House price predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction of house prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following notebook uses an array of house features to create a model to predict the house price.  \n",
    "\n",
    "\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:95% !important; }</style>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "output_type": "display_data"
    }
   ],
   "source": [
    "#display\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:95% !important; }</style>\"))\n",
    "\n",
    "#machine learning libraries\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#insight libraries\n",
    "import eli5\n",
    "from eli5.sklearn import PermutationImportance\n",
    "\n",
    "\n",
    "#data visulasation\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "#needed for more than on of the above\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setup\n",
    "file_path = 'train_data.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def selectiveColumnsTraining():\n",
    "#target object\n",
    "    home_data = pd.read_csv(file_path, index_col='Id')\n",
    "    y = home_data.SalePrice\n",
    "\n",
    "    features = ['OpenPorchSF', 'LowQualFinSF', 'GrLivArea', 'LotArea', 'YearBuilt',\n",
    "           'OverallQual', 'OverallCond', '1stFlrSF', '2ndFlrSF', 'FullBath',\n",
    "           'BedroomAbvGr', 'TotRmsAbvGrd', 'Fireplaces','MiscVal','OpenPorchSF','PoolArea','WoodDeckSF']\n",
    "\n",
    "    #optimum arrangement found - including na columns in test\n",
    "    #features = ['OpenPorchSF','GarageCars','BsmtFullBath','LowQualFinSF','GrLivArea','TotalBsmtSF','BsmtFinSF2','BsmtFinSF1','LotArea',\n",
    "    #            'YearBuilt', 'OverallQual', 'OverallCond', '1stFlrSF', \n",
    "    #            '2ndFlrSF','FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd','Fireplaces']\n",
    "    \n",
    "    #cols_with_missing = [col for col in X_train.columns\n",
    "#                      if X_train[col].isnull().any()] \n",
    "\n",
    "    X = home_data[features]\n",
    "\n",
    "    # Split into validation and training data\n",
    "    train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)\n",
    "    return train_X, val_X, train_y, val_y;\n",
    "\n",
    "def selectiveColumnsTesting():\n",
    "    home_data = pd.read_csv(file_path, index_col='Id')\n",
    "    y = home_data.SalePrice\n",
    "\n",
    "    features = ['OpenPorchSF', 'LowQualFinSF', 'GrLivArea', 'LotArea', 'YearBuilt',\n",
    "           'OverallQual', 'OverallCond', '1stFlrSF', '2ndFlrSF', 'FullBath',\n",
    "           'BedroomAbvGr', 'TotRmsAbvGrd', 'Fireplaces','MiscVal','OpenPorchSF','PoolArea','WoodDeckSF']\n",
    "\n",
    "    #optimum arrangement found - including na columns in test\n",
    "    #features = ['OpenPorchSF','GarageCars','BsmtFullBath','LowQualFinSF','GrLivArea','TotalBsmtSF','BsmtFinSF2','BsmtFinSF1','LotArea',\n",
    "    #            'YearBuilt', 'OverallQual', 'OverallCond', '1stFlrSF', \n",
    "    #            '2ndFlrSF','FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd','Fireplaces']\n",
    "\n",
    "    X = home_data[features]\n",
    "    return X, y, features;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permuation Importance\n",
    "\n",
    "Throughout this process, we will not only wish to explore what models give the best predictions, but also investigate what features have the biggest impact on predictions. To do explore this concept of feature importance, we shall calculate the permutation importance.  \n",
    "\n",
    "Permutation importance nicely tackles this question by asking \"If I randomly shuffle a single column of the validation data, leaving the target and all other columns in place, how would that affect the accuracy of predictions in that now-shuffled data?\". Randomly re-ordering a single column should cause less accurate predictions, since the resulting data no longer corresponds to anything observed in the real world. Model accuracy especially suffers if we shuffle a column that the model relied on heavily for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelPermImportance(model, val_X, val_y):\n",
    "    perm = PermutationImportance(model, random_state=1).fit(val_X, val_y)\n",
    "    return eli5.show_weights(perm, feature_names = val_X.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Regressor Model\n",
    "\n",
    "First we will explore the accuracy of the decision tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    <style>\n",
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       "\n",
       "\n",
       "\n",
       "    \n",
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       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.6718\n",
       "                \n",
       "                    &plusmn; 0.1154\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallQual\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.06%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1480\n",
       "                \n",
       "                    &plusmn; 0.0399\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GrLivArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.55%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1049\n",
       "                \n",
       "                    &plusmn; 0.0082\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                2ndFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 95.90%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0699\n",
       "                \n",
       "                    &plusmn; 0.0341\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YearBuilt\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.84%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0279\n",
       "                \n",
       "                    &plusmn; 0.0246\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallCond\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.95%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0259\n",
       "                \n",
       "                    &plusmn; 0.0108\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                1stFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.51%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0164\n",
       "                \n",
       "                    &plusmn; 0.0103\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Fireplaces\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.11%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0078\n",
       "                \n",
       "                    &plusmn; 0.0132\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.38%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0047\n",
       "                \n",
       "                    &plusmn; 0.0015\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.48%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0036\n",
       "                \n",
       "                    &plusmn; 0.0025\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotRmsAbvGrd\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.74%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0013\n",
       "                \n",
       "                    &plusmn; 0.0029\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FullBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LowQualFinSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MiscVal\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PoolArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.78%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0011\n",
       "                \n",
       "                    &plusmn; 0.0011\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BedroomAbvGr\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.25%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0062\n",
       "                \n",
       "                    &plusmn; 0.0031\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.23%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0064\n",
       "                \n",
       "                    &plusmn; 0.0170\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                WoodDeckSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
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   "source": [
    "def DTRmodel(leaf_node_limit=None):\n",
    "    train_X, val_X, train_y, val_y = selectiveColumnsTraining()\n",
    "    DTmodel = DecisionTreeRegressor(max_leaf_nodes=leaf_node_limit, random_state=1)\n",
    "    DTmodel.fit(train_X, train_y)\n",
    "    display(modelPermImportance(DTmodel, val_X, val_y))\n",
    "    val_predictions = DTmodel.predict(val_X)\n",
    "    val_mae = mean_absolute_error(val_predictions, val_y)\n",
    "    return val_mae\n",
    "DTRmodel(leaf_node_limit=150)\n"
   ]
  },
  {
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   "execution_count": 8,
   "metadata": {},
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     "data": {
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       "Text(0, 0.5, 'MAE')"
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#which number of leaf nodes creates the best prediction?\n",
    "leaf_nodes= [(limit, DTRmodel(leaf_node_limit=limit)) for limit in range (50,750,25)]\n",
    "leaves=pd.DataFrame(leaf_nodes, columns = [\"Max Leaf Nodes\",\"MAE\"]).set_index(\"Max Leaf Nodes\")\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.lineplot(data=leaves, markers='o')\n",
    "plt.title('Changes in MAEs by Max Leaf Nodes')\n",
    "plt.ylabel(\"MAE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this data, we can see that 150 is a \"sweet spot\" where the MAE is locally minimised"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Regressor Model\n",
    "def RFRmodel(train_mode=selectiveColumnsTraining(), trees=10, leaf_limit=None,cross_val_folds=2):\n",
    "    train_X, val_X, train_y, val_y = train_mode\n",
    "    rf_model = RandomForestRegressor(random_state=1, n_estimators=trees, max_leaf_nodes=leaf_limit)\n",
    "    if cross_val_folds!=2:\n",
    "        scores = -1 * cross_val_score(rf_model, train_X, train_y,\n",
    "                              cv=cross_val_folds,\n",
    "                              scoring='neg_mean_absolute_error')\n",
    "        return scores.mean()\n",
    "    rf_model.fit(train_X, train_y)\n",
    "    display(modelPermImportance(rf_model, val_X, val_y))\n",
    "    rf_val_predictions = rf_model.predict(val_X)\n",
    "    rf_val_mae = mean_absolute_error(rf_val_predictions, val_y)\n",
    "    return rf_val_mae"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
       "        filter: brightness(85%);\n",
       "    }\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
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       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.6805\n",
       "                \n",
       "                    &plusmn; 0.1638\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallQual\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.31%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1425\n",
       "                \n",
       "                    &plusmn; 0.0378\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GrLivArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.51%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1072\n",
       "                \n",
       "                    &plusmn; 0.0087\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                2ndFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 95.58%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0788\n",
       "                \n",
       "                    &plusmn; 0.0813\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                WoodDeckSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 95.89%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0710\n",
       "                \n",
       "                    &plusmn; 0.0366\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YearBuilt\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.39%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0371\n",
       "                \n",
       "                    &plusmn; 0.0265\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                1stFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.24%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0212\n",
       "                \n",
       "                    &plusmn; 0.0258\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallCond\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.61%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0151\n",
       "                \n",
       "                    &plusmn; 0.0088\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Fireplaces\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.12%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0079\n",
       "                \n",
       "                    &plusmn; 0.0019\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.58%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0027\n",
       "                \n",
       "                    &plusmn; 0.0027\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FullBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.69%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0018\n",
       "                \n",
       "                    &plusmn; 0.0061\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.73%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0015\n",
       "                \n",
       "                    &plusmn; 0.0022\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotRmsAbvGrd\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.87%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0005\n",
       "                \n",
       "                    &plusmn; 0.0036\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LowQualFinSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PoolArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MiscVal\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.57%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0028\n",
       "                \n",
       "                    &plusmn; 0.0014\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BedroomAbvGr\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 98.68%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0141\n",
       "                \n",
       "                    &plusmn; 0.0373\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
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     "name": "stdout",
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     "text": [
      "Validation MAE when not specifying max_leaf_nodes: 24,603\n"
     ]
    },
    {
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       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.6718\n",
       "                \n",
       "                    &plusmn; 0.1154\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallQual\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.06%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1480\n",
       "                \n",
       "                    &plusmn; 0.0399\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GrLivArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.55%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1049\n",
       "                \n",
       "                    &plusmn; 0.0082\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                2ndFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 95.90%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0699\n",
       "                \n",
       "                    &plusmn; 0.0341\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YearBuilt\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.84%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0279\n",
       "                \n",
       "                    &plusmn; 0.0246\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallCond\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.95%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0259\n",
       "                \n",
       "                    &plusmn; 0.0108\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                1stFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.51%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0164\n",
       "                \n",
       "                    &plusmn; 0.0103\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Fireplaces\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.11%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0078\n",
       "                \n",
       "                    &plusmn; 0.0132\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.38%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0047\n",
       "                \n",
       "                    &plusmn; 0.0015\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.48%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0036\n",
       "                \n",
       "                    &plusmn; 0.0025\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotRmsAbvGrd\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.74%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0013\n",
       "                \n",
       "                    &plusmn; 0.0029\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FullBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LowQualFinSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MiscVal\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PoolArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.78%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0011\n",
       "                \n",
       "                    &plusmn; 0.0011\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BedroomAbvGr\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.25%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0062\n",
       "                \n",
       "                    &plusmn; 0.0031\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.23%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0064\n",
       "                \n",
       "                    &plusmn; 0.0170\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                WoodDeckSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
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      "Validation MAE for best value of max_leaf_nodes: 21,877\n"
     ]
    },
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       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.5440\n",
       "                \n",
       "                    &plusmn; 0.1076\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallQual\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 91.63%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1567\n",
       "                \n",
       "                    &plusmn; 0.0183\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GrLivArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 95.07%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0735\n",
       "                \n",
       "                    &plusmn; 0.0055\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                2ndFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 95.52%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0643\n",
       "                \n",
       "                    &plusmn; 0.0157\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YearBuilt\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.35%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0479\n",
       "                \n",
       "                    &plusmn; 0.0074\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                1stFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.46%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0140\n",
       "                \n",
       "                    &plusmn; 0.0057\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallCond\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.58%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0124\n",
       "                \n",
       "                    &plusmn; 0.0084\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.64%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0117\n",
       "                \n",
       "                    &plusmn; 0.0046\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Fireplaces\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.65%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0115\n",
       "                \n",
       "                    &plusmn; 0.0029\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotRmsAbvGrd\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.86%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0004\n",
       "                \n",
       "                    &plusmn; 0.0010\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FullBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.93%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0002\n",
       "                \n",
       "                    &plusmn; 0.0022\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                WoodDeckSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.95%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0001\n",
       "                \n",
       "                    &plusmn; 0.0001\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MiscVal\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.97%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0001\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LowQualFinSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PoolArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.86%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0005\n",
       "                \n",
       "                    &plusmn; 0.0022\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BedroomAbvGr\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.82%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0007\n",
       "                \n",
       "                    &plusmn; 0.0044\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.81%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0007\n",
       "                \n",
       "                    &plusmn; 0.0043\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MAE for Random Forest Model: 18,716\n"
     ]
    }
   ],
   "source": [
    "print(\"Validation MAE when not specifying max_leaf_nodes: {:,.0f}\".format(DTRmodel()))\n",
    "print(\"Validation MAE for best value of max_leaf_nodes: {:,.0f}\".format(DTRmodel(leaf_node_limit=150)))\n",
    "print(\"Validation MAE for Random Forest Model: {:,.0f}\".format(RFRmodel()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "Interestingly, the order of importance for each features is very similar, with the Overall Quality, Ground Floor Living Area and 2nd Floor Square Feet being the most important factors to the model. There are slight differents in the level of importance these features take but nothing noticable, as the erro intervals overlap. We also see that Pool Area, Bedrooms above ground and Open Porch Square feet consistently don't add anything to the model, so if trying to reduce the data mass collected and processed, a good start would be to stop including those columns\n",
    "\n",
    "## Using the model on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tofun\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To improve accuracy, a Random Forest model is trained on all training data\n",
    "X, y, features= selectiveColumnsTesting()\n",
    "rf_model_on_full_data = RandomForestRegressor(random_state=1)\n",
    "rf_model_on_full_data.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_path = 'test_data.csv'\n",
    "test_data = pd.read_csv(test_data_path)\n",
    "test_X = test_data[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>120220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>165240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>189130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>188800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>192195.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  SalePrice\n",
       "0  1461   120220.0\n",
       "1  1462   165240.0\n",
       "2  1463   189130.0\n",
       "3  1464   188800.0\n",
       "4  1465   192195.6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds = rf_model_on_full_data.predict(test_X)\n",
    "output = pd.DataFrame({'Id': test_data.Id,\n",
    "                      'SalePrice': test_preds})\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model is good, as according to Kaggle, the mean absolute error of the predicted house prices made from the test data is 19050.25253.\n",
    "\n",
    "However, as seen in In[2] a lot of data from the file is not used, so there is much room for improvement \n",
    "\n",
    "\n",
    "## Improving the Random Forest Model\n",
    "\n",
    "How many trees do we use in the random forest model? right now we use the default value of RandomForestRegressor (which is 10), but what would value would optimise the MAE is we did?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#which number trees creates the best prediction?\n",
    "def plotTreesvsMAE(start,stop,step=1, train_mode=selectiveColumnsTraining(),train_mode2=None, labels=(\"method 1 MAE\",\"method 2 MAE\"), cross_val_folds=2):\n",
    "    tree_list =[]\n",
    "    if train_mode2==None:\n",
    "        tree_list= [(tree_No, RFRmodel(trees=tree_No, train_mode=train_mode,cross_val_folds=cross_val_folds)) for tree_No in range (start,stop,step)]\n",
    "        tree_df=pd.DataFrame(tree_list, columns = [\"Number of trees\",\"MAE\"]).set_index(\"Number of trees\")\n",
    "\n",
    "    else:\n",
    "        tree_list= [(tree_No, RFRmodel(trees=tree_No, train_mode=train_mode), RFRmodel(trees=tree_No, train_mode=train_mode2, cross_val_folds=cross_val_folds)) for tree_No in range (start,stop,step)]\n",
    "        tree_df=pd.DataFrame(tree_list, columns = [\"Number of trees\",labels[0], labels[1]]).set_index(\"Number of trees\")\n",
    "    plt.figure(figsize=(12,8))\n",
    "    sns.lineplot(data=tree_df, markers=True)\n",
    "    plt.title('Changes in MAEs by number of estimators (trees)')\n",
    "    plt.ylabel(\"MAE\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotTreesvsMAE(10,300,step=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this data, we can see that 80 is a \"sweet spot\" where the MAE is locally minimised\n",
    "\n",
    "Now that we have a broad idea of where the minimum is, we, will test values around the area to see if the value can get any lower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotTreesvsMAE(70,90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further analysis shows that there is another local minimum at 74, so we shall use this number of trees in our RFR models from now on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MAE for Random Forest Model: 18,716\n",
      "Validation MAE for Random Forest Model with optimum number of trees: 17,654\n"
     ]
    }
   ],
   "source": [
    "print(\"Validation MAE for Random Forest Model: {:,.0f}\".format(RFRmodel()))\n",
    "print(\"Validation MAE for Random Forest Model with optimum number of trees: {:,.0f}\".format(RFRmodel(trees=74)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with the missing values - Imutptation \n",
    "\n",
    "currently, we carefully select what columns in the csv's are used for according supposed useful and whether they is any data missing. In other words we will automatically not consider any coulmns with missing values, making us miss out on a lot of meaningful data. To improve on this, we will use a techinque called imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ImputedColsTraining():\n",
    "    X_full = pd.read_csv('train_data.csv', index_col='Id')\n",
    "    # Remove rows with missing target, separate target from predictors\n",
    "    X_full.dropna(axis=0, subset=['SalePrice'], inplace=True)\n",
    "    y = X_full.SalePrice\n",
    "    X_full.drop(['SalePrice'], axis=1, inplace=True)\n",
    "\n",
    "    # we'll use only numerical predictors\n",
    "    X = X_full.select_dtypes(exclude=['object'])\n",
    "\n",
    "    # Break off validation set from training data\n",
    "    X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2,\n",
    "                                                          random_state=1)\n",
    "    #imputation\n",
    "    my_imputer = SimpleImputer(strategy='median')\n",
    "    imputed_X_train = pd.DataFrame(my_imputer.fit_transform(X_train))\n",
    "    imputed_X_valid = pd.DataFrame(my_imputer.transform(X_valid))\n",
    "\n",
    "    # imputation removed column names; put them back\n",
    "    imputed_X_train.columns = X_train.columns\n",
    "    imputed_X_valid.columns = X_valid.columns\n",
    "    \n",
    "    #imputed_X_train.index = X_train.index\n",
    "    #imputed_X_valid.index = X_valid.index\n",
    "    return imputed_X_train, imputed_X_valid, y_train, y_valid;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
       "        filter: brightness(85%);\n",
       "    }\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.4702\n",
       "                \n",
       "                    &plusmn; 0.0679\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallQual\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 92.57%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1142\n",
       "                \n",
       "                    &plusmn; 0.0227\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GrLivArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.42%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0402\n",
       "                \n",
       "                    &plusmn; 0.0017\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                2ndFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.15%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0291\n",
       "                \n",
       "                    &plusmn; 0.0052\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtFinSF1\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.44%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0249\n",
       "                \n",
       "                    &plusmn; 0.0069\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotalBsmtSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.06%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0168\n",
       "                \n",
       "                    &plusmn; 0.0120\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GarageCars\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.56%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0109\n",
       "                \n",
       "                    &plusmn; 0.0052\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.60%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0105\n",
       "                \n",
       "                    &plusmn; 0.0033\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                1stFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.61%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0105\n",
       "                \n",
       "                    &plusmn; 0.0022\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotRmsAbvGrd\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.83%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0081\n",
       "                \n",
       "                    &plusmn; 0.0036\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YearBuilt\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.88%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0077\n",
       "                \n",
       "                    &plusmn; 0.0024\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallCond\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.06%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0060\n",
       "                \n",
       "                    &plusmn; 0.0030\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YearRemodAdd\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.10%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0056\n",
       "                \n",
       "                    &plusmn; 0.0019\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Fireplaces\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.18%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0049\n",
       "                \n",
       "                    &plusmn; 0.0021\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GarageArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.21%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0047\n",
       "                \n",
       "                    &plusmn; 0.0008\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MasVnrArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.53%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0022\n",
       "                \n",
       "                    &plusmn; 0.0006\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                HalfBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.54%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0021\n",
       "                \n",
       "                    &plusmn; 0.0012\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FullBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.62%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0016\n",
       "                \n",
       "                    &plusmn; 0.0011\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MSSubClass\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.63%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0016\n",
       "                \n",
       "                    &plusmn; 0.0019\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.78%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0008\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YrSold\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.78%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0007\n",
       "                \n",
       "                    &plusmn; 0.0025\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MoSold\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.80%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0007\n",
       "                \n",
       "                    &plusmn; 0.0012\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BedroomAbvGr\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.87%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0004\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                KitchenAbvGr\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.89%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0012\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GarageYrBlt\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.92%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0002\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtFinSF2\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.93%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0002\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PoolArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.95%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0001\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtHalfBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.98%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LowQualFinSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.98%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MiscVal\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.99%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0000\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                ScreenPorch\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.96%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0001\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtUnfSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.95%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0001\n",
       "                \n",
       "                    &plusmn; 0.0002\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                3SsnPorch\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.94%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0001\n",
       "                \n",
       "                    &plusmn; 0.0002\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                EnclosedPorch\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.93%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0001\n",
       "                \n",
       "                    &plusmn; 0.0007\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtFullBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.87%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0004\n",
       "                \n",
       "                    &plusmn; 0.0021\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                WoodDeckSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 98.69%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0096\n",
       "                \n",
       "                    &plusmn; 0.0026\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotFrontage\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MAE for Random Forest Model with imputation: 17,095\n"
     ]
    }
   ],
   "source": [
    "print(\"Validation MAE for Random Forest Model with imputation: {:,.0f}\".format(RFRmodel(trees=74, train_mode=ImputedColsTraining())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "but does changing this have an affect on the number of trees needed? let's investigate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotTreesvsMAE(70,250,step=10, train_mode=ImputedColsTraining())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "interestingly, it appears that with imputed data, a higher number of trees tend to yield better results, with a local minimum around 230. Zooming in..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotTreesvsMAE(220,240,train_mode=ImputedColsTraining())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we see the minimum is actually at 232"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
       "        filter: brightness(85%);\n",
       "    }\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.4696\n",
       "                \n",
       "                    &plusmn; 0.0656\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallQual\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 92.55%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1146\n",
       "                \n",
       "                    &plusmn; 0.0213\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GrLivArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.25%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0276\n",
       "                \n",
       "                    &plusmn; 0.0163\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GarageCars\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.43%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0250\n",
       "                \n",
       "                    &plusmn; 0.0056\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtFinSF1\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.44%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0249\n",
       "                \n",
       "                    &plusmn; 0.0016\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                2ndFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.46%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0246\n",
       "                \n",
       "                    &plusmn; 0.0072\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotalBsmtSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.24%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0146\n",
       "                \n",
       "                    &plusmn; 0.0039\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                1stFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.38%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0130\n",
       "                \n",
       "                    &plusmn; 0.0058\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.79%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0085\n",
       "                \n",
       "                    &plusmn; 0.0023\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotRmsAbvGrd\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.81%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0084\n",
       "                \n",
       "                    &plusmn; 0.0041\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YearBuilt\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.01%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0064\n",
       "                \n",
       "                    &plusmn; 0.0022\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallCond\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.12%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0054\n",
       "                \n",
       "                    &plusmn; 0.0023\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YearRemodAdd\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.19%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0048\n",
       "                \n",
       "                    &plusmn; 0.0026\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GarageArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.25%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0043\n",
       "                \n",
       "                    &plusmn; 0.0019\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Fireplaces\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.26%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0042\n",
       "                \n",
       "                    &plusmn; 0.0006\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MasVnrArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.57%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0020\n",
       "                \n",
       "                    &plusmn; 0.0008\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FullBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.69%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0012\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                HalfBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.69%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0012\n",
       "                \n",
       "                    &plusmn; 0.0020\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MoSold\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.71%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0011\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MSSubClass\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.84%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0005\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.86%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0004\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                KitchenAbvGr\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.87%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0004\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BedroomAbvGr\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.87%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YrSold\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.89%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0010\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtUnfSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.93%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0001\n",
       "                \n",
       "                    &plusmn; 0.0006\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GarageYrBlt\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.94%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0001\n",
       "                \n",
       "                    &plusmn; 0.0002\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                ScreenPorch\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.96%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0001\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtHalfBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.97%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MiscVal\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.97%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0002\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtFinSF2\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.98%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PoolArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.99%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LowQualFinSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.98%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0000\n",
       "                \n",
       "                    &plusmn; 0.0001\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                EnclosedPorch\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.96%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0001\n",
       "                \n",
       "                    &plusmn; 0.0002\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                3SsnPorch\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.72%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0011\n",
       "                \n",
       "                    &plusmn; 0.0020\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                WoodDeckSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.70%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0012\n",
       "                \n",
       "                    &plusmn; 0.0014\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtFullBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 98.60%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0105\n",
       "                \n",
       "                    &plusmn; 0.0013\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotFrontage\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MAE for Random Forest Model with imputation - optimised trees: 16,902\n"
     ]
    }
   ],
   "source": [
    "print(\"Validation MAE for Random Forest Model with imputation - optimised trees: {:,.0f}\".format(RFRmodel(trees=232, train_mode=ImputedColsTraining())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "Again we see that the Overall Quality, Ground Floor Living Area and 2nd Floor Square Feet are still some the most important factors to the model, but it seems the importance is spread more as there are more features to consider. The increase in features as also led to a rise in reduandant features, which are all categorical but one. The values overall comfirm what the MAE says, the extra features to bring a bit of extra insight, but not much\n",
    "\n",
    "\n",
    "when using the new model on the test set and sending the submissions file into kaggle, the MAE was 16991.14489"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Catergorical variables\n",
    "\n",
    "Currently our training data includes drops catergorical variables, but these columns can be very helpful in identifying trends. In repsonse we will explore a number of ultising catergorical variables in our model\n",
    "\n",
    "\n",
    "### Label Encoding\n",
    "Label encoding will assign as number to each of the unique values in a catergorical column, but problems can arises when the validation data has different values to the test data in the column, so we mush first identify these columns and make sure not to label encode them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LabelEncodingTraining():\n",
    "    X = pd.read_csv('train_data.csv', index_col='Id') \n",
    "    #X_test = pd.read_csv('../input/test.csv', index_col='Id')\n",
    "\n",
    "    # Remove rows with missing target, separate target from predictors\n",
    "    X.dropna(axis=0, subset=['SalePrice'], inplace=True)\n",
    "    y = X.SalePrice\n",
    "    X.drop(['SalePrice'], axis=1, inplace=True)\n",
    "\n",
    "    # To keep things simple, we'll drop columns with missing values\n",
    "    cols_with_missing = [col for col in X.columns if X[col].isnull().any()] \n",
    "    X.drop(cols_with_missing, axis=1, inplace=True)\n",
    "    \n",
    "   # X_test.drop(cols_with_missing, axis=1, inplace=True)\n",
    "\n",
    "    # Break off validation set from training data\n",
    "    X_train, X_valid, y_train, y_valid = train_test_split(X, y,\n",
    "                                                          train_size=0.8, test_size=0.2,\n",
    "                                                          random_state=0)\n",
    "    \n",
    "    # All categorical columns\n",
    "    object_cols = [col for col in X_train.columns if X_train[col].dtype == \"object\"]\n",
    "\n",
    "    # Columns that can be safely label encoded\n",
    "    good_label_cols = [col for col in object_cols if \n",
    "                       set(X_train[col]) == set(X_valid[col])]\n",
    "    # Problematic columns that will be dropped from the dataset\n",
    "    bad_label_cols = list(set(object_cols)-set(good_label_cols))\n",
    "    \n",
    "    # Drop categorical columns that will not be encoded\n",
    "    label_X_train = X_train.drop(bad_label_cols, axis=1)\n",
    "    label_X_valid = X_valid.drop(bad_label_cols, axis=1)\n",
    "\n",
    "    # Apply label encoder \n",
    "    label_encoder = LabelEncoder()\n",
    "    for col in good_label_cols:\n",
    "        label_X_train[col] = label_encoder.fit_transform(X_train[col])\n",
    "        label_X_valid[col] = label_encoder.transform(X_valid[col])\n",
    "#    X_cat =  X = X_full.select_dtypes(exclude=['int', 'float64'])\n",
    "    return label_X_train, label_X_valid, y_train, y_valid;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
       "        filter: brightness(85%);\n",
       "    }\n",
       "</style>\n",
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       "\n",
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       "\n",
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       "    \n",
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       "    \n",
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       "    \n",
       "\n",
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       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.4844\n",
       "                \n",
       "                    &plusmn; 0.0619\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallQual\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 92.81%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1123\n",
       "                \n",
       "                    &plusmn; 0.0293\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GrLivArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.29%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0278\n",
       "                \n",
       "                    &plusmn; 0.0042\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotalBsmtSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.87%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0197\n",
       "                \n",
       "                    &plusmn; 0.0078\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtFinSF1\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.14%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0162\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                2ndFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.54%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0115\n",
       "                \n",
       "                    &plusmn; 0.0081\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                1stFlrSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.66%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0102\n",
       "                \n",
       "                    &plusmn; 0.0034\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GarageCars\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.75%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0092\n",
       "                \n",
       "                    &plusmn; 0.0045\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YearRemodAdd\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.05%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0063\n",
       "                \n",
       "                    &plusmn; 0.0008\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.11%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0057\n",
       "                \n",
       "                    &plusmn; 0.0037\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                GarageArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.21%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0048\n",
       "                \n",
       "                    &plusmn; 0.0009\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LandContour\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.37%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0035\n",
       "                \n",
       "                    &plusmn; 0.0010\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OverallCond\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.45%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0029\n",
       "                \n",
       "                    &plusmn; 0.0036\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YearBuilt\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.54%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0022\n",
       "                \n",
       "                    &plusmn; 0.0026\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtUnfSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.58%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0019\n",
       "                \n",
       "                    &plusmn; 0.0018\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                WoodDeckSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.62%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0017\n",
       "                \n",
       "                    &plusmn; 0.0013\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BedroomAbvGr\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.64%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0016\n",
       "                \n",
       "                    &plusmn; 0.0007\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                KitchenQual\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.70%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0012\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                SaleCondition\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.71%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0011\n",
       "                \n",
       "                    &plusmn; 0.0011\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FullBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.73%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0011\n",
       "                \n",
       "                    &plusmn; 0.0008\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OpenPorchSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.76%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0009\n",
       "                \n",
       "                    &plusmn; 0.0002\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                CentralAir\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.77%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0008\n",
       "                \n",
       "                    &plusmn; 0.0009\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MSZoning\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.77%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0008\n",
       "                \n",
       "                    &plusmn; 0.0042\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtHalfBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.84%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0005\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                YrSold\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.85%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0004\n",
       "                \n",
       "                    &plusmn; 0.0008\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MSSubClass\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.86%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0004\n",
       "                \n",
       "                    &plusmn; 0.0012\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtFullBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.88%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0011\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TotRmsAbvGrd\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.90%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0009\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                HalfBath\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.90%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                KitchenAbvGr\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.90%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0002\n",
       "                \n",
       "                    &plusmn; 0.0023\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotShape\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.92%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0002\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                ExterQual\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.97%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0002\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LotConfig\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.97%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                3SsnPorch\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.99%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                LowQualFinSF\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.99%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MiscVal\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0001\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PavedDrive\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0000\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Street\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.99%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0000\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PoolArea\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.99%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0000\n",
       "                \n",
       "                    &plusmn; 0.0001\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BldgType\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.99%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0000\n",
       "                \n",
       "                    &plusmn; 0.0006\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                ScreenPorch\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.97%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0000\n",
       "                \n",
       "                    &plusmn; 0.0001\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BsmtFinSF2\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.97%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0000\n",
       "                \n",
       "                    &plusmn; 0.0001\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                EnclosedPorch\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.94%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0001\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                HouseStyle\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 99.83%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0005\n",
       "                \n",
       "                    &plusmn; 0.0009\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Fireplaces\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 98.58%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                -0.0110\n",
       "                \n",
       "                    &plusmn; 0.0043\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MoSold\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MAE for Random Forest Model with Label Encoding: 17,215\n"
     ]
    }
   ],
   "source": [
    "print(\"Validation MAE for Random Forest Model with Label Encoding: {:,.0f}\".format(RFRmodel(trees=232, train_mode=LabelEncodingTraining())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "even though imputation hasn't been untilised yet, we can see the resukts are still better than the \"plain\" random forest model\n",
    "\n",
    "\n",
    "### One-Hot encoding\n",
    "\n",
    "Our next approcah to consider is to make a column for all unique values in a catergorical column and put a 1 that column for a row that has the celected categorical value in it.\n",
    "However it would not be wise to ulitise this approach with a catorgical column that has a high cardinality, as it would make too many extra columns, so will only use the method with columns of a cardinality under 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OneHotEncodingTraining():\n",
    "    X = pd.read_csv('train_data.csv', index_col='Id') \n",
    "    #X_test = pd.read_csv('../input/test.csv', index_col='Id')\n",
    "\n",
    "    # Remove rows with missing target, separate target from predictors\n",
    "    X.dropna(axis=0, subset=['SalePrice'], inplace=True)\n",
    "    y = X.SalePrice\n",
    "    X.drop(['SalePrice'], axis=1, inplace=True)\n",
    "\n",
    "    # To keep things simple, we'll drop columns with missing values\n",
    "    cols_with_missing = [col for col in X.columns if X[col].isnull().any()] \n",
    "    X.drop(cols_with_missing, axis=1, inplace=True)\n",
    "    \n",
    "   # X_test.drop(cols_with_missing, axis=1, inplace=True)\n",
    "\n",
    "    # Break off validation set from training data\n",
    "    X_train, X_valid, y_train, y_valid = train_test_split(X, y,\n",
    "                                                          train_size=0.8, test_size=0.2,\n",
    "                                                          random_state=0)    \n",
    "    # All categorical columns\n",
    "    object_cols = [col for col in X_train.columns if X_train[col].dtype == \"object\"]\n",
    "    \n",
    "    # Columns that will be one-hot encoded\n",
    "    low_cardinality_cols = [col for col in object_cols if X_train[col].nunique() < 10]\n",
    "    \n",
    "    OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
    "    OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[low_cardinality_cols]))\n",
    "    OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[low_cardinality_cols]))\n",
    "\n",
    "    OH_cols_train.index = X_train.index\n",
    "    OH_cols_valid.index = X_valid.index\n",
    "\n",
    "    # Remove categorical columns (will replace with one-hot encoding)\n",
    "    num_X_train = X_train.drop(object_cols, axis=1)\n",
    "    num_X_valid = X_valid.drop(object_cols, axis=1)\n",
    "\n",
    "    # Add one-hot encoded columns to numerical features\n",
    "    OH_X_train = pd.concat([num_X_train, OH_cols_train], axis=1)\n",
    "    OH_X_valid = pd.concat([num_X_valid, OH_cols_valid], axis=1)\n",
    "    \n",
    "    test_X_train = num_X_train+OH_cols_train\n",
    "    test_X_valid = num_X_valid+OH_cols_valid\n",
    "    \n",
    "    return OH_X_train, OH_X_valid, y_train, y_valid;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MAE for Random Forest Model with One-Hot Encoding: 16,978\n"
     ]
    }
   ],
   "source": [
    "print(\"Validation MAE for Random Forest Model with One-Hot Encoding: {:,.0f}\".format(RFRmodel(trees=232, train_mode=OneHotEncodingTraining())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we see one hot encoding has actually worked a bit better, but which will prevail once the number of trees is optimised?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotTreesvsMAE(70,300,15, train_mode=LabelEncodingTraining(),train_mode2=OneHotEncodingTraining(), labels=('Label Encoding','One Hot Encoding'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "interestingly, on its own, one-hot encoding triumphs over label encoding by far, zooming in (on One-Hot Encoding)..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotTreesvsMAE(220,250, train_mode=OneHotEncodingTraining())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we see that at 229 trees, one-hot encoding is optimised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MAE for Random Forest Model with One-Hot Encoding -optimised trees: 16,964\n"
     ]
    }
   ],
   "source": [
    "print(\"Validation MAE for Random Forest Model with One-Hot Encoding - optimised trees: {:,.0f}\".format(RFRmodel(trees=229, train_mode=OneHotEncodingTraining())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "interestingly, we see this value is still not as low as imputation or one-hot encoding by themselves, suggesting that the data is being overfitted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipelining - combining methods\n",
    "\n",
    "So what happens when we combine the last two techinques by one-hot coding the catergorical data and imputing the numerical missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipelineTrainingAndmodel(trees,cross_val_folds=2, model_type=\"RFR\",learning_rate =0.3 ):\n",
    "    # Read the data\n",
    "    X_full = pd.read_csv('train_data.csv', index_col='Id')\n",
    "    X_test_full = pd.read_csv('test_data.csv', index_col='Id')\n",
    "\n",
    "    # Remove rows with missing target, separate target from predictors\n",
    "    X_full.dropna(axis=0, subset=['SalePrice'], inplace=True)\n",
    "    y = X_full.SalePrice\n",
    "    X_full.drop(['SalePrice'], axis=1, inplace=True)\n",
    "\n",
    "    # Break off validation set from training data\n",
    "    X_train_full, X_valid_full, y_train, y_valid = train_test_split(X_full, y, \n",
    "                                                                    train_size=0.8, test_size=0.2,\n",
    "                                                                    random_state=0)\n",
    "\n",
    "    # \"Cardinality\" means the number of unique values in a column\n",
    "    # Select categorical columns with relatively low cardinality (convenient but arbitrary)\n",
    "    categorical_cols = [cname for cname in X_train_full.columns if\n",
    "                        X_train_full[cname].nunique() < 10 and \n",
    "                        X_train_full[cname].dtype == \"object\"]\n",
    "\n",
    "    # Select numerical columns\n",
    "    numerical_cols = [cname for cname in X_train_full.columns if \n",
    "                    X_train_full[cname].dtype in ['int64', 'float64']]\n",
    "\n",
    "    # Keep selected columns only\n",
    "    my_cols = categorical_cols + numerical_cols\n",
    "    X_train = X_train_full[my_cols].copy()\n",
    "    X_valid = X_valid_full[my_cols].copy()\n",
    "    X_test = X_test_full[my_cols].copy()\n",
    "    \n",
    "    # Preprocessing for numerical data\n",
    "    numerical_transformer = SimpleImputer(strategy='median') # Your code here\n",
    "\n",
    "    # Preprocessing for categorical data\n",
    "    categorical_transformer = Pipeline(steps=[\n",
    "        ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "        ('one-hot', OneHotEncoder(handle_unknown='ignore', sparse=False))\n",
    "    ]) \n",
    "    # Bundle preprocessing for numerical and categorical data\n",
    "    preprocessor = ColumnTransformer(\n",
    "        transformers=[\n",
    "            ('num', numerical_transformer, numerical_cols),\n",
    "            ('cat', categorical_transformer, categorical_cols)\n",
    "        ])\n",
    "\n",
    "    # Define model\n",
    "    \n",
    "    if model_type == \"Gradient Boosting\":\n",
    "        model = XGBRegressor(random_state=0, n_estimators=trees, learning_rate=learning_rate, n_jobs=4) # Your code here\n",
    "        my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                                      ('model', model)\n",
    "                                     ])\n",
    "        my_pipeline.fit(X_train, y_train)\n",
    "\n",
    "        \n",
    "    else:\n",
    "        model = RandomForestRegressor(n_estimators=trees, random_state=0)\n",
    "        my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                                      ('model', model)\n",
    "                                     ])\n",
    "        my_pipeline.fit(X_train, y_train)\n",
    "    \n",
    "    if cross_val_folds!=2:\n",
    "        scores = -1 * cross_val_score(my_pipeline, X_full, y,\n",
    "                              cv=cross_val_folds,\n",
    "                              scoring='neg_mean_absolute_error')\n",
    "        return scores.mean()\n",
    "    \n",
    "    else:\n",
    "        # Preprocessing of validation data, get predictions\n",
    "        preds = my_pipeline.predict(X_valid)\n",
    "        # Evaluate the model\n",
    "        score = mean_absolute_error(y_valid, preds)\n",
    "        return score\n",
    "    \n",
    "    #print('MAE:', score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17316.210769454967"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipelineTrainingAndmodel(235)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MAE')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_list= [(tree_No, pipelineTrainingAndmodel(tree_No)) for tree_No in range (70,350,15)]\n",
    "tree_df=pd.DataFrame(tree_list, columns = [\"Number of trees\",\"MAE\"]).set_index(\"Number of trees\")\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.lineplot(data=tree_df, markers=True)\n",
    "plt.title('Changes in MAEs by number of estimators (trees)')\n",
    "plt.ylabel(\"MAE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that 235 is a sweet spot where the MAEs is minimised. zooming in..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MAE')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z8PNXQvjG43vQE9Ewv64Sn7lyEd57zoJZXaff78fOnTuhaRoqKyvxu9/9DgsWnHidkUgEL7/8MqqqqnDgwIET+sH/+q//Gp/+9KdnFQMRERERFR8r5NP081dCuPPhHQhFNCgAoYiGOx/egZ+/Epr1dV911VV47LHHAAAbNmzA+vXrTzj9oYcewrXXXot169bhgQcemPXtEREREZH5WCEf5Uu/eg2v98TGPf2VzgiG05kTjmnJND77s+3Y8FLnmJc5a34NvnDtkklve926dfjyl7+MNWvWYPv27fjIRz6C3//+9yOnb9iwAV/4whfQ2NiI97///bjzzjtHTrvrrrtw//33AwACgQCefvrpSW+PiIiIiMzHhHyaRifjkx2fjmXLlqGjowMbNmzA1VdffcJphw8fxhtvvIGLL74YIgKXy4WdO3fi7LPPBsCWFSIiIiKrYkI+ymSV7Iv+6SmEItpJxxfUVWLj7ReOcYnpue666/DpT38amzZtQl9f38jxjRs3IhwOj/SNx2IxPPDAA/jqV78669skIiIiIvOwh3yaPnPlIlS6nSccq3Q78ZkrF+Xl+j/ykY/g85//PJYuXXrC8Q0bNuA3v/kNOjo60NHRga1bt7KPnIiIiMgGmJBP03vPWYB/vGEpFtRVQqBXxv/xhqWznrKS1dzcjE9+8pMnHOvo6EBnZycuuOCCkWNtbW2oqanBiy++CEDvIc8de9jR0ZGXeIiIiIiosEQpZXYMRbVy5Uq1ZcuWE47t2rULixcvNimi4imX+0n2k8ko9A0OYziVhsflRL3fA4eDm10REdmFXf/Oi8hWpdTKyc7HHnIiKmmZjMKewwO47d4t6A5raA5U4p5bVmJRY7Ut/lgTEZU7/p1nywoRlbi+weGRP9IA0B3WcNu9W9A3OGxyZERElA/8O8+EnIhK3HAqPfJHOqs7rGE4lTYpIiIiyif+nWdCPsLuvfR2v39kXx6XE82ByhOONQcq4XE5x7kEERFZyXh/592u8klTy+eeTsDr9aKvr8+2SatSCn19ffB6vWaHQjRt9X4P7rll5cgf6+ZAJb69dgXq/R6TIyMionwY6+/819+3DBte7EQqDxsvWgEXdUIfNdjd3Y3e3l6zQykYr9eL5uZms8MgmjaHQ7CosRrfeP8yVBh7AHz10dfxjRuX49RglcnRERHRbDkcgrZ6H/7fmrPQOseHhioPntp1BHc9sQ+7Dw3g2+tWoMLmn4oyIQfgdrtHdsAkotLjcAi+8fge+Dwu3LV2BXYfGsBdv9uLf//AuWaHRkREedATTeD2+7biX25ajhvObcba1a04NpzGVx59HfF7t+I/P3QeKj32TcrZskJElhDVkqipdCFYXYGPXNSGR7cfxGs9UbPDIiKiPAhF9EWdC+qO95J/9OI2fP19S/Hsvl58+IcvYSCRNCu8gmNCTkSWEEukUFvpBgDc9o521Hhd+NZv95ocFRER5UPImLIyv+7ExZ1rV7XiO+vOwcudYXzwBy8ibNNRiEzIicgS9Aq5npDXVrrx8UtOxVO7j2DrW/0mR0ZERLMVimhwCDCv9uQBFNcun4/v3Xwedh8awNrvv4AjsYQJERYWE3IiKnmJZBrDqQxqvO6RY7e+bSEaqirwz7/ZY9sJSURE5SIU0TCvxgu3c+zU9LLFjfjxravQHdZw0/deQHc4XuQIC4sJORGVvJim9w1mW1YAwOdx4ROXnoYXD/TjD28cNSs0IiLKg1BYO6ldZbS3ndaA+//sfPQPDuPG/3wB+3uPFSm6wmNCTkQlL2ok5DU5CTkArFvdggV1lfjG46ySExFZWSiiYUFg4oQcAM5tDWDDxy7AcCqDm773AnYdjBUhusJjQk5EJS+WOLlCDgAVLic+efnp2N4dxeOvHTYjNCIimqV0RuFQNHHChJWJLJlfi423Xwi304G133sBr3SGCxxh4TEhJ6KSN1Ih9568dcIN5yxAe9CPb/12D9IZVsmJiKzmyEACqYyatGUl12lzq/DT2y9Enc+DD/7gRTz/prVbF5mQE1HJi2kpACdXyAHA5XTgb65YhH1HjuEX20LFDo2IiGYpO/JwKi0ruVrm+PDgxy/EgrpK/OmPNuOp3db9pJQJORGVvOgYizpzXXX2PCyZX4O7ntiL4VSmmKEREdEsZTcFap5GhTyrscaLjbdfiDMaq/Gxe7fi0e09+Q6vKJiQE1HJG29RZ5bDIfj0lYvQ1a9h45auYoZGRESzlE3Ip9OykmuO34Of3HY+zmmtw19ueAU/3Wy9/weYkBNRyYtpSfg8znHn0wLAJWcEsWphAP/25D5ow+kiRkdERLMRCmuo87nhrzh5ndBU1Xjd+O+PrMZFpzXgsw9txw//cCCPERYeE3IiKnlRLXnCpkBjERF85sozcWRgCPe+0FGUuIiIaPZCEW3KE1Ym4vO48IMPr8SVSxrx5Udfx09f6kTvQAKhcBy9A0PIlPDCfybkRFTyYonkuP3juVa3zcE7zwjiP555c2RUIhERlbaePCXkgD4O97sfOBd/ddlpWDDHh+vvfh4Xff1pXH/3c9hzeKBkk3Im5ERU8qJaEjWVU/so89N/sgiReBI/+L21Pq4kIipHSqkp7dI5HS6nAx+44BT87UPb0W1McOkOa7jt3i3oGxzO2+3kU8ESchH5oYgcEZGdOcc2isg246tDRLYZx1fnHH9VRK4fdV1OEXlFRB7NOfZjETmQc7kVhbovRGSumJaaUoUcAJY21+LqpfPwX7/fj75jQwWOjIiIZiOqJTE4nEbzNEceTiaZyowk41ndYQ3DqdJcY1TICvmPAbw794BSaq1SaoVSagWAhwA8bJy0E8BK4/i7AXxPRHLLYZ8EsGuM2/hM9vqUUtvyfg+IqCToFfKpJeQA8KkrzoCWTOM/Nr1ZwKiIiGi2shNW8tWykuVxOU9K8psDlfC4nHm9nXwpWEKulHoWQP9Yp4mIALgJwAbjvHGlVMo42QtA5Zy3GcA1AH5QqFiJqLTFprCoM9dpc6txw7nNuPePb+FgVJv8AkREZIrspkD5bFkBgHq/B/fcsnIkKW8OVOKeW1ai3u/J6+3ki1k95G8HcFgptS97QETOF5HXAOwA8PGcBP3bAD4LYKzdPr4mIttF5C4RqSh41ERUdOmMwsDQ1FtWsj552elQSuE7T75RoMiIiGi2RirkeW5ZcTgEixqr8cgdF+G5v30XHrnjIixqrIbDIXm9nXwxKyFfD6M6nqWUelEptQTAKgB3iohXRNYAOKKU2jrGddwJ4Ezj/HMA/O14NyYiHxORLSKypbe3N293gogKbyAx8aZA42mZ48P61a346ZYudBwdLERoREQ0Sz0RDRUuR0Eq1w6HIFhdgQUBH4LVFSWbjAMmJORGb/gNADaOdbpSaheAQQBnA7gIwHUi0gHgAQCXisj9xvkOKt0QgB8BWD3ebSqlvq+UWqmUWhkMBvN6f4iosGKa/mHZdCvkAPB/3nUa3E7BXU/szXdYRESUB9kZ5Ho3c/kyo0J+OYDdSqnu7AERacsu4hSRUwAsAtChlLpTKdWslFoIYB2Ap5RSHzLO12T8KwDeC31hKBHZTFQzKuTe6e/gNrfGi1vf1oZfvtqD3Ydi+Q6NiIhmKRTW8t6uYkWFHHu4AcALABaJSLeIfNQ4aR1GtasAuBjAq8YYxEcA3KGUOjrJTfxERHZA7zlvAPDV/EVPRKUiu8HPTCrkAPDxd7ajyuPCt37LKjkRUakJRRJ5n7BiRdMvOU2RUmr9OMdvHePYfQDum+T6NgHYlPPzpbMKkIgsIVshr/XNLCGv83nwsXe041u/24tXOsM4pzWQz/CIiGiGEsk0jh4byvuEFSviTp1EVNKOt6zMLCEHgD+9uA31fg+++ds9+QqLiIhmqadAM8itiAk5EZW0mDa7lhUAqKpw4Y53nYbn3ujDc29M1g1HRETFUKiRh1bEhLzAMhmF3oEhhMJx9A4MIZNRk1+IiEZEtSScDoHPM7vd1T54fiuaar34xuN7oBRfh0REZmOF/Dgm5AWUySjsOTyA6+9+Dhd9/Wlcf/dz2HN4gEk50TTEEknUVrpnPRLL63bik5edjm1dETyx60ieoiMiopkKhTU4BJhX6zU7FNMxIS+gvsFh3HbvFnQb28J2hzXcdu8W9A0OmxwZkXVEtdSMRh6O5X3nNaOtwY9vPr6Hb4yJiEzWHdHQWOOF28l0lI9AAQ2n0iPJeFZ3WMNwKm1SRETWE9OSs+ofz+V2OvDXV5yBPYcH8KvtPXm5TiIimpkeY1MgYkJeUB6XE82jFio0Byrhcc2uF5aonES1JGrylJADwJqlTThzXjX+5Xd7kUxn8na9REQ0PaGIxpGHBibkBVTv9+CeW1aOJOXNgUp89wPnot7vMTkyIuuI5TkhdzgEn7lyEd7qi+PBLd2TX4CIiPIunVE4GElwwoqhYBsDkf4f/6LGajxyx0UYTqWx59AAfrkthOUtdWaHRmQZ2UWd+XTpmXOxdmUz5tVUoDscR4XLiXq/Bw7H7BaOEhHR1PQODCGVUWxZMbBCXmAOhyBYXYEFAR9e7ozgR893jIz5IaKJKaX0lpVZbAo09vUCH7zgFHz+l6/hYk5AIiIqulAkDoAjD7OYkBfR2lUtyCjwY3KiKUokM0imVd4r5H2Dw7jjJy9zAhIRkUmyf3/ZsqJjQl5ELXN8ePvpDfjpli6kWYkjmlTU2KWzpjK/3XWcgEREZK6eSAIAuKjTwIS8yNatakUoouH3+3rNDoWo5MUSekKe7wo5JyAREZkrFImjttKNqgouZwSYkBfdFWc1ot7vwQMvdZkdClHJy1bI852QjzUB6fs3r+QEJCKiIgmFOYM8F9+WFJnH5cD7zmvGD/9wAL0DQwhWV5gdElHJisaNlpU8L+rMnYAUSyTxxpFjiMSH4HDU5PV2iIhobKGIhlPq/WaHUTJYITfB2lUtSGUUfraVizuJJlKolhXg+ASkU+b48LmHd+B/+KkVEVFRKKVYIR+FCbkJTg1WYXXbHGzc3AmluLiTaDzHF3XmPyHPcjkdePfZ8/DkriOID6cKdjtERKSLaSkMDqeZkOdgQm6S9atb0NEXxwv7+8wOhahkxTQ9Qa7xFra77pplTdCSaTy9m4utiYgKrTs7g5wjD0cwITfJVWc3ocbr4uJOoglEtST8HidczsL+qTq/rR4NVRV4dHtPQW+HiIiOjzxkhfw4JuQm8bqduP6cBfjNzkMIczMSojHFEsmC9I+P5nQIrl46D0/tPoLBIbatEBEVUiisV8g5g/w4JuQmWre6FcPpDB55JWR2KEQlKaolC9o/nmvNsvkYSmXwxK7DRbk9IqJyFYpoqHA50FDFUbNZTMhNtLipBstb6vAAF3cSjamYCfnKUwJorKnAY9sPFuX2iIjKVU8kgQV1lRARs0MpGUzITbZ+VQv2Hj6GlzsjZodCVHJiWnFaVgB9DOLVS5uwaW8vBoxxi0RElH/dEY3tKqMwITfZtcvnw+9x4oGXOs0OhajkxLRk3jcFmsiaZfMxzLYVIqKC4gzykzEhN5m/woXrVszHo9sPsipHNEoskSpahRwAzmmpw/xaLx59lW0rRESFkEimcfTYEEcejsKEvASsW9UKLZnGL7Zx5BpRViqdwbGhFGoqCzuDPJfDIbhmWROe3dc7sikRERHlz8GoPvKQLSsnYkJeApY112JxUw0e2My2FaKsgYQ+frCYFXIAuGbZfCTTCr997VBRb5eIqByEwhoAziAfjQl5CRARrF/dgp2hGHaGomaHQ1QSshXqYifky5tr0RyoxKOctkJElHchY5fOZrasnIAJeYl4z4oF8Lod2MDFnUQAjifkxVzUCehvkK9Z1oTn3jjKTbuIiPIsFElABGis8ZodSklhQl4iaivduHppE36xrQfxYe4USBQzFjnX+oqbkAPAtcvmI5VReJxtK0REeRUKa2is9sLjYgqai49GCVm3qhXHhlL8qJwI5lXIAWDJ/BqcUu/DYzv4WiQiyqdQJM4JK2NgQl5CVi0M4NSgnzPJiQDENHMWdQJ628qaZU14/s0+9B0bKvrtExHZVXaXTjoRE/ISIiJYt6oVL3dGsPfwgNnhEJlqpEJexLGHua5ZOh/pjMIPnX0OAAAgAElEQVRv2LZCRJQXmYzCwSh36RwLE/ISc8O5C+B2Ch54qcvsUIhMFUsk4XYKKt1OU25/cVM12oN+bhJERJQnRwaGkEwrtqyMgQl5iamvqsCfLJmHh1/pRiKZNjscItNEtSRqK90QEVNuX0SwZmkTXjzQhyMDCVNiICKyk1BEn0HezAr5SZiQl6D1q1oRiSc54YHKWlRLmrKgM9ea5fORUcBvdvK1SEQ0W9mEnC0rJ2NCXoLedmo9WuZUsm2FylpMS6LGhAWduc5orMbpc6s4+YiIKA9Gdulky8pJmJCXIIdDX9z5wv4+dBwdNDscIlOUQkIOAGuWzcfmjn4cjrFthYhoNnoiGmor3aiqMGexfiljQl6ibjyvGU6H4IHNrJJTeYolUqaMPBztmmVNUAr4NWeSExHNSijCCSvjYUJeoubWeHHpmXPxs63dSKYzZodDVHR6D7n5VZTT5lbhzHnVbFshIpqlUFjjDPJxMCEvYetXt+DosSE8ueuw2aEQFZVSCjFjykopWLOsCVvfCqPHWJBERETTo5RCKKKhmf3jY2JCXsLecXoQ82q82MDFnVRm4sNppDKqZBLya5bNB8C2FSKimYolUjg2lML8Oq/ZoZQkJuQlzOV04KaVzXh2Xy+6w3GzwyEqmuO7dJZGQt7W4MeS+TVsWyEimqGRCSt1PpMjKU1MyEvcTataAAAPbuk2ORKi4okl9IS8VCrkgD5tZVtXBF39fHNMRDRd2RnkHHk4NibkJa454MPbTw/iwS1dSGeU2eEQFUU0blTITd4YKNc1S5sAsG2FiGgmsmtwuKhzbEzILWD9qhb0RBN4dm+v2aEQFUUskQJQWhXy1nofljfXsm2FiGgGQhENHpcD9X6P2aGUJCbkFnDZ4kY0VHmw4aVOs0MhKorjPeTmjz3Mdc2yJuwIRfFWHzfsIiKajuzIQ4dDzA6lJDEhtwCPy4H3ndeMJ3cfwRHuFkhlIKaVXg85AFxttK2wSk5END2hCGeQT4QJuUWsW9WKdEbhwa1c3En2l62QV5dQDzmgr+k4p7UOjzEhJyKaFn2XTo48HA8Tcotoa/DjgvY52Li5Cxku7iSbi2pJVFe44CzBjzbXLJuP1w/GsL/3mNmhEBFZQiKZRu/AEEceToAJuYWsX92Kzv44XtjfZ3YoRAUVSyRLZgb5aFcvnQcArJITEU3RwajebsuRh+NjQm4hVy6Zh9pKNxd3ku3FtNJNyJtqK7FqYYB95EREU5QdeciWlfExIbcQr9uJ689ZgN++dhj9g8Nmh0NUMDEthdoSm7CSa82y+dhzeAD7Dg+YHQoRUcnL7tLZzJaVcTEht5j1q1sxnM7g4Ze5uJPsK6olS2pToNGuOnseRDhthYhoKrojGkSAebWskI+HCbnFLJpXjXNa6/DA5i4oxcWdZE+xRLLkRh7mmlvjxfltc/Do9h6+DomIJtET0TC3ugIeF9PO8RT0kRGRH4rIERHZmXNso4hsM746RGSbcXx1zvFXReT6UdflFJFXROTRnGNtIvKiiOwzrrcstn+6452n4jNXLsKBo4PoHRji1BWynahW2gk5AFyzbD7e7B3EHratEBFNKLspEI2v0G9Vfgzg3bkHlFJrlVIrlFIrADwE4GHjpJ0AVhrH3w3geyKS20T6SQC7Rl3/1wHcpZQ6HUAYwEfzfxdKSyajsCBQia88+jou/dYzuP7u57Dn8EDZJOWZjELvwBBC4TjfjNhUMp1BfDhdsos6s646ex4cAjz6KttWiIgmEopoWBBg//hECpqQK6WeBdA/1mkiIgBuArDBOG9cKZUyTvYCUDnnbQZwDYAfjLr8pQB+Zhz6bwDvzfNdKDl9g8P42H1b0W0skOgOa7jt3i3oK4NFnpmMwp7DA7j+7udw0defLrs3I+WiVHfpHK2hqgIXnlqPx3YcZNsKEdE4MhmFg1FuCjQZM5t53g7gsFJqX/aAiJwvIq8B2AHg4zkJ+rcBfBZAJufy9QAiOefpBrCg8GGbaziVHknGs7rDGoZTaZMiKp6+wWHcdu+WsnwzUk6yu3TWlPCUlaw1y+bjwNFBvNYTMzsUIqKS1HtsCMm0QjNbViZkZkK+HkZ1PEsp9aJSagmAVQDuFBGviKwBcEQptXXU5cfawm/MMpWIfExEtojIlt7e3nzEbhqPy4nmUYP1mwOV8LicJkVUPOX8ZqScxBL6e+xSr5AD+t4ATofgsR1sWyEiGkv2/21uCjQxUxJyozf8BgAbxzpdKbULwCCAswFcBOA6EekA8ACAS0XkfgBHAdTl9Jk3A+gZ5/q+r5RaqZRaGQwG83pfiq3e78E9t6wcScqbA5W455aVqPfbfz1rOb8ZKScjFfISHnuYNcfvwUWnNXDaChHROLKbAi3gDPIJmVUhvxzAbqXUyDBtY2KKy/j+FACLAHQope5USjUrpRYCWAfgKaXUh5T+v9/TAN5vXMWHAfyimHfCDA6HYFFjNR654yI89OcX4ls3Lseixmo4HGN9YGAv5fxmpJxYpYc8a83SJnT1a9gRipodChFRyQlxl84pKfTYww0AXgCwSES6RSQ7BWUdRrWrALgYwKvGGMRHANyhlDo6yU38LYBPicgb0HvK/yt/0Zcuh0MQrK7Af2x6E1/45WtlkYwDx9+M/MP1S7HxYxfgh7euKps3I+UkarGE/Mol8+B2CjcJIiIaQyisocbrQrUFPvU0U0FXTSml1o9z/NYxjt0H4L5Jrm8TgE05P+8HsHo2MVpZe7AKv993FJmMKpukVAS44ycv49hQCl+7/myc0VhtdkiUZ8cXdVrjj3etz42LT2vAY9sP4s6rzoQ+AIqIiACOPJwqbplkYW0NfgylMuiJapOf2SYi8SSODaVGvif7iSWS8Lgc8LqtszZgzbL5CEU0vNIVMTsUIqKS0hPRsIDtKpNiQm5hbQ1+AMD+3kGTIymezv74yPeROMcd2lFMS1piQWeuK5Y0wuN04DG2rRARnYC7dE4NE3ILaw/qCfmBo+WZkIdZIbelmJZCrQVmkOeq8brxjjOCeGz7QW5URURkiGpJDAylOPJwCpiQW1iwqgJVFa6ySsi7wnpCfkq9jxVym4pqScv0j+das6wJh2IJvNwZNjsUIqKS0DMyYYUJ+WSYkFuYiKA96MebvcfMDqVouvrjaKjyYH5tJSvkNhVLJC0zYSXX5Wc1wuNycNoKEZEhlN0UiAn5pJiQW1xbg7+8KuT9GpoDPgT8boRZIbelqGbNhLyqwoV3LQri1zsOIs22FSKikRnkbFmZHBNyi2tr8CMU0ZBIlsf28Z39cbTO8aHO5+GUFZuKWnBRZ9YHz2/FV957Nt7qG0TvwBD7yYmorPVENHicDjT4K8wOpeQxIbe49mAVlALe6otPfmaLS6UzCEU0tM7xIeBzIxIfZsJjM0opxCxaIc9kFBqqKvCVR1/Hpd96Btff/Rz2HB7g7ygRla3uiIb5dd6y2StlNpiQW1z7yOhD+/eRH4wmkM4otMypRMDnQUYBA8ZMcrKHY0MpZBRQY7EpKwDQNziMj923Fd1Gz2R3WMNt925B3yBbq4ioPIXCGttVpogJucWNzCIvgz7yLmPkYcsc30gFlZNW7CWW0N9gWbFCPpxKjyTjWd1hDcOp8mgnIyIaTd8UiAn5VDAhtzh/hQuNNRVlsbAzO4Ncb1nxAOAscruJGs+nFXvIPS4nmkdVgpoDlfC4rLPjKBFRvgyl0jgyMMSRh1PEhNwG2hr8ZdGy0hWOw+UQNNVWIuDXEzZOWrGXWEJPyK1YIa/3e3DPLStHkvLmQCXuuWUl6v0ekyMjIiq+g5EEAI48nCrrNWrSSdqDVfjfHfaffdzZr/eiOR2COqNCzpYVe4lqRoXcggm5wyFY1FiNR+64CH2DQ3irL47aShcXMxFRWeLIw+lhhdwG2hv8CMeTCNt88Vh25CGA4y0rg2xZsZNsQm7FCjmgJ+XB6gr43C7cft9W/O/Ow2aHRERkipGEnBXyKWFCbgPtwfJY2NndH0dzQE/IayvdEAEiGhNyO4lZuEKeq7XehzMaq/DE60zIiag8hcIaRICmWibkU8GE3AbaGqoA2Hv04bGhFPoGh0cq5E6HoMbrZsuKzcS0JESA6grrd9NdtrgRmzv6R6r+RETlJBTRMLe6Ah4XU82p4KNkAy2BSrgcYutJK105E1ayAj43p6zYTCyRQnWFPfquL188F6mMwjN7e80OhYio6HoiGiesTAMTchtwOR1orfeVRULeMuf4i7vW52GF3GaiWtLy7SpZK1oCqPd72LZCRGUpxBnk08KE3CbaG/zY32vfhLxz3Ao5E3I7iWlJyy7oHM3pELzrzLnYtOcIkumM2eEQERVNJqNwMJLghJVpYEJuE+3BKhzoG0Qmo8wOpSC6+uOo9rpOSNYCPg+nrNhM1EYJOaC3rcQSKWzpCJsdChFR0Rw9NoThdIYV8mlgQm4TbQ1+DKcyI2OG7KYrrKEl4IPI8d7iOh8XddpNVEtacpfO8bz99CA8Tgee2MW2FSIqH90ceThtTMhtor1BH31o1z7y3BnkWQGfB4PDaQyn2A5gF7GEvSrk/goXLjy1Hk/uOgyl7PnpFRHRaKEwNwWaLibkNtEWtG9CrpRCV3/8hAWdgN5DDgARjVVyu9AXdVp/5GGuyxfPRUdfHG/aeI0HEVGuHlbIp40JuU0EqypQVeGy5Szy3oEhDKUyJ1XI64zdOiMcfWgLQ6k0EsmMrSrkgD6PHADbVoiobIQiGqq9LlTbqAWx0JiQ24SIoD3ot+VunZ0jIw9HJ+T6Cz08yAq5HcS0FADr79I52vy6SpzVVIMnmZATUZkIhTnycLqYkNtIm01HH46XkAeMCjk3B7KHWEJ/Hu1WIQf0tpWtb4XRzzePRFQGQhENzewfnxYm5DbS3lCFnqiGRDJtdih51dWvQeTkXrRshZyTVuwhu8W83SrkAHD5WY3IKODp3UfMDoWIqOBC3KVz2piQ20hb0A+lgI4+e1XJO/vjmFfjhdftPOE4K+T2MpKQ27Dn8Oz5tZhbXYEnd7NthYjsLZZIYiCRYsvKNDEht5GR0Yc2a1vp6o+jJeA76bjP44TH6eCUFZuIafZtWXE4BJctnotn9x7FUMpen2AREeXiyMOZYUJuI21GQm63hZ1d4fhJ/eOAvpC1zudGhLt12kJspGXFXmMPsy5f3IhjQym8uL/f7FCIiAomO/KQLSvTw4TcRvwVLjTWVNhqYWcimcahWOKkkYdZAZ8HYfaQ20IsYUxZsWHLCgBcdFoDvG4Hp60Qka1ldwxvZkI+LUzIbaa9oQoHjtpnFnkookEpnLQpUFatz8055DYR1ZKocDlOWitgF163Exef1oAndh3hrp1EZFuhsAaP04GGqgqzQ7EUJuQ202azWeRdxsjD8SvkblbIbSKmJW3ZP57r8sWNCEU07Dk8YHYoREQFEYpoaKrzwuEQs0OxFCbkNtPe4EcknrTNZjmTJ+QeTlmxiWgZJOSXnjkXAPDE62xbISJ7CkW4KdBMMCG3mfagvRZ2dvbHUeFyIFg99kdfdT4PIvFhtgDYQFRL2nIGea65NV4sb67FE7s4j5yI7Im7dM4ME3KbaWuoAgDs77VHH3lXv4aWOT6IjP3RV8DnRiqjMDjMUXJWF0vYv0IO6G0rr3ZHcGQgYXYoRER5NZRK48jAEEcezgATcptpCVTC5RAcsFGFfLx2FSBncyCbtOiUs6iWRI3XniMPc122uBGKu3YSkQ0diuqFBo48nD4m5DbjcjrQWu+zxehDpZSxKdD4L+w6n15R5aQV64tpqbKokC9uqsb8Wi/bVojIdrKbAnHk4fQxIbchffSh9RPyqJbEwFBqzE2BsuqyFXJOWrG0TEYhlrB/Dzmgb2h1+VmN+MO+o0gk2WpFRPbRHeEunTPFhNyG2oN+HOgbRDpj7YWOnZNMWAH0HnKACbnVHRtOQSmURYUc0NtWtGQaz7951OxQiIjyJrtL57xar8mRWA8Tchtqa/BjOJUZeWFYVVe/Hv9UKuRsWbG2qPH8lUOFHAAuaJ8Dv8fJthUispVQWMPc6gpUuOy5wVshMSG3ofYGffSh1dtWshXyiRNyVsjtIKoZCbm3PBLyCpcT7zgjiCd3HebITiKyjVBEY7vKDDEht6G27Cxyi48+7OyPo97vQVXF+JM33E4HqitcrJBbXCyhP3/l0rIC6G0rh2ND2BmKmR0KEVFe9EQ0TliZISbkNhSsqkB1hcvyFfLucBzNE1THs+r8bkRYIbe0WLZCXmn/sYdZ71oUhAjwxC7u2klE1pfJKPREEpywMkNMyG1IRNAW9Ft+t87JZpBnBXwehFkht7SYlgJQXhXy+qoKnNcawJO7mZATkfUdPTaE4XSGLSszxITcptob/JaeRZ7OKITCGlrnTP7Crq1khdzqRnrIyyghB/S2lZ2hGA5Grb0Am4goZAySmF/LhHwmmJDbVFtDFXqimmXnHB+MakhlFFoCrJCXg1giCYcAVZ7yaVkBgMsXzwUAPMlpK0RkcSHOIJ8VJuQ21Rb0Qymgo8+aVfKpzCDPCvjcnLJicVFN3xTI4RCzQymq0+ZW4ZR6H55kHzkRWVx2l04m5DPDhNymRkYfWrRtpWsKIw+z6nweDCRSSKUzhQ6LCiSqJctm5GEuEcFlZzbiuTf7EB9OmR0OEdGM9UQ0VFe4yvJveT4wIbepNiMht+rCzq5+DU6HoGkKu31ld+vM9iGT9cS0ZFkt6Mx1+eK5GE5l8Pt93LWTiKyLM8hnhwm5TfkrXJhX47Xsws7O/jgW1FXC5Zz8VzTg13frZB+5dektK+XVP561qm0Oqr0utq0QkaV1hzUs4MjDGWNCbmNtDX7sP2rNzYGmOvIQ0FtWAHDSioXFEqmyrZC7nQ5csmguntp9BJkMd+0kImtihXx2mJDbWFvQb9nNgbrDcbRMYeQhcLxlhRVy6yrXHvKsyxfPxdFjw9jWHTE7FCKiaYslkhhIpLhL5ywwIbex9gY/IvEkwoPWqhwPDqVw9NjwlBZ0AkBdZbZlxVr3k44r5x5yALjkjLlwOoRtK0RkST3ZkYdMyGeMCbmNtQezCzut1bbSFTYmrExhBjkA1Pn1RI4tK9aUSKYxlMqU3aZAuWp9bqxaGMATr3MeORFZD0cezh4Tchtrb6gCAMst7Ozq11/YU+0hr65wweUQtqxYVKxMd+kc7fLFjdhzeGBk5CcRkVWwQj57BUvIReSHInJERHbmHNsoItuMrw4R2WYcX51z/FURud447hWRl4xjr4nIl3Ku68ciciDncisKdV+sqjlQCZdDLDf6cDqbAgH6LOc6n5sVcouKJfSEvJxbVgDgssWNAMC2FSKynO6IBo/TgWBVhdmhWFYhK+Q/BvDu3ANKqbVKqRVKqRUAHgLwsHHSTgArjePvBvA9EXEBGAJwqVJqOYAVAN4tIhfkXOVnstenlNpWwPtiSS6nA631PsttDtTVH0dVhQt1vqknaHU+DyKskFtSdn58jbc8xx5mtTX4cWrQjyd2sW2FiKwlFNbQVOctu92W86lgCblS6lkA/WOdJiIC4CYAG4zzxpVS2W3qvACUcVwppbIN0G7ji3PBpqG9ocp6PeT9cbTM8UH/NZmagM/NRZ0WFdP0l365V8gBvW3lxQN9GEjwzSURWUdPRMP8WrarzIZZPeRvB3BYKbUve0BEzheR1wDsAPDxbIIuIk6jteUIgN8ppV7MuZ6vich2EblLRPg5yRjag3509MWRttB8Y30G+fRe2KyQW1eUPeQjLlvciGRa4dm93LWTiKyDM8hnz6yEfD2M6niWUupFpdQSAKsA3CkiXuN42mhlaQawWkTONi5yJ4AzjfPPAfC3492YiHxMRLaIyJbe3t7835sS1t7gx3AqM7LgotQppdAVjk95wkpWXSUr5FbFHvLjzm2tQ8DnxhPsIyciixhOZXBkYIgLOmep6Am50Rt+A4CNY52ulNoFYBDA2aOORwBsgtGXrpQ6aLS0DAH4EYDV492mUur7SqmVSqmVwWAwL/fDKtoasqMPrdFH3ntsCIlkBq3100vIA34PwvEklLLOJwGki8azPeRMyF1OB961aC6e3nMEqXTG7HCIiCZ1MKpBKU5YmS0zKuSXA9itlOrOHhCRNiNRh4icAmARgA4RCYpInXG8MntZ4+cm418B8F7oC0NplPagPvrwQK81+sizI9+muilQVp3PjeFUBloyXYiwqICiWhKVbic8Lk5hBfS2lUg8iZc7uWsnEZW+UIQzyPOhkGMPNwB4AcAiEekWkY8aJ63DqHYVABcDeNXoFX8EwB1KqaMAmgA8LSLbAWyG3kP+qHGZn4jIDug95w0Avlqo+2JlDVUeVFe4LFMhz448nG7LSsCX3a2TfeRWE0uU9y6do73jjAa4ncK2FSKyhJFNgVghn5WCzRlTSq0f5/itYxy7D8B9YxzfDuCcca7n0lmGWBZEBG1BPw5YJCHPbgrUPM132gHf8d06+UfBWqJaEjWV5T3yMFe1140L2uvxxK7D+NzVi80Oh4hoQtkKeVOd1+RIrI2fEZeB9ga/ZXbr7OyPY16NF163c1qXqzMq5Jy0Yj0xLcUK+SiXnTkX+3sHLfNGmojKV09EQ7C6AhWu6f2/TSdiQl4G2hqq0BPVkLBAf3Vnfxwt0xx5COS2rHDSitVEtSQXdI7CXTuJyCpCEY2fTOcBE/Iy0B70Qymgo6/0q23dxqZA05Xd1ZM95NbDHvKTtczx4cx51fjd60zIiai0hcKcQZ4PTMjLwMjowxJvWxlKpXEwlkDrLBLyyCAr5Faj95AzIR/tssVzseWt8MhYSCKiUpPJKPREE6yQ5wET8jKQTchLvR81FNZnmU53wgoAVLic8HmcrJBbTDqjMJBIMSEfw+WLG5HOKGzae8TsUIiIxnR0cAjDqQwT8jxgQl4G/BUuzKvx4s0Sn0XeZYxOmu6mQFkBnwcR9pBbyrFECgB36RzL8uY6NFRVsG2FiEoWRx7mDxPyMtHWUPqjD7MzyGfSsgLobSsRjRVyK4lq2V06OfZwNIdDcOmZQTyztxdJ7tpJRCWoJ5IAAMxnQj5rTMjLRLsFZpF39cfhcTkQrKqY0eUDPg+nrFhMLKEn5KyQj+3yxY0YSKSw+UC/2aEQEZ0kFNELaVzUOXtMyMtEW4MfkXgS/SW86LGrP46WQCUcDpnR5et8bs4ht5iRCjkT8jFdfHoDPC4Hfsfxh0RUgkJhDdUVLhZV8oAJeZloD2YXdpZuH3lnf3zG7SqAnpCzQm4tMY0V8on4PC5cdGo9ntx1BEops8MhIjpBKKKxXSVP2LhZJtobqgAAb/YO4rxT5pgczdi6+uM475TAjC8f8HkQ1ZJIZxScM6yyU3FFmZBP6qZVLXCI4K2+OPwVLtT7PTP+FImIKJ9CkQTbVfKECXmZaA5Uwu2Uku0jj8aTiCVSs6yQe6CUXnUN+D15jI4KhS0rE8tkFFrn+HD7fVvRHdbQHKjEPbesxKLGaiblRGS6UDiOlbMopNFxbFkpEy6nA61zfDhQopsDZSeszGSXzqzAyG6dbFuxilgiCadD4Pc4zQ6lJPUNDo8k4wDQHdZw271b0FfCa0GIqDwMJPRCGivk+cGEvIy0NVRhf4n2kHeFjYR8BpsCZQV8elWcow+tI6olUeN1QYTV3rEMp9IjyXhWd1jDcCptUkRERDqOPMwvJuRl5NSgHx19caQzpbc47HiFfOYv7DqjQs7NgawjpqXYPz4Bj8uJ5lHVp+ZAJTwufqJARObJZBScDmDjxy7A0gW1yJRgXmE1TMjLSFuDH8OpDHoi2uRnLrLO/jjm+D2o9s48OctWyMODrJBbRVRLsn98AvV+D+65ZeVIUp7tIa/nGgkiMkkmo7Dn8ABu/dFmrP3+H3Hzf72IPYcHmJTPEhd1lpG2Bn304f6jg7Pq1S6E7Azy2ahjD7nlxBJJVsgn4HAIFjVW45E7LkJn/yCSacUFnURkqr7BYdx275aT1rY8csdFCFbPbGM/YoW8rLQH9dGH+3tLr4+8qz8+6zcJNV43HAJuDmQhrJBPzuEQBKsr8JM/duITG14B2+2JyExc21IYTMjLSEOVB9UVrpIbfZjOKIQi2qxGHgJ64lJbyc2BrCSmJVEzizalcrKitQ69A0PoiSbMDoWIyhjXthQGE/IyIiJoD/pLLiE/FEsgmVZ5aaMJ+DyskFuEUoqLOqdheXMdAODVrojJkRBROav3e/Cv687h2pY8Yw95mWlr8GNzR9jsME7Q2adPWJlthRzQ+8gjGivkVpBIZjCczqCmkn+GpmJxUw08Lge2dUVw9dIms8MhojKVVgrfeXIvvnnjcrQYlXHuIDx7rJCXmbaGKoQiGhLJ0un16urPX0Ie8Hk4ZcUiYgn9eWKFfGo8LgeWzK/Btk5WyInIPE/uOoJn9h7F4FAKCwI+BKsrmIznARPyMtMe1CetlFLbSlc4DqdD0FTrnfV11fk8nENuEVFjAyf2kE/d8uY67AhFkUpnzA6FiMrUxs2daKypwDvPCJodiq0wIS8z2dGHpZSQd/bHMb/OC5dz9r+OdT43wuwht4SYxgr5dJ3TWgctmcbew6U3KYmI7O9gVMMze3tx43ktefk/m47jo1lmRmaRl9Dow87+OFoC+ZmLHvC5oSXTJdWSQ2OLMiGfthUt+sLObVzYSUQm+NmWbmQUcNPKFrNDsR0m5GXGX+HCvBov9pdQhbyrf/YjD7PqjN06OWml9I20rDAhn7LWOT4EfG5OWiGiostkFDZu6cLbTq1Ha31pbS5oB0zIy1BbQ+mMPowPp3D02FDedg4NGAk5Z5GXPrasTJ+IYHlLHSvkRFR0z7/Zh+6whrWrWB0vBCbkZag96Mf+3kEopcwOBV39+m5f+UvI9eSOFfLSF9VSAIBqL8ceTseKljrsPTKAY0Mps0MhojKycUsXahAHLZ4AACAASURBVCvduHLJPLNDsSUm5GWorcGPqJYsicWP+Rx5COS2rLBCXupiiST8HifcXBg0Lctb6qAUsKM7anYoRFQmwoPDeHznIVx/zgJ43dyRsxD4P2EZOjVYBaA0FnZ25jkhD/j1CnkpvNmgiUW1JPvHZ2BFMxd2ElFxPfJKCMPpDNtVCogJeRkambRSAn3knf1x+D3OkVaT2WIPuXXEtCT7x2cg4PdgYb0P27pKa8ddIrInpRQ2bu7C8uZaLG6qMTsc25owIReRcR95EWnNfzhUDM2BSridUhILO7vDcbTM8UEkP7t8ed1OVLgcbFmxAFbIZ255Sx1e7WLLChEV3qvdUew5PIC1q5j2FdJkFfJN2W9E5MlRp/0879FQUbicDrTO8ZVMy0q+2lWyAj4PW1YsIKoluUvnDK1oqcOhWAKHogmzQyEim9u4uROVbieuXd5kdii2NllCnlu2nDPBaWQxbQ1VplfIlVLo6tfyNmElq87nZoXcAgYSKbaszNDxDYLYtkJEhTM4lMIvt/XgmmVNqGYBpaAmS8jVON+P9TNZyKlBPzr64khnzHsajx4bhpZMF6RCzrGHpU9vWeHIw5lY3FQDt1OwjW0rRFRAj+04iMHhNNZxMWfBTfa/4VwR+RT0anj2exg/BwsaGRVUW4Mfw6kMeiL5r1BPVb4nrGQF/G7sOTSQ1+uk/EqlMzg2xAr5THndTpzVVMMKOREV1MbNXWgP+nHeKQGzQ7G9ySrk9wCoBlCV83325x8UNjQqpHZj9OGbJvaRZ2eQt8ypzOv11rFCXvIGEvqmNuwhn7kVLXXY0R019VMuIrKvN44MYOtbYaxb1ZK3wQs0vgkr5EqpL413moisyn84VCzZ0YcHjg7ikkXmxJBNyJsD+W5ZcSOiJaGU4h+REhVL6G+YWCGfueUtdfjvF97CG0eOYdG8arPDISKb2bi5Cy6H4IZzm80OpSxMaw65iJwlIl8WkX0A/qNAMVERNFR5UF3hMnVhZ2d/HI01FXnf9auu0oN0RiGW4NbipSqqMSGfLS7sJKJCGU5l8NDLIVxxViMaqirMDqcsTJqQi8gpIvJ/ReRVAPcBuAPAFUqplQWPjgpGRNAe9GN/r7kJeUueq+OAPmUFACetlLCYZrSsMCGfsbYGP2q8Lu7YSUR598Suw+gfHMZNXMxZNJNtDPQ8gF8DcAN4v1LqPAADSqmOIsRGBdbW4De1Qt4d1vK+oBPI3a2TfeSlihXy2RMRLG+p46QVIsq7jZu70FTrxTtO5/yOYpmsQt4LfRFnI45PVeEKIptoD1YhFNGgDaeLftvDqQx6ooWZ8BLws0Je6rIJOccezs45LXXYcyiG+DDbs4goP0IRDc/u68WNK1vgdHAdVrFMmJArpd4DYCmAlwF8SUQOAAiIyOpiBEeFlV3Y2dFX/Cp5KKJBKRQkIa8zKuSctFK6uKgzP1a01iGjgB3drJITUX48uKULAHDjeVzMWUyT9pArpaJKqR8qpa4AcAGALwD4toh0FTw6Kqj24PFJK8XWVaAZ5EBuywor5KUqqiXhcggq87ygt9wsb9YXdr7azT5yIpq9dEbhwS3duPi0BtP2KClX05qyopQ6rJT6jlLqbQAuLlBMVCQL6/WEfL8Js8gLtSkQcLzqyh7y0hXTkqitdHMs5SzVV1WgZU4lF3YSUV4898ZRhCIa1nIxZ9FN2MApIr+c5PLX5TEWKjJ/hQvzarzYb0aFPByHx+XA3Or8j1NyOgQ1Xhd7yEtYVEtywkqerGgJYGtHv9lhEJENbNzchYDPjSvOajQ7lLIz2YqqCwF0AdgA4EUALGfZjFmjD7v642gOVMJRoAUjAb+HFfISFkukmJDnyfLmWvzq1R4ciSUwt8ZrdjhEZFF9x4bw29cP4eYLFqLCxXbCYpusZWUegM8BOBvAvwK4AsBRpdQzSqlnCh0cFV5bgx/7e49BqeIOz+nsjxekXSWrzudhhbyERY2WFZq9c1qzGwSxbYWIZu6RV0JIphXbVUwy2ZSVtFLqN0qpD0Nf0PkGgE0i8omiREcF1x6sQiyRQv9gcZPXrn6tIJsCZQV8bi7qLGExLYkaL0ce5sOS+bVwOYQJORHNmFIKGzd34ZzWOiyaV212OGVpKjt1VojIDQDuB/AXAL4D4OFCB0bF0d5Q/Ekr0XgSUS1Z0Ap5wOfh2MMSFmOFPG+8bifObKrmpBUimrGXOyPYd+QY1rE6bprJdur8bwDPAzgXwJeUUquUUl9RSoWKEh0VXHYWeTEXdnaF9QkrhRypVOdzMyEvUUopLurMsxUtddjeFUUmw33biGj6frq5Cz6PE9csm292KGVrsgr5zQDOAPBJAM+LSMz4GhCRWOHDo0JrDlT+f/buPL6t8swb/u/WvlqSZVvxlsQ7SQwx4ASSQCG0FLpNS2lZ2hJoIdDyTPftmeedzsz7zNuZYTrdt4G0UKAdoFum+7QsoZSQBJKShMSOLTmbHTuSV+2ylnO/f0hHcRI78aJzjqRzfT+ffJrKsnQ72Nal61wL9Foma2OnOIO8sdIs2XO4LAZEptNIpgXJnoMsTjyVQVrglCEvoK5GF8LTaQwoMMKUEFLaItNp/ObgMN51WR1sRiolVMrFasg1nHN77k/FjD92znmFXIck0tFpNVheacGxMfleyE9OyJMhB4CpONWRF5tgPHvlosJEAXmhdDU6AFBjJyFk4X57YBixZAa3r6dyFSUtaDEQKU/N1TZZM+QnJ2JwWfSSBmTO3LZOKlspPqF4GgAoQ15AzVU22E06CsgJIQv2zN5BtNXYcHmjU+mjqBoF5ATNVVacGI8hI1P96eBkXPKVvK5chnxS5ukx5OLEDDkF5IWj0TCsbXBSQE4IWZC+02G8fnIKt69rpM3JCpM0IGeMPcoYCzDGDs247RnG2P7cn+OMsf2529fPuP0AY+yW3O0mxtirudsOM8b+3xmP1cQY28MY8+Ye1yDl11OumqqsSGYEnJqMy/J8gxMxGQLy7LcCLQcqPvmSFTPVKhbS2kYHjpwOI5HKKH0UQkiJeOa1Qei1DO+9okHpo6ie1BnyHwG4eeYNnPPbOeddnPMuAL/AmRGKhwB0526/GcDDjDEdgGkAN3DO1wLoAnAzY+zq3Oc8BODrnPM2AJMA7pX46ylLzdU2AMBRGerIMwLH0KS0S4GAMzXkQaohLzohypBLoqvRhYzAcehUUOmjEEJKwHQ6g1++PoS3rlmGSivlM5UmaUDOOX8JwMRsH2PZayO3AXgqd98Y5zyd+7AJAM/dzjnnYqSoz/3huc+/AcDPcx97HMB7pPg6yl2TjLPI/aEEUhku6VIggDLkxYyaOqWxlho7CSEL8GyPH1OxFG7vpmbOYqBkDfm1APycc694A2PsKsbYYQBvAPioGKAzxrS50pYAgGc553sAuAFMzQjihwDUy/oVlIkqmwF2k06Wxk5xworUGXKLQQuDVkPbOotQKJENyO20qbOgauwm1DvNFJATQublmdcGUe8045rWKqWPQqBsQH4nctlxEed8D+d8DYB1AP6OMWbK3Z7JlbI0AFjPGOsEMFv3waxdiYyx+xljexlje0dHRwv6RZQDxhiaq6yyZMhPyjCDHMh+TQ6LHlNRypAXm2A8BZtRB52WesoLrauRGjsJIRc3OBHDX7xjuK27ERoNNXMWA0VeEXO14e8F8MxsH+ec9wKIAug85/YpAC8iW2M+BsCZeywgG6wPz/F4j3DOuznn3dXV1QX5GspNdvSh9DXkQxMxaBhQ55Q2IAeyk1YoQ158QvE01Y9LpKvRiaHJOMYi00ofhRBSxH62bwiMAe/rpmbOYqFUiuotAI5wzofEG3ITU3S5v68A0AHgOGOsmjHmzN1unvG5HMAOAO/LPcTdAH4l49dQVpqqrBgOJhBPSjuh4eREDHVOM/QyZEedFgPNIS9CwXgKFRSQS2Jtbo7w/pOUJSeEzC4jcPxs7yDe1FaNehmSY2R+pB57+BSAXQA6GGNDjDFxCsodOKdcBcA1AA7kasW3A3iQcz4GoBbADsbYQQCvIVtD/tvc53wRwGcYYz5ka8p/KOXXU87Exs7j49KWrZyciEne0CmiDHlxCsVTqKD6cUlcWu+AVsNwYIgCckLI7F7yjmIkmMAd66iZs5hI+qrIOb9zjtvvmeW2JwE8OcvtBwFcPsfjHAWwfmmnJADQXJ0NyI+ORrGqtkKy5xmcjOOGjhrJHn8ml8WAqTgFJsUmlEhJ3tSrVmaDFh0eO9WRX4AgcIxHk0imMzDotHBbDVRDS1Tlp68Nwm014M2rPEofhcxAaSoCYOboQ+nqyOPJDEbD01julicYy5asJME5pw1kRYRKVqS1ttGJ3x4chiBwCjTPIQgcff4wtj6xF0OTcTS4zNi2pRsdHjv9WxFVGItM49kePz68aSUMOmqsLyb0X4MAACwGHWodJklHHw5OZiesNLjkqVlzWfRIZTiiEtfFk4UJxVPU1CmhyxudCCfSOCZx+VkpGo8m88E4AAxNxrH1ib0Yj1JpG1GHX/51CGmB43YqVyk6lCEnALKZo6++fy1MBi1Gw9OSXMYdlGkGuSi/HCiahM1I3+rFIJUREE1maCmQhLqWn2nsbMlt4SVZyXQmH4yLhibjSKbpTTspf5xzPP3aILpXuNBaY1f6OOQclCEn+cu4X/jFQbz3e6/glu/tRJ8/DEGYdaz7osm1FEjksGSDPpq0UjzCieweL4eZ3iBJpaXaBqtBS3XkszDotOddoWtwmWHQaRU6ESHy2XdiEkdHo5QdL1IUkBPZLuOenIjBYtCi0moo6OPOJZ8hp0krRSMYz745Et8skcLTahgua3DSpJVZuK0GbNvSnQ/KxRpyt0y/kwhRgiBwjIanYdRrsW3LlXjHpbVKH4nMgtJURLbLuIMTcSyvtMjWYOnKBX0UkBcPMSCnkhVpdS134gd/OYpEKgOTnrK/Io2GodFlxpfeuRpOsx7TaYEaOklZo0bm0kEZcjLnZdxMgUtWBidiaJRx3J0zlyEXg0CivJCYIaemTkmtbXAileE4PBxS+ihFxzcaxQNP7sO//uEItjz6KiboDTspY9TIXDooICezXsb9yvsuw9//9yH0nQ4X5Dk457IuBQIAp5ghj1JAXizyGXIKyCV1ea6x8wDVkZ/H68/+TtuyYQUAYPfRcSWPQ4ikqJG5dFBATqDRMHR47Nj+4Cbs/OJmbH9wExorLejzh/GBbbvzL2BLMR5NIp7KYHmlfGt69VoN7EYdlawUkVCCMuRy8FSYUOswUWPnLHyjERi0Grz90lrYjDq8MkABOSlfBp2GGplLBAXkBEA2KK+2G1HvsqDabkSDy4L/2no1NBqGO7ftgS+wtIVB+QkrMi0FEjmtekxRQF40qIZcPmsbnBSQz2IgEMHKKgtMei2uaqrELgrISRk7MDiFh269jBqZSwAF5GROLdU2PLX1KgDAB7btxtHRxQfl4gxyOUtWAMBpNmCSxh4WjVA8DYNWA5OefvVIrWu5EycnYpigWtGz+AIRtOVmMG9ocePYWBQjwfhFPouQ0jM4EcOnnjmA3x8cxi8f3Ji/Ak4NncWJXhXJBbXW2PFfW69CRuC4c9tuHB9b3PY/MSBvkDsgt1CGvJgE4ylUmPWyTdpRs65GqiM/VyKVwcmJGFpqsguTNrS4AYCy5KTsCALH5352AADwsc2tqLGb8lfAKRgvThSQk4tq99jxk61XIZkWcOe23Tg5HlvwY5yciKHGboTZIG/dmstCGfJiEoqnUEFLgWRxab0DGga8TgF53rGxKAQOtOYC8lXLKuC06KmOnJSdH758DHuOTeAf37Va9kQYWRwKyMm8XLKsAj+572rEUxncuW13PuM9X4MTcVlHHopclCEvKqFEiho6ZWI16tDusVOGfAaxF6YtF5BrNAwbmt3YNTAOzgs75pUQpfSdDuMrf+zDW1d78L4rG5Q+DpknCsjJvK2uq8CP770K4UQKd27bjaHJ+QflJydiWK5AQO60GBBKpJHOCLI/NzlfMJ6ihk4ZdTVmN3ZSsJnlDUSgYUBTlTV/28YWN05NxfON54SUsmRawKef2Y8Ksw7/8t5LqTywhFBAThaks96BH993FYLxFD6wbQ+Gpy7eDJXKCBgJKpchB2g5ULEIxSlDLqe1jU5MxVI4vogys3I0EIigsdJy1vbSDS1VAEBlK6QsfPP5fvSMhPCv770MVTaj0schC0ABOVmwyxqcePLeqzAZTeLObbtxOpi44P2Hp+IQONDokm8GuciVG+1EdeTFIUg15LKixs6z+QIRtFbbzrqtpdqKGruRAnJS8vadmMD3XxzAbd0NuHG1R+njkAWigJwsSlejE4/fux7jkWxQHgjNHZTnZ5ArkCEXs7FUR648zjlCiTRlyGXU7rHDYtDSPHIA6YyAY2NRtHrODsgZY9jY4saugTEq7SElKzqdxmd+egB1TjO+9M7VSh+HLAIF5GTRrljuwo8+vA7+UCIblIdnD8qVWgoEZKesAJQhLwbRZAYZgVNALiOthqGz3kGTVpD9PZTMCOdlyAFgY0sVxiJJeJe4AI0QpXz59704ORHD127rgp36dEoSBeRkSbpXVuKxe9ZheCqBD27bg7HI9Hn3GZyIw6DVwGM3yX6+MwE5ZciVRls6lXF5oxO9wyFMpzNKH0VR4oQVceThTOI88ld8Y7KeiZBC2HEkgP/acxL3X9uM9U2VSh+HLBIF5GTJrmp249F71mFwMoYPbtuD8XOC8sGJGBpcZkWWETituaZOypArLpQLyClDLq+uRieSGQG9I2Glj6IoX27TcMssAXljpQWNlWbsOkp15KS0TEST+MIvDqLDY8enb2xX+jhkCSggJwWxocWNH969DsfHo/jgD/Zgcsa67pMTMUUmrACA3aiDTsMoQ14E8hlyCshltTbX2Ln/5KTCJ1GWLxDBsgrTnFdoNjZXYffRCWQEqiMnpYFzjr//7zcwFUvi67d3nTU9iJQeCshJwWxqrcK2Ld04OpYNysVGysHJGBor5Z+wAmQbtpwWPdWQFwHKkCuj1mFCjd2IA0NBpY+iKF8gMmu5imhDixvBeAq9IyEZT0XI4v1q/zB+/8ZpfPrGdqyuq1D6OGSJKCAnBfWm9mo8cteV8AUi+L+/PYyhyRge/tCVuGPdcggKZZ6cFgNNWSkCVEOuDMYYuhqdqp60wjnHwDwCcgB4ZYDqyEnxG56K40u/OoTuFS488KYWpY9DCoACclJw13fU4Mf3rcetVzTijkd24/ZHduOjP96HPn9YkaDcadZTyUoRCCXSAChDroSu5U4cG4uq9o3pSDCBaDIza/24yFNhQku1leaRk6InCByf//kBZASOr962FloF+rNI4VFATiTRVGXDF39xEEOT2U2eQ5NxbH1iL8aj8gcE2Qw5lawoLRhPgTHAbqLFQHLrasgtCFJp2Yo4YaXtAgE5kB1/+OqxCaQyghzHImRRHt91HDt94/jSO1djhduq9HFIgVBATiSRTGfywbhoaDKOpAKj11wWypAXg1A8BZtRp8i0HbW7tMEBxoD9J9VZtuK9wMjDmTa2uBFLZnBwSJ3/TqT4+QJh/NsfjuCGS2pwx7pGpY9DCogCciIJg06LBtfZjZwNLjMMOvm7wF1WypAXg1A8ReUqCrGb9GirsWH/oDonrfgCETgterithgve7+pmcR45la2Q4pPKCPj0MwdgMWjxb7deCsYouVFOKCAnknBbDdi2pTsflDe4zNi2pfuiL4hScFr0mE4LiCfVvRhFacF4iho6FbS2wYkDQ0FVrocfCETQWm27aADjshqwuraC6shJUfrOCz68cSqIf7nlUtQosGiPSIuKOYkkNBqGDo8d2x/chGQ6A4NOC7fVoEi5wsxtnWaDMuMXCRBKUIZcSV3LnfjZviEMTsSx3K3MXgCl+EYjuGmNZ1733djixhO7TyCRytBcZ1I09g9O4Ts7fHjv5fV426W1Sh+HSIAy5EQyGg1Dtd2IepcF1XajYrXDLks2CKQ6cmUF4ylUmCkHoJSu3IKg11VWtjIemcZENImW6gvXj4s2trqRTAv46wl1/TuR4hVPZvCZZ/bDYzfin969RunjEIlQQE7KnsOczZBTHbmyQvE0ZcgV1OGxw6TX4MCguiat+ObZ0Clat7ISWg3DrqNUtkKKw7/9oRdHx6L4j/evpbK/MkYBOSl7LitlyItBkJo6FaXTanBpvUN1jZ2+0YUF5HaTHpc1OKiOnBSFv3hH8fiuE/jIpiZsbK1S+jhEQhSQk7J3poacMuRKSaYFxFMZyu4orKvRiUPDISTT6pmz7QtEYDFoUeeYf//IhmY3DgxOITKdlvBkhFxYMJbC5392EK01Nnzh5g6lj0MkRgE5KXvOXA15kDLkigklsm+GHBYKyJW0ttGJZFpA3+mw0keRjS8QQUu1bUE9LBtbqpAWOF47PiHhyQi5sC/96hDGItP4+m1d1GCsAhSQk7Jn1GlhMWgpQ66gYDz7b08ZcmWJjZ1qKlvxBSLzLlcRXbnCBYNWg11UtkJkJggco+FpeP1hvOOyWnz5PZ24tMGh9LGIDGjkAVEFl8Ugaw25IHCMR5OKj3wsFqFcQE415Mqqd5pRZTPi9cEp3LVB6dNILzKdxkgwseCA3GzQ4vLlTrwyMCbRyQg5nyBw9PnD2PrEXgxNxrP7O+7qhiBwVb9+qAVlyIkqOC162aasiL9Ub/neTmx6aAdu+d5O9PnDEAT1LWQR5TPkNPZQUYwxdDU6cGBQHavhBxY4YWWmjS1VODwcwhSVuhGZjEeT+WAcAIYm49j65F6MR+l7UA0oICeq4LToZcuQz/pL9Ql1/1INJbLNcZQhV15XoxMDo9H8m6Ry5l1KQN7qBufA7qNUR07kkUxn8q8boqHJOJJp2jKtBhSQE1VwWgyyZcjpl+r5zmTIKSBXWlejCwBwcKj8s+S+QAR6LcOKyoVvJl3b4IRZr8UuKlshMjHotGhwnT0NqMFlhkFHDZ1qQAE5UQWXjBnyuX6p6rXq/XELUVNn0RAbxNRQtuILRLDSbYVuET97Bp0G65oqaR45kY3basDDd12Zf/1ocJmxbUs33FaDwicjclBvhEBUxWUxIBhPyVLH7bYasG1L91m/VB+69TI8tvMYplWaJQ/FUzDqNDS6qwg4zHq0VFuxXwUB+cBoBG2ehZeriDa2uOENRDAani7gqQiZnUbDkEwL+NI7V+OFz16H7Q9uQofHTg2dKkEBOVEFp8UAzs/Mw5aSRsOwstKCL71zNf7wiWux/cFNGI9M4/t/Por7Ht+LWFJ9y0aC8RSVqxSRrkYX9g9OgfPybTROpDI4MR5Fa/XSAnIA2HWUsuREHq+fnMIDT+6D3aRHtd1IwbiKUEBOVMGVW0gj1yzyvkAEDzy5DycmYqi2G/E3XfX491svw07fGLb88FVVNNTNFEqkqKGziHQ1OjAWSeLUVPzidy5Rx8ejEDjQsoiGTtGaOgfsJh3VkRPZeAMROC16VNmoTEVtKCAnquCyZH+5yVVH3jMcAgCsqavI33bbukZ85wNX4MDQFO58ZDfGIuq5DB6Mp1BhopGHxUJs7CznshVfbsJKW4190Y+h1TBc1eSmOnIiG68/jPYaOxijzLjaUEBOVMGZy5DLNVO4ZyQIu1F3XnPn2y+txbYt3Tg6FsFtD+/CSLB8M5QzheJpypAXkY5lNjxy15VoclsxGp4uyxn5vkAEjAHN1dYlPc7GFjdOjMcwNBkr0MkImR3nHN5ABK1L6HsgpYsCcqIKTjFDHpWnVKRnOIRVdRWzZjmu76jBEx+5CqOhabzv+7twfCwqy5mUFIxTyUqxEASOgdEo/u9ve/COb79ctourvIEIGl2WJTcSb2zN1ZFTlpxIbDQyjWA8hfYllFmR0kUBOVGFMzXk0mfIMwLHkdNhrK6tmPM+65sq8dT9VyOeyuD9D+/CkdMhyc+lJGrqLB5qWVw1EIgsaiHQudpr7HBbDRSQE8l5/bkyK8/iy6xI6aKAnKhChUkPDYMsy4FOjEcRS2awum7ugBwAOusd+OkDV0PDgNsf3o3XT05KfjYlCAJHmJo6i4YaFldlBI6jY1G0FSAg12gYrm7J1pGX81QaojyvPwwABfm+JaWHAnKiChoNg8Osx1Rc+ixgz0g2232hDLmotcaOn390IxxmPT74gz14pQynOUSSaQiclgIVCzVsAxyciCGZFpY0YWWmjS1unA4lcEwF5WVEOf2BCBzm7LhDoj4UkBPVcFkMsow97BkOQadh815I0lhpwc8/ugENLjPueew1PNfjl/iE8hK3dFKGvDjMtriq3LYBenMTVgpRsgIAG1uqAICmrRBJ+fwRtNXYaMKKSlFATlTDadHLMmWlZySE1hobjAvIONZUmPDM/RuwapkdD/x4H361/5SEJ5SXOHO9wkxjD4uBRsPQ4bFj+4Ob8POPbsDXbusqu22AvgIH5CvdFtQ6TLQgiEiGc47+QJjqx1WMAnKiGi6LQZYpKz3DoYvWj8/GZTXgJ1uvRvcKFz71zH78ZM8JCU4nv1A8u5mUmjqLh0bDUG034rs7fPiHXx0qq2AcyAbkngpjwcqkGGPY0OLG7oHxsptGQ4rDWCSJqViK6sdVjAJyohoOGTLko+FpBMLT86ofn43NqMPjH1mPzR01+H+2H8J//nmgwCeUX5BKVopWu8eOo6NRpDOC0kcpKN9oYSaszLSxpQrj0ST6A+GCPi6RhyBwjIancWoyVpSz98WGznbKkKsWBeRENeSoIe8VGzoXkSEXmfRaPHzXlXjX2jr82x+O4N//50hJT3cQa8ipqbP4tHnsSGYEnJgon6U3nPPsyMPqwgbkG1qy88hf8VHZSqkRBI4+fxi3fG8nNj20oyhn74t9D/PtPSLlhwJyohouix7xVAaJlHTj3RYyYeVC9FoNvnF7F+5c34jvvTiACKOvEwAAIABJREFUf/z14aJ68ViIUCKXIbdQQF5s2nMv/mJ2rhycDiUQmU4XPENe7zRjhdtCjZ0lqBRm7/f7w6gw6VBDE1ZUS7KAnDH2KGMswBg7NOO2Zxhj+3N/jjPG9uduXz/j9gOMsVtytzcyxnYwxnoZY4cZY5+c8Vj/xBg7NePz3i7V10LKg7itUyyhkELPcAj1TnP+uZZCq2H4l1suxf1vasYTu07gW8/3IxBKFO0l17kE4ykwBtgM1NRZbMSgtT+3kKQcnGnoLPyl/40tbuw5Ol52JT7lrhRm73sDEbR57DRhRcWkfIX8EYDvAHhCvIFzfrv4d8bYVwEEc//3EIBuznmaMVYL4ABj7DcA0gA+yzn/K2PMDmAfY+xZznlP7vO+zjn/Dwm/BlJGXLkgeTKWhKfCJMlz9I6EsKq2cIEAYwx/97ZL0FptRb3Lgvd+/xUMTcbzo+pKYTpGKJ7KLmYq8nOqkcWgQ2OlGf1llCEv9ISVmTa0VOGpVwdxeDiEtY3Ogj/+TILAMR5NIpnOwKDTwm010M/QIomz92cG5cU0e59zDq8/jJs7lyl9FKIgyTLknPOXAEzM9jGWfQt4G4CncveNcc7TuQ+bAPDc7SOc87/m/h4G0AugXqozk/LmypVMSDVpJZHKYGA0suRylXMxxrD5Eg+++IuDRX3JdS7BeIpGHhax9hp7fmV3OfDmlqtU2Qo/V31Dc66OXOKylVKoeS4lbqsB3//gFUU7e388msRkLCXJVR1SOpSqIb8WgJ9z7hVvYIxdxRg7DOANAB+dEaCLH18J4HIAe2bc/LeMsYO58hiX9McmpUwsI5Fq0krf6TAEvrSGzrmUwiXXuYQSaZqwUsRaPTYcHYuUTRmGL5CdsCLFpf9quxHtHpvkG3VLoea5lGg0DPsHJ/Gld67GM/dfje/ceXlRXV3sz09YoYZONVMqIL8Tuey4iHO+h3O+BsA6AH/HGMvXFDDGbAB+AeBTnPNQ7ubvA2gB0AVgBMBX53oyxtj9jLG9jLG9o6Ojhf1KSMlwihlyiSatnGnodBT8sUt53XkwnqKAvIi119iRynAcHy+PSSsDgYiks5w3tlRh7/FJJNPSvYEp5Tfgxeq/Xh3CD18+hi//vhdf+VNf0QTjwJkyqzbKkKua7AE5Y0wH4L0Anpnt45zzXgBRAJ25++uRDcZ/wjn/5Yz7+TnnGc65AGAbgPVzPSfn/BHOeTfnvLu6urpwXwwpKTNryKXQMxyC3ag7L3AuhNnWnT9815VFc8n1QoK5GnJSnMS5x+UwaWUimsR4NClJ/bhoQ4sb8VQGB4amJHsOrVZTsm/Ai9HpYAK9IyFs7qjBmjoHDp0KFdUo2X5/GHaTDp4KmrCiZkpkyN8C4AjnfEi8gTHWlAvUwRhbAaADwPFcrfkPAfRyzr8280FyzZ+iW5BtDCVkTmaDFkadRrKSlZ6REFbVVkiSeZm57vzPn78eX3rnavQMh4oqyzOXEGXIi1q2vKM8Jq2ImcYWCQPyq5vcYEy6eeTpjIBtfx7AQ7dedtYb8P/8UGm8AS9GL/YFAAA3XFKDzvoKBOOp865AKMnrz17VoQkr6ibl2MOnAOwC0MEYG2KM3Zv70B04p1wFwDXITlbZD2A7gAc552MANgG4C8ANs4w3/HfG2BuMsYMANgP4tFRfCykfLosBUxKUrAgCR+9ISJL6cZG47nyF24pHXz6Gb73gLYkmr2xTJwXkxcps0KLRZSmLDZT5CSsFXgo0k8OiR2edQ5I6cs45/s/2N/DDnccRSqSw/cFNeOkL1+Mf3rkaz/WeLok34MXohSMB1DlMaPfY0FmXLSk8PBy8yGfJxxeI0IZOIt3YQ875nXPcfs8stz0J4MlZbn8ZwKy/gTjndy3xiESFnBa9JDXkJyZiiCUzBZ+wMpcPXLUcn3x6P14ZGMc1bVWyPOdiJFIZTKcFypAXuXaPrSxKVnyBCMx6LeqdhS8bm2ljixuP7TyOeDIDs6FwZSRff7YfP907hE/c0Iq3dYoXgY14/JUTeGznMby7qwFNVdaCPZ8aTKcz2Okbw7svrwdjDB3L7NBqGA6dCuHmztqLP4DExiPTkpdZkdJAmzqJqmQz5IUvWekZzjV0Spghn+mmNcvgtOjx1KsnZXm+xRK3dFaYaOxhMWvz2HFsLIpUiU9a8QbCaKmxSp5J3tDiRjIjYN+JyYI95k/2nMC3XvDhtu4GfPrG9rM+9sB1zTDoNPj28945PpvMZe/xSUSTGdzQUQMAMOm1aKux4VCRZMi9YkMnZchVjwJyoiouq16Sps6ekSB0GiZblsOk1+K9lzfgTz2nMR6ZluU5FyMUz04vpZKV4tbusWUnrYxFlT7KkgwEIpKWq4jWrayETsMKVrbyp8On8aX/PoTNHdX48i2XnldLXGM34UNXrcB/7z+Fo6OlX+svpxeOBGDQabCx1Z2/rbPegUOngkXR2OmlkYckhwJyoioOszQ15D3DIbTW2GDSyzcF4c71jUhlOH7x16GL31khwXj235pKVoqbOG5NzNaVouh0GsPBhCxviq1GHdY2OguyIGjfiQl8/KnXcWmDE9/94BXQa2d/WX7gupZslvwF35KfU0129AVwdbMbFsOZq3Rr6iowFkkiEFY+meENRGA36rBMou3RpHRQQE5UxWXRYyqeKnhmpGckJFv9uKjNY0f3CheefnWwKDI9swnlAnLKkBe3lmpx0krp1pEP5DLHcm073NjixsGhqXxZ1mL4AhHc+/he1DpMePTu7rOCxnNV24246+oV+NX+U/mvlVzYifEojo5Gsbnj7HHHnfXZxs5Dp5QvW+n3h9HqoQkrhAJyojIuiwEZgSOUSF/8zvM0FpmGPzQtW/34THesX46jY1HsOTYh+3PPhxisUIa8uJkNWiyvtMBbwqMPxbPLVTa2ocUNgQOvLfJnzx9K4O5HX4VOw/DER66C23bxGdT5LDnVks/LjiPZcYebc/XjolW1FWAMODwcmu3TZOWTeJEVKR0UkBNVEbd1BgtYttKb39Apf0D+jktrYTfpira5UyxZocVAxa+txl7SGXLfaAQ6DcMKt0WW57tiuQsGnWZRZSuhRAr3PPYapmJJPHbPeiyf55mrbEZs2bASvz4wnB/xSOa2o28UzVVWrDxnMo3NqENTlVXxDPlENImxSJJGHhIAFJATlZFiW6c4YWWVAgG52aDFLZfX4w+HTmMyKs3Co6U4U7JCU1aKXbvHhmNjUUlXwkvJF4hgZZV1zhrsQjPptehe4cKuBQbkybSAjz65D15/GN//0JW4tMGxoM+//03NMOq0+PYLlCW/kFgyjV1Hx3H9OdlxUWedQ/EMudjQSSMPCUABOVEZlzWbqS1oQD4SQp3DBJdCW/TuWLccybSAX75+SpHnv5BgPAWTXgMjrfwueu0eO9ICx/Hx0py0MqDApf+NLW70jITm/WZYEDg+97MDeGVgHA/dehne1F598U86R5XNiC0bV1CW/CJ2DYwjmRaw+ZLZ/4076ytwaiqOCQUTGf25/36UIScABeREZZy5DHkhJ630DEu7ofNiVtdVYG2jE0+/erLomjtD8TTVj5eIttzYtVIsW5lOZ3B8PCp7pnFDS3Yp1+6j88uS/+sfevHrA8P4ws0duPXKhkU/7/3XNsOs1+JbVEs+px19AVgMWqxvqpz148WwsdPnD8Nm1KHWQRNWCAXkRGWc5sJmyBOpDAZGI4rUj89057pGeAORgi4qKYRgPEUBeYloqbZBw4D+EmzsPD4Wg8Dlv/R/WYMDVoN2XnXkP/jLUWz7yzHcvWEFPnZdy5Ke152rJf/NwWH4AqX3BkpqnHPsODKKTa1Vc16dW1MnTlpRrmyl3x9Baw1NWCFZFJATVXHkA/LCZMj7TochcPk2dM7lXWvrYDVo8dSrg4qe41zBeIoaOkuESZ+dtFKKAZ5YuiF3QK7XarC+qfKiC4J+c2AY/9/vevG2zmX4h3etKUgAdv+bslnybz5Pc8nP5Q1EcGoqft50lZkcFj0aK82Kbuz00oQVMgMF5ERVdFoNKkw6TBUoQ96Tn7CysMasQrMadfibrnr87o3h/GSTYhBKUIa8lLR57CWZIfcFImAsm+WX28aWKgyMRuEPJWb9+CsDY/jsTw9g/cpKfP32Lmg1hcmGVloNuHvjSvz24HC+OZBk5ccdzlE/Luqsc+CwQpNWJqNJjEWmqX6c5FFATlTHZS3cts6e4RDsRh0aXOaCPN5SfGD9ciRSAn61v3iaO4PxFC0FKiHtHhuOl+CkFW8gjAaXWdZNuaINLdmV7LNNW+kdCeGBJ/ZhhduCbVu6C36+rdc2w6LX4ptUS36WF44EcMkyO2odF/69vKauAsfHY0ta7rRY4lbcVg9lyEkWBeREdZwWQ8FqyHtGQlhVWwFNgbJeS3FpgwNr6irwX3uKp7kzRDXkJUWctHJsrLQmrfgCEbQqkB0HsuNOHWb9eWUrp6biuOexV2E16vD4R9bDYSn8z4GYJf/dGyMl2YwrhVAihb0nJnHDJXOXq4jW5DZ29iow/lD870UZciKigJyojsuiL0iGXBA4ekeUnbByrjvXL8eR02EcGFJ+JbQgcISn06gw0QzyUtGWWztfSsFdRuA4OhZFm0KBjVbDcHVz5VmNnVOxJO5+9FXEkhn86CPrUOeU7goaZcnP9rJ3DBmBY/M8AnJx0sohBQJyXyACq0GLOpqwQnIoICeq4ypQhvzERAyxZEbxCSszvburDma9Fk8XwebO8HQanINKVkpIc7UVGoaSqkkemowhmRYUy5AD2Tryock4BidiSKQyuO/xvTg5HsMjd3XjkmXS/n5wWQ24Z9NK/P6NEfSdLp3/blJ54UgADrMelzc6L3rfarsRngqjInXk3kCYJqyQs1BATlTHWaAMubihs5gy5HaTHu9aW4tfHxhGZDqt6FnELZ1UslI6THotVrqtJdXY6c2dtUXBaRXXd1Tj4buuRCyZwf7BKWQEjq/f3pWvL5fafdc0w2rQqX4uuSBwvNg3ije1V0M3z42tnXUORSat9Psjil3VIcWJAnKiOk6zAZHp9JIb13pGgtBpWNGtPb5j/XLEkhn8ev+woucQp71Qhry0tNbY0F9Cow99o8qMPBQJAkcsmcE//7YHN33jJXzuZwfwD+9ajbd1LpPtDC6rAffkasnVnCU/PBzCWGQamzvmvwF1Tb0DvkAE8WRGwpOdbSqWxGh4mkYekrNQQE5Ux2XNBohT8aWVrfQMh9BaY1NkssOFXN7oxCXL7Hj6NWXLVihDXpraPXacGI9hOi1fgLIUvkAENXajYt9n49Ektj6xF0OTcQDA0GQcH3/qdYzLvJL9vmubYDPq8M3n+2V93mLywpEAGAOua59/QN5ZVwGBA72n5asjFyesUEMnmYkCcqI6TosBABBcYtlKz0ioqOrHRYwx3LGuEQeHgjik0IxdYEaGnBYDlZQ2jw2ZEpq04gtEFL1KlUxn8sG4aGgyjqTMb2icFgM+vGklfv/GaRyRMbgsJjv6Aljb4ITbZpz353TmJq3IWUcullkV29VVoiwKyInquCxL39Y5FpmGPzRdVPXjM91yeQOMOo2iWXJxtq8U496IdMSsXSnUkXPOFQ/IDTrteXsIGlxmGOZY2S6le69pgt2owzefU18t+XhkGgeGpi64nXM2tQ4TKq0GHDol35uYfn8YFoMW9RJO3yGlhwJyojquXIZ8KZNWevMbOoszIHdY9HjHpbX479eHEUsq09x5JkNOYw9LSXO1FVoNK4lJK/7QNCLTaUUDcrfVgG1buvNBeYPLjG1buuG2GmQ/i5gl/8Oh0/nfUWrx5/5RcH7x7ZznYoxhTV2FrI2d4pvIYthfQYoHBeREdZy5jO3UEgJyccLKqiINyIFsc2dkOo3fHhxR5PlD8TS0GgabkQLyUmLUabHCbSmJWeS+gPKX/jUahg6PHdsf3ISdX9yM7Q9uQofHrliwde81zarMku/oG0WVzZifLb4Qa+oc6PeHZdtQ2+8P52f+EyKigJyozpkM+eJLVnpGQqhzmOBSIAs2X+tWutBSbcVTCs0kD8ZTqDDpaM5uCWqvsefrXIuZLzcNRulaXI2GodpuRL3Lgmq7UdHMp8Oix4evacL/HD6dTxyUu3RGwEv9o7i+o3pR//ad9RVIZbgsb0KDsRQC4Wm0eah+nJyNAnKiOhaDFnotW1LJSs9wcW3onA1jDHeuX47XT04p0uQVjKdo5GGJavfYcHw8ikSquCeteAMRVJh0qF5AE58a3HtNE+wm9UxceX1wCsF4CjfMYzvnbMSs+mEZyla8uTeR7RSQk3NQQE5UhzEGp8WAqejiMuSJVAYDo5GirR+f6b1XNMCg1eDpVwdlf+5QIkUjD0tUq8cOgQNHR4t70opYi0tXYc7mMOvxkU1N+ONhvyxBptJ2HAlAq2G4pq1qUZ+/vNICu1EnS2OnOPKQSlbIuSggJ6rksugXPYe873QYAi+uDZ1zqbQacFPnMvzyr0OyZzuzJSsUkJciMXvnLfIFQQOjEQps5vARMUuuglryF44E0L3CtejfNxoNw2qZGjv7/WGY9TRhhZyPAnKiSk6LYdE15D35CSsLbx5Swp3rGxFKpPGHQ/I2d4bilCEvVU1V4qSV4q0jn4wmMRZJKl4/XqwcZj3uvaYJf+rxK7qPQGojwTiOnA4vulxF1FnvQO9ICOmMtI2dNGGFzIUCcqJKLot+0VNWeoZDsBt1580eLlYbmt1Y6bbgqT3ylq0E42lUmGnCSiky6rRYWeSTVnyjyk9YKXYf3tSECpMO33y+fLPkL/aNAgA2Lzkgr0AiJeCoxAux+v1haugks6KAnKiSa4kZ8lW1FSWT4WCM4Y71y/Hq8Yn8mDg5hBLU1FnK2j32fL1rMSqGkYfFLpslb8azZZwlf+FIAPVOM9qW+H0gNnZK+e8UjKfgD01TmRWZFQXkRJWcFgOmYklwzhf0eYLA0TtS/BNWznXrFQ3QaRielmkEYiKVQTItUMlKCWvz2HGiiCet+AIRmPQaqsW9iA9fsxIVJh2+UYa15NPpDHb6xrD5kuolN/Y2V9tg0mskbez00YQVcgEUkBNVclr0SGU4osmFBRsnJmKIJTMlMWFlpmq7ETeu9uAXfx3CdFr6AOvMlk4KyEtVu8cGgWcbJ4uRLxBBSzXV4l5MhUmP+65txnO9frwxVF5Z8teOTSKWzGBzx9LKVQBAq2FYVSttY6fYk0EZcjIbCsiJKrly2zonowurIxcXbZRahhwA7ly/HJOxFP542C/5c4VyATllyEtXuycbNBRrY6fYHEcu7sObVsJh1pfdXPIXjgRg0GmwocVdkMfrrHOgdzgEQVjYldP58uau6pRK/xGRFwXkRJWcuW2dYiZ3vnpGgtBpWEkGAte0VqHBZZalbCWfIaeAvGStdFuh07CibOyMTqdxaiqO1urS+zlUgt2kx9Zrm/BcbwAHh6aUPk7BvNgXwIZmNyyGwjSPd9ZXIDydxsmJWEEe71z9/jBNWCFzooCcqJIrF5AvdFtnz3AIrTU2mPRaKY4lKY2G4Y51jXhlYBzHJZwkIAgcTosez9x/NVZUWiTLNhFpGXQarKyyor8IM+TiwiKaVjF/d29ciWta3YhNZ3BqMobR8HRJ/2weH4vi6FgUmzuqC/aYa8TGTonKVnwBmptP5kYBOVGlfMnKAiet9IyESq5+fKb3dzdCq2F4+jVpRiAKAkefP4x7HnsNtz+yGx/64R70+cMl/cKvZu0eW74RrZj4RrNnKsUrVUqxGnT4zI0d+NzPD2DTQztwy/d2lvTP5o6+AICljzucqd1jh17LJGnsDCVSGAkm6E0kmRMF5ESVxJKVhcwiH4tMwx+aLsn6cZGnwoQbLqnBz/cNIpku/AKM8WgSW5/Yi6HJOABgaDKOrU/sxfgCa/VJcWirsePERKzoJq14/RHoNAwr3Falj1IyxqNJfOLp18vmZ3NH3yiaq60F/R4w6DToWGbHYQky5OKYTsqQk7lQQE5UyZlv6px/hrw3v6GzdANyILu5cyySxPO9hW3uDIQTGA0n8i/4oqHJOJIyTHYhhdfusYNzyDq/fj58gQhWuC3Qa+klbL6S6UzZ/GzGkmnsPjpekOkq5+qsc+DQqeCCR+JejNdPIw/JhdFvM6JKeq0GNqNuQTXk4oSVVSUekF/XXoNahwlPFahs5dRUHP/4q0O49qEdGJyMnzdBoMFlhkFXejX35Ezw4C2yshXfKNXiLpRBpy2bn81XfONIpgXcUMByFdGaegcmYykMBxMFfVyvPwKjToMGl6Wgj0vKBwXkRLWcFv2CSlZ6RkKoc5jgshokPJX0tBqG27ob8RfvKAaXME3g+FgUX/z5QVz/lR34yZ6TeE9XPdbUVWDblu78C3+Dy4xtW7rhLvF/M7VaWWWFXsuKqrEzmRZwYjxG9eML5LYazvvZ/N4HryjJn80dfQFYDVp0r3QV/LE7cyWJhd7Y2Z8b06mlCStkDoWZFURICXJZDAtq6uwZLr0NnXO5bV0jvv2CFz/dO4jPvrVjQZ/b7w/juzt8+M2BYei0Gnxg/XLcf11LfmOiIHBsf3ATkukMDDot3FYDjfkqUXqtBk1V1vzl9mJwfDyKjMApIF8gjYahw2PH9gc3YTqdgdcfwWM7j+Gr7+9S+mgLwjnHi32j2NRaBaME2f1LllVAw4DDp4K4ac2ygj2uzx/G+qbKgj0eKT8UkBPVclr0mJrnHPJEKoOB0Qje1lm4X9BKqneacV17NX66dxCffHMbdPOoxX1jKIjv7PDij4f9sBi0uO/aZtx3TRNqKkxn3U+jYai2G6U6OpFZW40dbxQ4W7gUYj07BeQLN/Nn89VjE9j++jDevMqDd15Wp/DJ5q/fH8GpqTg+fkOrJI9vNmjRWmPD4eHCTVoJJ7IlMG0eKrMic6OSFaJaLoth3iUrfafDEHhpbuicyx3rl8MfmsaOvtEL3u+14xO4+9FX8a7vvIxXBsbxiRtasfOLN+D/vH3VecE4KT9tHhsGJ2OIJ4uj+c8XiIAxoIWWAi3Ju7vq0e6x4Wt/6kc6U/iJS1IRxx1eL0FDp6izzlHQWeRnJqzQ9yyZGwXkRLVcFj0m5znyqyc/YcUh5ZFkdcMlNaixG2fd3Mk5x8veMdz+8C68/z934Y1TQXz+pg7s/N834DNv7Sj5Onoyf+KklYHR4qgj9wUiqHeaYTaUXjNiMdFqGD731g4cHYvi5/uGlD7OvO04EsCq2gosc0iXDFhT74A/NI1AuDCNnd5cD0Y7ZcjJBVDJClEtp8WAUCKNdEa4aMlGz3AINqPuvCkFpUyv1eDjN7SgpsKMk+NRmA06VFr02NE3im/v8OHA4BQ8FUZ86Z2rcef6xoKtpyalRZy00u8Po7Ne+Tek3lxzHFm6G1d7cPlyJ775vBfvuby+6DcQB+Mp7D0xiY9e1yzp84iNnYeHQ6jpWHrg7w2EYdRp0FhJE1bI3ChDTlRLnEUenEcdec9ICKtq7WXVnCgIHFcsr8Q//7YHb/rKi7jlezvx2olJfHeHD+ORaXz5lk689IXNuPeaJgrGVWyFu3gmrWQEjqOjEbr0XyCMMXz+pg6MBBP48e4TSh/nol72jiEjcEnmj88kliYeLlDvRL8/gpZqmrBCLoxeZYlquXLbOidjKbhtczchCgJH70gI77+yQa6jyWI8msQDP9531ua+z/3sAH5wdzdaqm20dIUAyF5Jaa6yFcWklVOTcUynBcqQF9DGlipc21aF7+7w4fZ1jbCb9EofaU47+gJwmPXoanRK+jx2kx5NVVYcOlWYxk5fICLJiEZSXugVl6iWmCG/WGPnyYkYYslMWTV0AnNv7rMbdRSMk7O0eWzoL4LlQOKCIgrIC+vzN3VgMpbCD/5yTOmjzEkQOF7sC+C69up5TYVaqjV1FQVp7IxMp3FqKk5XdchF0asuUS0xQz51kVnk5djQCZTX5j4irXaPHYMTccSSaUXPkR95WE3NcYV0WYMTb+tchh/85SjGI9NKH2dWh4aDGIsksfmSalmer7PegaHJ+IKWx80mP2GFGjrJRVBATlTrTMnKhX/h9gyHoNUwtHnKK8Mx2+Y+2qpJZiNm98TgQim+QATVdiMcluItqyhVn31rB+KpDL734oDSR5nVjiOjYAy4rl3a+nFRZ102AbPUeeRiqRdlyMnFUA05US2nVSxZuXiGvLXaVvQTCBZq5uY+2qpJLkTM7vX7I7isQdr63QvxjUbQSvPHJdFaY8P7rmzAk7tP4N5rmlDnLK6JUi/0BdDV6ESlTAmDNbkSxUOngtjUWrXox/EGIjDoNFhOE1bIRVCGnKiW3aiDVsPmlSEvt/pxkbi5r95lQbXdSME4mdVKtwUGrSZfw60Ezjl8fhp5KKVPvqUd4MA3n/MqfZSzjEWmcXBoSvLpKjO5rAbUO80FyZA3V1llqXsnpY2+Q4hqMcbgNOsxeYEM+XhkGqdDCayuLc+AnJD50Gk1aK625hecKCEQnkZ4Ok0BuYTqnWZ86OoV+Nm+QcXLk2b6c98oOM8uM5NTIRo7+/0RWghE5oUCcqJqTov+gk07vSPZjGC5ZsgJma82jx39Co4+pPXj8vhfm1tg1mvxtWf7lD5K3o6+AKrtRtkTI531DhwbiyIyvbhm5ihNWCELQAE5UTWXxXDBkpWekWx2ZBVlyInKtdfYMDQZR3SRwclS5SesUHAjKbfNiHuvbcbv3ziNN4YKsxhnKdIZAS/1j+L69mrZS+o66yvAOdA7sriyFZqwQhZCsoCcMfYoYyzAGDs047ZnGGP7c3+OM8b2525fP+P2A4yxW3K3NzLGdjDGehljhxljn5zxWJWMsWcZY97c/9LUfbJgTovhgk2dPcMh1DpMsjUSEVKsxKBCqVIGbyAMu0mHavvcS7xIYWy9tgkuix7//scjSh8Ffz05hVAiLXsJK6qgAAAbUElEQVS5CnBm0sqhRW7s9OYDcnoTSS5Oygz5jwDcPPMGzvntnPMuznkXgF8A+GXuQ4cAdOduvxnAw4wxHYA0gM9yzlcBuBrA/2KMrc59zv8G8DznvA3A87n/T8iCuCz6CwfkIyGqHycEZ4IKpcpWfIFsQydj1HgsNbtJjwevb8VfvGPYNTCu6Fl29AWg0zBsalv8pJPFqqkwodpuXPTGTq8/DINWgxU0YYXMg2QBOef8JQATs32MZX+j3gbgqdx9Y5xz8TqoCQDP3T7COf9r7u9hAL0A6nP3ezeAx3N/fxzAeyT4MkiZc1nnLllJpDIYGI1S/TghAFZUipNWlMmQ+wJRqsWV0V0bVmBZhQn//scj4Jwrdo4dRwLoXulChUmZ2fOddRU4vMjGTm8gguZqmrBC5kep75JrAfg55/nZSoyxqxhjhwG8AeCjMwJ08eMrAVwOYE/uJg/nfATIBu4A5L+eRUqe06LHdFpAPJk572P9/jAyAqcMOSE4M2lFiQz5VCyJscg01Y/LyKTX4pNvacPrJ6fwXG9AkTMMT8Vx5HRYkXIVUWe9A95ABInU+a8RF9PvD1P9OJk3pQLyO5HLjos453s452sArAPwd4wxk/gxxpgN2RKXT3HOF3ztiDF2P2NsL2Ns7+jo6BKPTsrJhbZ19uTmz1KGnJCsdo9dkdGH1NCpjPdf2YCmKiv+4499yAjyZ8lf7Mu+Xss5f/xca+oqkBE4jpxe2BvRWDKNoUmasELmT/aAPFcb/l4Az8z2cc55L4AogM7c/fXIBuM/4Zz/csZd/Yyx2tx9agHM+Raec/4I57ybc95dXV1dmC+ElAWnOXsZdNaAfCQEm1GHRhfV/xECAO0eG05NyT9pJR+QV1O2UU46rQaffWs7+vxh/PrAKdmff0dfAA0us6JvxNbkGjsXWrYifs+2U0MnmSclMuRvAXCEcz4k3sAYa8oF6mCMrQDQAeB4rtb8hwB6OedfO+dxfg3g7tzf7wbwK8lPTsqOM5chn62xs2c4hFW1dtpeSUiOePld7jpyXyACk16DeldxrXNXg7d31mJNXQW+9mw/kmlBlucUBA5/KIGt1zbhW3dcDgVL2NHgMsNh1i+4sVO8ktRaQ28iyfxIOfbwKQC7AHQwxoYYY/fmPnQHzilXAXANgAO5MYjbATzIOR8DsAnAXQBumDEW8e25z/k3ADcyxrwAbsz9f0IWxGXNZsjPDcgFgaOXJqwQchZx46DcdeS+0Qiaq2zQ0ptj2Wk0DJ+/qQODE3E889pJyZ9PEDj6/GHc+v1XcNvDu/GJp19Hnz8MQYGSGSC70bmzfuGNnf2BMPRahpVuusJK5kcn1QNzzu+c4/Z7ZrntSQBPznL7ywBm/Q3MOR8H8OalnZKo3Vw15CcnYogmM1Q/TsgMyystMOg08MockHv9EVy5glZNKOW69mqsb6rEt17w4dYrG2AxSBY6YDyaxNYn9mJoMg4AGJqMY+sTe7H9wU2KzaDvrHPgsZ3HkcoI0M9zYorPn30TSRNWyHzRdwpRNadFzJCfHZD35Dazra51yH4mQoqVVsPQUm1Dv4yNnbFkdv04NXQqhzGGL97cgdHwNH70ynHJnieWTCMQTuSDcdHQZBzJ9MKnnBTKmnoHkhlhQQ3N/YEwWql+nCwABeRE1Yw6LSwGLSbPKVnpGQ5Bq2G0YY2Qc7R7bLJlyAWBYzQ8jWfuvxpv61ymWNkCAa5cUYk3X1KD/3xxAMELLFNbrD/3j+KtX38JQ5NxNJzTK9DgMsOg0xb8OeerM3el9NA8y1bECSvtVD9OFoACcqJ6Lsv5y4F6RkJorbbBpFfuRYCQYtTusWM4mEA4UfigbCaxlviDP9iD2x/ZjQ//6DVFa4kJ8LmbOhCeTuM/Xxoo2GOORabxyadfx92PvgqDToM6hwnbtnTng/IGlxnbtnTDbTUU7DkXaqXbCqtBi8On5heQHx2NgnNQQocsiHSFYISUCIdZf15TZ89wCBta3AqdiJDiJc5V9gUiuHy5dHXdxVhLrHaraivwN2vr8NjOY/jwxpWoqTBd/JPmwDnHz/YN4cu/60UsmcYn39yGBze3wKjTQhA4tj+4Ccl0BgadFm6rQdFpVxoNw5o6Bw4Nz2/Sitj0TCMPyUJQhpyonsuqPytDPh6ZxulQgiasEDILcdKK1AuCkulM0dUSE+AzN7YjneH49gu+RT/GsbEoPrBtD77w84No99jw+09ci0/f2A5jrixFo2GothtR77Kg2m4sitGza+or0DMcmteCJG8gAr2WYYXbKsPJSLmggJyontNiOKsmsnckm92gCSuEnK+x0gKjTiP56EODTlt0tcQEWOG24vZ1jXjq1ZM4OR5b0Ocm0wK+u8OHm77xEg4NB/Evt1yKZ+7fUBLr5dfUORBPZXBsLHrR+3r9YTRVWec9kYUQgAJyQuCynJ0h7xnJ1gmuogw5IefRahhaa2zol3g5UDyVxkO3XlZUtcQk6xNvboNOy/D15/rn/Tn7TkziXd9+GV/5Yx/esqoGz3/mOnzgquVFkf2ej8767OvBfOaRewMRtFFDJ1kgqiEnqueyGBCMpyAIHBoNQ89wCLUOEyrphZ+QWbV77Nh9dFzS53jof/owHp7Gzx7YAIHzoqglJlmeChPu3rgSj7x0FA9c14xLls2dvAgnUvjKH/vw5O4TWFaRbdi8cbVHxtMWRmu1DUadBodOBfHurvo57xdPZnByIoZbLp/7PoTMhjLkRPWcFgMEDoRyUyN6aEMnIRfUWmPDSDCR/5kptMPDQfzu4Ai6V1ai1mkuqlpikvWx61pgM+rwH3+cO0v+x8OncePXXsKTu0/g7g0r8exnrivJYBwAdFoNLqmtwKFTF27sHBiNZCesUIacLBAF5ET1XLnlQJOxFBKpDAZGo1Q/TsgFSN3Y+fVn+1Fh0mHrm5oleXyydE6LAR+9rgXP9fqx78TkWR87HUzggSf34oEn98Fp0WP7g5vwT3+zBjZjaV+U76yrwKHhIDifu7HTG6AJK2RxSvung5ACcOYD8iTCiRQyAqcMOSEXIAYbXn+44Cvt/3pyEs/1BvD5mzrgMOsL+tiksD68aSVePTaBRCqDU5Mx6HUavHZsAl/8xRtIZQR88eZLcN+1TWXT3NhZ78BP9pzE4EQcy92WWe/j9Ueg09CEFbJwFJAT1XNasrXiU7EkAqFpADRhhZALaXRZYNJr4JWgsfOrf+qD22rAPRtXFvyxSWGZdFp86i1t+PhTr+c3bD5062W49Yp6fOSaprILSjvrHACyGzvnCsj7/RE0VVlh0JXHmxAiH/qOIarnygfkKfSMhGAz6tDomv2XLSEkOye6tcZW8NGHrwyMYadvHB+7vgXWEi9vUIPxaDIfjAPZOfFf/MVB/O0NrWUXjANA+zIbdBqGQxfY2OkLhGlDJ1kUCsiJ6s2sIe8ZDmFVrZ2axwi5iPYae0FryDnn+Oqf+rGswoQPXb2iYI9LpDP38iZBoRNJy6jTot1jn3NjZyKVwYmJGDV0kkWhgJyoXoVJDw0DJqNJ9NKEFULmpc1jx+lQAsF4YSatvNg3in0nJvHxN7fCpKflP6VAjcub1tRV4PCp2Rs78xNWKENOFoECcqJ6Gg2Dw6zHgaEpRJMZqh8nZB7Exk5fYOllK4LA8R9/6sPySgtu625c8uMRebitBmzb0q2q5U2d9Q6MR5Pw5/qNZhKvGFGGnCwGFekRgmwd+WvHJwAAq2sdCp+GkOInBh39/giuXFG5pMf6n8OncXg4hK++f23ZTORQA42GocNjx/YHNyGZzqhieZO4sfPQqSCWOUxnfcwbCEOrYWiqKr/6eSI9+s1HCACHRY9ESoBWw+hyIyHz0OAyw6zXLrmxMyNwfO3ZfrTW2PAe2m5YcjQahmq7UTXLm1bVVoCx7KSVc/X7I1jpttCEFbIo9F1DCM5MWmmttlH9KiHzIE5aWWpj56/2n4IvEMFnbmyHtsyDOVL6LAYdWqpts27s9AUi+aVZhCwUlawQ1RMEjo9d34IH3tQMDWMQBF72WR5CCqHNY8NO39iiPz+VEfCN57xYU1eBm9csK+DJCJFOZ10F9hybOOu2RCqDE+NRvOuyWoVORUodZciJqgkCR58/jE8/sx+3P7Ibn/7pfvT5wxCEuVcjE0Ky2j12+EPTi5608tO9gzg5EcNn39pOb4JJyeisd2AkmMBY5Exj59HRKASenT5EyGJQQE5UbTyaxNYn9p612GLrE3sxHk0qfDJCip84acW7iDryRCqDbz/vwxXLndjcUVPooxEimTW5jZ2HZ8wj9+amDVEPElksCsiJqs292CKj0IkIKR0zJ60s1E/2nMTpUAKfu6kDjFF2nJQOcTTuzI2dXn+EJqyQJaGAnKiaGhdbEFIo9c7FTVqJTqfxvR0+bGp1Y2NLlUSnI0QaDrMeK9wWHJ4xacUbCGOF2wIjvXaQRaKAnKiaGhdbEFIomtyYUO8ClwP96JXjGI8m8dm3dkh0MkKktaau4uySFX8E7bQQiCwBTVkhqqbGxRaEFFJbjR0veUfnff9gPIWH/zyAN19SgyuWuyQ8GSHSWVPnwO/fOI1gPAWTXoPj41G8gyaskCWgDDlRPbUttiCkkNo9NoyGpzEVm18j9A/+chShRBqfeWu7xCcjRDqd9dnGzp7hEE1YIQVBATkhhJBFExeheAMXb+wcj0zj0ZeP4R2X1eYnVRBSitbkGjsPDwfz3/ttNTRhhSweBeSEEEIWTRzzNp/Gzu+/OIB4KoNPv4Wy46S0VdmMqHWYcOhUEF5/GBoGNFfThBWyeFRDTgghZNHqnWZYDVp4LzL68HQwgSd3n8AtlzeglTKJpAysqXPg0HAIiZSAlW4rTVghS0IZckIIIYvGGEOrx37RDPl3dnghcI5PvaVNppMRIq3O+goMjEZwcGiKFgKRJaOAnBBCyJK01dguuBxocCKGp18dxO3rGtFYaZHxZIRIp7POAc6B4WAivySLkMWikhVCCCFL0u6x4ef7hjAZTcI1ywz/bzznhVbD8LebKTtOysfaRgcevutKOM16OC0GCAKnKV1k0SggJ4QQsiTiuLd+fxhXNbvP+pgvEMH214fwkU1NWOYwKXE8QgpOEDjGIkn88297MDQZzy+V6/DYKSgni0IlK4QQQpZEHH3YP8vow68/1w+zXouPXd8i97EIkcx4NImtT+zF0GQcADA0GcfWJ/ZiPDq/efyEnIsCckIIIUtS5zDBZtTBd05j5+HhIH53cAQfuaYJbptRodMRUnjJdCYfjIuGJuNIpjMKnYiUOgrICSGELAljDK2zNHZ+7U/9qDDpcN+1zQqdjBBpGHRaNLjMZ93W4DLDQKMPySJRQE4IIWTJ2j02eANnMuR/PTmJ548E8MB1LXCY9QqejJDCc1sN2LalOx+UizXk7lmamgmZD2rqJIQQsmTtHjt+uncIE9EkKq0GfPVPfXBbDbhn40qlj0ZIwWk0DB0eO7Y/uAnJdAYGnRZuq4EaOsmiUUBOCCFkycTtm/3+MATOsdM3jr9/xypYjfQyQ8qTRsNQbafeCFIY9JuSEELIkomTVrz+MLa/fgrLKkz40NUrFD4VIYSUBgrICSGELJnHbsQPtlwJl9WAmgoTBM5h0lODGyGEzAcF5IQQQpZEEDj6AxH8029mLEm5q5s2FxJCyDzRlBVCCCFLMuuSlCdpSQohhMwXBeSEEEKWhJakEELI0lBATgghZEloSQohhCwNBeSEEEKWhJakEELI0lBTJyGEkCWhJSmEELI0FJATQghZMlqSQgghi0clK4QQQv7/9u49Vo6yjOP490epYLgIpkVulVMqIJdAqaVCuBVDCiGEm9xqERESKVBupl4qSirGSCEVA1GuApIAUsNdS1piKhoDLbWcc9pS7tQAVijUcBc57eMf864Op3v2nD1nd2bb/X2SSWffmXnnnfPk3T777js7ZmZWIifkZmZmZmYlckJuZmZmZlYiJ+RmZmZmZiVyQm5mZmZmViIn5GZmZmZmJXJCbmZmZmZWoqYl5JJulfSGpGW5snskdaZlpaTOVD4hV94l6cRa9aTymZJeyx13TLOuxczMzMysWZo5Qn47cHS+ICJOi4ixETEWuBe4L21aBoxP5UcDN0ratK96cq6p1BcRcxt9AWZmZmZmzda0hDwi/gysqbZNkoBTgbvTvh9ERE/avDkQA6nHzMzMzGxDV9Yc8kOB1yPi+UqBpC9LWg4sBabmEvRapknqTtNatm1WY83MzMzMmqWshHwyaXS8IiIWRsTewAHADEmb91PH9cAYYCywCpjd146SviVpsaTFq1evHlrLzczMzMwaqPCEPM0NPwm4p9r2iFgBvA/sU6ueiHg9ItZGxDrgZmBCjX1viojxETF+5MiRg2+8mZmZmVmDlTFCfiTwTES8WimQNLpyE6ekXYA9gJW1KpG0Q+7liWQ3hpqZmZmZbVCa+bOHdwOPA3tIelXSOWnT6fSargIcAnSln0G8Hzg/It7sp56rJC2V1A0cAVzarGsxMzMzM2sWRUT/e21EJK0G/l7CqUcAb5ZwXhs4x6j1OUatzzFqfY5R63OMWt9AY7RLRPQ7X7rtEvKySFocEePLbof1zTFqfY5R63OMWp9j1Poco9bX6BiV9SsrZmZmZmaGE3IzMzMzs1I5IS/OTWU3wPrlGLU+x6j1OUatzzFqfY5R62tojDyH3MzMzMysRB4hNzMzMzMrkRPyBpE0StICSSskLZd0cSq/WtIzkrol3S9pm9wxMyS9IOlZSUeV1/r2UG+MJHVI+lBSZ1puKPcKNn41YvSTFJ9OSfMl7ZjKJena1I+6JY0r9wo2boOIz0RJb+f60OXlXkF76CtOue3TJYWkEem1+1GBBhEf96OC1XivmynptVwsjskdM7ScLiK8NGABdgDGpfWtgOeAvYBJwKapfBYwK63vBXQBmwGjgReBYWVfx8a8DCJGHcCystvdTkuNGG2d2+ci4Ia0fgzwCCDgQGBh2dewMS+DiM9E4Pdlt7vdlr7ilF6PAuaRPY9jRCpzP2rt+LgftUiMgJnA9Cr7Dzmn8wh5g0TEqohYktbfBVYAO0XE/IjoSbs9Aeyc1o8HfhsRH0XEy8ALwISi291OBhEjK1iNGL2T220LoHLzy/HAHZF5AthG0g6FNrqNDCI+VoK+4pQ2XwN8l0/GyP2oQIOIjxWsnxhVM+Sczgl5E0jqAPYHFvbadDbZKARkgX0lt+1VagfbGmiAMQIYLekpSY9JOrSg5hnrx0jSTyW9AkwBKl/Zuh+VZIDxAThIUpekRyTtXXhD21w+TpKOA16LiK5eu7kflWSA8QH3o9JUyRempaldt0raNpUNuQ85IW8wSVsC9wKX5EeNJF0G9AB3VoqqHO5PxAWoI0argM9HxP7At4G7JG1ddHvbUbUYRcRlETGKLD7TKrtWOdz9qMnqiM8SssdG7wdcBzxQRnvbVT5OZO9tl/HJD0v/27VKmftRk9URH/ejklR5r7seGAOMJcsRZld2rXJ4XX3ICXkDSRpOFrg7I+K+XPk3gGOBKZEmG5F9ehqVO3xn4B9FtbVd1ROj9NXTW2n9b2RzwnYvvtXtpa8Y5dwFfDWtux8VrJ74RMQ7EfFeWp8LDK/cqGbNVSVOY8jmtnZJWknWV5ZI2h73o8LVEx/3o3JUe6+LiNcjYm1ErANu5v/TUobch5yQN4gkAb8GVkTEz3PlRwPfA46LiA9yhzwEnC5pM0mjgd2ARUW2ud3UGyNJIyUNS+u7ksXopWJb3V5qxGi33G7HAc+k9YeAM9OvRBwIvB0RqwprcJupNz6Stk/HIGkC2f85bxXX4vZULU4RsTQitouIjojoIEsgxkXEP3E/KlS98XE/Kl6N97r8vRUnAsvS+pBzuk2H1mTLORj4OrBUUmcq+wFwLdldt4+m/vREREyNiOWS5gBPk31VdUFErC2h3e2krhgBhwFXSOoB1gJTI2JN8c1uK33F6BxJewDryH59YGraNpfsFyJeAD4Avllsc9tOvfE5GTgv9aEPgdNz3xJa81SNUxpdrcb9qFj1xsf9qHh9vddNljSWbDrKSuBcgEbkdH5Sp5mZmZlZiTxlxczMzMysRE7IzczMzMxK5ITczMzMzKxETsjNzMzMzErkhNzMzMzMrEROyM3MWoCkkDQ793q6pJkNqvt2SSc3oq5+znOKpBWSFvQq75D0tWaf38xsQ+WE3MysNXwEnNRqT+CrPBxrgM4Bzo+II3qVdwBVE3JJfh6GmbU9J+RmZq2hB7gJuLT3ht4j3JLeS/9OlPSYpDmSnpN0paQpkhZJWippTK6aIyX9Je13bDp+mKSrJT0pqVvSubl6F0i6C1hapT2TU/3LJM1KZZcDhwA3SLq61yFXAodK6pR0qaSzJP1O0sPA/HT8d3Lt+HHuXGek6+mUdGNq87D0N1mW2rHe38zMbEPikQkzs9bxS6Bb0lV1HLMfsCewBngJuCUiJki6GLgQuCTt1wEcDowBFkj6AnAm2WPSD5C0GfBXSfPT/hOAfSLi5fzJJO0IzAK+BPwLmC/phIi4QtJXgOkRsbhXG7+fyisfBM4CDgL2jYg1kiaRPWp6AiDgIUmHAauB04CDI+JjSb8CpgDLgZ0iYp9U3zZ1/L3MzFqOE3IzsxYREe9IugO4iOwR2QPxZESsApD0ImnEmWxkOz91ZE5ErAOel/QS8EVgErBvbvT9M2SJ8X+ARb2T8eQA4E8RsTqd807gMOCBAba34tGIWJPWJ6XlqfR6y9SOfckS/yclAXwaeAN4GNhV0nXAH3LXbGa2QXJCbmbWWn4BLAFuy5X1kKYYKstMP5Xb9lFufV3u9To++R4fvc4TZKPRF0bEvPwGSROB9/ton/q9goHJ1y/gZxFxY692XAj8JiJmrNcIaT/gKOAC4FTg7Aa1y8yscJ5DbmbWQtKo8RyyGyQrVpKNFAMcDwwfRNWnSNokzSvfFXgWmAecJ2k4gKTdJW3RTz0LgcMljUg3fE4GHuvnmHeBrWpsnwecLWnL1I6dJG0H/BE4Oa0j6bOSdkk3vm4SEfcCPwLG9XN+M7OW5hFyM7PWMxuYlnt9M/CgpEVkSWpfo9e1PEuWOH8OmBoR/5Z0C9nc8iVp5H01cEKtSiJilaQZwAKyke25EfFgP+fuBnokdQG3k809z9c5X9KewONpasp7wBkR8bSkH5LNU98E+JhsRPxD4LZUBrDeCLqZ2YZEEb2/xTQzMzMzs6J4yoqZmZmZWYmckJuZmZmZlcgJuZmZmZlZiZyQm5mZmZmVyAm5mZmZmVmJnJCbmZmZmZXICbmZmZmZWYmckJuZmZmZlei/Xu4BTG0QX+cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_list= [(tree_No, pipelineTrainingAndmodel(tree_No)) for tree_No in range (220,250)]\n",
    "tree_df=pd.DataFrame(tree_list, columns = [\"Number of trees\",\"MAE\"]).set_index(\"Number of trees\")\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.lineplot(data=tree_df, markers=True)\n",
    "plt.title('Changes in MAEs by number of estimators (trees)')\n",
    "plt.ylabel(\"MAE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "235 is indeed the number of trees that minises the MAE with this method. Interestingly, we see this value is still not as low as imputation or one-hot encoding by themselves, suggesting that the data is being overfitted. However when the whole dataset is used to train the model and a file of similar size is predicted the MAE plummets to 15999.95255, suggesting that the method is valid for a bigger dataset\n",
    "\n",
    "\n",
    "## Cross-Validation\n",
    "whilst we cannot gain a bigger dataset, we may use other techinques, such as cross-validation to mesure the models performance better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17809.35328739443"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipelineTrainingAndmodel(235,cross_val_folds=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using 5 folds, we can see the MAE is actually a little bit higher, but how will it stack up compared to the other two leading models?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation MAE for Random Forest Model with One-Hot Encoding : 17,849\n",
      "Cross-Validation MAE for Random Forest Model with Imputation Encoding : 18,118\n"
     ]
    }
   ],
   "source": [
    "print(\"Cross-Validation MAE for Random Forest Model with One-Hot Encoding : {:,.0f}\".format(RFRmodel(trees=229, train_mode=OneHotEncodingTraining(), cross_val_folds=5)))\n",
    "print(\"Cross-Validation MAE for Random Forest Model with Imputation Encoding : {:,.0f}\".format(RFRmodel(trees=232, train_mode=ImputedColsTraining(), cross_val_folds=5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the Cross-Validation MAE's is higher than the normal MAE's by a fair amount, suggesting that this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotTreesvsMAE(220,250, train_mode=OneHotEncodingTraining(),train_mode2=ImputedColsTraining(), cross_val_folds=5, labels = (\"One-Hot Encoding\",\"Imputation\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suprisingly, the One-Hot Encoding fair exceeds Imputation but stills "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost\n",
    "\n",
    "Another popular method of modelling data is called gradient boosting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tofun\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:07:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tofun\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:07:09] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tofun\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:07:11] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tofun\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:07:16] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tofun\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:07:19] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tofun\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:07:21] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "Cross-Validation MAE for Random Forest Model with Imputation Encoding : 15,953\n"
     ]
    }
   ],
   "source": [
    "print(\"Cross-Validation MAE for Random Forest Model with Imputation Encoding : {:,.0f}\".format(pipelineTrainingAndmodel(1000, cross_val_folds=5, model_type=\"Gradient Boosting\", learning_rate=0.05)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see, even without the parameter tuning XGBoost outperforms all models used already significantly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "def XGBR(cross_val_folds=5, early_stopping=5):\n",
    "    X = pd.read_csv('train_data.csv', index_col='Id')\n",
    "    # Remove rows with missing target, separate target from predictors\n",
    "    X.dropna(axis=0, subset=['SalePrice'], inplace=True)\n",
    "    y = X.SalePrice\n",
    "    X.drop(['SalePrice'], axis=1, inplace=True)\n",
    "    X_train, X_valid, y_train, y_valid = train_test_split(X,y,random_state=1)\n",
    "     # Preprocessing for numerical data\n",
    "    numerical_transformer = SimpleImputer(strategy='median') # Your code here\n",
    "\n",
    "    #model = XGBRegressor(random_state=0, n_estimators=trees, learning_rate=learning_rate, n_jobs=4) # Your code here\n",
    "\n",
    "#         my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "#                                       ('model', XGBRegressor())\n",
    "#                                      ])\n",
    "    my_pipeline = Pipeline([('numerical_transformer', numerical_transformer), ('xgbrg', XGBRegressor())])\n",
    "\n",
    "    param_grid = {\n",
    "        \"xgbrg__n_estimators\":list(range(100,1500,100)),\n",
    "        \"xgbrg__learning_rate\": [0.05, 0.1, 0.2,0.25,0.3],\n",
    "    }\n",
    "\n",
    "    fit_params = {\"xgbrg__eval_set\": [(X_valid, y_valid)], \n",
    "                  \"xgbrg__early_stopping_rounds\": early_stopping, \n",
    "                  \"xgbrg__verbose\": False} \n",
    "\n",
    "    searchCV = GridSearchCV(my_pipeline, cv=cross_val_folds, param_grid=param_grid, fit_params=fit_params, n_jobs=4, scoring= 'neg_mean_absolute_error')\n",
    "    searchCV.fit(X_train, y_train)  \n",
    "    preds = searchCV.predict(X_valid)\n",
    "\n",
    "#     scores = -1 * cross_val_score(searchCV,X_full, y,\n",
    "#                           cv=cross_val_folds,\n",
    "#                           scoring='neg_mean_absolute_error')\n",
    "    \n",
    "    \n",
    "    return (-1 * searchCV.score(preds,y_valid))\n",
    "    \"\"\""
   ]
  }
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