housing_modeling.ipynb

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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1-final"
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
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 },
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 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# Importing Packages"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "\n",
    "# import modules for preprocessing\n",
    "from sklearn import tree\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge, LassoCV, LassoLarsCV, LassoLarsIC\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.feature_selection import RFE, SelectKBest, f_regression, RFECV\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler,OneHotEncoder\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, make_scorer\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import validation_curve, GridSearchCV,RandomizedSearchCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "def rmse(y, y_pred):\n",
    "    return round(np.sqrt(mean_squared_error(y, y_pred)), 2)\n",
    "def bias(y, y_pred):\n",
    "\treturn round(np.mean(y_pred - y), 2)\n",
    "def variance(y_pred):\n",
    "\treturn np.mean([yi**2 for yi in y_pred]) - np.mean(y_pred)**2\n",
    "\n",
    "def train_test_metrics(y_tr, y_tt, y_tr_pred, y_tt_pred):\n",
    "\tprint('Training R^2 Score: ', round(r2_score(y_tr, y_tr_pred), 4))\n",
    "\tprint('Training MAE: ', int(mean_absolute_error(y_tr, y_tr_pred)))\n",
    "\tprint('Training MSE: ', \"{:.4e}\".format(mean_squared_error(y_tr, y_tr_pred)))\n",
    "\tprint('Training RMSE: ', int(rmse(y_tr, y_tr_pred)))\n",
    "\tprint('Training Bias: ', round(bias(y_tr, y_tr_pred), 2))\n",
    "\tprint('Training Variance: ', \"{:.4e}\".format(variance(y_tr_pred)))\n",
    "\tprint(\"\")\n",
    "\tprint('Testing R^2 Score: ', round(r2_score(y_tt, y_tt_pred), 4))\n",
    "\tprint('Testing MAE: ', int(mean_absolute_error(y_tt, y_tt_pred)))\n",
    "\tprint('Testing MSE: ', \"{:.4e}\".format(mean_squared_error(y_tt, y_tt_pred)))\n",
    "\tprint('Testing RMSE: ', int(rmse(y_tt, y_tt_pred)))\n",
    "\tprint('Testing Bias: ', round(bias(y_tt, y_tt_pred), 2))\n",
    "\tprint('Testing Variance: ', \"{:.4e}\".format(variance(y_tt_pred)))\n",
    "\n",
    "from mlxtend.evaluate import bias_variance_decomp\n",
    "\n",
    "# import module for object serialization\n",
    "import pickle\n",
    "\n"
   ]
  },
  {
   "source": [
    "# Feature Engineering Continued\n",
    "\n",
    "## One-Hot Encoding/Dummy Variables\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/modeling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
       "       'waterfront', 'condition', 'grade', 'sqft_above', 'zipcode', 'lat',\n",
       "       'long', 'sqft_living15', 'sqft_lot15', 'sale_age', 'age', 'renovated',\n",
       "       'basement', 'viewed'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "# Get index of the columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   bdr_1  bdr_2  bdr_3  bdr_4  bdr_5  bdr_6  bdr_7  bdr_8  bdr_9  bdr_10  ...  \\\n",
       "0      0      0      0      1      0      0      0      0      0       0  ...   \n",
       "1      0      0      0      0      1      0      0      0      0       0  ...   \n",
       "2      0      0      0      1      0      0      0      0      0       0  ...   \n",
       "3      0      0      1      0      0      0      0      0      0       0  ...   \n",
       "4      0      0      1      0      0      0      0      0      0       0  ...   \n",
       "\n",
       "   zip_98146  zip_98148  zip_98155  zip_98166  zip_98168  zip_98177  \\\n",
       "0          0          0          0          0          0          0   \n",
       "1          0          0          0          0          0          0   \n",
       "2          0          0          0          0          0          0   \n",
       "3          0          0          0          0          0          0   \n",
       "4          0          0          0          0          0          0   \n",
       "\n",
       "   zip_98178  zip_98188  zip_98198  zip_98199  \n",
       "0          0          0          0          0  \n",
       "1          0          0          0          0  \n",
       "2          0          0          0          0  \n",
       "3          0          0          0          0  \n",
       "4          0          0          0          0  \n",
       "\n",
       "[5 rows x 129 columns]"
      ],
      "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>bdr_1</th>\n      <th>bdr_2</th>\n      <th>bdr_3</th>\n      <th>bdr_4</th>\n      <th>bdr_5</th>\n      <th>bdr_6</th>\n      <th>bdr_7</th>\n      <th>bdr_8</th>\n      <th>bdr_9</th>\n      <th>bdr_10</th>\n      <th>...</th>\n      <th>zip_98146</th>\n      <th>zip_98148</th>\n      <th>zip_98155</th>\n      <th>zip_98166</th>\n      <th>zip_98168</th>\n      <th>zip_98177</th>\n      <th>zip_98178</th>\n      <th>zip_98188</th>\n      <th>zip_98198</th>\n      <th>zip_98199</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 129 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "# Grab indices of columns for creating dummy variables and create dataframe with dummy variables\n",
    "dum_feat = df[['bedrooms', 'bathrooms', 'floors', 'condition', 'grade', 'zipcode']]\n",
    "dum_index = dum_feat.columns\n",
    "# To prevent what they call the dummy variable trap (related to multicollinearity), drop one of the dummy variable, as well as  the original categorical variable used in creating the dummy variables\n",
    "df_dum = pd.get_dummies(data=dum_feat, columns=dum_index, drop_first=True, prefix=['bdr', 'bth', 'flr', 'cnd', 'grd', 'zip'])\n",
    "df_dum.head()"
   ]
  },
  {
   "source": [
    "## Polynomial and Interaction Features"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sqft_living  sqft_lot  waterfront  sqft_above  sqft_living15  sqft_lot15  \\\n",
       "0       2070.0    8893.0         0.0      2070.0         2390.0      7700.0   \n",
       "1       2900.0    6730.0         0.0      1830.0         2370.0      6283.0   \n",
       "2       3770.0   10893.0         0.0      3770.0         3710.0      9685.0   \n",
       "3       4560.0   14608.0         0.0      4560.0         4050.0     14226.0   \n",
       "4       2550.0    5376.0         0.0      2550.0         2250.0      4050.0   \n",
       "\n",
       "   sale_age   age  renovated  basement  ...  zip_98146  zip_98148  zip_98155  \\\n",
       "0      28.0  28.0        0.0       0.0  ...          0          0          0   \n",
       "1      37.0  37.0        0.0       1.0  ...          0          0          0   \n",
       "2      17.0  17.0        0.0       0.0  ...          0          0          0   \n",
       "3      25.0  25.0        0.0       0.0  ...          0          0          0   \n",
       "4      10.0  10.0        0.0       0.0  ...          0          0          0   \n",
       "\n",
       "   zip_98166  zip_98168  zip_98177  zip_98178  zip_98188  zip_98198  zip_98199  \n",
       "0          0          0          0          0          0          0          0  \n",
       "1          0          0          0          0          0          0          0  \n",
       "2          0          0          0          0          0          0          0  \n",
       "3          0          0          0          0          0          0          0  \n",
       "4          0          0          0          0          0          0          0  \n",
       "\n",
       "[5 rows x 206 columns]"
      ],
      "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>sqft_living</th>\n      <th>sqft_lot</th>\n      <th>waterfront</th>\n      <th>sqft_above</th>\n      <th>sqft_living15</th>\n      <th>sqft_lot15</th>\n      <th>sale_age</th>\n      <th>age</th>\n      <th>renovated</th>\n      <th>basement</th>\n      <th>...</th>\n      <th>zip_98146</th>\n      <th>zip_98148</th>\n      <th>zip_98155</th>\n      <th>zip_98166</th>\n      <th>zip_98168</th>\n      <th>zip_98177</th>\n      <th>zip_98178</th>\n      <th>zip_98188</th>\n      <th>zip_98198</th>\n      <th>zip_98199</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2070.0</td>\n      <td>8893.0</td>\n      <td>0.0</td>\n      <td>2070.0</td>\n      <td>2390.0</td>\n      <td>7700.0</td>\n      <td>28.0</td>\n      <td>28.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2900.0</td>\n      <td>6730.0</td>\n      <td>0.0</td>\n      <td>1830.0</td>\n      <td>2370.0</td>\n      <td>6283.0</td>\n      <td>37.0</td>\n      <td>37.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3770.0</td>\n      <td>10893.0</td>\n      <td>0.0</td>\n      <td>3770.0</td>\n      <td>3710.0</td>\n      <td>9685.0</td>\n      <td>17.0</td>\n      <td>17.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4560.0</td>\n      <td>14608.0</td>\n      <td>0.0</td>\n      <td>4560.0</td>\n      <td>4050.0</td>\n      <td>14226.0</td>\n      <td>25.0</td>\n      <td>25.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2550.0</td>\n      <td>5376.0</td>\n      <td>0.0</td>\n      <td>2550.0</td>\n      <td>2250.0</td>\n      <td>4050.0</td>\n      <td>10.0</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 206 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# Grab columns for polynominal and interaction features from the original dataframe without dummy variables\n",
    "poly_feat = df.drop(['price', 'bedrooms', 'bathrooms', 'floors', 'condition', 'grade', 'zipcode', 'lat', 'long'], axis=1)\n",
    "y = df['price']\n",
    "# Use PolynomialFeatures to create binomial and interaction features\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "poly_data = poly.fit_transform(poly_feat)\n",
    "poly_columns = poly.get_feature_names(poly_feat.columns)\n",
    "df_poly = pd.DataFrame(poly_data, columns=poly_columns)\n",
    "# Concatenating two dataframes together for input into linear regression model\n",
    "X = pd.concat([df_poly, df_dum], axis=1)\n",
    "X.head()"
   ]
  },
  {
   "source": [
    "# Modeling Process\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Intercept : 109486.98346740508\nR^2 Score :  0.8711630640637245\nCoefficients : [ 5.02242884e+03  1.09136674e+00  5.82518547e+04 -4.98278447e+03\n -4.65714422e+01  1.05592581e+00  7.04187993e+01 -5.10159080e+03\n  1.00196734e+05 -5.75850976e+04 -3.33614587e+04 -1.93129705e-02\n -6.16870151e-04  2.92659686e+02  5.32866436e-02  2.63920511e-02\n -9.54044846e-04 -2.48542722e+00  1.97127832e+00 -3.00898702e+01\n -4.95314567e+03 -2.62714930e+00 -1.80254233e-07  2.57626338e-01\n  3.07911742e-04  1.61643014e-04  1.15564615e-06  1.55335558e-03\n -8.72289068e-03  5.27228306e-01  1.69974517e-01 -1.75759132e-01\n  5.82667475e+04 -6.52328732e+01  3.32493174e+01 -4.47041904e+00\n -6.87276885e+03  4.15383323e+03 -4.09118955e+05 -1.29193958e+05\n  5.82473784e+04 -1.96499795e-02 -1.80373354e-02  8.40017892e-04\n  2.88136292e-01  6.88371661e-01 -8.12354312e+01  4.98063801e+03\n  3.42439718e+01  7.29335989e-04 -3.66521646e-04  4.47392745e-01\n  6.72946537e-01  7.89111535e+01  3.20791872e+00  1.97641823e+01\n -1.02350896e-07 -1.35143768e-02  9.35375220e-03 -1.47097996e+00\n  7.67474867e-01 -2.52098127e-01  5.03457282e+00 -7.31356157e+00\n  1.37375602e+03  1.50036862e+03  8.12844510e+02  2.23704581e+01\n -3.77254622e+03 -4.32991707e+02 -1.17533242e+02  1.00204064e+05\n -2.95637353e+04  6.11030983e+04 -5.75707392e+04  3.87123104e+04\n -3.33732891e+04  3.60397373e+04  4.31643962e+04  5.83074081e+04\n  5.69357147e+04  4.36134280e+04 -2.32420579e+03 -8.15164475e+04\n  6.14922270e+02 -2.07244978e+05 -2.52409574e+05 -3.00205306e+04\n -8.25133141e+04 -4.08818758e+04 -3.22233617e+04 -1.32273992e+05\n -4.11131374e+04 -3.53047006e+04 -3.85994149e+04 -1.90463676e+04\n -2.20750044e+04 -2.27947062e+04 -7.42538560e+03  2.20788415e+04\n -3.68822436e+03  5.30007620e+04  6.73294989e+04  9.79143061e+04\n  4.74653102e+04  1.95538589e+05  2.17869547e+05  1.46573935e+05\n  2.55861426e+05  4.43780814e+05  6.20530272e+05  3.20306475e+05\n  2.37825496e+05 -6.78352564e+05 -1.12015339e+05  1.95908697e+06\n  9.31670066e+05 -2.32581844e+04 -4.08331671e+04 -2.72243434e+04\n -1.24215699e+05 -6.65144328e+04  7.82551668e+04  9.50894862e+04\n  1.23867468e+05  1.69968605e+05  3.63706728e+04  1.06668333e+04\n -1.70787797e+03 -4.68454404e+03  5.19197744e+03  3.79051061e+04\n  1.21384679e+05  2.31827418e+05  3.71382365e+05  6.54222259e+05\n  1.27442173e+06  2.76906000e+03 -9.74162127e+03  7.51738139e+05\n  3.09153450e+05  2.57106838e+05  2.63706994e+05  2.59267028e+05\n  5.78711111e+04  1.46043532e+05  8.78372936e+04  8.74385055e+04\n -7.02593434e+03 -2.70173852e+04  1.75928135e+05  1.73800971e+05\n  1.35811211e+05  2.14867058e+05  1.23795746e+03  1.53721181e+04\n  1.19975941e+04  3.49409774e+05  2.03778136e+05  3.04560550e+04\n  1.13656284e+06  4.49381181e+05  6.57771934e+02  1.02716188e+05\n  2.52968298e+05  2.14104385e+05  4.56578516e+04  9.06955831e+04\n  4.01513171e+04  9.04401625e+04  1.01796958e+05  6.76333507e+04\n  1.67270968e+05  1.93439660e+05  1.85741069e+05  1.28052262e+05\n -2.71839696e+04  4.40752426e+05  3.48509696e+05  4.48364862e+05\n  1.30646202e+05  3.55920153e+05  1.24028388e+05  4.73245582e+05\n  5.79752632e+05  3.38159595e+05  3.11374646e+05  3.32591145e+05\n  1.66658216e+05  4.88142387e+05  3.24885975e+05  2.12048546e+05\n  2.00469568e+05  1.70016705e+05  2.69425911e+05  2.55533006e+05\n  1.15272848e+05  6.27142998e+04  1.45897339e+05  7.09597472e+04\n  5.87494447e+04  2.35732213e+05  4.99463293e+04  4.17966781e+04\n  2.12515244e+04  3.75887647e+05]\n"
     ]
    }
   ],
   "source": [
    "# Use scikit-learn to instantiate a linear regression object and fit the model to the data\n",
    "lm = LinearRegression().fit(X, y)\n",
    "# We use the value of R_squared as an indication of the fit \n",
    "print('Intercept :', lm.intercept_)\n",
    "print('R^2 Score : ', lm.score(X, y))\n",
    "print('Coefficients :', lm.coef_)"
   ]
  },
  {
   "source": [
    "## **Train-Test Split**"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.8733\n",
      "Training MAE:  81864\n",
      "Training MSE:  1.7850e+10\n",
      "Training RMSE:  133605\n",
      "Training Bias:  0.0\n",
      "Training Variance:  1.2307e+11\n",
      "\n",
      "Testing R^2 Score:  0.8526\n",
      "Testing MAE:  81199\n",
      "Testing MSE:  1.9607e+10\n",
      "Testing RMSE:  140024\n",
      "Testing Bias:  -2394.05\n",
      "Testing Variance:  1.0973e+11\n",
      "\n",
      "Average MSE:  2.0628e+10\n",
      "Average Bias:  1.9419e+10\n",
      "Average Variance:  1.2089e+09\n"
     ]
    }
   ],
   "source": [
    "X_tr, X_tt, y_tr, y_tt = train_test_split(X, y, random_state=42, test_size=0.2, shuffle=True)\n",
    "model = LinearRegression()\n",
    "lm = model.fit(X_tr, y_tr)\n",
    "y_tr_pred = lm.predict(X_tr)\n",
    "y_tt_pred = lm.predict(X_tt)\n",
    "\n",
    "train_test_metrics(y_tr, y_tt, y_tr_pred, y_tt_pred)\n",
    "print(\"\")\n",
    "av_mse, av_bias, av_var = bias_variance_decomp(model, X_tr.values, y_tr.values, X_tt.values, y_tt.values, loss='mse', random_seed=42)    \n",
    "print('Average MSE: ', \"{:.4e}\".format(av_mse))\n",
    "print('Average Bias: ', \"{:.4e}\".format(av_bias))\n",
    "print('Average Variance: ', \"{:.4e}\".format(av_var))\n",
    "\n"
   ]
  },
  {
   "source": [
    "**Initial thoughts:**\n",
    "\n",
    "Comparing the training and testing RMSE, there is expected increase in RMSE from the training to the testing set.  The model still includes all the polynomial and interaction features, so it is definitely still overfit."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "\n",
    "## Check for the Linear Regression assumptions\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this defines residuals as the sample estimate of the error for each observation\n",
    "residuals = (y_tt - y_tt_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This checks for the normal distribution of the residuals or error term.  By satisfying this assumption, you are able to generate more reliable confidence and prediction intervals.\n",
    "# plt.hist(residuals)\n",
    "# plt.savefig('images/residuals.png')"
   ]
  },
  {
   "source": [
    "<img src='images/residuals.png'>"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # We use residplot to check for heteroscedasticity, which is the case where the residuals have a non-constant variance\n",
    "# sns.residplot(y_tt_pred, y_test, lowess=True, color='g')\n",
    "# plt.savefig('images/residplot.png')"
   ]
  },
  {
   "source": [
    "<img src='images/residplot.png'>"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# Feature Selection"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## KBest"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['sqft_living',\n",
       " 'sqft_above',\n",
       " 'sqft_living15',\n",
       " 'viewed',\n",
       " 'sqft_living^2',\n",
       " 'sqft_living waterfront',\n",
       " 'sqft_living sqft_above',\n",
       " 'sqft_living sqft_living15',\n",
       " 'sqft_living age',\n",
       " 'sqft_living basement',\n",
       " 'sqft_living viewed',\n",
       " 'waterfront sqft_above',\n",
       " 'sqft_above^2',\n",
       " 'sqft_above sqft_living15',\n",
       " 'sqft_above basement',\n",
       " 'sqft_above viewed',\n",
       " 'sqft_living15^2',\n",
       " 'sqft_living15 viewed',\n",
       " 'viewed^2',\n",
       " 'grd_11']"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "# Instantiate SelectKBest object and fit training data where k is the number of features you want to select\n",
    "X_tr, X_tt, y_tr, y_tt = train_test_split(X, y, random_state=42, test_size=0.2, shuffle=True)\n",
    "kb = SelectKBest(f_regression, k=20)\n",
    "kbest = kb.fit(X_tr, y_tr)\n",
    "sel_columns = X_tr.columns[kbest.get_support()]\n",
    "rem_columns = X_tr.columns[~kbest.get_support()]\n",
    "list(sel_columns)"
   ]
  },
  {
   "source": [
    "sqft_living, sqft_above, viewed, sqft_living15, grd_11 seem to have a strong correlation "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.6584\n",
      "Training MAE:  147198\n",
      "Training MSE:  4.8142e+10\n",
      "Training RMSE:  219412\n",
      "Training Bias:  -0.0\n",
      "Training Variance:  9.2775e+10\n",
      "\n",
      "Testing R^2 Score:  0.6393\n",
      "Testing MAE:  145770\n",
      "Testing MSE:  4.7994e+10\n",
      "Testing RMSE:  219074\n",
      "Testing Bias:  -1326.19\n",
      "Testing Variance:  7.8672e+10\n",
      "\n",
      "Average MSE:  2.0628e+10\n",
      "Average Bias:  1.9419e+10\n",
      "Average Variance:  1.2089e+09\n"
     ]
    }
   ],
   "source": [
    "# Instantiate linear regression object and fit the linear regression to the data\n",
    "kbest = LinearRegression().fit(X_tr[sel_columns], y_tr)\n",
    "y_kb_tr_pred = kbest.predict(X_tr[sel_columns])\n",
    "y_kb_tt_pred = kbest.predict(X_tt[sel_columns])\n",
    "\n",
    "train_test_metrics(y_tr, y_tt, y_kb_tr_pred, y_kb_tt_pred)\n",
    "print(\"\")\n",
    "kb_mse, kb_bias, kb_var = bias_variance_decomp(kbest, X_tr.values, y_tr.values, X_tt.values, y_tt.values, loss='mse', random_seed=42)    \n",
    "print('Average MSE: ', \"{:.4e}\".format(kb_mse))\n",
    "print('Average Bias: ', \"{:.4e}\".format(kb_bias))\n",
    "print('Average Variance: ', \"{:.4e}\".format(kb_var))"
   ]
  },
  {
   "source": [
    "## RFECV\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Optimal number of features : 201\n"
     ]
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "rfe = RFECV(estimator=lm, step=1, cv=5, n_jobs=-1, scoring='neg_mean_squared_error')\n",
    "rfecv = rfe.fit(X_tr, y_tr)\n",
    "selected = X_tr.columns[rfe.support_]\n",
    "print(\"Optimal number of features : %d\" % rfe.n_features_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['sqft_lot^2', 'sqft_lot sqft_lot15', 'sqft_lot15^2', 'bth_7.5', 'grd_3']"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "removed = X_tr.columns[~rfe.support_]\n",
    "list(removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.8731\n",
      "Training MAE:  81848\n",
      "Training MSE:  1.7876e+10\n",
      "Training RMSE:  133699\n",
      "Training Bias:  -0.0\n",
      "Training Variance:  1.2306e+11\n",
      "\n",
      "Testing R^2 Score:  0.8529\n",
      "Testing MAE:  81160\n",
      "Testing MSE:  1.9570e+10\n",
      "Testing RMSE:  139893\n",
      "Testing Bias:  -2334.33\n",
      "Testing Variance:  1.0980e+11\n",
      "\n",
      "Average expected loss:  2.1964e+10\n",
      "Average bias:  1.9415e+10\n",
      "Average variance:  2.5486e+09\n"
     ]
    }
   ],
   "source": [
    "rfecv = LinearRegression().fit(X_tr[selected], y_tr)\n",
    "y_tr_pred_rfe = rfecv.predict(X_tr[selected])\n",
    "y_tt_pred_rfe = rfecv.predict(X_tt[selected])\n",
    "\n",
    "train_test_metrics(y_tr, y_tt, y_tr_pred_rfe, y_tt_pred_rfe)\n",
    "print(\"\")\n",
    "rfe_mse, rfe_bias, rfe_var = bias_variance_decomp(rfe, X_tr.values, y_tr.values, X_tt.values, y_tt.values, loss='mse', random_seed=42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(rfe_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(rfe_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(rfe_var))\n"
   ]
  },
  {
   "source": [
    "# Second Run"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['price']\n",
    "dum_feat = df[['bathrooms', 'condition', 'grade', 'zipcode']]\n",
    "dum_index = dum_feat.columns\n",
    "df_dum = pd.get_dummies(data=dum_feat, columns=dum_index, drop_first=True, prefix=['bth', 'cnd', 'grd', 'zip'])\n",
    "poly_feat = df.drop(['price', 'bedrooms', 'bathrooms', 'floors', 'condition', 'grade', 'zipcode', 'sqft_lot15', 'sqft_above', 'lat', 'long', 'sqft_lot'], axis=1)\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "poly_data = poly.fit_transform(poly_feat)\n",
    "poly_columns = poly.get_feature_names(poly_feat.columns)\n",
    "df_poly = pd.DataFrame(poly_data, columns=poly_columns)\n",
    "X = pd.concat([df_poly, df_dum], axis=1)"
   ]
  },
  {
   "source": [
    "- Removed `sqft_lot15` and `sqft_lot` as poorly correlated due to RFECV\n",
    "- Removed `lat` and `long` as `zipcode` is better indicator\n",
    "- Removed `bedrooms` and `floors` as they are weaker indicators\n",
    "- Removed `sqft_above` due to multicollinearity"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sqft_living  waterfront  sqft_living15  sale_age   age  renovated  \\\n",
       "0       2070.0         0.0         2390.0      28.0  28.0        0.0   \n",
       "1       2900.0         0.0         2370.0      37.0  37.0        0.0   \n",
       "2       3770.0         0.0         3710.0      17.0  17.0        0.0   \n",
       "3       4560.0         0.0         4050.0      25.0  25.0        0.0   \n",
       "4       2550.0         0.0         2250.0      10.0  10.0        0.0   \n",
       "\n",
       "   basement  viewed  sqft_living^2  sqft_living waterfront  ...  zip_98146  \\\n",
       "0       0.0     0.0      4284900.0                     0.0  ...          0   \n",
       "1       1.0     0.0      8410000.0                     0.0  ...          0   \n",
       "2       0.0     1.0     14212900.0                     0.0  ...          0   \n",
       "3       0.0     1.0     20793600.0                     0.0  ...          0   \n",
       "4       0.0     0.0      6502500.0                     0.0  ...          0   \n",
       "\n",
       "   zip_98148  zip_98155  zip_98166  zip_98168  zip_98177  zip_98178  \\\n",
       "0          0          0          0          0          0          0   \n",
       "1          0          0          0          0          0          0   \n",
       "2          0          0          0          0          0          0   \n",
       "3          0          0          0          0          0          0   \n",
       "4          0          0          0          0          0          0   \n",
       "\n",
       "   zip_98188  zip_98198  zip_98199  \n",
       "0          0          0          0  \n",
       "1          0          0          0  \n",
       "2          0          0          0  \n",
       "3          0          0          0  \n",
       "4          0          0          0  \n",
       "\n",
       "[5 rows x 157 columns]"
      ],
      "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>sqft_living</th>\n      <th>waterfront</th>\n      <th>sqft_living15</th>\n      <th>sale_age</th>\n      <th>age</th>\n      <th>renovated</th>\n      <th>basement</th>\n      <th>viewed</th>\n      <th>sqft_living^2</th>\n      <th>sqft_living waterfront</th>\n      <th>...</th>\n      <th>zip_98146</th>\n      <th>zip_98148</th>\n      <th>zip_98155</th>\n      <th>zip_98166</th>\n      <th>zip_98168</th>\n      <th>zip_98177</th>\n      <th>zip_98178</th>\n      <th>zip_98188</th>\n      <th>zip_98198</th>\n      <th>zip_98199</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2070.0</td>\n      <td>0.0</td>\n      <td>2390.0</td>\n      <td>28.0</td>\n      <td>28.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4284900.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2900.0</td>\n      <td>0.0</td>\n      <td>2370.0</td>\n      <td>37.0</td>\n      <td>37.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>8410000.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3770.0</td>\n      <td>0.0</td>\n      <td>3710.0</td>\n      <td>17.0</td>\n      <td>17.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>14212900.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4560.0</td>\n      <td>0.0</td>\n      <td>4050.0</td>\n      <td>25.0</td>\n      <td>25.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>20793600.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2550.0</td>\n      <td>0.0</td>\n      <td>2250.0</td>\n      <td>10.0</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6502500.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 157 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.8631\n",
      "Training MAE:  84320\n",
      "Training MSE:  1.9295e+10\n",
      "Training RMSE:  138906\n",
      "Training Bias:  0.0\n",
      "Training Variance:  1.2162e+11\n",
      "\n",
      "Testing R^2 Score:  0.8499\n",
      "Testing MAE:  82078\n",
      "Testing MSE:  1.9971e+10\n",
      "Testing RMSE:  141320\n",
      "Testing Bias:  -2879.55\n",
      "Testing Variance:  1.0970e+11\n",
      "\n",
      "Average expected loss:  2.0858e+10\n",
      "Average bias:  1.9846e+10\n",
      "Average variance:  1.0126e+09\n"
     ]
    }
   ],
   "source": [
    "X_tr2, X_tt2, y_tr2, y_tt2 = train_test_split(X, y, random_state=42, test_size=0.2)\n",
    "lm2 = LinearRegression().fit(X_tr2, y_tr2)\n",
    "y_tr_pred2 = lm2.predict(X_tr2)\n",
    "y_tt_pred2 = lm2.predict(X_tt2)\n",
    "\n",
    "train_test_metrics(y_tr2, y_tt2, y_tr_pred2, y_tt_pred2)\n",
    "print(\"\")\n",
    "av2_mse, av2_bias, av2_var = bias_variance_decomp(model, X_tr2.values, y_tr2.values, X_tt2.values, y_tt2.values, loss='mse', random_seed=42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(av2_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(av2_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(av2_var))"
   ]
  },
  {
   "source": [
    "Results are deceptively good."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Ridge Regression"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()\n",
    "X_train_scaled = ss.fit_transform(X_tr2)\n",
    "X_test_scaled = ss.fit_transform(X_tt2)\n",
    "X_tr2_sc = pd.DataFrame(X_train_scaled, columns=X_tr2.columns)\n",
    "X_tt2_sc = pd.DataFrame(X_test_scaled, columns=X_tt2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.8631\n",
      "Training MAE:  84298\n",
      "Training MSE:  1.9296e+10\n",
      "Training RMSE:  138909\n",
      "Training Bias:  0.0\n",
      "Training Variance:  1.2158e+11\n",
      "\n",
      "Testing R^2 Score:  0.8432\n",
      "Testing MAE:  85263\n",
      "Testing MSE:  2.0859e+10\n",
      "Testing RMSE:  144427\n",
      "Testing Bias:  9477.89\n",
      "Testing Variance:  1.1986e+11\n",
      "\n",
      "Average expected loss:  1.0514e+35\n",
      "Average bias:  1.1354e+33\n",
      "Average variance:  1.0401e+35\n"
     ]
    }
   ],
   "source": [
    "ridge = Ridge(alpha=1).fit(X_tr2_sc, y_tr2)\n",
    "y_ridge_tr = ridge.predict(X_tr2_sc)\n",
    "y_ridge_tt = ridge.predict(X_tt2_sc)\n",
    "\n",
    "train_test_metrics(y_tr2, y_tt2, y_ridge_tr, y_ridge_tt)\n",
    "print(\"\")\n",
    "rdg_mse, rdg_bias, rdg_var = bias_variance_decomp(model, X_tr2_sc.values, y_tr2.values, X_tt2_sc.values, y_tt2.values, loss='mse', random_seed=42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(rdg_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(rdg_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(rdg_var))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Optimal Alpha Value: 391\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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8AGBKfq8k8atf/UrPPfdclcf0799f/fv3r3Wngiqiofc2MygAMCXDnge1aNEi9e/fX/Hx8YqPj9fgwYO1fv36oLfrM4Oikg8ATMmvGdS8efOqdVKbzaaHHnqoymNatGihp556Su3bt1d5ebmWL1+u22+/XRs3btQVV1xRrfaqJZL1+AAgFPgVUNOnT/c8nND9vxLtqvgTUMOHD/fa/vOf/6wlS5YoLy8vuAFVsUiCe1AAYEp+34Nq2LChhg4dqtGjRwc8QFwul9544w2dPHlSvXv3Dui5K3JHet+D4hIfAJiTXwH1ySefKCcnR//5z3/03//+V4mJibrlllt08803q1WrVjVu/IsvvlBaWppKSkoUFRWlZcuWKTk5ucr3OByOGrcnSSdLy3TJedsH9u/V8YaXXPB4XFxt/03gizENDsa15i40drUZ04SEhCpftzmdzotfszvPxx9/7Akqp9Op3r1769Zbb1V6erqio6Or1bnS0lIVFBTo2LFjWrNmjbKysrR27VolJSVV6zz+cjgc6rJ+mcLzfv5GdMnEDJ3tc21Q2rMCh8Nx0V8yVA9jGhyMa/Wcvzi4JDmdTp9jgj2m1a7i69evn55//nl9/fXX+ve//63LL79cjz/+uBITE/Wvf/2rWueKjIxUu3bt1K1bN2VkZKhLly5Bf3qv26fMnEt8AGBGNX6ibnh4uAYMGCCn06mCggLl5eXp4MGDtepMeXl5pQ9FDChWkgCAkFDtgHK5XHrvvfeUk5Ojd955Ry6XSwMHDtTSpUs1bNgwv88zffp0paWlqWXLliouLtaqVau0efNm5eTkVLdL1UKRBACEBr8D6lyhxBtvvKGjR4+qT58+evrppzVy5Mhq33uSpMLCQt133306dOiQmjVrpuTkZK1atcrn8R0B51NmzgwKAMzIr4BKSUnRt99+q8TERE2ePLnW1XuStGDBglq9v8ZYSQIALqqyooi65ldA7d+/X40bN5bL5dLy5cu1fPnyKo+32Wzatm1bQDoYaO6KK0nwRV0AMCW/Aqp///6elSRCnk+RBAEFAGbkV0C99dZb1Tqpy+WqUWfqAkUSABAaArqa+ZkzZ7Ro0SJ17949kKcNLIokACAk+F3Fd+bMGb3zzjvau3evoqOjNXToUMXFxUmSTp06pYULF2rBggX68ccf1a5du6B1uNb4oi4AhAS/AuqHH37Q8OHDtW/fPs9q5lFRUVqxYoUaNGige++9VwcOHFDv3r01Z84cXX/99UHtdG34Pg+KGRQAVDRlyhSv7eo+dikQ/AqomTNn6ttvv9VDDz2k/v37a//+/Zo1a5amTJmiH3/8UUlJSVqyZIn69u0b7P7WHkUSAHBRWVlZXtumDaiNGzfq9ttv1/Tp0z37mjdvrnHjxiktLU3Lly9XgwaGPZy3WnyKJLgHBQCm5FeqHDp0SD179vTa16tXL0nSHXfcETLhJIkZFACECL+SxeVyqVGjRl77zm03a9Ys8L0KJh75DgAhwe8qvn379mn79u2e7ePHj0v66XkgTZs29Tm+R48eAehe4PkUSbCSBACYkt8BlZmZqczMTJ/9f/zjH7223W63bDabjhw5UvveBQOX+AAgJPgVUPPnzw92P+qM70oSFEkAgBn5FVC33XZbsPtRd5hBAUBICKHyuwCpLKD+9+VjAIB5WC+gGoTJHfbzxNHmdktnywzsEACgMtYLKEmKZD0+ADA7SwaUb6k5hRIAYDaWDCgKJQDA/KwZUKwmAQCm5/cXdesT30dulIo6PgD42caNG43ugjUDiocWAkDVUlJSjO6CNS/xUSQBAOZnWEA9//zzuvbaaxUfH6/27dvr1ltv1Zdfflk3jVMkAQCmZ1hAbd68Wffcc4/Wr1+vNWvWKDw8XCNHjtTRo0eD33jF70ExgwIA0zHsHtTq1au9thcuXKjWrVtr27ZtGjZsWFDbrqxIAgBgLqYpkiguLlZ5ebnsdnvwG2MlCQCo0iuvvOK1PW7cuDrvg83pdJqiwnrcuHH65ptvtHHjRoWFhV3wOIfDUeu2Wq3LVuz2jZ7t74aM1eFeA2t9XgCoL3r16uW1nZeXF/A2EhISqnzdFDOoxx9/XNu2bdM777xTZThJF/9AVXE4HEpISFDk/2nutb+5/RJF1+K8VnZuTBE4jGlwMK61U9nYBXtMDQ+oxx57TKtXr9abb76ptm3b1k2jVPEBgOkZGlDTpk3T6tWrtXbtWnXs2LHO2qVIAgDMz7CAevjhh7Vy5UotW7ZMdrtdhYWFkqSoqCg1bdo0uI1TZg4ApmfY96AWL16sEydOKD09XZ06dfL8vPDCC8FvnBkUAJieYTMop9NpVNNyU2YOAKZnybX4fIokuMQHAKZjyYCiSAIAzM+SAeX7uA1mUABgNtYMqEhmUABgdpYMqIqX+CiSAADzsWRA+RZJEFAAYDaWDKiKZeZc4gMA8zF8LT5DUGYOAFWaO3eu0V0goCRxDwoAKjDi+U8VcYlPko0ycwAwHUsGFDMoADA/awZUWLjctp8/us3lklxnDewQAKAiawaUzcYsCgBMzppFEtJPAVVa8vN2WanUqIlx/QEAE9m5c6fXdkpKSp33wbIB5Y6MlO3kz9u20lK5jesOAJjKNddc47VtxCOSrHmJT+ISHwCYnGUDitUkAMDcLBtQrCYBAOZm4YDise8AYGaWDSjfp+oygwIAM7FsQFEkAQDmZtmA8imS4B4UAJiKZQOKGRQAmJuhAbVlyxaNGTNGnTt3lt1uV3Z2dt01HklAAYCZGRpQJ0+eVFJSkp599lk1bty4Ttv2KZLgse8AYCqGLnWUlpamtLQ0SdLEiRPrtnEu8QGAqVn2HpQ7gocWAoCZhdxisQ6HIyDvb378uFqet//ooUIdqOW5raq2/ybwxZgGB+Nacxcau9qMaUJCQpWvh1xAXewDVcXhcHjeH7F3t9dr0VFNFFWLc1vV+WOKwGBMg4NxrZ677rrLa7uysQv2mIZcQAWK70oS3IMCgHPmzZtndBesew9KkazFBwBmZugMqri4WPn5+ZKk8vJyFRQUaPfu3YqOjlZ8fHxQ2/YtM6dIAgDMxNAZ1I4dOzRgwAANGDBAp0+fVmZmpgYMGKBnnnkm+I1TZg4ApmboDOrXv/61IY8RlsRKEgBgcpa9B8VKEgBgbpat4uOBhQBwYXa73WvbiKtdlp1BVbwHxUoSAGAulg2ois+DYgYFAOZi2YDyqeKjzBwATMWyAcVKEgBgbpYNKN+VJJhBAYCZWDegwiO8Nm1lZZLbbVBnAAAVWTegGjSQO8I7pCiUAADzsG5ASSx3BAAmZumAolACAMzL0gHls5oEpeYAYBoWDygu8QGAWVk6oCquJsElPgAwD0sHFKtJAIB5WXc1czGDAoALufLKK43ugrUDyvceFDMoAJCkTZs2Gd0FLvF5YQYFAKZh6YDiqboAYF6WDihmUABgXpYOKIokAMC8LB1QlJkDgHkZHlCLFy9W165dFRcXp9TUVG3durXuGucSHwBUKjU11evHCIaWma9evVqPPvqo5syZo759+2rx4sUaPXq0tm3bpvj4+KC373OJjxkUAEiSdu3aZXQXjA2o+fPn67bbbtNdd90lSZo9e7Y++OADLV26VBkZGcHvQIUZVIMD+xX2f7cEv916pNkPBxR24pDR3ahXGNPgYFxrp7K/jeGKrOTIwDEsoEpLS7Vz505NnjzZa//AgQP1ySefBKXNXpubSJu/92zf//1pzT/v9fAdWxS+g4CqjvZGd6AeYkyDg3Gtncbz/uSzr+lN90vdewWtTcPuQRUVFcnlcik2NtZrf2xsrA4dqpv/yzkVFtz0BwDUnOFLHdlsNq9tt9vts+98DoejFq018dra2qyjzqqBwlVei3MCgHXV5m9yQkJCla8bFlAxMTEKCwvzmS0dPnzYZ1Z1vot9oCqdd3lPkr5pcpmu7/pH/ebgR/qFq6Tm5wWAemen19abMd19jujU9JLa/U2+CMMCKjIyUikpKdqwYYNGjhzp2b9hwwbdcMMNQWkz7+pTlQxmS0lDg9KeFTgcjqD+gloRYxocjGs1LV3qtXnt88/7HFK7K1oXZ+glvkmTJun+++9Xjx491KdPHy1dulQHDx7U+PHjjewWAMAEDA2om266SUeOHNHs2bNVWFiozp07KycnR61btzayWwAAEzC8SGLChAmaMGGC0d0AAJiM4UsdAQBQGQIKAGBKBBQAwJRsTqfTbXQnAACoiBkUAMCUCCgAgCkRUAAAUyKgAACmREABAEzJEgG1ePFide3aVXFxcUpNTdXWrVuN7pJpbdmyRWPGjFHnzp1lt9uVnZ3t9brb7VZmZqYSExN12WWXafjw4frqq6+8jjlz5oweeeQRtWvXTi1atNCYMWP0/ffeK8lbyfPPP69rr71W8fHxat++vW699VZ9+eWXXscwrtW3aNEi9e/fX/Hx8YqPj9fgwYO1fv16z+uMae3NmTNHdrtdjzzyiGdfXY5rvQ+o1atX69FHH9Uf/vAHffTRR+rdu7dGjx6t7777zuiumdLJkyeVlJSkZ599Vo0bN/Z5fd68eZo/f77++te/6sMPP1RsbKxuvPFGnThxwnPMY489pjfffFNLlizR22+/rRMnTujWW2+Vy+Wqy49iGps3b9Y999yj9evXa82aNQoPD9fIkSN19OhRzzGMa/W1aNFCTz31lDZt2qQNGzZowIABuv322/X5559LYkxrKy8vT1lZWUpOTvbaX5fjWu+/BzVo0CAlJyfr73//u2df9+7dlZ6eroyMDAN7Zn4tW7bUrFmzdPvtt0v66f+cEhMTde+99+rhhx+WJJ0+fVoJCQmaOXOmxo8fr2PHjqlDhw6aP3++brnlFklSQUGBunTpolWrVmnQoEGGfR6zKC4uVuvWrZWdna1hw4YxrgHUtm1bZWRkaNy4cYxpLRw7dkypqamaN2+eZs2apaSkJM2ePbvOf1fr9QyqtLRUO3fu1MCBA732Dxw4UJ988olBvQpd+/fvV2Fhodd4Nm7cWP379/eM586dO1VWVuZ1TKtWrdSpUyfG/H+Ki4tVXl4uu90uiXENBJfLpddee00nT55U7969GdNamjp1qtLT05Wamuq1v67H1fDVzIOpqKhILpfL5wm9sbGxPk/yxcUVFhZKUqXj+cMPP0iSDh06pLCwMMXExPgcw5j/5NFHH1WXLl3Uu3dvSYxrbXzxxRdKS0tTSUmJoqKitGzZMiUnJ3v+EDKm1ZeVlaX8/HwtXLjQ57W6/l2t1wF1js1m89p2u90+++C/mownY/6Txx9/XNu2bdM777yjsLAwr9cY1+pLSEhQbm6ujh07pjVr1uiBBx7Q2rVrPa8zptXjcDg0Y8YMrVu3TpGRkRc8rq7GtV5f4ouJiVFYWJhPah8+fNjn/wBwcXFxcZJU5Xg2b95cLpdLRUVFFzzGqh577DG99tprWrNmjdq2bevZz7jWXGRkpNq1a6du3bopIyNDXbp00YsvvsiY1tCnn36qoqIi9evXTzExMYqJidGWLVu0ePFixcTE6NJLL5VUd+NarwMqMjJSKSkp2rBhg9f+DRs2qE+fPgb1KnS1adNGcXFxXuNZUlKijz/+2DOeKSkpioiI8Drm+++/19dff23pMZ82bZpWrVqlNWvWqGPHjl6vMa6BU15ertLSUsa0hoYPH66tW7cqNzfX89OtWzeNGjVKubm56tChQ52Oa72/xDdp0iTdf//96tGjh/r06aOlS5fq4MGDGj9+vNFdM6Xi4mLl5+dL+uk/9oKCAu3evVvR0dGKj4/XAw88oDlz5ighIUEdOnTQc889p6ioKN18882SpEsuuUR33nmnnnzyScXGxio6Olp/+tOflJycrGuuucbAT2achx9+WCtXrtSyZctkt9s91/GjoqLUtGlT2Ww2xrUGpk+frrS0NLVs2VLFxcVatWqVNm/erJycHMa0hux2u6d455wmTZooOjpaSUlJklSn41rvA+qmm27SkSNHNHv2bBUWFqpz587KyclR69atje6aKe3YsUMjRozwbGdmZiozM1Njx47VggULNGXKFJ0+fVqPPPKInE6nevToodWrV+sXv/iF5z3PPPOMwsLCNH78eJWUlGjAgAF66aWXfO65WMXixYslSenp6V77p02bpscee0ySGNcaKCws1H333adDhw6pWbNmSk5O9ipjZkyDoy7Htd5/DwoAEJrq9T0oAEDoIqAAAKZEQAEATImAAgCYEgEFADAlAgoAYEoEFFAHunTpogceeKBG733ggQc8S/cAVkJAAQHw6quvym63q1u3bkZ3Bag3CCggAM6tTrJ37159+umnRncHqBcIKKCWDh48qI8++khPPPGE4uPjlZOTY3SXgHqBgAJq6T//+Y8aNWqk6667TqNGjdLq1atVVlZW5Xtyc3Nlt9uVk5OjZ555RomJibr88ss1cuRIORyOSt9z6NAhjR8/XvHx8WrTpo2mTJmikpISr2Oys7OVnp6ujh07qnnz5urRo4fmzp2r8vLygH1eoK4QUEAtrVy5UkOGDFHTpk11880368iRI3r//ff9eu/cuXO1Zs0aPfjgg5o0aZK2b9+uESNG6MiRI17HlZeX68Ybb1RERISeeuopDR8+XFlZWZo1a5bXcYsWLVLz5s01efJkT/BNnz5df/nLXwL2eYG6Uu9XMweC6auvvtLnn3+uadOmSZKuuOIKz4r5w4YNu+j7f/zxR+Xl5XkecfDrX/9a6enp+sc//qEnn3zSc1xZWZmGDRumJ554QpJ09913y+l0Kisry+u4t99+W02aNPFsT5gwQZMnT9bChQs1bdo0NWzYMBAfG6gTzKCAWli5cqWaNWumtLQ0z75Ro0Zp3bp1On78+EXfP2bMGK/n76Smpqpz58569913fY695557vLavuuoqFRUV6cSJE55958LJ5XLJ6XSqqKhIV199tU6ePHnBS4eAWTGDAmrI7XZr1apVuuqqq3Tw4EHP/l69eqmkpERr1qzRHXfcUeU52rdvX+m+3Nxcr30RERG6/PLLvfadC7ajR496nsXz8ccfa8aMGdq+fbtKS0u9jj927Jjfnw0wAwIKqKHc3FwVFBSooKBA69at83k9JyfnogFls9l89rndvo9oa9Dgwhc7zh2/b98+3XjjjWrXrp0yMzPVqlUrNWzYULt27VJGRgaFEgg5BBRQQzk5OYqOjtYLL7zg89qmTZu0ZMkSHThwQC1atLjgOfbs2eOzLz8/X/Hx8dXuz9tvv62SkhKtWLHC64nR+/fvr/a5ADPgHhRQA+cu4Q0ePFjXX3+9z8+kSZNUXl6uVatWVXmeFStWyOl0erY3bdqkr776SoMHD652n849Tvv8GdiZM2f08ssvV/tcgBkwgwJq4FwRxHXXXVfp623btlXnzp21cuVKPfTQQxc8T2xsrIYOHao77rhDx44d00svvaTmzZvrwQcfrHafBg0apMjISI0ZM0bjxo1TaWmpVqxYUeXlQcDM+M0FamDlypWKjIzUoEGDLnjM0KFD9cUXX+jzzz+/4DFTp07ViBEj9I9//EMvvPCCunfvrjfffFMxMTHV7lOHDh2UnZ2t8PBwZWRk6KWXXtLQoUM1Y8aMap8LMAOb0+n0vSMLIKhyc3M1YsQILVmyRKNGjTK6O4ApMYMCAJgSAQUAMCUCCgBgStyDAgCYEjMoAIApEVAAAFMioAAApkRAAQBMiYACAJgSAQUAMKX/D45ib4nxqLn3AAAAAElFTkSuQmCC\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "tr_rmse_ridge = []\n",
    "tt_rmse_ridge = []\n",
    "alphas_ridge = []\n",
    "\n",
    "for alpha in np.linspace(0, 400, num=50):\n",
    "    ridge = Ridge(alpha=alpha)\n",
    "    ridge.fit(X_tr2_sc, y_tr2)\n",
    "    tr_pred = ridge.predict(X_tr2_sc)\n",
    "    tr_rmse_ridge.append(np.sqrt(mean_squared_error(y_tr2, tr_pred)))\n",
    "    tt_pred = ridge.predict(X_tt2_sc)\n",
    "    tt_rmse_ridge.append(np.sqrt(mean_squared_error(y_tt2, tt_pred)))\n",
    "    alphas_ridge.append(alpha)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alphas_ridge, tr_rmse_ridge, label=\"Train\")\n",
    "ax.plot(alphas_ridge, tt_rmse_ridge, label=\"Test\")\n",
    "ax.set_xlabel(\"Alpha\")\n",
    "ax.set_ylabel(\"RMSE\")\n",
    "optimal_alpha = alphas_ridge[np.argmin(tt_rmse_ridge)]\n",
    "ax.axvline(optimal_alpha, color=\"black\", linestyle=\"--\")\n",
    "print(f'Optimal Alpha Value: {int(optimal_alpha)}')\n",
    "plt.savefig('images/optimal_ridge.png')\n",
    "\n",
    "# Optimal Alpha Value: 391"
   ]
  },
  {
   "source": [
    "<img src=\"images/optimal_ridge.png\">"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.8587\n",
      "Training MAE:  84903\n",
      "Training MSE:  1.9905e+10\n",
      "Training RMSE:  141083\n",
      "Training Bias:  0.0\n",
      "Training Variance:  1.1803e+11\n",
      "\n",
      "Testing R^2 Score:  0.8478\n",
      "Testing MAE:  84424\n",
      "Testing MSE:  2.0248e+10\n",
      "Testing RMSE:  142296\n",
      "Testing Bias:  9477.89\n",
      "Testing Variance:  1.1605e+11\n",
      "\n",
      "Average expected loss:  1.0514e+35\n",
      "Average bias:  1.1354e+33\n",
      "Average variance:  1.0401e+35\n"
     ]
    }
   ],
   "source": [
    "ridge = Ridge(alpha=391, normalize=False)\n",
    "ridge.fit(X_tr2_sc, y_tr2)\n",
    "y_best_ridge_tr = ridge.predict(X_tr2_sc)\n",
    "y_best_ridge_tt = ridge.predict(X_tt2_sc)\n",
    "\n",
    "train_test_metrics(y_tr2, y_tt2, y_best_ridge_tr, y_best_ridge_tt)\n",
    "print(\"\")\n",
    "rdgb_mse, rdgb_bias, rdgb_var = bias_variance_decomp(model, X_tr2_sc.values, y_tr2.values, X_tt2_sc.values, y_tt2.values, loss='mse', random_seed=42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(rdgb_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(rdgb_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(rdgb_var))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                          importance\n",
       "zip_98004               70828.327238\n",
       "sqft_living waterfront  68649.541461\n",
       "zip_98039               50283.847517\n",
       "zip_98112               48713.201931\n",
       "grd_11                  46598.172716\n",
       "sqft_living^2           46251.790675\n",
       "sqft_living             45261.686548\n",
       "grd_10                  42766.071827\n",
       "grd_12                  39533.385732\n",
       "sqft_living age         38363.803677\n",
       "zip_98040               36384.362192\n",
       "grd_13                  34820.203826\n",
       "sale_age                33063.052700\n",
       "zip_98033               32955.583912\n",
       "age                     32823.613157\n",
       "zip_98119               32189.488616\n",
       "zip_98103               31388.299615\n",
       "zip_98115               31375.466484\n",
       "sqft_living15 age       30601.266746\n",
       "zip_98105               29971.195358"
      ],
      "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>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>zip_98004</th>\n      <td>70828.327238</td>\n    </tr>\n    <tr>\n      <th>sqft_living waterfront</th>\n      <td>68649.541461</td>\n    </tr>\n    <tr>\n      <th>zip_98039</th>\n      <td>50283.847517</td>\n    </tr>\n    <tr>\n      <th>zip_98112</th>\n      <td>48713.201931</td>\n    </tr>\n    <tr>\n      <th>grd_11</th>\n      <td>46598.172716</td>\n    </tr>\n    <tr>\n      <th>sqft_living^2</th>\n      <td>46251.790675</td>\n    </tr>\n    <tr>\n      <th>sqft_living</th>\n      <td>45261.686548</td>\n    </tr>\n    <tr>\n      <th>grd_10</th>\n      <td>42766.071827</td>\n    </tr>\n    <tr>\n      <th>grd_12</th>\n      <td>39533.385732</td>\n    </tr>\n    <tr>\n      <th>sqft_living age</th>\n      <td>38363.803677</td>\n    </tr>\n    <tr>\n      <th>zip_98040</th>\n      <td>36384.362192</td>\n    </tr>\n    <tr>\n      <th>grd_13</th>\n      <td>34820.203826</td>\n    </tr>\n    <tr>\n      <th>sale_age</th>\n      <td>33063.052700</td>\n    </tr>\n    <tr>\n      <th>zip_98033</th>\n      <td>32955.583912</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>32823.613157</td>\n    </tr>\n    <tr>\n      <th>zip_98119</th>\n      <td>32189.488616</td>\n    </tr>\n    <tr>\n      <th>zip_98103</th>\n      <td>31388.299615</td>\n    </tr>\n    <tr>\n      <th>zip_98115</th>\n      <td>31375.466484</td>\n    </tr>\n    <tr>\n      <th>sqft_living15 age</th>\n      <td>30601.266746</td>\n    </tr>\n    <tr>\n      <th>zip_98105</th>\n      <td>29971.195358</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "top_feat_ridge = pd.DataFrame(np.abs(ridge.coef_), index=np.array(X_tr2_sc.columns), columns=['importance']).sort_values('importance', ascending=False)\n",
    "top_feat_ridge.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                      importance\n",
       "zip_98077             918.701522\n",
       "zip_98019             883.466010\n",
       "zip_98108             859.384422\n",
       "bth_0.75              836.315150\n",
       "bth_2.25              607.925230\n",
       "zip_98065             544.369445\n",
       "zip_98070             514.810798\n",
       "zip_98106             409.487747\n",
       "bth_0.5               401.640598\n",
       "zip_98014             356.789094\n",
       "sqft_living basement  340.938326\n",
       "bth_3.0               302.104133\n",
       "waterfront viewed     296.404157\n",
       "waterfront^2          296.404157\n",
       "waterfront            296.404157\n",
       "bth_5.75              269.905592\n",
       "bth_2.75               36.326469\n",
       "zip_98045              27.261180\n",
       "bth_7.5                 0.000000\n",
       "grd_3                   0.000000"
      ],
      "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>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>zip_98077</th>\n      <td>918.701522</td>\n    </tr>\n    <tr>\n      <th>zip_98019</th>\n      <td>883.466010</td>\n    </tr>\n    <tr>\n      <th>zip_98108</th>\n      <td>859.384422</td>\n    </tr>\n    <tr>\n      <th>bth_0.75</th>\n      <td>836.315150</td>\n    </tr>\n    <tr>\n      <th>bth_2.25</th>\n      <td>607.925230</td>\n    </tr>\n    <tr>\n      <th>zip_98065</th>\n      <td>544.369445</td>\n    </tr>\n    <tr>\n      <th>zip_98070</th>\n      <td>514.810798</td>\n    </tr>\n    <tr>\n      <th>zip_98106</th>\n      <td>409.487747</td>\n    </tr>\n    <tr>\n      <th>bth_0.5</th>\n      <td>401.640598</td>\n    </tr>\n    <tr>\n      <th>zip_98014</th>\n      <td>356.789094</td>\n    </tr>\n    <tr>\n      <th>sqft_living basement</th>\n      <td>340.938326</td>\n    </tr>\n    <tr>\n      <th>bth_3.0</th>\n      <td>302.104133</td>\n    </tr>\n    <tr>\n      <th>waterfront viewed</th>\n      <td>296.404157</td>\n    </tr>\n    <tr>\n      <th>waterfront^2</th>\n      <td>296.404157</td>\n    </tr>\n    <tr>\n      <th>waterfront</th>\n      <td>296.404157</td>\n    </tr>\n    <tr>\n      <th>bth_5.75</th>\n      <td>269.905592</td>\n    </tr>\n    <tr>\n      <th>bth_2.75</th>\n      <td>36.326469</td>\n    </tr>\n    <tr>\n      <th>zip_98045</th>\n      <td>27.261180</td>\n    </tr>\n    <tr>\n      <th>bth_7.5</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>grd_3</th>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "top_feat_ridge.tail(20)"
   ]
  },
  {
   "source": [
    "## Lasso Regression"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.863\n",
      "Training MAE:  84308\n",
      "Training MSE:  1.9304e+10\n",
      "Training RMSE:  138937\n",
      "Training Bias:  0.0\n",
      "Training Variance:  1.2159e+11\n",
      "\n",
      "Testing R^2 Score:  0.8432\n",
      "Testing MAE:  85244\n",
      "Testing MSE:  2.0857e+10\n",
      "Testing RMSE:  144420\n",
      "Testing Bias:  9477.89\n",
      "Testing Variance:  1.1950e+11\n",
      "\n",
      "Average expected loss:  1.0514e+35\n",
      "Average bias:  1.1354e+33\n",
      "Average variance:  1.0401e+35\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso(alpha=1).fit(X_tr2_sc, y_tr2)\n",
    "y_lasso_tr = lasso.predict(X_tr2_sc)\n",
    "y_lasso_tt = lasso.predict(X_tt2_sc)\n",
    "\n",
    "train_test_metrics(y_tr2, y_tt2, y_lasso_tr, y_lasso_tt)\n",
    "print(\"\")\n",
    "lss_mse, lss_bias, lss_var = bias_variance_decomp(model, X_tr2_sc.values, y_tr2.values, X_tt2_sc.values, y_tt2.values, loss='mse', random_seed=42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(lss_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(lss_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(lss_var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Optimal Alpha Value: 857\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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g165dDBs2zB7z5Zdf8sADD7B//37CwsIASE1NZdq0aWRmZuLj48OqVauYO3cuR44cwWCwbSj/8ssv884773Dw4EGUBvgBn5mZSVRUlNNft7FScrNw37oJ97QtKCV1rwi2hN1I5R1DqOxzN6qv/3WqYePT0j4jlyPt4Ujao7qGbpNGvfq3srKS5ORkUlJSiI6Ovmx8SUkJq1evJiwsjIgI22rT9PR0vL296dmzpz2uV69eeHl5sXv3bntMdHS0PaECxMfHYzKZ2Ldvnz2md+/e9oRaFZOdnc2JEyec8XZbPDUoDPPYSZQsWUv5xL9R2blbrbGarF/Rpb6J5xP3o180E+2u/8p9r0IIl2vUC5Xmz5+Pn58fjz32WJ1xK1euZM6cOZSUlBAVFcWmTZvQ6WwrR/Py8vD393foSSqKQps2bcjLy7PHBAQEOLymv78/Go3GISY0NNQhpuqavLw8IiMja6xbZua1bc13rdc3WW3aQeJkdOdy8d+3gxt+3In771YNAyiqFe3+dLT707HoDBg730ZhlzguREaDm8YFFb/+WuxnpBbSHo6kPaq7lja5XC+30SbVHTt28OGHH5KWlnbZ2Pvvv58BAwaQk5PDsmXLePjhh/niiy/w9PQEqHFoVlXVaom2JnXFVC1Sqmvo91qGGWToBiAKet6JqbKSU5//i4jMvWj2p6NYrdUiNaYy/H/cif+PO7H6+FEZ15/KngOxduwCbo16UOaqyWfEkbSHI2mP6hq6TRptUk1LSyMnJ8dh2NdisTBnzhyWL1/OwYMH7eWtW7emdevWdOjQgR49ehAZGcmmTZsYM2YMgYGB5OfnOyRRVVUpKCiw9zQDAwPtQ8FVCgoKsFgsDjFVvdYq+fn5ANV6uaIBaLWc73wb5SOSUIwFaL/binbnF2hOHq0x3K2oEI+v1uPx1Xqs/kFU9hxAZVx/rJHRssBJCNFgGm1STU5OZuTIkQ5lCQkJJCQk8PDDD9d6naqqqKpqvx0mLi6O4uJi0tPT7fOq6enplJSU2I/j4uJYtGgRp0+fpm3btgBs27YNnU5Ht27d7DFz586lvLwcvV5vjwkJCaFdu3ZOfe+ibqqvPxVD76di6P24nfwF7c4v0O76ErfzhTXGuxXk4rH5Yzw2f4z1hgAqb70Dy613YInpBlr361t5IZqgqrUlVap+LorqXJpUi4uLOXbsGABWq5WsrCwyMjLw8/MjPDy8Wg9Qq9USFBRk77ofO3aMTZs20b9/f/z9/Tlz5gyLFy/Gw8ODIUOGABAdHc2gQYOYMWMGS5cuRVVVZsyYwZAhQ+yvM3DgQGJiYpg4cSLz5s2jsLCQ2bNnM27cOHx8fABITExkwYIFTJo0iZSUFI4ePcqSJUuYOXNmg6z8FfVjjeiAOWIS5gfGozn4g60H+30aSllJjfFu587i8d8N8N8NqAYvKm/uieW2O2x7FNfwdB0hBPTv39/h+Pf3+YvfuDSp7t27lxEjRtiP58+fz/z58xk7dizLly+/7PUeHh7s2LGD1157jfPnzxMYGEifPn348ssvCQr6bVOBFStWMGvWLEaPHg3AsGHDWLhwof28RqMhNTWVlJQUhg4dil6vJzExkXnz5tljWrduzfr160lJSWHAgAH4+voyefJkpkyZ4oymENdKo8VyUxyWm+IwPTwDzf50tLu3ot37LUotq4KVshLcd2/FffdWVEXBGt4eS/QtWKJvxtrpZtTWN1znNyGEaOoazX2qojpZZODoqtqjvBTt3l1o93yNZv8eFHN5vS+1Bodjib4Zy42dsbaNxNo2stH1ZuUz4kjaw5Gz2sPX19fhuCn3VFvsQiUhnELvSWXveCp7x4PZhObg92i/34Fm3y7cimqeg63ilnMKt5xTuG//zF5m9W1jT7DWtpGoQW2xtglG9QsArfx3EqKlk58CouXw0GHp1gdLtz5gteD2yyG0P+xEk/Edmqxf6/USbsZ83Iz5cOD/HMpVRUH19Uf1D8baJgjVPxDVLwCrXwCqnz+qXxvbcLJG/ssJ0ZzJ/3DRMrlpsEZ1xRzVFZImQHERmiP70Rz+Ec2R/bgdP1zjvbC1UVQVpTAfCvPRHP2pxhhVUVB9/GwJtlVrVO/WqF6twNvH/nfV2we19Q22BN3Kt9neXytEcyVJVQgAbx8st92B5bY7bMflpWiOHsTt6AHcsn7F7fRx3HJPoVgsV/0tFFVFOX8Ozp+rV7zq5obqcwOq78Uk29r/YjL2sX15+eBlLELx8rAlZp0B3D0kEQvhQpJUhaiJ3hNL19uxdL39t7LKCtxysmwJ9vRxlOyTuBXkoOTn4lbPRHklFKsVxZgPxvxaYzrVUKZ66EGnQ9XpwUOPqtOjGrzA4IVq8EL19AKDN6rB8+Kx98Vzng5x6A2yUYYQV0iSqhD1pXXHGnYj1rAbq58zm1DOncWtIBelINf2Z2EBijEfpdD25XbBeF2qqZjLwVyOcuH8Nb2OqriB3oCqN4DeE1XviWrwvFjm9VuP+eJQNlVD2hfLcfdw0jsSoumQpCqEM3joUIPDsASH1R5TWYFiLLB9FZ9HKb6AUlx08e9FUHIB5YIRt/PnbDGlxdev/jVQVCuUldS6kcblqHpPh6SrXpw7xssb1fOSL69W4Olt6zUbvEDvKSupRZMln1whrhetO2qbYNQ2wfWLN5tQqhKsscA2xFxywZ6ElZILmPJzMVRW2J4/aypHqTA37Hu4Akp5KUp5KeTnXPG1qrvHxaFoWw8ZgyeqznBxaNs2pI3eYJtH1ulR3T3A3QO/gnNozufYeskeHrZyrbut7bXu4H7J37XuMgfdUlgtUFz9KVcNQZKqEI2Vhw41IAQ1IASAmpZIVbuR3Wq1Df2aTbYkayqD8jKUslJbr7O0GKWq91laglJW/Nu5slKU8qry0ivaKMPZlAqz7ReEy9xL/HuRV/G9VHd3cNeheugu/mlL0Gg9bOd+n5TdPWxJXWewXaPT/5bcq8r1nheHzW1/R2eQ3rczqCpUmG2f6dILKBfOO4z22L8uGC9+nUe5YLT9MqqqVPQfAXf+oUGrKP/KQjQnbm72+U+Aa9ouzVJp++FVXmpLvOWlKOVltj9LSy7+ILv4w+ySH25cOI9SUnRFtyS5klJRARUVDT7crmq0toR8sVft+PdLes+X9qS1WtuxRmv7u8b2pWo0vx27uYHiZitz09iOL5b5nj2LpvD0b2VubqBoQGP7Ut1++ztul5QpCihutj9r6MkrZ7Mvvin1ki+rbXW81QKX/KlYKqGyEioroOqXpcoK+5+2X/7KwVR28c+LvwxeLFfKS23nystsf17LCvzrsK5BkqoQomYaLXi1st0/e6XXWi/Oxxafv5hwL+lJlBZDaTFKSbGt51x1XHrB9gO0rMw2n9vMKJZKsFTa3uN1UsOSOqfwShnbQK/csCSpCiGaJje33xJyUB2Lt2qiqrYh7Kph6aqe8u96MA7HFWaoMFN8roBWeo/fekQVZlsvtLLC1iuqrLjk2GzrpYrLSm7n7+oqXDPV09s2PN/AJKkKIRoXRbHNS+oM4Ot/Rb3k41e6WbrVejHxmm3z0BUmFPPFZFxpRqmshIqLCfhiQqbChGIy/TZcaf5dki8vQ6kariwvtZU18d73m93CXV0FO1Wjtc1Ve7b67bauql3JLm6OQtXfW/n+dstX1Zx2ZmaD1k+SqhCi5XJzA93FFcUXixrksV2qau8p/9aDNl9M4CZb79lyMWlXVqJUmu3zkEplhW1+22KxDR9XVtj+XvWnagWrFcU+l2m1zWdarRQXnaeVp+fFMuvF2IsxVfOdVddVHatWsKq2lrDa5kpRVVucogDKxTlXfvs7iuMcrZsGtFVzvBrHlddV88hVc8f2BV76i6u7Db8d6w2244urv9EbbHPNjZgkVSGEaGiK8tviJIOXvbihn7t5xT13cc3kBi0hhBDCSSSpCiGEEE4iSVUIIYRwEplTFUIIUSdfX1+HY6PR6JJ6NAXSUxVCCCGcRJKqEEII4SSSVIUQQggnkaQqhBBCOEm9kmppaSnWej5xwmg0sm/fvmupkxBCCNEk1SuphoWF8a9//ct+XFRUxMCBA9m7d2+12P/85z8MHDjQeTUUQgghmoh6JVVVddxMq7Kykr1791JUVNQglRJCCCGaIplTFUIIIZzEpUl1586djBkzhpiYGHx9fVm9enWtsdOnT8fX15dly5bZywoLC3nqqafo0aMHwcHBdOnShSeffJJz5845XGs0Ghk/fjwRERFEREQwfvz4ajcvnzp1iqSkJEJDQ2nfvj0zZ87EbDY7xBw4cIDhw4cTHBxMTEwMCxYsqNaLF0II0XK5NKmWlJQQGxvLSy+9hMFgqDVu48aN/PDDD4SEhDiUZ2dnk52dzXPPPce3337LW2+9xbfffstjjz3mEJecnExGRgZr165l3bp1ZGRkMGHCBPt5i8VCUlISxcXFbN68mVWrVrFp0yb++te/2mOKiooYNWoUgYGBbN26lZdeeolly5bx2muvOak1hBBCNHX13qZQUZR6lV2JwYMHM3jwYAAmTZpUY8zJkyd5+umn2bBhA4mJiQ7nYmNj+eCDD+zH7du35/nnnycpKYmioiJ8fHw4fPgwX331FVu2bKFnz54ALF68mGHDhpF58bFIW7du5dChQ+zfv5+wsDAAnnvuOaZNm8azzz6Lj48Pa9eupaysjOXLl2MwGIiNjeXIkSO88cYbTJky5ZrbQgghRNNX757q5MmTCQkJISQkhC5dugBw//3328uqvqZOneq0ylVWVpKcnExKSgrR0dH1uubChQvodDo8PT0BSE9Px9vb255QAXr16oWXlxe7d++2x0RHR9sTKkB8fDwmk8l+e1B6ejq9e/d26FHHx8eTnZ3NiRMnrvWtCiGEaAbq1VMdO3ZsQ9ejRvPnz8fPz6/acG5tjEYjL7zwAuPGjUOrtb21vLw8/P39HXqSiqLQpk0b8vLy7DEBAQEOr+Xv749Go3GICQ0NdYipuiYvL4/IyMireo9CCCGaj3ol1TfeeKOh61HNjh07+PDDD0lLS6tXfElJCWPHjiUkJITnn3/e4VxNQ7OqqlZLtDWpK6ZqkVJdQ7+ZmZmXr3wdrvX65kbaozppE0fSHo6c0R6dO3d2+mu60rXUPyoqqs7zjfbRb2lpaeTk5DgM+1osFubMmcPy5cs5ePCgvby4uJj7778fgNTUVPR6vf1cYGAg+fn5DklUVVUKCgrsPc3AwED7UHCVgoICLBaLQ0xVr7VKfn4+QLVe7qUu9w9Ql6o5X2Ej7VGdtIkjaQ9HzmqP7777zgm1aRwa+jNS720KT506Ve0WE4APP/yQkSNH0rNnT/74xz/WuMvS1UhOTmbnzp2kpaXZv0JCQpg0aRIbN260x124cIHExESsVitr1qzB29vb4XXi4uIoLi4mPT3dXpaenk5JSYl9njUuLo7Dhw9z+vRpe8y2bdvQ6XR069bNHrNr1y7Ky8sdYkJCQmjXrp1T3rMQQoimrV5J9eWXXyYuLo7S0lKH8sWLFzNlyhTS0tLIz8/n008/5d5773XoRdaluLiYjIwMMjIysFqtZGVlkZGRwalTpwgICCA2NtbhS6vVEhQUZP8t48KFC4wePRqj0cgbb7xBaWkpubm55Obm2n8BiI6OZtCgQcyYMYM9e/aQnp7OjBkzGDJkiP11Bg4cSExMDBMnTuTHH3/k66+/Zvbs2YwbNw4fHx8AEhMTMRgMTJo0iYMHD7Jp0yaWLFnCpEmTZOWvEEIIoJ5JdceOHQwZMsTh6e/FxcUsWLCA4OBgvv/+e3755Re++uortFotS5Ysqdc337t3L3379qVv376UlZUxf/58+vbty4svvliv6/ft28eePXv4+eef6d69O9HR0favS4dzV6xYQdeuXRk9ejQJCQl07dqVt956y35eo9GQmpqKp6cnQ4cO5dFHH+Xee+9l3rx59pjWrVuzfv16srOzGTBgAE899RSTJ09mypQp9aqrEEII17oem/XUa071xIkT3HPPPQ5l//3vfzGZTEydOpUbb7wRgO7duzN27Fg+/fTTen3zu+66q9rORnXZv3//VV3v5+fH22+/XWdMeHg4qampdcZ06dKFzz///LLfTwghhGupqsqJYgvf5pjYlWtmV66ZlFta0b2Bv2+9kmpRURE33HCDQ9nOnTtRFIX4+HiH8tjYWN555x3n1VAIIYS4DItV5aCxku9yq5KoiexSx0eWfptrontgw9ajXkk1NDSUX3/91aHs22+/xdfXl06dOjmUV1ZW4uXl5bwaCiGEcKl+/fo5HG/fvt1FNflNaaWV789W8F2uid15ZtLzzBRV1D28uyvXzNTGkFR79+7N6tWrefjhh4mMjOTrr7/mwIEDPPDAA9Vif/rpJ9q2bev0igohhHCNH3/80dVV4EKFle9yzezINrEz18S+/Aoqr3CK9NeiSooqG6Z+VeqVVGfNmsUnn3zC7bffTnBwMDk5ORgMBp588kmHuIqKCj799FNGjx7dIJUVQgjRMlyaRHfkmNhXUIHlCpOoXgO3B3jQO0hHnyAPegR6kH38l4ap8EX1SqoRERFs376d119/nePHjzN48GAmTpxYbeh3z5493HrrrSQkJDRIZYUQQjRfFVaVL7PK+eeRUr7MKr/inqifTqFnoI7eQR70DvKgm78HHprre8tjvXdUuvHGG1m0aFGdMX369KFPnz7XXCkhhBAtxxFjBR9klvLxL6XklVkvf8FFN7bS0CtIR69AD3oFeRDVWoubi/cNaLTbFAohhGi+iius/PvXMj44Ukr62eq79dWkU2stdwbruCvENqQb7Klp4FpeuXol1aVLl17RiyqKwrRp066qQkIIIZonVVXZm1/Be0dK+NexMoovM77bvpWG/qF67gz24I5gHUGNMIn+Xr2S6ty5cx02o78cSapCCCGqnDdbWftLKe8dKWX/uYo6Y33cFRLaG3goyovb2rg3uW1g6z38q9PpGDp0KPfffz9du3ZtyDoJIYRo4lRVZc9ZM+8eLmX9r2WUXWbp7p3BHjwU5cUfIvV4auu1g26jVK+kunv3btasWcPatWvZuHEjnTt35oEHHiAxMZGwsLCGrqMQQogmwmiykvpLKe8dLuGgse6bQoMNbjwY5cmDUV6092keS3zq9etAp06d+Nvf/saPP/7IZ599Rq9evVi2bBm33HILw4YN491336WwsLCh6yqEEKIRUlWV73JNTPzmHJ1Ts5m1+3ytCdVNgSFhOlYPvIGfHgjm2e6tm01ChXom1Uv17t2bV199lcOHD/PPf/6TkJAQ/vKXv9C5c2fef//9hqijEEKIRqjQZGX5gWL6bMhj6OZ8Pv6ljHJLzbFhXhqeubUVGYlBpN7dhnvaGdC6Na350vq46l8PtFotffv2xWg0kpWVxZ49e8jJyXFm3YQQQjRCadkm3j9SwqYTZZhqSaIAGgWGhut5uJMX8W11aJphEv29K06qFouFL7/8kjVr1rBlyxYsFgsDBw7knXfeYdiwYQ1RRyGEEI3IiC35dZ4P99bwcCcvHozyJKQJ3AbjTPVOqlWLlTZs2EBhYSE9e/bkhRde4L777sPPz68h6yiEEMIFqrYNrA+NAsPC9TwS7cWA0JbRK61JvZJqt27dOHnyJJ07d2bq1Kmy6lcIIZqxQ4UVrM4sZc2xi9sGLj5Qa2xkKw3jOnkxtmPL65XWpF5J9cSJExgMBiwWCx999BEfffRRnfGKovDdd985pYJCCCEantFk2zZwdWYJ3+fXvUGDhxvc287Aw508uStE5/L9dhuTeiXVPn36NLldLYQQQtStwqryVVY5H/9SyucnyzFfZi/76NZa/tjJk7EdPfHXS6+0JvVKqp999tkVvajFUsdyMCGEEC6jqir7Cir4+Ggp646VUWCqO5P6uCuMvtHAQ5286N4Etw283px6x63JZOL999/ntddeaxRPihdCCGFzqriStcfKSD1ayuHzde90BNAvRMeDUZ7c265pbxt4vdU7qZpMJrZs2cKvv/6Kn58fQ4cOJSgoCIDS0lLeeustli9fztmzZ2nfvn2DVVgIIUT9FFfCP4+UkPpLKTtzzFzucSg3ttKQ1ME2vNuuVfPZ5eh6qlerZWdnc88993D8+HH7U2q8vLz4+OOPcXNz409/+hNnzpwhLi6OV155hXvvvbdBKy2EEKJmFVaVbadNpP5SyqcnDJisxjrjW3soJNzoSVIHA3GBHjUO706fPt3h+EofB9qS1Cup/v3vf+fkyZNMmzaNPn36cOLECRYuXMj06dM5e/YssbGxrFq1il69ejV0fYUQQvyOVVVJzzOz7lgZ63+9dJ605vlPrQJ3h+kZ09GTIWF69Nq650nfe+89h2NJqrWrV1L9+uuvefDBB5k7d669LDAwkEceeYTBgwfz0Ucf4eYmY+5CCHE9HSysYN2xUtYeK+NU8eUXiN7Wxp2kDp4ktDfQRlbvNoh6JdW8vDxuv/12h7IePXoA8NBDD0lCFUKI6+SX85WsP17Gv38t5WDh5RcchXtrSGrvyQMdDHTydb8ONWzZ6pVULRYLer3eoazq2MfHx/m1EkIIYXf8QiUbfi3j37+WkXGu7o0ZwDZPOjLSwB26c9zfvb1sznAd1Xt51/Hjx/n+++/tx0VFRQBkZmbi7e1dLb579+5OqJ4QQrRMxy9U8skJ2xzpD5fZ4QhAr4Fh4Qbu72Agvq0enUYhMzNfEup1Vu9x2/nz53P33XfbvxISEgCYOXOmQ/mgQYO4++676/WaO3fuZMyYMcTExODr68vq1atrjZ0+fTq+vr4sW7bMofzdd9/l3nvvJSIiAl9fX06cOFHtWqPRyPjx44mIiCAiIoLx48djNBodYk6dOkVSUhKhoaG0b9+emTNnYjabHWIOHDjA8OHDCQ4OJiYmhgULFthXQwshxLU6bKxg0Y8X6Lsxj27rcnl2T1GdCVWjwKC2Ot68y4/MsSH8vwE3MDzCgE4jidRV6tVTff311xvkm5eUlBAbG8vYsWOZOHFirXEbN27khx9+ICQkpNq50tJSBg4cyPDhw/nLX/5S4/XJyclkZWWxdu1aFEVh2rRpTJgwgdTUVMA2vJ2UlISfnx+bN2+msLCQxx9/HFVVefnllwFbz3zUqFH06dOHrVu3kpmZyeTJk/H09GTq1KlOaA0hREujqioZ5yr45EQ5nxwvq9emDApwR7AHo2/0ZEQ7PQEGWXDUmNQrqf7P//xPg3zzwYMHM3jwYAAmTZpUY8zJkyd5+umn2bBhA4mJidXOV123d+/eGq8/fPgwX331FVu2bKFnz54ALF68mGHDhpGZmUlUVBRbt27l0KFD7N+/3/70neeee45p06bx7LPP4uPjw9q1aykrK2P58uUYDAZiY2M5cuQIb7zxBlOmTJGtu4QQ9VJWqfJNtoktp8r44lQ5Z0ovs+EutkTaK8iDUZEGRkYaCJKnwTRajXrLjMrKSpKTk0lJSSE6OvqqXiM9PR1vb297QgXo1asXXl5e7N69m6ioKNLT04mOjnZ4nF18fDwmk4l9+/bRt29f0tPT6d27NwaDwSHmhRde4MSJE0RGRl71+xRCNG/ZpRb+c6qcz0+Vs/2MiTLL5aeNFKB3kAcj2tkSaaiXJNKmoFEn1fnz5+Pn58djjz121a+Rl5eHv7+/Q09SURTatGlDXl6ePSYgIMDhOn9/fzQajUNMaGioQ0zVNXl5ebUm1czMzKuuuzOub26kPaqTNnHUGNrDqsLPxW7sOKch7ZyGn0vqt3xFo6j0aG1lQJtK+t1gwd+jFDBScgau9l01RHs0hja+FtdS/6ioqDrPN9qkumPHDj788EPS0tKu+bVqGppVVbVaor3ctb+PqVqkVNfQ7+X+AepSNTwtbKQ9qpM2ceTK9iiusPL1GRNfnCrnP1nl5JZdflgXwFOrMCBUx73tDAwL1+Orc959/w3VHk35M9fQn5FGm1TT0tLIyclxGPa1WCzMmTOH5cuXc/DgwXq9TmBgIPn5+Q5JVFVVCgoK7D3NwMBAdu/e7XBdQUEBFovFIaaq11olPz8foFovVwjRMhwrqrQn0Z05pss+j7RKmJeGoeF6hoTruStYd9ltAkXT0WiTanJyMiNHjnQoS0hIICEhgYcffrjerxMXF0dxcTHp6en2edX09HRKSkrsx3FxcSxatIjTp0/Ttm1bALZt24ZOp6Nbt272mLlz51JeXm7f+GLbtm2EhITQrl27a327QogmwGxR2ZVr4ouscv5zysTRosuv1gXb/Gj3AHeGhhsYEq6nq59WFjc2Uy5NqsXFxRw7dgwAq9VKVlYWGRkZ+Pn5ER4eXq0HqNVqCQoKcui65+bmkpuby9GjRwHbat/z588THh6On58f0dHRDBo0iBkzZrB06VJUVWXGjBkMGTLE/joDBw4kJiaGiRMnMm/ePAoLC5k9ezbjxo2z7xiVmJjIggULmDRpEikpKRw9epQlS5Ywc+ZM+c8hRDNlsarsP1dBWraJb7JNfJtrpqSyfveme2sVBrbVMSRcz+AwufWlpXBpUt27dy8jRoywH8+fP5/58+czduxYli9fXq/XeOedd1iwYIH9+IEHHgBs99Y++OCDAKxYsYJZs2YxevRoAIYNG8bChQvt12g0GlJTU0lJSWHo0KHo9XoSExOZN2+ePaZ169asX7+elJQUBgwYgK+vL5MnT2bKlClX3wBCiEZFVVUOn68kLdvE9jMmduSYMJrrv8FLZCvbsO7QcD19gnR4NJNNGL7++mtXV6HJUIxGo2wJ1EjJIhRH0h7VSZs4utL2UFWVX4oqScs2k5ZjS6J59VxgBLZHqPUJ1jE4zNYj7ejTuIZ15fNRXYtdqCSEEA3hdImFrafLScs2kZZjIrsemy9cKsjgxqAw25DugFAdPh7ylC7xG0mqQohmraxS5dtcE/89Xc7W0yZ+NtZvcVEVXw+FO4N19AvV0TdER6fWjas3KhoXSapCiGZFVVUOFlay9Uw5206b+DbXRPnln99t56VV6B3kQb8QHXeF6LjpBnc0bpJERf1IUhVCNHnZpRa+PmNi02EPfvg+p94bLwAYNAq9gjy4M1jHXSEe3NrGA3dJouIqSVIVQjQ5Fyqs7MyxrdD9+oyJQ/YhXS1Qd0J1U6BHgAcDQm1Dut3beDSbVboN5d1333U4fuSRR1xSj6ZAkqoQotEzW1T2nDXz9Rnb/aLfnzVTz9tFAdsORvFtdQxsq6dfiM6pWwG2BE888YTDsSTV2klSFUI0Ohar7Tmj31zcdGFXrpnSK8ii3lqFO0J0DAjVMTBUR5QsLhLXiSRVIYTLWS8uLqrauWhnromiK9h0wU2B7m3cuVlfSkLXEG4PkCFd4RqSVIUQ151VVTlQWMnOHBM7Lm7/d850ZfeLdmqtpV+Ijr6hOu4Ktg3pZmZmEhWsa6BaC3F5klSFEA2u0qry07kKvs01syPHxK5cE4WmK9vMLcTTjb4hOvqH2uZF5aHdojGSpCqEcDqjycqes2Z255nZnWvi+/yKK5oTBbhB58ZdIR7cFWy7X1Q2XRBNgSRVIcQ1yyquZFeumW9zTXyXa77kFpf683FX6BNs27XorhAdXfy0uEkSFU2MJFUhxBVRVZVjRRZ25pr4Nsc2H3qy+Aq2LLqotYdCnyAddwTbNl6QnYtEcyBJVQhxWadLLGw/U872i6tzr3QTeoAAvRs9Az3oE6zjzmAPuvhJEhXNjyRVIUQ1hSar/R7R7WdMHC26suFcBYjx09Iz0IOegTp6BnoQ2Uojc6Ki2ZOkKoSgpMLKrlyzvSeaUVDBlSwr8nCD29p40CfYg95BOnoEeMiuRaJFkqQqRAtUYVX5v0u2/fu/s2YqrmBE11OrEBfoQZ8g23Bu9zYeGLTSCxVCkqoQLUDV49C+zjax/Uw5O3PMlFzBLS5aBW4P8KBvqI5+IbaeqOxYJER1klSFaKZKKqx8fqqcL07ZFhjlXcHj0AC6+GnpF6qjf4ie3sEetHKX4VwhLkeSqhDNSIVVZetpE+uOlfLZyfIr2nChfSsN/UJ/u0+0jV52LBI2S5YscXUVmgxJqkI0cVZV5btcM+uOlbHheFm999ANMrjRL0RnT6Th3vLjQNRMHvVWf/K/SIgmyGiysvV0Of864kH6/+VwtvzyibTqcWj9Q3T0D9XR2Ve2/RPC2SSpCtEEWFWVjIIKvjpt4suscvacNWNVwfZfuPaEGurpRkJ7T4ZH6Lk9wAN32WxBiAYlSVWIRiq71MK20+VsO2Pi6zOmevVGAXw9FO6LNJDYwZM+QR6yf64Q15EkVSEaifJKlbQcE9vOlLPttOmKNqVv5a4wOEzP/R0MDAzVy+0uQriIJFUhXMiqqnybayb1aCkbj5dRVFH/1bqxflpu9yzj/ptC6Bko942KhrNv3z6H427durmkHk2BJFUhXOCwsYLUX0pZ80sZWSX1e8KLj7tC3xAdd4fpiW+rI8xbS2ZmJlEhugaurWjp+vfv73BsNBpdUo+mwKV3c+/cuZMxY8YQExODr68vq1evrjV2+vTp+Pr6smzZModyk8nEU089Rfv27QkNDWXMmDGcPn3aIcZoNDJ+/HgiIiKIiIhg/Pjx1T4Up06dIikpidDQUNq3b8/MmTMxm80OMQcOHGD48OEEBwcTExPDggULUNUre/CyaLmyiit5/UAxAz7Jo+f6PF7NKK4zobopEBfgwaxurdgyvA2//E8IH8T783C0F2Fy+4sQjZJL/2eWlJQQGxvL2LFjmThxYq1xGzdu5IcffiAkJKTauWeeeYbNmzezatUq/Pz8+Otf/0pSUhLbt29Ho7HdvJ6cnExWVhZr165FURSmTZvGhAkTSE1NBcBisZCUlISfnx+bN2+msLCQxx9/HFVVefnllwEoKipi1KhR9OnTh61bt5KZmcnkyZPx9PRk6tSpDdA6ojk4XWJh4/EyNvxaRvpZ82XjA/Ru3NtOz4BQPX1DdLIpvRBNjEuT6uDBgxk8eDAAkyZNqjHm5MmTPP3002zYsIHExESHc+fPn+ef//wnr7/+OgMGDADgrbfe4qabbuLrr78mPj6ew4cP89VXX7FlyxZ69uwJwOLFixk2bJht6Cwqiq1bt3Lo0CH2799PWFgYAM899xzTpk3j2WefxcfHh7Vr11JWVsby5csxGAzExsZy5MgR3njjDaZMmSL3+wm70yUWNh0vY+PxMr7Lu3wiNWgU7mmnJ6mDJwNCdWjlthchmqxG/WtwZWUlycnJpKSkEB0dXe38vn37qKioYODAgfaysLAwoqOj2b17NwDp6el4e3vbEypAr1698PLycoiJjo62J1SA+Ph4TCaTfYI+PT2d3r17YzAYHGKys7M5ceKEU9+3aHqOX6jkH/svMOjTPLqsyeGZ9PN1JlQF6Bui4/U7fTk8JpiV/W7g7jC9JFQhmrhGPTEzf/58/Pz8eOyxx2o8n5eXh0ajwd/f36E8ICCAvLw8e4y/v79DT1JRFNq0aeMQExAQ4PAa/v7+aDQah5jQ0NBq36fqXGRk5NW/UdEkHTZWsOl4GZtOlLP/XEW9rukR4M59N3pyX6SBtl6yt64QzU2jTao7duzgww8/JC0t7YqvVVW1WhK9mpjfl/8+pmqRUl1Dv5mZmfWrdANd39y4uj0qrfDfAg0fndZyoLh+SbFrKwvx/hbi21gI0ZcC5yk9A856J65uk8ZG2sNRQ7RHU2/ja6l/VFRUnecbbVJNS0sjJyfHYdjXYrEwZ84cli9fzsGDBwkMDMRisVBQUECbNm3scfn5+fTp0weAwMBA8vPzHZKoqqoUFBTYe5qBgYH2oeAqBQUFWCwWh5iqXuul3weo1su91OX+AepSNecrbFzZHufKLbx7pJSVh4o5U3r5nY1uD3DnD+0MjIw00K5Vw/03k8+II2kPRw3VHk25jRv6M9Jok2pycjIjR450KEtISCAhIYGHH34YsN2A7O7uzrZt27j//vsBOH36NIcPH7bPocbFxVFcXEx6erq9LD09nZKSEoeYRYsWcfr0adq2bQvAtm3b0Ol09puc4+LimDt3LuXl5ej1entMSEgI7dq1a9jGEC7zs7GCtw4W8/HRMsostd8+pQC9gzz4Q6SBeyP0csuLEC2US//nFxcXc+zYMQCsVitZWVlkZGTg5+dHeHh4tR6gVqslKCjI/ltG69at+eMf/8js2bMJCAiw31LTpUsX+83K0dHRDBo0iBkzZrB06VJUVWXGjBkMGTLE/joDBw4kJiaGiRMnMm/ePAoLC5k9ezbjxo3Dx8cHgMTERBYsWMCkSZNISUnh6NGjLFmyhJkzZ8rK32bosLGC574vYvPJ8jrj+oXouC/SwD3t9AQaZI5UiJbOpUl17969jBgxwn48f/585s+fz9ixY1m+fHm9XuPFF19Eo9Hw6KOPUl5eTt++fXnzzTft96gCrFixglmzZjF69GgAhg0bxsKFC+3nNRoNqamppKSkMHToUPR6PYmJicybN88e07p1a9avX09KSgoDBgzA19eXyZMnM2XKlGttBtGIZJdamL+3iA8ySy8+BaY6T63C/3T0ZEKsF1Gt3a9vBYUQjZpiNBplS6BGSuaHHDVkexSZrfxjfzGvHyiudZi3raeG8bFePNzJq9FsyiCfEUfSHo6c1R6+vr4Ox015m8IWO6cqxPVgtqi8c7iEl/ddoMBU8wKk29q4M7WrN/e2M8jzSIUQdZKkKlqk7FIL7x4u4d3DJeSW1ZxMO/homN29NX9op5d5cyFEvUhSFS2GqqrszjOz4lAJG4+XUVnLxEegwY1Z3VoxrpOX9EyFAPsdF+LyJKmKZq+4wsr6X8t4+1BJnTsfeWkVpnb1ZkpXb7zdG8ecqRCNwdKlS11dhSZDkqpoli5UWPnPqXI2HC/jy6xyyut4ZKm3VuHBKE/+fEsruS1GCHFNJKmKZuNChZUtJ22J9L+n606kAFGttfypsxdjOnri4yE9UyHEtZOkKpq0SqvK1tMmPjpayuZTZZguk0gVYGi4ngmxXvQL0ckCJCGEU0lSFU3SwcIKPjpayppfSmtdvXupcG8NCTcaeDTaq0H34hVCtGzy00U0GQVm2HqwmA+PlvJjweUftRbhreG+SAP3RRq4tY279EqFEA1Okqpo1M6WWfjkRDnrfy1lZ44BK+frjG9XlUhvNNDNXxKpEM7QnHZUamiSVEWjU5VINxwvY0eO6ZI9eGtOkK3cFe6LNPA/UZ70CvSQRCqEcBlJqsLlVFXlp8JK/nOqnC+zykk/a651M/sqCtA/VMfYjp7c206Pp1ZW7wohXE+SqnCJCxVWtp8x8Z8sWyLNrseDvwFuusGdUTcaeKC9QZ5ZKoRodOSnkriujhgrWLK/mHXHSjHXL4/S9QZ3RkUa6KbkEn9z24atoBBCXANJquK62Jtv5tWMC3x6opz6PGuwi5+WUTd6cl+kno4Xn1mamZnTsJUUQohrJElVNBhVVfkm28SrGcVszzbVGeupVegXomNwmJ67w3QytCuEaJLkJ5dwurJKlU9OlPHWwWK+z6/9ftIIbw3DI/QMDtPTJ0iHXiurdoUQTZskVeE0+89V8P6REtb8Usp5c+2DvDff4M6TN7diRDs9Gnm0mhCiGZGkKq5JkdnKv46V8X5mCXvr6JUC3BHswZM3t2JgqOy5K4RoniSpiqty/EIlr/9UzOqjpZTW9rTvi4aG63nyZm/iAnXXqXZCCOEaklTFFdmXb+YfPxWz4XhZnRs0+OvcGNvRkz928iTa1/36VVAIIVxIkqq4LFVV2XrGxD/2172KVwEGhOoY18mL4RF6PDQyxCuEaFkkqYpamS0q64+XseynYn46V/t8aainGw918uLBjp7yWDUhRIsmPwFFNYUmK+8eLuHtQ8V1bh8Y66tl2k2tGH2jQXqlQjRjt9xyi6ur0GRIUhV2x4oqWX6wmNWZdS8+ujPYg+k3tWJQW1nFK0RLsH37dldXocmQpNrCqarKt7lmlh8o5rOTtW8hqAB/iNQzrWsrugd4XM8qCiFEkyFJtYUqq1RZd6yUtw6V1Dlf6qlVeDDKk8djvWnvIx8XIYSoi/yUbGGyiit553AJ7x4u5Zyp9vnSEE83xsd482i0F746eVapEELUh0t/Wu7cuZMxY8YQExODr68vq1evdjg/b948evToQWhoKO3ateMPf/gDu3fvdoj59ddfefDBB+nQoQPh4eE88sgj5OXlOcQYjUbGjx9PREQEERERjB8/HqPR6BBz6tQpkpKSCA0NpX379sycOROz2ewQc+DAAYYPH05wcDAxMTEsWLAAVa3PM1dcq7TSyicnynh4WwG3rMvl1YziWhPqTTe48+ZdfvyYGMyMm1tJQhVCiCvg0p5qSUkJsbGxjB07lokTJ1Y7HxUVxaJFi2jXrh1lZWW88cYbJCYm8v333xMYGEhJSQmjRo0iNjaWjRs3oigKL7zwAmPGjOGrr77Czc2WEJKTk8nKymLt2rUoisK0adOYMGECqampAFgsFpKSkvDz82Pz5s0UFhby+OOPo6oqL7/8MgBFRUWMGjWKPn36sHXrVjIzM5k8eTKenp5MnTr1+jVaPRlNVv6TVc4nJ8r4KstEmaX25O+mwPBwPeNjvbkr2EMWHwkhxFVyaVIdPHgwgwcPBmDSpEnVziclJTkcv/DCC/zzn/9k//79xMfHs3v3bk6cOMHXX3+Nr68vAMuXLycyMpJvvvmG/v37c/jwYb766iu2bNlCz549AVi8eDHDhg0jMzOTqKgotm7dyqFDh9i/fz9hYWEAPPfcc0ybNo1nn30WHx8f1q5dS1lZGcuXL8dgMBAbG8uRI0d44403mDJlissTkVVVOWysZFeumc0ny9iebaLiMg8Bb+2hMK6TF8mdveT+UiFErfr16+dwLKuBa9dkfpKazWbee+89fHx8uOmmmwAwmUwoioJO99uesnq9Hjc3N3bt2kX//v1JT0/H29vbnlABevXqhZeXF7t37yYqKor09HSio6PtCRUgPj4ek8nEvn376Nu3L+np6fTu3RuDweAQ88ILL3DixAkiIyMbvhEuUWS28kO+md15ZtLzzOw5a6aojifDXKqzr5YJMd480MGAl7sM7woh6vbjjz+6ugpNRqNPqlu2bOGxxx6jtLSU4OBg1q9fT2BgIAA9evTA29ub2bNnM3fuXADmzp2LxWIhNzcXgLy8PPz9/R16koqi0KZNG/vca15eHgEBAQ7f19/fH41G4xATGhrqEFN1TV5eXq1JNTMz86red7kFvi7Q8NHpXzFWKhRWQKFZobBSodCscKpcQaX+veMgnZX+N1gY2MbCrT5WFKWIM8evqmoudbXt2ZxJmziS9nDUEO3R1Nv4WuofFRVV5/lGn1Tvuusu0tLSKCgo4L333uORRx7hyy+/JDg4mDZt2vDuu+/y5JNPsnLlStzc3EhISOCWW25Bo9HYX6OmoVlVVasl2prUFVO1SKmuod/L/QPUxmiy8uyu7Ku6tkpHHy1/iNQzop2Bbv7uLh+ivlZVw/XiN9ImjqQ9HDVUezTlNm7oz0ijT6peXl60b9+e9u3b06NHD2677Tbef/99Zs6cCcDAgQPZt28fBQUFaDQafH196dSpE+3atQMgMDCQ/Px8hySqqioFBQX2nmZgYGC1VcUFBQVYLBaHmN+vKs7Pzweo1st1htYeChpFxaLWPxH6eij0CPCgV5COe9rpiW6tbfKJVAghmpJGn1R/z2q1VrvVBWzDtWCbQD979izDhg0DIC4ujuLiYtLT0+3zqunp6ZSUlNiP4+LiWLRoEadPn6Zt27YAbNu2DZ1OR7du3ewxc+fOpby8HL1eb48JCQmxJ3BnUhQFXy0U1PHc7+jWWuICPegR6EHPQA+iWmtxkyQqhBAu49KkWlxczLFjxwBbsszKyiIjIwM/Pz9at27NP/7xD4YOHUpQUBAFBQWsWLGCM2fOcN9999lf44MPPqBTp04EBASQnp7O008/zaRJk+zd++joaAYNGsSMGTNYunQpqqoyY8YMhgwZYo8ZOHAgMTExTJw4kXnz5lFYWMjs2bMZN24cPj4+ACQmJrJgwQImTZpESkoKR48eZcmSJcycObPBeoNDAiox+PjSRu9m//LXa2ijdyPUUyP3kAohRCPj0qS6d+9eRowYYT+eP38+8+fPZ+zYsbzyyiscOnSIDz74gHPnznHDDTdw6623snnzZrp27Wq/5ujRozz//PMUFhYSERHBn//8ZyZPnuzwfVasWMGsWbMYPXo0AMOGDWPhwoX28xqNhtTUVFJSUhg6dCh6vZ7ExETmzZtnj2ndujXr168nJSWFAQMG4Ovry+TJk5kyZUpDNQ8z2lcQFeXbYK8vhBDCuRSj0dj4twRqoWTRhSNpj+qkTRxJezhyVntU7QNQ5fc70jUlDf0ZkfFDIYQQwkkkqQohhBBOIklVCCGEcBJJqkIIIYSTSFIVQgghnERW/wohhBBOIj1VIYQQwkkkqQohhBBOIklVCCGEcBJJqkIIIYSTSFIVQgghnESSaiO0cuVKbr75ZoKCgujXrx/ffvutq6vUIF599VUGDBhAeHg4HTp0ICkpiYMHDzrEqKrK/Pnz6dy5M8HBwdxzzz0cOnTIIcZkMvHUU0/Rvn17QkNDGTNmDKdPn76eb6VBvPLKK/j6+vLUU0/Zy1pie+Tk5DBx4kQ6dOhAUFAQPXv2ZMeOHfbzLalNLBYL8+bNs/98uPnmm5k3bx6VlZX2mObeHjt37mTMmDHExMTg6+vL6tWrHc476/0bjUbGjx9PREQEERERjB8/vl57HktSbWT+/e9/8/TTT/PnP/+Zb775hri4OO6//35OnTrl6qo53Y4dO3jsscf44osv2LRpE1qtlvvuu4/CwkJ7zNKlS3n99ddZsGABW7duJSAggFGjRnHhwgV7zDPPPMMnn3zCqlWr2Lx5MxcuXCApKQmLxeKKt+UUe/bs4b333qNLly4O5S2tPYxGI0OGDEFVVdasWcPu3btZuHAhAQEB9piW1CZLlixh5cqVLFiwgPT0dF566SVWrFjBq6++ao9p7u1RUlJCbGwsL730EgaDodp5Z73/5ORkMjIyWLt2LevWrSMjI4MJEyZctn5yn2ojEx8fT5cuXfjHP/5hL7vtttsYOXIkc+bMcWHNGl5xcTERERGsXr2aYcOGoaoqnTt35k9/+hMpKSkAlJWVERUVxd///nceffRRzp8/T8eOHXn99dd54IEHAMjKyuKmm25i3bp1xMfHu/ItXZXz58/Tr18/li5dysKFC4mNjeXll19uke3x/PPPs3PnTr744osaz7e0NklKSsLPz48333zTXjZx4kQKCwtJTU1tce3Rtm1bFi5cyIMPPgg47/Nw+PBhevbsyZYtW+jVqxcAu3btYtiwYezZs6fOp9xIT7URMZvN7Nu3j4EDBzqUDxw4kN27d7uoVtdPcXExVqvV/pipEydOkJub69AeBoOBPn362Ntj3759VFRUOMSEhYURHR3dZNvsiSeeYOTIkfTr18+hvCW2x2effUb37t159NFH6dixI3feeSdvv/02qmrrC7S0NunVqxc7duzgyJEjAPz888+kpaVx9913Ay2vPX7PWe8/PT0db29vevbsaY/p1asXXl5el20jlz6kXDgqKCjAYrE4DG0BBAQEkJeX56JaXT9PP/00N910E3FxcQDk5uYC1Nge2dnZAOTl5aHRaPD3968W0xTb7L333uPYsWO89dZb1c61xPY4fvw4q1atYtKkSTzxxBPs37+fWbNmATB+/PgW1yZPPPEExcXF9OzZE41GQ2VlJSkpKSQnJwMt8zNyKWe9/7y8PPz9/VEUxX5eURTatGlz2TaSpNoIXfoPCbYhjd+XNTd/+ctf+O6779iyZQsajcbh3NW0R1Nss8zMTJ5//nk+//xzPDw8ao1rKe0BYLVaufXWW+1TH7fccgvHjh1j5cqVjB8/3h7XUtrk3//+Nx9//DErV66kc+fO7N+/n6effpqIiAjGjRtnj2sp7VEbZ7z/muLr8zoy/NuI+Pv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     },
     "metadata": {}
    }
   ],
   "source": [
    "tr_rmse_lasso = []\n",
    "tt_rmse_lasso = []\n",
    "alphas_lasso = []\n",
    "\n",
    "for alpha in np.linspace(0, 1000, num=50):\n",
    "    lasso = Lasso(alpha=alpha)\n",
    "    lasso.fit(X_tr2_sc, y_tr2)\n",
    "    tr_pred = lasso.predict(X_tr2_sc)\n",
    "    tr_rmse_lasso.append(np.sqrt(mean_squared_error(y_tr2, tr_pred)))\n",
    "    tt_pred = lasso.predict(X_tt2_sc)\n",
    "    tt_rmse_lasso.append(np.sqrt(mean_squared_error(y_tt2, tt_pred)))\n",
    "    alphas_lasso.append(alpha)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alphas_lasso, tr_rmse_lasso, label=\"Train\")\n",
    "ax.plot(alphas_lasso, tt_rmse_lasso, label=\"Test\")\n",
    "ax.set_xlabel(\"Alpha\")\n",
    "ax.set_ylabel(\"RMSE\")\n",
    "optimal_alpha = alphas_lasso[np.argmin(tt_rmse_lasso)]\n",
    "ax.axvline(optimal_alpha, color=\"black\", linestyle=\"--\")\n",
    "print(f'Optimal Alpha Value: {int(optimal_alpha)}')\n",
    "plt.savefig('images/optimal_lasso.png')\n",
    "\n",
    "# Optimal Alpha Value: 857"
   ]
  },
  {
   "source": [
    "<img src=\"images/optimal_lasso.png\">"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.8581\n",
      "Training MAE:  85350\n",
      "Training MSE:  1.9992e+10\n",
      "Training RMSE:  141393\n",
      "Training Bias:  0.0\n",
      "Training Variance:  1.1785e+11\n",
      "\n",
      "Testing R^2 Score:  0.8474\n",
      "Testing MAE:  84932\n",
      "Testing MSE:  2.0309e+10\n",
      "Testing RMSE:  142508\n",
      "Testing Bias:  9477.89\n",
      "Testing Variance:  1.1707e+11\n",
      "\n",
      "Average expected loss:  1.0514e+35\n",
      "Average bias:  1.1354e+33\n",
      "Average variance:  1.0401e+35\n"
     ]
    }
   ],
   "source": [
    "best_lasso = Lasso(alpha=857, normalize=False).fit(X_tr2_sc, y_tr2)\n",
    "y_best_lasso_tr = best_lasso.predict(X_tr2_sc)\n",
    "y_best_lasso_tt = best_lasso.predict(X_tt2_sc)\n",
    "\n",
    "train_test_metrics(y_tr2, y_tt2, y_best_lasso_tr, y_best_lasso_tt)\n",
    "print(\"\")\n",
    "lssb_mse, lssb_bias, lssb_var = bias_variance_decomp(model, X_tr2_sc.values, y_tr2.values, X_tt2_sc.values, y_tt2.values, loss='mse', random_seed=42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(lssb_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(lssb_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(lssb_var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"52.099609\" xlink:href=\"#DejaVuSans-113\"/>\n       <use x=\"115.576172\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"149.03125\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"188.240234\" xlink:href=\"#DejaVuSans-95\"/>\n       <use x=\"238.240234\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"266.023438\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"293.806641\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"352.986328\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"380.769531\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"444.148438\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"507.625\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"539.412109\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"598.591797\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"626.375\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"687.898438\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"769.685547\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"831.208984\" xlink:href=\"#DejaVuSans-100\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_17\">\n     <g id=\"line2d_33\">\n      <path clip-path=\"url(#p5973da8408)\" d=\"M 319.336572 728.35845 \nL 319.336572 28.51845 \n\" style=\"fill:none;stroke:#cbcbcb;\"/>\n     </g>\n     <g id=\"line2d_34\"/>\n     <g id=\"text_17\">\n      <!-- waterfront^2 -->\n      <g transform=\"translate(323.199697 825.680325)rotate(-90)scale(0.14 -0.14)\">\n       <use xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"81.787109\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"143.066406\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"182.275391\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"243.798828\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"284.912109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"320.117188\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"358.980469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use 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xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"1062.162109\" xlink:href=\"#DejaVuSans-49\"/>\n       <use x=\"1125.785156\" xlink:href=\"#DejaVuSans-53\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_19\">\n     <g id=\"line2d_37\">\n      <path clip-path=\"url(#p5973da8408)\" d=\"M 341.126925 728.35845 \nL 341.126925 28.51845 \n\" style=\"fill:none;stroke:#cbcbcb;\"/>\n     </g>\n     <g id=\"line2d_38\"/>\n     <g id=\"text_19\">\n      <!-- waterfront sale_age -->\n      <g transform=\"translate(344.795362 870.94845)rotate(-90)scale(0.14 -0.14)\">\n       <use xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"81.787109\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"143.066406\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"182.275391\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"243.798828\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"284.912109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"320.117188\" xlink:href=\"#DejaVuSans-114\"/>\n       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id=\"line2d_40\"/>\n     <g id=\"text_20\">\n      <!-- waterfront age -->\n      <g transform=\"translate(355.885226 835.5722)rotate(-90)scale(0.14 -0.14)\">\n       <use xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"81.787109\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"143.066406\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"182.275391\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"243.798828\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"284.912109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"320.117188\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"358.980469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"420.162109\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"483.541016\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"522.75\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"554.537109\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"615.816406\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"679.292969\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_21\">\n     <g id=\"line2d_41\">\n      <path clip-path=\"url(#p5973da8408)\" d=\"M 362.917277 728.35845 \nL 362.917277 28.51845 \n\" style=\"fill:none;stroke:#cbcbcb;\"/>\n     </g>\n     <g id=\"line2d_42\"/>\n     <g id=\"text_21\">\n      <!-- waterfront renovated -->\n      <g transform=\"translate(366.780402 880.840325)rotate(-90)scale(0.14 -0.14)\">\n       <use xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"81.787109\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"143.066406\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"182.275391\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"243.798828\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"284.912109\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"320.117188\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"358.980469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"420.162109\" xlink:href=\"#DejaVuSans-110\"/>\n       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xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"792.916016\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"890.328125\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"951.851562\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"1015.230469\" xlink:href=\"#DejaVuSans-116\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_23\">\n     <g id=\"line2d_45\">\n      <path clip-path=\"url(#p5973da8408)\" d=\"M 384.70763 728.35845 \nL 384.70763 28.51845 \n\" style=\"fill:none;stroke:#cbcbcb;\"/>\n     </g>\n     <g id=\"line2d_46\"/>\n     <g id=\"text_23\">\n      <!-- waterfront viewed -->\n      <g transform=\"translate(388.570755 859.2322)rotate(-90)scale(0.14 -0.14)\">\n       <use xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"81.787109\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"143.066406\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"182.275391\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"243.798828\" xlink:href=\"#DejaVuSans-114\"/>\n       <use 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "importance = np.abs(best_lasso.coef_)\n",
    "names = np.array(X_tr2_sc.columns)\n",
    "fig, ax = plt.subplots(figsize=(30, 12))\n",
    "plt.bar(height=importance, x=names)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title(\"Feature Importances via Coefficients\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                          importance\n",
       "sqft_living waterfront  83233.800097\n",
       "zip_98004               71822.370745\n",
       "age                     66466.034012\n",
       "sqft_living^2           57350.791426\n",
       "zip_98039               51253.316016\n",
       "grd_11                  50911.500044\n",
       "sqft_living             49971.591416\n",
       "grd_10                  49131.142935\n",
       "zip_98112               48944.218152\n",
       "sqft_living15 age       44327.834348\n",
       "sqft_living age         42430.692600\n",
       "grd_12                  41596.893644\n",
       "grd_13                  37916.763007\n",
       "zip_98040               36935.865405\n",
       "zip_98033               33014.509616\n",
       "zip_98119               32504.484725\n",
       "zip_98103               32189.685455\n",
       "zip_98115               31676.201225\n",
       "zip_98105               30096.203236\n",
       "grd_9                   30031.070245"
      ],
      "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>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sqft_living waterfront</th>\n      <td>83233.800097</td>\n    </tr>\n    <tr>\n      <th>zip_98004</th>\n      <td>71822.370745</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>66466.034012</td>\n    </tr>\n    <tr>\n      <th>sqft_living^2</th>\n      <td>57350.791426</td>\n    </tr>\n    <tr>\n      <th>zip_98039</th>\n      <td>51253.316016</td>\n    </tr>\n    <tr>\n      <th>grd_11</th>\n      <td>50911.500044</td>\n    </tr>\n    <tr>\n      <th>sqft_living</th>\n      <td>49971.591416</td>\n    </tr>\n    <tr>\n      <th>grd_10</th>\n      <td>49131.142935</td>\n    </tr>\n    <tr>\n      <th>zip_98112</th>\n      <td>48944.218152</td>\n    </tr>\n    <tr>\n      <th>sqft_living15 age</th>\n      <td>44327.834348</td>\n    </tr>\n    <tr>\n      <th>sqft_living age</th>\n      <td>42430.692600</td>\n    </tr>\n    <tr>\n      <th>grd_12</th>\n      <td>41596.893644</td>\n    </tr>\n    <tr>\n      <th>grd_13</th>\n      <td>37916.763007</td>\n    </tr>\n    <tr>\n      <th>zip_98040</th>\n      <td>36935.865405</td>\n    </tr>\n    <tr>\n      <th>zip_98033</th>\n      <td>33014.509616</td>\n    </tr>\n    <tr>\n      <th>zip_98119</th>\n      <td>32504.484725</td>\n    </tr>\n    <tr>\n      <th>zip_98103</th>\n      <td>32189.685455</td>\n    </tr>\n    <tr>\n      <th>zip_98115</th>\n      <td>31676.201225</td>\n    </tr>\n    <tr>\n      <th>zip_98105</th>\n      <td>30096.203236</td>\n    </tr>\n    <tr>\n      <th>grd_9</th>\n      <td>30031.070245</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "top_feat_lasso = pd.DataFrame(np.abs(best_lasso.coef_),\n",
    "                              index=np.array(X_tr2_sc.columns),\n",
    "                              columns=['importance']).sort_values('importance', ascending=False)\n",
    "top_feat_lasso.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['sqft_living15', 'bth_1.0', 'bth_0.5', 'viewed^2', 'renovated viewed',\n",
       "       'bth_2.75', 'renovated^2', 'age basement', 'age renovated',\n",
       "       'sale_age viewed', 'sale_age basement', 'sale_age age',\n",
       "       'sqft_living15 sale_age', 'bth_2.5', 'sqft_living basement',\n",
       "       'renovated', 'cnd_3', 'zip_98019', 'zip_98014', 'waterfront^2',\n",
       "       'waterfront age', 'grd_8', 'waterfront', 'grd_3', 'zip_98077',\n",
       "       'zip_98045', 'bth_8.0', 'bth_7.5', 'viewed', 'zip_98106', 'bth_5.75',\n",
       "       'zip_98108', 'bth_3.0', 'waterfront viewed'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "lasso_zero = top_feat_lasso.loc[top_feat_lasso.importance == 0]\n",
    "lasso_zero.index"
   ]
  },
  {
   "source": [
    "## Lasso Regression with AIC and BIC"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "model_bic = LassoLarsIC(criterion=\"bic\")\n",
    "model_bic.fit(X_tr2_sc, y_tr2)\n",
    "alpha_bic_ = model_bic.alpha_\n",
    "\n",
    "model_aic = LassoLarsIC(criterion=\"aic\")\n",
    "model_aic.fit(X_tr2_sc, y_tr2)\n",
    "alpha_aic_ = model_aic.alpha_\n",
    "\n",
    "def plot_ic_criterion(model, name, color):\n",
    "    alpha_ = model.alpha_\n",
    "    alphas_ = model.alphas_\n",
    "    criterion_ = model.criterion_\n",
    "    plt.plot(-np.log10(alphas_), criterion_, '--', color=color, linewidth=2, label=name)\n",
    "    plt.axvline(-np.log10(alpha_), color=color, linewidth=2, label='alpha for %s ' % name)\n",
    "    plt.xlabel('-log(alpha)')\n",
    "    plt.ylabel('criterion')\n",
    "\n",
    "plt.figure()\n",
    "plot_ic_criterion(model_aic, 'AIC', 'green')\n",
    "plot_ic_criterion(model_bic, 'BIC', 'blue')\n",
    "plt.legend()\n",
    "plt.title(\"Information Criterion for model selection\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.863\n",
      "Training MAE:  84297\n",
      "Training MSE:  1.9304e+10\n",
      "Training RMSE:  138938\n",
      "Training Bias:  0.0\n",
      "Training Variance:  1.2156e+11\n",
      "\n",
      "Testing R^2 Score:  0.8434\n",
      "Testing MAE:  85211\n",
      "Testing MSE:  2.0841e+10\n",
      "Testing RMSE:  144362\n",
      "Testing Bias:  9477.89\n",
      "Testing Variance:  1.1946e+11\n",
      "\n",
      "Average expected loss:  1.0514e+35\n",
      "Average bias:  1.1354e+33\n",
      "Average variance:  1.0401e+35\n"
     ]
    }
   ],
   "source": [
    "aic_lasso = Lasso(alpha=model_aic.alpha_).fit(X_tr2_sc, y_tr2)\n",
    "aic_tr_pred = aic_lasso.predict(X_tr2_sc)\n",
    "aic_tt_pred = aic_lasso.predict(X_tt2_sc)\n",
    "\n",
    "train_test_metrics(y_tr2, y_tt2, aic_tr_pred, aic_tt_pred)\n",
    "print(\"\")\n",
    "aic_mse, aic_bias, aic_var = bias_variance_decomp(model, X_tr2_sc.values, y_tr2.values, X_tt2_sc.values, y_tt2.values, loss = 'mse', random_seed = 42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(aic_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(aic_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(aic_var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                           importance\n",
       "sqft_living age         237418.331989\n",
       "sqft_living sale_age    203971.277577\n",
       "age renovated           111589.019719\n",
       "waterfront^2             91128.721222\n",
       "sqft_living waterfront   89143.485841\n",
       "zip_98004                84971.092709\n",
       "waterfront               81302.207138\n",
       "sqft_living              80616.206841\n",
       "sale_age                 79093.398626\n",
       "sqft_living renovated    71252.412835\n",
       "cnd_4                    69497.307660\n",
       "basement^2               64543.945197\n",
       "cnd_3                    61785.248419\n",
       "zip_98112                61711.918685\n",
       "zip_98039                57934.982982\n",
       "sqft_living15 age        55699.106111\n",
       "cnd_5                    55529.200162\n",
       "viewed^2                 53150.033603\n",
       "zip_98040                50638.529852\n",
       "zip_98115                50420.859333"
      ],
      "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>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sqft_living age</th>\n      <td>237418.331989</td>\n    </tr>\n    <tr>\n      <th>sqft_living sale_age</th>\n      <td>203971.277577</td>\n    </tr>\n    <tr>\n      <th>age renovated</th>\n      <td>111589.019719</td>\n    </tr>\n    <tr>\n      <th>waterfront^2</th>\n      <td>91128.721222</td>\n    </tr>\n    <tr>\n      <th>sqft_living waterfront</th>\n      <td>89143.485841</td>\n    </tr>\n    <tr>\n      <th>zip_98004</th>\n      <td>84971.092709</td>\n    </tr>\n    <tr>\n      <th>waterfront</th>\n      <td>81302.207138</td>\n    </tr>\n    <tr>\n      <th>sqft_living</th>\n      <td>80616.206841</td>\n    </tr>\n    <tr>\n      <th>sale_age</th>\n      <td>79093.398626</td>\n    </tr>\n    <tr>\n      <th>sqft_living renovated</th>\n      <td>71252.412835</td>\n    </tr>\n    <tr>\n      <th>cnd_4</th>\n      <td>69497.307660</td>\n    </tr>\n    <tr>\n      <th>basement^2</th>\n      <td>64543.945197</td>\n    </tr>\n    <tr>\n      <th>cnd_3</th>\n      <td>61785.248419</td>\n    </tr>\n    <tr>\n      <th>zip_98112</th>\n      <td>61711.918685</td>\n    </tr>\n    <tr>\n      <th>zip_98039</th>\n      <td>57934.982982</td>\n    </tr>\n    <tr>\n      <th>sqft_living15 age</th>\n      <td>55699.106111</td>\n    </tr>\n    <tr>\n      <th>cnd_5</th>\n      <td>55529.200162</td>\n    </tr>\n    <tr>\n      <th>viewed^2</th>\n      <td>53150.033603</td>\n    </tr>\n    <tr>\n      <th>zip_98040</th>\n      <td>50638.529852</td>\n    </tr>\n    <tr>\n      <th>zip_98115</th>\n      <td>50420.859333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "top_feat_aic = pd.DataFrame(np.abs(aic_lasso.coef_),\n",
    "                            index = np.array(X_tr2_sc.columns),\n",
    "                            columns = ['importance']).sort_values('importance', ascending = False)\n",
    "top_feat_aic.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.8629\n",
      "Training MAE:  84240\n",
      "Training MSE:  1.9320e+10\n",
      "Training RMSE:  138995\n",
      "Training Bias:  0.0\n",
      "Training Variance:  1.2134e+11\n",
      "\n",
      "Testing R^2 Score:  0.8441\n",
      "Testing MAE:  84999\n",
      "Testing MSE:  2.0743e+10\n",
      "Testing RMSE:  144022\n",
      "Testing Bias:  9477.89\n",
      "Testing Variance:  1.1941e+11\n",
      "\n",
      "Average expected loss:  1.0514e+35\n",
      "Average bias:  1.1354e+33\n",
      "Average variance:  1.0401e+35\n"
     ]
    }
   ],
   "source": [
    "bic_lasso = Lasso(alpha=model_bic.alpha_).fit(X_tr2_sc, y_tr2)\n",
    "bic_tr_pred = bic_lasso.predict(X_tr2_sc)\n",
    "bic_tt_pred = bic_lasso.predict(X_tt2_sc)\n",
    "\n",
    "train_test_metrics(y_tr2, y_tt2, bic_tr_pred, bic_tt_pred)\n",
    "print(\"\")\n",
    "bic_mse, bic_bias, bic_var = bias_variance_decomp(model, X_tr2_sc.values, y_tr2.values, X_tt2_sc.values, y_tt2.values, loss='mse', random_seed=42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(bic_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(bic_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(bic_var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                           importance\n",
       "sqft_living age         211000.374635\n",
       "sqft_living sale_age    178380.958748\n",
       "sqft_living waterfront   89162.347015\n",
       "age renovated            87615.195255\n",
       "zip_98004                83564.667358\n",
       "sqft_living              75365.774543\n",
       "zip_98112                60318.788484\n",
       "sqft_living renovated    58308.201529\n",
       "zip_98039                57177.918625\n",
       "sale_age                 57010.877375\n",
       "sqft_living15 age        54233.142453\n",
       "cnd_4                    53956.650692\n",
       "grd_11                   52449.313935\n",
       "grd_10                   49634.807922\n",
       "zip_98040                49138.082467\n",
       "zip_98115                48352.155277\n",
       "zip_98103                47851.918935\n",
       "zip_98033                47436.294589\n",
       "age                      46733.974976\n",
       "cnd_5                    46020.779847"
      ],
      "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>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sqft_living age</th>\n      <td>211000.374635</td>\n    </tr>\n    <tr>\n      <th>sqft_living sale_age</th>\n      <td>178380.958748</td>\n    </tr>\n    <tr>\n      <th>sqft_living waterfront</th>\n      <td>89162.347015</td>\n    </tr>\n    <tr>\n      <th>age renovated</th>\n      <td>87615.195255</td>\n    </tr>\n    <tr>\n      <th>zip_98004</th>\n      <td>83564.667358</td>\n    </tr>\n    <tr>\n      <th>sqft_living</th>\n      <td>75365.774543</td>\n    </tr>\n    <tr>\n      <th>zip_98112</th>\n      <td>60318.788484</td>\n    </tr>\n    <tr>\n      <th>sqft_living renovated</th>\n      <td>58308.201529</td>\n    </tr>\n    <tr>\n      <th>zip_98039</th>\n      <td>57177.918625</td>\n    </tr>\n    <tr>\n      <th>sale_age</th>\n      <td>57010.877375</td>\n    </tr>\n    <tr>\n      <th>sqft_living15 age</th>\n      <td>54233.142453</td>\n    </tr>\n    <tr>\n      <th>cnd_4</th>\n      <td>53956.650692</td>\n    </tr>\n    <tr>\n      <th>grd_11</th>\n      <td>52449.313935</td>\n    </tr>\n    <tr>\n      <th>grd_10</th>\n      <td>49634.807922</td>\n    </tr>\n    <tr>\n      <th>zip_98040</th>\n      <td>49138.082467</td>\n    </tr>\n    <tr>\n      <th>zip_98115</th>\n      <td>48352.155277</td>\n    </tr>\n    <tr>\n      <th>zip_98103</th>\n      <td>47851.918935</td>\n    </tr>\n    <tr>\n      <th>zip_98033</th>\n      <td>47436.294589</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>46733.974976</td>\n    </tr>\n    <tr>\n      <th>cnd_5</th>\n      <td>46020.779847</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "top_feat_bic = pd.DataFrame(np.abs(bic_lasso.coef_),\n",
    "                            index = np.array(X_tr2_sc.columns),\n",
    "                            columns = ['importance']).sort_values('importance', ascending=False)\n",
    "top_feat_bic.head(20)"
   ]
  },
  {
   "source": [
    "## Decision Tree Regressor with GridSearchCV and Pipeline"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.9999\n",
      "Training MAE:  295\n",
      "Training MSE:  1.7111e+07\n",
      "Training RMSE:  4136\n",
      "Training Bias:  -0.0\n",
      "Training Variance:  1.4090e+11\n",
      "\n",
      "Testing R^2 Score:  0.6855\n",
      "Testing MAE:  119947\n",
      "Testing MSE:  4.1847e+10\n",
      "Testing RMSE:  204565\n",
      "Testing Bias:  -1349.82\n",
      "Testing Variance:  1.3112e+11\n",
      "\n",
      "Average expected loss:  1.0514e+35\n",
      "Average bias:  1.1354e+33\n",
      "Average variance:  1.0401e+35\n"
     ]
    }
   ],
   "source": [
    "dtr = DecisionTreeRegressor(random_state=42).fit(X_tr2, y_tr2)\n",
    "y_tr_pred3 = dtr.predict(X_tr2) \n",
    "y_tt_pred3 = dtr.predict(X_tt2)\n",
    "\n",
    "train_test_metrics(y_tr2, y_tt2, y_tr_pred3, y_tt_pred3)\n",
    "print(\"\")\n",
    "dtr_mse, dtr_bias, dtr_var = bias_variance_decomp(model, X_tr2_sc.values, y_tr2.values, X_tt2_sc.values, y_tt2.values, loss='mse', random_seed=42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(dtr_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(dtr_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(dtr_var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Fitting 10 folds for each of 600 candidates, totalling 6000 fits\n",
      "Training Score: nan\n",
      "Pipeline(steps=[('decisiontreeregressor',\n",
      "                 DecisionTreeRegressor(max_depth=1, max_leaf_nodes=5,\n",
      "                                       min_samples_split=5, random_state=1))])\n"
     ]
    }
   ],
   "source": [
    "rmse_scorer = make_scorer('root_mean_squared_error', \n",
    "                           mean_squared_error,\n",
    "                           greater_is_better=False, \n",
    "                           squared=False)\n",
    "pipe_tree = make_pipeline(tree.DecisionTreeRegressor(random_state=1))\n",
    "dtm = DecisionTreeRegressor()\n",
    "param_grid = [{\n",
    "               'decisiontreeregressor__max_depth': [1, 2, 5, 10, 15, 20],\n",
    "               'decisiontreeregressor__min_samples_leaf': [1, 5, 10, 20, 50],\n",
    "               'decisiontreeregressor__max_leaf_nodes': [5, 10, 20, 50, 100],\n",
    "               'decisiontreeregressor__min_samples_split': [5, 10, 20, 40]\n",
    "             }]\n",
    "gs = GridSearchCV(pipe_tree, param_grid=param_grid, cv=10, scoring=rmse_scorer, verbose=1)\n",
    "gs.fit(X_tr2, y_tr2)\n",
    "print(f\"Training Score: {gs.best_score_}\")\n",
    "print(gs.best_estimator_)\n",
    "\n",
    "# Fitting 10 folds for each of 600 candidates, totalling 6000 fits\n",
    "# Training Score: -175078.03997151004\n",
    "# Pipeline(steps=[('decisiontreeregressor',\n",
    "#                  DecisionTreeRegressor(max_depth=10, max_leaf_nodes=100,\n",
    "#                                        min_samples_split=40, random_state=1))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.8579\nTraining MAE:  88207.83\nTraining MSE:  2.0018e+10\nTraining RMSE:  141486.22\nTraining Bias:  0.0\nTraining Variance:  1.2090e+11\n\nTesting R^2 Score:  0.7933\nTesting MAE:  95884.01\nTesting MSE:  2.7501e+10\nTesting RMSE:  165834.58\nTesting Bias:  -1342.05\nTesting Variance:  1.0614e+11\n"
     ]
    }
   ],
   "source": [
    "gs_model = gs.best_estimator_\n",
    "gs_model.fit(X_tr2, y_tr2)\n",
    "gs_tr_pred = gs_model.predict(X_tr2)\n",
    "gs_tt_pred = gs_model.predict(X_tt2) \n",
    "\n",
    "train_test_metrics(y_tr2, y_tt2, y_tr_pred3, y_tt_pred3)\n",
    "print(\"\")\n",
    "dtr_mse, dtr_bias, dtr_var = bias_variance_decomp(model, X_tr2_sc.values, y_tr2.values, X_tt2_sc.values, y_tt2.values, loss='mse', random_seed=42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(dtr_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(dtr_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(dtr_var))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# print('Training R^2 Score: ', round(r2_score(y_tr2, gs_tr_pred), 4))\n",
    "# print('Training MAE: ', round(mean_absolute_error(y_tr2, gs_tr_pred), 2))\n",
    "# print('Training MSE: ', \"{:.4e}\".format(mean_squared_error(y_tr2, gs_tr_pred)))\n",
    "# print('Training RMSE: ', round(np.sqrt(mean_squared_error(y_tr2, gs_tr_pred)), 2))\n",
    "# print('Training Bias: ', round(bias(y_tr2, gs_tr_pred), 2))\n",
    "# print('Training Variance: ', \"{:.4e}\".format(variance(gs_tr_pred)))\n",
    "# print(\"\")\n",
    "# print('Testing R^2 Score: ', round(r2_score(y_tt2, gs_tt_pred), 4))\n",
    "# print('Testing MAE: ', round(mean_absolute_error(y_tt2, gs_tt_pred), 2))\n",
    "# print('Testing MSE: ', \"{:.4e}\".format(mean_squared_error(y_tt2, gs_tt_pred)))\n",
    "# print('Testing RMSE: ', round(np.sqrt(mean_squared_error(y_tt2, gs_tt_pred)), 2))\n",
    "# print('Testing Bias: ', round(bias(y_tt2, gs_tt_pred), 2))\n",
    "# print('Testing Variance: ', \"{:.4e}\".format(variance(gs_tt_pred)))\n",
    "\n",
    "# Training R^2 Score:  0.8579\n",
    "# Training MAE:  88207.83\n",
    "# Training MSE:  2.0018e+10\n",
    "# Training RMSE:  141486.22\n",
    "# Training Bias:  0.0\n",
    "# Training Variance:  1.2090e+11\n",
    "\n",
    "# Testing R^2 Score:  0.7933\n",
    "# Testing MAE:  95884.01\n",
    "# Testing MSE:  2.7501e+10\n",
    "# Testing RMSE:  165834.58\n",
    "# Testing Bias:  -1342.05\n",
    "# Testing Variance:  1.0614e+11\n",
    "\n",
    "dtr = DecisionTreeRegressor(random_state=42).fit(X_tr2, y_tr2)\n",
    "y_tr_pred3 = dtr.predict(X_tr2) \n",
    "y_tt_pred3 = dtr.predict(X_tt2)\n",
    "\n",
    "train_test_metrics(y_tr2, y_tt2, y_tr_pred3, y_tt_pred3)\n",
    "print(\"\")\n",
    "dtr_mse, dtr_bias, dtr_var = bias_variance_decomp(model, X_tr2_sc.values, y_tr2.values, X_tt2_sc.values, y_tt2.values, loss='mse', random_seed=42)    \n",
    "print('Average expected loss: ', \"{:.4e}\".format(dtr_mse))\n",
    "print('Average bias: ', \"{:.4e}\".format(dtr_bias))\n",
    "print('Average variance: ', \"{:.4e}\".format(dtr_var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtr = DecisionTreeRegressor(max_depth=10, max_leaf_nodes=100,\n",
    "                            min_samples_leaf=5,\n",
    "                            min_samples_split=40,\n",
    "                            random_state=1).fit(X_tr2, y_tr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                           importance\n",
       "sqft_living lat              0.475641\n",
       "lat^2                        0.160390\n",
       "sqft_living sqft_living15    0.117406\n",
       "sqft_living basement         0.051939\n",
       "long                         0.031739\n",
       "lat                          0.025609\n",
       "long^2                       0.023008\n",
       "sqft_living15 age            0.013337\n",
       "sqft_above sqft_living15     0.013225\n",
       "sqft_above sqft_lot15        0.010962\n",
       "long viewed                  0.008737\n",
       "waterfront sqft_above        0.008226\n",
       "lat long                     0.007445\n",
       "lat viewed                   0.005338\n",
       "sqft_living long             0.004990\n",
       "zip_98004                    0.004215\n",
       "sqft_living sqft_above       0.004076\n",
       "grd_12                       0.003782\n",
       "grd_7                        0.002827\n",
       "sqft_living viewed           0.002675"
      ],
      "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>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sqft_living lat</th>\n      <td>0.475641</td>\n    </tr>\n    <tr>\n      <th>lat^2</th>\n      <td>0.160390</td>\n    </tr>\n    <tr>\n      <th>sqft_living sqft_living15</th>\n      <td>0.117406</td>\n    </tr>\n    <tr>\n      <th>sqft_living basement</th>\n      <td>0.051939</td>\n    </tr>\n    <tr>\n      <th>long</th>\n      <td>0.031739</td>\n    </tr>\n    <tr>\n      <th>lat</th>\n      <td>0.025609</td>\n    </tr>\n    <tr>\n      <th>long^2</th>\n      <td>0.023008</td>\n    </tr>\n    <tr>\n      <th>sqft_living15 age</th>\n      <td>0.013337</td>\n    </tr>\n    <tr>\n      <th>sqft_above sqft_living15</th>\n      <td>0.013225</td>\n    </tr>\n    <tr>\n      <th>sqft_above sqft_lot15</th>\n      <td>0.010962</td>\n    </tr>\n    <tr>\n      <th>long viewed</th>\n      <td>0.008737</td>\n    </tr>\n    <tr>\n      <th>waterfront sqft_above</th>\n      <td>0.008226</td>\n    </tr>\n    <tr>\n      <th>lat long</th>\n      <td>0.007445</td>\n    </tr>\n    <tr>\n      <th>lat viewed</th>\n      <td>0.005338</td>\n    </tr>\n    <tr>\n      <th>sqft_living long</th>\n      <td>0.004990</td>\n    </tr>\n    <tr>\n      <th>zip_98004</th>\n      <td>0.004215</td>\n    </tr>\n    <tr>\n      <th>sqft_living sqft_above</th>\n      <td>0.004076</td>\n    </tr>\n    <tr>\n      <th>grd_12</th>\n      <td>0.003782</td>\n    </tr>\n    <tr>\n      <th>grd_7</th>\n      <td>0.002827</td>\n    </tr>\n    <tr>\n      <th>sqft_living viewed</th>\n      <td>0.002675</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 74
    }
   ],
   "source": [
    "top_feat_dt = pd.DataFrame(dtr.feature_importances_,\n",
    "                           index=X_tr2.columns,\n",
    "                           columns=['importance']).sort_values('importance', ascending=False)\n",
    "top_feat_dt.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['bth_6.5',\n",
       " 'zip_98014',\n",
       " 'zip_98032',\n",
       " 'bth_6.0',\n",
       " 'zip_98031',\n",
       " 'bth_7.5',\n",
       " 'bth_6.75',\n",
       " 'zip_98029',\n",
       " 'zip_98028',\n",
       " 'zip_98022',\n",
       " 'zip_98023',\n",
       " 'zip_98024',\n",
       " 'zip_98027',\n",
       " 'zip_98030',\n",
       " 'bth_6.25',\n",
       " 'zip_98019',\n",
       " 'bth_8.0',\n",
       " 'zip_98011',\n",
       " 'zip_98010',\n",
       " 'grd_9',\n",
       " 'grd_5',\n",
       " 'grd_4',\n",
       " 'grd_3',\n",
       " 'zip_98033',\n",
       " 'cnd_5',\n",
       " 'cnd_4',\n",
       " 'grd_13',\n",
       " 'cnd_3',\n",
       " 'zip_98002',\n",
       " 'zip_98003',\n",
       " 'cnd_2',\n",
       " 'zip_98005',\n",
       " 'zip_98006',\n",
       " 'zip_98007',\n",
       " 'zip_98008',\n",
       " 'bth_7.75',\n",
       " 'sqft_living',\n",
       " 'zip_98059',\n",
       " 'zip_98034',\n",
       " 'zip_98136',\n",
       " 'zip_98115',\n",
       " 'zip_98116',\n",
       " 'zip_98117',\n",
       " 'zip_98118',\n",
       " 'zip_98119',\n",
       " 'zip_98122',\n",
       " 'zip_98125',\n",
       " 'zip_98126',\n",
       " 'zip_98133',\n",
       " 'zip_98144',\n",
       " 'zip_98108',\n",
       " 'zip_98146',\n",
       " 'zip_98148',\n",
       " 'zip_98155',\n",
       " 'zip_98166',\n",
       " 'zip_98168',\n",
       " 'zip_98177',\n",
       " 'zip_98178',\n",
       " 'zip_98188',\n",
       " 'zip_98198',\n",
       " 'zip_98109',\n",
       " 'zip_98107',\n",
       " 'zip_98038',\n",
       " 'bth_5.5',\n",
       " 'zip_98039',\n",
       " 'zip_98040',\n",
       " 'zip_98042',\n",
       " 'zip_98045',\n",
       " 'zip_98052',\n",
       " 'zip_98053',\n",
       " 'zip_98055',\n",
       " 'zip_98056',\n",
       " 'zip_98058',\n",
       " 'zip_98065',\n",
       " 'zip_98106',\n",
       " 'zip_98070',\n",
       " 'zip_98072',\n",
       " 'zip_98074',\n",
       " 'zip_98075',\n",
       " 'zip_98077',\n",
       " 'zip_98092',\n",
       " 'zip_98102',\n",
       " 'zip_98103',\n",
       " 'zip_98105',\n",
       " 'bth_5.75',\n",
       " 'bth_1.5',\n",
       " 'bth_5.25',\n",
       " 'sqft_above age',\n",
       " 'waterfront long',\n",
       " 'waterfront sqft_lot15',\n",
       " 'waterfront sale_age',\n",
       " 'waterfront age',\n",
       " 'waterfront renovated',\n",
       " 'waterfront basement',\n",
       " 'waterfront viewed',\n",
       " 'sqft_above^2',\n",
       " 'sqft_above sale_age',\n",
       " 'sqft_above renovated',\n",
       " 'bth_5.0',\n",
       " 'sqft_above viewed',\n",
       " 'lat sqft_lot15',\n",
       " 'lat sale_age',\n",
       " 'lat age',\n",
       " 'lat renovated',\n",
       " 'lat basement',\n",
       " 'long sqft_living15',\n",
       " 'long sqft_lot15',\n",
       " 'long sale_age',\n",
       " 'sqft_lot viewed',\n",
       " 'sqft_lot basement',\n",
       " 'sqft_lot renovated',\n",
       " 'sqft_lot sale_age',\n",
       " 'waterfront',\n",
       " 'sqft_above',\n",
       " 'sale_age',\n",
       " 'age',\n",
       " 'renovated',\n",
       " 'basement',\n",
       " 'viewed',\n",
       " 'sqft_living^2',\n",
       " 'sqft_living sqft_lot',\n",
       " 'sqft_living waterfront',\n",
       " 'sqft_living sqft_lot15',\n",
       " 'sqft_living sale_age',\n",
       " 'sqft_living age',\n",
       " 'sqft_living renovated',\n",
       " 'sqft_lot^2',\n",
       " 'sqft_lot waterfront',\n",
       " 'sqft_lot lat',\n",
       " 'sqft_lot long',\n",
       " 'sqft_lot sqft_lot15',\n",
       " 'long age',\n",
       " 'long renovated',\n",
       " 'long basement',\n",
       " 'renovated viewed',\n",
       " 'viewed^2',\n",
       " 'bth_0.5',\n",
       " 'bth_0.75',\n",
       " 'bth_1.0',\n",
       " 'bth_1.25',\n",
       " 'sqft_lot',\n",
       " 'bth_1.75',\n",
       " 'bth_2.0',\n",
       " 'bth_2.25',\n",
       " 'bth_2.5',\n",
       " 'bth_2.75',\n",
       " 'bth_3.0',\n",
       " 'bth_3.25',\n",
       " 'bth_3.5',\n",
       " 'bth_3.75',\n",
       " 'bth_4.0',\n",
       " 'bth_4.25',\n",
       " 'bth_4.5',\n",
       " 'bth_4.75',\n",
       " 'basement^2',\n",
       " 'renovated basement',\n",
       " 'sqft_living15^2',\n",
       " 'renovated^2',\n",
       " 'sqft_living15 sqft_lot15',\n",
       " 'sqft_living15 sale_age',\n",
       " 'sqft_living15 renovated',\n",
       " 'sqft_living15 basement',\n",
       " 'sqft_living15 viewed',\n",
       " 'sqft_lot15^2',\n",
       " 'sqft_lot15 sale_age',\n",
       " 'sqft_lot15 age',\n",
       " 'sqft_lot15 renovated',\n",
       " 'sqft_lot15 basement',\n",
       " 'sqft_lot15 viewed',\n",
       " 'sale_age^2',\n",
       " 'sale_age renovated',\n",
       " 'sale_age basement',\n",
       " 'sale_age viewed',\n",
       " 'age^2',\n",
       " 'age renovated',\n",
       " 'age basement',\n",
       " 'age viewed',\n",
       " 'zip_98199']"
      ]
     },
     "metadata": {},
     "execution_count": 98
    }
   ],
   "source": [
    "dt_zero = top_feat_dt.loc[top_feat_dt.importance == 0]\n",
    "dt_zero.index.tolist()"
   ]
  },
  {
   "source": [
    "## Random Forest Regressor with RandomSearchCV and GridSearchCV"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.9458\nTraining MAE:  42564.3\nTraining MSE:  7.6441e+09\nTraining RMSE:  87430.82\nTraining Bias:  190.05\nTraining Variance:  1.1999e+11\n\nTesting R^2 Score:  0.8705\nTesting MAE:  71651.21\nTesting MSE:  1.7237e+10\nTesting RMSE:  131289.66\nTesting Bias:  -1104.78\nTesting Variance:  1.0170e+11\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=100, \n",
    "                           max_features=\"auto\", \n",
    "                           max_depth=100, \n",
    "                           min_samples_leaf=4, \n",
    "                           min_samples_split=10, \n",
    "                           random_state=1).fit(X_tr2, y_tr2)\n",
    "rf_tr_pred = rf.predict(X_tr2)\n",
    "rf_tt_pred = rf.predict(X_tt2)\n",
    "\n",
    "print('Training R^2 Score: ', round(r2_score(y_tr2, rf_tr_pred), 4))\n",
    "print('Training MAE: ', round(mean_absolute_error(y_tr2, rf_tr_pred), 2))\n",
    "print('Training MSE: ', \"{:.4e}\".format(mean_squared_error(y_tr2, rf_tr_pred)))\n",
    "print('Training RMSE: ', round(np.sqrt(mean_squared_error(y_tr2, rf_tr_pred)), 2))\n",
    "print('Training Bias: ', bias(y_tr2, rf_tr_pred))\n",
    "print('Training Variance: ', \"{:.4e}\".format(variance(rf_tr_pred)))\n",
    "print(\"\")\n",
    "print('Testing R^2 Score: ', round(r2_score(y_tt2, rf_tt_pred), 4))\n",
    "print('Testing MAE: ', round(mean_absolute_error(y_tt2, rf_tt_pred), 2))\n",
    "print('Testing MSE: ', \"{:.4e}\".format(mean_squared_error(y_tt2, rf_tt_pred)))\n",
    "print('Testing RMSE: ', round(np.sqrt(mean_squared_error(y_tt2, rf_tt_pred)), 2))\n",
    "print('Testing Bias: ', round(bias(y_tt2, rf_tt_pred), 2))\n",
    "print('Testing Variance: ', \"{:.4e}\".format(variance(rf_tt_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                           importance\n",
       "sqft_living lat              0.307188\n",
       "sqft_living sqft_living15    0.155893\n",
       "lat^2                        0.075278\n",
       "lat                          0.070771\n",
       "sqft_living long             0.049169\n",
       "lat long                     0.028410\n",
       "long^2                       0.022321\n",
       "long                         0.021555\n",
       "sqft_living basement         0.021085\n",
       "sqft_living viewed           0.020610\n",
       "sqft_above sqft_living15     0.017319\n",
       "sqft_living15 age            0.017024\n",
       "sqft_living^2                0.013846\n",
       "lat viewed                   0.013360\n",
       "sqft_living sqft_above       0.012049\n",
       "sqft_above viewed            0.009553\n",
       "waterfront lat               0.008827\n",
       "long viewed                  0.005946\n",
       "zip_98004                    0.005652\n",
       "sqft_above basement          0.005430"
      ],
      "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>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sqft_living lat</th>\n      <td>0.307188</td>\n    </tr>\n    <tr>\n      <th>sqft_living sqft_living15</th>\n      <td>0.155893</td>\n    </tr>\n    <tr>\n      <th>lat^2</th>\n      <td>0.075278</td>\n    </tr>\n    <tr>\n      <th>lat</th>\n      <td>0.070771</td>\n    </tr>\n    <tr>\n      <th>sqft_living long</th>\n      <td>0.049169</td>\n    </tr>\n    <tr>\n      <th>lat long</th>\n      <td>0.028410</td>\n    </tr>\n    <tr>\n      <th>long^2</th>\n      <td>0.022321</td>\n    </tr>\n    <tr>\n      <th>long</th>\n      <td>0.021555</td>\n    </tr>\n    <tr>\n      <th>sqft_living basement</th>\n      <td>0.021085</td>\n    </tr>\n    <tr>\n      <th>sqft_living viewed</th>\n      <td>0.020610</td>\n    </tr>\n    <tr>\n      <th>sqft_above sqft_living15</th>\n      <td>0.017319</td>\n    </tr>\n    <tr>\n      <th>sqft_living15 age</th>\n      <td>0.017024</td>\n    </tr>\n    <tr>\n      <th>sqft_living^2</th>\n      <td>0.013846</td>\n    </tr>\n    <tr>\n      <th>lat viewed</th>\n      <td>0.013360</td>\n    </tr>\n    <tr>\n      <th>sqft_living sqft_above</th>\n      <td>0.012049</td>\n    </tr>\n    <tr>\n      <th>sqft_above viewed</th>\n      <td>0.009553</td>\n    </tr>\n    <tr>\n      <th>waterfront lat</th>\n      <td>0.008827</td>\n    </tr>\n    <tr>\n      <th>long viewed</th>\n      <td>0.005946</td>\n    </tr>\n    <tr>\n      <th>zip_98004</th>\n      <td>0.005652</td>\n    </tr>\n    <tr>\n      <th>sqft_above basement</th>\n      <td>0.005430</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 77
    }
   ],
   "source": [
    "top_feat_rf = pd.DataFrame(rf.feature_importances_,\n",
    "                           index=X_tr2.columns,\n",
    "                           columns=['importance']).sort_values('importance', ascending=False)\n",
    "top_feat_rf.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "importance\n",
       "0.000000      17\n",
       "0.000032       1\n",
       "0.000050       1\n",
       "0.000050       1\n",
       "0.000050       1\n",
       "              ..\n",
       "0.000738       1\n",
       "0.000688       1\n",
       "0.000687       1\n",
       "0.000684       1\n",
       "0.000217       1\n",
       "Length: 201, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 99
    }
   ],
   "source": [
    "top_feat_rf.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'n_estimators': 150,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_depth': 50}"
      ]
     },
     "metadata": {},
     "execution_count": 55
    }
   ],
   "source": [
    "random_grid = {'n_estimators': [20, 50, 100, 150, 200],\n",
    "               'max_depth': [10, 20, 50, 100],\n",
    "               'min_samples_split': [2, 5, 10],\n",
    "               'min_samples_leaf': [1, 2, 4]}\n",
    "rf = RandomForestRegressor()\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
    "rf_random.fit(X_tr2, y_tr2)\n",
    "rf_random.best_params_\n",
    "\n",
    "\n",
    "# Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
    "# {'n_estimators': 150,\n",
    "#  'min_samples_split': 2,\n",
    "#  'min_samples_leaf': 1,\n",
    "#  'max_depth': 50}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training R^2 Score:  0.9801\nTraining MAE:  27058.29\nTraining MSE:  2.8010e+09\nTraining RMSE:  52924.47\nTraining Bias:  -14.82\nTraining Variance:  1.2696e+11\n\nTesting R^2 Score:  0.8778\nTesting MAE:  70402.12\nTesting MSE:  1.6264e+10\nTesting RMSE:  127528.55\nTesting Bias:  -1052.86\nTesting Variance:  1.0265e+11\n"
     ]
    }
   ],
   "source": [
    "best_random = rf_random.best_estimator_\n",
    "rf_random_tr = rf_random.predict(X_tr2)\n",
    "rf_random_tt = rf_random.predict(X_tt2)\n",
    "\n",
    "print('Training R^2 Score: ', round(r2_score(y_tr2, rf_random_tr), 4))\n",
    "print('Training MAE: ', round(mean_absolute_error(y_tr2, rf_random_tr), 2))\n",
    "print('Training MSE: ', \"{:.4e}\".format(mean_squared_error(y_tr2, rf_random_tr)))\n",
    "print('Training RMSE: ', round(np.sqrt(mean_squared_error(y_tr2, rf_random_tr)), 2))\n",
    "print('Training Bias: ', round(bias(y_tr2, rf_random_tr), 2))\n",
    "print('Training Variance: ', \"{:.4e}\".format(variance(rf_random_tr)))\n",
    "print(\"\")\n",
    "print('Testing R^2 Score: ', round(r2_score(y_tt2, rf_random_tt), 4))\n",
    "print('Testing MAE: ', round(mean_absolute_error(y_tt2, rf_random_tt), 2))\n",
    "print('Testing MSE: ', \"{:.4e}\".format(mean_squared_error(y_tt2, rf_random_tt)))\n",
    "print('Testing RMSE: ', round(np.sqrt(mean_squared_error(y_tt2, rf_random_tt)), 2))\n",
    "print('Testing Bias: ', round(bias(y_tt2, rf_random_tt), 2))\n",
    "print('Testing Variance: ', \"{:.4e}\".format(variance(rf_random_tt)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                           importance\n",
       "sqft_living lat              0.297823\n",
       "sqft_living sqft_living15    0.131081\n",
       "lat^2                        0.072726\n",
       "lat                          0.069335\n",
       "sqft_living long             0.061776\n",
       "lat long                     0.029113\n",
       "long^2                       0.022179\n",
       "long                         0.021591\n",
       "sqft_above sqft_living15     0.019045\n",
       "sqft_living viewed           0.017634\n",
       "sqft_living15 age            0.015766\n",
       "sqft_living basement         0.015192\n",
       "sqft_living sqft_above       0.014782\n",
       "lat viewed                   0.011066\n",
       "sqft_living^2                0.010187\n",
       "sqft_above viewed            0.009593\n",
       "waterfront lat               0.008907\n",
       "sqft_above basement          0.008370\n",
       "long viewed                  0.006598\n",
       "zip_98004                    0.005170"
      ],
      "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>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sqft_living lat</th>\n      <td>0.297823</td>\n    </tr>\n    <tr>\n      <th>sqft_living sqft_living15</th>\n      <td>0.131081</td>\n    </tr>\n    <tr>\n      <th>lat^2</th>\n      <td>0.072726</td>\n    </tr>\n    <tr>\n      <th>lat</th>\n      <td>0.069335</td>\n    </tr>\n    <tr>\n      <th>sqft_living long</th>\n      <td>0.061776</td>\n    </tr>\n    <tr>\n      <th>lat long</th>\n      <td>0.029113</td>\n    </tr>\n    <tr>\n      <th>long^2</th>\n      <td>0.022179</td>\n    </tr>\n    <tr>\n      <th>long</th>\n      <td>0.021591</td>\n    </tr>\n    <tr>\n      <th>sqft_above sqft_living15</th>\n      <td>0.019045</td>\n    </tr>\n    <tr>\n      <th>sqft_living viewed</th>\n      <td>0.017634</td>\n    </tr>\n    <tr>\n      <th>sqft_living15 age</th>\n      <td>0.015766</td>\n    </tr>\n    <tr>\n      <th>sqft_living basement</th>\n      <td>0.015192</td>\n    </tr>\n    <tr>\n      <th>sqft_living sqft_above</th>\n      <td>0.014782</td>\n    </tr>\n    <tr>\n      <th>lat viewed</th>\n      <td>0.011066</td>\n    </tr>\n    <tr>\n      <th>sqft_living^2</th>\n      <td>0.010187</td>\n    </tr>\n    <tr>\n      <th>sqft_above viewed</th>\n      <td>0.009593</td>\n    </tr>\n    <tr>\n      <th>waterfront lat</th>\n      <td>0.008907</td>\n    </tr>\n    <tr>\n      <th>sqft_above basement</th>\n      <td>0.008370</td>\n    </tr>\n    <tr>\n      <th>long viewed</th>\n      <td>0.006598</td>\n    </tr>\n    <tr>\n      <th>zip_98004</th>\n      <td>0.005170</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 63
    }
   ],
   "source": [
    "top_feat_rf_rcv = pd.DataFrame(best_random.feature_importances_,\n",
    "                           index=X_tr2.columns,\n",
    "                           columns=['importance']).sort_values('importance', ascending=False)\n",
    "top_feat_rf_rcv.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "importance\n",
       "0.000000      3\n",
       "0.000104      1\n",
       "0.000098      1\n",
       "0.000095      1\n",
       "0.000090      1\n",
       "             ..\n",
       "0.000995      1\n",
       "0.000993      1\n",
       "0.000988      1\n",
       "0.000953      1\n",
       "0.000298      1\n",
       "Length: 215, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 100
    }
   ],
   "source": [
    "top_feat_rf_rcv.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Fitting 3 folds for each of 288 candidates, totalling 864 fits\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'max_depth': 110,\n",
       " 'max_features': 3,\n",
       " 'min_samples_leaf': 3,\n",
       " 'min_samples_split': 8,\n",
       " 'n_estimators': 200}"
      ]
     },
     "metadata": {},
     "execution_count": 47
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'bootstrap': [True],\n",
    "    'max_depth': [80, 90, 100, 110],\n",
    "    'max_features': [2, 3],\n",
    "    'min_samples_leaf': [3, 4, 5],\n",
    "    'min_samples_split': [8, 10, 12],\n",
    "    'n_estimators': [100, 200, 300, 1000]\n",
    "}\n",
    "rf = RandomForestRegressor()\n",
    "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, cv = 3, n_jobs = -1, verbose = 2)\n",
    "grid_search.fit(X_tr2, y_tr2)\n",
    "grid_search.best_params_\n",
    "\n",
    "# Fitting 3 folds for each of 288 candidates, totalling 864 fits\n",
    "# {'bootstrap': True,\n",
    "#  'max_depth': 110,\n",
    "#  'max_features': 3,\n",
    "#  'min_samples_leaf': 3,\n",
    "#  'min_samples_split': 8,\n",
    "#  'n_estimators': 200}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_grid = grid_search.best_estimator_\n",
    "rf_gs_tr = best_grid.predict(X_tr2)\n",
    "rf_gs_tt = best_grid.predict(X_tt2)\n",
    "\n",
    "print('Training R^2 Score: ', round(r2_score(y_tr2, rf_gs_tr), 4))\n",
    "print('Training MAE: ', round(mean_absolute_error(y_tr2, rf_gs_tr), 2))\n",
    "print('Training MSE: ', \"{:.4e}\".format(mean_squared_error(y_tr2, rf_gs_tr)))\n",
    "print('Training RMSE: ', round(np.sqrt(mean_squared_error(y_tr2, rf_gs_tr)), 2))\n",
    "print('Training Bias: ', round(bias(y_tr2, rf_gs_tr), 2))\n",
    "print('Training Variance: ', \"{:.4e}\".format(variance(rf_gs_tr)))\n",
    "print(\"\")\n",
    "print('Testing R^2 Score: ', round(r2_score(y_tt2, rf_gs_tt), 4))\n",
    "print('Testing MAE: ', round(mean_absolute_error(y_tt2, rf_gs_tt), 2))\n",
    "print('Testing MSE: ', \"{:.4e}\".format(mean_squared_error(y_tt2, rf_gs_tt)))\n",
    "print('Testing RMSE: ', round(np.sqrt(mean_squared_error(y_tt2, rf_gs_tt)), 2))\n",
    "print('Testing Bias: ', round(bias(y_tt2, rf_gs_tt), 2))\n",
    "print('Testing Variance: ', \"{:.4e}\".format(variance(rf_gs_tt)))\n",
    "\n",
    "# Training R^2 Score:  0.8427\n",
    "# Training MAE:  83705.74\n",
    "# Training MSE:  2.2172e+10\n",
    "# Training RMSE:  148904.28\n",
    "# Training Bias:  -239.46\n",
    "# Training Variance:  8.0044e+10\n",
    "\n",
    "# Testing R^2 Score:  0.7613\n",
    "# Testing MAE:  98035.45\n",
    "# Testing MSE:  3.1765e+10\n",
    "# Testing RMSE:  178228.29\n",
    "# Testing Bias:  -1378.33\n",
    "# Testing Variance:  6.5898e+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                           importance\n",
       "sqft_living15^2              0.038026\n",
       "sqft_living sqft_above       0.032620\n",
       "sqft_living sqft_living15    0.029391\n",
       "sqft_living lat              0.027265\n",
       "sqft_living long             0.026596\n",
       "lat^2                        0.026509\n",
       "sqft_above lat               0.023884\n",
       "lat                          0.023300\n",
       "sqft_above sqft_living15     0.023174\n",
       "long sqft_living15           0.022903\n",
       "sqft_above basement          0.022653\n",
       "sqft_above long              0.021898\n",
       "sqft_living                  0.021702\n",
       "sqft_above^2                 0.021088\n",
       "sqft_living15                0.019718\n",
       "sqft_above                   0.019080\n",
       "sqft_living15 basement       0.018808\n",
       "sqft_living^2                0.017950\n",
       "sqft_living basement         0.017726\n",
       "lat sqft_living15            0.016611"
      ],
      "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>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sqft_living15^2</th>\n      <td>0.038026</td>\n    </tr>\n    <tr>\n      <th>sqft_living sqft_above</th>\n      <td>0.032620</td>\n    </tr>\n    <tr>\n      <th>sqft_living sqft_living15</th>\n      <td>0.029391</td>\n    </tr>\n    <tr>\n      <th>sqft_living lat</th>\n      <td>0.027265</td>\n    </tr>\n    <tr>\n      <th>sqft_living long</th>\n      <td>0.026596</td>\n    </tr>\n    <tr>\n      <th>lat^2</th>\n      <td>0.026509</td>\n    </tr>\n    <tr>\n      <th>sqft_above lat</th>\n      <td>0.023884</td>\n    </tr>\n    <tr>\n      <th>lat</th>\n      <td>0.023300</td>\n    </tr>\n    <tr>\n      <th>sqft_above sqft_living15</th>\n      <td>0.023174</td>\n    </tr>\n    <tr>\n      <th>long sqft_living15</th>\n      <td>0.022903</td>\n    </tr>\n    <tr>\n      <th>sqft_above basement</th>\n      <td>0.022653</td>\n    </tr>\n    <tr>\n      <th>sqft_above long</th>\n      <td>0.021898</td>\n    </tr>\n    <tr>\n      <th>sqft_living</th>\n      <td>0.021702</td>\n    </tr>\n    <tr>\n      <th>sqft_above^2</th>\n      <td>0.021088</td>\n    </tr>\n    <tr>\n      <th>sqft_living15</th>\n      <td>0.019718</td>\n    </tr>\n    <tr>\n      <th>sqft_above</th>\n      <td>0.019080</td>\n    </tr>\n    <tr>\n      <th>sqft_living15 basement</th>\n      <td>0.018808</td>\n    </tr>\n    <tr>\n      <th>sqft_living^2</th>\n      <td>0.017950</td>\n    </tr>\n    <tr>\n      <th>sqft_living basement</th>\n      <td>0.017726</td>\n    </tr>\n    <tr>\n      <th>lat sqft_living15</th>\n      <td>0.016611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 101
    }
   ],
   "source": [
    "top_feat_rf_gs = pd.DataFrame(best_grid.feature_importances_,\n",
    "                              index=X_tr2.columns,\n",
    "                              columns=['importance']).sort_values('importance', ascending=False)\n",
    "top_feat_rf_gs.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "importance\n",
       "0.000000      10\n",
       "0.032620       1\n",
       "0.000379       1\n",
       "0.000366       1\n",
       "0.000335       1\n",
       "              ..\n",
       "0.004418       1\n",
       "0.004411       1\n",
       "0.004376       1\n",
       "0.004358       1\n",
       "0.001655       1\n",
       "Length: 208, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 102
    }
   ],
   "source": [
    "top_feat_rf_gs.value_counts()"
   ]
  },
  {
   "source": [
    "# Results"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_rmse = [[rmse(y_tr, y_tr_pred), rmse(y_tt, y_tt_pred)],\n",
    "             [rmse(y_tr, y_kb_tr_pred), rmse(y_tt, y_kb_tt_pred)],\n",
    "             [rmse(y_tr, y_tr_pred_rfe), rmse(y_tt, y_tt_pred_rfe)],\n",
    "             [rmse(y_tr2, y_tr_pred2), rmse(y_tt2, y_tt_pred2)],\n",
    "             [rmse(y_tr2, y_ridge_tr), rmse(y_tt2, y_ridge_tt)],\n",
    "             [rmse(y_tr2, y_best_ridge_tr), rmse(y_tt2, y_best_ridge_tt)],\n",
    "             [rmse(y_tr2, y_lasso_tr), rmse(y_tt2, y_lasso_tt)],\n",
    "             [rmse(y_tr2, y_best_lasso_tr), rmse(y_tt2, y_best_lasso_tt)],\n",
    "             [rmse(y_tr2, aic_tr_pred), rmse(y_tt2, aic_tt_pred)],\n",
    "             [rmse(y_tr2, bic_tr_pred), rmse(y_tt2, bic_tt_pred)],\n",
    "             [rmse(y_tr2, y_tr_pred3), rmse(y_tt2, y_tt_pred3)],\n",
    "             [rmse(y_tr2, gs_tr_pred), rmse(y_tt2, gs_tt_pred)],\n",
    "             [rmse(y_tr2, rf_tr_pred), rmse(y_tt2, rf_tt_pred)],\n",
    "             [rmse(y_tr2, rf_random_tr), rmse(y_tt2, rf_random_tt)],\n",
    "             [rmse(y_tr2, rf_gs_tr), rmse(y_tt2, rf_gs_tt)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [\"Baseline 1\", \"K-Best\", \"RFECV\", \"Baseline 2\", \"Ridge\", \"Best Ridge\", \"Lasso\", \"Best Lasso\", \"Lasso AIC\", \"Lasso BIC\", \"Decision Tree\", \"DT GridSearchCV\", \"Random Forest\", \"RF RandomSearchCV\", \"RF GridSearchCV\"]\n",
    "columns = [\"Training RMSE\", \"Testing RMSE\"]\n",
    "rmse_df = pd.DataFrame(data=list_rmse, index=index, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   Training RMSE  Testing RMSE\n",
       "Baseline 1             128094.52     134097.92\n",
       "K-Best                 209795.48     202748.99\n",
       "RFECV                  127719.34     133569.89\n",
       "Baseline 2             131279.78     135544.37\n",
       "Ridge                  134418.16     142728.44\n",
       "Best Ridge             132583.55     141224.64\n",
       "Lasso                  135006.92     142731.09\n",
       "Best Lasso             136264.04     140134.98\n",
       "Lasso AIC              135009.20     142691.23\n",
       "Lasso BIC              135157.01     141539.03\n",
       "Decision Tree            3757.56     176773.42\n",
       "DT GridSearchCV        141486.22     165834.58\n",
       "Random Forest           87430.82     131289.66\n",
       "RF RandomSearchCV       52924.47     127528.55\n",
       "RF GridSearchCV        148904.28     178228.29"
      ],
      "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>Training RMSE</th>\n      <th>Testing RMSE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Baseline 1</th>\n      <td>128094.52</td>\n      <td>134097.92</td>\n    </tr>\n    <tr>\n      <th>K-Best</th>\n      <td>209795.48</td>\n      <td>202748.99</td>\n    </tr>\n    <tr>\n      <th>RFECV</th>\n      <td>127719.34</td>\n      <td>133569.89</td>\n    </tr>\n    <tr>\n      <th>Baseline 2</th>\n      <td>131279.78</td>\n      <td>135544.37</td>\n    </tr>\n    <tr>\n      <th>Ridge</th>\n      <td>134418.16</td>\n      <td>142728.44</td>\n    </tr>\n    <tr>\n      <th>Best Ridge</th>\n      <td>132583.55</td>\n      <td>141224.64</td>\n    </tr>\n    <tr>\n      <th>Lasso</th>\n      <td>135006.92</td>\n      <td>142731.09</td>\n    </tr>\n    <tr>\n      <th>Best Lasso</th>\n      <td>136264.04</td>\n      <td>140134.98</td>\n    </tr>\n    <tr>\n      <th>Lasso AIC</th>\n      <td>135009.20</td>\n      <td>142691.23</td>\n    </tr>\n    <tr>\n      <th>Lasso BIC</th>\n      <td>135157.01</td>\n      <td>141539.03</td>\n    </tr>\n    <tr>\n      <th>Decision Tree</th>\n      <td>3757.56</td>\n      <td>176773.42</td>\n    </tr>\n    <tr>\n      <th>DT GridSearchCV</th>\n      <td>141486.22</td>\n      <td>165834.58</td>\n    </tr>\n    <tr>\n      <th>Random Forest</th>\n      <td>87430.82</td>\n      <td>131289.66</td>\n    </tr>\n    <tr>\n      <th>RF RandomSearchCV</th>\n      <td>52924.47</td>\n      <td>127528.55</td>\n    </tr>\n    <tr>\n      <th>RF GridSearchCV</th>\n      <td>148904.28</td>\n      <td>178228.29</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "source": [
    "rmse_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                           importance\n",
       "sqft_living lat              0.475641\n",
       "lat^2                        0.160390\n",
       "sqft_living sqft_living15    0.117406\n",
       "sqft_living basement         0.051939\n",
       "long                         0.031739\n",
       "...                               ...\n",
       "age^2                        0.000000\n",
       "age renovated                0.000000\n",
       "age basement                 0.000000\n",
       "age viewed                   0.000000\n",
       "zip_98199                    0.000000\n",
       "\n",
       "[217 rows x 1 columns]"
      ],
      "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>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sqft_living lat</th>\n      <td>0.475641</td>\n    </tr>\n    <tr>\n      <th>lat^2</th>\n      <td>0.160390</td>\n    </tr>\n    <tr>\n      <th>sqft_living sqft_living15</th>\n      <td>0.117406</td>\n    </tr>\n    <tr>\n      <th>sqft_living basement</th>\n      <td>0.051939</td>\n    </tr>\n    <tr>\n      <th>long</th>\n      <td>0.031739</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>age^2</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>age renovated</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>age basement</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>age viewed</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>zip_98199</th>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>217 rows × 1 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 79
    }
   ],
   "source": [
    "top_feat_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'sqft_living lat'"
      ]
     },
     "metadata": {},
     "execution_count": 106
    }
   ],
   "source": [
    "top_feat_ridge.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_top3 = [[top_feat_ridge.index[0], top_feat_ridge.index[1], top_feat_ridge.index[2]],\n",
    "             [top_feat_lasso.index[0], top_feat_lasso.index[1], top_feat_lasso.index[2]],\n",
    "             [top_feat_dt.index[0], top_feat_dt.index[1], top_feat_dt.index[2]],\n",
    "             [top_feat_rf.index[0], top_feat_rf.index[1], top_feat_rf.index[2]],\n",
    "             [top_feat_rf_rcv.index[0], top_feat_rf_rcv.index[1], top_feat_rf_rcv.index[2]],\n",
    "             [top_feat_rf_gs.index[0], top_feat_rf_gs.index[1], top_feat_rf_gs.index[2]]]\n",
    "index = [\"Ridge\", \"Lasso\", \"Decision Tree\", \"Random Forest\", \"RF RandomSearch\", \"RF GridSearch\"]\n",
    "columns = [\"First\", \"Second\", \"Third\". \"Fourth\", \"Fifth\"]\n",
    "top3_df = pd.DataFrame(data=list_top3, index=index, columns=columns)                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                  First                     Second  \\\n",
       "Ridge                   sqft_living lat                sqft_living   \n",
       "Lasso            sqft_living waterfront                        age   \n",
       "Decision Tree           sqft_living lat                      lat^2   \n",
       "Random Forest           sqft_living lat  sqft_living sqft_living15   \n",
       "RF RandomSearch         sqft_living lat  sqft_living sqft_living15   \n",
       "RF GridSearch           sqft_living15^2     sqft_living sqft_above   \n",
       "\n",
       "                                     Third  \n",
       "Ridge                           sqft_above  \n",
       "Lasso                      sqft_living age  \n",
       "Decision Tree    sqft_living sqft_living15  \n",
       "Random Forest                        lat^2  \n",
       "RF RandomSearch                      lat^2  \n",
       "RF GridSearch    sqft_living sqft_living15  "
      ],
      "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>First</th>\n      <th>Second</th>\n      <th>Third</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Ridge</th>\n      <td>sqft_living lat</td>\n      <td>sqft_living</td>\n      <td>sqft_above</td>\n    </tr>\n    <tr>\n      <th>Lasso</th>\n      <td>sqft_living waterfront</td>\n      <td>age</td>\n      <td>sqft_living age</td>\n    </tr>\n    <tr>\n      <th>Decision Tree</th>\n      <td>sqft_living lat</td>\n      <td>lat^2</td>\n      <td>sqft_living sqft_living15</td>\n    </tr>\n    <tr>\n      <th>Random Forest</th>\n      <td>sqft_living lat</td>\n      <td>sqft_living sqft_living15</td>\n      <td>lat^2</td>\n    </tr>\n    <tr>\n      <th>RF RandomSearch</th>\n      <td>sqft_living lat</td>\n      <td>sqft_living sqft_living15</td>\n      <td>lat^2</td>\n    </tr>\n    <tr>\n      <th>RF GridSearch</th>\n      <td>sqft_living15^2</td>\n      <td>sqft_living sqft_above</td>\n      <td>sqft_living sqft_living15</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 108
    }
   ],
   "source": [
    "top3_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#instantiate a linear regression object\n",
    "rf_final = RandomForestRegressor(n_estimators=100, \n",
    "                           max_features=\"auto\", \n",
    "                           max_depth=100, \n",
    "                           min_samples_leaf=4, \n",
    "                           min_samples_split=10, \n",
    "                           random_state=1).fit(X_tr2, y_tr2)\n",
    "rf_final.fit(X_tr2, y_tr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm_final.coef_"
   ]
  },
  {
   "source": [
    "## Gradient Boosting Regression Trees (GBRT)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor "
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbrt = GradientBoostingRegressor(n_estimators=100).fit(X_tr2, y_tr2) \n",
    "y_pred_gbrt_tr = gbrt.predict(X_tr2)\n",
    "y_pred_gbrt_tt = gbrt.predict(X_tt2)\n",
    "\n",
    "print(\"Feature Importances: \", gbrt.feature_importances_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "source": [
    "# Pickle"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_out = open(\"model.pickle\",\"wb\")\n",
    "pickle.dump(lm_final, pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_out = open(\"scaler.pickle\", \"wb\")\n",
    "pickle.dump(scaler, pickle_out)\n",
    "pickle_out.close"
   ]
  }
 ]
}