A1_Analysis_Team_10_BMBAND1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "09f97406",
   "metadata": {},
   "source": [
    "# Investing in the Heartland: Unlocking Ames House Market Potential \n",
    "\n",
    "Sep, 2010"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c146d19a",
   "metadata": {},
   "source": [
    "## Maximize Your Profits by Building in Ames' Thriving Somerset Neighborhood\n",
    "Considering constructing new homes in Ames? Northridge Heights is the only place to look at. As the city has grown and Iowa State University's relevance has increased, Northridge Heights has become a sought-after location for future developments. It is a desirable location for builders to invest in due to its large, well-kept properties, welcoming atmosphere, and top-notch schools. For any developer wishing to take advantage of the expanding market in Ames, developing new homes in Northridge Heights promises great demand and speedy sales. Indeed, the area has an average sale price of more than 177k, making it one of the most desirable neighborhoods of the city. Because of the properties sizing, Northridge Heights is a sought-after neighborhood for families looking for a welcoming place to call home. Moreover, the neighborhood offers parks, shopping centers, and restaurants within walking distance (Hunziker Development). \n",
    "It is also important to note that following the sales boom of 2009, customers were willing to buy older homes at the same price as new buildings to secure a property in the area.  This suggests that the neighborhood is highly demanded and bound to grow as new constructions spring up. \n",
    "\n",
    "## Boost Your Sales: Build New Houses in Ames with Features that Sell\n",
    "To construct new homes in Ames, it is essential to keep an eye on common varieties and amenities. House structure seems to be extremely crucial to pricing in this hidden Heartland town. According to historical trends, a spacious three-car garage with capacity for additional activities, exceptional quality of the materials used and a very spacious house maximize the sales profit potential. Together with these characteristics, four rooms to accommodate large families are the most essential features that enable properties to sell more quickly and for a higher price. By including these features into your newly developed properties in Northridge Heights, you can ensure that your properties will be stand out on the market. These will indeed appeal to potential buyers, and they will sell quickly. <i>See appendix 1 for a the relative role each feature plays in predicting the houses sale price.</i>\n",
    "\n",
    "## Attracting the Perfect Buyers: Marketing Your Ames' Constructions to Families and Professionals\n",
    "Ames offers a close-knit community that preserves recreational activities and a young energy from students and academics, making it the ideal setting to develop pure creativity (Best Places to Live in the US in 2022, 2022). Consider large families and professionals desiring a peaceful area with simple access to first-rate facilities when attempting to choose the ideal customer for your Northridge Heights new property. Particularly, Northridge Heights is a wonderful neighborhood for parents seeking a healthy environment in which to raise their children, thanks to its excellent schools, parks, and community activities. Also, young professionals looking for a handy spot to plant roots may find this community to be intriguing. Evaluate employing mailing lists, adverts in local newspapers, and billboards in the North-South Railroad area to promote your new developments and highlight their features and amenities in order to attract these suitable consumers. <i>See appendix 2 for the impact each neighborhood has on price.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e244f92",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d963f74d",
   "metadata": {},
   "source": [
    "Real estate development is a highly competitive sector that needs meticulous planning, exhaustive research, and an in-depth knowledge of the market for success. As a real estate developer, you must constantly be on the lookout for advantageous investment options that might yield a substantial return. This is especially true in a market as dynamic as Ames, Iowa, where property prices can change dramatically based on a variety of reasons. Using cutting-edge statistical methodologies, a comprehensive study of the Ames, Iowa real estate market was necessary to determine the most influential factors affecting property values. This extensive research provides valuable insight into the major elements driving Ames real estate values, such as property size, number of bedrooms, and general quality and condition. By adopting the insights provided in this research, you will be able to find lucrative investment opportunities and optimize your investing methods for the highest possible results. Don't pass out on the potential of the Ames real estate market; read on to learn how to maximize it. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eabfd57e",
   "metadata": {},
   "source": [
    "## The Outcomes "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e7beeea",
   "metadata": {},
   "source": [
    "### Ames' Thriving Suburban Market: New Frontiers\n",
    "\n",
    "According to a Real Estate research, Ames is moving farther and farther out into the suburbs (Olsen B. C. & Stokes, J. R., 2015) . This is because there is less room for new development due to the high concentration of real estate in more central locations, such as Old Town. Investors are therefore seeking for fresh possibilities in the nearby, hitherto non-residential neighborhoods. \n",
    "Among these, Northridge Heights is particularly intriguing because it has experienced significant growth recently and is highly coveted by both young and large families. As a result, it has the second-highest average price in the entire city.\n",
    "\n",
    "On the other hand, College Creek and North Ames stand out when examining the number of properties sold over the last two years. The former was not taken into consideration because, being a college neighborhood, it has a high turnover rate and is not an attractive location for families because of the high student population. On the other hand, North Ames has experienced very strong sales in the most recent era; as a result, it's highly possible that the market will be saturated by the time of construction. For this reason, it is wise to make investments in a growing area where families will be more likely to purchase and value a high-quality structure.\n",
    " <i>See appendix 3 and 4 for sales figures</i>\n",
    "\n",
    "### Observe Emerging Patterns in Features\n",
    "\n",
    "Based on the analysis of Northridge Heights housing market, the overall quality of a home has a substantial impact on its price. Particular emphasis is placed on the use of exceptionally durable, high-quality materials. In addition, a spacious three-car garage that provides room for additional hobbies, such as woodworking or a gym, is highly prized by the market and can improve the property's worth. Houses with four rooms are in high demand among bigger American families. Furthermore, the market rewards bigger residences, favoring new styles such as an open space connecting the kitchen and living room, where significant memories may be created with loved ones. <i> See appendix 1</i>\n",
    "\n",
    "A larger living area raises the price, with a recommended range of 1300 to 2500 square feet for new constructions. Similarly, an increase in total basement square footage, particularly favored by Americans to develop recreational activities, is decisive, and should be contained between 1000 and 2000 square feet. \n",
    "Lastly, extending the square footage on the first floor to create extra-large bedrooms boosts the price, with a proposed range of 1000 to 2150 square feet. <i>See appendices 5, 6 and 7.</i>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7bfb527",
   "metadata": {},
   "source": [
    "## The Conclusion "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e8df15",
   "metadata": {},
   "source": [
    "This model will allow you to create residential masterpieces that will not only meet but exceed the expectations of the clientele of the future. The real estate market in Ames is changing and it's time to align with the times and follow new trends. In fact, carefully assessing consumer preferences will empower you to maximize the likelihood of selling your buildings and making the highest profit. \n",
    "To build a more sustainable business, consider investing in the Northridge Heights area. Indeed, believing in emerging neighborhoods will give you the opportunity to target families who value open spaces and a lot large enough to accommodate multiple rooms. In addition, the area has gained a lot of interest over time and properties are always in high demand, resulting in a considerable increase in market price. Ames is the place to grow. Are you ready to join us?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e5790f",
   "metadata": {},
   "source": [
    "# Assumptions "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f58f4dfb",
   "metadata": {},
   "source": [
    "The following presumptions were adhered to in order to offer a thorough analysis of the Ames housing market: \n",
    "\n",
    "1) The dataset offered for analysis is presumed to be an exhaustive list of all homes sold in Ames between 2006 and 2010. The dataset is thought to be extensive and trustworthy because it was carefully compiled and checked for precision. Real estate developers can use the general trends and patterns indicated by this dataset as a strong foundation for making decisions about their investments in Ames.                              \n",
    "\n",
    "2) Given that the dataset contains information up to July 2010, it is believed that this report was written in September 2010. September is noteworthy because it signals the start of the work cycle after summer vacations for businesses and students, making it a crucial period for businesses to assess their potential for growth and investment. \n",
    "\n",
    "3) Because of Iowa State University's expansion and the city's alluring amenities, the real estate market in Ames has remained steady and resilient despite the economic downturn. By making an investment today, real estate developers may take advantage of the rising demand and set themselves up for long-term success. This window of opportunity might not exist indefinitely, and more competition might result in greater costs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43197497",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9901bb33",
   "metadata": {},
   "source": [
    "The selling prices of residential properties in Ames, Iowa, were examined and forecasted using the House Prices dataset. For 1,460, houses sold between 2006 and 2010, the dataset contains 79 explanatory factors and 1 target variable: SalePrice. Both investors and real estate professionals can benefit from this dataset because it has the potential to forecast property prices.  The dataset can also be used to pinpoint crucial elements that increase a home's worth and enables investors to make better choices when edifying the chosen lot. The dataset can be accessed in CSV format from the Kaggle competition website.\n",
    "The dataset includes details on a number of features of the sold properties, including their size, number of bathrooms and bedrooms, and general quality. The dataset also includes an assortment of categorical factors, such as the area where the house is situated. To better understand the dataset, a preliminary examination was performed.  This involved using multiple plots to visualize the data, including scatter plots, heat maps, and boxplots. During this process, outliers and missing values were also found and treated appropriately. The dataset's short 5-year span was the major limitation when drawing conclusions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b92a9da",
   "metadata": {},
   "source": [
    "# Problem Solving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eda644d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:03.935108Z",
     "start_time": "2023-02-27T04:28:56.636627Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np  # Importing NumPy package for numerical computations\n",
    "import pandas as pd  # Importing Pandas package for data manipulation and analysis\n",
    "\n",
    "from sklearn.linear_model import ElasticNet, Lasso, GammaRegressor, Ridge, RidgeCV, LinearRegression  # Importing ElasticNet, GammaRegressor and Lasso linear regression models from scikit-learn\n",
    "import lightgbm as lgb  # Importing LightGBM package for gradient boosting\n",
    "import xgboost as xg  # Importing XGBoost package for gradient boosting\n",
    "\n",
    "import seaborn as sns  # Importing Seaborn package for visualization\n",
    "import matplotlib.pyplot as plt  # Importing Matplotlib package for visualization\n",
    "\n",
    "from scipy.stats import probplot, skew  # Importing probability plot, skewness function from SciPy\n",
    "from sklearn.metrics import mean_squared_error, make_scorer, r2_score  # Importing mean squared error and make scorer functions from scikit-learn\n",
    "from sklearn.preprocessing import OrdinalEncoder, StandardScaler  # Importing ordinal encoder and standard scaler from scikit-learn\n",
    "from sklearn.model_selection import cross_validate, GridSearchCV, KFold, RepeatedKFold, train_test_split, cross_val_score  # Importing cross-validation functions from scikit-learn\n",
    "from sklearn.feature_selection import f_classif, mutual_info_classif, mutual_info_regression  # Importing feature selection functions from scikit-learn\n",
    "\n",
    "from sklearn.svm import LinearSVR, SVR  # Importing Support Vector Regression models from scikit-learn\n",
    "from sklearn.kernel_ridge import KernelRidge  # Importing Kernel Ridge Regression model from scikit-learn\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, VotingRegressor  # Importing Ensemble Regression models from scikit-learn\n",
    "from sklearn.tree import DecisionTreeClassifier # importing Decision tree classifier\n",
    "from sklearn.neighbors import KNeighborsRegressor #importing KNeighborsRegressor\n",
    "\n",
    "SEED=0  # Setting random seed for reproducibility\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1236f240",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:03.951107Z",
     "start_time": "2023-02-27T04:29:03.940689Z"
    }
   },
   "outputs": [],
   "source": [
    "# this part ignores all the warnings \n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3c1b2b",
   "metadata": {},
   "source": [
    "## Importing Data\n",
    "\n",
    "CSV files containing test and train data are read from the same directory as the notebook. These come from kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "39cc2594",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:04.120607Z",
     "start_time": "2023-02-27T04:29:03.955108Z"
    }
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
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       "<p>5 rows × 80 columns</p>\n",
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      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig  ... PoolArea PoolQC Fence MiscFeature  \\\n",
       "0         Lvl    AllPub    Inside  ...        0    NaN   NaN         NaN   \n",
       "1         Lvl    AllPub       FR2  ...        0    NaN   NaN         NaN   \n",
       "2         Lvl    AllPub    Inside  ...        0    NaN   NaN         NaN   \n",
       "3         Lvl    AllPub    Corner  ...        0    NaN   NaN         NaN   \n",
       "4         Lvl    AllPub       FR2  ...        0    NaN   NaN         NaN   \n",
       "\n",
       "  MiscVal MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0       0      2    2008        WD         Normal     208500  \n",
       "1       0      5    2007        WD         Normal     181500  \n",
       "2       0      9    2008        WD         Normal     223500  \n",
       "3       0      2    2006        WD        Abnorml     140000  \n",
       "4       0     12    2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('./house-prices-advanced-regression-techniques/train.csv')   #read the train file\n",
    "df_test = pd.read_csv(\"./house-prices-advanced-regression-techniques/test.csv\")     #read the test file\n",
    "\n",
    "df_train_b = df_train.drop(['Id'], axis=1)  #create a copy and drop all the ID\n",
    "df_test_b = df_test.drop(['Id'], axis=1)    #create a copy and drop all the ID\n",
    "df_train_b.head()                           #look at the train data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10cfcbf5",
   "metadata": {},
   "source": [
    "## EDA\n",
    "- Preliminary EDA is undertaken to find values that are missing in both the test and train datasets.\n",
    "- Columns containing these missing values are identified.\n",
    "- The correlation between features is analyzed.\n",
    "- Numerical fields are assessed to check if they are normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73dfcb86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:04.197559Z",
     "start_time": "2023-02-27T04:29:04.125244Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "(TRAIN) Number of missing values:\n",
      "========================================\n",
      "MSZoning           4\n",
      "LotFrontage      227\n",
      "Alley           1352\n",
      "Utilities          2\n",
      "Exterior1st        1\n",
      "Exterior2nd        1\n",
      "MasVnrType        16\n",
      "MasVnrArea        15\n",
      "BsmtQual          44\n",
      "BsmtCond          45\n",
      "BsmtExposure      44\n",
      "BsmtFinType1      42\n",
      "BsmtFinSF1         1\n",
      "BsmtFinType2      42\n",
      "BsmtFinSF2         1\n",
      "BsmtUnfSF          1\n",
      "TotalBsmtSF        1\n",
      "BsmtFullBath       2\n",
      "BsmtHalfBath       2\n",
      "KitchenQual        1\n",
      "Functional         2\n",
      "FireplaceQu      730\n",
      "GarageType        76\n",
      "GarageYrBlt       78\n",
      "GarageFinish      78\n",
      "GarageCars         1\n",
      "GarageArea         1\n",
      "GarageQual        78\n",
      "GarageCond        78\n",
      "PoolQC          1456\n",
      "Fence           1169\n",
      "MiscFeature     1408\n",
      "SaleType           1\n",
      "dtype: int64\n",
      "========================================\n",
      "(TEST) Number of missing values:\n",
      "========================================\n",
      "LotFrontage      259\n",
      "Alley           1369\n",
      "MasVnrType         8\n",
      "MasVnrArea         8\n",
      "BsmtQual          37\n",
      "BsmtCond          37\n",
      "BsmtExposure      38\n",
      "BsmtFinType1      37\n",
      "BsmtFinType2      38\n",
      "Electrical         1\n",
      "FireplaceQu      690\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "PoolQC          1453\n",
      "Fence           1179\n",
      "MiscFeature     1406\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#this function checks all the mission values in the test and train data\n",
    "\n",
    "def check_missing_values(train, test):\n",
    "    print(\"=\"*40)\n",
    "    print(f\"(TRAIN) Number of missing values:\")\n",
    "    print(\"=\"*40)\n",
    "    print(train.isna().sum()[train.isna().sum()>0])\n",
    "    print(\"=\"*40)\n",
    "    print(f\"(TEST) Number of missing values:\")\n",
    "    print(\"=\"*40)\n",
    "    print(test.isna().sum()[test.isna().sum()>0])    \n",
    "    return\n",
    "\n",
    "check_missing_values(df_test_b, df_train_b)  #calling the abve function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d90314a4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:04.213559Z",
     "start_time": "2023-02-27T04:29:04.201019Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "Columns to fill missing values:\n",
      "========================================\n",
      "{'Bsmt': ['BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'], 'MasVnr': ['MasVnrType', 'MasVnrArea'], 'Pool': ['PoolArea', 'PoolQC'], 'Fireplace': ['Fireplaces', 'FireplaceQu'], 'Misc': ['MiscFeature', 'MiscVal'], 'Garage': ['GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual', 'GarageCond']}\n"
     ]
    }
   ],
   "source": [
    "HSE_FEAT = [\"Bsmt\", \"MasVnr\", \"Pool\", \"Fireplace\", \"Misc\", \"Garage\"]    #creating a list of prefix\n",
    "\n",
    "RELATED_COL = {                                                         #look at the columns related to the prefix\n",
    "    feat: [col for col in df_train_b if feat in col]\n",
    "    for feat in HSE_FEAT\n",
    "}\n",
    "\n",
    "print(\"=\"*40)\n",
    "print(\"Columns to fill missing values:\")\n",
    "print(\"=\"*40)\n",
    "print(RELATED_COL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe902410",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:05.133761Z",
     "start_time": "2023-02-27T04:29:04.217039Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x3000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "****INFO****\n",
      "count      1460\n",
      "unique       25\n",
      "top       NAmes\n",
      "freq        225\n",
      "Name: Neighborhood, dtype: object\n",
      "\n",
      "****VALUE COUNTS****\n",
      "NAmes      225\n",
      "CollgCr    150\n",
      "OldTown    113\n",
      "Edwards    100\n",
      "Somerst     86\n",
      "Gilbert     79\n",
      "NridgHt     77\n",
      "Sawyer      74\n",
      "NWAmes      73\n",
      "SawyerW     59\n",
      "BrkSide     58\n",
      "Crawfor     51\n",
      "Mitchel     49\n",
      "NoRidge     41\n",
      "Timber      38\n",
      "IDOTRR      37\n",
      "ClearCr     28\n",
      "StoneBr     25\n",
      "SWISU       25\n",
      "MeadowV     17\n",
      "Blmngtn     17\n",
      "BrDale      16\n",
      "Veenker     11\n",
      "NPkVill      9\n",
      "Blueste      2\n",
      "Name: Neighborhood, dtype: int64\n",
      "\n",
      "****VALUE AVG SALE PRICE****\n",
      "Neighborhood\n",
      "MeadowV     98576.470588\n",
      "IDOTRR     100123.783784\n",
      "BrDale     104493.750000\n",
      "BrkSide    124834.051724\n",
      "Edwards    128219.700000\n",
      "OldTown    128225.300885\n",
      "Sawyer     136793.135135\n",
      "Blueste    137500.000000\n",
      "SWISU      142591.360000\n",
      "NPkVill    142694.444444\n",
      "NAmes      145847.080000\n",
      "Mitchel    156270.122449\n",
      "SawyerW    186555.796610\n",
      "NWAmes     189050.068493\n",
      "Gilbert    192854.506329\n",
      "Blmngtn    194870.882353\n",
      "CollgCr    197965.773333\n",
      "Crawfor    210624.725490\n",
      "ClearCr    212565.428571\n",
      "Somerst    225379.837209\n",
      "Veenker    238772.727273\n",
      "Timber     242247.447368\n",
      "StoneBr    310499.000000\n",
      "NridgHt    316270.623377\n",
      "NoRidge    335295.317073\n",
      "Name: SalePrice, dtype: float64\n",
      "\n",
      "****TEST INFO****\n",
      "count      1459\n",
      "unique       25\n",
      "top       NAmes\n",
      "freq        218\n",
      "Name: Neighborhood, dtype: object\n",
      "\n",
      "****VALUE COUNTS****\n",
      "NAmes      218\n",
      "OldTown    126\n",
      "CollgCr    117\n",
      "Somerst     96\n",
      "Edwards     94\n",
      "NridgHt     89\n",
      "Gilbert     86\n",
      "Sawyer      77\n",
      "SawyerW     66\n",
      "Mitchel     65\n",
      "NWAmes      58\n",
      "IDOTRR      56\n",
      "Crawfor     52\n",
      "BrkSide     50\n",
      "Timber      34\n",
      "NoRidge     30\n",
      "StoneBr     26\n",
      "SWISU       23\n",
      "MeadowV     20\n",
      "ClearCr     16\n",
      "BrDale      14\n",
      "NPkVill     14\n",
      "Veenker     13\n",
      "Blmngtn     11\n",
      "Blueste      8\n",
      "Name: Neighborhood, dtype: int64\n",
      "\n",
      "Only in Train: []\n",
      "Only in Test: []\n"
     ]
    }
   ],
   "source": [
    "# Function to perform EDA for each feature\n",
    "def analyse_feature(feature_name):\n",
    "    \n",
    "    sns.jointplot(data=df_train_b, y=\"SalePrice\", x=feature_name)  #setting up a joint plot between sale price and the feature\n",
    "    plt.figure(figsize=(20, 30))\n",
    "    plt.show()\n",
    "    print(\"\\n****INFO****\")\n",
    "    print(df_train_b[feature_name].describe()) #descriptive stats for the feature\n",
    "    print(\"\\n****VALUE COUNTS****\")\n",
    "    print(df_train_b[feature_name].value_counts(dropna=False)) #gives the value counts for the unique values in the feature\n",
    "    print(\"\\n****VALUE AVG SALE PRICE****\")\n",
    "    print(df_train_b.groupby(feature_name)['SalePrice'].mean().sort_values()) #mean sales price for unique value of the feature\n",
    "    if df_train_b[feature_name].dtype!=\"O\":\n",
    "        print(\"\\nSkewness:\",str(skew(df_train_b[feature_name]))) #checkes the skewness of the feature\n",
    "\n",
    "    print(\"\\n****TEST INFO****\")\n",
    "    print(df_test_b[feature_name].describe()) #descriptive stats for the feature\n",
    "    print(\"\\n****VALUE COUNTS****\")\n",
    "    print(df_test_b[feature_name].value_counts()) #gives the value counts for the unique values in the feature\n",
    "    \n",
    "    #checks if the data is only in train DF\n",
    "    print(\"\\nOnly in Train: \"+str(list(set(df_train_b[feature_name].value_counts().index.values) - set(df_test_b[feature_name].value_counts().index.values))))\n",
    "    #checks if the data is only in train DF\n",
    "    print(\"Only in Test: \"+ str(list(set(df_test_b[feature_name].value_counts().index.values) - set(df_train_b[feature_name].value_counts().index.values))))\n",
    "    \n",
    "analyse_feature(\"Neighborhood\")  #enter the feature name to look at the EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e9ec50a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:05.953906Z",
     "start_time": "2023-02-27T04:29:05.138274Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2500x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# calculate correlation matrix\n",
    "correlation = df_train_b.corr().round(2)[-1:-2:-1]\n",
    "\n",
    "# transforming correlations to absolute values\n",
    "correlation.loc[ : , 'SalePrice' ].apply(func = abs).sort_values(ascending = False)\n",
    "\n",
    "# create heatmap\n",
    "fig, ax = plt.subplots(figsize=(25, 2))\n",
    "sns.heatmap(correlation, annot=True, cmap='Blues', square=True, linewidths=1, ax=ax)\n",
    "\n",
    "# set axis labels and title\n",
    "ax.set_xlabel('Features')\n",
    "ax.set_ylabel('Target')\n",
    "ax.set_title('Correlation Matrix')\n",
    "\n",
    "# rotate x-axis labels\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a2f2a33",
   "metadata": {},
   "source": [
    "Here we can see that the highly correlated features are OverallQuality, GrLivArea, GarageCars, TotalBsmtSF, FullBath, YearBuilt and first floor SF.\n",
    "Initial assumption is that these feature will have huge impact on predicting the price. We need to furture look at the linearity of these features with the sales price to further understand their relationship with the sale price"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b518fcc",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "\n",
    "- Missing values are filled with approporiate substitutes.\n",
    "- Outliers are identified and adjusted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab65072c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:06.032220Z",
     "start_time": "2023-02-27T04:29:05.957426Z"
    }
   },
   "outputs": [],
   "source": [
    "#AFTER ANALYSING EACH COLUMN -> FILL MISSING VALUES\n",
    "df_test_b['MSZoning']=df_test_b['MSZoning'].fillna(\"C (all)\")   \n",
    "df_test_b['GarageCars']=df_test_b['GarageCars'].fillna(0) \n",
    "df_test_b['GarageArea']=df_test_b['GarageArea'].fillna(0) \n",
    "df_test_b['Functional']=df_test_b['Functional'].fillna(\"Typ\") \n",
    "df_test_b['SaleType']=df_test_b['SaleType'].fillna(\"WD\") \n",
    "df_test_b['SaleCondition']=df_test_b['SaleCondition'].fillna(\"Normal\")\n",
    "df_train_b['Fence']=df_train_b['Fence'].fillna(\"None\") \n",
    "df_test_b['Fence']=df_test_b['Fence'].fillna(\"None\") \n",
    "df_train_b['Electrical'] = df_train_b['Electrical'].fillna(\"SBrkr\")\n",
    "df_train_b['FireplaceQu'] = df_train_b['FireplaceQu'].fillna(\"None\")\n",
    "df_test_b['FireplaceQu'] = df_test_b['FireplaceQu'].fillna(\"None\")\n",
    "df_train_b['GarageType'] = df_train_b['GarageType'].fillna(\"None\")\n",
    "df_test_b['GarageType'] = df_test_b['GarageType'].fillna(\"None\")\n",
    "df_train_b['GarageQual'] = df_train_b['GarageQual'].fillna(\"None\")\n",
    "df_test_b['GarageQual'] = df_test_b['GarageQual'].fillna(\"None\")\n",
    "df_train_b['GarageCond'] = df_train_b['GarageCond'].fillna(\"None\")\n",
    "df_test_b['GarageCond'] = df_test_b['GarageCond'].fillna(\"None\")\n",
    "df_train_b['GarageFinish'] = df_train_b['GarageFinish'].fillna(\"None\")\n",
    "df_test_b['GarageFinish'] = df_test_b['GarageFinish'].fillna(\"None\")\n",
    "df_test_b['Exterior1st'] = df_test_b['Exterior1st'].fillna(\"VinylSd\")\n",
    "df_test_b['Exterior2nd']= df_test_b['Exterior2nd'].fillna(\"VinylSd\")\n",
    "df_train_b['MasVnrType'] = df_train_b['MasVnrType'].fillna(\"None\")\n",
    "df_test_b['MasVnrType'] = df_test_b['MasVnrType'].fillna(\"None\")\n",
    "df_train_b['MasVnrArea'] = df_train_b['MasVnrArea'].fillna(0)\n",
    "df_test_b['MasVnrArea'] = df_test_b['MasVnrArea'].fillna(0)\n",
    "df_test_b['BsmtHalfBath']=df_test_b['BsmtHalfBath'].fillna(0) \n",
    "df_test_b['BsmtFullBath']=df_test_b['BsmtFullBath'].fillna(0) \n",
    "df_test_b['KitchenQual']=df_test_b['KitchenQual'].fillna(\"Gd\") \n",
    "df_test_b['TotalBsmtSF']=df_test_b['TotalBsmtSF'].fillna(0) \n",
    "df_test_b['BsmtUnfSF']=df_test_b['BsmtUnfSF'].fillna(0)\n",
    "df_test_b['BsmtFinSF1']=df_test_b['BsmtFinSF1'].fillna(0) \n",
    "df_test_b['BsmtFinSF2']= df_test_b['BsmtFinSF2'].fillna(0) \n",
    "df_test_b['BsmtQual']=df_test_b['BsmtQual'].fillna(\"None\") \n",
    "df_train_b['BsmtQual']=df_train_b['BsmtQual'].fillna(\"None\")\n",
    "df_test_b['BsmtCond']= df_test_b['BsmtCond'].fillna(\"None\")\n",
    "df_train_b['BsmtCond']= df_train_b['BsmtCond'].fillna(\"None\")\n",
    "df_test_b['BsmtExposure']= df_test_b['BsmtExposure'].fillna(\"None\") \n",
    "df_train_b['BsmtExposure']= df_train_b['BsmtExposure'].fillna(\"None\") \n",
    "df_test_b['Utilities']= df_test_b['Utilities'].fillna(\"AllPub\") \n",
    "df_train_b['GarageYrBlt'] = df_train_b['GarageYrBlt'].fillna(1895)\n",
    "df_test_b['GarageYrBlt'] = df_test_b['GarageYrBlt'].fillna(1895)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7bd2d8e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:06.079713Z",
     "start_time": "2023-02-27T04:29:06.035206Z"
    }
   },
   "outputs": [],
   "source": [
    "#AFTER ANALYSING EACH COLUMN -> Fixing Outliers\n",
    "df_test_b.loc[df_test.GarageYrBlt==2207,'GarageYrBlt'] = 2007 \n",
    "df_test_b.loc[df_test_b.LotArea==1533,'LotFrontage'] = 21 \n",
    "df_train_b.loc[df_train_b.LotArea>40000,'LotArea'] = 40000\n",
    "df_test_b.loc[df_test_b.LotArea>40000,'LotArea'] = 40000\n",
    "df_train_b.loc[df_train_b.LotFrontage>150,'LotFrontage'] = 150\n",
    "df_test_b.loc[df_test_b.LotFrontage>150,'LotFrontage'] = 150\n",
    "df_train_b.loc[df_train_b[\"1stFlrSF\"]>3000,'1stFlrSF'] = 3000\n",
    "df_test_b.loc[df_test_b[\"1stFlrSF\"]>3000,'1stFlrSF'] = 3000\n",
    "df_train_b.loc[df_train_b[\"GrLivArea\"]>4000,'GrLivArea'] = 4000\n",
    "df_test_b.loc[df_test_b[\"GrLivArea\"]>4000,'GrLivArea'] = 4000\n",
    "df_train_b.loc[df_train_b[\"TotRmsAbvGrd\"]>12,'TotRmsAbvGrd'] = 12\n",
    "df_test_b.loc[df_test_b[\"TotRmsAbvGrd\"]>12,'TotRmsAbvGrd'] = 12\n",
    "df_train_b.loc[df_train_b[\"BsmtFinSF1\"]>2200,'BsmtFinSF1'] = 2200\n",
    "df_test_b.loc[df_test_b[\"BsmtFinSF1\"]>2200,'BsmtFinSF1'] = 2200\n",
    "df_train_b.loc[df_train_b[\"TotalBsmtSF\"]>2500,'TotalBsmtSF'] = 2500\n",
    "df_test_b.loc[df_test_b[\"TotalBsmtSF\"]>2500,'TotalBsmtSF'] = 2500\n",
    "df_train_b.loc[df_train_b[\"GarageCars\"]>3,'GarageCars'] = 3\n",
    "df_test_b.loc[df_test_b[\"GarageCars\"]>3,'GarageCars'] = 3\n",
    "df_train_b.loc[df_train_b[\"GarageArea\"]>1000,'GarageArea'] = 1000\n",
    "df_test_b.loc[df_test_b[\"GarageArea\"]>1000,'GarageArea'] = 1000\n",
    "\n",
    "#Simple Linear Reg to fill missing LotFrontage values\n",
    "df_test_b.loc[df_test_b[\"LotFrontage\"].isnull(),[\"LotFrontage\"]]=(df_test_b.loc[df_test_b[\"LotFrontage\"].isnull(),[\"LotArea\"]]*0.00885-15.17).values\n",
    "df_train_b.loc[df_train_b[\"LotFrontage\"].isnull(),[\"LotFrontage\"]]=(df_train_b.loc[df_train_b[\"LotFrontage\"].isnull(),[\"LotArea\"]]*0.00885-15.17).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3b7ec09",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:06.127170Z",
     "start_time": "2023-02-27T04:29:06.086673Z"
    }
   },
   "outputs": [],
   "source": [
    "#Update some values that is not exist one of two df_trainsets\n",
    "df_test_b['MSSubClass'] = df_test_b['MSSubClass'].replace([\"150\"], [\"160\"])\n",
    "df_train_b['HouseStyle'] = df_train_b['HouseStyle'].replace([\"2.5Fin\"], [\"2Story\"])\n",
    "df_train_b['Exterior1st'] = df_train_b['Exterior1st'].replace(['Stone', 'ImStucc'],['CemntBd', 'Stucco'])\n",
    "df_train_b['Electrical'] = df_train_b['Electrical'].replace([\"Mix\",\"FuseF\",\"FuseP\"], [\"SBrkr\",\"FuseA\",\"FuseA\"])\n",
    "df_test_b['Electrical'] = df_test_b['Electrical'].replace([\"Mix\",\"FuseF\",\"FuseP\"], [\"SBrkr\",\"FuseA\",\"FuseA\"])\n",
    "df_test_b['BsmtCond'] = df_test_b['BsmtCond'].replace([\"Ex\"], [\"Gd\"])\n",
    "df_train_b['GarageQual'] = df_train_b['GarageQual'].replace([\"Ex\"], [\"Gd\"])\n",
    "df_train_b['GarageCond'] = df_train_b['GarageCond'].replace([\"Ex\"], [\"Gd\"])\n",
    "df_test_b['GarageCond'] = df_test_b['GarageCond'].replace([\"Ex\"], [\"Gd\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60b1bc2",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "\n",
    "- New features are created.\n",
    "- Useless Features are dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18b4d0b7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:06.173817Z",
     "start_time": "2023-02-27T04:29:06.131146Z"
    }
   },
   "outputs": [],
   "source": [
    "#Feature Extraction (Converting Years to Ages)\n",
    "df_train_b[\"Age\"] = 2011 - df_train_b[\"YearBuilt\"]\n",
    "df_test_b[\"Age\"] = 2011 - df_test_b[\"YearBuilt\"]\n",
    "\n",
    "df_train_b[\"RemodAfter\"] = df_train_b[\"YearRemodAdd\"] - df_train_b[\"YearBuilt\"]\n",
    "df_train_b[\"AgeRemodAdd\"] = 2011 - df_train_b[\"YearRemodAdd\"]\n",
    "\n",
    "df_test_b[\"RemodAfter\"] = df_test_b[\"YearRemodAdd\"] - df_test_b[\"YearBuilt\"]\n",
    "df_test_b.loc[df_test_b.RemodAfter<0,'RemodAfter'] = 0\n",
    "df_test_b[\"AgeRemodAdd\"] = 2011 - df_test_b[\"YearRemodAdd\"]\n",
    "\n",
    "df_train_b[\"Age_Sold\"] = df_train_b[\"YrSold\"] - df_train_b[\"YearBuilt\"] \n",
    "df_test_b[\"Age_Sold\"] = df_test_b[\"YrSold\"] - df_test_b[\"YearBuilt\"]\n",
    "df_test_b.loc[df_test_b.Age_Sold<0,'Age_Sold'] = 0\n",
    "\n",
    "df_train_b[\"Sold_before\"] = 2011 - df_train_b[\"YrSold\"]\n",
    "df_test_b[\"Sold_before\"] = 2011 - df_test_b[\"YrSold\"] \n",
    "\n",
    "df_train_b['Age_Garage'] = 2011 - df_train_b['GarageYrBlt']\n",
    "df_test_b['Age_Garage'] = 2011 - df_test_b['GarageYrBlt']\n",
    "df_train_b['Age_Garage_Sold'] = df_train_b[\"YrSold\"] - df_train_b['GarageYrBlt']\n",
    "df_test_b['Age_Garage_Sold'] = df_test_b[\"YrSold\"] - df_test_b['GarageYrBlt']\n",
    "\n",
    "#adding all the porches together to get totalporch\n",
    "df_train_b['TotalPorch']=df_train_b['EnclosedPorch'] + df_train_b['OpenPorchSF'] + df_train_b['ScreenPorch'] + df_train_b['3SsnPorch'] \n",
    "df_test_b['TotalPorch']=df_test_b['EnclosedPorch'] + df_test_b['OpenPorchSF'] + df_test_b['ScreenPorch'] + df_test_b['3SsnPorch'] \n",
    "\n",
    "#adding all the baths together \n",
    "df_train_b['TotalBath'] = df_train_b['FullBath']+ (df_train_b['HalfBath']*0.5)+ df_train_b['BsmtFullBath']+ (df_train_b['BsmtHalfBath']*0.5)\n",
    "df_test_b['TotalBath'] = df_test_b['FullBath']+ (df_test_b['HalfBath']*0.5)+ df_test_b['BsmtFullBath']+ (df_test_b['BsmtHalfBath']*0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a1291acd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:06.204762Z",
     "start_time": "2023-02-27T04:29:06.178295Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train_b[\"Has2ndFlr\"] = df_train_b[\"2ndFlrSF\"].apply(lambda x: \"Yes\" if x>0 else \"No\") #check if there is 2nd floor in the house\n",
    "df_test_b[\"Has2ndFlr\"] = df_test_b[\"2ndFlrSF\"].apply(lambda x: \"Yes\" if x>0 else \"No\")   #check if there is 2nd floor in the house\n",
    "\n",
    "df_train_b[\"HasBsmt\"] = df_train_b['TotalBsmtSF'].apply(lambda x: \"Yes\" if x>0 else \"No\") #check if there is bsmt in the house\n",
    "df_test_b[\"HasBsmt\"] = df_test_b['TotalBsmtSF'].apply(lambda x: \"Yes\" if x>0 else \"No\")   #check if there is bsmt in the house"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0832d00f",
   "metadata": {},
   "source": [
    "### Dropping Useless Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "10569acc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:06.268131Z",
     "start_time": "2023-02-27T04:29:06.208245Z"
    }
   },
   "outputs": [],
   "source": [
    "#Drop Missing Columns\n",
    "df_train_b = df_train_b.dropna(axis=1) \n",
    "df_test_b = df_test_b.dropna(axis=1)\n",
    "\n",
    "#AFTER ANALYSING EACH COLUMN ->  Drop insignificant columns\n",
    "df_train_b = df_train_b.drop([\"YearBuilt\",\"YearRemodAdd\",\"Street\",\"Condition2\",\"LowQualFinSF\",\"RoofMatl\",\"Exterior2nd\",\"OpenPorchSF\",\"EnclosedPorch\",\n",
    "                    \"3SsnPorch\",\"ScreenPorch\",\"PoolArea\",\"MiscVal\",\"YrSold\",\"Utilities\",\"Heating\",\"KitchenAbvGr\",\"GarageYrBlt\"], axis=1) \n",
    "df_test_b = df_test_b.drop([\"YearBuilt\",\"YearRemodAdd\",\"Street\",\"Condition2\",\"LowQualFinSF\",\"RoofMatl\",\"Exterior2nd\",\"OpenPorchSF\",\"EnclosedPorch\",\n",
    "                    \"3SsnPorch\",\"ScreenPorch\",\"PoolArea\",\"MiscVal\",\"YrSold\",\"Utilities\",\"Heating\",\"KitchenAbvGr\",\"GarageYrBlt\"], axis=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aca4d4b",
   "metadata": {},
   "source": [
    "## First Level Analysis on cleaned and feature engineered data set\n",
    "\n",
    "- Visualization of how key features affect the sale price."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49587ccc",
   "metadata": {},
   "source": [
    "### Appendix 2 - Neighbourhood Average Sale Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8bb7ad93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:06.984725Z",
     "start_time": "2023-02-27T04:29:06.272199Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort neighborhoods by median sale price\n",
    "neighborhood_order = df_train_b.groupby('Neighborhood')['SalePrice'].median().sort_values().index\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "sns.boxplot(x='Neighborhood', y='SalePrice', data=df_train_b, order=neighborhood_order)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Neighborhood vs SalePrice', fontsize=16)\n",
    "plt.xlabel('Neighborhood', fontsize=14)\n",
    "plt.ylabel('SalePrice', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e4ed8b",
   "metadata": {},
   "source": [
    "Here it can be seen that different neighborhoods have different sale price. NridgHt is one of those neighborhoods where the sale price is very high. Though there are a couple of outliers, which are not impacting the average price that much."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24882ba3",
   "metadata": {},
   "source": [
    "### Appendix 3 - Neighbourhood v Age of House"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1d853077",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:08.332230Z",
     "start_time": "2023-02-27T04:29:06.988283Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort neighborhoods by count\n",
    "neighborhood_order = df_train_b['Neighborhood'].value_counts().index\n",
    "\n",
    "# Create figure and axes objects\n",
    "fig, ax1 = plt.subplots(figsize=(20, 8))\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# Create countplot on primary axis\n",
    "sns.countplot(x='Neighborhood', data=df_train_b, order=neighborhood_order, palette='Blues_d', ax=ax1)\n",
    "\n",
    "# Create lineplot on secondary axis\n",
    "sns.lineplot(x='Neighborhood', y='Age', data=df_train_b, sort=False, color='black', ax=ax2)\n",
    "\n",
    "# Set axis labels and titles\n",
    "ax1.set_xlabel('Neighborhood', fontsize=14)\n",
    "ax1.set_ylabel('Count', fontsize=14)\n",
    "ax2.set_ylabel('Age', fontsize=14)\n",
    "ax1.set_title('Neighborhood vs Age', fontsize=16)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44504294",
   "metadata": {},
   "source": [
    "### Appendix 4 - Heatmap of Sale Numbers for each Neighborhood\n",
    "The ages referes the the number of years before 2011 that the sale took place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2265edd7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:09.374486Z",
     "start_time": "2023-02-27T04:29:08.336803Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ftLW1ZenKysr44osvEB4eXuKhbEV5/VpfvHgR8fHxGDp0KHJzc2VLfn4+OnXqhKtXryItLQ3p6enw8/PDgAEDZEMLi/Lvv/+iffv2qFatmtz+OnfuDADw8/OTK9+1a1coKyvL1ktyjd6kVq1a6NatG1avXi2rvzt27EBcXBy++eabN27bvHlzJCYmYuDAgdi/f3+RT18qbR0okJaWhqtXr6JPnz5QV1eXpevo6KB79+4lPj8dHZ1C9erzzz9Hfn4+zp49K5feo0cPqKqqytaDg4Px4sULfPHFF1BS+u/jira2Nvr27YtLly4hPT1dbh+DBg2SWx8wYABUVFRkw0eOHTuG3NxcDBkyRO71VldXh6ura5ET25fm/V6Uktaxkryeb1Lca3X27FnZsL+BAwfC1NRUNlQSAFauXAkTExN8+umnJTrO2+4TZbnGBQquxYABA+TS+/XrV+xcZSW9b5XkXnz69Gk4ODigefPmcuW8vLwghMDp06cLHfvVOvu2uDIzM2VP7izY1+tD6/r37w8tLS3ZEMKCuvt6uebNm6NevXqycv7+/sjMzCz0HmjdujWsrKyKjPF1N2/eRI8ePWBkZARlZWWoqqpiyJAhyMvLw4MHD+TKmpubF7pOTk5Octf9zJkzxV73kihJfQ0JCcH9+/dl5/1qnevSpQsiIiLk/gatXbsWjRs3hrq6OlRUVKCqqopTp04hKCio0PHf9PoSEX0M2FhERFVCWloa4uLiUK1atWLL2Nra4uTJkzA1NcXYsWNha2sLW1tb/N///V+pjmVhYVHisubm5sWmxcXFleq4pRUXF1dkrAXX6PXjGxkZya0XTOSZkZFR7DESEhIghCjVcUrj9f1GRUUBePnlUVVVVW5ZtGgRhBCIj49HQkIC8vLy3jp5blRUFA4ePFhoX/Xr1weAQl/Yy3KN3ubbb7/Fw4cPceLECQAv5w1p1aoVGjdu/MbtvvjiC2zYsAFPnz5F3759YWpqihYtWsj2A5S+DhRISEhAfn7+G+tvSRRMNl/U9q8f+/U4C/KLiz8/Px8JCQlvjE1FRQVGRkayfRXUn2bNmhV6zXft2lXo9dbU1ISuru5bz/NNSlrHSvJ6vklxr1V2djZSU1MBvKyvo0aNwo4dO5CYmIiYmBjs3r0bI0aMKPHEvW97D5T2Gr+q4HV6vd4UvI5liadASe7FpX2/vOlvwdviiouLg4qKSqHGbIlEAnNzc7mYijtWtWrVCpUr63s2LCwM7dq1w/Pnz/F///d/OHfuHK5evSprqHn9ehb1ekilUrlycXFx73QPKUl9LahvU6ZMKVTfxowZA+C/99iyZcswevRotGjRAv/88w8uXbqEq1evolOnTkXew0vzt56IqCriI2WIqEo4dOgQ8vLy4Obm9sZy7dq1Q7t27ZCXl4dr165h5cqVmDBhAszMzPDZZ5+V6FjFTWRdlMjIyGLTCj5sF/QGeHXSZaBwQ0VpGRkZISIiolB6weSvxsbG77R/ADAwMICSklK5Hef1a12wr5UrVxb71CkzMzPk5eVBWVm50ASwrzM2NoaTkxMWLFhQZP6bGh/flw4dOqBBgwb47bffoK2tjRs3bmDbtm0l2vbLL7/El19+ibS0NJw9exZz5sxBt27d8ODBA1hZWZW5DhgYGEAikbyx/pZEwRe5orZ//cvm6691QX5x8SspKcHAwKDQvi0tLWXrubm5iIuLk+2r4Hz//vvvEvW2KM17vTilqWNvez3fpLjXSk1NTa7X3+jRo/Hzzz9jw4YNyMzMRG5uLr7++usynl1hpb3Gryp4naKioop8Hd9FSe7FpX2/vEv9MDIyQm5uLmJiYuQajIQQiIyMRLNmzeRii4iIKNT4/eLFC1lMBeWKO883PVAAAHx8fJCWloa9e/fKvW63bt0q9bkVMDIyeud7yNvqa8H5z5gxA3369ClyH/b29gCAbdu2wc3NDWvWrJHLL27C7ffx/iciqszYs4iIKr2wsDBMmTIFenp6RT5ZqijKyspo0aKF7FfTgmEI76OnyKsCAgJw+/ZtubQdO3ZAR0dH1nOk4EP8nTt35ModOHCg0P5e/+X2Tdzd3XH69OlCTwbasmULNDU138sjvrW0tNCiRQvs3btXLq78/Hxs27YN1atXR506dd75OAXatGkDfX19BAYGomnTpkUuampq0NDQgKurK/bs2fPGRrdu3brh3r17sLW1LXJfZWksKksdGj9+PA4dOoQZM2bAzMwM/fv3L9UxtbS00LlzZ8yaNQvZ2dkICAgAUPY6oKWlhebNm2Pv3r3IzMyUpaekpODgwYMljislJaVQPd6xYweUlJTg4uLyxm3t7e1haWmJHTt2yA0xTUtLwz///CN7Qtqrtm/fLre+e/du5ObmyhqRPT09oaKigkePHhVbf0qiNO/DstSx4l7PNynutWrXrp3c0EkLCwv0798fq1evxtq1a9G9e3fUrFmzROdSEu9yjQvqxK5du+TS//7773d+LH1J7sXu7u4IDAwsNES44Olb7du3f6cYXuXu7g4AhRqG//nnH6SlpcnyO3ToUGS5q1evIigoSFauZcuWUFdXL/QeuHjxYomGyRY0jLzaw0wIgT/++KM0pyWnffv2xV73knpbfbW3t4ednR1u375dbH3T0dGRnePrPeju3LlT7HBcIqKPHXsWEVGlcu/ePdl8BNHR0Th37hw2btwIZWVl7Nu3743z06xduxanT59G165dUbNmTWRmZmLDhg0AgI4dOwJ4Oc+HlZUV9u/fD3d3dxgaGsLY2Pitv8oWp1q1aujRowfmzp0LCwsLbNu2DSdOnMCiRYtkX3SbNWsGe3t7TJkyBbm5uTAwMMC+fftw/vz5QvtzdHTE3r17sWbNGjRp0gRKSkrFfvmaM2eObL6U2bNnw9DQENu3b8ehQ4ewePFi6OnplemcXuft7Q0PDw+0b98eU6ZMgZqaGlavXo179+5h586d7/XXWW1tbaxcuRJDhw5FfHw8+vXrB1NTU8TExOD27duIiYmR/Wq8bNkytG3bFi1atMB3332H2rVrIyoqCgcOHMC6deugo6OD+fPn48SJE2jdujXGjx8Pe3t7ZGZm4smTJzh8+DDWrl371qFsr3N0dAQALFq0CJ07d4aysjKcnJygpqZW7DaDBw/GjBkzcPbsWXz//fdvLFtg5MiR0NDQQJs2bWBhYYHIyEh4e3tDT09P1ivhXerAjz/+iE6dOsHDwwOTJ09GXl4eFi1aBC0tLcTHx5foWhgZGWH06NEICwtDnTp1cPjwYfzxxx8YPXr0WxsolJSUsHjxYgwaNAjdunXDqFGjkJWVhV9++QWJiYn4+eefC22zd+9eqKiowMPDAwEBAfjhhx/g7OwsmwPH2toa8+fPx6xZs/D48WN06tQJBgYGiIqKwpUrV6ClpYV58+a99bxK8z4saR0ryev5JsrKyvDw8MCkSZOQn5+PRYsWITk5ucjz+fbbb9GiRQsAwMaNG9+679J4l2tcv359DBw4EEuXLoWysjI6dOiAgIAALF26FHp6enJzV5VWSe7FEydOxJYtW9C1a1fMnz8fVlZWOHToEFavXo3Ro0e/14ZvDw8PeHp6Yvr06UhOTkabNm1w584dzJkzB40aNcIXX3wB4GVjyFdffYWVK1dCSUkJnTt3xpMnT/DDDz+gRo0amDhxIoCXvQGnTJmCn376CSNGjED//v3x7NkzzJ07t0TDvjw8PKCmpoaBAwdi2rRpyMzMxJo1awoN9SyNCRMmYMOGDejatSt++uknmJmZYfv27bh//36p9vO2+rpu3Tp07twZnp6e8PLygqWlJeLj4xEUFIQbN25gz549AF423P7444+YM2cOXF1dERwcjPnz58PGxuadGyOJiKokxc2tTURUcgVPRSlY1NTUhKmpqXB1dRULFy4s8olXrz9px9/fX/Tu3VtYWVkJqVQqjIyMhKurqzhw4IDcdidPnhSNGjUSUqlU7gk8Bft79VHFxR1LiJdP4Onatav4+++/Rf369YWampqwtrYWy5YtK7T9gwcPxCeffCJ0dXWFiYmJGDdunDh06FChJ8bEx8eLfv36CX19fSGRSOSOiSKe4nb37l3RvXt3oaenJ9TU1ISzs7PcE6+E+O/JY3v27JFLL+oJWcU5d+6c6NChg9DS0hIaGhqiZcuW4uDBg0XurzRPQ3s9pgJ+fn6ia9euwtDQUKiqqgpLS0vRtWvXQuUDAwNF//79hZGRkVBTUxM1a9YUXl5eco+ljomJEePHjxc2NjZCVVVVGBoaiiZNmohZs2aJ1NTUt8b++nXPysoSI0aMECYmJrLXqOBJd68/De1VXl5eQkVFRYSHh7/1+gghxObNm0X79u2FmZmZUFNTE9WqVRMDBgwQd+7ckStXkjpQ3Gt94MAB4eTkJLt2P//8c5F1vSiurq6ifv36wtfXVzRt2lRIpVJhYWEhZs6cKffUorfVCx8fH9GiRQuhrq4utLS0hLu7u7hw4YJcmYKYrl+/Lrp37y60tbWFjo6OGDhwoIiKiipyn+3btxe6urpCKpUKKysr0a9fP3Hy5ElZmaFDhwotLa0iYyrt+7Akdaykr+frCq7fokWLxLx580T16tWFmpqaaNSokTh27Fix21lbW8s9PfJtinsiZVFPbRSiZNe4qLqUmZkpJk2aJExNTYW6urpo2bKl8Pf3F3p6enJPuytNPKW5Fz99+lR8/vnnwsjISKiqqgp7e3vxyy+/yD1R6011tri/E0U99TIjI0NMnz5dWFlZCVVVVWFhYSFGjx4tEhIS5LbNy8sTixYtEnXq1BGqqqrC2NhYDB48WDx79kyuXH5+vvD29hY1atQQampqwsnJSRw8eFC4urqW6GloBw8eFM7OzkJdXV1YWlqKqVOniiNHjhS6ngXv7dcNHTq00FMCAwMDhYeHh1BXVxeGhoZi+PDhYv/+/SV6Gtqr3lZfb9++LQYMGCBMTU2FqqqqMDc3Fx06dBBr166VlcnKyhJTpkwRlpaWQl1dXTRu3Fj4+PgUirs0f6uIiKoyiRBveXwQERERlavs7GxYW1ujbdu22L17t6LDeS/c3NwQGxuLe/fulfux5s6di3nz5iEmJua9zMVV1d25cwfOzs5YtWqVbBLgiuzixYto06YNtm/fXuInaVHVUdnqKxFRVcFhaERERAoSExOD4OBgbNy4EVFRUfjuu+8UHRJVYY8ePcLTp08xc+ZMWFhYFHoce0Vw4sQJ+Pv7o0mTJtDQ0MDt27fx888/w87OrtgJjKlqqgz1lYioKuME10RERApy6NAhtGvXDkeOHMHq1atlE+0SlYcff/wRHh4eSE1NxZ49ewpNEF4R6Orq4vjx4/jiiy/g6emJxYsXo3PnzvDz85M9OZI+DpWhvhIRVWUchkZERERERERERDLsWURERERERERERDJsLCIiIiIiIiIiIhk2FhERERERERERkQwbi4iIiIiIiIiISEZF0QFQybmv9Fd0CETl7uqFh4oOgajc5UQ8UXQIROUvOVrRERCVO40GrRUdAlG5i9/6uaJD+CA0Gn2jsGNn3PxNYccuDnsWERERERERERGRDHsWEREREREREdHHTcK+NK/i1SAiIiIiIiIiIhk2FhERERERERERkQyHoRERERERERHRx00iUXQEFQp7FhERERERERERkUyV6Fnk5eWFzZs3w9vbG999950s3cfHB71794YQQq68vb09QkNDERoaCktLyw8dLlUg3RuYoYejGcx0pQCAp3EZ2Ho1HFeeJgIAhjSvjvZ1jGGirYbcPIEHManY4P8M96NSFRg1UdlN6tkAcwc2xurDgfhuyzUAwJrRrTHItbZcuasPY+D+wxFFhEhUJrO8XPH9l65yaZFxqbDps0yuzPDujaGvo46rgc8xYfkRBD2J+dChEpXZrFFd8P3XXeTSImOTYeMxU5bf37MxqpsbIDsnDzeDwjD3t4O4eu+pIsIlemcTujtg9oCGWHv0PmZuv1Eof9mXzeDVwQ4zt13H2mPBCoiQqhROcC2nSjQWAYC6ujoWLVqEUaNGwcDAoNhy58+fR2ZmJvr3749NmzZh1qxZHzBKqmhiU7Pxx8UwvEjKBAB8UtcE87vaY9Rfd/A0PgPhiZlY6ReKiKRMqKkooV9DCyzqWQ9DttxEUmaugqMnKp3GtYzg5W6Hu0/jC+WduPUco9dckK3n5OZ/yNCI3ouAx9HoOnmrbD0v778fiyYPbI3xA1riK+/9eBgeh+++aIdDSwfDafAqpGZkKyJcojIJCHmBrl+vlK3n5f9Xz0OeRmPioj0IDY+FhlQV4wZ3wMHV36BBz3mITeAPXVS5NLIxxND2tXEvLKHI/C5NqqOJrTFexKd/4MiIPg5VpumsY8eOMDc3h7e39xvLrV+/Hp9//jm++OILbNiwoVCvI2tra/z0008YMmQItLW1YWVlhf379yMmJgY9e/aEtrY2HB0dce3aNbntLl68CBcXF2hoaKBGjRoYP3480tLSZPmrV6+GnZ0d1NXVYWZmhn79+r2/k6cy83+SgCtPExGemInwxExsuPQMGTn5cDDXAQCcfhCLG8+SEJGchafxGVhz7im0pSqoZayp4MiJSkdLqoI/x7XD+N8vITGt8BfjrJw8RCdlypaEIsoQVXS5efmIik+TLbFJ/32BGNu/BRZvPYf95+4jMDQGI7z3Q0Oqik87NlBgxESll5uXj6i4FNnyaiPQrqPXcOZyMJ48j0PQ40hMX7oXejoaaGBXTYERE5WellQF60a3xoT1l4v83GJhoIHFQ5pi1JqLyM3jD1z0nkgkilsqoCrTWKSsrIyFCxdi5cqVCA8PL7JMSkoK9uzZg8GDB8PDwwNpaWnw9fUtVO7XX39FmzZtcPPmTXTt2hVffPEFhgwZgsGDB+PGjRuoXbs2hgwZImtounv3Ljw9PdGnTx/cuXMHu3btwvnz5/HNN98AAK5du4bx48dj/vz5CA4OxtGjR+Hi4lJu14LKRkkCtLczgrqqEgIjUgrlqyhJ0LWBKVKzcvEolr9gUOWydFgLHLsZDt97EUXmt3Uwx6N1/XHj115YMbIVjHXVP3CERO+udnVDPP5nIoL+Gocts/vA2kIfAGBtoQ8LIx2cvPZYVjY7Jw/nbj9FywY1FBQtUdnUrmmCx8cXIOjfudjy85ewtjQqspyqijKG92mDxJR03H3w/ANHSfRuFg9tihO3X8AvIKpQnkQCrPm6FVYeCsL950kKiI7o41BlhqEBQO/evdGwYUPMmTMH69evL5T/119/wc7ODvXr1wcAfPbZZ1i/fj3at28vV65Lly4YNWoUAGD27NlYs2YNmjVrhv79+wMApk+fjlatWiEqKgrm5ub45Zdf8Pnnn2PChAkAADs7O6xYsQKurq5Ys2YNwsLCoKWlhW7dukFHRwdWVlZo1KjRG88lKysLWVlZcmn5OdlQUlUr07Wh4tkYaWJlvwZQU1FCRk4e5hwKxtOEDFl+S2t9fO9ZB1JVJcSn5WCaTyCSOQSNKpG+razRsJYhXGceKjL/xK0X8Ln0FGExabAy1cb3Axri3x884DLjELI5HI0qiatBzzFioQ8ehsfD1EAL333RDmdWDUMTrzUwN9QGAETHyw/DiU5IRU0zfQVES1Q2V+89wYgftuLh02iYGunguxGdcGbTZDTptwDxSS97tHdu1wBbfv4SmuqqiIxNRrevf0NcYtpb9kxUcfRpaYWGNoboMPtokfnfdnNAXp7AuuOco4ioPFWZnkUFFi1ahM2bNyMwMLBQ3vr16zF48GDZ+uDBg7F3714kJibKlXNycpL938zMDADg6OhYKC06OhoAcP36dWzatAna2tqyxdPTE/n5+QgNDYWHhwesrKxQq1YtfPHFF9i+fTvS09/cM8Xb2xt6enpyy5MTW0p3MahEniVk4Ku/7uCbPXdx4G4UpnvUhpWBhiz/VngyvvrrDsbvuYerTxPxQ6c60NeoUu2sVIVZGmli0dBmGLHyPLJyim742ev/BMduPkdQeCKO3ghH359PobaFLjwbVf/A0RKV3fHLIfA5ex8Bj6Nx5nooen+3EwAwuJOzrMxrI88hkUgKDUcnqsiOXwiEz6lbCAh5gTOXg9F73BoAwODuLWRl/K4+QIvPvNHeaxmOXwzEtsXDYGKgraiQiUrF0lATCwc3xldrLhb5ucXZ2gCjPrHH2N8vKSA6qvIkSopbKqCKGdU7cHFxgaenJ2bOnCmXHhgYiMuXL2PatGlQUVGBiooKWrZsiYyMDOzcuVOurKqqquz/kv+NHywqLT8/X/bvqFGjcOvWLdly+/ZtPHz4ELa2ttDR0cGNGzewc+dOWFhYYPbs2XB2di7USPWqGTNmICkpSW6x9hjyTteGipabL/AiKRMPotOw3j8Mj2LT0KehhSw/MzcfL5IyERSViiWnHyFPCHR2MFVgxEQl19DGCKb6Gjjr3RXx2wcjfvtgtHMwx9ed6iF++2AoFTFGOioxA89i0mBroaOAiInej/TMHASERsO2uiEi/9ejyMxI/guzib4WohPY44Iqr/TMbASEvIBtTRO5tMfPYnHl7hOMnrcDuXn5GNq7tQKjJCo5ZxtDmOpp4Mz8Toje9BmiN32GtvXM8NUn9rL/m+iq487ynrL8miba+PHzRri1rIeiwyeqUqpk9whvb280atQIderUkaWtX78eLi4uWLVqlVzZrVu3Yv369Rg9enSZj9e4cWMEBASgdu3axZZRUVFBx44d0bFjR8yZMwf6+vo4ffo0+vTpU2R5qVQKqVQql8YhaB+GBBKoKhc/ydjL/CrXzkpVlN+9CLSYckAubc3o1njwIgm/7g9AfhG9Kgy1pbA00kLUK8MxiSobNVVl1K1pjAt3wvAkIhERcSlwb1oLtx9GAgBUVZTQztkK3687qeBIicpOTVUFdW3McOFmSLFlJJBAqlolP/JTFXQ2IBJtZsgPm185siUevkjGikOBiEzMwOm78vMv7pnaHrsvhGLH2ccgeicVdKJpRamSfzmcnJwwaNAgrFz58rGiOTk52Lp1K+bPn48GDeSfejJixAgsXrwYt2/fhrOzc1G7e6vp06ejZcuWGDt2LEaOHAktLS0EBQXhxIkTWLlyJf799188fvwYLi4uMDAwwOHDh5Gfnw97e/t3Pld6N8Nb1cCVp4mITsmGppoy2tsZwdlSFzMOBEFdRQmDmlni4uMExKVnQ09dFT0czWCirQa/kDhFh05UIqmZuQgKT5RLS8vKRXxKFoLCE6ElVcGM/s44cPkpIhMzUNNEG3M+a4S4lEwcvBqmmKCJysB7tAcOXXyAZ1FJMDXQwvQh7aCjJcX2o7cBAKv2XMbUQW0REh6HkPB4TBvcFhlZOdh18p6CIycqOe+JvXHo7F08i0iAqaE2po/oBB0tdWw/eBma6mqYPsITh/zuIjI2CYZ6WvhqgAsszfSx98QNRYdOVCIvP7fIT1qdnpWLhNQsWXpCqvzT0XLz8hGdlImQyMIPqCGisquSjUUA8OOPP2L37t0AgAMHDiAuLg69e/cuVM7Ozg6Ojo5Yv349VqxYUaZjOTk5wc/PD7NmzUK7du0ghICtrS0+/fRTAIC+vj727t2LuXPnIjMzE3Z2dti5c6dsom1SHAMNNXznURuGWmpIy8rD47g0zDgQhOvPkqCqLEENAw3M7WIKXQ0VJGfkIjg6FRP+uYen8exxQVVDXr5A/RoGGNiuFvS01BCZkIFzgZHw+r+zSOVE7lSJWJroYMvsPjDS00RsYhquBD6H6+j1CIt6+eVi6c6LUJeqYvnELjDQ1sDVoOfoNmUbUjMKP5KZqKKyNNPHFu8vYaSvhdiEVFy5+wSuQ5ciLCIBUjUV2FubYXD3FjDS10J8UjquBTxFx2G/IuhxpKJDJyKiSkYiOLNjpeG+0l/RIRCVu6sXHio6BKJylxPxRNEhEJW/5GhFR0BU7jQacD4oqvrit36u6BA+CI2W0xV27IxLixR27OJw4hUiIiIiIiIiIpKpssPQiIiIiIiIiIhKhBNcy2HPIiIiIiIiIiIikmHPIiIiIiIiIiL6uEnYl+ZVvBpERERERERERCTDxiIiIiIiIiIiIpLhMLRKZEGXeooOgajcfRGWpOgQiMqdvmNNRYdAVO5qVddTdAhE5S4lI0fRIRDR+8IJruWwZxEREREREREREcmwZxERERERERERfdw4wbUcXg0iIiIiIiIiIpJhYxEREREREREREclwGBoRERERERERfdw4wbUc9iwCMHfuXDRs2FC27uXlhV69eiksHiIiIiIiIiIiRakSPYsiIyOxYMECHDp0CM+fP4epqSkaNmyICRMmwN3d/YPFERISggULFuDEiROIiYlBtWrV0LJlS0yePBlNmzb9YHFQyd2/exNH/tmGJyH3kRgfi/HfL0aT1q6y/H3b/sDlsycQFxMFFVVVWNeui35DvoZt3QYKjJqodD5vVQMDW9VEdQMNAMDDqFT8diIEZ4NjAQBG2mqY1tUebeyMoKuhiquh8ZjvE4SnsemKDJuoVPo2roa+javBQl8dAPA4Jg3rzz/FxUfxsjLWRpoY16EWGtfUh0QCPI5Nx4y9AYhKzlJU2ESl4mFvDI86xjDRVgMAhCdm4p87kbj1PFlWpp+zOdzrGENbTRkPY9Ow4XI4whMzFRUyUal1cTBF1/qmMNORAgCexmdg5/XnuPYsCcpKEgxpZolmNfVhritFWnYeboUnY+PlZ4hPz1Fw5FTpVYIJrufOnYt58+bJpZmZmSEyMhIAIITAvHnz8PvvvyMhIQEtWrTAqlWrUL9+/VIfq9I3Fj158gRt2rSBvr4+Fi9eDCcnJ+Tk5ODYsWMYO3Ys7t+//0HiuHbtGtzd3dGgQQOsW7cOdevWRUpKCvbv34/JkyfDz8+vyO1ycnKgqqr6QWKkwrIyM1DDxg7tPLph5YLvCuWbW9bEF6OnwMTcEtnZWTi2byd++X48Fq//B7p6BgqImKj0IhMzseRwsKzxp3dTS6zxaoyeyy8iJCoVa7waIzcvH6M33UBqVh6GuVhj81fN0PmX88jIyVNw9EQlE52Shd/OPEZ4QgYAoKuTOZb0b4DBf17D49h0WOqr448hjXDgdgTWnX2CtKxcWBtrIjs3X8GRE5VcXFo2dtx4gaiUlw2cLraGmNreBtP/DUZ4YiZ6NDBFVwdTrLnwFBHJWejjZI5ZHrUxcV8gMlnXqZKITcvGxsvPEJH0sp672xvjh052GPd3AGLTslHbRAs7b7zA49h0aEuVMaqNFeZ0qoNv9wYoOHKiD6N+/fo4efKkbF1ZWVn2/8WLF2PZsmXYtGkT6tSpg59++gkeHh4IDg6Gjo5OqY5T8ZvO3mLMmDGQSCS4cuUK+vXrhzp16qB+/fqYNGkSLl26BAAICwtDz549oa2tDV1dXQwYMABRUVElPkZKSgoGDRoELS0tWFhY4Ndff4WbmxsmTJgA4GXrnZeXF+zs7HDu3Dl07doVtra2aNiwIebMmYP9+/cDeNmwJZFIsHv3bri5uUFdXR3btm1779eESs65WWv0G/o1mrZpX2R+q/aeqN+oOUwtLFHdqhY+/+pbZKSn4VloyAeOlKjsTgfFwO9+LJ7EpuNJbDp+PfoQ6dm5aFhTD9bGmmhkpY/ZewNxNzwZoTFpmLM3AJpqyujWyELRoROV2LmHcbj4KB5h8RkIi8/AGt9QpGfnoYGlLgBgjFstXHwUh5WnH+NBVCqeJ2biQkg8EvhLNFUiN8KTcet5MiKSsxCRnIVdNyOQmZsPO2NNAECXeqbYdzcSV8KS8CwxE6vOP4VURYK2tfgDF1UeV54m4lpYEp4nZeJ5Uia2XAlHZk4+6pppIT07D7P+Dca5R/F4npSJ4Og0rDn/FHamWrIed0RlJlFS3FIKKioqMDc3ly0mJiYAXrZLLF++HLNmzUKfPn3QoEEDbN68Genp6dixY0epL0elbiyKj4/H0aNHMXbsWGhpaRXK19fXhxACvXr1Qnx8PPz8/HDixAk8evQIn376aYmPM2nSJFy4cAEHDhzAiRMncO7cOdy4cUOWf+vWLQQEBGDy5MlQUip8SfX19eXWp0+fjvHjxyMoKAienp4lP2FSqNycHJw54gNNLW3UtLFTdDhEZaIkAbo6m0NTTQW3niZCTeXlPSs7978eRPkCyMnLR1MbfrmgyklJAng4mEJDVRl3nydDAqBNbUOExWdgxWdOODahNTZ6NYZrHWNFh0pUZhIJ0NpaH1IVJTyISYepthoMNFVx50WKrExuvkBgZCrqmBT+nExUGShJXvagU1dVQlBUapFltNSUkS8EUrNyP3B0RO9PVlYWkpOT5ZasrKKHyT98+BDVqlWDjY0NPvvsMzx+/BgAEBoaisjISHzyySeyslKpFK6urrh48WKpY6rUw9BCQkIghEDdunWLLXPy5EncuXMHoaGhqFGjBgBg69atqF+/Pq5evYpmzZq98RgpKSnYvHkzduzYIZv/aOPGjahWrZqszMOHDwHgjXG8asKECejTp0+JypLi3bp8HqsXfY/srEzoGRpj6oKV0NHTV3RYRKVSx1wbu79pCamKEtKz8zBm8w2ERKdBRUmC8PgMTO5cBz/8E4CM7Dx86WINU111mPxvrgCiysLWRAsbvBpDTUUJGdl5mPr3PYTGpsNISw1aUhUMbVUTa/xC8duZx2hVyxCL+9XH6G23cCMsSdGhE5VYDX11/NSlDlSVlZCZm4clZ0LxPClT1iCUlCHfWy4pMxcmWuxxQZWLtaEGlvZ2gJqyEjJy8vDjsYd4llB47i1VZQm+bFEDvg/jkJHDoZZUeXl7exeai2jOnDmYO3euXFqLFi2wZcsW1KlTB1FRUfjpp5/QunVrBAQEyOYtMjMzk9vGzMwMT58+LXVMlbqxSAgBAJC84RF3QUFBqFGjhqyhCAAcHBygr6+PoKCgtzYWPX78GDk5OWjevLksTU9PD/b29qWK41Ulmew6KyurUEtidlYW1KT88vah1XNugh9/24qU5ET4Hd2PVd4zMefXDdDVN1R0aEQlFhqThh6/XoSuhgo8Hc2x+FMnDFpzGSHRafhmy014D2iA6/M7IjcvHxdD4uAbFKPokIlK7WlcOgb9eQ066iroYG+Mud3rYtS2W0jJfPlrs9+DWOy8Eg4AeBCVCqfquujTuBobi6hSeZGchWkH70NLTRktrPQxtm1NzD363/B48Vp5SRFpRBVdeGImvtlzD9pSFbSxMcDk9rUw7UCQXIORspIE33WsDYkEWHXuieKCpapDqWTf58vDjBkzMGnSJLk0aRHf/Tt37iz7v6OjI1q1agVbW1ts3rwZLVu2BFC4XUIIUeK2ildV6mFodnZ2kEgkCAoKKrZMcRempBesuIaggnQAqFOnDgC8MY5XFTVk7nXe3t7Q09OTW7as/bVE+6f3S6quAbNqNVC7riOGT/geysrK8Dt2QNFhEZVKTp5AWFw67oUnY+mRBwiKSMbQdtYAgIDnyejx60U0+uEk2vx4BsP/vA4DLVWEJ/BpaFS55OYLhCdkICgiBat8Q/EwOg2fNauOxPQc5OblI/S1J/yFxqbDXFddQdESlU1evkBUSjYex2Vg540IPI3PRJd6Jkj8X48ifQ35B6foqqsU6m1EVNHl5gtEJGfhYUwaNl0Jx+O4dPR0NJflKytJMMPDFmY6Usz6N5i9iqjSk0ql0NXVlVuKaix6nZaWFhwdHfHw4UOYm798jxT0MCoQHR1dqLdRSVTqxiJDQ0N4enpi1apVSEtLK5SfmJgIBwcHhIWF4dmzZ7L0wMBAJCUloV69em89hq2tLVRVVXHlyhVZWnJysmzoGQA0bNgQDg4OWLp0KfLzC9+oEhMTS3lmL1sWk5KS5JYhX08s9X7o/RPi5fxFRJWZBBLZfEUFUjNzEZ+WAytjTTSorodTAdEKio7o/ZAAUFNWejlvS0QKrIw05PJrGmkgIomPFKdKTgKoKEsQnZqNhPQcOFn897QbZSUJHMy18SCm8OdkospEgpdDzoD/Goqq6alj5r/3kcK5iuh9qSQTXL8qKysLQUFBsLCwgI2NDczNzXHixAlZfnZ2Nvz8/NC6detS77tSD0MDgNWrV6N169Zo3rw55s+fDycnJ+Tm5uLEiRNYs2YNAgMD4eTkhEGDBmH58uXIzc3FmDFj4OrqWqLhYDo6Ohg6dCimTp0KQ0NDmJqaYs6cOVBSUpL1NpJIJNi4cSM6duwIFxcXzJw5E3Xr1kVqaioOHjyI48ePw8/Pr1TnJZVKC7UkqknZYv6+ZWakI+pFuGw9JuoFnj56AG0dXWjr6uHAXxvRqGU76BsYIzUlCaf+/QcJsdFo1s5dgVETlc6kTnY4GxyLiMRMaEmV0bWhBVrYGmL4n9cAAJ2czBCfmoOIxAzUsdDB9z3q4WRAFM4/iFNw5EQlN8bNBhcfxSMqOQuaasr4pL4pGlvpY/xfdwAAWy89w8LeDrgZloRrTxPRytYQ7eyM8fXWW4oNnKgUPmtkgVvPkxGXlgN1VSW0tjFAfTNtLDz5CABwOCgavZzMEJGShcjkLPRyNENWrsD5xwkKjpyo5IY2r45rYYmIScuGpqoyXGobwbGaLmYfDoaSBJjpURu1TTQx98gDKEskMPhfb7qUrFzk5nPQJVVtU6ZMQffu3VGzZk1ER0fjp59+QnJyMoYOHQqJRIIJEyZg4cKFsLOzg52dHRYuXAhNTU18/vnnpT5WpW8ssrGxwY0bN7BgwQJMnjwZERERMDExQZMmTbBmzRpIJBL4+Phg3LhxcHFxgZKSEjp16oSVK1eW+BjLli3D119/jW7dukFXVxfTpk3Ds2fPoK7+X9f15s2b49q1a1iwYAFGjhyJ2NhYWFhYoHXr1li+fHk5nDm9D6EPg/Dzd2Nk6zv/WA4AaNuxK4Z+Mx0R4U9xfsFhpCYlQltXDzZ16mHmL+tQ3aqWgiImKj1jHSl++cwJprpSpGTm4H5ECob/eQ0XHr5sDDLVUcfM7nVhpC1FTEoWfK4/x6r/ffEgqiwMtdQwr0c9GGurITUrFyHRaRj/1x1cCX35Jdk3OBbeRx7Aq3VNTP6kNsLiMzD9n3u4Hc75iqjy0NNQxdh2VjDQUEV6dh7CEjKx8OQj3I14+QS0A/eioaashOEtakBLqoyQmDQsPBGCzFz+4EiVh76GKqa428JQUxVp2XkIjUvH7MPBuBmeDFMdNbT639NaV/V3lNtu+oEg3H3laYBEVVF4eDgGDhyI2NhYmJiYoGXLlrh06RKsrKwAANOmTUNGRgbGjBmDhIQEtGjRAsePH4eOjs5b9lyYRLw6+Q6VSFpaGiwtLbF06VIMHz78gx330qPED3YsIkX5Yu0lRYdAVO709TlPDlV9tarrKToEonKXwvmg6CNw+Ovmby9UBWi4L1TYsTNOzVTYsYtT6XsWfQg3b97E/fv30bx5cyQlJWH+/PkAgJ49eyo4MiIiIiIiIiKi94uNRSW0ZMkSBAcHQ01NDU2aNMG5c+dgbGys6LCIiIiIiIiI6F29w0TTVREbi0qgUaNGuH79uqLDICIiIiIiIiIqd2wsIiIiIiIiIqKP2/+edk4vsZ8VERERERERERHJsLGIiIiIiIiIiIhkOAyNiIiIiIiIiD5unOBaDhuLKpGcPKHoEIjK3SAPW0WHQFTuMnN5P6eqT12Fcz9Q1ZeYkafoEIiIygUbi4iIiIiIiIjo48YJruWwnxUREREREREREcmwsYiIiIiIiIiIiGQ4DI2IiIiIiIiIPm6c4FoOrwYREREREREREclUmcYia2trLF++/I1lJBIJfHx8Pkg8RERERERERFRJSCSKWyqgSjEM7dmzZ5g7dy6OHDmC2NhYWFhYoFevXpg9ezaMjIxKvb9Nmzbhyy+/fGOZM2fOwM3NrYwRU2Xx4N5NHN27DU8fBSMpPhZjZy5Co1auRZbd8tvPOHvMB5+OmACPnp994EiJyi7g+G6E3/ZHclQ4lFXVYGxTDw17ekHXrHqR5a/89RseXTiKRn1Gom77nh84WqKyCT65By/uXERq9HMoqarByLou6nf3go7pf/X8+o5fEXb1tNx2Blb2cJuw5EOHS1QmvJ/Tx6CjnSGcLHRgqqOGnDyBJ/EZOBgYg+jUbLlyZtpq6O5gAltjTUgkQGRyNjZde47EjFwFRU5UtVT4xqLHjx+jVatWqFOnDnbu3AkbGxsEBARg6tSpOHLkCC5dugRDQ8NS7fPTTz9Fp06dZOt9+vRBgwYNMH/+fFlaafdJlVNWZgZq2NihTcduWOM9o9hyN/39EPogAPqGJh8wOqL3IzrkHuzadYWRlR3y8/Jw59+tOLPqB3SdtQYqUnW5suG3/RH3JBgaerwHUuUS++gearXtCoMadhD5+Qg4vAUX1s5Gx+mr5eq5Wd3GaDxwgmxdSbnCfxQikuH9nD4GtkaaOB+aiLDEDChJJOhazwRft6qBn08/RnaeAAAYaapifDsrXHqaiCPBscjMyYeZjhpy/5dPVCacs0hOhb8aY8eOhZqaGo4fPw5XV1fUrFkTnTt3xsmTJ/H8+XPMmjWryO0ePnwIFxcXqKurw8HBASdOnJDlaWhowNzcXLaoqalBU1NTti6VSjFixAgYGBhAU1MTnTt3xsOHDwEAQgiYmJjgn3/+ke2vYcOGMDU1la37+/tDVVUVqampAF4Of/vzzz/Ru3dvaGpqws7ODgcOHCiPy0Wl5Ni0NXp/8TWatG5fbJmEuGjsWLcEIybPg7KK8geMjuj9aD9mPmq17Ag9CysYVK+FFoMmID0hBvHPQuTKpSfG4trfa9F66BR+gaZKp82oebBq3hG6FlbQs7RBk4ETkJEQg8Rw+XqupKIKdV0D2aKmpaOgiIlKj/dz+hisuxSOK8+SEJmSjRfJWdhxMwKGmqqorv9fg2jXeiYIjErFwcAYPE/KQlx6DgKj0pCanafAyImqlgrdWBQfH49jx45hzJgx0NDQkMszNzfHoEGDsGvXLggh34Kcn5+PPn36QFlZGZcuXcLatWsxffr0Eh/Xy8sL165dw4EDB+Dv7w8hBLp06YKcnBxIJBK4uLjA19cXAJCQkIDAwEDk5OQgMDAQAODr64smTZpAW1tbts958+ZhwIABuHPnDrp06YJBgwYhPj6+jFeGPpT8/HysXzYPnn0Gw9KqlqLDIXovcjLTAABqmv/do0R+Pvy3LEM99z7Qs7BSVGhE701ORkE9l28Mig25h0M/DMbxhaNwY9dKZKUkKiA6oveD93P6GGiovvzKmv6/hiAJAAdzLcSkZuPrVtXxY6famOhiBUdz7TfshYhKq0I3Fj18+BBCCNSrV6/I/Hr16iEhIQExMTFy6SdPnkRQUBC2bt2Khg0bwsXFBQsXLizxMQ8cOIA///wT7dq1g7OzM7Zv347nz5/LJsd2c3OTNRadPXsWzs7O6NChgyzN19e30HxHXl5eGDhwIGrXro2FCxciLS0NV65cKTaOrKwsJCcnyy3Z2VklOgd6f47+sxVKSspw7z5A0aEQvRdCCNzc+ydMajlAv5q1LD3w5N9QUlZGHdceiguO6D0RQuDu/vUwsnGA7itfls3qNUXTwZPRbswCOPYYjsSwhzi3ehbycnMUGC1R2fB+Th+LXvVN8SguHZEpL+cs0pYqQ11FGe52RgiKSsPai89wJyIFXza3hK2Rxlv2RvQGnOBaToVuLHqbgh5FktcublBQEGrWrInq1f+b7K9Vq1Yl2mdQUBBUVFTQokULWZqRkRHs7e0RFBQE4GVjUUBAAGJjY+Hn5wc3Nze4ubnBz88Pubm5uHjxIlxd5SdJdnJykv1fS0sLOjo6iI6OLjYOb29v6OnpyS3b1v1aonOg9+NJyH2cPLALwyb8UKiOEVVW1/esReKLJ2jtNU2WFh8Wgge+B9Bi8ATWdaoSbv+zFskvnqDZkKly6dUbtYN5/WbQtbCCRYPmaP3VXKTGvEBk4FUFRUpUdryf08egr5MZqumpY8u1F7K0grp9LzIFfo8T8Dw5C6cexiMwMhVtrA0UFSpRlVOhBzHXrl0bEokEgYGB6NWrV6H8+/fvw8DAAMbGxnLprw9LAwo3KBWnqG0L0gv20aBBAxgZGcHPzw9+fn6YP38+atSogQULFuDq1avIyMhA27Zt5bZXVVUtFE9+fn6xccyYMQOTJk2SS7sall6ic6D342HALaQkJWDasF6ytPz8POzesAInD/yFRet9FBYbUVlc27MWz+9ehvu3P0PT4L/7ZvSjAGSmJuHA7P+eEiny83Fr33o88N2PHvM2KCJcojK5/c86RAZcQbtvvKGhb/zGsup6htA0MEFazIs3liOqaHg/p49BH0czNDDXxsrzYUjK/O8JZ2lZucjLF7KeRgWiUrNhY8ieRfQOOMG1nArdWGRkZAQPDw+sXr0aEydOlJu3KDIyEtu3b8eQIUMKNQQ5ODggLCwML168QLVq1QC8nHS6JBwcHJCbm4vLly+jdevWAIC4uDg8ePBANhyuYN6i/fv34969e2jXrh10dHSQk5ODtWvXonHjxtDRebcJM6VSKaRSqVyamhonbPuQWrXvDIeGzeTSfp09AS3bd0Lbjt0UFBVR6QkhcH3PWoTf8Yf7eG9oG5vL5ds0bw9ze2e5NN/Vs2HdrANqtez4IUMlKjMhBO7sXYcXd/3Rbqw3tIzM37pNVloyMhJjoa7Lp0VR5cD7OX0s+jqawdFCG79dCEN8uvxQ4TwBhCVmwFRbTS7dRFsNCRkcVkz0vlToxiIA+O2339C6dWt4enrip59+go2NDQICAjB16lRYWlpiwYIFhbbp2LEj7O3tMWTIECxduhTJycnFPjXtdXZ2dujZsydGjhyJdevWQUdHB9999x0sLS3Rs2dPWTk3NzdMnDgRjRo1gq6uLgDAxcUF27dvL9QjiCquzIx0REeEy9Zjol4g7PEDaGnrwsjUHNq6enLllVWUoWdgBPPqnDCSKo9ru9fg6XU/uIz8HirqmshITgAAqKprQkVNCqmWLqRaunLbKCmrQF3XALpm1YvaJVGFc/ufNQi/fhYth8+CilQDma/Uc2U1KXKzMhB0dAeqObeBuq4B0uOjEXhoC9S0dGHh2FLB0ROVDO/n9DHo52SGJtV18eflcGTl5kNH+vJpxJk5+cjJfzkK5HRIPIY2tcSjuHSExKajrqkW6pu9bFwiovejwjcW2dnZ4dq1a5g7dy4+/fRTxMXFwdzcHL169cKcOXNgaFj410AlJSXs27cPw4cPR/PmzWFtbY0VK1agU6dOJTrmxo0b8e2336Jbt27Izs6Gi4sLDh8+LDeUrH379sjLy5ObyNrV1RU+Pj6F5iuiiutJSBCWzBwrW9+9/v8AAK07dMGwibMVFRbRexVy/jAA4NSKGXLpLQZN4C/NVGWEXjgCADi3aqZceuOB38KqeUdIJEpIjniKsGtnkJORBnVdA5jUdkSzIdOgqq6piJCJSo33c/oYtLV5Oe/QuLbyP87uuBGBK8+SAAB3I1Kx53YkOtoZoY+jGWJSs7Hx6nOExmd88HipCuEwNDkSUdwkPVThnHuQoOgQiMrdqSexig6BqNxl5vJPL1V96iqcYJmqvsQMThNBVd/ynnUVHcIHodF9tcKOnXFwjMKOXZwK37OIiIiIiIiIiKhc8SmSctjPioiIiIiIiIiIZNhYREREREREREREMhyGRkREREREREQfN05wLYdXg4iIiIiIiIiIZNiziIiIiIiIiIg+bpzgWg57FhERERERERERkQx7FlUi2lK+XFT1RaXkKDoEonInVeFvNVT1tayur+gQiMpdWk6uokMgoveFcxbJ4dUgIiIiIiIiIiIZNhYREREREREREZEMxzURERERERER0ceNE1zLYc8iIiIiIiIiIiKSYc8iIiIiIiIiIvqoSdizSE6V61kkkUjg4+Oj6DDg5eWFXr16KToMIiIiIiIiIqJSqdA9i7y8vLB58+ZC6Z6enjh69KgCIqKqJujODRzcsxWhD4OQEB+LyXOWoFkbN1n+lfOncfLQXoQ+DEJKchJ+XrMd1rb2iguYqAw87Y3QsJouzHXUkJMn8Cg+HT53oxGVmi0rs6avQ5Hb7r0bhRMP4j5UqERl1tHOEE4WOjD9Xz1/Ep+Bg4ExiH6lngOAmbYaujuYwNZYExIJEJmcjU3XniMxg4+/porvceBtnD2wE+GPHyAlIQ5Dpv6E+s3byfKn93ctcrsug7+Ga8+BHypMonfyNOgOLv67CxGhD5GaGIcBE+ehbrO2svzUpHic2vkHHt25jsz0VFjVdUKnod/AyKK6AqMmqnoqdGMRAHTq1AkbN26US5NKpQqKprCcnByoqqoqOgwqo8zMDFjVsoObZ3csmz+tyHz7+s5o6dIRv//6kwIiJHp3dsZa8Hscj6fxmVBSAnrWN8W4tjUx/8QjZOcJAMD0f4Pltqlvro3BTarh5vNkRYRMVGq2Rpo4H5qIsMQMKEkk6FrPBF+3qoGfTz+W1XMjTVWMb2eFS08TcSQ4Fpk5+TDTUUPu//KJKrrsrAxYWNVG0/ZdsHXJD4Xyv/99r9z6/VuX8c+axWjQsuhGJKKKKDsrA2ZWtmjo2gl7ls+VyxNCYNfS2VBWUcGnk+dDqqGFS4f3YJv3VIxevAFq6hqKCZqqBA5Dk1fhh6FJpVKYm5vLLQYGBgCAhw8fwsXFBerq6nBwcMCJEyfktu3bty/GjRsnW58wYQIkEgkCAgIAALm5udDR0cGxY8cAAEePHkXbtm2hr68PIyMjdOvWDY8ePZJt/+TJE0gkEuzevRtubm5QV1fHtm3bkJeXh0mTJsm2mzZtGoSQ/+D5999/w9HRERoaGjAyMkLHjh2RlpZWLteMSq5R8zb49MsxaN62Q5H5Lh27ou/gkWjQqPkHjozo/fntQhguPU1CREoWnidlYcu1FzDSUkNNg/8+UCVn5cktTtV08CAmHbFpOQqMnKjk1l0Kx5VnSYhMycaL5CzsuBkBQ01VVNdXl5XpWs8EgVGpOBgYg+dJWYhLz0FgVBpSs/MUGDlRydVt1BKeA0egQQuXIvN1DIzklsCrF1CrfiMYmVX7wJESlZ1dwxboMGAY6r3Sa65AfGQ4nocEocuwCbC0rQvjajXQZdi3yM7MwD3/0wqIlqjqqvCNRcXJz89Hnz59oKysjEuXLmHt2rWYPn26XBk3Nzf4+vrK1v38/GBsbAw/Pz8AwNWrV5GZmYk2bdoAANLS0jBp0iRcvXoVp06dgpKSEnr37o38/Hy5/U6fPh3jx49HUFAQPD09sXTpUmzYsAHr16/H+fPnER8fj3379snKR0REYODAgRg2bBiCgoLg6+uLPn36FGpQIiL6EDRUX97604v5gqwjVYajuQ4uPkn4kGERvVev13MJAAdzLcSkZuPrVtXxY6famOhiBUdzbQVGSVR+UhLjcf+GP5p16KLoUIjem9yclz9iqaiqydKUlJShrKKKZ8H3FBUWVRUSBS4VUIUfhvbvv/9CW1v+g9z06dPRokULBAUF4cmTJ6he/eX41IULF6Jz586ycm5ubvj2228RGxsLZWVlBAQEYM6cOfD19cWYMWPg6+uLJk2ayPbft29fueOsX78epqamCAwMRIMGDWTpEyZMQJ8+fWTry5cvx4wZM2Tbr127VtZbCXjZWJSbm4s+ffrAysoKAODo6PjG887KykJWVpZcWnZWNtQq0BA8Iqqc+jmZIyQ2HS+Ss4rMb2mlj8zcfNx8nvKBIyN6f3rVN8WjuHREprycs0hbqgx1FWW42xnhcFAMDgbEoK6ZFr5sbolVF8LwKC5DwRETvV/X/Y5Cqq5ZbC8kosrIuFpN6Bmb4fRff6Lr8IlQU1eH/+G/kZoYj5SEeEWHR1SlVPieRe3bt8etW7fklrFjxyIoKAg1a9aUNRQBQKtWreS2bdCgAYyMjODn54dz587B2dkZPXr0kPUs8vX1havrf2O4Hz16hM8//xy1atWCrq4ubGxsAABhYWFy+23atKns/0lJSYiIiJA7toqKilwZZ2dnuLu7w9HREf3798cff/yBhIQ3/2Lv7e0NPT09uWXD6qUlvWxEREX6rKE5LPWkWH8lvNgyra31cSUsCbn57P1IlVNfJzNU01PHlmsvZGkF8xDci0yB3+MEPE/OwqmH8QiMTEUbawNFhUpUbq6dPoJG7TpCVY0/NFLVoayigv4T5iIuMhy/fNULC7264GngbdR2bg4lpQr/1ZYqOIlEorClIqrwPYu0tLRQu3btQulFDeF6/SJLJBK4uLjA19cXampqcHNzQ4MGDZCXl4e7d+/i4sWLmDBhgqx89+7dUaNGDfzxxx+oVq0a8vPz0aBBA2Rnyz9JRUtLq1TnoKysjBMnTuDixYs4fvw4Vq5ciVmzZuHy5cuyBqnXzZgxA5MmTZJLC4rMLrIsEVFJDHA2h6OFDpb5PSn2yU+1jTRhriPFn5eLb0wiqsj6OJqhgbk2Vp4PQ1Lmf/U8LSsXeflC1tOoQFRqNmwMOSEqVS2hQbcR8yIMn0+co+hQiN67arXqYJT378hMT0Vebi60dPXx5w9jUa1WHUWHRlSlVNrmVwcHB4SFheHFi/9+NfT39y9UrmDeIl9fX7i5uUEikaBdu3ZYsmQJMjIyZPMVxcXFISgoCN9//z3c3d1Rr169t/b+AQA9PT1YWFjg0qVLsrTc3Fxcv35drpxEIkGbNm0wb9483Lx5E2pqanLzGr1OKpVCV1dXbuEQNCIqq08bmqORpQ6Wn3uKuPTiJ61uba2PpwkZeJ5U9BA1ooqsr6MZnCy0sepCGOJfq+d5AghLzICptppcuom2GhIyOJE7VS1XTx2GZS17VLMu/IMrUVWhrqkNLV19xEWEI+LxA9g3aaPokIiqlArfsygrKwuRkZFyaSoqKujYsSPs7e0xZMgQLF26FMnJyZg1a1ah7QvmLVJRUUG7du1kaZMnT0bjxo2hq6sLADAwMICRkRF+//13WFhYICwsDN99912JYvz222/x888/w87ODvXq1cOyZcuQmJgoy798+TJOnTqFTz75BKamprh8+TJiYmJQr169Ml4Vel8yM9IR+eKZbD068jmePAqGto4ejE3NkZqchNiYSCTExQAAXjx7CgDQNzCCvqGxQmImKq3PGpqjWQ09rPV/hqycPOhKlQEAGTn5yHllqJm6ihIaV9fFP3eiFBUqUZn1czJDk+q6+PNyOLJy86Hzv3qe+Uo9Px0Sj6FNLfEoLh0hsemoa6qF+mba+O1C2Jt2TVRhZGWkIy7yuWw9PjoCL0IfQkNbFwYmZgCAzPQ03Lnki25DxigqTKJ3kp2ZgfhX6nliTCQin4RAQ1sHesZmCLzkB01dPegZmSL6WSiOblkF+6ZtYOvU9A17JXq7ijocTFEqfGPR0aNHYWFhIZdmb2+P+/fvY9++fRg+fDiaN28Oa2trrFixAp06dZIr26BBAxgbG8PKykrWMOTq6oq8vDy5+YqUlJTw119/Yfz48WjQoAHs7e2xYsUKuLm5vTXGyZMnIyIiAl5eXlBSUsKwYcPQu3dvJCUlAQB0dXVx9uxZLF++HMnJybCyssLSpUvlJuMmxXj0IBA/Tv1atr513a8AABePbhgzdS6uXTqLtUvmyfJXLJwJAOg7eCT6Dxn1YYMlKiNXW0MAwCRXa7n0zdee49LTJNl60xq6kAC4+iwJRJVNW5uX8w6Na2sll77jRgSu/K9O341IxZ7bkehoZ4Q+jmaISc3GxqvPERrPya2pcgh/HIzf506Qrf+7eRUAoIlrJwz4ZgYA4PaFU4AQcG7jrogQid7Zi8fB2PLTZNn68W1rAADOLp+g59fTkZIYh+Pb1iA1KQE6BoZwavsJXPoMVlS4RFWWRPD57ZXGzad8MhFVfb9fe/b2QkSVnFSl0o4CJyoxNxt9RYdAVO7ScoqeA5CoKhnUpPrbC1UBup9tUdixk/8aorBjF4efVomIiIiIiIiISIaNRUREREREREREJFPh5ywiIiIiIiIiIipPnOBaHnsWERERERERERGRDHsWEREREREREdHHjR2L5LBnERERERERERERybBnUSWipa6s6BCIyl0HW31Fh0BU7lL4qGX6CNyKTFF0CETlrrmlnqJDIKL3hHMWyWPPIiIiIiIiIiIikmFjERERERERERERyXAYGhERERERERF91DgMTR57FhERERERERERkQx7FhERERERERHRR409i+SxZxEREREREREREclUip5F0dHR+OGHH3DkyBFERUXBwMAAzs7OmDt3Llq1aqXo8ErFy8sLiYmJ8PHxUXQoVIS83Fzs2LgOvicOIzE+DgZGxnDv3B2fDhkJJSW2rVLlFBp4G+cP7sKL0AdISYjD51N+hEOztrL8rMwMHN/xO4Kunkd6SjIMTMzRsnMftPikpwKjJiod/wM78eDqecRHPIOKmhSWdg5w/XQEjKrVkJURQuDC3q24feYQMtNSYWFbFx5e42BS3VpxgROVQsDx3Qi/7Y/kqHAoq6rB2KYeGvb0gq5Z9SLLX/nrNzy6cBSN+oxE3fa8p1Pl8CjgFk7v34nwR8FITojDsOkL4NjCRa5MVPgTHNyyFo8Cb0Hk58O8hg2GTpkPAxMzBUVNVPVUisaivn37IicnB5s3b0atWrUQFRWFU6dOIT4+XtGhycnJyYGqqqqiw6B38PeOTThy4G9MnDkfNa1tERIcgP/zngstLR306P+5osMjKpOcrEyYW9misVsn7Fw2p1D+4c2rEBpwE/2+mQUDE3OE3LmKg+uXQ9fACPVeaVQiqsieBd1BY48eMK9lD5GXh7N7NmL3ou8wfNGfUFPXAABc/ncXrh75B11GTYGheXVc3L8Du3+ejhG/bIRUQ1PBZ0D0dtEh92DXriuMrOyQn5eHO/9uxZlVP6DrrDVQkarLlQ2/7Y+4J8HQ0DNUULREZZOdlQlL69po0aELNi7+vlB+bORzrJg5Fi06dkWnz4ZBXVMbUeFPoKKqpoBoqSrhMDR5Fb6rRGJiIs6fP49Fixahffv2sLKyQvPmzTFjxgx07doVABAWFoaePXtCW1sburq6GDBgAKKiomT7mDt3Lho2bIgNGzagZs2a0NbWxujRo5GXl4fFixfD3NwcpqamWLBggdyxk5KS8NVXX8HU1BS6urro0KEDbt++XeR+a9WqBalUCiEE/v77bzg6OkJDQwNGRkbo2LEj0tLSMHfuXGzevBn79++HRCKBRCKBr6/vB7mOVDL3A+6gZRtXNGvVDmYW1dDGzQMNm7XEw+BARYdGVGZ1GrWAx2fDUf+1X+UKPHsQgEaunqhVvyEMTM3RrGN3mFvZ4vnjBx84UqKyGzDdG44unjCpbg1TK1t0+WoKkuOiEfXkIYCXvYquHd2HVj0Hwr5ZO5jUsEHXUVORk52FoIunFRw9Ucm0HzMftVp2hJ6FFQyq10KLQROQnhCD+GchcuXSE2Nx7e+1aD10CpSUK8Vvw0Qy9Rq3RJfPR8KppWuR+Ye3/456TVqix5AxqF6rDozNq6F+09bQ0Tf4wJESVW0VvrFIW1sb2tra8PHxQVZWVqF8IQR69eqF+Ph4+Pn54cSJE3j06BE+/fRTuXKPHj3CkSNHcPToUezcuRMbNmxA165dER4eDj8/PyxatAjff/89Ll26JNtv165dERkZicOHD+P69eto3Lgx3N3d5Xo0hYSEYPfu3fjnn39w69YtREZGYuDAgRg2bBiCgoLg6+uLPn36QAiBKVOmYMCAAejUqRMiIiIQERGB1q1bl+8FpFJxcGyI2zeu4PmzpwCA0JBgBN29haYt2yg4MqLyY1XXEfevXURyfAyEEHh87yZiI8JR27mZokMjKrOs9DQAgLqWDgAgKSYSaUnxsHFsKiujoqqGGnWd8PwhfxCgyikn82U9V9PUlqWJ/Hz4b1mGeu59oGdhpajQiMpFfn4+Aq/7w9SiBtbOn4QfvLrj1+lf4e7ls4oOjaoCiQKXCqjC/9SgoqKCTZs2YeTIkVi7di0aN24MV1dXfPbZZ3BycsLJkydx584dhIaGokaNl/MSbN26FfXr18fVq1fRrNnLLzv5+fnYsGEDdHR04ODggPbt2yM4OBiHDx+GkpIS7O3tsWjRIvj6+qJly5Y4c+YM7t69i+joaEilUgDAkiVL4OPjg7///htfffUVACA7Oxtbt26FiYkJAODGjRvIzc1Fnz59YGX18g+0o6Oj7Hw0NDSQlZUFc3PzD3YNqeT6DfoS6WmpGD24N5SUlJGfn4cvRo6Fa8fOig6NqNx0/XIcfNYtweLRA6CkrAyJRAm9Rk2BdV3Ht29MVAEJIXB6+1pUr9MAJjVsAACpiS9/6NHU05crq6VngKTYqNd3QVThCSFwc++fMKnlAP1q1rL0wJN/Q0lZGXVceyguOKJykpqUgKzMDJzatx2dPx+B7l+MRtDNy9i4+HuMmf9/qF2/kaJDJKoyKnxjEfByzqKuXbvi3Llz8Pf3x9GjR7F48WL8+eefSE5ORo0aNWQNRQDg4OAAfX19BAUFyRqLrK2toaOjIytjZmYGZWVluUmLzczMEB0dDQC4fv06UlNTYWRkJBdLRkYGHj16JFu3srKSNRQBgLOzM9zd3eHo6AhPT0988skn6NevHwwMStctMisrq1BPquysPKj9r+GKyse508fge/wwpsxeiJrWtngcEow/Vy6BoZEJ3DvzQxdVTZeO7EX4wyAMnrYA+sZmeBJ0BwfXL4eOvhFqOzVRdHhEpXZi80pEPwvFoB9+LZQnee3nOyFEoTSiyuD6nrVIfPEEHScslqXFh4Xgge8BeE7/P869QVWSEAIA0KB5W7h1fzmSxNLGDk/u38PFY/vZWETvhPdNeRV+GFoBdXV1eHh4YPbs2bh48SK8vLwwZ86clx/yinhRX09/feJpiURSZFp+fj6Alz2RLCwscOvWLbklODgYU6dOlW2jpaUltw9lZWWcOHECR44cgYODA1auXAl7e3uEhoaW6ny9vb2hp6cnt6xbsaRU+6DS27h6OfoN+hIu7p1gbWuHDp7d0LP/IOzZvlHRoRGVi5zsLJzY+Sc6DxmNuk1aw9zKFi079YZjq/a48O8uRYdHVGonNv+GkBuXMHDmL9A1+u/HHG39l5P8piUlyJVPT06Elh7nuaDK5dqetXh+9zI6jFsITQNjWXr0owBkpibhwOwv8de3PfDXtz2QFh+NW/vW48CcYQqMmOj90NLRg5KyMsxee4qlWXUrJMSwlyjR+1QpehYVxcHBAT4+PnBwcEBYWBiePXsm610UGBiIpKQk1KtXr8z7b9y4MSIjI6GiogJra+tSbSuRSNCmTRu0adMGs2fPhpWVFfbt24dJkyZBTU0NeXl5b93HjBkzMGnSJLm0sMS3b0fvJisrExIl+cZHJWUliP81IhJVNXm5ucjLy4VEIv/bgURJCfn/+/WOqDIQQuDklt/w4NoFDJy1BPqmFnL5eibm0NIzxJN712FmXRsAkJebg2f378Dt0xGKCJmo1IQQuL5nLcLv+MN9vDe0jeWnNbBp3h7m9s5yab6rZ8O6WQfUatnxQ4ZKVC5UVFVRs3Y9RL8Ik0uPefEMhqac5oPofarwjUVxcXHo378/hg0bBicnJ+jo6ODatWtYvHgxevbsiY4dO8LJyQmDBg3C8uXLkZubizFjxsDV1RVNmzZ9+wGK0bFjR7Rq1Qq9evXCokWLYG9vjxcvXuDw4cPo1atXsfu+fPkyTp06hU8++QSmpqa4fPkyYmJiZA1X1tbWOHbsGIKDg2FkZAQ9Pb1CPZwAQCqVyuZKKqCWkV7m86GSadbaBbu3roeJmcXLYWgP78Nn1zZ4dOml6NCIyiwrMwPxkc9l6wnREYh4EgINbR3oG5vB2sEZR7ethYqaFPomZngSeBu3zh5H5yFjFBg1Uemc2LQSgf6n0WfiPKipa8rmKJJqakFVTQqJRIKmnXrD/8BOGJhZwsDcEv4HdkJVTYp6rTsoOHqikrm2ew2eXveDy8jvoaKuiYzklz3lVNU1oaImhVRLF1ItXbltlJRVoK5rAF2z6ooImajUsjLSEfvK55a46Ag8D30ITW1dGJiYoX3PgdiybA5sHZxRu0Fj3L95GQHXLmLsjysUGDVVBRyGJq/CNxZpa2ujRYsW+PXXX/Ho0SPk5OSgRo0aGDlyJGbOnAmJRAIfHx+MGzcOLi4uUFJSQqdOnbBy5cp3Oq5EIsHhw4cxa9YsDBs2DDExMTA3N4eLiwvMzMyK3U5XVxdnz57F8uXLkZycDCsrKyxduhSdO7+cIHnkyJHw9fVF06ZNkZqaijNnzsDNze2dYqX3Z9SE6dj+52qsWbYQSQkJMDQ2Qace/fCZ11eKDo2ozJ4/CsaG+RNl60e2rAYANHL1RN8x3+HTb2fj+I4/sGflAmSkJkPfxAwenw1Hcw/O00WVx81TBwEAOxdMkUvv8tUUOLp4AgBadPsUudnZOL5pJTLTU1DNti4GTP8ZUg3NDx4vUVmEnD8MADi1YoZceotBE9hziKqMZ4+CsWr2eNn6/o2/AQCate+Ez8fNglNLF/QfNQUn927DvvX/B5NqNeE17UfUquekqJCJqiSJEBxnUFk8iGLPIqr6bkckKjoEonKXkpOr6BCIyl1YQtbbCxFVcs0t9RQdAlG561LfVNEhfBCmw3Yr7NjRGwYo7NjFqTQTXBMRERERERERUfljYxEREREREREREclU+DmLiIiIiIiIiIjKFee3lsOeRUREREREREREJMOeRURERERERET0UZNI2LXoVexZREREREREREREMuxZREREREREREQfNfYsksfGokpEXVVZ0SEQlbuUnFxFh0BU7ppZGCo6BKJyJ1VKVHQIROXuyvMkRYdAVO661DdVdAikAByGRkREREREREREMuxZREREREREREQfNQ5Dk8eeRUREREREREREJMOeRURERERERET0UWPPInnsWURERERERERERDIfbWORRCKBj48PAODJkyeQSCS4desWAMDX1xcSiQSJiYkKi4+IiIiIiIiISBGq5DC0yMhIeHt749ChQwgPD4eenh7s7OwwePBgDBkyBJqamoiIiICBgcEHj+3JkyewsbHBzZs30bBhww9+fHq72Ogo/Ll6Oa74n0d2VhYsa1ph8sx5qFPXQdGhEZWJ/4GdeHD1POIjnkFFTQpLOwe4fjoCRtVqyMoIIXBh71bcPnMImWmpsLCtCw+vcTCpbq24wIlKIfDODezftQWPHwYhIS4W0+YtQfO27WX5Qgjs3vI7Th7ai7SUFNSu1wAjx09HDWtbBUZNVDpPg+7g4r+7EBH6EKmJcRgwcR7qNmsry09NisepnX/g0Z3ryExPhVVdJ3Qa+g2MLKorMGqi0gk4vhvht/2RHBUOZVU1GNvUQ8OeXtA1K7oeX/nrNzy6cBSN+oxE3fY9P3C0VKVwFJqcKtez6PHjx2jUqBGOHz+OhQsX4ubNmzh58iQmTpyIgwcP4uTJkwAAc3NzSKXSDxpbdnb2Bz0elV5KcjImjBoKZRUVLFy2Gn/u3IdR4yZDW1tH0aERldmzoDto7NEDg+euwKfTf0Z+Xh52L/oO2ZkZsjKX/92Fq0f+Qceh32DI/N+gpW+I3T9PR1ZGugIjJyq5zIwMWNvWwfBx04vM9/lrM/79ezuGj5uOn1dvgb6BEeZPG4OM9LQPHClR2WVnZcDMyhadvcYVyhNCYNfS2UiIjsCnk+fjq4XroGdsim3eU+Xu90QVXXTIPdi164pPJi9B+7E/QuTn4cyqH5CblVmobPhtf8Q9CYaGnqECIiWq2qpcY9GYMWOgoqKCa9euYcCAAahXrx4cHR3Rt29fHDp0CN27dwcgPwytOBcuXICzszPU1dXRokUL3L17Vy7/4sWLcHFxgYaGBmrUqIHx48cjLe2/D53W1tb46aef4OXlBT09PYwcORI2NjYAgEaNGkEikcDNze29nj+9m13bNsDEzAxTv/8Rdes7wtzCEo2btUS16jXevjFRBTVgujccXTxhUt0apla26PLVFCTHRSPqyUMAL79gXDu6D616DoR9s3YwqWGDrqOmIic7C0EXTys4eqKSadyiDQYOG4OW7ToUyhNC4NDeHejz+TC0bNcBNW1qY9z0ecjKzMS5U0cVEC1R2dg1bIEOA4ahXvN2hfLiI8PxPCQIXYZNgKVtXRhXq4Euw75FdmYG7vnzXk6VR/sx81GrZUfoWVjBoHottBg0AekJMYh/FiJXLj0xFtf+XovWQ6dASblKDpihD0wikShsqYiqVGNRXFwcjh8/jrFjx0JLS6vIMqV5IaZOnYolS5bg6tWrMDU1RY8ePZCTkwMAuHv3Ljw9PdGnTx/cuXMHu3btwvnz5/HNN9/I7eOXX35BgwYNcP36dfzwww+4cuUKAODkyZOIiIjA3r17y3i2VB78z/miTt36mD9zMvp3ccXXQwbg8P6/FR0W0XuV9b+eFOpaL3vMJcVEIi0pHjaOTWVlVFTVUKOuE54/DFRIjETvU3TEcyTGx8G5aUtZmqqaGhycmyA44LYCIyN6f3L/9xlVRVVNlqakpAxlFVU8C76nqLCI3llO5svPLWqa2rI0kZ8P/y3LUM+9D/QsrBQVGlGVVqUai0JCQiCEgL29vVy6sbExtLW1oa2tjenTi+6eXpQ5c+bAw8MDjo6O2Lx5M6KiorBv3z4ALxuBPv/8c0yYMAF2dnZo3bo1VqxYgS1btiAz878ukh06dMCUKVNQu3Zt1K5dGyYmJgAAIyMjmJubw9Cw6C6TWVlZSE5OlluysrJKe0molCJehOPgvt2wrFET3r+uRbfe/bFq2SKcOHxA0aERvRdCCJzevhbV6zSASY2XPR1TE+MBAJp6+nJltfQMkJoU/6FDJHrvEhLiAAD6BkZy6foGhkj8Xx5RZWdcrSb0jM1w+q8/kZGagrzcHJw/sBOpifFISeC9nConIQRu7v0TJrUcoF/NWpYeePJvKCkro45rD8UFR1UOexbJq1KNRQVev9hXrlzBrVu3UL9+/VI1uLRq1Ur2f0NDQ9jb2yMoKAgAcP36dWzatEnWCKWtrQ1PT0/k5+cjNDRUtl3Tpk0L7bckvL29oaenJ7esXr64TPuikhP5+bCrUw/DR3+L2vb10K13f3Tp2RcH9+1WdGhE78WJzSsR/SwU3cfOLJQneW1WPyFEoTSiyuz1z2JCiAr7AY2otJRVVNB/wlzERYbjl696YaFXFzwNvI3azs2hpFQlP/LTR+D6nrVIfPEErb2mydLiw0LwwPcAWgyewHs4UTmqUoM7a9euDYlEgvv378ul16pVCwCgoaHxzscouCHl5+dj1KhRGD9+fKEyNWvWlP2/uOFwbzNjxgxMmjRJLi2Kc3CWO0NjE9S0qSWXVtPaBufOnFRQRETvz4nNvyHkxiV8/v1S6BqZyNK19V/2cExLSoD2Kz0v0pMToaX34Z8aSfS+GfyvXifEx8HglbqflJgAPX1OikpVR7VadTDK+3dkpqciLzcXWrr6+POHsahWq46iQyMqtWt71uL53ctw//ZnaBoYy9KjHwUgMzUJB2Z/KUsT+fm4tW89HvjuR495GxQRLlGVU6Uai4yMjODh4YHffvsN48aNK3NDTYFLly7JGn4SEhLw4MED1K1bFwDQuHFjBAQEoHbt2qXap5ray3HkeXl5bywnlUoLPa0tMZfD0MpbfceGCA97IpcWHvYUZuYWigmI6D0QQuDklt/w4NoFDJy1BPqm8vVZz8QcWnqGeHLvOsysX97T8nJz8Oz+Hbh9OkIRIRO9V6YWltA3NMKd65dRy+7l3/GcnBwE3r6OwSML/+hDVNmp/29ul7iIcEQ8foD2/b98yxZEFYcQAtf3rEX4HX+4j/eGtrG5XL5N8/Ywt3eWS/NdPRvWzTqgVsuOHzJUqmLYU01elWosAoDVq1ejTZs2aNq0KebOnQsnJycoKSnh6tWruH//Ppo0aVLifc2fPx9GRkYwMzPDrFmzYGxsjF69egEApk+fjpYtW2Ls2LEYOXIktLS0EBQUhBMnTmDlypXF7tPU1BQaGho4evQoqlevDnV1dejp6b3radN70vezL/DtV0OwY9MfcHX3RHDgXRze/zcmfDdH0aERldmJTSsR6H8afSbOg5q6pmyOIqmmFlTVpJBIJGjaqTf8D+yEgZklDMwt4X9gJ1TVpKjXuvCTpYgqooyMdEQ+fyZbj4p8gdCQYGjr6MLEzAJd+3yOvTs2wKJ6DVhY1sTeHRsgVVdHO/dOCoyaqHSyMzMQH/lctp4YE4nIJyHQ0NaBnrEZAi/5QVNXD3pGpoh+FoqjW1bBvmkb2DqVbVoEIkW4tnsNnl73g8vI76GiromM5AQAgKq6JlTUpJBq6UKqpSu3jZKyCtR1DaBrVl0RIRNVSVWuscjW1hY3b97EwoULMWPGDISHh0MqlcLBwQFTpkzBmDFjSryvn3/+Gd9++y0ePnwIZ2dnHDhwQNYzyMnJCX5+fpg1axbatWsHIQRsbW3x6aefvnGfKioqWLFiBebPn4/Zs2ejXbt28PX1fZdTpvfI3qEB5v78K9av+T9s27gO5haWGD1hGtw9uyo6NKIyu3nqIABg54IpculdvpoCRxdPAECLbp8iNzsbxzetRGZ6CqrZ1sWA6T9DqqH5weMlKotHwYGYO3mUbH3zmmUAALdPuuGb6fPQ67OhyM7Owh//9zPSUlJgV68Bfli0Chqa79YLmehDevE4GFt+mixbP75tDQDA2eUT9Px6OlIS43B82xqkJiVAx8AQTm0/gUufwYoKl6hMQs4fBgCcWjFDLr3FoAnsOUTlix2L5EiEEELRQVDJhMVzGBpVfScfRSk6BKJy18yC8+RQ1XcnKlHRIRCVu4dxGYoOgajczf3ETtEhfBA1vtmvsGM/+62nwo5dHD4agYiIiIiIiIiIZKrcMDQiIiIiIiIiotLgBNfy2LOIiIiIiIiIiIhk2LOIiIiIiIiIiD5q7Fkkjz2LiIiIiIiIiIhIho1FREREREREREQkw8YiIiIiIiIiIvqoSSQShS1l5e3tDYlEggkTJsjShBCYO3cuqlWrBg0NDbi5uSEgIKDU++acRZVIZk6eokMgKneJGbmKDoGo3GXl5Cs6BKJyp6bM3ySp6mtorqPoEIjoI3X16lX8/vvvcHJykktfvHgxli1bhk2bNqFOnTr46aef4OHhgeDgYOjolPyexb/iRERERERERPRRq0w9i1JTUzFo0CD88ccfMDAwkKULIbB8+XLMmjULffr0QYMGDbB582akp6djx44dpToGG4uIiIiIiIiIiBQkKysLycnJcktWVlax5ceOHYuuXbuiY8eOcumhoaGIjIzEJ598IkuTSqVwdXXFxYsXSxUTG4uIiIiIiIiI6OMmUdzi7e0NPT09ucXb27vIMP/66y9cv369yPzIyEgAgJmZmVy6mZmZLK+kOGcREREREREREZGCzJgxA5MmTZJLk0qlhco9e/YM3377LY4fPw51dfVi9/f60DYhRKmHu310PYu8vLzQq1evN5Zxc3OTm02ciIiIiIiIiKg8SKVS6Orqyi1FNRZdv34d0dHRaNKkCVRUVKCiogI/Pz+sWLECKioqsh5Fr/ciio6OLtTb6G0qZc8iLy8vbN68Gd7e3vjuu+9k6T4+PujduzeEEMVu+3//939vzC8ra2trTJgwoVAj09y5c+Hj44Nbt27JYk9MTISPj897j4HeXV5uLnZsXAffE4eRGB8HAyNjuHfujk+HjISS0kfXtkpVxM3DuxB64wISI8OhrKYGc1sHtOg7DPrm1QG8rPdXfTbj2b1rSI6JgJqGFizrNUKLvl9CS99IwdETlUzQ3Rs49PdWhD68j8T4WEyc/QuatnYDAOTm5mLP5jW4dfUCYiKeQ0NLGw0aNcdnw76BgZGJYgMnKoXQwNs4f3AXXoQ+QEpCHD6f8iMcmrWV5WdlZuD4jt8RdPU80lOSYWBijpad+6DFJz0VGDVR6TwOvI2zB3Yi/PHLej5k6k+o37ydLH96f9cit+sy+Gu49hz4ocKkKuhdHmH/obi7u+Pu3btyaV9++SXq1q2L6dOno1atWjA3N8eJEyfQqFEjAEB2djb8/PywaNGiUh2rUjYWAYC6ujoWLVqEUaNGyc3+XZy8vDxIJBLo6el9gOiosvp7xyYcOfA3Js6cj5rWtggJDsD/ec+FlpYOevT/XNHhEZXJiwd3Ub99d5hY14HIz8OVfZtx6NdZGDB/HVSl6sjNzkJs2CM07joQRjVqISstBRd3rcPR3+ah7/crFB0+UYlkZWagpk0duHp0x/KfpsvlZWdl4knIffT+fDhq2tghLTUFW9ctw9K5k/HTyi0Kipio9HKyMmFuZYvGbp2wc9mcQvmHN69CaMBN9PtmFgxMzBFy5yoOrl8OXQMj1HulUYmoIsvOyoCFVW00bd8FW5f8UCj/+9/3yq3fv3UZ/6xZjAYti25EIqpKdHR00KBBA7k0LS0tGBkZydInTJiAhQsXws7ODnZ2dli4cCE0NTXx+eel+z5babtKdOzYEebm5sVO+rRp0ybo6+vj33//hYODA6RSKZ4+fVpoGFpaWhqGDBkCbW1tWFhYYOnSpYX2FRERga5du0JDQwM2NjbYsWMHrK2tsXz58lLFPHfuXGzevBn79++XPSLP19e3VPug8nU/4A5atnFFs1btYGZRDW3cPNCwWUs8DA5UdGhEZdZ1wk+wb+MBQ0srGNWoBbcvJyI1PhoxTx8CAKSaWug2aSFsm7lA37w6zGzroc3A0Yh9+hApcdEKjp6oZBo2a4MBXqPRrG2HQnmaWtqY4b0KLV08UK2GNezqOWLo6CkIfRiE2OjSTfZIpEh1GrWAx2fDUb+FS5H5zx4EoJGrJ2rVbwgDU3M069gd5la2eP74wQeOlKjs6jZqCc+BI9CgmHquY2AktwRevYBa9RvByKzaB46UqpqyPvb+fSzv07Rp0zBhwgSMGTMGTZs2xfPnz3H8+HHo6OiUaj+VtrFIWVkZCxcuxMqVKxEeHl5kmfT0dHh7e+PPP/9EQEAATE1NC5WZOnUqzpw5g3379uH48ePw9fXF9evX5coMGTIEL168gK+vL/755x/8/vvviI4u/ReoKVOmYMCAAejUqRMiIiIQERGB1q1bl3o/VH4cHBvi9o0reP7sKQAgNCQYQXdvoWnLNgqOjOj9yc5IBwCoaxX/ByM7Ix2QSCDV1PpQYRF9UBlpqZBIJNDU0lZ0KETvjVVdR9y/dhHJ8TEQQuDxvZuIjQhHbedmig6NqFykJMbj/g1/NOvQRdGhECmMr6+vXEcWiUSCuXPnIiIiApmZmfDz8yvUG6kkKu0wNADo3bs3GjZsiDlz5mD9+vWF8nNycrB69Wo4OzsXuX1qairWr1+PLVu2wMPDAwCwefNmVK9eXVbm/v37OHnyJK5evYqmTZsCAP7880/Y2dkV2t/06dPx/fffy6VlZ2fDwcEBAKCtrQ0NDQ1kZWXB3Ny8bCdN5arfoC+RnpaK0YN7Q0lJGfn5efhi5Fi4duys6NCI3gshBPx3/w7z2vVhaGldZJncnGxc3rsRtZu7QU2DjUVU9WRnZ+GvjavQ2s2TjUVUpXT9chx81i3B4tEDoKSsDIlECb1GTYF1XUdFh0ZULq77HYVUXbPYXkhEVHaVurEIABYtWoQOHTpg8uTJhfLU1NTg5ORU7LaPHj1CdnY2WrVqJUszNDSEvb29bD04OBgqKipo3LixLK127dpFzpM0depUeHl5yaWtWLECZ8+eLc0pAQCysrKQlZUll5adlQe1ImZEp/fn3Olj8D1+GFNmL0RNa1s8DgnGnyuXwNDIBO6deyg6PKJ3dn7HasSFh6LntCVF5ufl5uLU7z8DIh/tBo39wNERlb/c3Fz85j0LIj8fXt9Mf/sGRJXIpSN7Ef4wCIOnLYC+sRmeBN3BwfXLoaNvhNpOTRQdHtF7d+30ETRq1xGqavyORO+uEsxv/UFV2mFoBVxcXODp6YmZM2cWytPQ0Hjj+L+SPBWtuDJFpRsbG6N27dpyi6Gh4VuPURRvb2/o6enJLetWFP3ljt6fjauXo9+gL+Hi3gnWtnbo4NkNPfsPwp7tGxUdGtE7O79jNZ7evoTukxdB27DwE6DycnNxct1CJMdGouvEhexVRFVObm4uVi6cgZjIF/jO+zf2KqIqJSc7Cyd2/onOQ0ajbpPWMLeyRctOveHYqj0u/LtL0eERvXehQbcR8yIMzdy7KToUoiqp0jcWAS8bVg4ePIiLFy+WarvatWtDVVUVly5dkqUlJCTgwYP/JgGsW7cucnNzcfPmTVlaSEgIEhMTyxSrmpoa8vLy3lpuxowZSEpKkltGjZ9SpmNSyWVlZUKiJN/AqKSsBJGfr6CIiN6dEALnd6xG6M2L6D75Z+iaFB4GW9BQlBT9At0mLYS6tq4CIiUqPwUNRZHPwzDDexV0dPUVHRLRe5WXm4u8vFxIJPIf7yVKSsgvwQ+kRJXN1VOHYVnLHtWsays6FKoiqsoE1+9LpR+GBgBOTk4YNGgQVq5cWarttLW1MXz4cEydOhVGRkYwMzPDrFmzoKT03x/ZunXromPHjvjqq6+wZs0aqKqqYvLkyW/ttVQca2trHDt2DMHBwTAyMoKenh5UVVULlZNKpZC+NuRM7X+T0lL5adbaBbu3roeJmcXLYWgP78Nn1zZ4dOml6NCIyuz8jlUIuewLz7GzoaqugfSkeACAmoYWVNSkyM/Lw4m1CxAbFoLO4+ZB5OfLyki1dKCsUvgeRVTRZGakI/LFM9l6TOQLPHkUDG0dPRgYGeP/fpqOJyH3MWX+r8jPz0NifCwAQFtHDypF/B0mqoiyMjMQH/lctp4QHYGIJyHQ0NaBvrEZrB2ccXTbWqioSaFvYoYngbdx6+xxdB4yRoFRE5VOVkY64l6p5/HREXgR+hAa2rowMDEDAGSmp+HOJV90Y90mKjdVorEIAH788Ufs3r271Nv98ssvSE1NRY8ePaCjo4PJkycjKSlJrsyWLVswfPhwuLi4wNzcHN7e3ggICIC6unqpjzdy5Ej4+vqiadOmSE1NxZkzZ+Dm5lbq/VD5GDVhOrb/uRprli1EUkICDI1N0KlHP3zm9ZWiQyMqs0DfQwCAg0vk52dx85oE+zYeSEuIxdPbL3tY/j1ffp6i7lMWoZp98XO/EVUUjx8EYcH0r2Xr237/FQDQrmNX9B38FW5cejl/4Mwxg+S2m7VoLRycOZcLVQ7PHwVjw/yJsvUjW1YDABq5eqLvmO/w6bezcXzHH9izcgEyUpOhb2IGj8+Go7kH512kyiP8cTB+nztBtv7v5lUAgCaunTDgmxkAgNsXTgFCwLmNuyJCpCqqgnbwURiJKMnEPSQnPDwcNWrUwMmTJ+Hu/uFuUA+i2LOIqr5/gyMVHQJRuXOpYazoEIjKXWhSqqJDICp3qkpVYlYPojfq5fRxPMm7zrSjCjv2g8WdFHbs4lSZnkXl6fTp00hNTYWjoyMiIiIwbdo0WFtbw8WFj2gkIiIiIiIioqqFjUUlkJOTg5kzZ+Lx48fQ0dFB69atsX379iLnGiIiIiIiIiKiyqWiTjStKGwsKgFPT094enoqOgwiIiIiIiIionLHxiIiIiIiIiIi+qixY5E8zshGREREREREREQybCwiIiIiIiIiIiIZDkMjIiIiIiIioo+akhLHob2KjUWVSEhsqqJDICp37a1MFB0CUbnLys1TdAhE5U5ViR3YiYiIKis2FhERERERERHRR40TXMvjTz5ERERERERERCTDnkVERERERERE9FGTsGuRHPYsIiIiIiIiIiIiGTYWERERERERERGRDIehEREREREREdFHjaPQ5FX6xqLo6Gj88MMPOHLkCKKiomBgYABnZ2fMnTsXrVq1UnR4VME9CriF0/t3IvxRMJIT4jBs+gI4tnCRKxMV/gQHt6zFo8BbEPn5MK9hg6FT5sPAxExBUROVTtCdGzi4ZytCHwYhIT4Wk+csQbM2brL8K+dP4+ShvQh9GISU5CT8vGY7rG3tFRcwURncv3sTR/7Zhich95EYH4vx3y9Gk9ausvx92/7A5bMnEBcTBRVVVVjXrot+Q76Gbd0GCoyaqHQeB97G2QM7Ef74AVIS4jBk6k+o37ydLH96f9cit+sy+Gu49hz4ocIkeies50QVQ6VvLOrbty9ycnKwefNm1KpVC1FRUTh16hTi4+MVHVqJCCGQl5cHFZVK/1JUStlZmbC0ro0WHbpg4+LvC+XHRj7Hiplj0aJjV3T6bBjUNbURFf4EKqpqCoiWqGwyMzNgVcsObp7dsWz+tCLz7es7o6VLR/z+608KiJDo3WVlZqCGjR3aeXTDygXfFco3t6yJL0ZPgYm5JbKzs3Bs30788v14LF7/D3T1DBQQMVHpZWdlwMKqNpq274KtS34olP/973vl1u/fuox/1ixGg5ZFf7kmqohYz0lROMG1vErdQpGYmIjz58/D19cXrq4vbw5WVlZo3ry5rMyyZcuwceNGPH78GIaGhujevTsWL14MbW1tCCFgamqKtWvXom/fvgCAhg0b4sWLF4iOjgYA+Pv7w8XFBQkJCRg/fjyio6Px77//yvafm5uL6tWrY+HChRg2bBiEEPjll1+wdu1aREREoE6dOvjhhx/Qr18/AICvry/at2+Po0ePYtasWbhz5w6OHTuG9u3bf6jLRq+o17gl6jVuWWz+4e2/o16TlugxZIwszdi82ocIjei9adS8DRo1b1NsvkvHrgCA6MgXHyokovfOuVlrODdrXWx+q/aecuuff/Utzh4/gGehIajfsFl5h0f0XtRt1BJ1GxX/uUXHwEhuPfDqBdSq3whGZvzsQpUH6zlRxVCpJ7jW1taGtrY2fHx8kJWVVWQZJSUlrFixAvfu3cPmzZtx+vRpTJv28pd1iUQCFxcX+Pr6AgASEhIQGBiInJwcBAYGAnjZuNOkSRNoa2tjxIgROHr0KCIiImT7P3z4MFJTUzFgwAAAwPfff4+NGzdizZo1CAgIwMSJEzF48GD4+fnJxTVt2jR4e3sjKCgITk5O7/vS0HuQn5+PwOv+MLWogbXzJ+EHr+74dfpXuHv5rKJDIyKid5Cbk4MzR3ygqaWNmjZ2ig6HqFykJMbj/g1/NOvQRdGhEJUb1nOi8lOpG4tUVFSwadMmbN68Gfr6+mjTpg1mzpyJO3fuyMpMmDAB7du3h42NDTp06IAff/wRu3fvluW7ubnJGovOnj0LZ2dndOjQQZbm6+sLNzc3AEDr1q1hb2+PrVu3yrbfuHEj+vfvD21tbaSlpWHZsmXYsGEDPD09UatWLXh5eWHw4MFYt26dXOzz58+Hh4cHbG1tYWQk3zpOFUNqUgKyMjNwat921G3UAl/PWQbHFi7YuPh7hATcVHR4RERUSrcun8dXfdwwolc7HPP5C1MXrISOnr6iwyIqF9f9jkKqrokGr83FSFSVsJ7T+ySRSBS2VESVurEIeDln0YsXL3DgwAF4enrC19cXjRs3xqZNmwAAZ86cgYeHBywtLaGjo4MhQ4YgLi4OaWlpAF42FgUEBCA2NhZ+fn5wc3ODm5sb/Pz8kJubi4sXL8qGuAHAiBEjsHHjRgAvJ9c+dOgQ/p+9u46LKu3bAH4N4RBDo4AK0gaKioWihIrYXWsidrcu6iomdue6BrauvbargoG6FhaIimIBohJK17x/+O48OwuugAOHuL7v53xezn3qmnnOjvCb+76Pl5cXACA4OBgpKSlwd3eX9XqSSCTYsWMHwsLC5HLXrVv3P19XamoqPn/+LLekp+Xce4oKhlQqBQBUr98Yru16oIKFDZp37oNqdRoh8OwxgdMREVFeVa1ZB3PX7sSMZZthX8cR63yn4XNc8ZjjkCivbl88jdpNmkO1jFjoKEQFhvc5UcEp9sUiAFBTU4O7uztmzpyJwMBAeHp6YtasWXj16hVat26N6tWr49ChQ7hz5w7WrVsHAEhPTwcAVK9eHQYGBggICJAVi1xcXBAQEIBbt24hOTkZjRs3ll2rX79+ePHiBa5fv45du3bB3NwcTZp8nZ0/KysLAHDy5EkEBQXJluDgYBw8eFAus6am5n++Jl9fX+jo6MgtBzavVth7Rt+nqaUDJWVlGFU0l2s3qlgJsR/eCxOKiIjyTaymDqPyprCuUgMDx82AsrIyAs4eFzoWkcK9DLmPDxGvUa9ZW6GjEBUY3uekaCKRcEtRVKwnuP6WatWq4ejRo7h9+zYyMjKwbNkyKCl9rYv9cwga8L95i44dO4ZHjx6hSZMm0NLSQnp6OjZu3AgHBwdoaWnJ9jcwMEDHjh2xbds2XL9+HQMGDJC7rlgsxuvXr+V6I+WHt7c3JkyYINd2KSz+h85JeaOiqgoz66qIjngt1/4h4g30yxkLlIqIiBRFKv06fxFRSXPrwilUsKyM8ubWQkchKjC8z4kKVrEuFn369AndunWDl5cX7O3toaWlhdu3b2Px4sXo0KEDrKyskJGRgTVr1qBdu3a4du0aNm7cmO08rq6uGD9+PGrXrg1tbW0AgLOzM3bv3p2tYAN8HYrWtm1bZGZmon///rJ2LS0tTJo0CePHj0dWVhYaN26Mz58/IzAwEBKJRG7f7xGLxRCL5btTqpZJyfXxlDupyUn4GPVOtv4pOhLvXj6DhkQbemWN4NbhJ+xYPgtW1WrCuroDnty7ice3AzFyLnt5UfGRkpyEqIg3svXoqHcIDwuFREsHhuWMkfA5Hh8/RCH20wcAQMSbVwAAXT0D6OobCpKZKK9SkpPwPuKtbP3D+wi8CnsKiZY2JNo6OL5vG2o7NoGuniESvsTjwolDiP0YjXpNmgmYmihvUpOT8Okfv7fEREci4uUzqP//7y0AkJKUiAc3/NH2H09yJSpOeJ+TUIrq3EFCKdbFIolEggYNGmDFihUICwtDeno6TE1NMXjwYEybNg3q6upYvnw5Fi1aBG9vbzg7O8PX1xf9+vWTO4+bmxsyMzNlE1kDgIuLC44ePZpjD6HmzZvDxMQEdnZ2KF9e/hGNc+fORbly5eDr64sXL15AV1cXDg4OmDZtWoG8B/Rj3oSFYt3MMbL1Y9vWAgDqubVEr9HTYe/ojG5DJ+HPw7twZMsqlC1vBs8pc2FZlU+wo+Ij7Gkw5k4eJlvfuWkFAMDZvS1GTPbB7RuXsXHpbNn21Qu+fl516TMY3foNLdywRPn08lkIFv78vz8a9m5eCQBo3LwN+o+aisi3r3B1/ikkxMdBoq0DC9uqmLZkEypWshQoMVHevX0Ril99xsnWT/h9nV6hjktLdB/lDQC4f+0CIJWiphMLoVQ88T4nKhpE0r9n8aVcS0pKQvny5bF161Z07ty50K576nF0oV2LSCgmEnWhIxAVuNSMTKEjEBW4qET2iCYiKgk62peOKThqz74o2LXvzWoq2LW/pVj3LCpsWVlZiIqKwrJly6Cjo4P27dsLHYmIiIiIiIiIfhBHocljsSgPXr9+DQsLC1SsWBHbt2+HigrfPiIiIiIiIiIqWVjtyANzc3Nw1B4RERERERFRycIJruUpCR2AiIiIiIiIiIiKDhaLiIiIiIiIiIhIhsPQiIiIiIiIiKhU4yg0eexZREREREREREREMuxZVIxUKactdASiAnfmWZTQEYgKXGV9idARiApcRS0NoSMQFbiX8QlCRyAiBeEE1/LYs4iIiIiIiIiIiGTYs4iIiIiIiIiISjV2LJLHnkVERERERERERCTDYhEREREREREREclwGBoRERERERERlWqc4FoeexYREREREREREZFMketZ5OnpCT8/P/j6+uLnn3+WtR89ehSdOnXCly9foKenh127dqFHjx6y7T169MCBAwfw/PlzWFlZydqtrKzQo0cPLFiwQNZWuXJlvHz5Ei9fvkSFChUK54VRsdC/aytER0Vka2/bqQdGTpwmQCKiH3frxD48v3MNsVFvoKJaBibW1dC420DomZjK9nl++yoe+p9C9KtnSEn4jF6z16OsmdV/nJWoaHn2OAjnj+zBm+dPEB/7CUO8fVHL0Vm2fceqebhx8bTcMea21TBlyebCjkqUbyEP7+LkwZ14+ewJ4mI+YvzMJajbyBUAkJGRgd/9NiDo1jV8iHwHdU0Jqteuj55eo6BnUFbY4ER58DL4Pq7+sR8RL5/iS+wn9Jo0F9XqNZZtT01Jxrk9vyLk1lUkffkMvbLGcGzVGQ1adBAwNZUE7Fgkr0j2LFJTU8OiRYsQGxubbZtEIkHdunVx6dIlufaAgACYmprKtb99+xYvXryAm5ubrO3q1atISUlBt27dsH379gJ7DVQ8rdq8G7uPXZAtC1ZsAgA0cXMXOBlR/r0LfYCazdqhx4yV6DTJF1lZmTiybBrSU1Nk+6SnpaC8TTU4dfUSMClR/qWlJKOiuTW6D53wzX2qOTjCd/tx2TJy5rJCTEj041JTkmFmYQvPEZOzbUtLTUH48yfo1Gsg5q3diXG/LEbku9dY5jNRgKRE+ZeemgLjSlZoO2BMjttP+a3Ds6C/0HXUdIxd7odGbbri5LbVCLl1tZCTEpVsRbJY1Lx5cxgbG8PX1zfH7W5ubvD395eth4SEIDk5GSNGjJBrv3TpElRVVeHk5CRr27JlC3r16oW+ffti69atkEqlcuc2NzfHvHnz0K9fP0gkElSqVAnHjh3Dhw8f0KFDB0gkEtSoUQO3b9+WOy4wMBDOzs5QV1eHqakpxowZg8TERNn29evXw8bGBmpqajAyMkLXrl1/4B2igqKrpw99A0PZcjPwMkwqmKJG7bpCRyPKt44TF6Ba4xYwqGCOsmZWcPeaiC+fohEd/ky2T9VGzdGgQx+Y2dUWMClR/tnVaYj2fYagdkPXb+6joqoKHT0D2aKppV14AYkUoFY9J3T3HI56jZtm26ahKYG37zo4OrujvKk5bKrWQP/hk/DyWQg+RkcJkJYof2xrN4B7z4Gwa+Cc4/Y3Tx+jtosHLO1qQa+cMeo1bwfjSlZ49+JpISclKtmKZLFIWVkZCxYswJo1a/D27dts293c3BAaGorIyEgAX4tCTZo0QdOmTbMVixo0aAANDQ0AwJcvX/D777+jT58+cHd3R2Jiotz+f1uxYgWcnJxw7949tGnTBn379kW/fv3Qp08f3L17F9bW1ujXr5+s0PTw4UN4eHigc+fOePDgAfbv34+rV69i1KhRAIDbt29jzJgxmDNnDkJDQ3HmzBk4O+f84UdFR3p6Oi6dO4kWbTpysjMqUdKSvxayxZpaAichKlzPHt3DlH5t4DO8J3avXYgvcdl7MBOVJMmJCRCJRNDQlAgdhUhhKlWpgSe3A/E55gOkUilePLqHj5FvYV2zntDRqJgTiUSCLUVRkSwWAUCnTp1Qq1YtzJo1K9s2JycnqKqqygo9/v7+cHFxgYODA+Lj4/Hs2TNZ+z+HoO3btw82Njaws7ODsrIyevbsiS1btmQ7f+vWrTF06FDY2Nhg5syZ+PLlC+rVq4du3brB1tYWU6dORUhICN6/fw8AWLJkCXr16oVx48bBxsYGjRo1wurVq7Fjxw6kpKTg9evX0NTURNu2bVGpUiXUrl0bY8bk3K3yb6mpqfj8+bPckpqamt+3k/Lh+uWLSEj4AvfW7YWOQqQwUqkUl/f9ivI2djCsaC50HKJCU83BEZ4TZmHc3DXoPGAUXj0PwcpfRiM9PU3oaEQFIi0tFfu2rUMjVw8Wi6hEaTNgNMpWrITFw7tjVm93+PlORbuB42BepYbQ0YhKlCJbLAKARYsWwc/PD8HBwXLtGhoaqF+/vqxYFBAQAFdXV6ioqMDJyQn+/v54/fo1Xr58iaZN/9dNd8uWLejTp49svU+fPjh8+DDi4uLkzm9vby/72cjICABQo0aNbG3R0dEAgDt37mD79u2QSCSyxcPDA1lZWXj58iXc3d1RqVIlWFpaom/fvti9ezeSkpL+87X7+vpCR0dHbtm4akku3zlShLMnj6BuAycYGJYTOgqRwvjvWoePb16i5TBvoaMQFaq6TZqjRt1GKF/JEvb1G2PkzGWIjniDR7cDhY5GpHAZGRlY6zsd0qwseI6aKnQcIoW6cfow3j4LQZ8p8zHCdxNa9R2OP7asxPMHd4SORsWcSCTcUhQV6WKRs7MzPDw8MG1a9qdQubm54dKlS3j8+DGSk5Ph4OAAAHBxccGlS5dw6dIlqKmpwdHREQAQHByMmzdvYsqUKVBRUYGKigocHR2RnJyMvXv3yp1bVVVV9vPfXcJyasvKypL9/6FDhyIoKEi23L9/H8+ePYOVlRW0tLRw9+5d7N27FyYmJpg5cyZq1qyZrUj1T97e3oiPj5dbho3NPpkhFYz3UREIun0TLdt1FjoKkcL471qHF/euo8vUxdDS55NxqHTT0TeEflljfIjIPtydqDjLyMjAmgXe+BAVgZ9917JXEZUo6WmpOL/3N7TqNxxV6jSCcSUrOLbshBoN3XDtxH6h4xGVKCpCB/geX19f1K5dG7a2tnLtbm5umDdvHvbs2YPGjRtDWVkZwNdi0Zo1ayAWi9GwYUOoqakB+NqryNnZGevWrZM7z86dO7FlyxYMHz483xkdHBzw+PFjWFtbf3MfFRUVNG/eHM2bN8esWbOgq6uLixcvonPnnIsRYrEYYrFYru3jP55cRAXr/Mlj0NHTR/2GTYSOQvTDpFIp/HetQ9jdQHSZugQ6ZY2FjkQkuITP8Yj9GA1tPQOhoxApzN+Foqh3rzF90UZoaesKHYlIoTIzMpCZmQGRSL7Pg0hJCVn/enARUV4V1bmDhFLki0X29vbo3bs31qxZI9feqFEjiMVirFmzBtOnT5e116tXD/Hx8Th06BAmT/7aEyc9PR07d+7EnDlzUL16dbnzDBo0CIsXL8b9+/dRs2bNfGWcOnUqHB0dMXLkSAwePBiampoICQnB+fPnsWbNGpw4cQIvXryAs7Mz9PT0cOrUKWRlZaFy5cr5uh4VrKysLJw/dQzNW7aDskqR/0+E6Lsu7VyL0BuX0G6MD8qoqyMxPgYAIFbXhEqZr0XplITP+BLzAQmxnwAAsZFvAAAaOnrQ1NEXJjhRHqQkJ+FD5P96CX16H4E3L55CU0sbGhJtnNy3FbUbukJHzwCfoiNxbOcmSLR1UMuRD5yg4iMlOQlREW9k6x+iIhAeFgqJlg70DAyxat5UhD9/gklzViArKxNxMR8BABItHaj8o5c8UVGWmpKMmKh3svXY6EhEhj+HukQLuoZGMK9WE2d2bYRKGTF0yxohPPg+gi6fQ6t+IwRMTVTyFIu/hOfOnYsDBw7Itf09xOzv+Yr+pqqqioYNG+LChQuyya2PHz+OT58+oVOnTtnObWNjgxo1amDLli1YvXp1vvLZ29sjICAA06dPR5MmTSCVSmFlZYUePXoAAHR1dXH48GH4+PggJSUFNjY22Lt3L+zs7PJ1PSpY927fQPT7SLRo01HoKEQK8fDSCQDAoUXyQ1ndB05EtcYtAAAvgm7g/JZlsm2nN/oCABp06APHjn0LKSlR/r1+/gQrZ4yWrR/a+vVLJsemrdBz2GREhIfh5qXTSE5MgI6eAWxrOGDg5DlQ09AUKjJRnr14GoL5U4fJ1nf9ugIA0KR5G3TpMwR3b1wGAEwb0VvuuOmLNqJazTqFF5ToB7wLC8XWOeNl66d3rAcA1HbxQJcRP6PH2Jk4t2czfl8zH8kJn6Fb1gjuPQeivjsfSkOkSCKplP31iosXHzgMjUq+M8+ihI5AVOAq63MOESr5dMRlhI5AVOBexicIHYGowHWrVV7oCIWi8dIrgl376qSiN/1JkZ7gmoiIiIiIiIiIClexGIZGRERERERERFRQOMG1PPYsIiIiIiIiIiIiGRaLiIiIiIiIiIhIhsPQiIiIiIiIiKhU4zA0eexZREREREREREREMuxZVIxEf04ROgJRgeMjxak00FcXCx2BqMBlZkmFjkBU4Gqa6AodgYgUhB2L5LFnERERERERERERybBYREREREREREREMhyGRkRERERERESlGie4lseeRUREREREREREJMOeRURERERERERUqrFjkTz2LCIiIiIiIiIiIpki07MoOjoav/zyC06fPo33799DT08PNWvWhI+PDxo2bCh0vFx58uQJqlatihs3bqBBgway9gYNGiAoKAixsbHQ0NAAAKSlpUFXVxcrV67EkCFDhIpc6j15eA+nD+1C+PMniIv5iDEzFqNOIxfZ9iO7NuPm5fP49OE9VFRVYW5dBV37DYNVleoCpibKm2ePg3D+yB68ef4E8bGfMMTbF7UcnWXbd6yahxsXT8sdY25bDVOWbC7sqET5FvLgLv74fSdePgtBbMxHTJy1FPWcXGXb/7p6EX+ePIyXz0Lw5XM8Fm7YDXOrysIFJsqHkId3cfLgTrx89vX3lvEzl6BuI1cAQEZGBn7324CgW9fwIfId1DUlqF67Pnp6jYKeQVlhgxP9gMyMDOzZtgn+508hLuYT9AwM0axVO/ToNxhKSuz7QIrDOYvkFZn/urp06YL79+/Dz88PT58+xfHjx+Hq6oqYmBiho+WKVCqFtbU1TExMcOnSJVl7QkIC7t27h3LlyiEwMFDWfvPmTSQnJ8PNzU2IuPT/UlOSYWphg77DJ+W43biCGfoOn4T56/dg+pJfYVjOBEtmjMHn+NhCTkqUf2kpyahobo3uQyd8c59qDo7w3X5ctoycuawQExL9uJSUZFSytMGAUVO+ub2yXU38NHB0IScjUpzUlGSYWdjCc8TkbNvSUlMQ/vwJOvUaiHlrd2LcL4sR+e41lvlMFCApkeIc3LMdp48fxLDxP2P9zsMYMHwsjuzdgROH9gkdjahEKxLFori4OFy9ehWLFi2Cm5sbKlWqhPr168Pb2xtt2rQBACxfvhw1atSApqYmTE1NMWLECCQkJAD4WqgpW7YsDh06JDtnrVq1UK5cOdn69evXoaqqioSEBHh5eaFt27ZyGTIyMmBsbIytW7fKzrl48WJYWlpCXV0dNWvWxMGDB2X7+/v7QyQS4ezZs6hbty7EYjGuXLkCV1dX+Pv7y/a7cuUKbG1t0b59e7l2f39/VKhQATY2Ngp7HynvatZrhK79h6GuU85Fu4ZuHrCrXR/lTCqgYiVL9BoyFslJiXjz8nkhJyXKP7s6DdG+zxDUbuj6zX1UVFWho2cgWzS1tAsvIJEC1K7vhB4DRqB+46Y5bndu3gZd+gxG9dr1CzkZkeLUqueE7p7DUS+H+1xDUwJv33VwdHZHeVNz2FStgf7DJ+HlsxB8jI4SIC2RYjx5/ACOTi6o17AJjEzKw8nVHbXqOeJZaLDQ0YhKtCJRLJJIJJBIJDh69ChSU1Nz3EdJSQmrV6/Go0eP4Ofnh4sXL2LKlK/fHopEIjg7O8uKMbGxsQgODkZ6ejqCg79+iPj7+6NOnTqQSCQYNGgQzpw5g8jISNn5T506hYSEBHTv3h0AMGPGDGzbtg0bNmzA48ePMX78ePTp0wcBAQFyuaZMmQJfX1+EhITA3t4ebm5uuHr1KjIyMgAAly5dgqurK1xcXOR6HF26dIm9ioqZjPR0XDp9FBqaEphZsMhHJcuzR/cwpV8b+Azvid1rF+JLHHvPEREVd8mJCRCJRNDQlAgdhSjfqtWohft3/8K7N68AAC+fhyLkYRDqOjoJnIxKGpFIuKUoKhJzFqmoqGD79u0YPHgwNm7cCAcHB7i4uKBnz56wt7cHAIwbN062v4WFBebOnYvhw4dj/fr1AABXV1f8+uuvAIDLly+jZs2aMDMzg7+/P6pVqwZ/f3+4uroCABo1aoTKlStj586dsoLTtm3b0K1bN0gkEiQmJmL58uW4ePGibL4kS0tLXL16FZs2bYKLy//mtJkzZw7c3d1l666urkhMTMStW7fQsGFD+Pv7Y/LkyXB2dkbfvn2RlJQEFRUV3LhxA2vXri2w95QUJ+jmVaxfNANpqSnQ0TfE5PlroKWjK3QsIoWp5uCI2k5NYVDWGB/fR+DEns1Y+cto/Lx8K1RVywgdj4iI8iEtLRX7tq1DI1cPFouoWOvaewCSEhMwvE8nKCkpIysrE30Hj4RL81ZCRyMq0YpEzyLg65xFEREROH78ODw8PODv7w8HBwds374dwNeeOO7u7qhQoQK0tLTQr18/fPr0CYmJiQC+FmkeP36Mjx8/IiAgAK6urnB1dUVAQAAyMjIQGBgoV+QZNGgQtm3bBuDr5NonT56El5cXACA4OBgpKSlwd3eX9XqSSCTYsWMHwsLC5HLXrVtXbt3GxgYVK1aEv78/Pn/+jHv37sHFxQVGRkawsLDAtWvXcOPGDSQnJ6Np05y7ygNAamoqPn/+LLekfaPXFRWsqjXrYO7anZixbDPs6zhine80fI4rHnNpEeVG3SbNUaNuI5SvZAn7+o0xcuYyREe8waPbgd8/mIiIipyMjAys9Z0OaVYWPEdNFToO0Q+5cvEs/M+dwqSZC7Dytz0YN20OjuzbiQunjwsdjUoYJZFIsKUoKjLFIgBQU1ODu7s7Zs6cicDAQHh6emLWrFl49eoVWrdujerVq+PQoUO4c+cO1q1bBwBIT08HAFSvXh0GBgYICAiQFYtcXFwQEBCAW7duITk5GY0bN5Zdq1+/fnjx4gWuX7+OXbt2wdzcHE2aNAEAZGVlAQBOnjyJoKAg2RIcHCw3bxEAaGpqZnsdrq6uuHTpEq5cuQIbGxvZ3El/D0W7dOkSKlWqBHNz82++F76+vtDR0ZFbdmxckf83l/JNrKYOo/KmsK5SAwPHzYCysjICzvIfJyq5dPQNoV/WGB8i3godhYiI8igjIwNrFnjjQ1QEfvZdy15FVOxtW78SXXsPgHOzljC3skFTj7bo0K03ft+9TehoRCVakRiG9i3VqlXD0aNHcfv2bWRkZGDZsmWyxyMeOHBAbt+/5y06duwYHj16hCZNmkBLSwvp6emyoW1aWlqy/Q0MDNCxY0ds27YN169fx4ABA+SuKxaL8fr1a7neSLnl5uaGMWPGoFq1arKhb8DXYtHatWshFov/s1cRAHh7e2PCBPknFwW9Tc5zFlI8qfTr/EVEJVXC53jEfoyGtp6B0FGIiCgP/i4URb17jemLNkJLW1foSEQ/LDU1BSIl+Z4XSspKkP7/F/xEVDCKRLHo06dP6NatG7y8vGBvbw8tLS3cvn0bixcvRocOHWBlZfX1H781a9CuXTtcu3YNGzduzHYeV1dXjB8/HrVr14a29tcn+Tg7O2P37t3ZCi/A16Fobdu2RWZmJvr37y9r19LSwqRJkzB+/HhkZWWhcePG+Pz5MwIDAyGRSOT2zYmbmxsSExOxdetWbN68Wdbu4uICT09PKCsry4a8fYtYLIZYLJZrKyPmB6KipSQn4f0/ek98eB+BV2FPIdHShkRbB8f3bUNtxybQ1TNEwpd4XDhxCLEfo1GvSTMBUxPlTUpyEj5E/u8+//Q+Am9ePIWmljY0JNo4uW8rajd0hY6eAT5FR+LYzk2QaOuglqOzgKmJ8iYlOQlREW9k69FR7xAeFgqJlg4Myxkj4XM8Pn6IQuynDwCAiP+fKFVXzwC6+oaCZCbKq3/f5x+iImT3uZ6BIVbNm4rw508wac4KZGVlIi7mIwBAoqUDFVVVoWIT/ZB6jZxxYOcWlDUygZm5FV48e4Kj+3fBvXVHoaNRCVNER4MJRiSVSqVCh0hNTYWPjw/OnTuHsLAwpKenw9TUFN26dcO0adOgrq6OFStWYMmSJYiLi4OzszN69+6Nfv36ITY2Frq6ugCAR48eoUaNGpg0aRKWLFkCAFi5ciXGjx+PEydOoE2bNnLXlUqlsLCwgJ2dHU6ePJlt25o1a7B+/Xq8ePECurq6cHBwwLRp02RPXnNzc5O7/j+Zm5vj1atXiIyMhLGxsazd2toaYWFhePPmDSpWrJin9+lGWFye9qfvC3lwBwt/HpGtvXHzNug/aio2Lp6JsNDHSIiPg0RbBxa2VdG+pxcsbasJkLZ0SEzPEDpCifP04V2snDE6W7tj01boOWwyNi34GW9ePkVyYgJ09AxgW8MBbXsNhn5ZIwHSlg766uLv70R58vj+bcydPCxbu7N7W4yY7AP/c39g49LZ2bZ36TMY3foNLYyIpU5mluC/YpY4wffvYP7U7Pd5k+Zt0KXPEIzz7JDjcdMXbUS1mnUKOl6ppK1RJL57L9GSkhKx+7f1uH7lIuJjY6FvWBbOzVqip+cQqLIIWihsjTSEjlAoWqy7Idi1z410FOza31IkikVCSUpKQvny5bF161Z07txZ6DjfxWIRlQYsFlFpwGIRlQYsFlFpwGIRlQalpVjksf6mYNc+O6KBYNf+llL56ZaVlYWoqCgsW7YMOjo6aN++vdCRiIiIiIiIiIiKhFJZLHr9+jUsLCxQsWJFbN++HSoqpfJtICIiIiIiIiIASpyzSE6prJKYm5ujFI++IyIiIiIiIiL6JiWhAxARERERERERUdFRKnsWERERERERERH9TSTiOLR/Ys8iIiIiIiIiIiKSYc8iIiIiIiIiIirV2LFIHotFxUjV8tpCRyAqcJeeRQsdgajAlVFSFjoCUYHT1VAVOgJRgXsTmyR0BKICZ2ukIXQEEgCHoRERERERERERkQx7FhERERERERFRqSYCx6H9E3sWERERERERERGRDHsWEREREREREVGppsSORXLYs4iIiIiIiIiIiGRKVbFo+/bt0NXV/aFz+Pj4oFatWv+5j6enJzp27PhD1yEiIiIiIiKiwiESiQRbiqJiOQzN09MTfn5+snV9fX3Uq1cPixcvhr29/Q+d+9ChQ1i8eDGePHmCrKwsmJmZoWXLlli2bBkAYNKkSRg9evQPXYOKru1bfsWlC+fxKvwFxGI11KhZG6PHTUQlcwuhoxHl24vg+7h8fC/evniKL7Gf0G/yPNjVbyLbPrWbS47Hte4zDC4dfiqsmEQ/5OmjezhzeBdehYUiPuYjRk5bhNoNc763d6xdiMtnj6LHoHFw79CzkJMS5V/wg7s4tn8HXjwLQeynj5gyeynqN3aTbZdKpTiw41f8efIwEr98gXXV6hg8ZipMza0ETE2UN88eB+H8kT148/wJ4mM/YYi3L2o5Osu271g1DzcunpY7xty2GqYs2VzYUYlKtGLbs6hly5aIjIxEZGQkLly4ABUVFbRt2/ab+6enp3/3nH/++Sd69uyJrl274q+//sKdO3cwf/58pKWlyfaRSCQwMDBQyGugoufunVvo1qMXtuzYhzUbtyAzMwOjhw9EcnKS0NGI8i0tNRkmlazRceC4HLfP+PWw3NJ1xFSIRCJUd8z5D22ioig1JRmmFjboNXTif+5373oAXj59DF39soWUjEhxUpKTYW5li4Gjp+a4/eg+P5w4uBsDR0/FwvU7oKtngDlTRiA5KbGQkxLlX1pKMiqaW6P70Anf3KeagyN8tx+XLSNnLivEhESlQ7HsWQQAYrEYxsbGAABjY2NMnToVzs7O+PDhAxITE2FhYYH9+/dj/fr1uHHjBjZs2JCte9enT5/QqlUrGBsb48CBAzhx4gQaN26MyZMny/axtbWVG1Lm4+ODo0ePIigoCACQmZmJyZMnY+vWrVBWVsbAgQMhlUrlriOVSrFkyRJs3LgRkZGRsLW1xS+//IKuXbsWzJtD+bZ6vfw3EjNnL4BHUyeEBD+GQ516AqUi+jFVajuiSm3Hb27X0pMvgAffugZLu9owMCpf0NGIFKZG3UaoUbfRf+4T+ykaezYtxbjZq7B6zrf/CCEqqhwaOMGhgVOO26RSKU4e3oPOvbzg2KQpAGD01NkY2NUdVy6cQYt2XQozKlG+2dVpCLs6Df9zHxVVVejo8Qt8UqwiOhpMMMW2Z9E/JSQkYPfu3bC2tpbr9TN16lSMGTMGISEh8PDwkDvm7du3aNKkCapUqYLDhw9DTU0NxsbGePz4MR49epTray9btgxbt27Fli1bcPXqVcTExODIkSNy+8yYMQPbtm3Dhg0b8PjxY4wfPx59+vRBQEDAj71wKnAJCV8AADo6OgInISocX+Ji8OTuddRr2lroKEQKlZWVhS3LZ8Ojcx9UqGQpdBwihYuOfIe4mE+oWfd/Xw6olimDajXrIPTxfQGTESnes0f3MKVfG/gM74ndaxfiS1ys0JGISpxi27PoxIkTkEgkAIDExESYmJjgxIkTUFL6X/1r3Lhx6Ny5c7Zjnz59Cnd3d3To0AGrVq2S9TgaPXo0rly5gho1aqBSpUpwdHREixYt0Lt3b4jF4hxzrFy5Et7e3ujS5eu3NRs3bsTZs2dl2xMTE7F8+XJcvHgRDRt+rZBbWlri6tWr2LRpE1xcch7mkZqaitTUVPm2LNVv5iDFk0qlWLlsEWrWrgMra1uh4xAVijsBZyBW00D1Bs7f35moGDlzaCeUlJTRrF13oaMQFYjY2E8AAN1/9bbQ1dPHh/eRQkQiKhDVHBxR26kpDMoa4+P7CJzYsxkrfxmNn5dvhapqGaHjUTGmxK5FcoptzyI3NzcEBQUhKCgIN2/eRIsWLdCqVSu8evVKtk/dunWzHZecnIzGjRujY8eOWL16tdzQNE1NTZw8eRLPnz/HjBkzIJFIMHHiRNSvXx9JSdnnrImPj0dkZKSsCAQAKioqctcNDg5GSkoK3N3dIZFIZMuOHTsQFhb2zdfn6+sLHR0duWX5koV5fp8o/5b4zsXzp6GYt3Cp0FGICs3ti6dRu0lzqJZhYZpKjvDnT/Dn8f3wGvdLkX3iCJGi/PsWl0qlvO+pRKnbpDlq1G2E8pUsYV+/MUbOXIboiDd4dDtQ6GhEJUqx7VmkqakJa2tr2XqdOnWgo6ODzZs3Y9CgQbJ9/k0sFqN58+Y4efIkJk+ejIoVK2bbx8rKClZWVhg0aBCmT58OW1tb7N+/HwMGDMhzzqysLADAyZMnUaFChWxZvsXb2xsTJsjPp5CSpZrn61P+LFk4D5cDLmHT1p0wMjIWOg5RoXgZch8fIl6j1/hZQkchUqhnj4PwJT4WU7w6ytqysjJxYOtq/Hl8HxZtOSpYNiJF0fv/HkWxMZ+gZ/C/Cdzj42Kho6svVCyiAqejbwj9ssb4EPFW6ChEJUqxLRb9m0gkgpKSEpKTk/9zPyUlJezcuRO9evVC06ZN4e/vj/Llvz2Jq7m5OTQ0NJCYmP0pEjo6OjAxMcGNGzfg7Px1yEZGRgbu3LkDBwcHAEC1atUgFovx+vXrbw45y4lYLM5WTJImZ+X6eMofqVSKpQvnwf/in9jwmx8qVMheTCQqqW5dOIUKlpVR3tz6+zsTFSMN3VqhWi35hxSsmDkOjm4t0bj5t5+kSlSclDOpAF19Azy4cxOWNlUAfH0acPD9O+gzeIzA6YgKTsLneMR+jIY2J7ymH8ROmPKKbbEoNTUVUVFRAIDY2FisXbsWCQkJaNeu3XePVVZWxu7du/HTTz/JCkbGxsbw8fFBUlISWrdujUqVKiEuLg6rV69Geno63N3dczzX2LFjsXDhQtjY2KBq1apYvnw54uLiZNu1tLQwadIkjB8/HllZWWjcuDE+f/6MwMBASCQS9O/fXyHvBynG4gVzcPb0SSxduRYampr4+PEDAEAi0YKamprA6YjyJzU5CZ+i3snWY6IjEfHyGdQl2tArawQASElKxIMb/mjbb4RQMYl+SEpyEqIj//et8of3EXj94ik0JdowKGcMibb8gwqUVZSho2cA44qVCjsqUb4lJych6t0b2fr7qAi8fB4KiZY2yhqZoE3nXji8ZytMKprCpIIZDu/ZCrGaGpo0aylgaqK8SUlOwod/fJ5/eh+BNy+eQlNLGxoSbZzctxW1G7pCR88An6IjcWznJki0dVDLkfMtEilSsS0WnTlzBiYmJgC+FmSqVKmC33//Ha6urggPD//u8SoqKti7dy969OghKxi5uLhg3bp16NevH96/fw89PT3Url0b586dQ+XKlXM8z8SJExEZGQlPT08oKSnBy8sLnTp1Qnx8vGyfuXPnoly5cvD19cWLFy+gq6sLBwcHTJs2TSHvBSnOod/3AQCGDZIv4s2cvQBtO3QSIhLRD3v7IhS/+oyTrZ/wWwcAqOPSEt1HeQMA7l+7AEilqOnUTIiIRD8s/HkIlk4bKVs/sGUVAKBR09bwGj9TqFhEChUWGgyfiUNl634blgMAXFu0xaips9GxZ3+kpaVi86qFSPzyBTZVq+OXReugrpF9agaiour18ydYOWO0bP3Q1jUAAMemrdBz2GREhIfh5qXTSE5MgI6eAWxrOGDg5DlQ431OP4jzu8kTSaVSaW52/Pf8Of9l+fLl+Q5E3xbPYWhUClx6Fi10BKICZ6DGCcSp5NPV4FyLVPJFJ6QIHYGowDWrYih0hELRddtdwa59cIBDrvbbsGEDNmzYIOsgY2dnh5kzZ6JVq1YAvk6rMnv2bPz666+IjY1FgwYNsG7dOtjZ2eU5U657Ft27d09u/c6dO8jMzJT1uHn69CmUlZVRp06dPIcgIiIiIiIiIhJKcehYVLFiRSxcuFD2sC8/Pz906NAB9+7dg52dHRYvXozly5dj+/btsLW1xbx58+Du7o7Q0FBoaWnl6Vq5LhZdunRJ9vPy5cuhpaUFPz8/6OnpAfg6b9CAAQPQpEmTPAUgIiIiIiIiIqL/9u85mufPn48NGzbgxo0bqFatGlauXInp06ejc+fOAL4Wk4yMjLBnzx4MHTo0p1N+k1J+Ai5btgy+vr6yQhEA6OnpYd68eVi2bFl+TklEREREREREVOqkpqbi8+fPcktqaup/HpOZmYl9+/YhMTERDRs2xMuXLxEVFYUWLVrI9hGLxXBxcUFgYGCeM+WrWPT582e8f/8+W3t0dDS+fPmSn1MSEREREREREQlCSSQSbPH19YWOjo7c4uvrm2POhw8fQiKRQCwWY9iwYThy5AiqVasme1q8kZGR3P5GRkaybXmRr6ehderUCQMGDMCyZcvg6OgIALhx4wYmT54s6+5ERERERERERET/zdvbO9tDxcTinB+IUrlyZQQFBSEuLg6HDh1C//79ERAQINv+76e6SaXSfD3pLV/Foo0bN2LSpEno06cP0tPTv55IRQUDBw7EkiVL8nNKIiIiIiIiIiJBCDm/tVgs/mZx6N/KlCkjm+C6bt26uHXrFlatWoWpU6cCAKKiomBiYiLbPzo6Oltvo9zI1zA0DQ0NrF+/Hp8+fcK9e/dw9+5dxMTEYP369dDU1MzPKYmIiIiIiIiIKA+kUilSU1NhYWEBY2NjnD9/XrYtLS0NAQEBaNSoUZ7Pm6+eRX/T1NSEvr4+RCIRi0SFICMrS+gIRAWusmHeHulIRERF093IWKEjEBW4aoY6QkcgolJk2rRpaNWqFUxNTfHlyxfs27cP/v7+OHPmDEQiEcaNG4cFCxbAxsYGNjY2WLBgATQ0NNCrV688XytfxaKsrCzZk88SEhIAAFpaWpg4cSKmT58OJaV8dVgiIiIiIiIiIip0+ZnXp7C9f/8effv2RWRkJHR0dGBvb48zZ87A3d0dADBlyhQkJydjxIgRiI2NRYMGDXDu3DloaeX9C/l8FYumT5+OLVu2YOHChXBycoJUKsW1a9fg4+ODlJQUzJ8/Pz+nJSIiIiIiIiKiHGzZsuU/t4tEIvj4+MDHx+eHr5WvYpGfnx9+++03tG/fXtZWs2ZNVKhQASNGjGCxiIiIiIiIiIiKDaWi37GoUOVrvFhMTAyqVKmSrb1KlSqIiYn54VBERERERERERCSMfBWLatasibVr12ZrX7t2LWrWrPnDoYiIiIiIiIiICotIJBJsKYryNQxt8eLFaNOmDf788080bNgQIpEIgYGBePPmDU6dOqXojEXWtWvXMGzYMDx58gRt2rTB0aNHhY5ERERERERERPRD8lUscnFxwdOnT7Fu3To8efIEUqkUnTt3xogRI1C+fHlFZ8wmKioK8+fPx8mTJ/Hu3TuUK1cOtWrVwrhx49CsWbMCv/7fJkyYgFq1auH06dOQSCSFdl0qOId/34cjv+9HZOQ7AICFpTW8hgxHQ6cmAicjUqzkpETs3roeN69eQnxsLCxsKmPQqMmwqWIndDQiheF9TiXNq5AHCDyxH5EvnyEh7hO6j5+NKvUay7YnxMfgwt7NCHtwBylJCahUxR4t+4+CgUlFAVMT5U3Ig7v44/edePksBLExHzFx1lLUc3KVbf/r6kX8efIwXj4LwZfP8Vi4YTfMrSoLF5iohMpXsQgAypcvL8hE1uHh4XBycoKuri4WL14Me3t7pKen4+zZsxg5ciSePHmS7Zj09HSoqqoqPEtYWBiGDRuGihXz/w9wWloaypQpo8BU9CPKlTPC8DHjUdHUDABw6o9jmDp+FLbvPQRLK2uB0xEpztolc/D6ZRjGec+FvmFZ+J8/hVmThmPNtoMwKFtO6HhECsH7nEqatNRkGFWyQi2Xlvh9pY/cNqlUiv3LZkJZRQU9Js6BWF0TN079jl2+kzF88VaUUVMXJjRRHqWkJKOSpQ1cPdph+ZwpOW6vbFcTjs7N8euKeQIkpJKqiI4GE0y+5iwCgLi4OCxbtgyDBg3C4MGDsWLFCsTHxysyW45GjBgBkUiEv/76C127doWtrS3s7OwwYcIE3LhxA8DXsYYbN25Ehw4doKmpiXnz5iEzMxMDBw6EhYUF1NXVUblyZaxatUp23ocPH0JJSQkfP34EAMTGxkJJSQndunWT7ePr64uGDRsiPDwcIpEInz59gpeXF0QiEbZv3w4ACAgIQP369SEWi2FiYoKff/4ZGRkZsnO4urpi1KhRmDBhAgwNDeHu7l7g7xnlXmMXNzRq7AyzSuYwq2SOYaPGQl1DA48f3hc6GpHCpKam4Prli+g/dCzsataBSQUz/OQ5DOWMy+PM8d+FjkekELzPqSSyqdUATbt7oWr97D2eY6Le4t3zELT2GocKVlVgWN4Urb3GIi0lGY+uXxQgLVH+1K7vhB4DRqB+46Y5bndu3gZd+gxG9dr1CzkZUemSr2LR7du3YWVlhRUrViAmJgYfP37E8uXLYWVlhbt37yo6o0xMTAzOnDmDkSNHQlNTM9t2XV1d2c+zZs1Chw4d8PDhQ3h5eSErKwsVK1bEgQMHEBwcjJkzZ2LatGk4cOAAAKB69eowMDBAQEAAAODy5cswMDDA5cuXZef09/eHi4sLTE1NERkZCW1tbaxcuRKRkZHo0aMH3r17h9atW6NevXq4f/8+NmzYgC1btmDePPmKt5+fH1RUVHDt2jVs2rSpAN4pUoTMzEycP3sKKcnJqG7Pidup5MjKzERWViZU/9WrUSwWI/hhkDChiBSM9zmVNhnp6QAAFdX/3fNKSspQVlHFm9BHQsUiIio2OMG1vHwNQxs/fjzat2+PzZs3Q0Xl6ykyMjIwaNAgjBs3Tq7AokjPnz+HVCpFlSpVvrtvr1694OXlJdc2e/Zs2c8WFhYIDAzEgQMH0L17d4hEIjg7O8Pf3x9dunSBv78/+vfvDz8/PwQHB8PW1haBgYEYP348lJWVYWxsDJFIBB0dHRgbGwMA1q9fD1NTU6xduxYikQhVqlRBREQEpk6dipkzZ0JJ6WttztraGosXL/7P/KmpqUhNTZVvy1CGWCzO1XtF+Rf27CmGePZCWloa1NU14LtsNSwsOQSNSg51DU1UtrPHgZ2/wbSSJXT09HHl4hk8DXkEk4pmQscjUgje51TaGJY3g46hES7u+w1tBo5HGTU1XD91EAlxMfgSGyN0PCIiKmby3bNo6tSpskIRAKioqGDKlCm4ffu2wsL9m1QqBYBcVd7q1q2brW3jxo2oW7cuypYtC4lEgs2bN+P169ey7a6urvD39wfwdTiZm5sbnJ2dERAQgFu3biE5ORlOTk7fvGZISIjs6XB/c3JyQkJCAt6+ffuf2f7N19cXOjo6csvKpYu+exz9ODNzc/jtPYRf/fagU7cemDdzGl6+eC50LCKFGuc9F5BK4dXNA91aOOLk4X1wbtZSVtQmKgl4n1Npoqyigm7jfPAp6i2WDOmIBZ6t8Sr4Pqxr1uc9T0REeZavnkXa2tp4/fp1th4+b968gZaWlkKC5cTGxgYikQghISHo2LHjf+7772FqBw4cwPjx47Fs2TI0bNgQWlpaWLJkCW7evCnbx9XVFWPHjsXz58/x6NEjNGnSBGFhYQgICEBcXBzq1Knzn69PKpVmK2TlVODKaQjdv3l7e2PChAlybQkZyt89jn6cqmoZVDSrBACoWq06Qh4/woE9uzB1ho+wwYgUyKSCKeav+g0pyclISkqAvkFZLJk9FUbGFYSORqQwvM+ptClvaYuhvr8iJSkBmRkZ0NTWxW+/jER5S1uhoxERFXlKRXM0mGDy9TVDjx49MHDgQOzfvx9v3rzB27dvsW/fPgwaNAg//fSTojPK6Ovrw8PDA+vWrUNiYmK27XFxcd889sqVK2jUqBFGjBiB2rVrw9raGmFhYXL7/D1v0bx581CzZk1oa2vDxcUFAQEBsvmK/ku1atUQGBgoKxABQGBgILS0tFChQt5+MRWLxdDW1pZbOARNGFKpFOnpaULHICoQaurq0Dcoi4Qvn3Hv1nXUd/rvzzmi4oj3OZU2ahoSaGrr4lPkW0S+eIrKdb7dM56IiCgn+epZtHTpUohEIvTr10/2pC9VVVUMHz4cCxcuVGjAf1u/fj0aNWqE+vXrY86cObC3t0dGRgbOnz+PDRs2ICQkJMfjrK2tsWPHDpw9exYWFhbYuXMnbt26BQsLC9k+f89btGvXLowfPx4AYG9vj7S0NFy4cAFjx479z2wjRozAypUrMXr0aIwaNQqhoaGYNWsWJkyYwO6/xcTGNSvh6NQERsbGSEpMxPmzp3Hvzi0sX8uJyKlkufdXIKSQooKpOSLfvcH2jStRwdQczVq1FzoakcLwPqeSJi0lGTFR72TrcR+iEBX+HOoSLegYGiH4RgA0tHWgY1AO0W9e4syOdahc1wlW9t+fAoGoqEhJTkJUxBvZenTUO4SHhUKipQPDcsZI+ByPjx+iEPvpAwAg4s0rAICungF09Q0FyUwlQ1GdaFoo+SoWlSlTBqtWrYKvry/CwsIglUphbW0NDQ0NRefLxsLCAnfv3sX8+fMxceJEREZGomzZsqhTpw42bNjwzeOGDRuGoKAg9OjRAyKRCD/99BNGjBiB06dPy+3n5uaGw4cPw9XVFcDXG6ZJkyY4ceIEGjdu/J/ZKlSogFOnTmHy5MmoWbMm9PX1MXDgQMyYMeOHXzcVjpiYT5jzy8/49PEDNCVasLaxxfK1m1DfsZHQ0YgUKjExATt/W4tPH95DS0sHDZ2bovfAkVBRURU6GpHC8D6nkibiRSh2zJsoWz+36+vvvjWdW6DDsKn4EvcJ53ZtQEJ8LLT09GHfuAWcO/cRKi5RvoQ9DcbcycNk6zs3rQAAOLu3xYjJPrh94zI2Lv3fg4tWL5gGAOjSZzC69RtauGGJSjCR9J9jpqhI+5SYIXQEogIXHZ/6/Z2IiKjIuxsZK3QEogJXzVBH6AhEBa52pYKbl7go8dr3ULBrb+1ZQ7Brf0u+ehYlJiZi4cKFuHDhAqKjo5GVlSW3/cWLFwoJR0REREREREREhStfxaJBgwYhICAAffv2hYmJCcf2ERERERERERGVEPkqFp0+fRonT56EkxOfrEBERERERERExZsSO8HIydcjuvT09KCvr6/oLEREREREREREJLB8FYvmzp2LmTNnIikpSdF5iIiIiIiIiIgKlUgk3FIU5XoYWu3ateXmJnr+/DmMjIxgbm4OVVX5R9DevXtXcQmJiIiIiIiIiKjQ5LpY1LFjxwKMQbmRkSkVOgJRgYtPThc6AlGB0xTna8pAomKFjxQnIiIqvnL92+qsWbMAABkZGZg/fz68vLxgampaYMGIiIiIiIiIiAoDn/IuL89zFqmoqGDp0qXIzMwsiDxERERERERERCSgfE1w3axZM/j7+ys4ChERERERERFR4eME1/LyNWlCq1at4O3tjUePHqFOnTrQ1NSU296+fXuFhCMiIiIiIiIiosKVr2LR8OHDAQDLly/Ptk0kEnGIGhERERERERFRMZWvYWhZWVnfXIpyocjV1RXjxo0T/Bw+Pj6oVavWD52DiIiIiIiIiBRDSSQSbCmK8lUsKko8PT0hEokwbNiwbNtGjBgBkUgET09PAMDhw4cxd+5c2XZzc3OsXLmykJJScbRr22Y416uO1csWCh2FKN+ePLyHFT4TMbZPG/Rv3QB3AgPkth/ZtRk/D+mOwZ1cMLx7cyyaNgphTx4JlJYof4If3IXv9HEY3N0DXZvVwV9XL8ltl0ql2O+3CYO7e6BXq0aYOWEI3oSHCZSWKH9CHtzF4l/GY3jPlujZoi5uXfOX2/7X1YtY4D0Kg7s2Q88WdREeFipMUKIfwPucqGjId7EoICAA7dq1g7W1NWxsbNC+fXtcuXJFkdlyzdTUFPv27UNycrKsLSUlBXv37oWZmZmsTV9fH1paWkJEpGIo5PFDHD96EFY2tkJHIfohqSnJMLWwQd/hk3LcblzBDH2HT8L89XswfcmvMCxngiUzxuBzfGwhJyXKv5TkZJhb2WLg6Kk5bj+6zw8nDu7GwNFTsXD9DujqGWDOlBFITkos5KRE+ZeSkoxKljYYMGrKN7dXtquJnwaOLuRkRIrD+5yEwgmu5eWrWLRr1y40b94cGhoaGDNmDEaNGgV1dXU0a9YMe/bsUXTG73JwcICZmRkOHz4sazt8+DBMTU1Ru3ZtWds/h5C5urri1atXGD9+PEQiEUT/+F/o2rVrcHFxgYaGBvT09ODh4YHY2P/90ZSVlYUpU6ZAX18fxsbG8PHxkcsTHx+PIUOGoFy5ctDW1kbTpk1x//79gnnxVCCSkpIwd+bPmDLNB1pa2kLHIfohNes1Qtf+w1DXyS3H7Q3dPGBXuz7KmVRAxUqW6DVkLJKTEvHm5fNCTkqUfw4NnPCT1wg4NmmabZtUKsXJw3vQuZcXHJs0hZmFNUZPnY3UlBRcuXBGgLRE+VO7vhN6DBiB+o2z3+cA4Ny8Dbr0GYzqtesXcjIixeF9TlQ05KtYNH/+fCxevBj79+/HmDFjMHbsWOzfvx8LFy6UG+ZVmAYMGIBt27bJ1rdu3QovL69v7n/48GFUrFgRc+bMQWRkJCIjIwEAQUFBaNasGezs7HD9+nVcvXoV7dq1k5uLyc/PD5qamrh58yYWL16MOXPm4Pz58wC+/kLapk0bREVF4dSpU7hz5w4cHBzQrFkzxMTEFNCrJ0VbsXgeGjo5o26DhkJHISpUGenpuHT6KDQ0JTCzsBE6DpFCREe+Q1zMJ9Ss6yhrUy1TBtVq1kHoY36ZQ0RERJB1IhFiKYry9TS0Fy9eoF27dtna27dvj2nTpv1wqPzo27cvvL29ER4eDpFIhGvXrmHfvn3w9/fPcX99fX0oKytDS0sLxsbGsvbFixejbt26WL9+vazNzs5O7lh7e3vMmjULAGBjY4O1a9fiwoULcHd3x6VLl/Dw4UNER0dDLBYDAJYuXYqjR4/i4MGDGDJkiIJfOSnahXOn8DQkGL/u2C90FKJCE3TzKtYvmoG01BTo6Bti8vw10NLRFToWkULExn4CAOjqGci16+rp48P7SCEiERERERVp+SoWmZqa4sKFC7C2tpZrv3DhAkxNTRUSLK8MDQ3Rpk0b+Pn5yXr3GBoa5vk8QUFB6Nat23/uY29vL7duYmKC6OhoAMCdO3eQkJAAAwP5X0iTk5MRFpb7iTRTU1ORmpr6rzYlWQGKCsb7qEisXrYQy9b8yveaSpWqNetg7tqd+PI5DgFnjmGd7zTMWrEV2rr6QkcjUph/f3EnlUqL7Ld5RERERELKV7Fo4sSJGDNmDIKCgtCoUSOIRCJcvXoV27dvx6pVqxSdMde8vLwwatQoAMC6devydQ51dfXv7qOqqiq3LhKJkJWVBeDrfEYmJiY59mjS1dXNdQ5fX1/Mnj1brm3izzMw2Xtmrs9Beff0STBiY2IwuF8PWVtmZibu37uDI7/vxZ/X7kJZWVnAhEQFQ6ymDqPypjAqbwrrKjUwZVAXBJw9jnY9PIWORvTD9P6/R1FszCfoGZSVtcfHxUKHBVEiIiJCCXhUvILlq1g0fPhwGBsbY9myZThw4AAAoGrVqti/fz86dOig0IB50bJlS6SlpQEAPDw8vrt/mTJl5OYiAr72Grpw4UK2Qk1uOTg4ICoqCioqKjA3N8/XOQDA29sbEyZMkGuLS+XtW9Dq1HPE9r1H5NoWzpkBM3ML9Oo3kIUiKjWk0q/zFxGVBOVMKkBX3wAP7tyEpU0VAEB6ejqC799Bn8FjBE5HREREVPTkq1gEAJ06dUKnTp0UmeWHKSsrIyQkRPbz95ibm+Py5cvo2bMnxGIxDA0N4e3tjRo1amDEiBEYNmwYypQpg0uXLqFbt265GtbWvHlzNGzYEB07dsSiRYtQuXJlRERE4NSpU+jYsSPq1q2bq9ciFouzDYNK/sw/3AqahqYmLK3lJ/VVU1eHto5utnai4iIlOQnvI97K1j+8j8CrsKeQaGlDoq2D4/u2obZjE+jqGSLhSzwunDiE2I/RqNekmYCpifImOTkJUe/eyNbfR0Xg5fNQSLS0UdbIBG0698LhPVthUtEUJhXMcHjPVojV1NCkWUsBUxPlTUpyEqIi/nefR0e9Q3hYKCRaOjAsZ4yEz/H4+CEKsZ8+AAAi3rwC8HW+Ll39vE/PQCQE3uckFA5Nl5fvYhEA3L59GyEhIRCJRKhatSrq1KmjqFz5pq2d+8ecz5kzB0OHDoWVlRVSU1MhlUpha2uLc+fOYdq0aahfvz7U1dXRoEED/PTTT7k6p0gkwqlTpzB9+nR4eXnhw4cPMDY2hrOzM4yMjPL7soiI8u3lsxAs/HmEbH3v5pUAgMbN26D/qKmIfPsKV+efQkJ8HCTaOrCwrYppSzahYiVLgRIT5V1YaDB8Jg6VrfttWA4AcG3RFqOmzkbHnv2RlpaKzasWIvHLF9hUrY5fFq2DuoamUJGJ8izsaTDmTh4mW9+5aQUAwNm9LUZM9sHtG5excen/esevXvD1wTNd+gxGt35DQVQc8D4nKhpEUqlUmteD3r59i59++gnXrl2TzcMTFxeHRo0aYe/evYJNcl3SvWfPIioFXn5IFDoCUYHTFP/QdzVExUJGZp5/xSQioiKodiUtoSMUijFHnwh27dUdqwh27W/J1yQ4Xl5eSE9PR0hICGJiYhATE4OQkBBIpVIMHDhQ0RmJiIiIiIiIiAqMkki4pSjK11ebV65cQWBgICpXrixrq1y5MtasWQMnJyeFhSMiIiIiIiIiosKVr2KRmZkZ0nN4Sk5GRgYqVKjww6GIiIiIiIiIiApLUe3hI5R8DUNbvHgxRo8ejdu3b+PvKY9u376NsWPHYunSpQoNSEREREREREREhSfXPYv09PTkHiWXmJiIBg0aQEXl6ykyMjKgoqICLy8vdOzYUeFBiYiIiIiIiIgKwj/rHZSHYtHKlSsLMAYRERERERERERUFuS4W9e/fvyBzEBERERERERFREZCvCa4BICsrC8+fP0d0dDSysrLktjk7O/9wMMouMTVT6AhEBU5fUkboCEQFTk1VWegIRAXu05c0oSMQFTg11XxNAUtERRAnuJaXr2LRjRs30KtXL7x69Uo2wfXfRCIRMjNZ1CAiIiIiIiIiKo7yVSwaNmwY6tati5MnT8LExIQTQRERERERERFRscWyhrx8FYuePXuGgwcPwtraWtF5iIiIiIiIiIhIQPkaZNugQQM8f/5c0VmIiIiIiIiIiEhgue5Z9ODBA9nPo0ePxsSJExEVFYUaNWpAVVVVbl97e3vFJSQiIiIiIiIiKkBKHIcmJ9fFolq1akEkEslNaO3l5SX7+e9txXmC6/DwcFhYWODevXuoVatWjvv4+/vDzc0NsbGx0NXVLdR8REREREREREQFLdfFopcvXxZkjm/y9PSEn58ffH198fPPP8vajx49ik6dOmV7Gtu3uLq6IiAgAACgqqoKU1NTdO/eHT4+PhCLxQAAU1NTREZGwtDQUPEvhIqF/l1bIToqIlt72049MHLiNAESESleZkYG9mzbBP/zpxAX8wl6BoZo1qodevQbDCUlPgKYSo6P0e/x2/qV+Ov6VaSlpqKCWSVMnDYbtlWqCR2NKF9CHtzFH7/vxMtnIYiN+YiJs5ainpOrbPtfVy/iz5OH8fJZCL58jsfCDbthblVZuMBECpKclIjdW9fj5tVLiI+NhYVNZQwaNRk2VeyEjkYlCH8LlpfrYlGlSpUKMsd/UlNTw6JFizB06FDo6enl+zyDBw/GnDlzkJaWhlu3bmHAgAEAAF9fXwCAsrIyjI2NFZKZiqdVm3cjKytLtv7qxXNMGz8UTdzcBUxFpFgH92zH6eMHMX7aHJiZW+F56GOs8vWBpqYW2nfrJXQ8IoX48vkzxg3tj5p16mHB8vXQ1ddHxNs3kEi0hI5GlG8pKcmoZGkDV492WD5nSo7bK9vVhKNzc/y6Yp4ACYkKxtolc/D6ZRjGec+FvmFZ+J8/hVmThmPNtoMwKFtO6HhEJVK+imfHjx/Pcfnjjz9w/vx5hfdCat68OYyNjWVFnZwcOnQIdnZ2EIvFMDc3x7Jly7Lto6GhAWNjY5iZmaFLly5wd3fHuXPnZNvDw8MhEokQFBQkazt16hRsbW2hrq4ONzc3hIeHZzvv5s2bYWpqCg0NDXTq1AnLly/PNkTtjz/+QJ06daCmpgZLS0vMnj0bGRkZeX4vqGDp6ulD38BQttwMvAyTCqaoUbuu0NGIFObJ4wdwdHJBvYZNYGRSHk6u7qhVzxHPQoOFjkakMPt3bUVZIyNMnjEXVexqwNikAhzqOaJ8RVOhoxHlW+36TugxYATqN26a43bn5m3Qpc9gVK9dv5CTERWc1NQUXL98Ef2HjoVdzTowqWCGnzyHoZxxeZw5/rvQ8agEEYmEW4qiXPcs+qeOHTtmm78IkJ+3qHHjxjh69OgP9QT6m7KyMhYsWIBevXphzJgxqFixotz2O3fuyIaU9ejRA4GBgRgxYgQMDAzg6emZ4znv37+Pa9euwdzc/JvXffPmDTp37oxhw4Zh+PDhuH37NiZOnCi3z7Vr1zBs2DAsWrQI7du3x59//olffvlFbp+zZ8+iT58+WL16NZo0aYKwsDAMGTIEADBr1qy8vyFUKNLT03Hp3El06tEXoqL6XzBRPlSrUQtnjh/EuzevUMG0El4+D0XIwyAMGj1J6GhECnP9ij/qNmiEOdMm4mHQbRgYGqF9l+5o3aGr0NGIiCgPsjIzkZWVCdUyZeTaxWIxgh8GCROKqBTIV8+i8+fPo169ejh//jzi4+MRHx+P8+fPo379+jhx4gQuX76MT58+YdIkxf3h0alTJ9SqVSvH4sry5cvRrFkz/PLLL7C1tYWnpydGjRqFJUuWyO23fv16SCQSiMVi1KpVCx8+fMDkyZO/ec0NGzbA0tISK1asQOXKldG7d+9sxac1a9agVatWmDRpEmxtbTFixAi0atVKbp/58+fj559/Rv/+/WFpaQl3d3fMnTsXmzZt+ua1U1NT8fnzZ7klNTU1F+8UKcr1yxeRkPAF7q3bCx2FSKG69h4A52YtMbxPJ3R0q4exA39C+2694NK81fcPJiomIiPe4o8jB1DB1Ay+KzaibaduWLd8Ec6fOi50NCIiygN1DU1UtrPHgZ2/IebjB2RmZsL//Ek8DXmE2JiPQscjKrHyVSwaO3asrECjpaUFLS0tNGvWDEuXLsXkyZPh5OSElStX4vz58woNu2jRIvj5+SE4WH6oREhICJycnOTanJyc8OzZM7kns/Xu3RtBQUG4fv06unfvDi8vL3Tp0uWb1wsJCYGjo6Ncr5KGDRvK7RMaGor69eW7+v57/c6dO5gzZw4kEolsGTx4MCIjI5GUlJTjtX19faGjoyO3bFy1JMd9qWCcPXkEdRs4wcCQ46CpZLly8Sz8z53CpJkLsPK3PRg3bQ6O7NuJC6f5RzSVHNKsLNjYVsXA4WNhXbkq2nbqhtYduuCPIweEjkZERHk0znsuIJXCq5sHurVwxMnD++DcrCUfzEEKpSQSCbYURfkahhYWFgZtbe1s7dra2njx4gUAwMbGBh8/KrbS6+zsDA8PD0ybNk2uh8/fQ9/+KaenpOno6MDa2hoAsGvXLtjZ2WHLli0YOHBgjtfLzZPWcnPtrKwszJ49G507d852vJqaWo7n9fb2xoQJE+Ta3n3O3ZPf6Me9j4pA0O2bmDF/udBRiBRu2/qVst5FAGBuZYMPUZH4ffc2NGvFnnRUMugbloWZhaVcm5m5Ba5c+lOgRERElF8mFUwxf9VvSElORlJSAvQNymLJ7KkwMq4gdDSiEitfxaI6depg8uTJ2LFjB8qWLQsA+PDhA6ZMmYJ69eoBAJ49e5ZtbiFF8PX1Re3atWFraytrq1atGq5evSq3X2BgIGxtbaGsrJzjeVRVVTFt2jR4e3vjp59+goaGRrZ9qlWrhqNHj8q13bhxQ269SpUq+Ouvv+Tabt++Lbfu4OCA0NBQWaEqN8RiMcRisVzbx9SUXB9PP+b8yWPQ0dNH/YZNhI5CpHCpqSkQKckXuZWUlSD9x5MAiYo7uxq18PZ1uFzb29evYGRsIkwgIiL6YWrq6lBTV0fCl8+4d+s6+g8dK3QkKkGKaAcfweSr396WLVvw8uVLVKxYEdbW1rCxsUHFihURHh6O3377DQCQkJCQbaJnRbC3t0fv3r2xZs0aWdvEiRNx4cIFzJ07F0+fPoWfnx/Wrl373TmTevXqBZFIhPXr1+e4fdiwYQgLC8OECRMQGhqKPXv2YPv27XL7jB49GqdOncLy5cvx7NkzbNq0CadPn5brbTRz5kzs2LEDPj4+ePz4MUJCQrB//37MmDEj/28EFZisrCycP3UMzVu2g7JKvuqpREVavUbOOLBzC25dv4L3kRG4fvkiju7fhYZNcn66DlFx1KVnX4Q8eog92zfj3ZvXuHj2JE4dO4j2XXsKHY0o31KSkxAeForwsFAAQHTUO4SHheJjdBQAIOFzPMLDQvHu9dee/hFvXiE8LBRxnNeFirl7fwXi7l/X8D7yHYJu38CM8UNQwdScPaKJCpBImpuxVjmQSqU4e/Ysnj59CqlUiipVqsDd3V3h40Y9PT0RFxcn18Pn1atXqFy5MlJTU2VDvg4dOoSZM2fi2bNnMDExwejRo+WKRa6urqhVqxZWrlwpd/4FCxZg5cqVePHiBT5+/AgLCwvcu3cPtWrVAgCcOHEC48ePx5s3b1C/fn0MGDAAXl5eiI2Nha6uLgBg8+bNmD17NmJiYuDh4YG6deti7dq1iIyMlF3n7NmzmDNnDu7duwdVVVVUqVIFgwYNwuDBg3P9Xrz4wJ5FheHOX4GYMWE4Nu85hopm5kLHKXUy2LulwCUlJWL3b+tx/cpFxMfGQt+wLJybtURPzyFQVVUVOl6poKaac69XUqwbVwOwZcMqvHv7GsYmFdD1p758Gloh+vQlTegIJc7j+7cxd/KwbO3O7m0xYrIP/M/9gY1LZ2fb3qXPYHTrN7QwIpY6aqqcM6cwXL10Djt/W4tPH95DS0sHDZ2bovfAkdCUaAkdrVSoWl5T6AiFYubZZ4Jde46HjWDX/pZ8F4vo2wYPHownT57gypUrCj0vi0VUGrBYRKUBi0VUGrBYRKUBi0VUGpSWYpHPOeGKRT4til6xKNdjbFavXo0hQ4ZATU0Nq1ev/s99x4wZ88PBipOlS5fC3d0dmpqaOH36NPz8/L45tI2IiIiIiIiIqCjLdc8iCwsL3L59GwYGBrCwsPj2CUUi2RPRSovu3bvD398fX758gaWlJUaPHo1hw7J3Ef5R7FlEpQF7FlFpwJ5FVBqwZxGVBuxZRKVBaelZNOf8c8GuPdM99w/DKiy57ln08uXLHH8m4MCBA0JHICIiIiIiIiJSiB8qhaelpSE0NBQZGRmKykNEREREREREVKhEIuGWoihfxaKkpCQMHDgQGhoasLOzw+vXrwF8nato4cKFCg1IRERERERERESFJ1/FIm9vb9y/fx/+/v5QU1OTtTdv3hz79+9XWDgiIiIiIiIiIipcuZ6z6J+OHj2K/fv3w9HREaJ/9JmqVq0awsLCFBaOiIiIiIiIiKigKRXR4WBCyVex6MOHDyhXrly29sTERLniESmWmE9boFIgI5VPQ6OSLyMzVw8iJSKiIk7Mp1sSUQmVr+pDvXr1cPLkSdn63wWizZs3o2HDhopJRkRERERERERUCEQC/l9RlK+eRb6+vmjZsiWCg4ORkZGBVatW4fHjx7h+/ToCAgIUnZGIiIiIiIiIiApJvnoWNWrUCNeuXUNSUhKsrKxw7tw5GBkZ4fr166hTp46iMxIRERERERERUSHJV88iAKhRowb8/PwUmYWIiIiIiIiIqNBxgmt5eSoWKSkpfXcCa5FIhIyMjB8KRUREREREREREwshTsejIkSPf3BYYGIg1a9ZAKi16T3jx8fHB0aNHERQUlO9zhIeHw8LCAvfu3UOtWrUUlo2IiIiIiIiIhMWeRfLyVCzq0KFDtrYnT57A29sbf/zxB3r37o25c+cqLFxufK+nU//+/bF27VqMHj26kBJRcbZ983rs+G2DXJuevgEOnfYXJhBRAcjMyMCebZvgf/4U4mI+Qc/AEM1atUOPfoOhpJSvqeyIipz+XVshOioiW3vbTj0wcuI0ARIR/biQB3fxx+878fJZCGJjPmLirKWo5+Qq2/7X1Yv48+RhvHwWgi+f47Fww26YW1UWLjCRAvDznEgY+Z6zKCIiArNmzYKfnx88PDxw79491KhRQ5HZciUyMlL28/79+zFz5kyEhobK2tTV1SGRSCCRSAo9W26kpaWhTJkyQsegfzC3tMbStZtl6/zjmUqag3u24/Txgxg/bQ7MzK3wPPQxVvn6QFNTC+279RI6HpFCrNq8G1lZWbL1Vy+eY9r4oWji5i5gKqIfk5KSjEqWNnD1aIflc6bkuL2yXU04OjfHryvmCZCQSPH4eU6F5XsdUUqbPP8VHB8fj6lTp8La2hqPHz/GhQsX8McffwhSKAIAY2Nj2aKjowORSJStzcfHR27omKenJzp27IgFCxbAyMgIurq6mD17NjIyMjB58mTo6+ujYsWK2Lp1a7brPXnyBI0aNYKamhrs7Ozg7+8vtz04OBitW7eGRCKBkZER+vbti48fP8q2u7q6YtSoUZgwYQIMDQ3h7s4PuaJGWVkZ+gaGskVXT1/oSEQK9eTxAzg6uaBewyYwMikPJ1d31KrniGehwUJHI1IYXT19uc/ym4GXYVLBFDVq1xU6GlG+1a7vhB4DRqB+46Y5bndu3gZd+gxG9dr1CzkZUcHh5zmRMPJULFq8eDEsLS1x4sQJ7N27F4GBgWjSpElBZStQFy9eREREBC5fvozly5fDx8cHbdu2hZ6eHm7evIlhw4Zh2LBhePPmjdxxkydPxsSJE3Hv3j00atQI7du3x6dPnwB87eXk4uKCWrVq4fbt2zhz5gzev3+P7t27y53Dz88PKioquHbtGjZt2lRor5ly592b1+jWpil6dWyJudMnI+Ldm+8fRFSMVKtRC/fv/oV3b14BAF4+D0XIwyDUdXQSOBlRwUhPT8elcyfRok1HfmtIRFSM8fOcqPDkaRjazz//DHV1dVhbW8PPzw9+fn457nf48GGFhCtI+vr6WL16NZSUlFC5cmUsXrwYSUlJmDbt67hXb29vLFy4ENeuXUPPnj1lx40aNQpdunQBAGzYsAFnzpzBli1bMGXKFGzYsAEODg5YsGCBbP+tW7fC1NQUT58+ha2tLQDA2toaixcv/s98qampSE1N/VebCGKxWCGvn3JW1a4Gfp41HxXNKiE25hN2bfsVowf1xdZ9R6Gjoyt0PCKF6Np7AJISEzC8TycoKSkjKysTfQePhEvzVkJHIyoQ1y9fRELCF7i3bi90FCIi+gH8PKeCxAmu5eWpWNSvX78SU8G1s7OTm4vGyMgI1atXl60rKyvDwMAA0dHRcsc1bNhQ9rOKigrq1q2LkJAQAMCdO3dw6dKlHOdHCgsLkxWL6tb9fpdJX19fzJ49W65t/NQZmPjzL7l4dZRfDRrJ95SrVqMm+nRujXMnj6Fbr/4CpSJSrCsXz8L/3ClMmrkAZuZWePE8FL+tWQp9g7Jo1oq/fFHJc/bkEdRt4AQDw3JCRyEioh/Az3OiwpOnYtH27dsLKEbhU1VVlVsXiUQ5tv1zMrVv+buAlpWVhXbt2mHRokXZ9jExMZH9rKmp+d1zent7Y8KECXJtH5NLRqGuOFFX14CltQ3evnktdBQihdm2fiW69h4A52YtAQDmVjb4EBWJ33dvY7GISpz3UREIun0TM+YvFzoKERH9AH6eU0ErIf1iFCbfT0MrrW7cuAFnZ2cAQEZGBu7cuYNRo0YBABwcHHDo0CGYm5tDReXH3lqxWJxtyNmXrLQfOiflXVpaGl69fIEaNR2EjkKkMKmpKRD9q5+tkrISpLkojhMVN+dPHoOOnj7qNyyecywSEdFX/DwnKlwsFuXRunXrYGNjg6pVq2LFihWIjY2Fl5cXAGDkyJHYvHkzfvrpJ0yePBmGhoZ4/vw59u3bh82bN0NZWVng9PQ9G1YtRaMmLihnbIK4mBjs3PYrkhIT0aJNB6GjESlMvUbOOLBzC8oamXwdhvbsCY7u3wX31h2FjkakUFlZWTh/6hiat2wH5R/8EoeoKEhJTkJUxP8evBEd9Q7hYaGQaOnAsJwxEj7H4+OHKMR++gAAiPj/Bxno6hlAV99QkMxEisDPc6LCx//S8mjhwoVYtGgR7t27BysrKxw7dgyGhl//8S1fvjyuXbuGqVOnwsPDA6mpqahUqRJatmwpNz8SFV0fo99j3i9TER8XCx09fVSzs8faLbthbFJe6GhECjN03FTs/m09NixfgPjYWOgblkXL9l3R03OI0NGIFOre7RuIfh+JFm06Ch2FSCHCngZj7uRhsvWdm1YAAJzd22LEZB/cvnEZG5f+b87L1Qu+PrilS5/B6NZvaOGGJVIgfp5TYVDiODQ5IqlUKhU6BOXOuzgOQ6OSLzE1Q+gIRAVOhV8gUCkQn5QudASiAqejofr9nYiKOcuyakJHKBQrr7wU7NrjmlgIdu1vYc8iIiIiIiIiIirVlNixSA6/2iQiIiIiIiIiIhn2LCIiIiIiIiKiUo1TFsljzyIiIiIiIiIiIpJhsYiIiIiIiIiIiGQ4DI2IiIiIiIiISjUlcBzaP7FYVIxkZkmFjkBU4DTF/Fiiki81PUvoCEQFjr+3EBERFV/8q4yIiIiIiIiISjVOcC2PcxYREREREREREZEMi0VERERERERERCTDYWhEREREREREVKopcRiaHPYsIiIiIiIiIiIiGfYsIiIiIiIiIqJSTYkzXMsplj2LPD090bFjR9nPIpEIIpEIqqqqMDIygru7O7Zu3YqsrOyPJg4MDETr1q2hp6cHNTU11KhRA8uWLUNmZiYAYPv27bLzfWvx9/fPtp+RkRHatWuHx48fZ8v69z4qKiowMzPD8OHDERsbW+DvE+XPx+j3WOjjjc4eTdDWtT6G9uuGp0+ChY5FpDDbN69H0wY15JYurVyFjkWkUP27tkKrxjWzLeuWLRA6GlG+hTy8i6WzxmNkr1bo3bIebgf6y7ZlZGRg75Y1mDqsJ7w6NMHIXq2wYcksxH76IFxgIgXg5zmRMEpEz6KWLVti27ZtyMzMxPv373HmzBmMHTsWBw8exPHjx6Gi8vVlHjlyBN27d8eAAQNw6dIl6Orq4s8//8SUKVNw48YNHDhwAD169EDLli1l5+7cuTOqV6+OOXPmyNr09fURHh4ObW1thIaGQiqV4t27d5gyZQratGmDp0+fokyZMtnyZWRkIDg4GF5eXoiLi8PevXsL702iXPny+TPGDe2PmnXqYcHy9dDV10fE2zeQSLSEjkakUOaW1li6drNsXUmpWH53QPRNqzbvlvvS6NWL55g2fiiauLkLmIrox6SmJMPMwhYu7u2wct5UuW1pqSkIf/4EnXoNhJmFDRITvmDnpuVY5jMR89bsECgx0Y/j5zmRMEpEsUgsFsPY2BgAUKFCBTg4OMDR0RHNmjXD9u3bMWjQICQmJmLw4MFo3749fv31V9mxgwYNgpGREdq3by8rFqmrq8u2lylTBhoaGrLz/5NIJJK1m5iYYPz48Wjfvj1CQ0NRo0aNHPNVrFgRPXr0wPbt2wviraAftH/XVpQ1MsLkGXNlbcYmFQRMRFQwlJWVoW9gKHQMogKjq6cvt35g11aYVDBFjdp1BUpE9ONq1XNCrXpOOW7T0JTA23edXFv/4ZMwc6wnPkZHwbBc9t9liYoDfp5TYeEoNHkl9qvkpk2bombNmjh8+DAA4Ny5c/j06RMmTZqUbd927drB1tb2h3r6xMXFYc+ePQAAVVXVb+734sULnDlz5j/3IeFcv+IP2yp2mDNtIrq1dsGwft1x6thBoWMRKdy7N6/RrU1T9OrYEnOnT0bEuzdCRyIqMOnp6bh07iRatOkIEX8TpFIkOTEBIpEIGpoSoaMQKQQ/z6m08/X1Rb169aClpYVy5cqhY8eOCA0NldtHKpXCx8cH5cuXh7q6OlxdXbNNl5MbJaJn0bdUqVIFDx48AAA8ffoUAFC1atVv7vv3PrkVHx8PiUQCqVSKpKQkAED79u1RpUoVuf1OnDgBiUSCzMxMpKSkAACWL1+ep2tR4YiMeIs/jhxAl5590av/IDwJfoR1yxdBVbUM3Fu3FzoekUJUtauBn2fNR0WzSoiN+YRd237F6EF9sXXfUejo6Aodj0jhrl++iISEL/wcp1IlLS0V+7atQyNXDxaLqMTg5zkVpOIwwXVAQABGjhyJevXqISMjA9OnT0eLFi0QHBwMTU1NAMDixYuxfPlybN++Hba2tpg3bx7c3d0RGhoKLa3cT69SootFUqk0W8VZKpXmet/v0dLSwt27d5GRkYGAgAAsWbIEGzduzLafm5sbNmzYgKSkJPz22294+vQpRo8e/Z/nTk1NRWpq6r/avg5po4IjzcqCbRU7DBw+FgBgXbkqXr0Mwx9HDvAfJSoxGjRqIrderUZN9OncGudOHkO3Xv0FSkVUcM6ePIK6DZxgYFhO6ChEhSIjIwNrfadDmpUFz1FTv38AUTHBz3Mq7c6cOSO3vm3bNpQrVw537tyBs7MzpFIpVq5cienTp6Nz584AAD8/PxgZGWHPnj0YOnRorq9VYoehAUBISAgsLCwAALa2trK2nDx58gQ2NjZ5Or+SkhKsra1RpUoVDB06FH379kWPHj2y7aepqQlra2vY29tj9erVSE1NxezZs//z3L6+vtDR0ZFb1q9cnKd8lHf6hmVhZmEp12ZmboHoqCiBEhEVPHV1DVha2+Dtm9dCRyFSuPdREQi6fRMt23UWOgpRocjIyMCaBd74EBWBn33XslcRlRj8PKeCJhIJt6SmpuLz589yy787j+QkPj4ewNeHcAHAy5cvERUVhRYtWsj2EYvFcHFxQWBgYJ7ejxJbLLp48SIePnyILl26AABatGgBfX19LFu2LNu+x48fx7Nnz/DTTz/90DXHjx+P+/fv48iRI/+536xZs7B06VJERER8cx9vb2/Ex8fLLSPGTfmhfPR9djVq4e3rcLm2t69fwcjYRJhARIUgLS0Nr16+gAEnvKYS6PzJY9DR00f9hk2+vzNRMfd3oSjq3Wt4+66Dlrau0JGIFIaf51SS5dRZxNfX9z+PkUqlmDBhAho3bozq1asDAKL+v5ODkZGR3L5GRkaybblVIopFqampiIqKwrt373D37l0sWLAAHTp0QNu2bdGvXz8AX3v3bNq0CceOHcOQIUPw4MEDhIeHY8uWLfD09ETXrl3RvXv3H8qhra2NQYMGYdasWd8c7gYArq6usLOzw4IFC765j1gshra2ttzCIWgFr0vPvgh59BB7tm/GuzevcfHsSZw6dhDtu/YUOhqRwmxYtRT3795CZMRbhDx6AB/vCUhKTESLNh2EjkakUFlZWTh/6hiat2wHZZUSPfKeSomU5CSEh4UiPOzrZKYfoiIQHhaKj9FRyMzMwKp5U/HiaTBGTJ2LrKxMxMV8RFzMR2SkpwucnOjH8POcSrqcOot4e3v/5zGjRo3CgwcPcnxQV07T8eR12p0S8V/amTNnYGJiAhUVFejp6aFmzZpYvXo1+vfvDyWl/9XDunbtikuXLmHBggVwdnZGcnIyrK2tMX36dIwbN04hM+qPHTsWq1evxu+///6fxacJEyZgwIABmDp1KkxNTX/4uqQYlatVh8/CFdiyYRV2bdsEY5MKGD5uCpp5tBE6GpHCfIx+j3m/TEV8XCx09PRRzc4ea7fshrFJeaGjESnUvds3EP0+Ei3adBQ6CpFCvHgagvlTh8nWd/26AgDQpHkbdOkzBHdvXAYATBvRW+646Ys2olrNOoUXlEjB+HlOhUHInjRisThPnUNGjx6N48eP4/Lly6hYsaKs3djYGMDXHkYmJv8bHRMdHZ2tt9H3iKT/1QWGipTXMd8fs0hU3CkrFf2nEBD9qNT0LKEjEBW4mIQ0oSMQFTh9SRmhIxAVOMuyakJHKBTbbwk3f6dnPbNc7SeVSjF69GgcOXIE/v7+2eZdlkqlKF++PMaPH48pU75OY5OWloZy5cph0aJFeZrgukT0LCIiIiIiIiIiyi9FjDQqaCNHjsSePXtw7NgxaGlpyeYh0tHRgbq6OkQiEcaNG4cFCxbAxsYGNjY2WLBgATQ0NNCrV688XYvFIiIiIiIiIiKiIm7Dhg0Avs6D/E/btm2Dp6cnAGDKlClITk7GiBEjEBsbiwYNGuDcuXPQ0tLK07U4DK0Y4TA0Kg04DI1KAw5Do9KAw9CoNOAwNCoNSsswNL/bbwS7dv+6RW8eY/YsIiIiIiIiIqJSjV9ZyxNywm8iIiIiIiIiIipi2LOIiIiIiIiIiEo1pWIwwXVhYs8iIiIiIiIiIiKSYc+iYkSswtoeEVFJkJjFCa6p5CunIxY6AhERUa6xX5E8Vh+IiIiIiIiIiEiGxSIiIiIiIiIiIpLhMDQiIiIiIiIiKtU4v7U89iwiIiIiIiIiIiIZ9iwiIiIiIiIiolJNxK5Fckp8zyKRSISjR48KHYOIiIiIiIiIqFgo9sWiqKgojB49GpaWlhCLxTA1NUW7du1w4cIFQfIcOnQIrq6u0NHRgUQigb29PebMmYOYmBhB8tCP2bVtM5zrVcfqZQuFjkJUYHifU0mUmZGBnZvXYWD3NujS3BGDerTF3u2bkJWVJXQ0IoX6GP0eC3280dmjCdq61sfQft3w9Emw0LGIFIr3OVHhK9bD0MLDw+Hk5ARdXV0sXrwY9vb2SE9Px9mzZzFy5Eg8efKkUHKkp6dDVVUV06dPx6JFizB+/HgsWLAA5cuXx7Nnz7Bx40bs3LkTY8eO/eaxVPSEPH6I40cPwsrGVugoRAWG9zmVVAf3bMfp4wcxftocmJlb4XnoY6zy9YGmphbad+sldDwihfjy+TPGDe2PmnXqYcHy9dDV10fE2zeQSLSEjkakMLzPqbAU+540Clas348RI0ZAJBLhr7/+QteuXWFraws7OztMmDABN27cyPGYd+/eoUePHtDT04OBgQE6dOiA8PBw2fZbt27B3d0dhoaG0NHRgYuLC+7evSt3DpFIhI0bN6JDhw7Q1NTEvHnz8Ndff2HBggVYtmwZlixZgkaNGsHc3Bzu7u44dOgQ+vfvDwDw8fFBrVq1sHXrVllvKKlUWmDvEeVPUlIS5s78GVOm+UBLS1voOEQFgvc5lWRPHj+Ao5ML6jVsAiOT8nBydUeteo54Fspvoqnk2L9rK8oaGWHyjLmoYlcDxiYV4FDPEeUrmgodjUhheJ8TCaPYFotiYmJw5swZjBw5Epqamtm26+rqZmtLSkqCm5sbJBIJLl++jKtXr0IikaBly5ZIS0sDAHz58gX9+/fHlStXcOPGDdjY2KB169b48uWL3LlmzZqFDh064OHDh/Dy8sLu3bshkUgwYsSIHPP+M8/z589x4MABHDp0CEFBQfl+D6jgrFg8Dw2dnFG3QUOhoxAVGN7nVJJVq1EL9+/+hXdvXgEAXj4PRcjDINR1dBI4GZHiXL/iD9sqdpgzbSK6tXbBsH7dcerYQaFjESkU73MqLCKRSLClKCq2w9CeP38OqVSKKlWq5PqYffv2QUlJCb/99pvsf5Bt27ZBV1cX/v7+aNGiBZo2bSp3zKZNm6Cnp4eAgAC0bdtW1t6rVy94eXnJ1p89ewZLS8tcDSlLS0vDzp07UbZs2W/uk5qaitTU1H+1KUEsFufqtVL+XTh3Ck9DgvHrjv1CRyEqMLzPqaTr2nsAkhITMLxPJygpKSMrKxN9B4+ES/NWQkcjUpjIiLf448gBdOnZF736D8KT4EdYt3wRVFXLwL11e6HjESkE73MiYRTbYtHfQ7fyUoW7c+cOnj9/Di0t+fGtKSkpCAsLAwBER0dj5syZuHjxIt6/f4/MzEwkJSXh9evXcsfUrVs3W57cZqlUqdJ/FooAwNfXF7Nnz5Zrm/jzDEz2npmra1D+vI+KxOplC7Fsza8szFGJxfucSoMrF8/C/9wpTJq5AGbmVnjxPBS/rVkKfYOyaNaKf1xQySDNyoJtFTsMHP51XkzrylXx6mUY/jhygH9EU4nB+5wKS9Hs3yOcYlsssrGxgUgkQkhICDp27JirY7KyslCnTh3s3r0727a/izeenp748OEDVq5ciUqVKkEsFqNhw4ayYWp/+/fQN1tbW1y9ejVXE1bnNGzu37y9vTFhwgS5trjUYjtqsNh4+iQYsTExGNyvh6wtMzMT9+/dwZHf9+LPa3ehrKwsYEKiH8f7nEqDbetXomvvAXBu1hIAYG5lgw9Rkfh99zYWi6jE0DcsCzMLS7k2M3MLXLn0p0CJiBSP9zmRMIptsUhfXx8eHh5Yt24dxowZk60AExcXl23eIgcHB+zfvx/lypWDtnbOk7leuXIF69evR+vWrQEAb968wcePH7+bp1evXli9ejXWr1+f41PPcsrzX8RicbZv/JM/p+f6eMqfOvUcsX3vEbm2hXNmwMzcAr36DeQf0FQi8D6n0iA1NQUiJfnvCJWUlSDNyhIoEZHi2dWohbevw+Xa3r5+BSNjE2ECERUA3udEwijWXVXWr1+PzMxM1K9fH4cOHcKzZ88QEhKC1atXo2HD7BO29u7dG4aGhujQoQOuXLmCly9fIiAgAGPHjsXbt28BANbW1ti5cydCQkJw8+ZN9O7dG+rq6t/N0qBBA0yZMgUTJ07ElClTcP36dbx69QoXLlxAt27d4Ofnp/DXT4qnoakJS2sbuUVNXR3aOrqwtLYROh6RQvA+p9KgXiNnHNi5BbeuX8H7yAhcv3wRR/fvQsMmTb9/MFEx0aVnX4Q8eog92zfj3ZvXuHj2JE4dO4j2XXsKHY1IYXifU2HhBNfyim3PIgCwsLDA3bt3MX/+fEycOBGRkZEoW7Ys6tSpgw0bNmTbX0NDA5cvX8bUqVPRuXNnfPnyBRUqVECzZs1kPY22bt2KIUOGoHbt2jAzM8OCBQswadKkXOVZtGgR6tSpg3Xr1mHjxo3IysqClZUVunbtiv79+yv0tRMREdG3DR03Fbt/W48NyxcgPjYW+oZl0bJ9V/T0HCJ0NCKFqVytOnwWrsCWDauwa9smGJtUwPBxU9DMo43Q0YgUhvc5kTBE0r9niqYi7z2HoRERlQjxyfw8p5JPTZVDWomISgIz/dLxQJTD9yMFu3bnmkVvWGWxHoZGRERERERERESKxWIRERERERERERHJFOs5i4iIiIiIiIiIflRRnWhaKOxZREREREREREREMuxZRERERERERESlGvsVyWPPIiIiIiIiIiIikmHPIiIiIiIiIiIq1ThlkTwWi4qRjCyp0BGIiEgBdNRVhY5AVOD4ewuVBpm8z4mohOIwNCIiIiIiIiIikmHPIiIiIiIiIiIq1ZQ4xbUc9iwiIiIiIiIiIiIZ9iwiIiIiIiIiolKNE1zLY88iIiIiIiIiIiKSYbGIiIiIiIiIiIhkilWxKDo6GkOHDoWZmRnEYjGMjY3h4eGB69evAwBEIhGOHj0qSDZPT0+IRCLZYmBggJYtW+LBgweC5KH82b55PZo2qCG3dGnlKnQsIoXifU6l0a5tm+FcrzpWL1sodBQiheHnOZUWH6PfY6GPNzp7NEFb1/oY2q8bnj4JFjoWlTAiAf+vKCpWcxZ16dIF6enp8PPzg6WlJd6/f48LFy4gJiZG6GgAgJYtW2Lbtm0AgKioKMyYMQNt27bF69evv3lMeno6VFVVCysi5YK5pTWWrt0sW1dSKlY1VaJc4X1OpUnI44c4fvQgrGxshY5CpHD8PKeS7svnzxg3tD9q1qmHBcvXQ1dfHxFv30Ai0RI6GlGJVmz+NYmLi8PVq1exaNEiuLm5oVKlSqhfvz68vb3Rpk0bmJubAwA6deoEkUgkWweADRs2wMrKCmXKlEHlypWxc+dOuXOLRCL89ttv6NSpEzQ0NGBjY4Pjx4/L7RMcHIzWrVtDIpHAyMgIffv2xcePH+X2+bu3k7GxMWrVqoWpU6fizZs3+PDhAwAgPDwcIpEIBw4cgKurK9TU1LBr1y7Fv1n0Q5SVlaFvYChbdPX0hY5EpHC8z6m0SEpKwtyZP2PKNB9oaWkLHYdI4fh5TiXd/l1bUdbICJNnzEUVuxowNqkAh3qOKF/RVOhoVMKIRMItRVGxKRZJJBJIJBIcPXoUqamp2bbfunULALBt2zZERkbK1o8cOYKxY8di4sSJePToEYYOHYoBAwbg0qVLcsfPnj0b3bt3x4MHD9C6dWv07t1b1mMpMjISLi4uqFWrFm7fvo0zZ87g/fv36N69+zfzJiQkYPfu3bC2toaBgYHctqlTp2LMmDEICQmBh4fHD70vpHjv3rxGtzZN0atjS8ydPhkR794IHYlI4XifU2mxYvE8NHRyRt0GDYWOQlQg+HlOJd31K/6wrWKHOdMmoltrFwzr1x2njh0UOhZRiSeSSqVSoUPk1qFDhzB48GAkJyfDwcEBLi4u6NmzJ+zt7QF87SF05MgRdOzYUXaMk5MT7Ozs8Ouvv8raunfvjsTERJw8eVJ23IwZMzB37lwAQGJiIrS0tHDq1Cm0bNkSM2fOxM2bN3H27FnZOd6+fQtTU1OEhobC1tYWnp6e2LVrF9TU1GTnMDExwYkTJ+Dg4ADga88iCwsLrFy5EmPHjv3P15qampqtKPYxWQSxWJzPd49y42bgFaSmpKCiWSXExnzCrm2/4nX4S2zddxQ6OrpCxyNSCN7nwlNRKqJfIZUwF86dwo4tv+LXHfshFosxZqgnrG2rYMzEn4WOVipkZBWbXzGLLX6eCy+T93mBa+1SFwDQpWdfuDRtgSfBj7Bh5WKMm/oL3Fu3Fzhd6WCmXzr+Bj3z+INg125pV1awa39LselZBHydsygiIgLHjx+Hh4cH/P394eDggO3bt3/zmJCQEDg5Ocm1OTk5ISQkRK7t74ITAGhqakJLSwvR0dEAgDt37uDSpUuy3k0SiQRVqlQBAISFhcmOc3NzQ1BQEIKCgnDz5k20aNECrVq1wqtXr+SuVbdu3e++Vl9fX+jo6Mgta1cs/u5x9GMaNGoC56busLS2RZ36DbFg+ToAwLmTxwRORqQ4vM+pNHgfFYnVyxbil7kL+UULlVj8PKfSQJqVBRvbqhg4fCysK1dF207d0LpDF/xx5IDQ0YhKtGI1wTUAqKmpwd3dHe7u7pg5cyYGDRqEWbNmwdPT85vHiP41CFAqlWZr+/ck0yKRCFlZWQCArKwstGvXDosWLcp2bhMTE9nPmpqasLa2lq3XqVMHOjo62Lx5M+bNmye33/d4e3tjwoQJcm0fk/lNdGFTV9eApbUN3r759iTlRMUd73MqiZ4+CUZsTAwG9+sha8vMzMT9e3dw5Pe9+PPaXSgrKwuYkEjx+HlOJZG+YVmYWVjKtZmZW+DKpT8FSkRUOhS7YtG/VatWDUePHgXwteCTmZkpt71q1aq4evUq+vXrJ2sLDAxE1apVc30NBwcHHDp0CObm5lBRyf1bJhKJoKSkhOTk5Fwf8zexWJztm9AvWWl5Pg/9mLS0NLx6+QI1ajoIHYWowPA+p5KoTj1HbN97RK5t4ZwZMDO3QK9+A1koohKJn+dUEtnVqIW3r8Pl2t6+fgUjY5OcDyDKp6I60bRQik2x6NOnT+jWrRu8vLxgb28PLS0t3L59G4sXL0aHDh0AAObm5rhw4QKcnJwgFouhp6eHyZMno3v37nBwcECzZs3wxx9/4PDhw/jzz9xXokeOHInNmzfjp59+wuTJk2FoaIjnz59j37592Lx5s+wXztTUVERFRQEAYmNjsXbtWiQkJKBdu3aKf0OoQGxYtRSNmrignLEJ4mJisHPbr0hKTESLNh2EjkakMLzPqTTQ0NSEpbWNXJuaujq0dXSztRMVV/w8p9KgS8++GDukH/Zs3wyXZh4IDX6IU8cOYtzPs4SORlSiFZtikUQiQYMGDbBixQqEhYUhPT0dpqamGDx4MKZNmwYAWLZsGSZMmIDNmzejQoUKCA8PR8eOHbFq1SosWbIEY8aMgYWFBbZt2wZXV9dcX7t8+fK4du0apk6dCg8PD6SmpqJSpUpo2bIllJT+N+3TmTNnZMPStLS0UKVKFfz+++95uhYJ62P0e8z7ZSri42Kho6ePanb2WLtlN4xNygsdjUhheJ8TEZUM/Dyn0qByterwWbgCWzaswq5tm2BsUgHDx01BM482QkejEoY9i+QVq6ehlXbv4jgMjYioJODT0Kg04NPQqDTg09CoNCgtT0M7FyLc09BaVOXT0IiIiIiIiIiIqAgrNsPQiIiIiIiIiIgKggjs+f1P7FlEREREREREREQy7FlERERERERERKUap5SUx55FREREREREREQkw55FRERERERERFSqcc4ieSwWFSNqquwIRiXfp4Q0oSMQEZECGGmrCR2BqMClpGcKHYGIqECw+kBERERERERERDLsWUREREREREREpZqIo9DksGcRERERERERERHJsGcREREREREREZVqnOBaHnsWERERERERERGRDItFREREREREREQkUyKLRdHR0Rg6dCjMzMwgFothbGwMDw8PXL9+HT179kSrVq3k9j99+jREIhF++eUXufa5c+eifPnyAIDw8HCIRCIEBQXJth86dAgNGjSAjo4OtLS0YGdnh4kTJ8q2+/j4oFatWtnyxcXFQSQSwd/fX2GvmRTj8O/70Ld7JzRvUh/Nm9TH4P69cP3aFaFjESlUZkYGdm5eh4Hd26BLc0cM6tEWe7dvQlZWltDRiBSG9zmVBtu3/Ir+vbrBtVEdeLg5YdK4UXgV/lLoWEQFate2zXCuVx2rly0UOgqVMEoi4ZaiqETOWdSlSxekp6fDz88PlpaWeP/+PS5cuICYmBi4ublh0qRJyMjIgIrK15fv7+8PU1NTXLp0Se48/v7+cHNzy/Eaf/75J3r27IkFCxagffv2EIlECA4OxoULFwr89VHBKVfOCMPHjEdFUzMAwKk/jmHq+FHYvvcQLK2sBU5HpBgH92zH6eMHMX7aHJiZW+F56GOs8vWBpqYW2nfrJXQ8IoXgfU6lwd07t9CtRy9UtauOzMxMbFi7EqOHD8T+wyegrq4hdDwihQt5/BDHjx6ElY2t0FGISrwSVyyKi4vD1atX4e/vDxcXFwBApUqVUL9+fQDA06dPkZCQgNu3b8PR0RHA16LQzz//jPHjxyMpKQkaGhpIS0vD9evXsXr16hyvc+LECTRu3BiTJ0+Wtdna2qJjx44F+wKpQDV2kS8ODhs1FkcO7sPjh/dZLKIS48njB3B0ckG9hk0AAEYm5RHw5xk8Cw0WOBmR4vA+p9Jg9frNcuszZy+AR1MnhAQ/hkOdegKlIioYSUlJmDvzZ0yZ5oMdWzcJHYdKIE5wLa/EDUOTSCSQSCQ4evQoUlNTs223tbVF+fLlZb2Ivnz5grt376Jbt26wsrLCtWvXAAA3btxAcnLyN3sWGRsb4/Hjx3j06FHBvRgSVGZmJs6fPYWU5GRUt68pdBwihalWoxbu3/0L7968AgC8fB6KkIdBqOvoJHAyIsXhfU6lUULCFwCAjo6OwEmIFG/F4nlo6OSMug0aCh2FqFQocT2LVFRUsH37dgwePBgbN26Eg4MDXFxc0LNnT9jb2wMAXF1d4e/vD29vb1y5cgW2trYoW7YsXFxc4O/vD3d3d9nQNCsrqxyvM3r0aFy5cgU1atRApUqV4OjoiBYtWqB3794Qi8WF+ZJJwcKePcUQz15IS0uDuroGfJethoUlexVRydG19wAkJSZgeJ9OUFJSRlZWJvoOHgmX5q2+fzBRMcH7nEobqVSKlcsWoWbtOrCy5hAdKlkunDuFpyHB+HXHfqGjEJUaJa5nEfB1zqKIiAgcP34cHh4e8Pf3h4ODA7Zv3w4AcHNzw7Vr15CevWjx+AAAQ45JREFUng5/f3+4uroCgKxYBHwdmta0adNvXkNTUxMnT57E8+fPMWPGDEgkEkycOBH169dHUlLSD7+G1NRUfP78WW7JqacUKZ6ZuTn89h7Cr3570KlbD8ybOQ0vXzwXOhaRwly5eBb+505h0swFWPnbHoybNgdH9u3EhdPHhY5GpDC8z6m0WeI7F8+fhmLewqVCRyFSqPdRkVi9bCF+mbuQX8pTgRKJhFuKohJZLAIANTU1uLu7Y+bMmQgMDISnpydmzZoF4GuxKDExEbdu3cKlS5dkcxu5uLjg1q1biImJwfXr1785BO2frKysMGjQIPz222+4e/cugoODsX//14q3trY24uPjsx0TFxcH4L+7CPv6+kJHR0duWbl0UV7fBsoHVdUyqGhWCVWrVcfw0eNhbVsZB/bsEjoWkcJsW78SXXsPgHOzljC3skFTj7bo0K03ft+9TehoRArD+5xKkyUL5+FywCWs/80PRkbGQschUqinT4IRGxODwf16wM2xJtwcayLo7m0c2r8bbo41kZmZKXREohKpxA1D+5Zq1arh6NGjAL4WeExNTXH8+HEEBQXJikUmJiYwNzfHsmXLkJKSkqti0T+Zm5tDQ0MDiYmJAIAqVarg7du3iIqKgrHx//7hvnXrFpSUlGBt/e2hTd7e3pgwYYJcW0KGcp7ykGJIpVKkp6cJHYNIYVJTUyD61zM6lZSVIOUjxakE4X1OpYFUKsXShfPgf/FPbPjNDxUqVBQ6EpHC1anniO17j8i1LZwzA2bmFujVbyCUlfk3EilGEe3gI5gSVyz69OkTunXrBi8vL9jb20NLSwu3b9/G4sWL0aFDB9l+bm5uWL9+PaytrWFkZCRrd3FxwZo1a2BpaQkzM7NvXsfHxwdJSUlo3bo1KlWqhLi4OKxevRrp6elwd3cHALRo0QJVq1ZFz549MX/+fJQvXx4PHjzApEmTMGzYMGhpaX3z/GKxOFs3y/TEjPy+LZRLG9eshKNTExgZGyMpMRHnz57GvTu3sHwtn7hAJUe9Rs44sHMLyhqZwMzcCi+ePcHR/bvg3rqj0NGIFIb3OZUGixfMwdnTJ7F05VpoaGri48cPAACJRAtqamoCpyNSDA1NTVha28i1qamrQ/v/2rvzuKjq/Y/j7xEREBAVRXBBRXBfMRdUFDfcSy21LJNyydLcKk0tU0Mxt2tKmZqCmbnlUtfMJRPX9CpGetVMza3CcEETVECY3x/9mtuEltbAgZnX8z7m8XC+Z3vPdO5w+PD5nvEqmm0cgO3YXbHIw8NDjRo10r/+9S+dPn1aGRkZKleunAYMGKCxY8da1mvZsqU++OADy/2KftOiRQu9//776tmz558ep0WLFnrnnXf09NNP6+eff1axYsVUr149bdmyRVWqVJH06822t2zZorFjx+rJJ59UUlKSypcvr/79+2vUqFE2f+34565evaJJr7+qK5cvyd3DU4FBlTUrer4aNm5idDTAZp4bPlrL3n9X82ZN0fXkZBUvUVLtH35Mj0cMNDoaYDOc53AEa1avkCQN6t/Xanz8xCnq/Eg3IyIBQL5VIK/ePMggJrPZbDY6BO7PFTqL4ACupDDlDwDsQakidLbA/t3O4H45sH+lijgbHSFXfHXqmmHHDgksatix78Vub3ANAAAAAACAB2d309AAAAAAAAAeBJPQrNFZBAAAAAAAAAs6iwAAAAAAgGOjtcgKnUUAAAAAAACwoFgEAAAAAAAAC6ahAQAAAAAAh2ZiHpoVikX5iKuzk9ERgBzn7sLHEuyfB+c5ANiF1LRMoyMAucDZ6AAwAFerAAAAAADAoZloLLLCPYsAAAAAAABgQWcRAAAAAABwaDQWWaOzCAAAAAAAABYUiwAAAAAAAGCRr4tFZ8+elclkUkJCgtFRAAAAAABAfmUy8JEH5el7FkVERGjJkiWW58WLF1eDBg00bdo01a5d28Bk2Z09e1YVK1bU119/rbp16xodB/9A/MED+iB2kY4fO6rLly5p5uxotWzdxuhYgM3ELnxXH7w/z2qsWHFvrfk8zphAQA6IXbRA27dt1bmz38vFxVW16tTTi8NfUvkKFY2OBtgM5zkcQd/HOijp4k/Zxjt366XBL401IBHgGPJ0sUiS2rdvr5iYGEnSxYsX9dprr6lz5846f/68wclgr27fuqXKlavq4a7d9cqIoUbHAXJEhYBAzYheaHleoEC+bjQFsjkUf0A9evVWtRo1lZmZqXnRs/Xi8/20cu0GubkVNjoeYBOc53AEby9cpqysLMvzc9+f0tgRzym0ZVsDU8EemfJqi49B8vxvBy4uLvL19ZWvr6/q1q2r0aNH68KFC7p06VK2dePi4mQymbR582bVq1dPbm5uatWqlZKSkvT555+rWrVqKlKkiJ544gndvHnTsl1YWJiGDh2qUaNGqXjx4vL19dWECROs9v3tt9+qWbNmcnV1VfXq1fXFF1/IZDJp/fr1kqSKFX/9C069evVkMpkUFhYm6dfuqK5du2rGjBny8/OTt7e3Bg8erIyMjBx5v/DPNQ1trsFDh6t1m3CjowA5xsnJScW9S1geRYsVNzoSYFNz3l2ozo90U6XAIFWuUlXjJ07RxcREHT921OhogM1wnsMRFC1W3OqaZf/enfIrU0616j1kdDTAruX5zqLfS0lJ0bJlyxQYGChvb2+lpqbedb0JEyYoOjpahQsXVs+ePdWzZ0+5uLjoo48+UkpKirp166a5c+dq9OjRlm2WLFmikSNHav/+/frqq68UERGhpk2bqm3btsrKylLXrl3l7++v/fv368aNG3rppZesjvmf//xHDRs21BdffKEaNWqoUKFClmXbt2+Xn5+ftm/frlOnTqlXr16qW7euBgwYkDNvFAD8hR8vnFePTq3k7FxI1WrUUr8Xhqp0mXJGxwJyTErKDUmSl5eXwUmAnMN5DnuXkZGh7Vs+U7defWQy0QUC5KQ8XyzasGGDPDw8JEmpqany8/PThg0b/nTKRGRkpJo2bSpJ6tevn8aMGaPTp08rICBAkvTYY49p+/btVsWi2rVr64033pAkBQUFKTo6Wtu2bVPbtm21ZcsWnT59WnFxcfL19ZUkTZ48WW3b/q/1sWTJkpIkb29vyzq/KVasmKKjo+Xk5KSqVauqU6dO2rZt258Wi9LS0pSWlmY1dsdUSC4uLn/+hgHAX6hWo5ZefWOyyvqXV/LVK/owZoFe7N9Hi1esl5dXUaPjATZnNps1e+ZbqlOvvioFVjY6DpAjOM/hCL7a+aVSUm6obceHjY4CO0T90Vqen4bWsmVLJSQkKCEhQfv371d4eLg6dOigc+fO3XOb39/8ulSpUipcuLClUPTbWFJS0j23kSQ/Pz/LOidOnFC5cuWsikANGza879dQo0YNOTk53XXf9xIVFSUvLy+rx4xpUfd9TAC4l0ZNQtW8VVsFBFZW/YYhmjLrHUnSls8+MTgZkDOmR72pU9+dUOTUGUZHAXIM5zkcwebP1umhRk3lXcLH6CiA3cvznUXu7u4KDAy0PK9fv768vLy0cOFC9e/f/67bODs7W/5tMpmsnv829vubpP1xmz+uYzab/1Gb4/0c/4/GjBmjkSNHWo3dMRW6x9oA8Pe5uRVWQGCQfrjAFwfA/kyfGqmdO7Zr/uKlKlXK9683APIhznM4gp8v/qSEg/v12uRZRkeBnaKxyFqeLxb9kclkUoECBXTr1q1cO2bVqlV1/vx5/fzzzypVqpQk6cCBA1br/HaPoszMTJsc08XFJduUs9R0s032DQC/l56ernNnvletOsFGRwFsxmw2a8bUSMV9+YXmvb9EZcqUNToSYHOc53AkWz/7RF7FiqthSKjRUQCHkOeLRWlpabp48aIkKTk5WdHR0UpJSVGXLl1yLUPbtm1VqVIl9e3bV9OmTdONGzc0btw4SbJ0HPn4+MjNzU2bNm1S2bJl5erqys0F86mbN1N14fz/Oix+/PEHnfj2uIp4ecnPr7SByQDbmPf2DDUJbSEfXz9du3pVS2MW6GZqqsI7PWJ0NMBmpk2ZpM2ff6YZs6NV2N1dly//+i2qHh6ecnV1NTgdYBuc53AUWVlZ2rrxE7Vp30VOBfP8r7DIr2gtspLn/5+2adMm+fn5SZI8PT1VtWpVrV69WmFhYTp79myuZHByctL69evVv39/NWjQQAEBAZo+fbq6dOli+UFcsGBBzZkzR5MmTdL48eMVGhqquLi4XMkH2zp29L8a+Gxfy/NZ06dKkro83FUTJ081KhZgM5eTflbk66N1/VqyvIoVV/UatRW9aJl8KYbCjqxZvUKSNKh/X6vx8ROnqPMj3YyIBNgc5zkcxdcH9ynp50SFd+pqdBTAYZjMZjNzm/6GPXv2qFmzZjp16pQqVaqUK8dkGhocwbWbGUZHAHKch0ue/1sNAOA+XElJNzoCkOMCSjpGp+Khc78Yduzg8kUMO/a9cLV6n9atWycPDw8FBQXp1KlTGjZsmJo2bZprhSIAAAAAAJAzTMxDs0Kx6D7duHFDo0aN0oULF1SiRAm1adNGM2fONDoWAAAAAACATTENLR9hGhocAdPQ4AiYhgYA9oFpaHAEjjINLeH8DcOOXdff07Bj30sBowMAAAAAAAAg76BYBAAAAAAAAAv64AEAAAAAgEPj9tbW6CwCAAAAAACABZ1F+cjtjEyjIwAAANwXrlvgCLwK8+sUYDdoLbJCZxEAAAAAAAAsKIUDAAAAAACHZqK1yAqdRQAAAAAAAPnAzp071aVLF5UuXVomk0nr16+3Wm42mzVhwgSVLl1abm5uCgsL09GjRx/4OBSLAAAAAAAA8oHU1FTVqVNH0dHRd10+bdo0zZo1S9HR0Tpw4IB8fX3Vtm1b3bhx44GOYzKbzWZbBM6vIiIidO3atWzVuLzoSuodoyMAOe52RpbREYAc5+HCLHDYP25wDUdQ0IlpK7B/3u6Ocd1y5IcUw45duaSz0tLSrMZcXFzk4uLyp9uZTCatW7dOXbt2lfRrV1Hp0qU1fPhwjR49WpKUlpamUqVK6a233tJzzz1335kM7SyKiIiQyWTSoEGDsi174YUXZDKZFBERkfvBbKRWrVrq37//XZctX75czs7O+vnnn3M5Ff7M2tUr1KdnN7UJbag2oQ01oG9vfbVnl9GxAJuKXfiuWjWqZfV4tEOY0bEAm4pdtEB9e/dQWJP6ateyqV4ePkTnzp4xOhaQoz6MWajmDWpqzsypRkcBbIbrcziCqKgoeXl5WT2ioqIeeD9nzpzRxYsXFR4ebhlzcXFRixYttHfv3gfal+ElwnLlymnFihX617/+JTc3N0nS7du3tXz5cvn7+xuc7p/p16+fxo8frzlz5qhw4cJWyxYvXqzOnTurVKlSBqXD3fj4lNLzQ0eobLlfz72N//5Eo0cMUezyNQqoFGhwOsB2KgQEakb0QsvzAgWYlQz7cij+gHr06q1qNWoqMzNT86Jn68Xn+2nl2g1ycyv81zsA8pnjR4/o0/Ufq1JQZaOjADbF9Tlyi5F9gmPGjNHIkSOtxv6qq+huLl68KEnZ6gylSpXSuXPnHmhfhv92EBwcLH9/f61du9YytnbtWpUrV0716tWzjJnNZk2bNk0BAQFyc3NTnTp19PHHH1uWZ2Zmql+/fqpYsaLc3NxUpUoVvf3221bHyszM1MiRI1W0aFF5e3tr1KhR+uMsvLS0NA0dOlQ+Pj5ydXVVs2bNdODAAcvy+vXra+bMmZbnXbt2VcGCBfXLL79I+vU/jslk0okTJ9SnTx+lpaVp9erVVsc4f/68vvzyS/Xr1+8fvHPICc1atFSTZs3lX76C/MtX0KAhw+RWuLCOHvnG6GiATTk5Oam4dwnLo2ix4kZHAmxqzrsL1fmRbqoUGKTKVapq/MQpupiYqOPHHvwGj0Bed/PmTb05/lWNGjtBnp5FjI4D2BTX53AELi4uKlKkiNXj7xSLfmMyWZe+zGZztrG/YnixSJKeeeYZxcTEWJ4vXrxYzz77rNU6r732mmJiYjRv3jwdPXpUI0aM0FNPPaUdO3ZIkrKyslS2bFmtWrVKx44d0/jx4zV27FitWrXKso+ZM2dq8eLFWrRokXbv3q2rV69q3bp1VscZNWqU1qxZoyVLlujQoUMKDAxUu3btdPXqVUlSWFiY4uLiJP36hu/atUvFihXT7t27JUnbt2+Xr6+vqlSpIm9vbz3yyCNWr02SYmJiVKpUKXXo0ME2byByRGZmprZu3qjbt26pZu06RscBbOrHC+fVo1Mr9e7aXm+Oe0U//XjB6EhAjkpJ+fWmjl5eXgYnAWzvX9MiFdK0uR5qFGJ0FCBHcX0O/DlfX19J/+sw+k1SUtIDz2oyfBqaJPXp00djxozR2bNnZTKZtGfPHq1YscJSlElNTdWsWbP05ZdfKiTk1x+CAQEB2r17t+bPn68WLVrI2dlZEydOtOyzYsWK2rt3r1atWqWePXtKkmbPnq0xY8bo0UcflSS999572rx5s2Wb1NRUzZs3T7GxsZZCzsKFC7V161YtWrRIr7zyisLCwrRo0SJlZWXpyJEjcnJy0lNPPaW4uDh17NhRcXFxatGihWWfzz77rDp27Kjvv/9eAQEBMpvNio2NVUREhJycnHL0fcXfc/rkdxoY0Vvp6elycyusqJlzVDGAFlfYj2o1aunVNyarrH95JV+9og9jFujF/n20eMV6eXkVNToeYHNms1mzZ76lOvXqq1IgU3RgX7Zt2ajvjh/Tgg9WGh0FyDFcnyNX2MH96itWrChfX19t3brVMlMrPT1dO3bs0FtvvfVA+8oTxaISJUqoU6dOWrJkicxmszp16qQSJUpYlh87dky3b99W27ZtrbZLT0+3mqr23nvv6f3339e5c+d069Ytpaenq27dupKk69evKzEx0VJskqSCBQvqoYceskxFO336tDIyMtS0aVPLOs7OzmrYsKGOHz8uSWrevLlu3Lihr7/+Wnv27FGLFi3UsmVLRUZGSpLi4uI0fPhwy/bh4eEqW7asYmJi9Oabb+rLL7/U2bNn9cwzz/zpe5KWlpbtbuhpd5z+USsa7o9/hQpasnyNbqTcUNy2rYocP1bvvB/LDyTYjUZNQq2eV69VR09176gtn32iHr37GpQKyDnTo97Uqe9OaEHsMqOjADb188VEzZk5VTPnLuAaEXaN63Pgf1JSUnTq1CnL8zNnzighIUHFixeXv7+/hg8frilTpigoKEhBQUGaMmWKChcurN69ez/QcfJEsUj6tQNnyJAhkqR33nnHallW1q9fpf3ZZ5+pTJkyVst++8G4atUqjRgxQjNnzlRISIg8PT01ffp07d+//74z/FY0+rP5fV5eXqpbt67i4uK0d+9etWrVSqGhoUpISNDJkyf13XffKSwszLJtgQIFFBERodjYWE2cOFExMTFq3ry5goKC/jRLVFSUVaeUJL0y5nWNHjf+vl8P/h5n50Iq619eklStek0dP/pfrfroQ41+bYKxwYAc4uZWWAGBQfrhwnmjowA2N31qpHbu2K75i5eqVClfo+MANvXdt8eUfPWqBjzdyzKWmZmpb76O17rVy/XFnkN0ssMucH2O3GDKJ61FBw8eVMuWLS3Pf7sxdt++fRUbG6tRo0bp1q1beuGFF5ScnKxGjRppy5Yt8vT0fKDj5JliUfv27ZWeni5JateundWy6tWry8XFRefPn7ea4vV7u3btUpMmTfTCCy9Yxk6fPm35t5eXl/z8/LRv3z41b95cknTnzh3Fx8crODhYkhQYGKhChQpp9+7dlqpbRkaGDh48aNUtFBYWpu3bt2v//v2aNGmSihYtqurVqysyMlI+Pj6qVq2aVbZnnnlGkZGRWrt2rdauXav33nvvL9+Pu90NPeUOP+yNYDablZGRbnQMIMekp6fr3JnvVatOsNFRAJsxm82aMTVScV9+oXnvL1GZMmWNjgTYXP0GjRW73Pr+m1MnvSb/ChXV++l+FIpgt7g+hyMLCwvL9kVdv2cymTRhwgRNmDDhHx0nzxSLnJycLFO9/viDzdPTUy+//LJGjBihrKwsNWvWTL/88ov27t0rDw8P9e3bV4GBgfrggw+0efNmVaxYUUuXLtWBAwdUsWJFy36GDRumqVOnKigoSNWqVdOsWbN07do1y3J3d3c9//zzeuWVVywtXNOmTdPNmzetvrksLCxMb7/9tooXL67q1atbxubOnavu3btne20VK1ZUq1atNHDgQDk7O+uxxx77y/fDxcUlWztxRuqdv34j8Y+8N3e2GjcNVSlfX91MTdXWzZ/r6/gDmhU93+hogM3Me3uGmoS2kI+vn65dvaqlMQt0MzVV4Z0eMToaYDPTpkzS5s8/04zZ0Srs7q7Lly9Jkjw8POXq6mpwOsA2Cru7KyDQulvd1c1NRbyKZhsH8iuuz5FbHvDLwuxenikWSVKRIvf+qs8333xTPj4+ioqK0vfff6+iRYsqODhYY8eOlSQNGjRICQkJ6tWrl0wmk5544gm98MIL+vzzzy37eOmll5SYmKiIiAgVKFBAzz77rLp166br169b1pk6daqysrLUp08f3bhxQw899JA2b96sYsWKWdb5rTOpRYsWlulpLVq00OzZs+/Z+dSvXz9t27ZNAwcOVOHChf/+m4QcdfXqFU16/VVduXxJ7h6eCgyqrFnR89WwcROjowE2cznpZ0W+PlrXryXLq1hxVa9RW9GLlsnXr7TR0QCbWbN6hSRpUH/r+3CNnzhFnR/pZkQkAMDfwPU5YAyT+c/6l5CnXKGzCA7gdkaW0RGAHOfhkqf+VgPkiNsZmUZHAHJcQSdaEWD/vN0d47rl2E+phh27eml3w459L47xXx0AAAAAAOAeKP1aK2B0AAAAAAAAAOQddBYBAAAAAADHRmuRFTqLAAAAAAAAYEGxCAAAAAAAABZMQwMAAAAAAA7NxDw0K3QWAQAAAAAAwILOonzE1dnJ6AhAjruTaTY6ApDjbmdkGh0ByHEFnfgLLewf1y2A/TDxY8sKnUUAAAAAAACwoLMIAAAAAAA4NBqLrNFZBAAAAAAAAAuKRQAAAAAAALBgGhoAAAAAAHBszEOzQmdRDjCZTFq/fr3RMQAAAAAAAB6YXRWLIiIiZDKZLA9vb2+1b99ehw8f/tPt4uLiLNsUKFBAXl5eqlevnkaNGqXExMRcSo+8Iv7gAQ0bMkjhrUIVXKuqtm/7wuhIQI76MGahmjeoqTkzpxodBcgxnOewR2tXr1Cfnt3UJrSh2oQ21IC+vfXVnl1GxwJyFJ/nyCkmA/+XF9lVsUiS2rdvr8TERCUmJmrbtm0qWLCgOnfufM/1MzIyLP8+ceKEfvrpJx04cECjR4/WF198oZo1a+rIkSO5ER15xO1bt1S5clWNHvu60VGAHHf86BF9uv5jVQqqbHQUIMdwnsNe+fiU0vNDR2jxh6u0+MNVqt+gkUaPGKLvT58yOhqQI/g8B3KP3RWLXFxc5OvrK19fX9WtW1ejR4/WhQsXdOnSJZ09e1Ymk0mrVq1SWFiYXF1d9eGHH1q29fHxka+vrypXrqzHH39ce/bsUcmSJfX8889b1jlw4IDatm2rEiVKyMvLSy1atNChQ4f+NNOPP/6oXr16qVixYvL29tYjjzyis2fP5tRbgH+oaWhzDR46XK3bhBsdBchRN2/e1JvjX9WosRPk6VnE6DhAjuA8hz1r1qKlmjRrLv/yFeRfvoIGDRkmt8KFdfTIN0ZHA2yOz3Mgd9ldsej3UlJStGzZMgUGBsrb29syPnr0aA0dOlTHjx9Xu3bt7rm9m5ubBg0apD179igpKUmSdOPGDfXt21e7du3Svn37FBQUpI4dO+rGjRt33cfNmzfVsmVLeXh4aOfOndq9e7c8PDzUvn17paen2/YFA8AD+Ne0SIU0ba6HGoUYHQXIMZzncBSZmZnaunmjbt+6pZq16xgdB7A5Ps+R00wm4x55kd19G9qGDRvk4eEhSUpNTZWfn582bNigAgX+VxcbPny4unfvbnn+3Xff3XN/VatWlSSdPXtWPj4+atWqldXy+fPnq1ixYtqxY8ddp7utWLFCBQoU0Pvvvy/T/58FMTExKlq0qOLi4hQefvfulbS0NKWlpVmN3TEVkouLy5+9fAC4L9u2bNR3x49pwQcrjY4C5BjOcziC0ye/08CI3kpPT5ebW2FFzZyjigGBRscCbIrPcyD32V1nUcuWLZWQkKCEhATt379f4eHh6tChg86dO2dZ56GHHrrv/ZnNZkmyFHqSkpI0aNAgVa5cWV5eXvLy8lJKSorOnz9/1+3j4+N16tQpeXp6ysPDQx4eHipevLhu376t06dP3/O4UVFRlv3/9pgxLeq+cwPAvfx8MVFzZk7V629OpQANu8V5DkfhX6GClixfowVLPlK3Hr0UOX6sznzPPYtgP/g8R24xGfjIi+yus8jd3V2Bgf/7a0r9+vXl5eWlhQsXqn///pZ17tfx48clSRUqVJD06zeuXbp0SbNnz1b58uXl4uKikJCQe04py8rKUv369bVs2bJsy0qWLHnP444ZM0YjR460GrtjKnTfuQHgXr779piSr17VgKd7WcYyMzP1zdfxWrd6ub7Yc0hOTk4GJgT+Oc5zOApn50Iq619eklStek0dP/pfrfroQ41+bYKxwQAb4fMcMIbdFYv+yGQyqUCBArp169YDb3vr1i0tWLBAzZs3txR2du3apXfffVcdO3aUJF24cEGXL1++5z6Cg4O1cuVK+fj4qEiR+78Rm4uLS7bKeWq6+YFfAwD8Uf0GjRW7fJ3V2NRJr8m/QkX1frofF1ywC5zncFRms1kZGdwXE/aDz3PAGHZXLEpLS9PFixclScnJyYqOjlZKSoq6dOnyl9smJSXp9u3bunHjhuLj4zVt2jRdvnxZa9eutawTGBiopUuX6qGHHtIvv/yiV155RW5ubvfc55NPPqnp06frkUce0aRJk1S2bFmdP39ea9eu1SuvvKKyZcv+8xcNm7p5M1UXfjet8Mcff9CJb4+riJeX/PxKG5gMsI3C7u4KCAyyGnN1c1MRr6LZxoH8ivMcjuC9ubPVuGmoSvn66mZqqrZu/lxfxx/QrOj5RkcDbIbPc+SavDofzCB2VyzatGmT/Pz8JEmenp6qWrWqVq9erbCwsL/8uvoqVarIZDLJw8NDAQEBCg8P18iRI+Xr62tZZ/HixRo4cKDq1asnf39/TZkyRS+//PI991m4cGHt3LlTo0ePVvfu3XXjxg2VKVNGrVu3fqBOI+SeY0f/q4HP9rU8nzV9qiSpy8NdNXHyVKNiAQAAWLl69Yomvf6qrly+JHcPTwUGVdas6Plq2LiJ0dEAAPmcyfzbHZyR5zENDY4g5fYdoyMAAGygoBN/ooX9u5PJ9TnsX6kizkZHyBXfX7pt2LEDSroadux7sbtvQwMAAAAAAMDfZ3fT0AAAAAAAAB6EiYZYK3QWAQAAAAAAwIJiEQAAAAAAACyYhgYAAAAAABwas9Cs0VkEAAAAAAAACzqLAAAAAACAY6O1yArFonzEqQBnL+yfV2FnoyMAOS417Y7REYAcdzsjy+gIQI5zdWaiBgD7xKcbAAAAAAAALOgsAgAAAAAADs3EPDQrdBYBAAAAAADAgs4iAAAAAADg0Ew0FlmhswgAAAAAAAAWFIseQEREhLp27Wp0DAAAAAAAYEMmAx95Ub4oFnXp0kVt2rS567KvvvpKJpNJhw4dyuVUsGcrly9Th/BWalCvlh7v0V2H4g8aHQmwOc5z2LO1q1eoT89uahPaUG1CG2pA3976as8uo2MBNhW78F21alTL6vFohzCjYwE2xec5YIx8cc+ifv36qXv37jp37pzKly9vtWzx4sWqW7eugoODDUr392VkZMjZ2dnoGPiDTZ9v1LSpURr3+huqWy9YH69aoReeG6B1n34mv9KljY4H2ATnOeydj08pPT90hMqW85ckbfz3Jxo9Yohil69RQKVAg9MBtlMhIFAzohdanhcokC/+FgzcNz7PAWPki58mnTt3lo+Pj2JjY63Gb968qZUrV6pfv37au3evmjdvLjc3N5UrV05Dhw5VamqqZd309HSNGjVKZcqUkbu7uxo1aqS4uDjL8tjYWBUtWlSbN29WtWrV5OHhofbt2ysxMfGeueLj4+Xj46PJkydLkq5fv66BAwfKx8dHRYoUUatWrfTNN99Y1p8wYYLq1q2rxYsXKyAgQC4uLjKbzbZ5k2AzS5fEqNujj6r7Yz0UUKmSRo0ZJ18/X61audzoaIDNcJ7D3jVr0VJNmjWXf/kK8i9fQYOGDJNb4cI6euSbv94YyEecnJxU3LuE5VG0WHGjIwE2xec5covJZNwjL8oXxaKCBQvq6aefVmxsrFVxZfXq1UpPT1edOnXUrl07de/eXYcPH9bKlSu1e/duDRkyxLLuM888oz179mjFihU6fPiwevToofbt2+vkyZOWdW7evKkZM2Zo6dKl2rlzp86fP6+XX375rpni4uLUunVrTZw4UePGjZPZbFanTp108eJFbdy4UfHx8QoODlbr1q119epVy3anTp3SqlWrtGbNGiUkJNj+zcI/kpGeruPHjiqkSTOr8ZAmTfVNwtcGpQJsi/McjiYzM1NbN2/U7Vu3VLN2HaPjADb144Xz6tGplXp3ba83x72in368YHQkIMfweQ7knnwxDU2Snn32WU2fPl1xcXFq2bKlpF+noHXv3l0LFy5U7969NXz4cElSUFCQ5syZoxYtWmjevHn68ccftXz5cv3www8q/f/TK15++WVt2rRJMTExmjJliqRfp4W99957qlSpkiRpyJAhmjRpUrYsn3zyifr06aP58+friSeekCRt375dR44cUVJSklxcXCRJM2bM0Pr16/Xxxx9r4MCBkn7tcFq6dKlKliz5p683LS1NaWlpVmNmJxfLvpEzkq8lKzMzU97e3lbj3t4ldPnyJYNSAbbFeQ5HcfrkdxoY0Vvp6elycyusqJlzVDGAKQuwH9Vq1NKrb0xWWf/ySr56RR/GLNCL/fto8Yr18vIqanQ8wGb4PEfuyKMtPgbJN8WiqlWrqkmTJlq8eLFatmyp06dPa9euXdqyZYuGDRumU6dOadmyZZb1zWazsrKydObMGf33v/+V2WxW5cqVrfaZlpZm9ctS4cKFLYUiSfLz81NSUpLVNvv379eGDRu0evVqdevWzTIeHx+vlJSUbL983bp1S6dPn7Y8L1++/F8WiiQpKipKEydOtBob9/obem38hL/cFv+c6Q+9gGazOdsYkN9xnsPe+VeooCXL1+hGyg3FbduqyPFj9c77sfyCAbvRqEmo1fPqteroqe4dteWzT9Sjd1+DUgG2x+c5kPvyTbFI+vVG10OGDNE777yjmJgYlS9fXq1bt1ZWVpaee+45DR06NNs2/v7+Onz4sJycnBQfHy8nJyer5R4eHpZ///Fm0yaTKds9hSpVqiRvb28tXrxYnTp1UqFChSRJWVlZ8vPzs7oP0m+KFi1q+be7u/t9vdYxY8Zo5MiRVmNmJ7qKclqxosXk5OSky5cvW41fvXpF3t4lDEoF2BbnORyFs3MhlfX/9YsxqlWvqeNH/6tVH32o0a9NMDYYkEPc3AorIDBIP1w4b3QUwKb4PAdyX74qFvXs2VPDhg3TRx99pCVLlmjAgAEymUwKDg7W0aNHFRh498pyvXr1lJmZqaSkJIWGht51nftVokQJrV27VmFhYerVq5dWrVolZ2dnBQcH6+LFiypYsKAqVKjwj44hSS4u2aec3b7zj3eLv+BcqJCqVa+hfXv3qHWbtpbxfXv3KqxVawOTAbbDeQ5HZTablZGRbnQMIMekp6fr3JnvVatO/vuWYOBB8HmOnECDvbV8cYPr33h4eKhXr14aO3asfvrpJ0VEREiSRo8era+++kqDBw9WQkKCTp48qU8//VQvvviiJKly5cp68skn9fTTT2vt2rU6c+aMDhw4oLfeeksbN2584Bw+Pj768ssv9e233+qJJ57QnTt31KZNG4WEhKhr167avHmzzp49q7179+q1117TwYMHbfk2IIf16fuM1q75WOvWfqzvT5/W9KlTlJiYqB69Hjc6GmAznOewd+/Nna2EQ/FK/OlHnT75nd6Lfltfxx9QeIfORkcDbGbe2zP0zaEDSvzpBx3/72FNGDNSN1NTFd7pEaOjATbD5zlgjHzVWST9OhVt0aJFCg8Pl7+/vySpdu3a2rFjh8aNG6fQ0FCZzWZVqlRJvXr1smwXExOjyMhIvfTSS/rxxx/l7e2tkJAQdezY8W/l8PX11ZdffqmwsDA9+eST+uijj7Rx40aNGzdOzz77rC5duiRfX181b95cpUqVsslrR+5o36Gjrl9L1oJ57+rSpSQFBlXWO+8tUOnSZYyOBtgM5zns3dWrVzTp9Vd15fIluXt4KjCosmZFz1fDxk2MjgbYzOWknxX5+mhdv5Ysr2LFVb1GbUUvWiZfv9JGRwNshs9z5BYai6yZzH+8KQ/yLKahAYB9SE3jAx3273ZGltERgBzn6pyvJmoAf4u3e77rMflbfrpm3NTG0kULGXbse3GM/+oAAAAAAAD3wD2LrFEKBwAAAAAAgAXFIgAAAAAAAFgwDQ0AAAAAADg0E7e4tkJnEQAAAAAAACzoLAIAAAAAAI6NxiIrFIvykTS+ghYO4HZGptERgBzn4cqPX9i/ggVoYIf9c3HmPAdgn/h0AwAAAAAAgAV/2gQAAAAAAA6NWWjW6CwCAAAAAACABZ1FAAAAAADAoZloLbJCZxEAAAAAAAAsKBb9QYUKFTR79ux/tI8JEyaobt26lucRERHq2rWr5XlYWJiGDx/+j44BAAAAAABsw2Tg//IiuykWRUREyGQyaerUqVbj69evl+n/+8ni4uJkMpksj5IlS6pDhw765ptv7usYXbp0UZs2be667KuvvpLJZNKhQ4f08ssva9u2bf/sBcEQsYsWqG/vHgprUl/tWjbVy8OH6NzZM0bHAnLUhzEL1bxBTc2ZOfWvVwbykfiDBzRsyCCFtwpVcK2q2r7tC6MjATbFdQscycrly9QhvJUa1Kulx3t016H4g0ZHAuya3RSLJMnV1VVvvfWWkpOT/3S9EydOKDExUZ999pmSk5PVvn17Xb9+/S/3369fP3355Zc6d+5ctmWLFy9W3bp1FRwcLA8PD3l7e//t1wHjHIo/oB69emvRBys0971Fysy8oxef76dbt24aHQ3IEcePHtGn6z9WpaDKRkcBbO72rVuqXLmqRo993egoQI7gugWOYtPnGzVtapQGDHxeKz9er+Dg+nrhuQFK/Okno6MBdsuuikVt2rSRr6+voqKi/nQ9Hx8f+fr6qmHDhpo5c6YuXryoffv23XXdmJgYeXl5aevWrercubN8fHwUGxtrtc7Nmze1cuVK9evXT1L2aWjIP+a8u1CdH+mmSoFBqlylqsZPnKKLiYk6fuyo0dEAm7t586beHP+qRo2dIE/PIkbHAWyuaWhzDR46XK3bhBsdBcgRXLfAUSxdEqNujz6q7o/1UEClSho1Zpx8/Xy1auVyo6PBnpgMfORBdlUscnJy0pQpUzR37lz98MMP97WNm5ubJCkjIyPbshkzZujll1/W5s2b1bZtWxUsWFBPP/20YmNjZTabLeutXr1a6enpevLJJ23zQpBnpKTckCR5eXkZnASwvX9Ni1RI0+Z6qFGI0VEAADbAdQvsUUZ6uo4fO6qQJs2sxkOaNNU3CV8blAqwf3ZVLJKkbt26qW7dunrjjTf+ct0rV65o4sSJ8vT0VMOGDa2WjRkzRrNmzVJcXJwaN25sGX/22Wd19uxZxcXFWcYWL16s7t27q1ixYjZ7HTCe2WzW7JlvqU69+qoUyBQd2JdtWzbqu+PHNHDwcKOjAABsgOsW2Kvka8nKzMzMdpsPb+8Sunz5kkGpYI9oLLJW0OgAOeGtt95Sq1at9NJLL911edmyZSVJqampCgoK0urVq+Xj42NZPnPmTKWmpurgwYMKCAiw2rZq1apq0qSJFi9erJYtW+r06dPatWuXtmzZYtPXkJaWprS0NOuxLGe5uLjY9Di4t+lRb+rUdye0IHaZ0VEAm/r5YqLmzJyqmXMX8JkCAHaC6xbYu9++tOg3ZrM52xgA27G7ziJJat68udq1a6exY8fedfmuXbv0zTff6Pr16/ruu+/Url07q+WhoaHKzMzUqlWr7rp9v379tGbNGv3yyy+KiYlR+fLl1bp1a5u+hqioKHl5eVk9Zk3nm4pyy/Spkdq5Y7vefX+JSpXyNToOYFPffXtMyVevasDTvdSycR21bFxHCYcOas3KZWrZuI4yMzONjggAeABct8CeFStaTE5OTrp8+bLV+NWrV+TtXcKgVID9s8vOIunXYku9evVUuXL2NtyKFSuqaNGi99y2YcOGevHFF9WuXTs5OTnplVdesVres2dPDRs2TB999JGWLFmiAQMG2LyqPWbMGI0cOdJq7HaWs02PgezMZrNmTI1U3JdfaN77S1SmTFmjIwE2V79BY8UuX2c1NnXSa/KvUFG9n+4nJycng5IBAB4E1y1wBM6FCqla9Rrat3ePWrdpaxnft3evwlrZ9g/2cGw0qlmz22JR7dq19eSTT2ru3Ll/a/uQkBB9/vnnat++vQoWLKgRI0ZYlnl4eKhXr14aO3asrl+/roiICBul/h8XF5ds00PMt7JsfhxYmzZlkjZ//plmzI5WYXd3yzxoDw9Pubq6GpwOsI3C7u4KCAyyGnN1c1MRr6LZxoH87ObNVF04f97y/Mcff9CJb4+riJeX/PxKG5gMsA2uW+Ao+vR9RuNeHaXqNWuqTp16WrN6pRITE9Wj1+NGRwPslt0WiyTpzTffvOdUsvvRtGlTffbZZ+rYsaOcnJw0dOhQy7J+/fpp0aJFCg8Pl7+/vy3iIg9Ys3qFJGlQ/75W4+MnTlHnR7oZEQkA8DcdO/pfDXz2f5/nv03n7vJwV02czNRu5H9ct8BRtO/QUdevJWvBvHd16VKSAoMq6533Fqh06TJGR4MdMeXZW00bw2T+/XfAI0+7TmcRHMDtDO6XA/vn4WrXf6sBJEl3MrnEhP1zcbbLW8ACVhzlsuVqqnG/hxR3z3u3gXCQ/+wAAAAAAAB3xz2LrFEKBwAAAAAAgAXFIgAAAAAAAFhQLAIAAAAAAIAFxSIAAAAAAABYcINrAAAAAADg0LjBtTU6iwAAAAAAAGBBZ1E+4uJMbQ/2r6ATJX0AsAdctwAAkH9RLAIAAAAAAA7NJP5o/Xv8yQcAAAAAAAAWdBYBAAAAAACHxg2urdFZBAAAAAAAAAs6iwAAAAAAgEOjscia3XYWnT17ViaTSQkJCUZHAQAAAAAAyDfybbEoIiJCJpPJ8vD29lb79u11+PBhwzJVqFBBs2fPNuz4sJ2Vy5epQ3grNahXS4/36K5D8QeNjgTYVPz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      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sales = pd.crosstab(df_train_b[\"Neighborhood\"], df_train_b[\"Sold_before\"])\n",
    "df_sales_sum = df_sales.sum(axis=1).sort_values(ascending=False)\n",
    "df_sales = df_sales.loc[df_sales_sum.index]\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "sns.heatmap(df_sales, cmap=\"Blues\", annot=True, fmt=\".0f\")\n",
    "plt.title(\"Distribution of recently sold properties by neighborhood and year\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Neighborhood\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d3e54a",
   "metadata": {},
   "source": [
    "## Nridght Analysis\n",
    "\n",
    "- Analysis of the North Ridge Heights Neighborhood based on the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "66b5fd8b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:09.436708Z",
     "start_time": "2023-02-27T04:29:09.379039Z"
    }
   },
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>...</th>\n",
       "      <th>RemodAfter</th>\n",
       "      <th>AgeRemodAdd</th>\n",
       "      <th>Age_Sold</th>\n",
       "      <th>Sold_before</th>\n",
       "      <th>Age_Garage</th>\n",
       "      <th>Age_Garage_Sold</th>\n",
       "      <th>TotalPorch</th>\n",
       "      <th>TotalBath</th>\n",
       "      <th>Has2ndFlr</th>\n",
       "      <th>HasBsmt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>102.0</td>\n",
       "      <td>12858</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NridgHt</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>133</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>94.0</td>\n",
       "      <td>12883</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NridgHt</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11520</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NridgHt</td>\n",
       "      <td>PosN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>74</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>79.0</td>\n",
       "      <td>14122</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NridgHt</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>119</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>110.0</td>\n",
       "      <td>14300</td>\n",
       "      <td>Reg</td>\n",
       "      <td>HLS</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Mod</td>\n",
       "      <td>NridgHt</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3.5</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    MSSubClass MSZoning  LotFrontage  LotArea LotShape LandContour LotConfig  \\\n",
       "15          60       RL        102.0    12858      IR1         Lvl    Inside   \n",
       "16          20       RL         94.0    12883      IR1         Lvl    Corner   \n",
       "17          20       RL         90.0    11520      Reg         Lvl    Inside   \n",
       "18          20       RL         79.0    14122      IR1         Lvl    Inside   \n",
       "19          20       RL        110.0    14300      Reg         HLS    Inside   \n",
       "\n",
       "   LandSlope Neighborhood Condition1  ... RemodAfter AgeRemodAdd  Age_Sold  \\\n",
       "15       Gtl      NridgHt       Norm  ...          1           1         1   \n",
       "16       Gtl      NridgHt       Norm  ...          1           1         1   \n",
       "17       Gtl      NridgHt       PosN  ...          0           6         5   \n",
       "18       Gtl      NridgHt       Norm  ...          1           5         5   \n",
       "19       Mod      NridgHt       Norm  ...          1           7         7   \n",
       "\n",
       "    Sold_before Age_Garage Age_Garage_Sold TotalPorch  TotalBath Has2ndFlr  \\\n",
       "15            1        2.0             1.0        133        2.5       Yes   \n",
       "16            1        2.0             1.0         35        2.0        No   \n",
       "17            1        6.0             5.0         74        2.0        No   \n",
       "18            1        6.0             5.0        119        2.0        No   \n",
       "19            1        8.0             7.0        150        3.5        No   \n",
       "\n",
       "   HasBsmt  \n",
       "15     Yes  \n",
       "16     Yes  \n",
       "17     Yes  \n",
       "18     Yes  \n",
       "19     Yes  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nridght = pd.DataFrame(df_train_b[df_train_b.Neighborhood == \"NridgHt\"])\n",
    "df_nridght_test = pd.DataFrame(df_test_b[df_test_b.Neighborhood == \"NridgHt\"])\n",
    "df_nridght_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f46c252",
   "metadata": {},
   "source": [
    "### Appendix 5 - Living Area vs Sales Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0efc9857",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:11.709843Z",
     "start_time": "2023-02-27T04:29:09.440608Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking linearity of highly correlated features\n",
    "ax = sns.pairplot(df_nridght[['SalePrice', 'GrLivArea']], \n",
    "             y_vars=['SalePrice'],\n",
    "             x_vars=['GrLivArea'],\n",
    "             height=2.5,\n",
    "             kind='reg') # 'scatter'\n",
    "ax.figure.set_size_inches(18, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "828a6e28",
   "metadata": {},
   "source": [
    "Here it can be seen that with the increase in living area the sale price also increases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cd0cd4",
   "metadata": {},
   "source": [
    "### Appendix 6 -  Total Basement Area v Sales Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ccfc9ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:13.971425Z",
     "start_time": "2023-02-27T04:29:11.713889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking linearity of highly correlated features\n",
    "ax = sns.pairplot(df_nridght[['SalePrice','TotalBsmtSF',]],\n",
    "             y_vars=['SalePrice'],\n",
    "             x_vars=['TotalBsmtSF'],\n",
    "             height=2.5,\n",
    "             kind='reg') # 'scatter'\n",
    "ax.figure.set_size_inches(18, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66272284",
   "metadata": {},
   "source": [
    "Though houses with bigger basements have higher prices it can be seen form the above graph that the average basement size is between 1000 to 2000 SF, in the neighborhood"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973aef75",
   "metadata": {},
   "source": [
    "### Appendix 7 - First Floor Area vs Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d4f11540",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:16.115157Z",
     "start_time": "2023-02-27T04:29:13.975282Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking linearity of highly correlated features\n",
    "ax = sns.pairplot(df_nridght[['SalePrice', '1stFlrSF']], \n",
    "             y_vars=['SalePrice'],\n",
    "             x_vars=['1stFlrSF'],\n",
    "             height=2.5,\n",
    "             kind='reg') # 'scatter'\n",
    "ax.figure.set_size_inches(18, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7451b5",
   "metadata": {},
   "source": [
    "## Feature Preparation For Regression\n",
    "\n",
    "- converting the target variable into a new list\n",
    "- creating dummy variables\n",
    "- normalizing the skewed variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2dea6860",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:16.131213Z",
     "start_time": "2023-02-27T04:29:16.121698Z"
    }
   },
   "outputs": [],
   "source": [
    "#creating a new list with saleprice\n",
    "y = df_train_b.pop(\"SalePrice\") \n",
    "y_nridght = df_nridght.pop(\"SalePrice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef6f790",
   "metadata": {},
   "source": [
    "### One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "306814ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:16.256892Z",
     "start_time": "2023-02-27T04:29:16.135794Z"
    }
   },
   "outputs": [],
   "source": [
    "# Convert Categorical variables into ONE HOT ENCODINGs\n",
    "df_train_b = pd.get_dummies(df_train_b) \n",
    "df_test_b = pd.get_dummies(df_test_b)\n",
    "df_nridght = pd.get_dummies(df_nridght)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "278b1436",
   "metadata": {},
   "source": [
    "### Log Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a65d3658",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:16.757735Z",
     "start_time": "2023-02-27T04:29:16.260142Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skew in numerical features: \n",
      "\n",
      "There are 227 skewed numerical features to Log1p transform\n"
     ]
    }
   ],
   "source": [
    "# Skewing all features (Log level transform)\n",
    "\n",
    "skewed_feats = df_train_b.apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
    "print(\"\\nSkew in numerical features: \\n\")\n",
    "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
    "skewness.head(10)\n",
    "\n",
    "#New data \n",
    "df_train_b_skewed = df_train_b.copy()\n",
    "df_test_b_skewed = df_test_b.copy()\n",
    "\n",
    "skewness = skewness[abs(skewness) > 1]\n",
    "print(\"There are {} skewed numerical features to Log1p transform\".format(skewness.shape[0]))\n",
    "\n",
    "skewed_features = skewness.index\n",
    "for feat in skewed_features:\n",
    "    df_train_b_skewed[feat] = np.log1p(df_train_b_skewed[feat])\n",
    "    df_test_b_skewed[feat] = np.log1p(df_test_b_skewed[feat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f19aae5a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:16.930325Z",
     "start_time": "2023-02-27T04:29:16.761674Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skew in numerical features: \n",
      "\n",
      "There are 104 skewed numerical features to Log1p transform\n"
     ]
    }
   ],
   "source": [
    "# Skewing all features (Log level transform)\n",
    "skewed_feats = df_nridght.apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
    "print(\"\\nSkew in numerical features: \\n\")\n",
    "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
    "skewness.head(10)\n",
    "\n",
    "#New data \n",
    "df_nridght_skewed = df_nridght.copy()\n",
    "\n",
    "skewness = skewness[abs(skewness) > 1]\n",
    "print(\"There are {} skewed numerical features to Log1p transform\".format(skewness.shape[0]))\n",
    "\n",
    "skewed_features = skewness.index\n",
    "for feat in skewed_features:\n",
    "    df_nridght_skewed[feat] = np.log1p(df_nridght_skewed[feat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bfe6198f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:16.976699Z",
     "start_time": "2023-02-27T04:29:16.935858Z"
    }
   },
   "outputs": [],
   "source": [
    "# Convert Target also Log level\n",
    "y_log = np.log1p(y)  #converts SalesPrice to Log\n",
    "y_nridght_log = np.log1p(y_nridght)\n",
    "#Convert -INF to Zero\n",
    "df_test_b_skewed=df_test_b_skewed.replace(-np.Inf, 0) #converts the -ve values to 0\n",
    "df_nridght_skewed=df_nridght_skewed.replace(-np.Inf, 0) #converts the -ve values to 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d996793",
   "metadata": {},
   "source": [
    "## Training Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f25684a",
   "metadata": {},
   "source": [
    "### Regression on Selected Neighborhood - NeridgHt\n",
    "\n",
    "This next section runs the training data through various models to get an RMSE score for each one. A dataframe is created to compare each models performance. Both parametric and non-parametric models are used in this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fbb49205",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:16.992150Z",
     "start_time": "2023-02-27T04:29:16.981622Z"
    }
   },
   "outputs": [],
   "source": [
    "# create list of model scores\n",
    "model = []\n",
    "score = []\n",
    "model_type = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518ebad0",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "78e45f37",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:17.287672Z",
     "start_time": "2023-02-27T04:29:16.995584Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training RMSE: 0.051\n",
      "Test RMSE: 0.120\n"
     ]
    }
   ],
   "source": [
    "def random_forest_model(X, y):\n",
    "    \"\"\"\n",
    "    Trains a random forest regressor on the given data and returns the model, RMSE score and model type.\n",
    "\n",
    "    Args:\n",
    "        X (pandas.DataFrame): The feature matrix.\n",
    "        y (pandas.Series): The target variable.\n",
    "\n",
    "    Returns:\n",
    "        Tuple: A tuple containing the model, RMSE score, and model type.\n",
    "    \"\"\"\n",
    "    # split data\n",
    "    x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=219)\n",
    "    \n",
    "    # create and fit model\n",
    "    model = RandomForestRegressor(n_estimators=100, random_state=420)\n",
    "    model.fit(x_train, y_train)\n",
    "    \n",
    "    # get model predictions\n",
    "    y_train_pred = model.predict(x_train)\n",
    "    y_test_pred = model.predict(x_test)\n",
    "\n",
    "    # evaluate predictions\n",
    "    train_rmse = np.sqrt(mean_squared_error(y_train, y_train_pred))\n",
    "    test_rmse = np.sqrt(mean_squared_error(y_test, y_test_pred))\n",
    "    print(f\"Training RMSE: {train_rmse:.3f}\")\n",
    "    print(f\"Test RMSE: {test_rmse:.3f}\")\n",
    "  \n",
    "\n",
    "    \n",
    "    return 'RandomForest', test_rmse, 'non-parametric'\n",
    "        \n",
    "ret_model, ret_score, ret_model_type  = random_forest_model(df_nridght_skewed, y_nridght_log)\n",
    "\n",
    "model.append(ret_model)\n",
    "score.append(ret_score)\n",
    "model_type.append(ret_model_type)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f87b65",
   "metadata": {},
   "source": [
    "#### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "35ad57de",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:17.349630Z",
     "start_time": "2023-02-27T04:29:17.302283Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training RMSE: 0.000\n",
      "Test RMSE: 0.329\n"
     ]
    }
   ],
   "source": [
    "def linear_model(X, y):\n",
    "    \"\"\"\n",
    "    Trains a linear regressor on the given data and returns the model, RMSE score and model type.\n",
    "\n",
    "    Args:\n",
    "        X (pandas.DataFrame): The feature matrix.\n",
    "        y (pandas.Series): The target variable.\n",
    "\n",
    "    Returns:\n",
    "        Tuple: A tuple containing the model, RMSE score, and model type.\n",
    "    \"\"\"\n",
    "    \n",
    "    # split data\n",
    "    x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=219)\n",
    "    \n",
    "    # create and fit model\n",
    "    model = LinearRegression()\n",
    "    model.fit(x_train, y_train)\n",
    "    \n",
    "    # get model predictions\n",
    "    y_train_pred = model.predict(x_train)\n",
    "    y_test_pred = model.predict(x_test)\n",
    "    \n",
    "    \n",
    "    # evaluate predictions\n",
    "    train_rmse = np.sqrt(mean_squared_error(y_train, y_train_pred))\n",
    "    test_rmse = np.sqrt(mean_squared_error(y_test, y_test_pred))\n",
    "    print(f\"Training RMSE: {train_rmse:.3f}\")\n",
    "    print(f\"Test RMSE: {test_rmse:.3f}\")\n",
    "  \n",
    "    return 'LinearRegression', test_rmse, 'parametric'\n",
    "        \n",
    "    \n",
    "ret_model, ret_score, ret_model_type = linear_model(df_nridght_skewed, y_nridght_log)\n",
    "\n",
    "model.append(ret_model)\n",
    "score.append(ret_score)\n",
    "model_type.append(ret_model_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cc16e0c",
   "metadata": {},
   "source": [
    "#### KNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "830d3486",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:17.396696Z",
     "start_time": "2023-02-27T04:29:17.353602Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training RMSE: 0.195\n",
      "Test RMSE: 0.179\n"
     ]
    }
   ],
   "source": [
    "def kneighbors_model(X, y):\n",
    "    \"\"\"\n",
    "    Trains a KNeighbours regressor on the given data and returns the model, RMSE score and model type.\n",
    "\n",
    "    Args:\n",
    "        X (pandas.DataFrame): The feature matrix.\n",
    "        y (pandas.Series): The target variable.\n",
    "\n",
    "    Returns:\n",
    "        Tuple: A tuple containing the model, RMSE score, and model type.\n",
    "    \"\"\"\n",
    "    # split data\n",
    "    x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=219)\n",
    "    \n",
    "    # create and fit model\n",
    "    model = KNeighborsRegressor(n_neighbors=5)\n",
    "    model.fit(x_train, y_train)\n",
    "    \n",
    "    # get model predictions\n",
    "    y_train_pred = model.predict(x_train)\n",
    "    y_test_pred = model.predict(x_test)\n",
    "\n",
    "    \n",
    "    # evaluate predictions\n",
    "    train_rmse = np.sqrt(mean_squared_error(y_train, y_train_pred))\n",
    "    test_rmse = np.sqrt(mean_squared_error(y_test, y_test_pred))\n",
    "    print(f\"Training RMSE: {train_rmse:.3f}\")\n",
    "    print(f\"Test RMSE: {test_rmse:.3f}\")\n",
    "    \n",
    "    return 'KNeighbors', test_rmse, 'non-parametric'\n",
    "\n",
    "        \n",
    "ret_model, ret_score, ret_model_type = kneighbors_model(df_nridght_skewed, y_nridght_log)\n",
    "\n",
    "model.append(ret_model)\n",
    "score.append(ret_score)\n",
    "model_type.append(ret_model_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce23d153",
   "metadata": {},
   "source": [
    "#### Kernel SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae168a27",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:17.445005Z",
     "start_time": "2023-02-27T04:29:17.403263Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training RMSE: 0.073\n",
      "Test RMSE: 0.158\n"
     ]
    }
   ],
   "source": [
    "def support_vector_model(X, y):\n",
    "    \"\"\"\n",
    "    Trains a SVR regressor on the given data and returns the model, RMSE score and model type.\n",
    "\n",
    "    Args:\n",
    "        X (pandas.DataFrame): The feature matrix.\n",
    "        y (pandas.Series): The target variable.\n",
    "\n",
    "    Returns:\n",
    "        Tuple: A tuple containing the model, RMSE score, and model type.\n",
    "    \"\"\"\n",
    "    # split data\n",
    "    x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=219)\n",
    "    \n",
    "    # create and fit model\n",
    "    model = SVR(kernel='linear', C=1, epsilon=0.1)\n",
    "    model.fit(x_train, y_train)\n",
    "    \n",
    "    # get model predictions\n",
    "    y_train_pred = model.predict(x_train)\n",
    "    y_test_pred = model.predict(x_test)\n",
    "    \n",
    "    # evaluate predictions\n",
    "    train_rmse = np.sqrt(mean_squared_error(y_train, y_train_pred))\n",
    "    test_rmse = np.sqrt(mean_squared_error(y_test, y_test_pred))\n",
    "    print(f\"Training RMSE: {train_rmse:.3f}\")\n",
    "    print(f\"Test RMSE: {test_rmse:.3f}\")\n",
    "    \n",
    "    return 'SupportVector', test_rmse, 'non-parametric'\n",
    "        \n",
    "    \n",
    "ret_model, ret_score, ret_model_type = support_vector_model(df_nridght_skewed, y_nridght_log)\n",
    "\n",
    "model.append(ret_model)\n",
    "score.append(ret_score)\n",
    "model_type.append(ret_model_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6809249e",
   "metadata": {},
   "source": [
    "#### Gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "058a7cb3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:17.492472Z",
     "start_time": "2023-02-27T04:29:17.449494Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training RMSE: 0.147\n",
      "Test RMSE: 0.149\n"
     ]
    }
   ],
   "source": [
    "def gamma_regressor_model(X, y):\n",
    "    \"\"\"\n",
    "    Trains a Gamma regressor on the given data and returns the model, RMSE score and model type.\n",
    "\n",
    "    Args:\n",
    "        X (pandas.DataFrame): The feature matrix.\n",
    "        y (pandas.Series): The target variable.\n",
    "\n",
    "    Returns:\n",
    "        Tuple: A tuple containing the model, RMSE score, and model type.\n",
    "    \"\"\"\n",
    "\n",
    "    # split data\n",
    "    x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=219)\n",
    "    \n",
    "    # create and fit model\n",
    "    model = GammaRegressor()\n",
    "    model.fit(x_train, y_train)\n",
    "    \n",
    "    # get model predictions\n",
    "    y_train_pred = model.predict(x_train)\n",
    "    y_test_pred = model.predict(x_test)\n",
    "    \n",
    "    # evaluate predictions\n",
    "    train_rmse = np.sqrt(mean_squared_error(y_train, y_train_pred))\n",
    "    test_rmse = np.sqrt(mean_squared_error(y_test, y_test_pred))\n",
    "    print(f\"Training RMSE: {train_rmse:.3f}\")\n",
    "    print(f\"Test RMSE: {test_rmse:.3f}\")\n",
    "    \n",
    "    return 'GammaRegressor', test_rmse, 'parametric'\n",
    "        \n",
    "    \n",
    "ret_model, ret_score, ret_model_type = gamma_regressor_model(df_nridght_skewed, y_nridght_log)\n",
    "\n",
    "model.append(ret_model)\n",
    "score.append(ret_score)\n",
    "model_type.append(ret_model_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80217c51",
   "metadata": {},
   "source": [
    "#### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8d5cbf71",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:17.524017Z",
     "start_time": "2023-02-27T04:29:17.495923Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training RMSE: 0.284\n",
      "Test RMSE: 0.265\n"
     ]
    }
   ],
   "source": [
    "def lasso_model(X, y):\n",
    "    \"\"\"\n",
    "    Trains a Lasso regressor on the given data and returns the model, RMSE score and model type.\n",
    "\n",
    "    Args:\n",
    "        X (pandas.DataFrame): The feature matrix.\n",
    "        y (pandas.Series): The target variable.\n",
    "\n",
    "    Returns:\n",
    "        Tuple: A tuple containing the model, RMSE score, and model type.\n",
    "    \"\"\"\n",
    "    \n",
    "    # split data\n",
    "    x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=219)\n",
    "    \n",
    "    # create and fit model\n",
    "    model = Lasso(alpha=0.1, max_iter=100000)\n",
    "    model.fit(x_train, y_train)\n",
    "    \n",
    "    # get model predictions\n",
    "    y_train_pred = model.predict(x_train)\n",
    "    y_test_pred = model.predict(x_test)\n",
    "    \n",
    "    # evaluate predictions\n",
    "    train_rmse = np.sqrt(mean_squared_error(y_train, y_train_pred))\n",
    "    test_rmse = np.sqrt(mean_squared_error(y_test, y_test_pred))\n",
    "    print(f\"Training RMSE: {train_rmse:.3f}\")\n",
    "    print(f\"Test RMSE: {test_rmse:.3f}\")\n",
    "    \n",
    "    return 'Lasso', test_rmse, 'parametric'\n",
    "        \n",
    "    \n",
    "ret_model, ret_score, ret_model_type = lasso_model(df_nridght_skewed, y_nridght_log)\n",
    "\n",
    "model.append(ret_model)\n",
    "score.append(ret_score)\n",
    "model_type.append(ret_model_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c023be6",
   "metadata": {},
   "source": [
    "#### Model Performance Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "40a3c87d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:17.555477Z",
     "start_time": "2023-02-27T04:29:17.527539Z"
    }
   },
   "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>model</th>\n",
       "      <th>rmse_score</th>\n",
       "      <th>model_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.120217</td>\n",
       "      <td>non-parametric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GammaRegressor</td>\n",
       "      <td>0.148834</td>\n",
       "      <td>parametric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SupportVector</td>\n",
       "      <td>0.158165</td>\n",
       "      <td>non-parametric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>0.179084</td>\n",
       "      <td>non-parametric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.265431</td>\n",
       "      <td>parametric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>0.329192</td>\n",
       "      <td>parametric</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              model  rmse_score      model_type\n",
       "0      RandomForest    0.120217  non-parametric\n",
       "4    GammaRegressor    0.148834      parametric\n",
       "3     SupportVector    0.158165  non-parametric\n",
       "2        KNeighbors    0.179084  non-parametric\n",
       "5             Lasso    0.265431      parametric\n",
       "1  LinearRegression    0.329192      parametric"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dataframe of results\n",
    "rmse_results = {'model': model,\n",
    "               'rmse_score': score,\n",
    "               'model_type': model_type}\n",
    "\n",
    "df_rmse_results = pd.DataFrame(rmse_results)\n",
    "df_rmse_results.sort_values('rmse_score', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7070793e",
   "metadata": {},
   "source": [
    "The above table shows the rmse score (prediction accuracy) of each model and model type. It can be seen, that Random Forest model has the lease error hence the same has been used to determine important features that drive the sale price of houses in North Ridgh Hight Neighborhood. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7df4ef",
   "metadata": {},
   "source": [
    "#### Feature Importance on based on the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ee1e73c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:17.864067Z",
     "start_time": "2023-02-27T04:29:17.559036Z"
    }
   },
   "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>FeatureCoef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>0.330585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>0.110845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>0.098242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>0.068385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>0.061067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>0.059136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>0.020937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0.018166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.017517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>0.017144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeRemodAdd</th>\n",
       "      <td>0.015062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual_Ex</th>\n",
       "      <td>0.013960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_Garage</th>\n",
       "      <td>0.013814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalPorch</th>\n",
       "      <td>0.013324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>0.013194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              FeatureCoef\n",
       "GrLivArea        0.330585\n",
       "OverallQual      0.110845\n",
       "GarageCars       0.098242\n",
       "GarageArea       0.068385\n",
       "1stFlrSF         0.061067\n",
       "LotArea          0.059136\n",
       "TotRmsAbvGrd     0.020937\n",
       "MasVnrArea       0.018166\n",
       "Age              0.017517\n",
       "TotalBsmtSF      0.017144\n",
       "AgeRemodAdd      0.015062\n",
       "BsmtQual_Ex      0.013960\n",
       "Age_Garage       0.013814\n",
       "TotalPorch       0.013324\n",
       "BsmtFinSF1       0.013194"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def neighborhood_reg_model(X, y):\n",
    "    \"\"\"\n",
    "    Trains a RandomForestRegressor model on the input data and returns the top 15 important features.\n",
    "\n",
    "    Args:\n",
    "    X: input feature data\n",
    "    y: output label data\n",
    "\n",
    "    Returns:\n",
    "    A pandas DataFrame containing the top 15 important features and their corresponding feature coefficients.\n",
    "    \"\"\"\n",
    "    \n",
    "    # split data\n",
    "    x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=219)\n",
    "    \n",
    "    # create and fit model\n",
    "    regression_model = RandomForestRegressor()\n",
    "    regression_model.fit(x_train, y_train)\n",
    "    \n",
    "    # feat feature names and importance\n",
    "    feature_names = df_nridght_skewed.columns\n",
    "    feature_importance_abs = np.abs(regression_model.feature_importances_)\n",
    "    feature_importance = regression_model.feature_importances_\n",
    "    \n",
    "    # create feature importance dictionary\n",
    "    sorted_idx = np.argsort(feature_importance)[::-1]\n",
    "    sorted_feature_importance_dict = {}\n",
    "    for i in sorted_idx:\n",
    "        sorted_feature_importance_dict[feature_names[i]] = [feature_importance[i]]\n",
    "        \n",
    "    # create absolute feature importance dictionary\n",
    "    sorted_idx = np.argsort(feature_importance_abs)[::-1]\n",
    "    sorted_feature_importance_dict_abs = {}\n",
    "    for i in sorted_idx:\n",
    "        sorted_feature_importance_dict_abs[feature_names[i]] = [feature_importance_abs[i]]\n",
    "    \n",
    "    # rearrange dictionary \n",
    "    sorted_feature_importance_dict_rearanged = {}\n",
    "    for i in sorted_feature_importance_dict_abs:\n",
    "        sorted_feature_importance_dict_rearanged[i] = sorted_feature_importance_dict[i]\n",
    "    \n",
    "    return pd.DataFrame.from_dict(sorted_feature_importance_dict_rearanged, orient='index', columns=['FeatureCoef'])[:15]\n",
    "\n",
    "        \n",
    "df_nridght_imp_features = neighborhood_reg_model(df_nridght_skewed, y_nridght_log)\n",
    "df_nridght_imp_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba14bf0b",
   "metadata": {},
   "source": [
    "List of important features driving housing prices in North RIdge Hights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f11c7891",
   "metadata": {},
   "source": [
    "##### Appendix 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "86cda80c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:18.348239Z",
     "start_time": "2023-02-27T04:29:17.867594Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart using seaborn\n",
    "colors = ['#003f5c', '#58508d', '#bc5090', '#ff6361']\n",
    "# 'Blues_d'\n",
    "\n",
    "# set chart size\n",
    "plt.figure(figsize=(16, 6))\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "ax = sns.barplot(x=df_nridght_imp_features.index, y='FeatureCoef', data=df_nridght_imp_features, palette='Blues_d')\n",
    "\n",
    "# Add labels and titles\n",
    "ax.set_xlabel('Category', fontsize=16)\n",
    "ax.set_ylabel('Coefficient', fontsize=16)\n",
    "ax.set_title('Feature Importance', fontsize=20)\n",
    "\n",
    "# Rotate x-axis labels by 45 degrees\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
    "\n",
    "# Adjust plot margins\n",
    "plt.subplots_adjust(bottom=0.2)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c68206b",
   "metadata": {},
   "source": [
    "### Model for Complete Train DataFrame (kaggle Model w/o Hyper Parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8b597887",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:18.410784Z",
     "start_time": "2023-02-27T04:29:18.353240Z"
    }
   },
   "outputs": [],
   "source": [
    "class ModelTuner:\n",
    "    def __init__(self, models, X, y, cv, loss):\n",
    "        \"\"\"\n",
    "        Initializes a model trainer object with the given parameters.\n",
    "\n",
    "        :param models: a list of machine learning models to train\n",
    "        :param X: the feature matrix for the training data\n",
    "        :param y: the target variable for the training data\n",
    "        :param cv: the number of cross-validation folds to use for training and evaluation\n",
    "        :param loss: the loss function to use for model evaluation\n",
    "        \"\"\"\n",
    "        # Make a copy of the initial models list and store it\n",
    "        self.init_models = models.copy()\n",
    "        self.models = models.copy()\n",
    "\n",
    "        # Store the training data and target variable\n",
    "        self.X_train = df_train_b_skewed.copy()\n",
    "        self.y_train = y_log.copy()\n",
    "\n",
    "        # Store the number of cross-validation folds and loss function\n",
    "        self.cv = cv\n",
    "        self.loss = loss\n",
    "\n",
    "        # Initialize dictionaries to store training and validation scores for each model\n",
    "        self.train_scores = {}\n",
    "        self.valid_scores = {}\n",
    "        \n",
    "    def tune_model(self, model_name, grid, verbose=1): #n_jobs=-1):\n",
    "        \"\"\"\n",
    "        Trains and tunes a specified machine learning model using a grid search over hyperparameters.\n",
    "\n",
    "        :param model_name: the name of the model to tune\n",
    "        :param grid: a dictionary of hyperparameters to search over using a grid search\n",
    "        :param verbose: the level of verbosity for the grid search (default 1)\n",
    "        :return: the trained and tuned machine learning model\n",
    "        \"\"\"\n",
    "        # Set the model's hyperparameters to the specified grid and train the model\n",
    "        self.models[model_name] = self.init_models[model_name]\n",
    "#         search = GridSearchCV(\n",
    "#             self.models[model_name],\n",
    "#             param_grid=grid,\n",
    "#             scoring=self.loss,\n",
    "#             cv=self.cv,\n",
    "#             return_train_score=True,\n",
    "#             verbose=verbose,\n",
    "#             n_jobs=n_jobs,\n",
    "#         )\n",
    "#         search.fit(self.X_train, self.y_train)\n",
    "#         self.models[model_name] = search.best_estimator_\n",
    "       \n",
    "        model = self.models[model_name].set_params(**grid)\n",
    "        model.fit(self.X_train, self.y_train)\n",
    "        rename_metric = {\n",
    "            \"mean_train_score\": \"mean_train_RMSE\",\n",
    "            \"mean_test_score\": \"mean_valid_RMSE\",\n",
    "        }        \n",
    "        \n",
    "#         cv_results = pd.DataFrame(search.cv_results_)\n",
    "#         columns = [f\"param_{param}\" for param, val in grid.items() if len(val)>1]\n",
    "#         columns.extend(rename_metric.keys())\n",
    "#         cv_results = cv_results[columns].rename(columns=rename_metric)\n",
    "#         cv_results[list(rename_metric.values())] = -cv_results[list(rename_metric.values())]\n",
    "#         cv_results = cv_results.sort_values(by=\"mean_valid_RMSE\", ignore_index=True)\n",
    "    \n",
    "#         train_score = cv_results.mean_train_RMSE.iloc[0]\n",
    "#         valid_score = cv_results.mean_valid_RMSE.iloc[0]\n",
    "#         self.train_scores[model_name] = train_score\n",
    "#         self.valid_scores[model_name] = valid_score\n",
    "        \n",
    "#         print(\"=\"*40)\n",
    "#         print(f\"Model: {search.best_estimator_}\")\n",
    "#         print(f\"Train RMSE: {train_score}\")\n",
    "#         print(f\"Valid RMSE: {valid_score}\")\n",
    "#         print(\"=\"*40)\n",
    "\n",
    "#         return cv_results\n",
    "        \n",
    "        # Compute the training and validation RMSE scores for the model  \n",
    "        train_rmse = np.sqrt(mean_squared_error(self.y_train, model.predict(self.X_train)))\n",
    "        valid_scores = cross_validate(\n",
    "            model,\n",
    "            X=self.X_train,\n",
    "            y=self.y_train,\n",
    "            cv=self.cv,\n",
    "            return_train_score=False,\n",
    "            scoring=self.loss,\n",
    "        )[\"test_score\"]\n",
    "        valid_rmse = -np.mean(valid_scores)\n",
    "        \n",
    "        # Update the trained model, training scores, and validation scores in the object's attributes\n",
    "        self.models[model_name] = model\n",
    "        self.train_scores[model_name] = train_rmse\n",
    "        self.valid_scores[model_name] = valid_rmse\n",
    "        \n",
    "        # Print the model and its training and validation scores\n",
    "        print(\"=\"*40)\n",
    "        print(f\"Model: {model}\")\n",
    "        print(f\"Train RMSE: {train_rmse}\")\n",
    "        print(f\"Valid RMSE: {valid_rmse}\")\n",
    "        print(\"=\"*40)\n",
    "        \n",
    "        # Return the trained and tuned model\n",
    "        return model\n",
    "    \n",
    "    def collate_results(self):\n",
    "        \"\"\"\n",
    "        Combines the training and validation RMSE scores for each trained machine learning model into a single Pandas DataFrame.\n",
    "\n",
    "        :return: a Pandas DataFrame containing the training and validation RMSE scores for each trained machine learning model\n",
    "        \"\"\"\n",
    "        # Create Pandas DataFrames for the training and validation RMSE scores\n",
    "        train_results = pd.DataFrame.from_dict(\n",
    "            self.train_scores,\n",
    "            orient=\"index\",\n",
    "            columns=[\"mean_train_RMSE\"],\n",
    "        )\n",
    "        valid_results = pd.DataFrame.from_dict(\n",
    "            self.valid_scores,\n",
    "            orient=\"index\",\n",
    "            columns=[\"mean_valid_RMSE\"],\n",
    "        )\n",
    "        \n",
    "        # Concatenate the training and validation RMSE DataFrames horizontally and sort by validation RMSE\n",
    "        \n",
    "        results = pd.concat(\n",
    "            [train_results, valid_results],\n",
    "            axis=1,\n",
    "        ).sort_values(by=\"mean_valid_RMSE\")\n",
    "        \n",
    "        # Return the concatenated results DataFrame\n",
    "        return results\n",
    "    \n",
    "    def get_models(self, model_names=None):\n",
    "        \"\"\"\n",
    "        Returns a list of trained machine learning models.\n",
    "\n",
    "        :param model_names: a list of model names to return; if None (default), \n",
    "                            all trained models are returned\n",
    "        :return: a list of tuples, where each tuple contains a model name and\n",
    "                 its corresponding trained machine learning model object\n",
    "        \"\"\"\n",
    "        # If no model names are provided, use all trained models\n",
    "        if not model_names:\n",
    "            model_names = self.models.keys()\n",
    "        # Create a list of tuples, where each tuple contains a model name and its corresponding trained machine learning model object    \n",
    "        models = [(name, self.models[name]) for name in model_names]\n",
    "        \n",
    "        # Return the list of model tuples\n",
    "        return models\n",
    "    \n",
    "    def run_cv(self, model, n_jobs=-1):\n",
    "        \"\"\"\n",
    "        This method runs cross-validation for a specified model.\n",
    "\n",
    "        Args:\n",
    "        model (str or estimator object): The name of the model in the self.models dict or the estimator object.\n",
    "        n_jobs (int, optional): Number of CPU cores used for parallelization. Default is -1, which means to use all available CPUs.\n",
    "\n",
    "        Returns:\n",
    "        None. The method prints the mean train RMSE and mean valid RMSE of the model.\n",
    "        \"\"\"\n",
    "        # If model is a string, get the corresponding estimator object from the models dictionary\n",
    "        model = self.models[model] if model in self.models else model\n",
    "        \n",
    "         # Run cross-validation on the model using the specified number of CPUs\n",
    "        scores = cross_validate(\n",
    "            model,\n",
    "            X=self.X_train,\n",
    "            y=self.y_train,\n",
    "            cv=self.cv,\n",
    "            return_train_score=True,\n",
    "            scoring=self.loss,\n",
    "            n_jobs=n_jobs,\n",
    "        )\n",
    "        # Extract the train and validation scores\n",
    "        train_scores = scores[\"train_score\"]\n",
    "        valid_scores = scores[\"test_score\"]\n",
    "        \n",
    "        # Print the results\n",
    "        print(f\"Model: {model}\")\n",
    "        print(f\"Train RMSE: {-np.mean(train_scores)}\")\n",
    "        print(f\"Valid RMSE: {-np.mean(valid_scores)}\")\n",
    "\n",
    "    \n",
    "    def model_predict(self, model, X_test):\n",
    "        \"\"\"\n",
    "        Predicts the sale price for the test dataset using the specified model.\n",
    "\n",
    "        Args:\n",
    "            model (str or sklearn model): The name of the model or a sklearn model instance to use for prediction.\n",
    "            X_test (pd.DataFrame): The test dataset to use for prediction.\n",
    "\n",
    "        Returns:\n",
    "            pd.DataFrame: A dataframe containing the predicted sale prices.\n",
    "        \"\"\"\n",
    "        # Get the model instance\n",
    "        model = self.models[model] if model in self.models else model\n",
    "        \n",
    "        # Fit the model on the training dataset\n",
    "        model.fit(self.X_train, self.y_train)\n",
    "        \n",
    "        # Predict the sale prices for the test dataset\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        # Convert the sale prices to the correct format and create a dataframe\n",
    "        y_pred = pd.DataFrame({\n",
    "            \"Id\": range(1461, 1461+len(y_pred)),\n",
    "            \"SalePrice\": np.expm1(y_pred),\n",
    "        })\n",
    "\n",
    "        return y_pred\n",
    "    \n",
    "    def perf_boxplot(self, add_models=None, cv=None, n_jobs=-1):\n",
    "        \"\"\"\n",
    "        Creates a box plot showing the distribution of validation RMSE scores for each model.\n",
    "\n",
    "        Parameters:\n",
    "        - add_models: a dictionary of additional models to include in the plot, in the format {model_name: model_instance}\n",
    "        - cv: the cross-validation strategy to use. If None, uses the strategy set in the constructor.\n",
    "        - n_jobs: the number of parallel jobs to run for cross-validation. -1 means using all processors.\n",
    "\n",
    "        Returns: None\n",
    "        \"\"\"\n",
    "    \n",
    "        # Combine the main models and the additional models, if any\n",
    "        models = self.models.copy()\n",
    "        if add_models:\n",
    "            for name, model in add_models:\n",
    "                models[name] = model\n",
    "\n",
    "        # Collect the validation scores for each model\n",
    "        perf_df = pd.DataFrame()\n",
    "        for name, model in models.items():\n",
    "            valid_scores = cross_validate(\n",
    "                model,\n",
    "                X=self.X_train,\n",
    "                y=self.y_train,\n",
    "                cv=cv if cv else self.cv,\n",
    "                scoring=self.loss,\n",
    "                n_jobs=n_jobs,\n",
    "            )[\"test_score\"]\n",
    "\n",
    "            # Store the validation scores for the model in a dataframe\n",
    "            model_perf = pd.DataFrame({\"Model\": name, \"Validation RMSE\": -valid_scores})\n",
    "            perf_df = pd.concat([perf_df, model_perf])\n",
    "\n",
    "        # Calculate the minimum median RMSE among all models\n",
    "        min_median = perf_df.groupby(\"Model\").median().min()[0]\n",
    "\n",
    "        # Plot the box plot\n",
    "        plt.figure(figsize=(18, 8))\n",
    "\n",
    "        sns.boxplot(\n",
    "            x=perf_df[\"Validation RMSE\"],\n",
    "            y=perf_df[\"Model\"],\n",
    "            flierprops={\"alpha\": 0.5},\n",
    "        )\n",
    "        plt.axvline(x=min_median, ls=\"--\", color=\"coral\")\n",
    "        plt.ylabel(\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f2d66446",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:18.441148Z",
     "start_time": "2023-02-27T04:29:18.415863Z"
    }
   },
   "outputs": [],
   "source": [
    "# define the number of folds for KFold cross-validation\n",
    "kfold = KFold(n_splits=10, shuffle=True, random_state=SEED)\n",
    "\n",
    "# define the loss function for scoring\n",
    "rmse_loss = make_scorer(mean_squared_error, squared=False, greater_is_better=False)\n",
    "\n",
    "# define the models to be tuned\n",
    "MODELS = {\n",
    "    \"Ridge\": Ridge(random_state=SEED),\n",
    "    \"Linear SVR\": LinearSVR(random_state=SEED),\n",
    "    \"Kernel Ridge\": KernelRidge(),\n",
    "    \"Kernel SVR\": SVR(),\n",
    "    \"Random Forest\": RandomForestRegressor(random_state=SEED),\n",
    "    \"Gradient Boosting\": GradientBoostingRegressor(random_state=SEED),\n",
    "}\n",
    "\n",
    "# create an instance of the ModelTuner class\n",
    "tuner = ModelTuner(MODELS, X=df_train_b_skewed, y=y_log, cv=kfold, loss=rmse_loss)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0082e9a9",
   "metadata": {},
   "source": [
    "#### Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6fdb9dad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:18.784949Z",
     "start_time": "2023-02-27T04:29:18.444094Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "Model: Ridge(alpha=[10.0], random_state=0)\n",
      "Train RMSE: 0.1107349878410315\n",
      "Valid RMSE: 0.12270452919387917\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>111626.845317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>157598.794978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>184863.511233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>197301.253271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>181686.635662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  111626.845317\n",
       "1  1462  157598.794978\n",
       "2  1463  184863.511233\n",
       "3  1464  197301.253271\n",
       "4  1465  181686.635662"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RR_PARAM = {\n",
    "    #\"alpha\": [1e-4, 1e-3, 1e-2, 1e-1, 1., 1e1],\n",
    "    \"alpha\": [1e1],\n",
    "}\n",
    "\n",
    "rr_results = tuner.tune_model(\"Ridge\", grid=RR_PARAM)\n",
    "\n",
    "y_pred = tuner.model_predict('Ridge', df_test_b_skewed)\n",
    "\n",
    "y_pred.to_csv(\n",
    "    \"Ridge_Prediction.csv\",\n",
    "    index=False,\n",
    ")\n",
    "\n",
    "y_pred.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "493232d6",
   "metadata": {},
   "source": [
    "#### Linear SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9327b62b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:20.152207Z",
     "start_time": "2023-02-27T04:29:18.789478Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "Model: LinearSVR(dual=False, loss='squared_epsilon_insensitive', random_state=0,\n",
      "          tol=1e-07)\n",
      "Train RMSE: 0.10562174633865347\n",
      "Valid RMSE: 0.127537367711245\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>115796.692949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>160474.334675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>188290.434985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>196370.896819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>192954.043550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  115796.692949\n",
       "1  1462  160474.334675\n",
       "2  1463  188290.434985\n",
       "3  1464  196370.896819\n",
       "4  1465  192954.043550"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVR_PARAM = {\n",
    "#     \"C\": [1e-4, 1e-3, 1e-2, 1e-1, 1., 1e1],\n",
    "#     \"epsilon\": [0., 1e-2, 1e-1],\n",
    "    \"C\": 1.,\n",
    "    \"epsilon\": 0.,\n",
    "    \"dual\": False,\n",
    "    \"loss\": \"squared_epsilon_insensitive\",\n",
    "    \"tol\": 1e-7,\n",
    "\n",
    "}\n",
    "\n",
    "svr_results = tuner.tune_model(\"Linear SVR\", grid=SVR_PARAM)\n",
    "\n",
    "y_pred = tuner.model_predict('Linear SVR', df_test_b_skewed)\n",
    "\n",
    "y_pred.to_csv(\n",
    "    \"Linear_SVR_Prediction.csv\",\n",
    "    index=False,\n",
    ")\n",
    "\n",
    "y_pred.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4886acac",
   "metadata": {},
   "source": [
    "#### Kernel Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "53ce1067",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:23.035080Z",
     "start_time": "2023-02-27T04:29:20.155905Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "Model: KernelRidge(alpha=0.01, gamma=0.001, kernel='poly')\n",
      "Train RMSE: 0.07970392979905375\n",
      "Valid RMSE: 0.11882541014902989\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>114976.963526</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>159849.778375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>182097.965440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>194594.853151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>186457.595611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  114976.963526\n",
       "1  1462  159849.778375\n",
       "2  1463  182097.965440\n",
       "3  1464  194594.853151\n",
       "4  1465  186457.595611"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KRR_PARAM = {\n",
    "#     \"alpha\": [1e-4, 1e-3, 1e-2, 1e-1, 1., 1e1],\n",
    "#     \"gamma\": [1e-4, 1e-3, 1e-2, 1e-1],\n",
    "#     \"kernel\": [\"poly\", \"rbf\"],\n",
    "    \"alpha\": 0.01,\n",
    "    \"gamma\": 0.001,\n",
    "    \"kernel\": \"poly\",\n",
    "\n",
    "}\n",
    "\n",
    "krr_results = tuner.tune_model(\"Kernel Ridge\", grid=KRR_PARAM)\n",
    "\n",
    "\n",
    "y_pred = tuner.model_predict('Kernel Ridge', df_test_b_skewed)\n",
    "\n",
    "y_pred.to_csv(\n",
    "    \"Kernel_Ridge_Prediction.csv\",\n",
    "    index=False,\n",
    ")\n",
    "\n",
    "y_pred.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32e2dec1",
   "metadata": {},
   "source": [
    "#### Kernel SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4f67ec67",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:31.922524Z",
     "start_time": "2023-02-27T04:29:23.038725Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "Model: SVR(C=10.0, epsilon=0.0, gamma=0.001)\n",
      "Train RMSE: 0.10631872819063351\n",
      "Valid RMSE: 0.12003846871183732\n",
      "========================================\n"
     ]
    },
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>160255.538356</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>190270.429281</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>195221.658060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>184330.281628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  118015.941198\n",
       "1  1462  160255.538356\n",
       "2  1463  190270.429281\n",
       "3  1464  195221.658060\n",
       "4  1465  184330.281628"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KSVR_PARAM = {\n",
    "#     \"C\": [1e-4, 1e-3, 1e-2, 1e-1, 1., 1e1],\n",
    "#     \"epsilon\": [0., 1e-2, 1e-1],\n",
    "#     \"gamma\": [1e-4, 1e-3, 1e-2, 1e-1],\n",
    "#     \"kernel\": [\"poly\", \"rbf\"],\n",
    "    \"C\": 10.0,\n",
    "    \"epsilon\": 0.,\n",
    "    \"gamma\": 0.001,\n",
    "    \"kernel\": \"rbf\",\n",
    "}\n",
    "\n",
    "ksvr_results = tuner.tune_model(\"Kernel SVR\", grid=KSVR_PARAM)\n",
    "\n",
    "\n",
    "y_pred = tuner.model_predict('Kernel SVR', df_test_b_skewed)\n",
    "\n",
    "y_pred.to_csv(\n",
    "    \"Kernel_SVR_Prediction.csv\",\n",
    "    index=False,\n",
    ")\n",
    "\n",
    "y_pred.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a7d1a74",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ee66858b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:29:43.999996Z",
     "start_time": "2023-02-27T04:29:31.927043Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "Model: RandomForestRegressor(bootstrap=False, max_depth=32, max_features='sqrt',\n",
      "                      random_state=0)\n",
      "Train RMSE: 1.3159452046596291e-14\n",
      "Valid RMSE: 0.1366428062370824\n",
      "========================================\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>122906.935447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>148298.757344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>177971.697942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>189879.161530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>184437.486013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  122906.935447\n",
       "1  1462  148298.757344\n",
       "2  1463  177971.697942\n",
       "3  1464  189879.161530\n",
       "4  1465  184437.486013"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF_PARAM = {\n",
    "#     \"max_depth\": [8, 16, 32, 64],\n",
    "#     \"n_estimators\": [100, 200, 400],\n",
    "    \"max_depth\": 32,\n",
    "    \"n_estimators\": 100,\n",
    "    \"max_features\": \"sqrt\",\n",
    "    \"bootstrap\": False,\n",
    "}\n",
    "\n",
    "rf_results = tuner.tune_model(\"Random Forest\", grid=RF_PARAM)\n",
    "\n",
    "y_pred = tuner.model_predict('Random Forest', df_test_b_skewed)\n",
    "\n",
    "y_pred.to_csv(\n",
    "    \"Random_Forest_Prediction.csv\",\n",
    "    index=False,\n",
    ")\n",
    "\n",
    "y_pred.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964adc27",
   "metadata": {},
   "source": [
    "#### Gradient Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "55881bcc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:31:27.208681Z",
     "start_time": "2023-02-27T04:29:44.003962Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "Model: GradientBoostingRegressor(learning_rate=0.01, max_depth=5, max_features='sqrt',\n",
      "                          min_samples_split=5, n_estimators=3200,\n",
      "                          random_state=0)\n",
      "Train RMSE: 0.024236247734292067\n",
      "Valid RMSE: 0.11607310725545832\n",
      "========================================\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>125885.387558</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>157338.921768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>187254.286691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>195872.256936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>183777.804181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  125885.387558\n",
       "1  1462  157338.921768\n",
       "2  1463  187254.286691\n",
       "3  1464  195872.256936\n",
       "4  1465  183777.804181"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GB_PARAM = {\n",
    "#     \"max_depth\": [3, 5, 10],\n",
    "#     \"min_samples_split\": [2, 5],\n",
    "#     \"min_samples_leaf\": [1, 4],\n",
    "#     \"learning_rate\": [1e-2, 1e-1],\n",
    "    \"max_depth\": 5,\n",
    "    \"min_samples_split\": 5,\n",
    "    \"min_samples_leaf\": 1,\n",
    "    \"learning_rate\": 0.01,\n",
    "    \"n_estimators\": 3200,\n",
    "    \"max_features\": \"sqrt\",\n",
    "}\n",
    "\n",
    "gb_results = tuner.tune_model(\"Gradient Boosting\", grid=GB_PARAM)\n",
    "\n",
    "y_pred = tuner.model_predict('Gradient Boosting', df_test_b_skewed)\n",
    "\n",
    "y_pred.to_csv(\n",
    "    \"Gradient Boosting.csv\",\n",
    "    index=False,\n",
    ")\n",
    "\n",
    "y_pred.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a817b55",
   "metadata": {},
   "source": [
    "#### Ensemble average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1a944a45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:31:27.240441Z",
     "start_time": "2023-02-27T04:31:27.211527Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_train_RMSE</th>\n",
       "      <th>mean_valid_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting</th>\n",
       "      <td>2.423625e-02</td>\n",
       "      <td>0.116073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kernel Ridge</th>\n",
       "      <td>7.970393e-02</td>\n",
       "      <td>0.118825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kernel SVR</th>\n",
       "      <td>1.063187e-01</td>\n",
       "      <td>0.120038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>1.107350e-01</td>\n",
       "      <td>0.122705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Linear SVR</th>\n",
       "      <td>1.056217e-01</td>\n",
       "      <td>0.127537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>1.315945e-14</td>\n",
       "      <td>0.136643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean_train_RMSE  mean_valid_RMSE\n",
       "Gradient Boosting     2.423625e-02         0.116073\n",
       "Kernel Ridge          7.970393e-02         0.118825\n",
       "Kernel SVR            1.063187e-01         0.120038\n",
       "Ridge                 1.107350e-01         0.122705\n",
       "Linear SVR            1.056217e-01         0.127537\n",
       "Random Forest         1.315945e-14         0.136643"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuner.collate_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1badab11",
   "metadata": {},
   "source": [
    "This table gives the accuracy score of each regression model. For the ensemble model top three best performing models are selected to make the final predictions. The three models are combined together to reduce the prediction error. Combining different models helps improving the prediction accuracy as they complement each other. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "64048a2d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:31:40.263682Z",
     "start_time": "2023-02-27T04:31:27.245997Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: VotingRegressor(estimators=[('Kernel Ridge',\n",
      "                             KernelRidge(alpha=0.01, gamma=0.001,\n",
      "                                         kernel='poly')),\n",
      "                            ('Kernel SVR',\n",
      "                             SVR(C=10.0, epsilon=0.0, gamma=0.001)),\n",
      "                            ('Gradient Boosting',\n",
      "                             GradientBoostingRegressor(learning_rate=0.01,\n",
      "                                                       max_depth=5,\n",
      "                                                       max_features='sqrt',\n",
      "                                                       min_samples_split=5,\n",
      "                                                       n_estimators=3200,\n",
      "                                                       random_state=0))])\n",
      "Train RMSE: 0.06461602665562831\n",
      "Valid RMSE: 0.11339627592563403\n"
     ]
    }
   ],
   "source": [
    "models_selected = [\"Kernel Ridge\", \"Kernel SVR\", \"Gradient Boosting\"]\n",
    "indv_models = tuner.get_models(model_names=models_selected)\n",
    "ensemble = VotingRegressor(indv_models)\n",
    "\n",
    "tuner.run_cv(ensemble)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710494ad",
   "metadata": {},
   "source": [
    "### Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4a53dc96",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:31:51.351906Z",
     "start_time": "2023-02-27T04:31:40.266711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>119538.814955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>159142.833128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>186510.256945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>195228.892818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>184851.625904</td>\n",
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       "</table>\n",
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      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  119538.814955\n",
       "1  1462  159142.833128\n",
       "2  1463  186510.256945\n",
       "3  1464  195228.892818\n",
       "4  1465  184851.625904"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_pred = tuner.model_predict(ensemble, df_test_b_skewed)\n",
    "\n",
    "final_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7339e42c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-27T04:31:51.383480Z",
     "start_time": "2023-02-27T04:31:51.355904Z"
    }
   },
   "outputs": [],
   "source": [
    "final_pred.to_csv(\n",
    "    \"submission.csv\",\n",
    "    index=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3398555",
   "metadata": {},
   "source": [
    "# Bibliography "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573bdbd5",
   "metadata": {},
   "source": [
    "eXp Realty. (2022). Best Places to Live in the US in 2022. Livability. Retrieved from https://livability.com/best-places/2022-top-100-best-places-to-live-in-the-us/top-100-2022-ames-ia/ \n",
    "\n",
    "Hunziker Development. Northridge Heights. Retrieved from https://www.hunzikerdevelopment.com/active-developments/northridge-heights/\n",
    " \n",
    "Olsen, B. C., & Stokes, J. R. (2015). Is farm real estate the next bubble? The Journal of Real Estate Finance and Economics, 50(3), 355–376. https://doi.org/10.1007/s11146-014-9469-9"
   ]
  }
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