduce a feature representing the product of these two key features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5db4d945",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create polynomial features based on 'Overall Qual' and 'Gr Liv Area'\n",
    "for f in [\"Overall Qual\", \"Gr Liv Area\"]:\n",
    "    dataset[f + \"_p2\"] = dataset[f] ** 2\n",
    "    dataset[f + \"_p3\"] = dataset[f] ** 3\n",
    "# Create interaction feature    \n",
    "dataset[\"OverallQual_GrLivArea\"] = \\\n",
    "    dataset[\"Overall Qual\"] * dataset[\"Gr Liv Area\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ad76a5",
   "metadata": {},
   "source": [
    "To address multicollinearity, we'll eliminate one feature from each pair of highly correlated predictors. We identified two such pairs: Garage Cars and Garage Area, and Gr Liv Area and TotRms AbvGrd. To mitigate this issue, we will remove Garage Cars from the first pair and TotRms AbvGrd from the second."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f830e99",
   "metadata": {},
   "source": [
    "Multicollinearity occurs when predictors in a regression model are highly correlated, leading to difficulties in isolating individual variable effects and potentially causing unstable estimates. It's often addressed by removing or combining correlated variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "efe17db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Address multicollinearity by dropping one feature from each pair of highly correlated predictors\n",
    "dataset.drop([\"Garage Cars\", \"TotRms AbvGrd\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff39187a",
   "metadata": {},
   "source": [
    "<a id=\"2-ordinal-variables-mapping\"></a>\n",
    "## 2. Ordinal Variables Mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7016629c",
   "metadata": {},
   "source": [
    "For ordinal variables like Bsmt Cond in our dataset, we'll convert their categorical rankings to numerical values for machine learning compatibility. For instance, Bsmt Cond values - \"No Basement\" to 0, \"Po\" (Poor) to 1, \"Fa\" (Fair) to 2, \"TA\" (Typical) to 3, \"Gd\" (Good) to 4, and \"Ex\" (Excellent) to 5 - will be mapped numerically.\n",
    "\n",
    "This approach will be applied to all ordinal features including Exter Qual, Bsmt Qual, and others, assigning numbers to their categorical values based on the dataset documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "47e59c40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in 'Bsmt Cond' column:\n",
      "['Gd', 'TA', 'No Basement', 'Po', 'Fa', 'Ex']\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique values in 'Bsmt Cond' column:\")\n",
    "print(dataset['Bsmt Cond'].unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "afb1bd2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mp = {'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0}\n",
    "dataset['Exter Qual'] = dataset['Exter Qual'].map(mp)\n",
    "dataset['Exter Cond'] = dataset['Exter Cond'].map(mp)\n",
    "dataset['Heating QC'] = dataset['Heating QC'].map(mp)\n",
    "dataset['Kitchen Qual'] = dataset['Kitchen Qual'].map(mp)\n",
    "\n",
    "mp = {'Ex':5,'Gd':4,'TA':3,'Fa':2,'Po':1,'No Basement':0}\n",
    "dataset['Bsmt Qual'] = dataset['Bsmt Qual'].map(mp)\n",
    "dataset['Bsmt Cond'] = dataset['Bsmt Cond'].map(mp)\n",
    "dataset['Bsmt Exposure'] = dataset['Bsmt Exposure'].map(\n",
    "    {'Gd':4,'Av':3,'Mn':2,'No':1,'No Basement':0})\n",
    "\n",
    "mp = {'GLQ':6,'ALQ':5,'BLQ':4,'Rec':3,'LwQ':2,'Unf':1,'No Basement':0}\n",
    "dataset['BsmtFin Type 1'] = dataset['BsmtFin Type 1'].map(mp)\n",
    "dataset['BsmtFin Type 2'] = dataset['BsmtFin Type 2'].map(mp)\n",
    "\n",
    "dataset['Central Air'] = dataset['Central Air'].map({'Y':1,'N':0})\n",
    "dataset['Functional'] = dataset['Functional'].map(\n",
    "    {'Typ':7,'Min1':6,'Min2':5,'Mod':4,'Maj1':3,\n",
    "     'Maj2':2,'Sev':1,'Sal':0})\n",
    "dataset['Fireplace Qu'] = dataset['Fireplace Qu'].map(\n",
    "    {'Ex':5,'Gd':4,'TA':3,'Fa':2,'Po':1,'No Fireplace':0})\n",
    "dataset['Garage Finish'] = dataset['Garage Finish'].map(\n",
    "    {'Fin':3,'RFn':2,'Unf':1,'No Garage':0})\n",
    "dataset['Garage Qual'] = dataset['Garage Qual'].map(\n",
    "    {'Ex':5,'Gd':4,'TA':3,'Fa':2,'Po':1,'No Garage':0})\n",
    "dataset['Garage Cond'] = dataset['Garage Cond'].map(\n",
    "    {'Ex':5,'Gd':4,'TA':3,'Fa':2,'Po':1,'No Garage':0})\n",
    "dataset['Pool QC'] = dataset['Pool QC'].map(\n",
    "    {'Ex':4,'Gd':3,'TA':2,'Fa':1,'No Pool':0})\n",
    "dataset['Land Slope'] = dataset['Land Slope'].map(\n",
    "    {'Sev': 2, 'Mod': 1, 'Gtl': 0})\n",
    "dataset['Fence'] = dataset['Fence'].map(\n",
    "    {'GdPrv':4,'MnPrv':3,'GdWo':2,'MnWw':1,'No Fence':0})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4daa2962",
   "metadata": {},
   "source": [
    "<a id=\"3-one-hot-encoding-categorical-features\"></a>\n",
    "## 3. One-Hot Encoding ( Categorical Features)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc9f224",
   "metadata": {},
   "source": [
    "We'll use one-hot encoding to transform categorical features into binary variables, with each unique category becoming a binary variable (0 or 1). We'll utilize Pandas' `get_dummies()` function for this purpose.\n",
    "\n",
    "As an illustration, consider the `Paved Drive` variable, which indicates driveway pavement type with values `Y` (Paved), `P` (Partial Pavement), and `N` (Dirt/Gravel). Let's examine the `Paved Drive` values in the first few rows of our dataset after applying one-hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f848b807",
   "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>Paved Drive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Paved Drive\n",
       "0           P\n",
       "1           Y\n",
       "2           Y\n",
       "3           Y\n",
       "4           Y"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['Paved Drive']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dec99f93",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.get_dummies(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "388bc87e",
   "metadata": {
    "scrolled": true
   },
   "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>Paved Drive_N</th>\n",
       "      <th>Paved Drive_P</th>\n",
       "      <th>Paved Drive_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Paved Drive_N  Paved Drive_P  Paved Drive_Y\n",
       "0          False           True          False\n",
       "1          False          False           True\n",
       "2          False          False           True\n",
       "3          False          False           True\n",
       "4          False          False           True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pavedDrive_oneHot = [c for c in dataset.columns if c.startswith(\"Paved\")]\n",
    "dataset[pavedDrive_oneHot].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2672f85c",
   "metadata": {},
   "source": [
    "Paved Drive column is converted to 1 in Paved Drive_P and zeros in Paved Drive_N and Paved Drive_Y after one-hot encoding."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55528545",
   "metadata": {},
   "source": [
    "<a id=\"modeling\"></a>\n",
    "# MODELING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5b1b35",
   "metadata": {},
   "source": [
    "<a id=\"setup\"></a>\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c726474e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n",
      "us-east-1\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import boto3\n",
    "import re\n",
    "import sagemaker\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import io\n",
    "import time\n",
    "import json\n",
    "import sagemaker.amazon.common as smac\n",
    "\n",
    "role = sagemaker.get_execution_role()\n",
    "region = boto3.Session().region_name\n",
    "s3 = boto3.client(\"s3\")\n",
    "\n",
    "\n",
    "# S3 bucket for saving code and model artifacts.\n",
    "bucket = \"final-project-ryhan\"\n",
    "\n",
    "prefix = (\n",
    "    \"sagemaker/Ames-housing-price-prediction\"  # place to upload training files within the bucket\n",
    ")\n",
    "print (region)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0ab235",
   "metadata": {},
   "source": [
    "<a id=\"prediction-type\"></a>\n",
    "# Prediction Type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7390d214",
   "metadata": {},
   "source": [
    "In this project, our goal is to predict a house's price based on its attributes. The target price is a continuous variable, meaning it can take any real number value. This is evident from the `SalePrice` variable in our dataset, which represents the price we aim to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "18c9faf1",
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>189900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalePrice\n",
       "0     215000\n",
       "1     105000\n",
       "2     172000\n",
       "3     244000\n",
       "4     189900"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['SalePrice']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fffc39c",
   "metadata": {},
   "source": [
    "#### That means that the prediction type that is appropriate to our problem is **regression**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6645ca4e",
   "metadata": {},
   "source": [
    "<a id=\"feature-scaling-with-standanrdscaler\"></a>\n",
    "## Feature Scaling with standanrdscaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "32a72030",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "# We need to fit the scaler to our data before transformation\n",
    "dataset.loc[:, dataset.columns != 'SalePrice'] = scaler.fit_transform(\n",
    "    dataset.loc[:, dataset.columns != 'SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8035714d",
   "metadata": {},
   "source": [
    "<a id=\"splitting-data-train-test-split\"></a>\n",
    "## SPLITTING DATA (TRAIN-Validation-TEST-SPLIT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f2e0f081",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# First, split the dataset into a larger training set and a temporary set (combining validation and test)\n",
    "train_df, temp_df = train_test_split(dataset, test_size=0.3, random_state=42)\n",
    "\n",
    "# Now, split the temporary set into validation and test sets\n",
    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
    "\n",
    "# Separating the features and the target variable for each set\n",
    "X_train = train_df.drop(columns=['SalePrice'])\n",
    "Y_train = train_df['SalePrice']\n",
    "\n",
    "X_val = val_df.drop(columns=['SalePrice'])\n",
    "Y_val = val_df['SalePrice']\n",
    "\n",
    "X_test = test_df.drop(columns=['SalePrice'])\n",
    "Y_test = test_df['SalePrice']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6acdde30",
   "metadata": {},
   "source": [
    "<a id=\"saving-train-data-to-s3\"></a>\n",
    "## Saving Train data to s3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cdf2d14",
   "metadata": {},
   "source": [
    "### I'll convert the dataset to the recordIO-wrapped protobuf format  before saving to s3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "054259b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker.amazon.common as smac\n",
    "\n",
    "# Convert pandas DataFrames to numpy arrays\n",
    "X_train_np = X_train.values.astype(\"float32\")\n",
    "Y_train_np = Y_train.values.astype(\"float32\")\n",
    "X_val_np = X_val.values.astype(\"float32\")\n",
    "Y_val_np = Y_val.values.astype(\"float32\")\n",
    "\n",
    "# Now, convert the datasets to the recordIO-wrapped protobuf format\n",
    "train_file = \"Project_XGB_train_data.csv\"\n",
    "val_file = \"Project_XGB_val_data.csv\"\n",
    "\n",
    "# Saving training data\n",
    "f_train = io.BytesIO()  # Create an in-memory bytes buffer object that can be used as a file object\n",
    "smac.write_numpy_to_dense_tensor(f_train, X_train_np, Y_train_np)\n",
    "f_train.seek(0)  # Reset the file's position to the beginning\n",
    "\n",
    "# Saving validation data\n",
    "f_val = io.BytesIO()\n",
    "smac.write_numpy_to_dense_tensor(f_val, X_val_np, Y_val_np)\n",
    "f_val.seek(0)\n",
    "\n",
    "# Upload the files to Amazon S3\n",
    "s3_resource = boto3.Session().resource(\"s3\")\n",
    "\n",
    "s3_resource.Bucket(bucket).Object(os.path.join(prefix, \"train\", train_file)).upload_fileobj(f_train)\n",
    "s3_resource.Bucket(bucket).Object(os.path.join(prefix, \"validation\", val_file)).upload_fileobj(f_val)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e3571fd",
   "metadata": {},
   "source": [
    "<a id=\"xgboost-model\"></a>\n",
    "## XGBOOST MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39eb4041",
   "metadata": {},
   "source": [
    "<a id=\"xgboost-container-image-and-training-job\"></a>\n",
    "### XGBoost container image and training job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e0dc35f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.image_uris import retrieve\n",
    "\n",
    "container = retrieve(framework=\"xgboost\", region=region, version=\"1.7-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "235a5328",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job name is: FinalProject-Ryhan-XGBoost-2023-11-26-19-18-34\n"
     ]
    }
   ],
   "source": [
    "XGB_job = \"FinalProject-Ryhan-XGBoost-\" + time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.gmtime())\n",
    "\n",
    "print(\"Job name is:\", XGB_job)\n",
    "\n",
    "xgb_training_params = {\n",
    "    \"TrainingJobName\": XGB_job,\n",
    "    \"AlgorithmSpecification\": {\n",
    "        \"TrainingImage\": container,\n",
    "        \"TrainingInputMode\": \"File\"\n",
    "    },\n",
    "    \"RoleArn\": role,\n",
    "    \"ResourceConfig\": {\"InstanceCount\": 1, \"InstanceType\": \"ml.c4.2xlarge\", \"VolumeSizeInGB\": 10},\n",
    "    \"HyperParameters\": {\n",
    "        \"max_depth\": \"5\",           # Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit.\n",
    "        \"eta\": \"0.2\",               # Step size shrinkage used in updates to prevent overfitting. After each boosting step, we can directly get the weights of new features.\n",
    "        \"gamma\": \"4\",               # Minimum loss reduction required to make a further partition on a leaf node of the tree.\n",
    "        \"min_child_weight\": \"6\",    # Minimum sum of instance weight (hessian) needed in a child.\n",
    "        \"verbosity\": \"0\",           # Verbosity of printing messages. Valid values are 0 (silent), 1 (warning), 2 (info), and 3 (debug).\n",
    "        \"objective\": \"reg:squarederror\",  # Objective function for regression.\n",
    "        # Removed num_class as it's not relevant for regression\n",
    "        \"num_round\": \"10\"           # The number of rounds for boosting\n",
    "    },\n",
    "    \"StoppingCondition\": {\n",
    "        \"MaxRuntimeInSeconds\": 86400\n",
    "    },\n",
    "    \"OutputDataConfig\": {\n",
    "        \"S3OutputPath\": f\"s3://{bucket}/{prefix}/xgboost\"\n",
    "    },\n",
    "    \"InputDataConfig\": [\n",
    "    {\n",
    "        \"ChannelName\": \"train\",\n",
    "        \"DataSource\": {\n",
    "            \"S3DataSource\": {\n",
    "                \"S3DataType\": \"S3Prefix\",\n",
    "                \"S3Uri\": f\"s3://{bucket}/{prefix}/train/\",\n",
    "                \"S3DataDistributionType\": \"FullyReplicated\"\n",
    "            }\n",
    "        },\n",
    "        \"ContentType\": \"application/x-recordio-protobuf\",\n",
    "        \"CompressionType\": \"None\",\n",
    "        \"RecordWrapperType\": \"None\"\n",
    "    },\n",
    "    {\n",
    "        \"ChannelName\": \"validation\",\n",
    "        \"DataSource\": {\n",
    "            \"S3DataSource\": {\n",
    "                \"S3DataType\": \"S3Prefix\",\n",
    "                \"S3Uri\": f\"s3://{bucket}/{prefix}/validation/\",\n",
    "                \"S3DataDistributionType\": \"FullyReplicated\"\n",
    "            }\n",
    "        },\n",
    "        \"ContentType\": \"application/x-recordio-protobuf\",\n",
    "        \"CompressionType\": \"None\",\n",
    "        \"RecordWrapperType\": \"None\"\n",
    "    }\n",
    "]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2080d8ce",
   "metadata": {},
   "source": [
    "<a id=\"training\"></a>\n",
    "## TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e14939b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InProgress\n",
      "CPU times: user 91.1 ms, sys: 5.51 ms, total: 96.6 ms\n",
      "Wall time: 4min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "sm = boto3.client(\"sagemaker\")\n",
    "\n",
    "sm.create_training_job(**xgb_training_params)\n",
    "\n",
    "# Checking training job status\n",
    "status = sm.describe_training_job(TrainingJobName=XGB_job)[\"TrainingJobStatus\"]\n",
    "print(status)\n",
    "\n",
    "sm.get_waiter(\"training_job_completed_or_stopped\").wait(TrainingJobName=XGB_job)\n",
    "if status == \"Failed\":\n",
    "    message = sm.describe_training_job(TrainingJobName=XGB_job)[\"FailureReason\"]\n",
    "    print(\"Training failed with the following error: {}\".format(message))\n",
    "    raise Exception(\"Training job failed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd28c36",
   "metadata": {},
   "source": [
    "<a id=\"hyperparameter-tuning\"></a>\n",
    "# HYPERPARAMETER TUNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "48eed706",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FinalProj-Ryhan-XGB-26-19-33-53\n"
     ]
    }
   ],
   "source": [
    "from time import gmtime, strftime, sleep\n",
    "\n",
    "tuning_job_name = \"FinalProj-Ryhan-XGB-\" + strftime(\"%d-%H-%M-%S\", gmtime())\n",
    "\n",
    "print(tuning_job_name)\n",
    "\n",
    "tuning_job_config = {\n",
    "    \"ParameterRanges\": {\n",
    "        \"ContinuousParameterRanges\": [\n",
    "            {\"MaxValue\": \"0.5\", \"MinValue\": \"0.01\", \"Name\": \"eta\"},\n",
    "            {\"MaxValue\": \"10\", \"MinValue\": \"1\", \"Name\": \"gamma\"},\n",
    "            {\"MaxValue\": \"1\", \"MinValue\": \"0.5\", \"Name\": \"subsample\"},\n",
    "            {\"MaxValue\": \"1\", \"MinValue\": \"0.5\", \"Name\": \"colsample_bytree\"}\n",
    "        ],\n",
    "        \"IntegerParameterRanges\": [\n",
    "            {\"MaxValue\": \"10\", \"MinValue\": \"3\", \"Name\": \"max_depth\"},\n",
    "            {\"MaxValue\": \"10\", \"MinValue\": \"1\", \"Name\": \"min_child_weight\"}\n",
    "        ]\n",
    "    },\n",
    "    \"ResourceLimits\": {\"MaxNumberOfTrainingJobs\": 20, \"MaxParallelTrainingJobs\": 3},\n",
    "    \"Strategy\": \"Bayesian\",\n",
    "    \"HyperParameterTuningJobObjective\": {\"MetricName\": \"validation:rmse\", \"Type\": \"Minimize\"}\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b35ef7d",
   "metadata": {},
   "source": [
    "## I'm using Root Mean Square Error on the validation dataset as the objective metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9235671",
   "metadata": {},
   "source": [
    "A lower RMSE value indicates better model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3f493858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'HyperParameterTuningJobArn': 'arn:aws:sagemaker:us-east-1:378639026377:hyper-parameter-tuning-job/FinalProj-Ryhan-XGB-26-19-33-53',\n",
       " 'ResponseMetadata': {'RequestId': '9167091f-3957-4bae-b007-4c2ea832d24a',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': '9167091f-3957-4bae-b007-4c2ea832d24a',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '132',\n",
       "   'date': 'Sun, 26 Nov 2023 19:37:09 GMT'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_image = retrieve(framework=\"xgboost\", region=region, version=\"1.5-1\")\n",
    "\n",
    "s3_input_train = f\"s3://{bucket}/{prefix}/train\"\n",
    "s3_input_validation = f\"s3://{bucket}/{prefix}/validation\"\n",
    "\n",
    "training_job_definition = {\n",
    "    \"AlgorithmSpecification\": {\"TrainingImage\": training_image, \"TrainingInputMode\": \"File\"},\n",
    "    \"InputDataConfig\": [\n",
    "        {\n",
    "            \"ChannelName\": \"train\",\n",
    "            \"CompressionType\": \"None\",\n",
    "            \"ContentType\": \"application/x-recordio-protobuf\",\n",
    "            \"DataSource\": {\n",
    "                \"S3DataSource\": {\n",
    "                    \"S3DataDistributionType\": \"FullyReplicated\",\n",
    "                    \"S3DataType\": \"S3Prefix\",\n",
    "                    \"S3Uri\": s3_input_train,\n",
    "                }\n",
    "            },\n",
    "        },\n",
    "        {\n",
    "            \"ChannelName\": \"validation\",\n",
    "            \"CompressionType\": \"None\",\n",
    "            \"ContentType\": \"application/x-recordio-protobuf\",\n",
    "            \"DataSource\": {\n",
    "                \"S3DataSource\": {\n",
    "                    \"S3DataDistributionType\": \"FullyReplicated\",\n",
    "                    \"S3DataType\": \"S3Prefix\",\n",
    "                    \"S3Uri\": s3_input_validation,\n",
    "                }\n",
    "            },\n",
    "        },\n",
    "    ],\n",
    "    \"OutputDataConfig\": {\"S3OutputPath\": f\"s3://{bucket}/{prefix}/output\"},\n",
    "    \"ResourceConfig\": {\"InstanceCount\": 1, \"InstanceType\": \"ml.m4.xlarge\", \"VolumeSizeInGB\": 10},\n",
    "    \"RoleArn\": role,\n",
    "    \"StaticHyperParameters\": {\n",
    "        \"eval_metric\": \"rmse\",\n",
    "        \"num_round\": \"100\",\n",
    "        \"objective\": \"reg:squarederror\",\n",
    "        \"rate_drop\": \"0.3\",\n",
    "        \"tweedie_variance_power\": \"1.4\",\n",
    "    },\n",
    "    \"StoppingCondition\": {\"MaxRuntimeInSeconds\": 43200},\n",
    "}\n",
    "\n",
    "sm = boto3.client(\"sagemaker\")\n",
    "sm.create_hyper_parameter_tuning_job(\n",
    "    HyperParameterTuningJobName=tuning_job_name,\n",
    "    HyperParameterTuningJobConfig=tuning_job_config,\n",
    "    TrainingJobDefinition=training_job_definition,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f61bc663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'InProgress'"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.describe_hyper_parameter_tuning_job(HyperParameterTuningJobName=tuning_job_name)[\"HyperParameterTuningJobStatus\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b0b84fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FinalProj-Ryhan-XGB-26-19-33-53'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.describe_hyper_parameter_tuning_job(HyperParameterTuningJobName=tuning_job_name)['HyperParameterTuningJobName']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c2f88aea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 training jobs have completed\n"
     ]
    }
   ],
   "source": [
    "# run this cell to check current status of hyperparameter tuning job\n",
    "tuning_job_result = sm.describe_hyper_parameter_tuning_job(\n",
    "    HyperParameterTuningJobName=tuning_job_name\n",
    ")\n",
    "\n",
    "status = tuning_job_result[\"HyperParameterTuningJobStatus\"]\n",
    "if status != \"Completed\":\n",
    "    print(\"Reminder: the tuning job has not been completed.\")\n",
    "\n",
    "job_count = tuning_job_result[\"TrainingJobStatusCounters\"][\"Completed\"]\n",
    "print(\"%d training jobs have completed\" % job_count)\n",
    "\n",
    "objective = tuning_job_result[\"HyperParameterTuningJobConfig\"][\"HyperParameterTuningJobObjective\"]\n",
    "is_minimize = objective[\"Type\"] != \"Maximize\"\n",
    "objective_name = objective[\"MetricName\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "3f0ab8d7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model found so far:\n",
      "{'CreationTime': datetime.datetime(2023, 11, 26, 19, 41, 11, tzinfo=tzlocal()),\n",
      " 'FinalHyperParameterTuningJobObjectiveMetric': {'MetricName': 'validation:rmse',\n",
      "                                                 'Value': 20404.921875},\n",
      " 'ObjectiveStatus': 'Succeeded',\n",
      " 'TrainingEndTime': datetime.datetime(2023, 11, 26, 19, 41, 58, tzinfo=tzlocal()),\n",
      " 'TrainingJobArn': 'arn:aws:sagemaker:us-east-1:378639026377:training-job/FinalProj-Ryhan-XGB-26-19-33-53-005-5a2863a9',\n",
      " 'TrainingJobName': 'FinalProj-Ryhan-XGB-26-19-33-53-005-5a2863a9',\n",
      " 'TrainingJobStatus': 'Completed',\n",
      " 'TrainingStartTime': datetime.datetime(2023, 11, 26, 19, 41, 16, tzinfo=tzlocal()),\n",
      " 'TunedHyperParameters': {'colsample_bytree': '0.5781199849818944',\n",
      "                          'eta': '0.09900286889914324',\n",
      "                          'gamma': '8.163769086435273',\n",
      "                          'max_depth': '5',\n",
      "                          'min_child_weight': '1',\n",
      "                          'subsample': '0.8229828833886836'}}\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "if tuning_job_result.get(\"BestTrainingJob\", None):\n",
    "    print(\"Best model found so far:\")\n",
    "    pprint(tuning_job_result[\"BestTrainingJob\"])\n",
    "else:\n",
    "    print(\"No training jobs have reported results yet.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c72f35c",
   "metadata": {},
   "source": [
    "### From the output we see:\n",
    "- Metric: The best model achieved a Root Mean Square Error (RMSE) of 20404.921875 on the validation set.\n",
    "\n",
    "- Hyperparameters: Optimal settings include a learning rate (eta) of 0.0990, max_depth of 5, min_child_weight of 1, and specific settings for colsample_bytree, gamma, and subsample."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "039771dc",
   "metadata": {},
   "source": [
    "<a id=\"inferencing\"></a>\n",
    "# INFERENCING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aca1b82",
   "metadata": {},
   "source": [
    "<a id=\"hosting-the-model\"></a>\n",
    "## HOSTING THE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c7bd6285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arn:aws:sagemaker:us-east-1:378639026377:model/finalproj-ryhan-xgb-26-19-33-53-005-5a2863a9\n"
     ]
    }
   ],
   "source": [
    "best_training_job_name = tuning_job_result[\"BestTrainingJob\"][\"TrainingJobName\"]\n",
    "# Retrieve the best model's S3 model artifacts\n",
    "best_model_artifacts = sm.describe_training_job(TrainingJobName=best_training_job_name)[\"ModelArtifacts\"][\"S3ModelArtifacts\"]\n",
    "\n",
    "XGB_hosting_container = {\n",
    "    \"Image\": container,\n",
    "    \"ModelDataUrl\": best_model_artifacts\n",
    "}\n",
    "\n",
    "# Create the SageMaker model for hosting\n",
    "create_model_response = sm.create_model(\n",
    "    ModelName=best_training_job_name,  # Use the best training job name as the model name\n",
    "    ExecutionRoleArn=role,\n",
    "    PrimaryContainer=XGB_hosting_container,\n",
    ")\n",
    "\n",
    "print(create_model_response[\"ModelArn\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf9263b",
   "metadata": {},
   "source": [
    "<a id=\"endpoint-config\"></a>\n",
    "## Endpoint Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a5f02d59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FinalProject-Ryhan-XGB-endpoint-config-2023-11-26-19-59-37\n",
      "Endpoint Config Arn: arn:aws:sagemaker:us-east-1:378639026377:endpoint-config/finalproject-ryhan-xgb-endpoint-config-2023-11-26-19-59-37\n"
     ]
    }
   ],
   "source": [
    "model_name = best_training_job_name\n",
    "\n",
    "XGB_endpoint_config = \"FinalProject-Ryhan-XGB-endpoint-config-\" + time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.gmtime())\n",
    "print(XGB_endpoint_config)\n",
    "\n",
    "create_endpoint_config_response = sm.create_endpoint_config(\n",
    "    EndpointConfigName=XGB_endpoint_config,\n",
    "    ProductionVariants=[\n",
    "        {\n",
    "            \"InstanceType\": \"ml.m4.xlarge\",\n",
    "            \"InitialInstanceCount\": 1,\n",
    "            \"ModelName\": model_name,\n",
    "            \"VariantName\": \"AllTraffic\",\n",
    "        }\n",
    "    ],\n",
    ")\n",
    "\n",
    "print(\"Endpoint Config Arn: \" + create_endpoint_config_response[\"EndpointConfigArn\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30774fe4",
   "metadata": {},
   "source": [
    "<a id=\"creating-endpoint\"></a>\n",
    "## Creating Endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "beace6e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FinalProject-Ryhan-XGB-endpoint-202311262001\n",
      "Endpoint Arn: arn:aws:sagemaker:us-east-1:378639026377:endpoint/finalproject-ryhan-xgb-endpoint-202311262001\n",
      "Arn: arn:aws:sagemaker:us-east-1:378639026377:endpoint/finalproject-ryhan-xgb-endpoint-202311262001\n",
      "Status: InService\n",
      "CPU times: user 80.5 ms, sys: 0 ns, total: 80.5 ms\n",
      "Wall time: 4min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Create a unique name for the SageMaker endpoint\n",
    "XGB_endpoint = \"FinalProject-Ryhan-XGB-endpoint-\" + time.strftime(\"%Y%m%d%H%M\", time.gmtime())\n",
    "print(XGB_endpoint)\n",
    "\n",
    "create_endpoint_response = sm.create_endpoint(\n",
    "    EndpointName=XGB_endpoint, \n",
    "    EndpointConfigName=XGB_endpoint_config  \n",
    ")\n",
    "print(\"Endpoint Arn: \" + create_endpoint_response[\"EndpointArn\"])\n",
    "\n",
    "# Wait for the endpoint to be in service\n",
    "sm.get_waiter(\"endpoint_in_service\").wait(EndpointName=XGB_endpoint)\n",
    "\n",
    "# Check the status of the endpoint\n",
    "resp = sm.describe_endpoint(EndpointName=XGB_endpoint)\n",
    "status = resp[\"EndpointStatus\"]\n",
    "print(\"Arn: \" + resp[\"EndpointArn\"])\n",
    "print(\"Status: \" + status)\n",
    "\n",
    "# Raise an exception if the endpoint is not in service\n",
    "if status != \"InService\":\n",
    "    raise Exception(\"Endpoint creation did not succeed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9edb7c3c",
   "metadata": {},
   "source": [
    "<a id=\"prediction\"></a>\n",
    "## PREDICTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1237f62f",
   "metadata": {},
   "source": [
    "### Function to convert an array to a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5dba6290",
   "metadata": {},
   "outputs": [],
   "source": [
    "def np2csv(arr):\n",
    "    csv = io.BytesIO() #the function gets an array (Numpy array) and creates an in-memory binary buffer named csv\n",
    "    np.savetxt(csv, arr, delimiter=\",\", fmt=\"%g\") # write the array 'arr' to csv object, columns should be seperated by commas. The fmt=\"%g\" removes unneccesary decimals when saving and use scientific notation.\n",
    "    # In the following line:\n",
    "    # csv.getvalue() retrieves the entire contents of the buffer csv as a byte string.\n",
    "    # .decode() converts the byte string into a normal Python string by decoding it using the default UTF-8 encoding.\n",
    "    #.rstrip() removes any trailing whitespace or newlines from the end of the string.\n",
    "    return csv.getvalue().decode().rstrip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "88155bb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import boto3\n",
    "from io import StringIO\n",
    "\n",
    "runtime = boto3.client(\"runtime.sagemaker\")\n",
    "\n",
    "# Assuming np2csv converts a numpy array to a CSV string\n",
    "payload = np2csv(X_test)  \n",
    "\n",
    "# Save X_test as a CSV for inspection\n",
    "X_test_df = pd.DataFrame(X_test)\n",
    "X_test_df.to_csv('X_test.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3433710e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw response body: 140587.515625\n",
      "208920.453125\n",
      "83030.9609375\n",
      "233810.203125\n",
      "201558.265625\n",
      "377092.46875\n",
      "196612.671875\n",
      "263335.21875\n",
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      "225024.28125\n",
      "137573.171875\n",
      "137601.15625\n",
      "400711.21875\n",
      "132449.53125\n",
      "192363.28125\n",
      "137028.09375\n",
      "147441.765625\n",
      "153926.78125\n",
      "217172.9375\n",
      "151716.234375\n",
      "347419.03125\n",
      "163031.53125\n",
      "193339.109375\n",
      "168385.3125\n",
      "178125.625\n",
      "109803.6640625\n",
      "203618.546875\n",
      "183732.203125\n",
      "184663.34375\n",
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      "280403.59375\n",
      "208290.921875\n",
      "236388.25\n",
      "160715.9375\n",
      "207789.09375\n",
      "113738.21875\n",
      "212152.71875\n",
      "164967.46875\n",
      "185999.921875\n",
      "270228.53125\n",
      "138819.96875\n",
      "209794.390625\n",
      "150787.1875\n",
      "133749.0\n",
      "327222.96875\n",
      "241940.265625\n",
      "99220.296875\n",
      "117751.3203125\n",
      "146774.640625\n",
      "231655.390625\n",
      "187462.765625\n",
      "135017.328125\n",
      "128284.2265625\n",
      "122762.546875\n",
      "140996.84375\n",
      "83765.578125\n",
      "152177.109375\n",
      "130699.4140625\n",
      "121643.9921875\n",
      "189581.28125\n",
      "218373.359375\n",
      "108981.3984375\n",
      "263885.84375\n",
      "210182.765625\n",
      "297836.09375\n",
      "158045.140625\n",
      "285096.65625\n",
      "181962.390625\n",
      "83547.5859375\n",
      "227298.515625\n",
      "219058.65625\n",
      "177664.703125\n",
      "208074.3125\n",
      "135426.5625\n",
      "143984.625\n",
      "236081.203125\n",
      "118466.921875\n",
      "298753.65625\n",
      "138112.03125\n",
      "142864.71875\n",
      "169049.453125\n",
      "139151.390625\n",
      "107833.96875\n",
      "90685.546875\n",
      "158645.4375\n",
      "110219.03125\n",
      "140915.03125\n",
      "176147.890625\n",
      "215183.390625\n",
      "159774.546875\n",
      "158167.90625\n",
      "115840.5078125\n",
      "117757.375\n",
      "150156.953125\n",
      "115133.3125\n",
      "43221.3671875\n",
      "193392.28125\n",
      "161343.734375\n",
      "123904.359375\n",
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      "324287.875\n",
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      "279087.46875\n",
      "111138.65625\n",
      "189111.078125\n",
      "130350.2421875\n",
      "117585.28125\n",
      "186373.71875\n",
      "177902.765625\n",
      "289358.96875\n",
      "139863.203125\n",
      "50002.390625\n",
      "168898.71875\n",
      "104339.6484375\n",
      "209626.15625\n",
      "95447.5859375\n",
      "189586.03125\n",
      "205520.984375\n",
      "190356.515625\n",
      "220529.703125\n",
      "177386.28125\n",
      "132685.265625\n",
      "168015.234375\n",
      "334635.84375\n",
      "137357.4375\n",
      "131526.109375\n",
      "182786.96875\n",
      "257628.59375\n",
      "207788.359375\n",
      "271179.8125\n",
      "188814.921875\n",
      "209696.296875\n",
      "120100.375\n",
      "229297.265625\n",
      "133618.375\n",
      "103702.7109375\n",
      "248221.265625\n",
      "251645.84375\n",
      "91064.28125\n",
      "138173.40625\n",
      "117917.3828125\n",
      "119276.9609375\n",
      "212011.78125\n",
      "141699.765625\n",
      "123593.015625\n",
      "223350.53125\n",
      "131840.796875\n",
      "177985.875\n",
      "266145.6875\n",
      "111988.1328125\n",
      "143789.234375\n",
      "119136.46875\n",
      "209341.796875\n",
      "141904.28125\n",
      "176853.609375\n",
      "231954.265625\n",
      "131971.90625\n",
      "287785.25\n",
      "333720.21875\n",
      "97936.65625\n",
      "258229.90625\n",
      "136049.953125\n",
      "191561.109375\n",
      "101455.796875\n",
      "144936.609375\n",
      "165730.1875\n",
      "154972.734375\n",
      "176051.796875\n",
      "143286.453125\n",
      "134166.75\n",
      "115236.1640625\n",
      "77647.453125\n",
      "142568.078125\n",
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      "159097.1875\n",
      "273361.75\n",
      "264217.0625\n",
      "223939.640625\n",
      "148113.53125\n",
      "174701.6875\n",
      "346165.28125\n",
      "158080.78125\n",
      "312339.40625\n",
      "118565.875\n",
      "195032.78125\n",
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      "117519.0234375\n",
      "144665.09375\n",
      "166637.75\n",
      "149307.484375\n",
      "137234.0\n",
      "125940.6875\n",
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      "84610.8671875\n",
      "81821.6796875\n",
      "87417.2109375\n",
      "221949.21875\n",
      "174128.0625\n",
      "226128.71875\n",
      "160529.515625\n",
      "163541.109375\n",
      "134696.328125\n",
      "128011.1953125\n",
      "170519.625\n",
      "322022.75\n",
      "160030.15625\n",
      "177573.578125\n",
      "309704.78125\n",
      "131288.8125\n",
      "170614.90625\n",
      "127459.8984375\n",
      "145149.328125\n",
      "135884.703125\n",
      "160637.921875\n",
      "132218.65625\n",
      "144866.03125\n",
      "133268.6875\n",
      "137198.578125\n",
      "206529.25\n",
      "96386.3359375\n",
      "286366.34375\n",
      "91541.765625\n",
      "287073.9375\n",
      "149055.0\n",
      "148684.34375\n",
      "184726.84375\n",
      "139012.109375\n",
      "117815.5390625\n",
      "247514.921875\n",
      "318791.375\n",
      "156812.296875\n",
      "242373.890625\n",
      "134649.828125\n",
      "91472.8984375\n",
      "132562.984375\n",
      "309635.5\n",
      "163396.453125\n",
      "134197.109375\n",
      "129563.734375\n",
      "197185.421875\n",
      "244993.546875\n",
      "167432.609375\n",
      "175238.03125\n",
      "330754.5625\n",
      "226859.21875\n",
      "287120.84375\n",
      "88449.4921875\n",
      "138419.125\n",
      "157738.53125\n",
      "200174.609375\n",
      "182806.8125\n",
      "221060.796875\n",
      "145099.65625\n",
      "97038.8671875\n",
      "307683.5625\n",
      "147425.234375\n",
      "152562.84375\n",
      "319951.0\n",
      "208743.203125\n",
      "338812.65625\n",
      "100754.4921875\n",
      "139728.59375\n",
      "147957.90625\n",
      "174802.640625\n",
      "135313.515625\n",
      "129682.421875\n",
      "468304.9375\n",
      "133270.90625\n",
      "126869.25\n",
      "240528.921875\n",
      "260508.6875\n",
      "135281.140625\n",
      "160444.375\n",
      "139356.0\n",
      "270514.15625\n",
      "190377.171875\n",
      "258301.703125\n",
      "472541.28125\n",
      "66105.4765625\n",
      "208344.40625\n",
      "221333.984375\n",
      "141077.5\n",
      "209690.71875\n",
      "349040.40625\n",
      "146183.890625\n",
      "134861.53125\n",
      "208578.46875\n",
      "171920.734375\n",
      "307898.46875\n",
      "322111.625\n",
      "113024.0078125\n",
      "233871.859375\n",
      "195909.0\n",
      "138344.015625\n",
      "289819.59375\n",
      "202260.484375\n",
      "222877.703125\n",
      "191013.09375\n",
      "266510.46875\n",
      "356888.09375\n",
      "125261.4453125\n",
      "172423.109375\n",
      "271868.78125\n",
      "113406.3671875\n",
      "250894.4375\n",
      "354416.75\n",
      "169116.234375\n",
      "408911.71875\n",
      "154064.203125\n",
      "138621.0\n",
      "134335.34375\n",
      "157942.015625\n",
      "140536.390625\n",
      "138164.0\n",
      "100276.96875\n",
      "281487.8125\n",
      "169705.359375\n",
      "36941.20703125\n",
      "216580.234375\n",
      "181348.46875\n",
      "175614.4375\n",
      "112912.9296875\n",
      "113716.9765625\n",
      "164766.015625\n",
      "181670.53125\n",
      "216894.015625\n",
      "129648.4296875\n",
      "151155.171875\n",
      "359983.75\n",
      "148852.390625\n",
      "131593.28125\n",
      "135803.40625\n",
      "232997.84375\n",
      "84088.1953125\n",
      "244361.203125\n",
      "112163.65625\n",
      "133125.203125\n",
      "205264.53125\n",
      "141442.328125\n",
      "124371.7421875\n",
      "188281.125\n",
      "271414.0625\n",
      "119717.4765625\n",
      "117460.8046875\n",
      "140208.546875\n",
      "208324.109375\n",
      "96386.5625\n",
      "117185.3984375\n",
      "174536.40625\n",
      "148652.359375\n",
      "182932.359375\n",
      "58149.90625\n",
      "186098.71875\n",
      "425633.28125\n",
      "121552.0\n",
      "215497.46875\n",
      "231197.8125\n",
      "134399.046875\n",
      "161516.734375\n",
      "102915.734375\n",
      "163602.609375\n",
      "380755.71875\n",
      "142109.546875\n",
      "178590.984375\n",
      "133276.5\n",
      "184855.125\n",
      "202714.671875\n",
      "324400.375\n",
      "143343.625\n",
      "188351.3125\n",
      "144756.390625\n",
      "140357.0625\n",
      "146434.09375\n",
      "99010.328125\n",
      "128364.875\n",
      "178299.96875\n",
      "132888.328125\n",
      "125721.53125\n",
      "243782.671875\n",
      "82200.3203125\n",
      "151743.40625\n",
      "174474.515625\n",
      "110934.0\n",
      "240921.09375\n",
      "234686.9375\n",
      "168219.984375\n",
      "325612.46875\n",
      "124773.0546875\n",
      "203392.171875\n",
      "327240.78125\n",
      "194231.078125\n",
      "188124.71875\n",
      "139031.0625\n",
      "234719.390625\n",
      "188481.984375\n",
      "136579.34375\n",
      "148674.53125\n",
      "76827.3515625\n",
      "397073.65625\n",
      "312160.65625\n",
      "222379.453125\n",
      "315442.4375\n",
      "236157.671875\n",
      "262925.15625\n",
      "137798.546875\n",
      "119931.5703125\n",
      "190309.703125\n",
      "165619.375\n",
      "357914.09375\n",
      "217484.9375\n",
      "144385.71875\n",
      "255248.109375\n",
      "149084.390625\n",
      "270090.8125\n",
      "126969.4609375\n",
      "185331.421875\n",
      "143666.640625\n",
      "\n",
      "CPU times: user 28.3 ms, sys: 0 ns, total: 28.3 ms\n",
      "Wall time: 260 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Invoke the SageMaker endpoint\n",
    "try:\n",
    "    response = runtime.invoke_endpoint(\n",
    "        EndpointName=XGB_endpoint, \n",
    "        ContentType=\"text/csv\", \n",
    "        Body=payload\n",
    "    )\n",
    "\n",
    "    response_body = response[\"Body\"].read().decode()\n",
    "    print(\"Raw response body:\", response_body)  # Print raw response for inspection\n",
    "\n",
    "    # Convert the response body to an array of predictions\n",
    "    test_pred = np.fromstring(response_body, sep='\\n')\n",
    "\n",
    "except Exception as e:\n",
    "    print(\"Error during prediction:\", e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "6cbc60b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([140587.515625  , 208920.453125  ,  83030.9609375 , 233810.203125  ,\n",
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       "       400711.21875   , 132449.53125   , 192363.28125   , 137028.09375   ,\n",
       "       147441.765625  , 153926.78125   , 217172.9375    , 151716.234375  ,\n",
       "       347419.03125   , 163031.53125   , 193339.109375  , 168385.3125    ,\n",
       "       178125.625     , 109803.6640625 , 203618.546875  , 183732.203125  ,\n",
       "       184663.34375   , 172024.828125  , 280403.59375   , 208290.921875  ,\n",
       "       236388.25      , 160715.9375    , 207789.09375   , 113738.21875   ,\n",
       "       212152.71875   , 164967.46875   , 185999.921875  , 270228.53125   ,\n",
       "       138819.96875   , 209794.390625  , 150787.1875    , 133749.        ,\n",
       "       327222.96875   , 241940.265625  ,  99220.296875  , 117751.3203125 ,\n",
       "       146774.640625  , 231655.390625  , 187462.765625  , 135017.328125  ,\n",
       "       128284.2265625 , 122762.546875  , 140996.84375   ,  83765.578125  ,\n",
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       "        83547.5859375 , 227298.515625  , 219058.65625   , 177664.703125  ,\n",
       "       208074.3125    , 135426.5625    , 143984.625     , 236081.203125  ,\n",
       "       118466.921875  , 298753.65625   , 138112.03125   , 142864.71875   ,\n",
       "       169049.453125  , 139151.390625  , 107833.96875   ,  90685.546875  ,\n",
       "       158645.4375    , 110219.03125   , 140915.03125   , 176147.890625  ,\n",
       "       215183.390625  , 159774.546875  , 158167.90625   , 115840.5078125 ,\n",
       "       117757.375     , 150156.953125  , 115133.3125    ,  43221.3671875 ,\n",
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       "       172158.859375  , 152281.        , 185885.125     , 165824.703125  ,\n",
       "       263594.875     , 127557.7578125 , 181043.375     , 128100.1328125 ,\n",
       "       142630.234375  , 149370.        , 124636.2265625 , 204853.078125  ,\n",
       "       324287.875     , 198845.0625    , 164432.03125   , 156315.5       ,\n",
       "       467768.40625   , 187546.140625  , 343045.4375    , 319785.96875   ,\n",
       "       182973.140625  , 135147.796875  , 354999.375     , 194177.453125  ,\n",
       "       279087.46875   , 111138.65625   , 189111.078125  , 130350.2421875 ,\n",
       "       117585.28125   , 186373.71875   , 177902.765625  , 289358.96875   ,\n",
       "       139863.203125  ,  50002.390625  , 168898.71875   , 104339.6484375 ,\n",
       "       209626.15625   ,  95447.5859375 , 189586.03125   , 205520.984375  ,\n",
       "       190356.515625  , 220529.703125  , 177386.28125   , 132685.265625  ,\n",
       "       168015.234375  , 334635.84375   , 137357.4375    , 131526.109375  ,\n",
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       "       188814.921875  , 209696.296875  , 120100.375     , 229297.265625  ,\n",
       "       133618.375     , 103702.7109375 , 248221.265625  , 251645.84375   ,\n",
       "        91064.28125   , 138173.40625   , 117917.3828125 , 119276.9609375 ,\n",
       "       212011.78125   , 141699.765625  , 123593.015625  , 223350.53125   ,\n",
       "       131840.796875  , 177985.875     , 266145.6875    , 111988.1328125 ,\n",
       "       143789.234375  , 119136.46875   , 209341.796875  , 141904.28125   ,\n",
       "       176853.609375  , 231954.265625  , 131971.90625   , 287785.25      ,\n",
       "       333720.21875   ,  97936.65625   , 258229.90625   , 136049.953125  ,\n",
       "       191561.109375  , 101455.796875  , 144936.609375  , 165730.1875    ,\n",
       "       154972.734375  , 176051.796875  , 143286.453125  , 134166.75      ,\n",
       "       115236.1640625 ,  77647.453125  , 142568.078125  , 150139.03125   ,\n",
       "       159097.1875    , 273361.75      , 264217.0625    , 223939.640625  ,\n",
       "       148113.53125   , 174701.6875    , 346165.28125   , 158080.78125   ,\n",
       "       312339.40625   , 118565.875     , 195032.78125   , 196990.15625   ,\n",
       "       117519.0234375 , 144665.09375   , 166637.75      , 149307.484375  ,\n",
       "       137234.        , 125940.6875    , 249345.59375   ,  84610.8671875 ,\n",
       "        81821.6796875 ,  87417.2109375 , 221949.21875   , 174128.0625    ,\n",
       "       226128.71875   , 160529.515625  , 163541.109375  , 134696.328125  ,\n",
       "       128011.1953125 , 170519.625     , 322022.75      , 160030.15625   ,\n",
       "       177573.578125  , 309704.78125   , 131288.8125    , 170614.90625   ,\n",
       "       127459.8984375 , 145149.328125  , 135884.703125  , 160637.921875  ,\n",
       "       132218.65625   , 144866.03125   , 133268.6875    , 137198.578125  ,\n",
       "       206529.25      ,  96386.3359375 , 286366.34375   ,  91541.765625  ,\n",
       "       287073.9375    , 149055.        , 148684.34375   , 184726.84375   ,\n",
       "       139012.109375  , 117815.5390625 , 247514.921875  , 318791.375     ,\n",
       "       156812.296875  , 242373.890625  , 134649.828125  ,  91472.8984375 ,\n",
       "       132562.984375  , 309635.5       , 163396.453125  , 134197.109375  ,\n",
       "       129563.734375  , 197185.421875  , 244993.546875  , 167432.609375  ,\n",
       "       175238.03125   , 330754.5625    , 226859.21875   , 287120.84375   ,\n",
       "        88449.4921875 , 138419.125     , 157738.53125   , 200174.609375  ,\n",
       "       182806.8125    , 221060.796875  , 145099.65625   ,  97038.8671875 ,\n",
       "       307683.5625    , 147425.234375  , 152562.84375   , 319951.        ,\n",
       "       208743.203125  , 338812.65625   , 100754.4921875 , 139728.59375   ,\n",
       "       147957.90625   , 174802.640625  , 135313.515625  , 129682.421875  ,\n",
       "       468304.9375    , 133270.90625   , 126869.25      , 240528.921875  ,\n",
       "       260508.6875    , 135281.140625  , 160444.375     , 139356.        ,\n",
       "       270514.15625   , 190377.171875  , 258301.703125  , 472541.28125   ,\n",
       "        66105.4765625 , 208344.40625   , 221333.984375  , 141077.5       ,\n",
       "       209690.71875   , 349040.40625   , 146183.890625  , 134861.53125   ,\n",
       "       208578.46875   , 171920.734375  , 307898.46875   , 322111.625     ,\n",
       "       113024.0078125 , 233871.859375  , 195909.        , 138344.015625  ,\n",
       "       289819.59375   , 202260.484375  , 222877.703125  , 191013.09375   ,\n",
       "       266510.46875   , 356888.09375   , 125261.4453125 , 172423.109375  ,\n",
       "       271868.78125   , 113406.3671875 , 250894.4375    , 354416.75      ,\n",
       "       169116.234375  , 408911.71875   , 154064.203125  , 138621.        ,\n",
       "       134335.34375   , 157942.015625  , 140536.390625  , 138164.        ,\n",
       "       100276.96875   , 281487.8125    , 169705.359375  ,  36941.20703125,\n",
       "       216580.234375  , 181348.46875   , 175614.4375    , 112912.9296875 ,\n",
       "       113716.9765625 , 164766.015625  , 181670.53125   , 216894.015625  ,\n",
       "       129648.4296875 , 151155.171875  , 359983.75      , 148852.390625  ,\n",
       "       131593.28125   , 135803.40625   , 232997.84375   ,  84088.1953125 ,\n",
       "       244361.203125  , 112163.65625   , 133125.203125  , 205264.53125   ,\n",
       "       141442.328125  , 124371.7421875 , 188281.125     , 271414.0625    ,\n",
       "       119717.4765625 , 117460.8046875 , 140208.546875  , 208324.109375  ,\n",
       "        96386.5625    , 117185.3984375 , 174536.40625   , 148652.359375  ,\n",
       "       182932.359375  ,  58149.90625   , 186098.71875   , 425633.28125   ,\n",
       "       121552.        , 215497.46875   , 231197.8125    , 134399.046875  ,\n",
       "       161516.734375  , 102915.734375  , 163602.609375  , 380755.71875   ,\n",
       "       142109.546875  , 178590.984375  , 133276.5       , 184855.125     ,\n",
       "       202714.671875  , 324400.375     , 143343.625     , 188351.3125    ,\n",
       "       144756.390625  , 140357.0625    , 146434.09375   ,  99010.328125  ,\n",
       "       128364.875     , 178299.96875   , 132888.328125  , 125721.53125   ,\n",
       "       243782.671875  ,  82200.3203125 , 151743.40625   , 174474.515625  ,\n",
       "       110934.        , 240921.09375   , 234686.9375    , 168219.984375  ,\n",
       "       325612.46875   , 124773.0546875 , 203392.171875  , 327240.78125   ,\n",
       "       194231.078125  , 188124.71875   , 139031.0625    , 234719.390625  ,\n",
       "       188481.984375  , 136579.34375   , 148674.53125   ,  76827.3515625 ,\n",
       "       397073.65625   , 312160.65625   , 222379.453125  , 315442.4375    ,\n",
       "       236157.671875  , 262925.15625   , 137798.546875  , 119931.5703125 ,\n",
       "       190309.703125  , 165619.375     , 357914.09375   , 217484.9375    ,\n",
       "       144385.71875   , 255248.109375  , 149084.390625  , 270090.8125    ,\n",
       "       126969.4609375 , 185331.421875  , 143666.640625  ])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293d9863",
   "metadata": {},
   "source": [
    "<a id=\"mae-comparison\"></a>\n",
    "## MAE Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "08dc2c02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE Baseline: 56598.845\n",
      "Test MAE XGBoost: 12491.507\n"
     ]
    }
   ],
   "source": [
    "# MAE Calculation\n",
    "test_mae_XGB = np.mean(np.abs(Y_test - test_pred))\n",
    "test_mae_baseline = np.mean(np.abs(Y_test - np.median(Y_train)))\n",
    "\n",
    "print(\"Test MAE Baseline:\", round(test_mae_baseline, 3))\n",
    "print(\"Test MAE XGBoost:\", round(test_mae_XGB, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c41f1e",
   "metadata": {},
   "source": [
    "<a id=\"prediction-accuracy-test-mae-rmse-and-r2\"></a>\n",
    "## Prediction Accuracy Test: MAE, RMSE and r^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "77739970",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 12491.507\n",
      "Root Mean Square Error (RMSE): 18191.345\n",
      "R-squared (R²): 0.943\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "import numpy as np\n",
    "\n",
    "# Assuming 'test_pred' contains your model predictions and 'Y_test' contains the true values\n",
    "\n",
    "# Calculate Mean Absolute Error (MAE)\n",
    "mae = mean_absolute_error(Y_test, test_pred)\n",
    "print(\"Mean Absolute Error (MAE):\", round(mae, 3))\n",
    "\n",
    "# Calculate Root Mean Square Error (RMSE)\n",
    "rmse = np.sqrt(mean_squared_error(Y_test, test_pred))\n",
    "print(\"Root Mean Square Error (RMSE):\", round(rmse, 3))\n",
    "\n",
    "# Calculate R-squared (R²)\n",
    "r2 = r2_score(Y_test, test_pred)\n",
    "print(\"R-squared (R²):\", round(r2, 3))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625be9a9",
   "metadata": {},
   "source": [
    "<a id=\"report\"></a>\n",
    "# REPORT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a021baa",
   "metadata": {},
   "source": [
    "## In plain English, the metrics for my model can be explained as follows:\n",
    "\n",
    "### - Mean Absolute Error (MAE): 12491.507\n",
    "        On average, the predictions of the house prices are about \\\\$12,491.51 off from their actual market values. \n",
    "        \n",
    "### - Root Mean Square Error (RMSE): 18191.345\n",
    "        The RMSE being \\\\$18,191.35 indicates that the typical error (how much the prediction deviates from the actual price) is usually around this amount. RMSE is higher than MAE, which suggests that there are some predictions with larger errors. This metric penalizes larger errors more heavily than smaller ones.\n",
    "\n",
    "### - R-squared (R²): 0.943\n",
    "        The R² value of 0.943, or 94.3\\%, shows how well the variables I've used in my model explain the variation in house prices. In this case, it suggests that 94.3% of the variability in house prices can be explained by my model. This is quite high, indicating my model fits the data well. \n",
    "        \n",
    "        An R² of 1 means perfect prediction with no error\n",
    "        Since my mode's r square value is 0.943 (pretty close to 1) - that means the model explains 94% of the variance in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c090f13a",
   "metadata": {},
   "source": [
    "# Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "917801d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': '701cf8b3-990d-44f9-966d-7554a8e26baa',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': '701cf8b3-990d-44f9-966d-7554a8e26baa',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '0',\n",
       "   'date': 'Sun, 26 Nov 2023 21:00:40 GMT'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Deleting the endpoint\n",
    "sm.delete_endpoint(EndpointName=XGB_endpoint)"
   ]
  }
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