final_housing_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# King County, Washington Housing Prices Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import pickle\n",
    "from datetime import datetime\n",
    "from scipy import stats                                                                 \n",
    "from scipy.stats import f_oneway                                                               #ANOVA\n",
    "from statsmodels.formula.api import ols                                                        #Regression Summary\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd        \n",
    "from statsmodels.stats import weightstats as stests                                            #Z-test code\n",
    "from statsmodels.stats.proportion import proportions_ztest                                     #Proportions z-test\n",
    "from sklearn.linear_model import LinearRegression                                              #Simple & MultiLinear Regression\n",
    "from sklearn.feature_selection import RFE, RFECV, SelectKBest, f_regression, mutual_info_regression     \n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler                           #Multilinear Regression\n",
    "from sklearn import metrics                                                                    #MAE, MSE\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline \n",
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project and EDA Goals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Analysis:** Identify features in the data that correlate with the price of homes sold to make accurate pricing predictions of future houses on the market in the county of Kings, WA.<br><br>\n",
    "\n",
    "- **Feature Engineering:** Create new data insights based on the data and information given to give greater accuracy and predictive power to our predictive models.<br><br>\n",
    "\n",
    "- **OLS and Sci-kit Learn Models:** Use our substantiated data to build models to predict future housing prices, while continuously tweaking our features and insights to analyze and conclude on the best model to put into production.<br><br>\n",
    "\n",
    "- **Test Our Optimal Model:** Test the best fit model against the holdout data to predict our housing prices to an accuracy of within 200,000k."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading & Checking Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17290, 22)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2591820310</td>\n",
       "      <td>20141006T000000</td>\n",
       "      <td>365000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2070</td>\n",
       "      <td>8893</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2070</td>\n",
       "      <td>0</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>98058</td>\n",
       "      <td>47.4388</td>\n",
       "      <td>-122.162</td>\n",
       "      <td>2390</td>\n",
       "      <td>7700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7974200820</td>\n",
       "      <td>20140821T000000</td>\n",
       "      <td>865000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2900</td>\n",
       "      <td>6730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1830</td>\n",
       "      <td>1070</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>98115</td>\n",
       "      <td>47.6784</td>\n",
       "      <td>-122.285</td>\n",
       "      <td>2370</td>\n",
       "      <td>6283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7701450110</td>\n",
       "      <td>20140815T000000</td>\n",
       "      <td>1038000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3770</td>\n",
       "      <td>10893</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>3770</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>98006</td>\n",
       "      <td>47.5646</td>\n",
       "      <td>-122.129</td>\n",
       "      <td>3710</td>\n",
       "      <td>9685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9522300010</td>\n",
       "      <td>20150331T000000</td>\n",
       "      <td>1490000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4560</td>\n",
       "      <td>14608</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>4560</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>98034</td>\n",
       "      <td>47.6995</td>\n",
       "      <td>-122.228</td>\n",
       "      <td>4050</td>\n",
       "      <td>14226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9510861140</td>\n",
       "      <td>20140714T000000</td>\n",
       "      <td>711000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2550</td>\n",
       "      <td>5376</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2550</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98052</td>\n",
       "      <td>47.6647</td>\n",
       "      <td>-122.083</td>\n",
       "      <td>2250</td>\n",
       "      <td>4050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0          id             date      price  bedrooms  bathrooms  \\\n",
       "0           0  2591820310  20141006T000000   365000.0         4       2.25   \n",
       "1           1  7974200820  20140821T000000   865000.0         5       3.00   \n",
       "2           2  7701450110  20140815T000000  1038000.0         4       2.50   \n",
       "3           3  9522300010  20150331T000000  1490000.0         3       3.50   \n",
       "4           4  9510861140  20140714T000000   711000.0         3       2.50   \n",
       "\n",
       "   sqft_living  sqft_lot  floors  waterfront  view  condition  grade  \\\n",
       "0         2070      8893     2.0           0     0          4      8   \n",
       "1         2900      6730     1.0           0     0          5      8   \n",
       "2         3770     10893     2.0           0     2          3     11   \n",
       "3         4560     14608     2.0           0     2          3     12   \n",
       "4         2550      5376     2.0           0     0          3      9   \n",
       "\n",
       "   sqft_above  sqft_basement  yr_built  yr_renovated  zipcode      lat  \\\n",
       "0        2070              0      1986             0    98058  47.4388   \n",
       "1        1830           1070      1977             0    98115  47.6784   \n",
       "2        3770              0      1997             0    98006  47.5646   \n",
       "3        4560              0      1990             0    98034  47.6995   \n",
       "4        2550              0      2004             0    98052  47.6647   \n",
       "\n",
       "      long  sqft_living15  sqft_lot15  \n",
       "0 -122.162           2390        7700  \n",
       "1 -122.285           2370        6283  \n",
       "2 -122.129           3710        9685  \n",
       "3 -122.228           4050       14226  \n",
       "4 -122.083           2250        4050  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:\\\\Users\\\\User\\\\Documents\\\\Flatiron\\\\Repository\\\\flatiron-p2-housing\\\\data\\\\kc_house_data_train.csv')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Observations of Data\n",
    "- **id:** Treat as a string since the number is exponentially large.\n",
    "- **date:** Convert to UTC and extrapolate year from the data.\n",
    "- **price:** Prediction target value. Leave alone for now.\n",
    "- **bedrooms:** Number of bedrooms. Possible outliers with at least one home having 33 bedrooms.\n",
    "- **bathrooms:** Leave alone for now. Float data type suggests half bathrooms (w/o tub or shower).\n",
    "- **sqft_living:** Square footage of the house. \n",
    "- **sqft_lot:** Possible outliers with one property having 14,000 sqft, yet only costing about 240,000k.\n",
    "- **floors:** Possible mezzanines, banisters, catwalks, attics, sub-basements, or garage floors that are float vals.\n",
    "- **waterfront:** Leave alone for now.\n",
    "- **view:** Leave alone for now.\n",
    "- **condition:** Value unknown if higher = better or reverse.\n",
    "- **sqft_above:** Square footage if house has a basement most likely. Combine with basement for feature later.\n",
    "- **sqft_basement:** Could be useful later on. Leave alone for now. Possible feature later.\n",
    "- **yr_renovated:** Could prove useful to get a value for years since renovation.\n",
    "- **zipcode:** Convert to string to prevent issues later?\n",
    "- **sqft_living15:** Closely correlated to sqft_living. Worth keeping around as we'll see later in the correlation chart."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Visualizations (a peek)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:**\n",
    "- The majority of homes built are on the newer end between 11-20 years old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the outlier values in Bedrooms\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df['price'], df['bedrooms'], alpha = 0.1, color = 'r')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:**\n",
    "- Doesn't look at first glance that bedrooms by themself have a huge impact on sale price. But let's get rid of that 33 bedroom value by imputing the mean # of bedrooms since the sale price is also on the low side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3724696356275303"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Take the mean and use for the value with 33 bedrooms\n",
    "df['bedrooms'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of Price Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 5))    \n",
    "sns.histplot(data = df['price'], bins = 100)\n",
    "\n",
    "# plt.savefig('price_dist.png', dpi = 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:**\n",
    "- The pricing data is skewed to the right positively, yet quite a few outliers still exist. Such a spread and wide price range could be dependent on a wide range of factors however.\n",
    "- Let's log the price target and view in a graph to see as a normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Log Transformation on Price to View as Normal Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skew is: 0.44658171492588483\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target = np.log(df['price'])\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.histplot(data = target, bins=100, kde=True)\n",
    "print (\"Skew is:\", target.skew())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning & Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Preparation and taking care of abnormal data types\n",
    "- Properties having 0 bathrooms may be errored. Those with 0 bedrooms may be listed as studio apartments, but there are some outliers with larger square footage that still have issues with the number of bedrooms and bathrooms in comparison to sqft.\n",
    "- Check for issues with sqft_lot values with high numbers despite selling for low prices.\n",
    "- Let's create a deep copy of the original dataframe before we start modifying the housing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Turn id into a string to prevent any numerical issues.\n",
    "df['id'] = df['id'].astype('str')\n",
    "\n",
    "#Turn zipcode into a string to prevent any numerical issues.\n",
    "df['zipcode'] = df['zipcode'].astype('str')\n",
    "\n",
    "#Convert the date column to something workable. We'll first use UTC then strip the year out afterwards.\n",
    "df['date'] = pd.to_datetime(df['date'], utc = True)\n",
    "\n",
    "#Taking care of the 33 bedroom property by imputing the mean number of bedrooms.\n",
    "df['bedrooms'] = np.where(df['bedrooms'] > 30, df['bedrooms'].mean(), df['bedrooms'])\n",
    "\n",
    "#Taking care of any properties with less than .5 baths. Using .75 as that code for baths has a shower.\n",
    "df['bathrooms'] = np.where(df['bathrooms'] < .75, .75, df['bathrooms'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Transforming the data from the date to get and extract the year in order to calcuate the age of the house.\n",
    "df['year_sold'] = pd.to_datetime(df['date']).dt.date.astype('datetime64[ns]')\n",
    "\n",
    "#Convert bedrooms to into to account for the mean transformation we did previously for the 33 bedroom value.\n",
    "df['bedrooms'] = df['bedrooms'].astype(int)\n",
    "\n",
    "#Create a column for whether or not a property has a basement\n",
    "df['has_basement'] = np.where(df['sqft_basement'] > 0, 1, 0)\n",
    "\n",
    "#Feature Engineering to extrapolate the year sold from the convoluted date format.\n",
    "df['year_sold'] = pd.DatetimeIndex(df['year_sold']).year # extract only year\n",
    "\n",
    "#Feature Engineering the age of the home\n",
    "df['age_built'] = (df['year_sold'] - df['yr_built']).astype(int)\n",
    "df['age_built'] = (df['age_built'] + 6) #bring up to 2021 values\n",
    "\n",
    "#Feature Engineering the time since the last renovation on home. No renovation will treat build year as the value.\n",
    "reno_cond = [df['yr_renovated'] == 0, df['yr_renovated'] != 0]\n",
    "reno_choices = [df['year_sold'] - df['yr_built'], df['year_sold'] - df['yr_renovated']]\n",
    "df['last_reno'] = np.select(reno_cond, reno_choices)\n",
    "df['last_reno'] = (df['last_reno'] + 6) #bring up to 2021 values\n",
    "\n",
    "#Get month property sold\n",
    "df['month_sold'] = pd.DatetimeIndex(df['date']).month\n",
    "\n",
    "#Feature Engineering to convert months to seasons (1=Winter, 2=Spring, 3=Summer, 4=Fall)\n",
    "season_cond = [df['month_sold'] <= 3, (df['month_sold'] >= 4) & (df['month_sold'] <=6), \n",
    "               (df['month_sold'] >= 7) & (df['month_sold'] <=9), (df['month_sold'] >= 10) & (df['month_sold'] <=12)]\n",
    "season_choices = [1, 2, 3, 4] \n",
    "df['season_sold'] = np.select(season_cond, season_choices)\n",
    "\n",
    "#Simple Boolean answer whether the home was renovated as (True/False)\n",
    "choices_reno_TF = [0, 1] #0 = True\n",
    "df['was_reno'] = np.select(reno_cond, choices_reno_TF)\n",
    "\n",
    "#Feature Engineering columns that show the ratio of beds and bath to home\n",
    "df['bed_sqft_ratio'] = df['bedrooms'] / df['sqft_living']\n",
    "df['bath_sqft_ratio'] = df['bathrooms'] / df['sqft_living']\n",
    "df['floors_sqft_ratio'] = df['floors'] / df['sqft_living']\n",
    "\n",
    "#Feature Engineering for grade values attributed to letter grade \n",
    "df['grade_lvl'] = df.grade.apply(lambda x: 'S+' if x == 13 else (\n",
    "                                           'S' if x == 12 else (\n",
    "                                           'S-' if x == 11 else (\n",
    "                                           'A+' if x == 10 else (\n",
    "                                           'A' if x ==  9 else (\n",
    "                                           'A-' if x == 8 else (\n",
    "                                           'B+' if x == 7 else (\n",
    "                                           'B' if x == 6 else (\n",
    "                                           'B-' if x == 5 else (\n",
    "                                           'C+' if x == 4 else (\n",
    "                                           'C' if x == 3 else (\n",
    "                                           'C-' if x == 2 else (\n",
    "                                           'D')))))))))))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANOVA of price against grade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **H0:** The means of housing prices per grade are equal (ie. mu_S+ = mu_A = mu_D)\n",
    "- **Ha:** The means of housing prices according to grades is not equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1187     3200000.0\n",
       "2232     5570000.0\n",
       "4217     2888000.0\n",
       "6611     3800000.0\n",
       "6667     2340000.0\n",
       "7519     6885000.0\n",
       "8021     2983000.0\n",
       "9763     3800000.0\n",
       "10366    7700000.0\n",
       "12597    2479000.0\n",
       "14076    2385000.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['grade_lvl'] == 'S+']['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1763.5110290471393, pvalue=0.0)"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove C- from final model dataframe as it is missing values\n",
    "\n",
    "grade_anova = stats.f_oneway(df['price'][df['grade_lvl'] == 'S+'],\n",
    "                df['price'][df['grade_lvl'] == 'S'], \n",
    "                df['price'][df['grade_lvl'] == 'S-'],\n",
    "                df['price'][df['grade_lvl'] == 'A+'],\n",
    "                df['price'][df['grade_lvl'] == 'A'],\n",
    "                df['price'][df['grade_lvl'] == 'A-'],\n",
    "                df['price'][df['grade_lvl'] == 'B+'],\n",
    "                df['price'][df['grade_lvl'] == 'B'],\n",
    "                df['price'][df['grade_lvl'] == 'B-'],\n",
    "                df['price'][df['grade_lvl'] == 'C+'],\n",
    "                df['price'][df['grade_lvl'] == 'C'],\n",
    "                df['price'][df['grade_lvl'] == 'D'])\n",
    "\n",
    "grade_anova"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:**\n",
    "- Because the pvalue is less than our alpha of .05, we reject the null hypothesis that the mean housing prices according to grade are equal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is There a Statistical Difference Between Waterfront and Non-Waterfront Properties?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **H0:** There is no statistical difference between the sale levels of waterfront and non-waterfront properties\n",
    "- **Ha:** There is a significant statistical difference between the sale levels of waterfront and non-waterfront properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=11.903695918563983, pvalue=6.940441256090536e-23)\n",
      "1.9601012146709136\n"
     ]
    }
   ],
   "source": [
    "#Create variables to pass to 2 sided t-test\n",
    "#Assume an alpha value of 0.5\n",
    "\n",
    "waterfront = df[df['waterfront'] == 1]['price']\n",
    "no_waterfront = df[df['waterfront'] == 0]['price']\n",
    "\n",
    "#Two Sample independent T-test\n",
    "water_2samp_t_test = stats.ttest_ind(waterfront, no_waterfront, equal_var=False, nan_policy='omit')\n",
    "\n",
    "#Critical value calculation\n",
    "water_2samp_crit_val = stats.t.ppf(1-(.05/2), df=len(waterfront)+len(no_waterfront)-2)  \n",
    "\n",
    "print(water_2samp_t_test)\n",
    "print(water_2samp_crit_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:**\n",
    "- Because the pvalue is < our alpha of 0.5, and the critical value is < our statistic, we reject the null hypothesis that there is no statistical difference between the sale prices of waterfront and non-waterfront properties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Inference of Homes with Basements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a 95% confidence interval for the population mean homes sqft_living."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"With 95% confidence, the population mean of homes' square feet of living space falls between the intervals of 2067.75017962079 and 2095.179028013681\""
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqft_mean = df['sqft_living'].mean()\n",
    "sqft_std = df['sqft_living'].std()\n",
    "sqft_n = len(df['sqft_living'])\n",
    "sqft_tval = stats.t.ppf(.975, sqft_n-1)\n",
    "sqft_margin_err = (sqft_tval * sqft_std) / (sqft_n ** .5)\n",
    "sqft_ci = (sqft_mean - sqft_margin_err, sqft_mean + sqft_margin_err)\n",
    "sqft_ci_print = \"With 95% confidence, the population mean of homes' square feet of living space falls between the intervals of \" + str(sqft_ci[0]) + \" and \" + str(sqft_ci[1])\n",
    "sqft_ci_print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a 95% confidence interval for the true proportion of homes with basements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'With 95% confidence, the true proportion houses with basements falls between the intervals of 0.3863549790033762 and 0.40092090300934796'"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_mean = df['has_basement'].mean()\n",
    "base_std = df['has_basement'].std()\n",
    "base_n = len(df['has_basement'])\n",
    "base_tval = stats.t.ppf(.975, sqft_n-1)\n",
    "base_margin_err = (base_tval * base_std) / (base_n ** .5)\n",
    "base_ci = (base_mean - base_margin_err, base_mean + base_margin_err)\n",
    "base_ci_print = \"With 95% confidence, the true proportion houses with basements falls between the intervals of \" + str(base_ci[0]) + \" and \" + str(base_ci[1])\n",
    "base_ci_print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization (a deeper dive)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at a few variables that look to correlate with price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x2880 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(4,figsize=(12,40))\n",
    "\n",
    "sns.boxplot(y = 'bedrooms', x = 'price', data = df, width = 0.8, orient = 'h', showmeans = True, fliersize = 3, ax = axarr[0])\n",
    "sns.boxplot(y = 'floors', x = 'price', data = df, width = 0.8, orient = 'h', showmeans = True, fliersize = 3, ax = axarr[1])\n",
    "sns.boxplot(y = 'view', x = 'price', data = df, width = 0.8, orient = 'h', showmeans = True, fliersize = 3, ax = axarr[2])\n",
    "sns.boxplot(y = 'condition', x = 'price', data = df, width = 0.8, orient = 'h', showmeans = True, fliersize = 3, ax = axarr[3])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age of Homes Built by Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [-2, 1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 100000]\n",
    "labels = ['<1', '1-10', '11-20', '20-30', '30-40', '40-50', '50-60', '60-70', '70-80', '80-90', '90-100', '>100'] # e.g., (1-10]\n",
    "df['age_built_binned'] = pd.cut(df['age_built'], bins = bins, labels = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qNIOxJEmSJKnTDMaSJEmSpE4zGEuSJEmSOs1gLEmSJEnqNIOxJEmSJKnTDMaSJEmSpE4bejBOskOSryX5XHu9R5IVSVa15/l9256WZHWS25Mc1Vd+WJKb2rqzk6SV75jkolZ+bZKD+uosb8dYlWT5sM9TkiRJkjQ3jeKK8cnAbX2vTwWurKpDgCvba5IcCiwDngccDXw4yQ6tzkeAE4BD2uPoVn488EBVPQv4EPCBtq89gNOBw4HFwOn9AVySJEmSpAlDDcZJFgKvBT7aV7wUOL8tnw8c01d+YVU9UlV3AKuBxUn2A3atqmuqqoCPT6kzsa9LgCXtavJRwIqq2lBVDwAr2BSmJUmSJEl6zLCvGP8pcArw076yfarqHoD2vHcr3x+4u2+7Na1s/7Y8tXxSnaraCDwI7DnDviZJckKSlUlW3n///dtwepIkSZKkuW5owTjJrwH3VdUNg1aZpqxmKN/WOpsKqs6pqkVVtWjBggUDNlOSJEmSNE6GecX4ZcDrktwJXAi8MsnfAve24dG05/va9muAA/rqLwTWtfKF05RPqpNkHrAbsGGGfUmSJEmSNMnQgnFVnVZVC6vqIHqTal1VVf8DuAyYmCV6OfDZtnwZsKzNNH0wvUm2rmvDrR9KckS7f/jNU+pM7OvYdowCrgCOTDK/Tbp1ZCuTJEmSJGmSebNwzPcDFyc5HrgLOA6gqm5JcjFwK7AROKmqHm11TgQ+BuwEXN4eAOcCFyRZTe9K8bK2rw1JzgCub9u9r6o2DPvEJEmSJElzz0iCcVVdDVzdlr8LLNnMdmcCZ05TvhJ4/jTlP6IF62nWnQect61tliRJkiR1wyi+x1iSJEmSpO2WwViSJEmS1GkGY0mSJElSpxmMJUmSJEmdZjCWJEmSJHWawViSJEmS1GkGY0mSJElSpw0cjJP8XJJXteWdkuwyvGZJkiRJkjQaAwXjJL8DXAL8VStaCHxmSG2SJEmSJGlkBr1ifBLwMuD7AFW1Cth7WI2SJEmSJGlUBg3Gj1TVjydeJJkH1HCaJEmSJEnS6AwajL+c5A+BnZL8N+BTwD8Mr1mSJEmSJI3GoMH4VOB+4Cbgd4HPA//nsBolSZIkSdKozBtwu52A86rqrwGS7NDKfjCshkmSJEmSNAqDXjG+kl4QnrAT8KUnvjmSJEmSJI3WoMH4qVX18MSLtvy04TRJkiRJkqTRGTQY/2eSl0y8SHIY8MPhNEmSJEmSpNEZ9B7jdwGfSrKuvd4PeMNQWiRJkiRJ0ggNFIyr6vok/wV4DhDgm1X1k6G2TJIkSZKkEZgxGCd5ZVVdleQ3pqw6JAlV9ekhtk2SJEmSpKHb0hXjXwGuAn59mnUFGIwlSZIkSXPajMG4qk5P8iTg8qq6eERtkiRJkiRpZLY4K3VV/RR4+wjaIkmSJEnSyA36dU0rkrw7yQFJ9ph4DLVlkiRJkiSNwKBf1/RWevcU/96U8mc+sc2RJEmSJGm0Bg3Gh9ILxb9ELyD/C/CXw2qUJEmSJEmjMmgwPh/4PnB2e/3GVvabw2iUJEmSJEmjMmgwfk5VvbDv9T8l+fowGiRJkiRJ0igNOvnW15IcMfEiyeHAvw2nSZIkSZIkjc6gV4wPB96c5K72+kDgtiQ3AVVVLxhK6yRJkiRJGrJBg/HRQ22FJEmSJEmzZKBgXFXfGXZDJEmSJEmaDYPeYyxJkiRJ0lgyGEuSJEmSOs1gLEmSJEnqNIOxJEmSJKnTDMaSJEmSpE4bWjBO8tQk1yX5epJbkvxxK98jyYokq9rz/L46pyVZneT2JEf1lR+W5Ka27uwkaeU7JrmolV+b5KC+OsvbMVYlWT6s85QkSZIkzW3DvGL8CPDKqnoh8CLg6CRHAKcCV1bVIcCV7TVJDgWWAc+j973JH06yQ9vXR4ATgEPaY+J7lY8HHqiqZwEfAj7Q9rUHcDpwOLAYOL0/gEuSJEmSNGFowbh6Hm4vn9weBSwFzm/l5wPHtOWlwIVV9UhV3QGsBhYn2Q/YtaquqaoCPj6lzsS+LgGWtKvJRwErqmpDVT0ArGBTmJYkSZIk6TFDvcc4yQ5JbgTuoxdUrwX2qap7ANrz3m3z/YG7+6qvaWX7t+Wp5ZPqVNVG4EFgzxn2JUmSJEnSJEMNxlX1aFW9CFhI7+rv82fYPNPtYobyba2z6YDJCUlWJll5//33z9A0SZIkSdK4Gsms1FX1PeBqesOZ723Do2nP97XN1gAH9FVbCKxr5QunKZ9UJ8k8YDdgwwz7mtquc6pqUVUtWrBgwbafoCRJkiRpzhrmrNQLkuzelncCXgV8E7gMmJglejnw2bZ8GbCszTR9ML1Jtq5rw60fSnJEu3/4zVPqTOzrWOCqdh/yFcCRSea3SbeObGWSJEmSJE0yb4j73g84v80s/STg4qr6XJJrgIuTHA/cBRwHUFW3JLkYuBXYCJxUVY+2fZ0IfAzYCbi8PQDOBS5IspreleJlbV8bkpwBXN+2e19VbRjiuUqSJEmS5qihBeOq+gbw4mnKvwss2UydM4EzpylfCfzM/clV9SNasJ5m3XnAeVvXakmSJElS14zkHmNJkiRJkrZXBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpQwvGSQ5I8k9JbktyS5KTW/keSVYkWdWe5/fVOS3J6iS3Jzmqr/ywJDe1dWcnSSvfMclFrfzaJAf11VnejrEqyfJhnackSZIkaW4b5hXjjcD/qqrnAkcAJyU5FDgVuLKqDgGubK9p65YBzwOOBj6cZIe2r48AJwCHtMfRrfx44IGqehbwIeADbV97AKcDhwOLgdP7A7gkSZIkSROGFoyr6p6q+mpbfgi4DdgfWAqc3zY7HzimLS8FLqyqR6rqDmA1sDjJfsCuVXVNVRXw8Sl1JvZ1CbCkXU0+ClhRVRuq6gFgBZvCtCRJkiRJjxnJPcZtiPOLgWuBfarqHuiFZ2Dvttn+wN191da0sv3b8tTySXWqaiPwILDnDPuSJEmSJGmSoQfjJE8H/h54V1V9f6ZNpymrGcq3tU5/205IsjLJyvvvv3+GpkmSJEmSxtVQg3GSJ9MLxZ+oqk+34nvb8Gja832tfA1wQF/1hcC6Vr5wmvJJdZLMA3YDNsywr0mq6pyqWlRVixYsWLCtpylJkiRJmsOGOSt1gHOB26rq/+1bdRkwMUv0cuCzfeXL2kzTB9ObZOu6Ntz6oSRHtH2+eUqdiX0dC1zV7kO+Ajgyyfw26daRrUySJEmSpEnmDXHfLwPeBNyU5MZW9ofA+4GLkxwP3AUcB1BVtyS5GLiV3ozWJ1XVo63eicDHgJ2Ay9sDesH7giSr6V0pXtb2tSHJGcD1bbv3VdWGIZ2nJEmSJGkOG1owrqp/Zfp7fQGWbKbOmcCZ05SvBJ4/TfmPaMF6mnXnAecN2l5JkiRJUjeNZFZqSZIkSZK2VwZjSZIkSVKnGYwlSZIkSZ1mMJYkSZIkdZrBWJIkSZLUaQZjSZIkSVKnGYwlSZIkSZ1mMJYkSZIkdZrBWJIkSZLUaQZjSZIkSVKnGYwlSZIkSZ1mMJYkSZIkdZrBWJIkSZLUaQZjSZIkSVKnGYwlSZIkSZ1mMJYkSZIkdZrBWJIkSZLUaQZjSZIkSVKnGYwlSZIkSZ1mMJYkSZIkdZrBWJIkSZLUaQZjSZIkSVKnGYwlSZIkSZ1mMJYkSZIkdZrBWJIkSZLUaQZjSZIkSVKnGYwlSZIkSZ1mMJYkSZIkdZrBWJIkSZLUaQZjSZIkSVKnGYwlSZIkSZ1mMJYkSZIkdZrBWJIkSZLUaQZjSZIkSVKnGYwlSZIkSZ1mMJYkSZIkdZrBWJIkSZLUaQZjSZIkSVKnDS0YJzkvyX1Jbu4r2yPJiiSr2vP8vnWnJVmd5PYkR/WVH5bkprbu7CRp5TsmuaiVX5vkoL46y9sxViVZPqxzlCRJkiTNfcO8Yvwx4OgpZacCV1bVIcCV7TVJDgWWAc9rdT6cZIdW5yPACcAh7TGxz+OBB6rqWcCHgA+0fe0BnA4cDiwGTu8P4JIkSZIk9RtaMK6qfwY2TCleCpzfls8Hjukrv7CqHqmqO4DVwOIk+wG7VtU1VVXAx6fUmdjXJcCSdjX5KGBFVW2oqgeAFfxsQJckSZIkCRj9Pcb7VNU9AO1571a+P3B333ZrWtn+bXlq+aQ6VbUReBDYc4Z9/YwkJyRZmWTl/fff/zhOS5IkSZI0V20vk29lmrKaoXxb60wurDqnqhZV1aIFCxYM1FBJkiRJ0ngZdTC+tw2Ppj3f18rXAAf0bbcQWNfKF05TPqlOknnAbvSGbm9uX5IkSZIk/YxRB+PLgIlZopcDn+0rX9Zmmj6Y3iRb17Xh1g8lOaLdP/zmKXUm9nUscFW7D/kK4Mgk89ukW0e2MkmSJEmSfsa8Ye04ySeBVwB7JVlDb6bo9wMXJzkeuAs4DqCqbklyMXArsBE4qaoebbs6kd4M1zsBl7cHwLnABUlW07tSvKzta0OSM4Dr23bvq6qpk4BJkiRJkgQMMRhX1Rs3s2rJZrY/EzhzmvKVwPOnKf8RLVhPs+484LyBGytJkiRJ6qztZfItSZIkSZJmhcFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR12lgH4yRHJ7k9yeokp852eyRJkiRJ25+xDcZJdgD+Ang1cCjwxiSHzm6rJEmSJEnbm7ENxsBiYHVVfbuqfgxcCCyd5TZJkiRJkrYz4xyM9wfu7nu9ppVJkiRJkvSYVNVst2EokhwHHFVVv91evwlYXFXv6NvmBOCE9vI5wO0jb+iW7QX8x2w3Yg6wnwZjPw3OvhqM/TQ4+2ow9tNg7KfB2VeDsZ8GZ18NZnvsp5+rqgXTrZg36paM0BrggL7XC4F1/RtU1TnAOaNs1NZKsrKqFs12O7Z39tNg7KfB2VeDsZ8GZ18Nxn4ajP00OPtqMPbT4Oyrwcy1fhrnodTXA4ckOTjJU4BlwGWz3CZJkiRJ0nZmbK8YV9XGJG8HrgB2AM6rqltmuVmSJEmSpO3M2AZjgKr6PPD52W7H47RdD/XejthPg7GfBmdfDcZ+Gpx9NRj7aTD20+Dsq8HYT4OzrwYzp/ppbCffkiRJkiRpEON8j7EkSZIkSVtkMJ4Dkrw9yeoklWSv2W7PKCU5L8l9SW7e2m2S7JFkRZJV7Xn+8Fs8OtOdd5LjktyS5KdJNjsLYJIPJvlmkm8kuTTJ7n3rTmvvt9uTHDXk0xi6JAck+ackt7W+ObmVD/T+SHJG66cbk3wxyTP61o1NXyV5apLrkny99dMft/Kt+jlK8u6pv6vGqZ/6JdkhydeSfK69HvQ99d4ka9t76sYkr+lbN1Z9leTOJDe181zZygZ+TyV5R+uLW5Kc1Vc+Vv0EkGT3JJe03823JXnpVrynLup7P92Z5Ma+dWPTV0me03eeNyb5fpJ3bUU/vSjJVybej0kW960bm34CSPL77efm5iSfbL/jB+2nFya5pv3s/kOSXfvWjUU/JTm59c0tSd7Vygbtn83+rbW5/klyWOvP1UnOTpKhnuAsyGbySnrObuu+keQlfeuObn21Osmps9PyPlXlYzt8AE8Bdm7LLwYOAu4E9prtto24H14OvAS4eWu3Ac4CTm3LpwIfmO3zGXbfAM+l953cVwOLZqh7JDCvLX9gom+AQ4GvAzsCBwPfAnaY7XN9nP20H/CStrwL8O/tPAd6fwC79i2/E/jLcewrIMDT2/KTgWuBI7bm54jeV+RdAXxn4nfVuPXTlPP9P4C/Az7XXg/6nnov8O5pyseur6b7f2sr+ulXgS8BO7bXe49rP7XzOh/47bb8FGD3bfl/DPgT4I/Gua/aue0ArAd+biveU18EXt2WXwNcPY79BOwP3AHs1F5fDPzPrein64FfactvBc4Yp34Cng/cDDyN3nxLXwIO2Yr+mfZvrZn6B7gOeCm9/2svn3gfzqUHMH8L66fNK+1n7fJ27kcA17byHVofPbP9zvs6cOhsnqNXjLczSZ6b5E+A24FnA1TV16rqzllt2Cypqn8GNmzjNkvp/aFBez7mCW3cLJvuvKvqtqq6fYC6X6yqje3lV+h9zzf0+uzCqnqkqu4AVgOLp9vHXFFV91TVV9vyQ8Bt9P5oGOj9UVXf73u5MzAxMcNY9VX1PNxePrk9iq37OfoQcAqb+gjGrJ8mJFkIvBb4aF/x4/2dM5Z9NY1B++lE4P1V9QhAVd3XV3+s+qldkXs5cC5AVf24qr7HVr6n2lWo3wQ+2YrGrq/6LAG+VVXfYfB+KmDi6uduwLq2PI79NA/YKck8egFwHYP303OAf27LK4D/3pbHpZ+eC3ylqn7Q/hb6MvB6Bv+7YHN/a03bP0n2o/ch+zXVS4Qf39y+t3Mrk/xdkldOd8V7hryyFPh4+zvjK8DurU8WA6ur6ttV9WPgwrbtrDEYbweS7JzkLUn+ld4fWbcBL6iqr81y0+a6farqHuiFI2DvWW7P9uqt9D7Jg15gvLtv3ZpWNhaSHETvE81r2Yr3R5Izk9wN/BbwR6147PoqvaHBNwL3ASuqauB+SvI6YG1VfX3KqrHrp+ZP6X0I8NO+sq35nfP2NqTsvL7heuPYVwV8MckNSU5oZYP207OBX05ybZIvJ/nFVj6O/fRM4H7gb9Ibnv/RJDuz9f+P/TJwb1Wtaq/Hsa8mLGPTBwCD9tO7gA+23+f/D3BaKx+rfqqqtfTO7y7gHuDBqvoig/fTzcDr2vJx9EYDwfj0083Ay5PsmeRp9K5oHsDj/7txc/2zf1ueWj7XPJveKKm3A7cm+cP03V42g5n6Zbt6PxmMtw/3AMfTG0L1sqr6aLuyJQ1VkvcAG4FPTBRNs9lYTF2f5OnA3wPvmnIVeIuq6j1VdQC9fnr7xC6n2/TxtXJ2VdWjVfUieiMIFid5/iD12h8W72HThwaTVk93qG1u5HYgya8B91XVDdu4i48APw+8iN7v/z+Z2PU0287pvgJeVlUvAV4NnJTk5VtRdx4wn97Quz8ALm5XKcaxn+bRuzXmI1X1YuA/6Q3l3FpvZFNYhPHsK5I8hV5w+9RWVj0R+P32+/z3aVfoGbN+ah+2LaU3nPcZwM5J/sdW7OKt9H5eb6B3C9KPJ3Y9zbZzrp+q6jZ6t5GtAL5AbwjvxhkrDWZz/TMu/fZoVX2uqn6D3giXZwJ39d+rvxlzpl8MxtuHY4G1wKVJ/ijJz812g7ZX6U2kNDHpxtu2sPm9bagG7fm+LWw/tpL8Teuzz/eVLQd+DfitNrQHep/WHdBXdSGbhprNWUmeTC8Uf6KqPt2Kp31/TNdXff6OTUPKxrKvANoQzquBoxmsn36e3h9gX09yJ72++GqSfRnPfnoZ8Lp2rhcCr0zytwz4nqqqe9sfGD8F/ppNQxHHrq+qal17vg+4lN65Dvqztwb4dBt+dx29q/N7MYb9RO+c1rRRGgCX0AvKA/+eakNmfwO4aMp+x62voPdBy1er6t72etB+Wg5M/B/wKcb3Z+9VwB1VdX9V/YTeOf9XBv8d9c2qOrKqDqP3Qcu32n7Hpp+q6tyqeklVvZzebWmr2La/C/ptrn/WsOmWtf7yOSfJbm30z2X0riAfD3xjC9Vm6pft6/009aZjH7P3APYETgZupDcRwEFT1t9Jxybfaud9EDNMvrW5bYAPMnkShbNm+1xG1TdsefKto4FbgQVTyp/H5Ikjvs0cnFhjyjmF3v08f7ot7w/gkL7ldwCXjGNfAQuA3dvyTsC/0PvgZKt/jvp/V41bP01zrq9g0+Rbg76n9utb/n1696SNXV/Ruyd/l77l/6/97hm0n94GvK8tP5vekLuMWz/1ne+/AM9py+9t/TTwz1/r2y9PKRvXvroQeEvf60HfU7cBr2jLS4AbxrGfgMOBW+jdWxx698u+Yyv6aWKiuyfR+//zrePWT33neCDwTXqjU7bq/zt+dvKtzfYPvQnNjmDT5Fuvme0+2IY++1t6H5K8n76/jabZ7k4mT771WiZPvnVdK5/X+uhgNk2+9bxZPcfZ7mQfm/mH6X2KeUBbfie9T1U20vsk5aOz3b4R9sMn6Q01/Enrg+MH3YbeBw1X0vsU8Epgj9k+n2H3Db3JI9YAjwD3Aldspu5qen9k3tgef9m37j3tF9/tzMFZE6c511+iNzTnG33n+5pB3x/0rjTf3Or/A7D/OPYV8ALga+08b2bTrLZb/XM0zX+KY9NP05zrK9gUjAd9T10A3NT6+jImB+Wx6St6w+y+3h63AO/Zyn56Cr0/xG4Gvgq8chz7qe+cXgSsbO+Lz9D7Q33gnz/gY8Dbpikfq76iF/a+C+zWVzboe+qXgBvae/Ja4LAx7qc/phf4bm6/c3bcin46md43OPw7vRCUcesneh9E3dreC0u28n202b+1Ntc/wKL2b/Et4M/7+3SuPOjdvjBvhvXT5hV6gfgv2rnfxOQPE17T3mffov0fMZuPtEZJkiRJktRJ3mMsSZIkSeo0g7EkSZIkqdMMxpIkSZKkTjMYS5IkSZI6zWAsSZIkSeo0g7EkSZIkqdMMxpIkzSFJ3pvk3VtZ56NJDm3LdybZK8nuSX5vC/VekeRzm1n3+SS7b007tlaSjyU5dpjHkCQJDMaSJI29qvrtqrp1SvHuwIzBeAv7fE1Vfe/xtEuSpO2FwViSpCFI8pkkNyS5JckJrez4JP+e5Ookf53kz1v5giR/n+T69njZFnb/wiRXJVmV5HfaPiZd3U3y50n+Z1u+OsmiKft4P/DzSW5M8sEZjrVrkkuT3JrkL5M8qe1z4srzQUlua+dzS5IvJtmp77gfSHJdO+9fbuU7JPlgO9dvJPndVp7W7luT/COw92C9LUnS4zNvthsgSdKYemtVbWgh8foW9P4v4CXAQ8BVwNfbtn8GfKiq/jXJgcAVwHNn2PcLgCOAnYGvtX1vrVOB51fVi7aw3WLgUOA7wBeA3wAumbLNIcAbq+p3klwM/Hfgb9u6eVW1OMlrgNOBVwHHAw9W1S8m2RH4tyRfBF4MPAf4BWAf4FbgvG04N0mStorBWJKk4Xhnkte35QOANwFfrqoNAEk+BTy7rX8VcGiSibq7Jtmlqh7azL4/W1U/BH6Y5J/ohdfvDeEcAK6rqm+3Nn8S+CV+NhjfUVU3tuUbgIP61n16mvIjgRf03T+8G71w/XLgk1X1KLAuyVVP3GlIkrR5BmNJkp5gSV5BL+y+tKp+kORq4HY2fxX4SW3bHw54iJrm9UYm3yL11EHbuw3HmuqRvuVHgZ2mWfcom/7uCPCOqrqifyftqvJ0+5ckaai8x1iSpCfebsADLRT/F3rDnp8G/EqS+Unm0RtuPOGLwNsnXiR50Rb2vzTJU5PsCbwCuJ7eUOdDk+yYZDdgyRb28RCwywDnsjjJwe3e4jcA/zpAnS25AjgxyZMBkjw7yc7APwPL2j3I+wG/+gQcS5KkLfKKsSRJT7wvAG9L8g16V4q/AqwF/m/gWmAdvftnH2zbvxP4i7b9PHoB8W0z7P864B+BA4EzqmodQLu/9xvAKuBrMzWwqr6b5N+S3AxcXlV/sJlNr6E3UdcvtHZdOtN+B/RResOqv5re+PH7gWPavl8J3AT8O/DlJ+BYkiRtUaocsSRJ0igkeXpVPdyuGF8KnFdVT0TQlCRJj4NDqSVJGp33JrkRuBm4A/jMrLZGkiQBXjGWJGm7lOQtwMlTiv+tqk4awrF+AbhgSvEjVXX4E30sSZK2RwZjSZIkSVKnOZRakiRJktRpBmNJkiRJUqcZjCVJkiRJnWYwliRJkiR1msFYkiRJktRp/z+/cPvuISBUOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_line = df['age_built_binned']\n",
    "y_line = df['price']\n",
    "\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.barplot(data = df, x = x_line, y = y_line)\n",
    "# plt.savefig('age_built.png', dpi = 100)\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sales by Season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "x_box = df['season_sold']\n",
    "y_box = df['price']\n",
    "\n",
    "sns.boxplot(x = x_box, y = y_box, data = df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "x_bar = df['season_sold']\n",
    "y_bar = df['price']\n",
    "\n",
    "sns.barplot(x = x_box, y = y_box, data = df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grade vs Price\n",
    "- Let's take a closer look at **grade** vs **price** since there was a pretty nice correlation observed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "orders = list(reversed(range(1, 14))) #order of grade values in reverse order\n",
    "sns.boxplot(x = df['grade'], y = df['price'], order = orders)\n",
    "\n",
    "# plt.savefig('grade_vs_price_rev.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see a pretty distinct association between the grade of a property, vs the price it fetched!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grade Counts by Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "x_bar_grade = df['grade'].value_counts().index\n",
    "y_bar_grade = df['grade'].value_counts()\n",
    "\n",
    "grade_bar = sns.barplot(x = x_bar_grade, y = y_bar_grade, data = df)\n",
    "grade_bar.set(xlabel = \"Grade Ranking\", ylabel = \"Homes Sold\")\n",
    "plt.savefig('grade_ranking.png')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:**\n",
    "- Ranks of 7 and 8 sold at a much higher rate than the other grade level rankings. Use 7-9 when creating feature interactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sales by Zipcode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking for the top performing zipcodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "x_bar_zip = df['zipcode'].value_counts().index\n",
    "y_bar_zip = df['zipcode'].value_counts()\n",
    "\n",
    "zip_bar = sns.barplot(x = x_bar_zip, y = y_bar_zip, data = df)\n",
    "zip_bar.set_xticklabels(zip_bar.get_xticklabels(), rotation=90)\n",
    "zip_bar.set(xlabel = \"Zipcodes\", ylabel = \"Homes Sold\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 20 Performing Zipcodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- These zipcodes will be used later as features in our predictive models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_20_zip = ['98052', '98115', '98103', '98038', '98117', '98034', '98042', '98023', '98118', '98133', '98006', '98058', '98059', '98074', '98033', '98155', '98056', '98027', '98053', '98125']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_corr = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>year_sold</th>\n",
       "      <th>has_basement</th>\n",
       "      <th>age_built</th>\n",
       "      <th>last_reno</th>\n",
       "      <th>month_sold</th>\n",
       "      <th>season_sold</th>\n",
       "      <th>was_reno</th>\n",
       "      <th>bed_sqft_ratio</th>\n",
       "      <th>bath_sqft_ratio</th>\n",
       "      <th>floors_sqft_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-0.012306</td>\n",
       "      <td>-0.010562</td>\n",
       "      <td>-0.005151</td>\n",
       "      <td>-0.008900</td>\n",
       "      <td>-0.003500</td>\n",
       "      <td>0.010599</td>\n",
       "      <td>-0.008845</td>\n",
       "      <td>-6.278161e-07</td>\n",
       "      <td>-0.001781</td>\n",
       "      <td>-0.005015</td>\n",
       "      <td>-0.003406</td>\n",
       "      <td>-0.012104</td>\n",
       "      <td>0.007496</td>\n",
       "      <td>-0.003423</td>\n",
       "      <td>0.002470</td>\n",
       "      <td>-0.006992</td>\n",
       "      <td>-0.008961</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>-0.003019</td>\n",
       "      <td>-0.010365</td>\n",
       "      <td>-0.007544</td>\n",
       "      <td>-0.008349</td>\n",
       "      <td>0.003139</td>\n",
       "      <td>-0.001181</td>\n",
       "      <td>-0.003421</td>\n",
       "      <td>-0.001004</td>\n",
       "      <td>0.003391</td>\n",
       "      <td>0.014655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-1.230630e-02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.313775</td>\n",
       "      <td>0.524838</td>\n",
       "      <td>0.703540</td>\n",
       "      <td>0.086803</td>\n",
       "      <td>0.256985</td>\n",
       "      <td>0.275779</td>\n",
       "      <td>3.954851e-01</td>\n",
       "      <td>0.036099</td>\n",
       "      <td>0.666215</td>\n",
       "      <td>0.607760</td>\n",
       "      <td>0.323312</td>\n",
       "      <td>0.056450</td>\n",
       "      <td>0.117770</td>\n",
       "      <td>0.302394</td>\n",
       "      <td>0.019327</td>\n",
       "      <td>0.585567</td>\n",
       "      <td>0.081423</td>\n",
       "      <td>-0.002334</td>\n",
       "      <td>0.176970</td>\n",
       "      <td>-0.056484</td>\n",
       "      <td>-0.104828</td>\n",
       "      <td>-0.004350</td>\n",
       "      <td>-0.002850</td>\n",
       "      <td>0.117409</td>\n",
       "      <td>-0.474124</td>\n",
       "      <td>-0.266844</td>\n",
       "      <td>-0.306713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>-1.056203e-02</td>\n",
       "      <td>0.313775</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.523016</td>\n",
       "      <td>0.588536</td>\n",
       "      <td>0.030674</td>\n",
       "      <td>0.179908</td>\n",
       "      <td>-0.008668</td>\n",
       "      <td>8.272411e-02</td>\n",
       "      <td>0.021376</td>\n",
       "      <td>0.366405</td>\n",
       "      <td>0.488420</td>\n",
       "      <td>0.307873</td>\n",
       "      <td>0.159936</td>\n",
       "      <td>0.014489</td>\n",
       "      <td>-0.003991</td>\n",
       "      <td>0.129593</td>\n",
       "      <td>0.401073</td>\n",
       "      <td>0.028038</td>\n",
       "      <td>-0.010211</td>\n",
       "      <td>0.163935</td>\n",
       "      <td>-0.160089</td>\n",
       "      <td>-0.168892</td>\n",
       "      <td>0.002985</td>\n",
       "      <td>0.002938</td>\n",
       "      <td>0.014182</td>\n",
       "      <td>0.022666</td>\n",
       "      <td>-0.245144</td>\n",
       "      <td>-0.437583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>-5.150856e-03</td>\n",
       "      <td>0.524838</td>\n",
       "      <td>0.523016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.755964</td>\n",
       "      <td>0.085569</td>\n",
       "      <td>0.498886</td>\n",
       "      <td>0.065876</td>\n",
       "      <td>1.891644e-01</td>\n",
       "      <td>-0.123519</td>\n",
       "      <td>0.664481</td>\n",
       "      <td>0.687623</td>\n",
       "      <td>0.282700</td>\n",
       "      <td>0.508450</td>\n",
       "      <td>0.048088</td>\n",
       "      <td>0.023026</td>\n",
       "      <td>0.222025</td>\n",
       "      <td>0.568747</td>\n",
       "      <td>0.086658</td>\n",
       "      <td>-0.030027</td>\n",
       "      <td>0.162319</td>\n",
       "      <td>-0.508897</td>\n",
       "      <td>-0.537872</td>\n",
       "      <td>0.012926</td>\n",
       "      <td>0.014045</td>\n",
       "      <td>0.047567</td>\n",
       "      <td>-0.530903</td>\n",
       "      <td>0.191606</td>\n",
       "      <td>-0.266709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>-8.900440e-03</td>\n",
       "      <td>0.703540</td>\n",
       "      <td>0.588536</td>\n",
       "      <td>0.755964</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166810</td>\n",
       "      <td>0.357169</td>\n",
       "      <td>0.109738</td>\n",
       "      <td>2.833358e-01</td>\n",
       "      <td>-0.061530</td>\n",
       "      <td>0.762929</td>\n",
       "      <td>0.876696</td>\n",
       "      <td>0.435529</td>\n",
       "      <td>0.319397</td>\n",
       "      <td>0.051448</td>\n",
       "      <td>0.052646</td>\n",
       "      <td>0.234453</td>\n",
       "      <td>0.755066</td>\n",
       "      <td>0.179570</td>\n",
       "      <td>-0.033606</td>\n",
       "      <td>0.203606</td>\n",
       "      <td>-0.319912</td>\n",
       "      <td>-0.343313</td>\n",
       "      <td>0.017737</td>\n",
       "      <td>0.019027</td>\n",
       "      <td>0.051159</td>\n",
       "      <td>-0.689571</td>\n",
       "      <td>-0.435071</td>\n",
       "      <td>-0.548514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>-3.500101e-03</td>\n",
       "      <td>0.086803</td>\n",
       "      <td>0.030674</td>\n",
       "      <td>0.085569</td>\n",
       "      <td>0.166810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002205</td>\n",
       "      <td>0.015356</td>\n",
       "      <td>7.305841e-02</td>\n",
       "      <td>-0.013715</td>\n",
       "      <td>0.110320</td>\n",
       "      <td>0.180755</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>0.054228</td>\n",
       "      <td>0.004520</td>\n",
       "      <td>-0.082029</td>\n",
       "      <td>0.226190</td>\n",
       "      <td>0.136959</td>\n",
       "      <td>0.701875</td>\n",
       "      <td>0.003141</td>\n",
       "      <td>-0.038675</td>\n",
       "      <td>-0.054174</td>\n",
       "      <td>-0.052856</td>\n",
       "      <td>-0.001722</td>\n",
       "      <td>-0.002698</td>\n",
       "      <td>0.004608</td>\n",
       "      <td>-0.135307</td>\n",
       "      <td>-0.112162</td>\n",
       "      <td>-0.122997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>1.059874e-02</td>\n",
       "      <td>0.256985</td>\n",
       "      <td>0.179908</td>\n",
       "      <td>0.498886</td>\n",
       "      <td>0.357169</td>\n",
       "      <td>-0.002205</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025132</td>\n",
       "      <td>2.588299e-02</td>\n",
       "      <td>-0.259230</td>\n",
       "      <td>0.458984</td>\n",
       "      <td>0.528509</td>\n",
       "      <td>-0.247481</td>\n",
       "      <td>0.483657</td>\n",
       "      <td>0.005985</td>\n",
       "      <td>0.048227</td>\n",
       "      <td>0.128795</td>\n",
       "      <td>0.283933</td>\n",
       "      <td>-0.008170</td>\n",
       "      <td>-0.024641</td>\n",
       "      <td>-0.260333</td>\n",
       "      <td>-0.484020</td>\n",
       "      <td>-0.499194</td>\n",
       "      <td>0.019629</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>0.005904</td>\n",
       "      <td>-0.322314</td>\n",
       "      <td>0.157785</td>\n",
       "      <td>0.465857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>-8.844970e-03</td>\n",
       "      <td>0.275779</td>\n",
       "      <td>-0.008668</td>\n",
       "      <td>0.065876</td>\n",
       "      <td>0.109738</td>\n",
       "      <td>0.015356</td>\n",
       "      <td>0.025132</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.075653e-01</td>\n",
       "      <td>0.009608</td>\n",
       "      <td>0.092234</td>\n",
       "      <td>0.077162</td>\n",
       "      <td>0.083433</td>\n",
       "      <td>-0.021981</td>\n",
       "      <td>0.094268</td>\n",
       "      <td>-0.013331</td>\n",
       "      <td>-0.042728</td>\n",
       "      <td>0.091090</td>\n",
       "      <td>0.026895</td>\n",
       "      <td>-0.000824</td>\n",
       "      <td>0.039484</td>\n",
       "      <td>0.021966</td>\n",
       "      <td>-0.005274</td>\n",
       "      <td>0.005685</td>\n",
       "      <td>0.007942</td>\n",
       "      <td>0.094648</td>\n",
       "      <td>-0.095148</td>\n",
       "      <td>-0.047045</td>\n",
       "      <td>-0.043048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>-6.278161e-07</td>\n",
       "      <td>0.395485</td>\n",
       "      <td>0.082724</td>\n",
       "      <td>0.189164</td>\n",
       "      <td>0.283336</td>\n",
       "      <td>0.073058</td>\n",
       "      <td>0.025883</td>\n",
       "      <td>0.407565</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.045060</td>\n",
       "      <td>0.249554</td>\n",
       "      <td>0.164747</td>\n",
       "      <td>0.279939</td>\n",
       "      <td>-0.049593</td>\n",
       "      <td>0.091291</td>\n",
       "      <td>0.003820</td>\n",
       "      <td>-0.077572</td>\n",
       "      <td>0.279999</td>\n",
       "      <td>0.073185</td>\n",
       "      <td>0.000701</td>\n",
       "      <td>0.187475</td>\n",
       "      <td>0.049601</td>\n",
       "      <td>0.020538</td>\n",
       "      <td>-0.004189</td>\n",
       "      <td>-0.004069</td>\n",
       "      <td>0.091448</td>\n",
       "      <td>-0.223084</td>\n",
       "      <td>-0.136022</td>\n",
       "      <td>-0.169409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>-1.780728e-03</td>\n",
       "      <td>0.036099</td>\n",
       "      <td>0.021376</td>\n",
       "      <td>-0.123519</td>\n",
       "      <td>-0.061530</td>\n",
       "      <td>-0.013715</td>\n",
       "      <td>-0.259230</td>\n",
       "      <td>0.009608</td>\n",
       "      <td>4.505961e-02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.142066</td>\n",
       "      <td>-0.157466</td>\n",
       "      <td>0.166923</td>\n",
       "      <td>-0.358921</td>\n",
       "      <td>-0.060969</td>\n",
       "      <td>-0.009927</td>\n",
       "      <td>-0.109200</td>\n",
       "      <td>-0.094307</td>\n",
       "      <td>-0.007125</td>\n",
       "      <td>-0.044871</td>\n",
       "      <td>0.131918</td>\n",
       "      <td>0.358184</td>\n",
       "      <td>0.391948</td>\n",
       "      <td>0.017099</td>\n",
       "      <td>0.013351</td>\n",
       "      <td>-0.060471</td>\n",
       "      <td>0.088656</td>\n",
       "      <td>-0.081619</td>\n",
       "      <td>-0.159788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>-5.014634e-03</td>\n",
       "      <td>0.666215</td>\n",
       "      <td>0.366405</td>\n",
       "      <td>0.664481</td>\n",
       "      <td>0.762929</td>\n",
       "      <td>0.110320</td>\n",
       "      <td>0.458984</td>\n",
       "      <td>0.092234</td>\n",
       "      <td>2.495544e-01</td>\n",
       "      <td>-0.142066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758247</td>\n",
       "      <td>0.165003</td>\n",
       "      <td>0.448003</td>\n",
       "      <td>0.007988</td>\n",
       "      <td>0.116196</td>\n",
       "      <td>0.198369</td>\n",
       "      <td>0.715745</td>\n",
       "      <td>0.116062</td>\n",
       "      <td>-0.036560</td>\n",
       "      <td>0.048776</td>\n",
       "      <td>-0.448558</td>\n",
       "      <td>-0.459327</td>\n",
       "      <td>0.015166</td>\n",
       "      <td>0.015325</td>\n",
       "      <td>0.007583</td>\n",
       "      <td>-0.601622</td>\n",
       "      <td>-0.215087</td>\n",
       "      <td>-0.288729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>-3.406218e-03</td>\n",
       "      <td>0.607760</td>\n",
       "      <td>0.488420</td>\n",
       "      <td>0.687623</td>\n",
       "      <td>0.876696</td>\n",
       "      <td>0.180755</td>\n",
       "      <td>0.528509</td>\n",
       "      <td>0.077162</td>\n",
       "      <td>1.647470e-01</td>\n",
       "      <td>-0.157466</td>\n",
       "      <td>0.758247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051199</td>\n",
       "      <td>0.424328</td>\n",
       "      <td>0.020413</td>\n",
       "      <td>0.001374</td>\n",
       "      <td>0.338192</td>\n",
       "      <td>0.731077</td>\n",
       "      <td>0.193874</td>\n",
       "      <td>-0.030934</td>\n",
       "      <td>-0.211387</td>\n",
       "      <td>-0.424795</td>\n",
       "      <td>-0.435537</td>\n",
       "      <td>0.017298</td>\n",
       "      <td>0.019802</td>\n",
       "      <td>0.020280</td>\n",
       "      <td>-0.611694</td>\n",
       "      <td>-0.353338</td>\n",
       "      <td>-0.348571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>-1.210401e-02</td>\n",
       "      <td>0.323312</td>\n",
       "      <td>0.307873</td>\n",
       "      <td>0.282700</td>\n",
       "      <td>0.435529</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>-0.247481</td>\n",
       "      <td>0.083433</td>\n",
       "      <td>2.799385e-01</td>\n",
       "      <td>0.166923</td>\n",
       "      <td>0.165003</td>\n",
       "      <td>-0.051199</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.130948</td>\n",
       "      <td>0.068611</td>\n",
       "      <td>0.106727</td>\n",
       "      <td>-0.146112</td>\n",
       "      <td>0.199521</td>\n",
       "      <td>0.010007</td>\n",
       "      <td>-0.011882</td>\n",
       "      <td>0.818269</td>\n",
       "      <td>0.130750</td>\n",
       "      <td>0.102270</td>\n",
       "      <td>0.004454</td>\n",
       "      <td>0.002447</td>\n",
       "      <td>0.068260</td>\n",
       "      <td>-0.286948</td>\n",
       "      <td>-0.242044</td>\n",
       "      <td>-0.486481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>7.495925e-03</td>\n",
       "      <td>0.056450</td>\n",
       "      <td>0.159936</td>\n",
       "      <td>0.508450</td>\n",
       "      <td>0.319397</td>\n",
       "      <td>0.054228</td>\n",
       "      <td>0.483657</td>\n",
       "      <td>-0.021981</td>\n",
       "      <td>-4.959272e-02</td>\n",
       "      <td>-0.358921</td>\n",
       "      <td>0.448003</td>\n",
       "      <td>0.424328</td>\n",
       "      <td>-0.130948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.222668</td>\n",
       "      <td>-0.145220</td>\n",
       "      <td>0.407768</td>\n",
       "      <td>0.332340</td>\n",
       "      <td>0.076624</td>\n",
       "      <td>0.004059</td>\n",
       "      <td>-0.167741</td>\n",
       "      <td>-0.999873</td>\n",
       "      <td>-0.910310</td>\n",
       "      <td>-0.002317</td>\n",
       "      <td>-0.000125</td>\n",
       "      <td>-0.223009</td>\n",
       "      <td>-0.303833</td>\n",
       "      <td>0.211343</td>\n",
       "      <td>0.081512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>-3.423178e-03</td>\n",
       "      <td>0.117770</td>\n",
       "      <td>0.014489</td>\n",
       "      <td>0.048088</td>\n",
       "      <td>0.051448</td>\n",
       "      <td>0.004520</td>\n",
       "      <td>0.005985</td>\n",
       "      <td>0.094268</td>\n",
       "      <td>9.129115e-02</td>\n",
       "      <td>-0.060969</td>\n",
       "      <td>0.007988</td>\n",
       "      <td>0.020413</td>\n",
       "      <td>0.068611</td>\n",
       "      <td>-0.222668</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027804</td>\n",
       "      <td>-0.061159</td>\n",
       "      <td>-0.010221</td>\n",
       "      <td>0.004489</td>\n",
       "      <td>-0.019506</td>\n",
       "      <td>0.049347</td>\n",
       "      <td>0.222343</td>\n",
       "      <td>-0.166332</td>\n",
       "      <td>0.009604</td>\n",
       "      <td>0.010684</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>-0.049760</td>\n",
       "      <td>-0.016776</td>\n",
       "      <td>-0.036928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>2.469575e-03</td>\n",
       "      <td>0.302394</td>\n",
       "      <td>-0.003991</td>\n",
       "      <td>0.023026</td>\n",
       "      <td>0.052646</td>\n",
       "      <td>-0.082029</td>\n",
       "      <td>0.048227</td>\n",
       "      <td>-0.013331</td>\n",
       "      <td>3.819731e-03</td>\n",
       "      <td>-0.009927</td>\n",
       "      <td>0.116196</td>\n",
       "      <td>0.001374</td>\n",
       "      <td>0.106727</td>\n",
       "      <td>-0.145220</td>\n",
       "      <td>0.027804</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.131514</td>\n",
       "      <td>0.054175</td>\n",
       "      <td>-0.086265</td>\n",
       "      <td>-0.027701</td>\n",
       "      <td>0.132996</td>\n",
       "      <td>0.144769</td>\n",
       "      <td>0.133355</td>\n",
       "      <td>0.013219</td>\n",
       "      <td>0.012848</td>\n",
       "      <td>0.027767</td>\n",
       "      <td>-0.064465</td>\n",
       "      <td>-0.032468</td>\n",
       "      <td>0.031165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>-6.991705e-03</td>\n",
       "      <td>0.019327</td>\n",
       "      <td>0.129593</td>\n",
       "      <td>0.222025</td>\n",
       "      <td>0.234453</td>\n",
       "      <td>0.226190</td>\n",
       "      <td>0.128795</td>\n",
       "      <td>-0.042728</td>\n",
       "      <td>-7.757235e-02</td>\n",
       "      <td>-0.109200</td>\n",
       "      <td>0.198369</td>\n",
       "      <td>0.338192</td>\n",
       "      <td>-0.146112</td>\n",
       "      <td>0.407768</td>\n",
       "      <td>-0.061159</td>\n",
       "      <td>-0.131514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.327882</td>\n",
       "      <td>0.253981</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>-0.239283</td>\n",
       "      <td>-0.407716</td>\n",
       "      <td>-0.384335</td>\n",
       "      <td>-0.007337</td>\n",
       "      <td>-0.005330</td>\n",
       "      <td>-0.061178</td>\n",
       "      <td>-0.192826</td>\n",
       "      <td>-0.067229</td>\n",
       "      <td>-0.151869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>-8.961176e-03</td>\n",
       "      <td>0.585567</td>\n",
       "      <td>0.401073</td>\n",
       "      <td>0.568747</td>\n",
       "      <td>0.755066</td>\n",
       "      <td>0.136959</td>\n",
       "      <td>0.283933</td>\n",
       "      <td>0.091090</td>\n",
       "      <td>2.799990e-01</td>\n",
       "      <td>-0.094307</td>\n",
       "      <td>0.715745</td>\n",
       "      <td>0.731077</td>\n",
       "      <td>0.199521</td>\n",
       "      <td>0.332340</td>\n",
       "      <td>-0.010221</td>\n",
       "      <td>0.054175</td>\n",
       "      <td>0.327882</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.178533</td>\n",
       "      <td>-0.026342</td>\n",
       "      <td>0.040876</td>\n",
       "      <td>-0.332739</td>\n",
       "      <td>-0.327809</td>\n",
       "      <td>0.006104</td>\n",
       "      <td>0.007945</td>\n",
       "      <td>-0.010349</td>\n",
       "      <td>-0.576077</td>\n",
       "      <td>-0.343957</td>\n",
       "      <td>-0.432579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>4.630729e-04</td>\n",
       "      <td>0.081423</td>\n",
       "      <td>0.028038</td>\n",
       "      <td>0.086658</td>\n",
       "      <td>0.179570</td>\n",
       "      <td>0.701875</td>\n",
       "      <td>-0.008170</td>\n",
       "      <td>0.026895</td>\n",
       "      <td>7.318502e-02</td>\n",
       "      <td>-0.007125</td>\n",
       "      <td>0.116062</td>\n",
       "      <td>0.193874</td>\n",
       "      <td>0.010007</td>\n",
       "      <td>0.076624</td>\n",
       "      <td>0.004489</td>\n",
       "      <td>-0.086265</td>\n",
       "      <td>0.253981</td>\n",
       "      <td>0.178533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005160</td>\n",
       "      <td>-0.044789</td>\n",
       "      <td>-0.076701</td>\n",
       "      <td>-0.074631</td>\n",
       "      <td>0.007212</td>\n",
       "      <td>0.006074</td>\n",
       "      <td>0.004554</td>\n",
       "      <td>-0.157011</td>\n",
       "      <td>-0.132310</td>\n",
       "      <td>-0.144161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_sold</th>\n",
       "      <td>-3.019177e-03</td>\n",
       "      <td>-0.002334</td>\n",
       "      <td>-0.010211</td>\n",
       "      <td>-0.030027</td>\n",
       "      <td>-0.033606</td>\n",
       "      <td>0.003141</td>\n",
       "      <td>-0.024641</td>\n",
       "      <td>-0.000824</td>\n",
       "      <td>7.006991e-04</td>\n",
       "      <td>-0.044871</td>\n",
       "      <td>-0.036560</td>\n",
       "      <td>-0.030934</td>\n",
       "      <td>-0.011882</td>\n",
       "      <td>0.004059</td>\n",
       "      <td>-0.019506</td>\n",
       "      <td>-0.027701</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>-0.026342</td>\n",
       "      <td>-0.005160</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002596</td>\n",
       "      <td>0.011879</td>\n",
       "      <td>0.020979</td>\n",
       "      <td>-0.781982</td>\n",
       "      <td>-0.742107</td>\n",
       "      <td>-0.019482</td>\n",
       "      <td>0.033937</td>\n",
       "      <td>0.005288</td>\n",
       "      <td>0.010863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_basement</th>\n",
       "      <td>-1.036513e-02</td>\n",
       "      <td>0.176970</td>\n",
       "      <td>0.163935</td>\n",
       "      <td>0.162319</td>\n",
       "      <td>0.203606</td>\n",
       "      <td>-0.038675</td>\n",
       "      <td>-0.260333</td>\n",
       "      <td>0.039484</td>\n",
       "      <td>1.874749e-01</td>\n",
       "      <td>0.131918</td>\n",
       "      <td>0.048776</td>\n",
       "      <td>-0.211387</td>\n",
       "      <td>0.818269</td>\n",
       "      <td>-0.167741</td>\n",
       "      <td>0.049347</td>\n",
       "      <td>0.132996</td>\n",
       "      <td>-0.239283</td>\n",
       "      <td>0.040876</td>\n",
       "      <td>-0.044789</td>\n",
       "      <td>-0.002596</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.167689</td>\n",
       "      <td>0.144874</td>\n",
       "      <td>0.003219</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.049120</td>\n",
       "      <td>-0.181769</td>\n",
       "      <td>-0.100491</td>\n",
       "      <td>-0.370597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_built</th>\n",
       "      <td>-7.543576e-03</td>\n",
       "      <td>-0.056484</td>\n",
       "      <td>-0.160089</td>\n",
       "      <td>-0.508897</td>\n",
       "      <td>-0.319912</td>\n",
       "      <td>-0.054174</td>\n",
       "      <td>-0.484020</td>\n",
       "      <td>0.021966</td>\n",
       "      <td>4.960080e-02</td>\n",
       "      <td>0.358184</td>\n",
       "      <td>-0.448558</td>\n",
       "      <td>-0.424795</td>\n",
       "      <td>0.130750</td>\n",
       "      <td>-0.999873</td>\n",
       "      <td>0.222343</td>\n",
       "      <td>0.144769</td>\n",
       "      <td>-0.407716</td>\n",
       "      <td>-0.332739</td>\n",
       "      <td>-0.076701</td>\n",
       "      <td>0.011879</td>\n",
       "      <td>0.167689</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910587</td>\n",
       "      <td>-0.010146</td>\n",
       "      <td>-0.011703</td>\n",
       "      <td>0.222685</td>\n",
       "      <td>0.304355</td>\n",
       "      <td>-0.211245</td>\n",
       "      <td>-0.081334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_reno</th>\n",
       "      <td>-8.349094e-03</td>\n",
       "      <td>-0.104828</td>\n",
       "      <td>-0.168892</td>\n",
       "      <td>-0.537872</td>\n",
       "      <td>-0.343313</td>\n",
       "      <td>-0.052856</td>\n",
       "      <td>-0.499194</td>\n",
       "      <td>-0.005274</td>\n",
       "      <td>2.053791e-02</td>\n",
       "      <td>0.391948</td>\n",
       "      <td>-0.459327</td>\n",
       "      <td>-0.435537</td>\n",
       "      <td>0.102270</td>\n",
       "      <td>-0.910310</td>\n",
       "      <td>-0.166332</td>\n",
       "      <td>0.133355</td>\n",
       "      <td>-0.384335</td>\n",
       "      <td>-0.327809</td>\n",
       "      <td>-0.074631</td>\n",
       "      <td>0.020979</td>\n",
       "      <td>0.144874</td>\n",
       "      <td>0.910587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014246</td>\n",
       "      <td>-0.016349</td>\n",
       "      <td>-0.165447</td>\n",
       "      <td>0.327347</td>\n",
       "      <td>-0.213034</td>\n",
       "      <td>-0.071843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_sold</th>\n",
       "      <td>3.138575e-03</td>\n",
       "      <td>-0.004350</td>\n",
       "      <td>0.002985</td>\n",
       "      <td>0.012926</td>\n",
       "      <td>0.017737</td>\n",
       "      <td>-0.001722</td>\n",
       "      <td>0.019629</td>\n",
       "      <td>0.005685</td>\n",
       "      <td>-4.188762e-03</td>\n",
       "      <td>0.017099</td>\n",
       "      <td>0.015166</td>\n",
       "      <td>0.017298</td>\n",
       "      <td>0.004454</td>\n",
       "      <td>-0.002317</td>\n",
       "      <td>0.009604</td>\n",
       "      <td>0.013219</td>\n",
       "      <td>-0.007337</td>\n",
       "      <td>0.006104</td>\n",
       "      <td>0.007212</td>\n",
       "      <td>-0.781982</td>\n",
       "      <td>0.003219</td>\n",
       "      <td>-0.010146</td>\n",
       "      <td>-0.014246</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.965160</td>\n",
       "      <td>0.009601</td>\n",
       "      <td>-0.021738</td>\n",
       "      <td>-0.004274</td>\n",
       "      <td>-0.003189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season_sold</th>\n",
       "      <td>-1.180642e-03</td>\n",
       "      <td>-0.002850</td>\n",
       "      <td>0.002938</td>\n",
       "      <td>0.014045</td>\n",
       "      <td>0.019027</td>\n",
       "      <td>-0.002698</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>0.007942</td>\n",
       "      <td>-4.068929e-03</td>\n",
       "      <td>0.013351</td>\n",
       "      <td>0.015325</td>\n",
       "      <td>0.019802</td>\n",
       "      <td>0.002447</td>\n",
       "      <td>-0.000125</td>\n",
       "      <td>0.010684</td>\n",
       "      <td>0.012848</td>\n",
       "      <td>-0.005330</td>\n",
       "      <td>0.007945</td>\n",
       "      <td>0.006074</td>\n",
       "      <td>-0.742107</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>-0.011703</td>\n",
       "      <td>-0.016349</td>\n",
       "      <td>0.965160</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010683</td>\n",
       "      <td>-0.022159</td>\n",
       "      <td>-0.004642</td>\n",
       "      <td>-0.000701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>was_reno</th>\n",
       "      <td>-3.421415e-03</td>\n",
       "      <td>0.117409</td>\n",
       "      <td>0.014182</td>\n",
       "      <td>0.047567</td>\n",
       "      <td>0.051159</td>\n",
       "      <td>0.004608</td>\n",
       "      <td>0.005904</td>\n",
       "      <td>0.094648</td>\n",
       "      <td>9.144820e-02</td>\n",
       "      <td>-0.060471</td>\n",
       "      <td>0.007583</td>\n",
       "      <td>0.020280</td>\n",
       "      <td>0.068260</td>\n",
       "      <td>-0.223009</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>0.027767</td>\n",
       "      <td>-0.061178</td>\n",
       "      <td>-0.010349</td>\n",
       "      <td>0.004554</td>\n",
       "      <td>-0.019482</td>\n",
       "      <td>0.049120</td>\n",
       "      <td>0.222685</td>\n",
       "      <td>-0.165447</td>\n",
       "      <td>0.009601</td>\n",
       "      <td>0.010683</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049746</td>\n",
       "      <td>-0.017044</td>\n",
       "      <td>-0.036750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bed_sqft_ratio</th>\n",
       "      <td>-1.003727e-03</td>\n",
       "      <td>-0.474124</td>\n",
       "      <td>0.022666</td>\n",
       "      <td>-0.530903</td>\n",
       "      <td>-0.689571</td>\n",
       "      <td>-0.135307</td>\n",
       "      <td>-0.322314</td>\n",
       "      <td>-0.095148</td>\n",
       "      <td>-2.230841e-01</td>\n",
       "      <td>0.088656</td>\n",
       "      <td>-0.601622</td>\n",
       "      <td>-0.611694</td>\n",
       "      <td>-0.286948</td>\n",
       "      <td>-0.303833</td>\n",
       "      <td>-0.049760</td>\n",
       "      <td>-0.064465</td>\n",
       "      <td>-0.192826</td>\n",
       "      <td>-0.576077</td>\n",
       "      <td>-0.157011</td>\n",
       "      <td>0.033937</td>\n",
       "      <td>-0.181769</td>\n",
       "      <td>0.304355</td>\n",
       "      <td>0.327347</td>\n",
       "      <td>-0.021738</td>\n",
       "      <td>-0.022159</td>\n",
       "      <td>-0.049746</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.332157</td>\n",
       "      <td>0.384841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bath_sqft_ratio</th>\n",
       "      <td>3.390585e-03</td>\n",
       "      <td>-0.266844</td>\n",
       "      <td>-0.245144</td>\n",
       "      <td>0.191606</td>\n",
       "      <td>-0.435071</td>\n",
       "      <td>-0.112162</td>\n",
       "      <td>0.157785</td>\n",
       "      <td>-0.047045</td>\n",
       "      <td>-1.360220e-01</td>\n",
       "      <td>-0.081619</td>\n",
       "      <td>-0.215087</td>\n",
       "      <td>-0.353338</td>\n",
       "      <td>-0.242044</td>\n",
       "      <td>0.211343</td>\n",
       "      <td>-0.016776</td>\n",
       "      <td>-0.032468</td>\n",
       "      <td>-0.067229</td>\n",
       "      <td>-0.343957</td>\n",
       "      <td>-0.132310</td>\n",
       "      <td>0.005288</td>\n",
       "      <td>-0.100491</td>\n",
       "      <td>-0.211245</td>\n",
       "      <td>-0.213034</td>\n",
       "      <td>-0.004274</td>\n",
       "      <td>-0.004642</td>\n",
       "      <td>-0.017044</td>\n",
       "      <td>0.332157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.570078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors_sqft_ratio</th>\n",
       "      <td>1.465490e-02</td>\n",
       "      <td>-0.306713</td>\n",
       "      <td>-0.437583</td>\n",
       "      <td>-0.266709</td>\n",
       "      <td>-0.548514</td>\n",
       "      <td>-0.122997</td>\n",
       "      <td>0.465857</td>\n",
       "      <td>-0.043048</td>\n",
       "      <td>-1.694087e-01</td>\n",
       "      <td>-0.159788</td>\n",
       "      <td>-0.288729</td>\n",
       "      <td>-0.348571</td>\n",
       "      <td>-0.486481</td>\n",
       "      <td>0.081512</td>\n",
       "      <td>-0.036928</td>\n",
       "      <td>0.031165</td>\n",
       "      <td>-0.151869</td>\n",
       "      <td>-0.432579</td>\n",
       "      <td>-0.144161</td>\n",
       "      <td>0.010863</td>\n",
       "      <td>-0.370597</td>\n",
       "      <td>-0.081334</td>\n",
       "      <td>-0.071843</td>\n",
       "      <td>-0.003189</td>\n",
       "      <td>-0.000701</td>\n",
       "      <td>-0.036750</td>\n",
       "      <td>0.384841</td>\n",
       "      <td>0.570078</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Unnamed: 0     price  bedrooms  bathrooms  sqft_living  \\\n",
       "Unnamed: 0         1.000000e+00 -0.012306 -0.010562  -0.005151    -0.008900   \n",
       "price             -1.230630e-02  1.000000  0.313775   0.524838     0.703540   \n",
       "bedrooms          -1.056203e-02  0.313775  1.000000   0.523016     0.588536   \n",
       "bathrooms         -5.150856e-03  0.524838  0.523016   1.000000     0.755964   \n",
       "sqft_living       -8.900440e-03  0.703540  0.588536   0.755964     1.000000   \n",
       "sqft_lot          -3.500101e-03  0.086803  0.030674   0.085569     0.166810   \n",
       "floors             1.059874e-02  0.256985  0.179908   0.498886     0.357169   \n",
       "waterfront        -8.844970e-03  0.275779 -0.008668   0.065876     0.109738   \n",
       "view              -6.278161e-07  0.395485  0.082724   0.189164     0.283336   \n",
       "condition         -1.780728e-03  0.036099  0.021376  -0.123519    -0.061530   \n",
       "grade             -5.014634e-03  0.666215  0.366405   0.664481     0.762929   \n",
       "sqft_above        -3.406218e-03  0.607760  0.488420   0.687623     0.876696   \n",
       "sqft_basement     -1.210401e-02  0.323312  0.307873   0.282700     0.435529   \n",
       "yr_built           7.495925e-03  0.056450  0.159936   0.508450     0.319397   \n",
       "yr_renovated      -3.423178e-03  0.117770  0.014489   0.048088     0.051448   \n",
       "lat                2.469575e-03  0.302394 -0.003991   0.023026     0.052646   \n",
       "long              -6.991705e-03  0.019327  0.129593   0.222025     0.234453   \n",
       "sqft_living15     -8.961176e-03  0.585567  0.401073   0.568747     0.755066   \n",
       "sqft_lot15         4.630729e-04  0.081423  0.028038   0.086658     0.179570   \n",
       "year_sold         -3.019177e-03 -0.002334 -0.010211  -0.030027    -0.033606   \n",
       "has_basement      -1.036513e-02  0.176970  0.163935   0.162319     0.203606   \n",
       "age_built         -7.543576e-03 -0.056484 -0.160089  -0.508897    -0.319912   \n",
       "last_reno         -8.349094e-03 -0.104828 -0.168892  -0.537872    -0.343313   \n",
       "month_sold         3.138575e-03 -0.004350  0.002985   0.012926     0.017737   \n",
       "season_sold       -1.180642e-03 -0.002850  0.002938   0.014045     0.019027   \n",
       "was_reno          -3.421415e-03  0.117409  0.014182   0.047567     0.051159   \n",
       "bed_sqft_ratio    -1.003727e-03 -0.474124  0.022666  -0.530903    -0.689571   \n",
       "bath_sqft_ratio    3.390585e-03 -0.266844 -0.245144   0.191606    -0.435071   \n",
       "floors_sqft_ratio  1.465490e-02 -0.306713 -0.437583  -0.266709    -0.548514   \n",
       "\n",
       "                   sqft_lot    floors  waterfront          view  condition  \\\n",
       "Unnamed: 0        -0.003500  0.010599   -0.008845 -6.278161e-07  -0.001781   \n",
       "price              0.086803  0.256985    0.275779  3.954851e-01   0.036099   \n",
       "bedrooms           0.030674  0.179908   -0.008668  8.272411e-02   0.021376   \n",
       "bathrooms          0.085569  0.498886    0.065876  1.891644e-01  -0.123519   \n",
       "sqft_living        0.166810  0.357169    0.109738  2.833358e-01  -0.061530   \n",
       "sqft_lot           1.000000 -0.002205    0.015356  7.305841e-02  -0.013715   \n",
       "floors            -0.002205  1.000000    0.025132  2.588299e-02  -0.259230   \n",
       "waterfront         0.015356  0.025132    1.000000  4.075653e-01   0.009608   \n",
       "view               0.073058  0.025883    0.407565  1.000000e+00   0.045060   \n",
       "condition         -0.013715 -0.259230    0.009608  4.505961e-02   1.000000   \n",
       "grade              0.110320  0.458984    0.092234  2.495544e-01  -0.142066   \n",
       "sqft_above         0.180755  0.528509    0.077162  1.647470e-01  -0.157466   \n",
       "sqft_basement      0.008065 -0.247481    0.083433  2.799385e-01   0.166923   \n",
       "yr_built           0.054228  0.483657   -0.021981 -4.959272e-02  -0.358921   \n",
       "yr_renovated       0.004520  0.005985    0.094268  9.129115e-02  -0.060969   \n",
       "lat               -0.082029  0.048227   -0.013331  3.819731e-03  -0.009927   \n",
       "long               0.226190  0.128795   -0.042728 -7.757235e-02  -0.109200   \n",
       "sqft_living15      0.136959  0.283933    0.091090  2.799990e-01  -0.094307   \n",
       "sqft_lot15         0.701875 -0.008170    0.026895  7.318502e-02  -0.007125   \n",
       "year_sold          0.003141 -0.024641   -0.000824  7.006991e-04  -0.044871   \n",
       "has_basement      -0.038675 -0.260333    0.039484  1.874749e-01   0.131918   \n",
       "age_built         -0.054174 -0.484020    0.021966  4.960080e-02   0.358184   \n",
       "last_reno         -0.052856 -0.499194   -0.005274  2.053791e-02   0.391948   \n",
       "month_sold        -0.001722  0.019629    0.005685 -4.188762e-03   0.017099   \n",
       "season_sold       -0.002698  0.022392    0.007942 -4.068929e-03   0.013351   \n",
       "was_reno           0.004608  0.005904    0.094648  9.144820e-02  -0.060471   \n",
       "bed_sqft_ratio    -0.135307 -0.322314   -0.095148 -2.230841e-01   0.088656   \n",
       "bath_sqft_ratio   -0.112162  0.157785   -0.047045 -1.360220e-01  -0.081619   \n",
       "floors_sqft_ratio -0.122997  0.465857   -0.043048 -1.694087e-01  -0.159788   \n",
       "\n",
       "                      grade  sqft_above  sqft_basement  yr_built  \\\n",
       "Unnamed: 0        -0.005015   -0.003406      -0.012104  0.007496   \n",
       "price              0.666215    0.607760       0.323312  0.056450   \n",
       "bedrooms           0.366405    0.488420       0.307873  0.159936   \n",
       "bathrooms          0.664481    0.687623       0.282700  0.508450   \n",
       "sqft_living        0.762929    0.876696       0.435529  0.319397   \n",
       "sqft_lot           0.110320    0.180755       0.008065  0.054228   \n",
       "floors             0.458984    0.528509      -0.247481  0.483657   \n",
       "waterfront         0.092234    0.077162       0.083433 -0.021981   \n",
       "view               0.249554    0.164747       0.279939 -0.049593   \n",
       "condition         -0.142066   -0.157466       0.166923 -0.358921   \n",
       "grade              1.000000    0.758247       0.165003  0.448003   \n",
       "sqft_above         0.758247    1.000000      -0.051199  0.424328   \n",
       "sqft_basement      0.165003   -0.051199       1.000000 -0.130948   \n",
       "yr_built           0.448003    0.424328      -0.130948  1.000000   \n",
       "yr_renovated       0.007988    0.020413       0.068611 -0.222668   \n",
       "lat                0.116196    0.001374       0.106727 -0.145220   \n",
       "long               0.198369    0.338192      -0.146112  0.407768   \n",
       "sqft_living15      0.715745    0.731077       0.199521  0.332340   \n",
       "sqft_lot15         0.116062    0.193874       0.010007  0.076624   \n",
       "year_sold         -0.036560   -0.030934      -0.011882  0.004059   \n",
       "has_basement       0.048776   -0.211387       0.818269 -0.167741   \n",
       "age_built         -0.448558   -0.424795       0.130750 -0.999873   \n",
       "last_reno         -0.459327   -0.435537       0.102270 -0.910310   \n",
       "month_sold         0.015166    0.017298       0.004454 -0.002317   \n",
       "season_sold        0.015325    0.019802       0.002447 -0.000125   \n",
       "was_reno           0.007583    0.020280       0.068260 -0.223009   \n",
       "bed_sqft_ratio    -0.601622   -0.611694      -0.286948 -0.303833   \n",
       "bath_sqft_ratio   -0.215087   -0.353338      -0.242044  0.211343   \n",
       "floors_sqft_ratio -0.288729   -0.348571      -0.486481  0.081512   \n",
       "\n",
       "                   yr_renovated       lat      long  sqft_living15  \\\n",
       "Unnamed: 0            -0.003423  0.002470 -0.006992      -0.008961   \n",
       "price                  0.117770  0.302394  0.019327       0.585567   \n",
       "bedrooms               0.014489 -0.003991  0.129593       0.401073   \n",
       "bathrooms              0.048088  0.023026  0.222025       0.568747   \n",
       "sqft_living            0.051448  0.052646  0.234453       0.755066   \n",
       "sqft_lot               0.004520 -0.082029  0.226190       0.136959   \n",
       "floors                 0.005985  0.048227  0.128795       0.283933   \n",
       "waterfront             0.094268 -0.013331 -0.042728       0.091090   \n",
       "view                   0.091291  0.003820 -0.077572       0.279999   \n",
       "condition             -0.060969 -0.009927 -0.109200      -0.094307   \n",
       "grade                  0.007988  0.116196  0.198369       0.715745   \n",
       "sqft_above             0.020413  0.001374  0.338192       0.731077   \n",
       "sqft_basement          0.068611  0.106727 -0.146112       0.199521   \n",
       "yr_built              -0.222668 -0.145220  0.407768       0.332340   \n",
       "yr_renovated           1.000000  0.027804 -0.061159      -0.010221   \n",
       "lat                    0.027804  1.000000 -0.131514       0.054175   \n",
       "long                  -0.061159 -0.131514  1.000000       0.327882   \n",
       "sqft_living15         -0.010221  0.054175  0.327882       1.000000   \n",
       "sqft_lot15             0.004489 -0.086265  0.253981       0.178533   \n",
       "year_sold             -0.019506 -0.027701  0.001626      -0.026342   \n",
       "has_basement           0.049347  0.132996 -0.239283       0.040876   \n",
       "age_built              0.222343  0.144769 -0.407716      -0.332739   \n",
       "last_reno             -0.166332  0.133355 -0.384335      -0.327809   \n",
       "month_sold             0.009604  0.013219 -0.007337       0.006104   \n",
       "season_sold            0.010684  0.012848 -0.005330       0.007945   \n",
       "was_reno               0.999968  0.027767 -0.061178      -0.010349   \n",
       "bed_sqft_ratio        -0.049760 -0.064465 -0.192826      -0.576077   \n",
       "bath_sqft_ratio       -0.016776 -0.032468 -0.067229      -0.343957   \n",
       "floors_sqft_ratio     -0.036928  0.031165 -0.151869      -0.432579   \n",
       "\n",
       "                   sqft_lot15  year_sold  has_basement  age_built  last_reno  \\\n",
       "Unnamed: 0           0.000463  -0.003019     -0.010365  -0.007544  -0.008349   \n",
       "price                0.081423  -0.002334      0.176970  -0.056484  -0.104828   \n",
       "bedrooms             0.028038  -0.010211      0.163935  -0.160089  -0.168892   \n",
       "bathrooms            0.086658  -0.030027      0.162319  -0.508897  -0.537872   \n",
       "sqft_living          0.179570  -0.033606      0.203606  -0.319912  -0.343313   \n",
       "sqft_lot             0.701875   0.003141     -0.038675  -0.054174  -0.052856   \n",
       "floors              -0.008170  -0.024641     -0.260333  -0.484020  -0.499194   \n",
       "waterfront           0.026895  -0.000824      0.039484   0.021966  -0.005274   \n",
       "view                 0.073185   0.000701      0.187475   0.049601   0.020538   \n",
       "condition           -0.007125  -0.044871      0.131918   0.358184   0.391948   \n",
       "grade                0.116062  -0.036560      0.048776  -0.448558  -0.459327   \n",
       "sqft_above           0.193874  -0.030934     -0.211387  -0.424795  -0.435537   \n",
       "sqft_basement        0.010007  -0.011882      0.818269   0.130750   0.102270   \n",
       "yr_built             0.076624   0.004059     -0.167741  -0.999873  -0.910310   \n",
       "yr_renovated         0.004489  -0.019506      0.049347   0.222343  -0.166332   \n",
       "lat                 -0.086265  -0.027701      0.132996   0.144769   0.133355   \n",
       "long                 0.253981   0.001626     -0.239283  -0.407716  -0.384335   \n",
       "sqft_living15        0.178533  -0.026342      0.040876  -0.332739  -0.327809   \n",
       "sqft_lot15           1.000000  -0.005160     -0.044789  -0.076701  -0.074631   \n",
       "year_sold           -0.005160   1.000000     -0.002596   0.011879   0.020979   \n",
       "has_basement        -0.044789  -0.002596      1.000000   0.167689   0.144874   \n",
       "age_built           -0.076701   0.011879      0.167689   1.000000   0.910587   \n",
       "last_reno           -0.074631   0.020979      0.144874   0.910587   1.000000   \n",
       "month_sold           0.007212  -0.781982      0.003219  -0.010146  -0.014246   \n",
       "season_sold          0.006074  -0.742107      0.000833  -0.011703  -0.016349   \n",
       "was_reno             0.004554  -0.019482      0.049120   0.222685  -0.165447   \n",
       "bed_sqft_ratio      -0.157011   0.033937     -0.181769   0.304355   0.327347   \n",
       "bath_sqft_ratio     -0.132310   0.005288     -0.100491  -0.211245  -0.213034   \n",
       "floors_sqft_ratio   -0.144161   0.010863     -0.370597  -0.081334  -0.071843   \n",
       "\n",
       "                   month_sold  season_sold  was_reno  bed_sqft_ratio  \\\n",
       "Unnamed: 0           0.003139    -0.001181 -0.003421       -0.001004   \n",
       "price               -0.004350    -0.002850  0.117409       -0.474124   \n",
       "bedrooms             0.002985     0.002938  0.014182        0.022666   \n",
       "bathrooms            0.012926     0.014045  0.047567       -0.530903   \n",
       "sqft_living          0.017737     0.019027  0.051159       -0.689571   \n",
       "sqft_lot            -0.001722    -0.002698  0.004608       -0.135307   \n",
       "floors               0.019629     0.022392  0.005904       -0.322314   \n",
       "waterfront           0.005685     0.007942  0.094648       -0.095148   \n",
       "view                -0.004189    -0.004069  0.091448       -0.223084   \n",
       "condition            0.017099     0.013351 -0.060471        0.088656   \n",
       "grade                0.015166     0.015325  0.007583       -0.601622   \n",
       "sqft_above           0.017298     0.019802  0.020280       -0.611694   \n",
       "sqft_basement        0.004454     0.002447  0.068260       -0.286948   \n",
       "yr_built            -0.002317    -0.000125 -0.223009       -0.303833   \n",
       "yr_renovated         0.009604     0.010684  0.999968       -0.049760   \n",
       "lat                  0.013219     0.012848  0.027767       -0.064465   \n",
       "long                -0.007337    -0.005330 -0.061178       -0.192826   \n",
       "sqft_living15        0.006104     0.007945 -0.010349       -0.576077   \n",
       "sqft_lot15           0.007212     0.006074  0.004554       -0.157011   \n",
       "year_sold           -0.781982    -0.742107 -0.019482        0.033937   \n",
       "has_basement         0.003219     0.000833  0.049120       -0.181769   \n",
       "age_built           -0.010146    -0.011703  0.222685        0.304355   \n",
       "last_reno           -0.014246    -0.016349 -0.165447        0.327347   \n",
       "month_sold           1.000000     0.965160  0.009601       -0.021738   \n",
       "season_sold          0.965160     1.000000  0.010683       -0.022159   \n",
       "was_reno             0.009601     0.010683  1.000000       -0.049746   \n",
       "bed_sqft_ratio      -0.021738    -0.022159 -0.049746        1.000000   \n",
       "bath_sqft_ratio     -0.004274    -0.004642 -0.017044        0.332157   \n",
       "floors_sqft_ratio   -0.003189    -0.000701 -0.036750        0.384841   \n",
       "\n",
       "                   bath_sqft_ratio  floors_sqft_ratio  \n",
       "Unnamed: 0                0.003391           0.014655  \n",
       "price                    -0.266844          -0.306713  \n",
       "bedrooms                 -0.245144          -0.437583  \n",
       "bathrooms                 0.191606          -0.266709  \n",
       "sqft_living              -0.435071          -0.548514  \n",
       "sqft_lot                 -0.112162          -0.122997  \n",
       "floors                    0.157785           0.465857  \n",
       "waterfront               -0.047045          -0.043048  \n",
       "view                     -0.136022          -0.169409  \n",
       "condition                -0.081619          -0.159788  \n",
       "grade                    -0.215087          -0.288729  \n",
       "sqft_above               -0.353338          -0.348571  \n",
       "sqft_basement            -0.242044          -0.486481  \n",
       "yr_built                  0.211343           0.081512  \n",
       "yr_renovated             -0.016776          -0.036928  \n",
       "lat                      -0.032468           0.031165  \n",
       "long                     -0.067229          -0.151869  \n",
       "sqft_living15            -0.343957          -0.432579  \n",
       "sqft_lot15               -0.132310          -0.144161  \n",
       "year_sold                 0.005288           0.010863  \n",
       "has_basement             -0.100491          -0.370597  \n",
       "age_built                -0.211245          -0.081334  \n",
       "last_reno                -0.213034          -0.071843  \n",
       "month_sold               -0.004274          -0.003189  \n",
       "season_sold              -0.004642          -0.000701  \n",
       "was_reno                 -0.017044          -0.036750  \n",
       "bed_sqft_ratio            0.332157           0.384841  \n",
       "bath_sqft_ratio           1.000000           0.570078  \n",
       "floors_sqft_ratio         0.570078           1.000000  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A look at correlation of all features\n",
    "housing_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing_corr['price'].sort_values(ascending = False).plot.bar(figsize=(15,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:**\n",
    "- We can clearly see the correlation between **price** and **sqft_living**, **grade**, **bathrooms**, and **view**.\n",
    "- **Sqft_living15** also indicates the immediate neighbors and property values of those homes are correlated to the price as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20,15))\n",
    "\n",
    "corr = housing_corr.iloc[1:,1:] #removes id column from heatmap\n",
    "c1 = corr.abs().unstack()\n",
    "c1.sort_values(ascending = False)\n",
    "\n",
    "sns.heatmap(corr, \n",
    "    annot=True, \n",
    "    annot_kws={\"fontsize\":8},\n",
    "    xticklabels = housing_corr.columns.values, # x labels\n",
    "    yticklabels = housing_corr.columns.values, # y labels\n",
    "    vmin = -1, # color scale the min value\n",
    "    vmax = 1, # color scale the max\n",
    "    center = 0, # color scale place at the center at 0\n",
    "    cmap = \"YlGnBu\", # color palette\n",
    "    square = True) # each cell will be square-shaped\n",
    "\n",
    "# plt.savefig('corr_heat.png', dpi = 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:**\n",
    "- **Month** and **season_sold** don't seem to be highly correlated to **price** here at all.\n",
    "- **Renovations** might not have much impact on price also. As we already saw, most homes were quite new (bet. 11-20 years old).\n",
    "- This helps solidify which features we should concentrate on looking at."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick Correlation Look at Initial Summary Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's look at the summary model for our initial dataframe first (df)\n",
    "- Since our initial inferences isolated grade, waterfront, and sqft_living as good indicators of correlation in relation to price, we'll take a look at those independent variables first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OLS Summary Model of Grade, Waterfront, and Sqft_living"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.579</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.579</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   5953.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Mar 2021</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:29:53</td>     <th>  Log-Likelihood:    </th> <td>-2.3888e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 17290</td>      <th>  AIC:               </th>  <td>4.778e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 17285</td>      <th>  BIC:               </th>  <td>4.778e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>-6.017e+05</td> <td> 1.44e+04</td> <td>  -41.725</td> <td> 0.000</td> <td> -6.3e+05</td> <td>-5.73e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade</th>       <td> 9.898e+04</td> <td> 2428.159</td> <td>   40.763</td> <td> 0.000</td> <td> 9.42e+04</td> <td> 1.04e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>  <td> 8.039e+05</td> <td> 2.09e+04</td> <td>   38.447</td> <td> 0.000</td> <td> 7.63e+05</td> <td> 8.45e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th> <td>  178.9822</td> <td>    3.109</td> <td>   57.577</td> <td> 0.000</td> <td>  172.889</td> <td>  185.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>was_reno</th>    <td> 1.384e+05</td> <td> 9242.380</td> <td>   14.972</td> <td> 0.000</td> <td>  1.2e+05</td> <td> 1.56e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13137.908</td> <th>  Durbin-Watson:     </th>  <td>   2.017</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>822210.647</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 3.110</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>36.206</td>   <th>  Cond. No.          </th>  <td>2.59e+04</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.59e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.579\n",
       "Model:                            OLS   Adj. R-squared:                  0.579\n",
       "Method:                 Least Squares   F-statistic:                     5953.\n",
       "Date:                Fri, 12 Mar 2021   Prob (F-statistic):               0.00\n",
       "Time:                        11:29:53   Log-Likelihood:            -2.3888e+05\n",
       "No. Observations:               17290   AIC:                         4.778e+05\n",
       "Df Residuals:                   17285   BIC:                         4.778e+05\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept   -6.017e+05   1.44e+04    -41.725      0.000    -6.3e+05   -5.73e+05\n",
       "grade        9.898e+04   2428.159     40.763      0.000    9.42e+04    1.04e+05\n",
       "waterfront   8.039e+05   2.09e+04     38.447      0.000    7.63e+05    8.45e+05\n",
       "sqft_living   178.9822      3.109     57.577      0.000     172.889     185.075\n",
       "was_reno     1.384e+05   9242.380     14.972      0.000     1.2e+05    1.56e+05\n",
       "==============================================================================\n",
       "Omnibus:                    13137.908   Durbin-Watson:                   2.017\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           822210.647\n",
       "Skew:                           3.110   Prob(JB):                         0.00\n",
       "Kurtosis:                      36.206   Cond. No.                     2.59e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.59e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_model = ols(formula='price~grade+waterfront+sqft_living+was_reno', data = df).fit()\n",
    "summary_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As expected we see a high correlation with these features in relation to price.\n",
    "- These variables only capture around 57% of all of our predictive values however."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at zipcodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.401</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.399</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   167.4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Mar 2021</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:29:54</td>     <th>  Log-Likelihood:    </th> <td>-2.4193e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 17290</td>      <th>  AIC:               </th>  <td>4.840e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 17220</td>      <th>  BIC:               </th>  <td>4.845e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    69</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>  2.82e+05</td> <td> 1.72e+04</td> <td>   16.421</td> <td> 0.000</td> <td> 2.48e+05</td> <td> 3.16e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98002]</th> <td>-4.971e+04</td> <td> 2.88e+04</td> <td>   -1.727</td> <td> 0.084</td> <td>-1.06e+05</td> <td> 6701.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98003]</th> <td> 8763.9215</td> <td> 2.59e+04</td> <td>    0.339</td> <td> 0.735</td> <td>-4.19e+04</td> <td> 5.95e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98004]</th> <td> 1.115e+06</td> <td> 2.55e+04</td> <td>   43.787</td> <td> 0.000</td> <td> 1.06e+06</td> <td> 1.16e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98005]</th> <td> 5.268e+05</td> <td> 2.99e+04</td> <td>   17.629</td> <td> 0.000</td> <td> 4.68e+05</td> <td> 5.85e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98006]</th> <td> 5.675e+05</td> <td> 2.26e+04</td> <td>   25.068</td> <td> 0.000</td> <td> 5.23e+05</td> <td> 6.12e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98007]</th> <td> 3.156e+05</td> <td> 3.13e+04</td> <td>   10.073</td> <td> 0.000</td> <td> 2.54e+05</td> <td> 3.77e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98008]</th> <td> 3.588e+05</td> <td> 2.58e+04</td> <td>   13.907</td> <td> 0.000</td> <td> 3.08e+05</td> <td> 4.09e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98010]</th> <td> 1.429e+05</td> <td> 3.78e+04</td> <td>    3.783</td> <td> 0.000</td> <td> 6.88e+04</td> <td> 2.17e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98011]</th> <td> 2.086e+05</td> <td> 2.84e+04</td> <td>    7.337</td> <td> 0.000</td> <td> 1.53e+05</td> <td> 2.64e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98014]</th> <td> 1.742e+05</td> <td> 3.35e+04</td> <td>    5.196</td> <td> 0.000</td> <td> 1.08e+05</td> <td>  2.4e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98019]</th> <td> 1.464e+05</td> <td> 2.95e+04</td> <td>    4.957</td> <td> 0.000</td> <td> 8.85e+04</td> <td> 2.04e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98022]</th> <td> 2.677e+04</td> <td> 2.72e+04</td> <td>    0.985</td> <td> 0.324</td> <td>-2.65e+04</td> <td>    8e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98023]</th> <td> 7573.8820</td> <td> 2.23e+04</td> <td>    0.339</td> <td> 0.735</td> <td>-3.62e+04</td> <td> 5.14e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98024]</th> <td>  3.09e+05</td> <td> 3.98e+04</td> <td>    7.765</td> <td> 0.000</td> <td> 2.31e+05</td> <td> 3.87e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98027]</th> <td> 3.253e+05</td> <td> 2.34e+04</td> <td>   13.907</td> <td> 0.000</td> <td> 2.79e+05</td> <td> 3.71e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98028]</th> <td> 1.864e+05</td> <td>  2.6e+04</td> <td>    7.163</td> <td> 0.000</td> <td> 1.35e+05</td> <td> 2.37e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98029]</th> <td> 3.337e+05</td> <td> 2.51e+04</td> <td>   13.295</td> <td> 0.000</td> <td> 2.84e+05</td> <td> 3.83e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98030]</th> <td>  1.44e+04</td> <td> 2.67e+04</td> <td>    0.540</td> <td> 0.589</td> <td>-3.79e+04</td> <td> 6.67e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98031]</th> <td>  1.86e+04</td> <td> 2.62e+04</td> <td>    0.711</td> <td> 0.477</td> <td>-3.27e+04</td> <td> 6.99e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98032]</th> <td> -3.04e+04</td> <td> 3.33e+04</td> <td>   -0.913</td> <td> 0.361</td> <td>-9.56e+04</td> <td> 3.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98033]</th> <td> 5.207e+05</td> <td>  2.3e+04</td> <td>   22.601</td> <td> 0.000</td> <td> 4.76e+05</td> <td> 5.66e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98034]</th> <td> 2.419e+05</td> <td> 2.21e+04</td> <td>   10.955</td> <td> 0.000</td> <td> 1.99e+05</td> <td> 2.85e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98038]</th> <td> 8.642e+04</td> <td> 2.19e+04</td> <td>    3.949</td> <td> 0.000</td> <td> 4.35e+04</td> <td> 1.29e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98039]</th> <td> 1.921e+06</td> <td> 4.78e+04</td> <td>   40.147</td> <td> 0.000</td> <td> 1.83e+06</td> <td> 2.01e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98040]</th> <td>  9.01e+05</td> <td> 2.57e+04</td> <td>   35.055</td> <td> 0.000</td> <td> 8.51e+05</td> <td> 9.51e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98042]</th> <td> 2.717e+04</td> <td> 2.22e+04</td> <td>    1.224</td> <td> 0.221</td> <td>-1.63e+04</td> <td> 7.07e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98045]</th> <td> 1.493e+05</td> <td> 2.78e+04</td> <td>    5.378</td> <td> 0.000</td> <td> 9.49e+04</td> <td> 2.04e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98052]</th> <td> 3.641e+05</td> <td> 2.17e+04</td> <td>   16.764</td> <td> 0.000</td> <td> 3.21e+05</td> <td> 4.07e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98053]</th> <td> 3.953e+05</td> <td> 2.35e+04</td> <td>   16.805</td> <td> 0.000</td> <td> 3.49e+05</td> <td> 4.41e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98055]</th> <td> 2.485e+04</td> <td> 2.63e+04</td> <td>    0.944</td> <td> 0.345</td> <td>-2.68e+04</td> <td> 7.65e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98056]</th> <td> 1.415e+05</td> <td> 2.33e+04</td> <td>    6.066</td> <td> 0.000</td> <td> 9.58e+04</td> <td> 1.87e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98058]</th> <td> 7.237e+04</td> <td> 2.26e+04</td> <td>    3.197</td> <td> 0.001</td> <td>  2.8e+04</td> <td> 1.17e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98059]</th> <td> 2.124e+05</td> <td> 2.28e+04</td> <td>    9.318</td> <td> 0.000</td> <td> 1.68e+05</td> <td> 2.57e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98065]</th> <td> 2.452e+05</td> <td> 2.51e+04</td> <td>    9.760</td> <td> 0.000</td> <td> 1.96e+05</td> <td> 2.94e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98070]</th> <td> 2.137e+05</td> <td> 3.38e+04</td> <td>    6.326</td> <td> 0.000</td> <td> 1.47e+05</td> <td>  2.8e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98072]</th> <td> 2.847e+05</td> <td> 2.62e+04</td> <td>   10.883</td> <td> 0.000</td> <td> 2.33e+05</td> <td> 3.36e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98074]</th> <td> 4.093e+05</td> <td>  2.3e+04</td> <td>   17.796</td> <td> 0.000</td> <td> 3.64e+05</td> <td> 4.54e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98075]</th> <td>  5.12e+05</td> <td> 2.42e+04</td> <td>   21.118</td> <td> 0.000</td> <td> 4.64e+05</td> <td> 5.59e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98077]</th> <td> 3.949e+05</td> <td> 2.85e+04</td> <td>   13.859</td> <td> 0.000</td> <td> 3.39e+05</td> <td> 4.51e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98092]</th> <td> 5.772e+04</td> <td> 2.42e+04</td> <td>    2.381</td> <td> 0.017</td> <td> 1.02e+04</td> <td> 1.05e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98102]</th> <td> 6.517e+05</td> <td> 3.66e+04</td> <td>   17.790</td> <td> 0.000</td> <td>  5.8e+05</td> <td> 7.23e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98103]</th> <td> 3.078e+05</td> <td> 2.18e+04</td> <td>   14.099</td> <td> 0.000</td> <td> 2.65e+05</td> <td> 3.51e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98105]</th> <td> 5.868e+05</td> <td> 2.81e+04</td> <td>   20.909</td> <td> 0.000</td> <td> 5.32e+05</td> <td> 6.42e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98106]</th> <td> 3.637e+04</td> <td> 2.47e+04</td> <td>    1.473</td> <td> 0.141</td> <td> -1.2e+04</td> <td> 8.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98107]</th> <td> 3.015e+05</td> <td> 2.63e+04</td> <td>   11.478</td> <td> 0.000</td> <td>  2.5e+05</td> <td> 3.53e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98108]</th> <td> 7.408e+04</td> <td> 2.91e+04</td> <td>    2.542</td> <td> 0.011</td> <td>  1.7e+04</td> <td> 1.31e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98109]</th> <td>  5.94e+05</td> <td> 3.58e+04</td> <td>   16.601</td> <td> 0.000</td> <td> 5.24e+05</td> <td> 6.64e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98112]</th> <td> 8.375e+05</td> <td> 2.61e+04</td> <td>   32.053</td> <td> 0.000</td> <td> 7.86e+05</td> <td> 8.89e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98115]</th> <td> 3.317e+05</td> <td> 2.18e+04</td> <td>   15.221</td> <td> 0.000</td> <td> 2.89e+05</td> <td> 3.74e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98116]</th> <td>  3.33e+05</td> <td> 2.47e+04</td> <td>   13.486</td> <td> 0.000</td> <td> 2.85e+05</td> <td> 3.81e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98117]</th> <td> 2.882e+05</td> <td> 2.21e+04</td> <td>   13.065</td> <td> 0.000</td> <td> 2.45e+05</td> <td> 3.31e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98118]</th> <td> 1.298e+05</td> <td> 2.23e+04</td> <td>    5.808</td> <td> 0.000</td> <td>  8.6e+04</td> <td> 1.74e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98119]</th> <td> 5.893e+05</td> <td> 2.93e+04</td> <td>   20.129</td> <td> 0.000</td> <td> 5.32e+05</td> <td> 6.47e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98122]</th> <td> 3.372e+05</td> <td> 2.55e+04</td> <td>   13.214</td> <td> 0.000</td> <td> 2.87e+05</td> <td> 3.87e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98125]</th> <td> 1.932e+05</td> <td> 2.36e+04</td> <td>    8.196</td> <td> 0.000</td> <td> 1.47e+05</td> <td> 2.39e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98126]</th> <td> 1.431e+05</td> <td> 2.44e+04</td> <td>    5.860</td> <td> 0.000</td> <td> 9.52e+04</td> <td> 1.91e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98133]</th> <td> 1.044e+05</td> <td> 2.25e+04</td> <td>    4.649</td> <td> 0.000</td> <td> 6.04e+04</td> <td> 1.48e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98136]</th> <td> 2.839e+05</td> <td> 2.62e+04</td> <td>   10.838</td> <td> 0.000</td> <td> 2.33e+05</td> <td> 3.35e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98144]</th> <td> 3.394e+05</td> <td> 2.46e+04</td> <td>   13.783</td> <td> 0.000</td> <td> 2.91e+05</td> <td> 3.88e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98146]</th> <td> 8.175e+04</td> <td> 2.52e+04</td> <td>    3.243</td> <td> 0.001</td> <td> 3.23e+04</td> <td> 1.31e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98148]</th> <td>-9916.4315</td> <td> 4.74e+04</td> <td>   -0.209</td> <td> 0.834</td> <td>-1.03e+05</td> <td> 8.29e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98155]</th> <td>  1.41e+05</td> <td> 2.31e+04</td> <td>    6.101</td> <td> 0.000</td> <td> 9.57e+04</td> <td> 1.86e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98166]</th> <td> 1.974e+05</td> <td> 2.64e+04</td> <td>    7.484</td> <td> 0.000</td> <td> 1.46e+05</td> <td> 2.49e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98168]</th> <td>-4.096e+04</td> <td> 2.62e+04</td> <td>   -1.566</td> <td> 0.117</td> <td>-9.22e+04</td> <td> 1.03e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98177]</th> <td> 3.733e+05</td> <td> 2.61e+04</td> <td>   14.323</td> <td> 0.000</td> <td> 3.22e+05</td> <td> 4.24e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98178]</th> <td> 3.068e+04</td> <td> 2.62e+04</td> <td>    1.173</td> <td> 0.241</td> <td>-2.06e+04</td> <td>  8.2e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98188]</th> <td> 7033.0686</td> <td> 3.26e+04</td> <td>    0.216</td> <td> 0.829</td> <td>-5.69e+04</td> <td> 7.09e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98198]</th> <td> 2.318e+04</td> <td>  2.6e+04</td> <td>    0.892</td> <td> 0.373</td> <td>-2.78e+04</td> <td> 7.41e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode[T.98199]</th> <td> 5.051e+05</td> <td> 2.47e+04</td> <td>   20.457</td> <td> 0.000</td> <td> 4.57e+05</td> <td> 5.54e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>17985.408</td> <th>  Durbin-Watson:     </th>  <td>   2.006</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>2528120.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 4.931</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>61.412</td>   <th>  Cond. No.          </th>  <td>    66.6</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.401\n",
       "Model:                            OLS   Adj. R-squared:                  0.399\n",
       "Method:                 Least Squares   F-statistic:                     167.4\n",
       "Date:                Fri, 12 Mar 2021   Prob (F-statistic):               0.00\n",
       "Time:                        11:29:54   Log-Likelihood:            -2.4193e+05\n",
       "No. Observations:               17290   AIC:                         4.840e+05\n",
       "Df Residuals:                   17220   BIC:                         4.845e+05\n",
       "Df Model:                          69                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept          2.82e+05   1.72e+04     16.421      0.000    2.48e+05    3.16e+05\n",
       "zipcode[T.98002] -4.971e+04   2.88e+04     -1.727      0.084   -1.06e+05    6701.740\n",
       "zipcode[T.98003]  8763.9215   2.59e+04      0.339      0.735   -4.19e+04    5.95e+04\n",
       "zipcode[T.98004]  1.115e+06   2.55e+04     43.787      0.000    1.06e+06    1.16e+06\n",
       "zipcode[T.98005]  5.268e+05   2.99e+04     17.629      0.000    4.68e+05    5.85e+05\n",
       "zipcode[T.98006]  5.675e+05   2.26e+04     25.068      0.000    5.23e+05    6.12e+05\n",
       "zipcode[T.98007]  3.156e+05   3.13e+04     10.073      0.000    2.54e+05    3.77e+05\n",
       "zipcode[T.98008]  3.588e+05   2.58e+04     13.907      0.000    3.08e+05    4.09e+05\n",
       "zipcode[T.98010]  1.429e+05   3.78e+04      3.783      0.000    6.88e+04    2.17e+05\n",
       "zipcode[T.98011]  2.086e+05   2.84e+04      7.337      0.000    1.53e+05    2.64e+05\n",
       "zipcode[T.98014]  1.742e+05   3.35e+04      5.196      0.000    1.08e+05     2.4e+05\n",
       "zipcode[T.98019]  1.464e+05   2.95e+04      4.957      0.000    8.85e+04    2.04e+05\n",
       "zipcode[T.98022]  2.677e+04   2.72e+04      0.985      0.324   -2.65e+04       8e+04\n",
       "zipcode[T.98023]  7573.8820   2.23e+04      0.339      0.735   -3.62e+04    5.14e+04\n",
       "zipcode[T.98024]   3.09e+05   3.98e+04      7.765      0.000    2.31e+05    3.87e+05\n",
       "zipcode[T.98027]  3.253e+05   2.34e+04     13.907      0.000    2.79e+05    3.71e+05\n",
       "zipcode[T.98028]  1.864e+05    2.6e+04      7.163      0.000    1.35e+05    2.37e+05\n",
       "zipcode[T.98029]  3.337e+05   2.51e+04     13.295      0.000    2.84e+05    3.83e+05\n",
       "zipcode[T.98030]   1.44e+04   2.67e+04      0.540      0.589   -3.79e+04    6.67e+04\n",
       "zipcode[T.98031]   1.86e+04   2.62e+04      0.711      0.477   -3.27e+04    6.99e+04\n",
       "zipcode[T.98032]  -3.04e+04   3.33e+04     -0.913      0.361   -9.56e+04    3.48e+04\n",
       "zipcode[T.98033]  5.207e+05    2.3e+04     22.601      0.000    4.76e+05    5.66e+05\n",
       "zipcode[T.98034]  2.419e+05   2.21e+04     10.955      0.000    1.99e+05    2.85e+05\n",
       "zipcode[T.98038]  8.642e+04   2.19e+04      3.949      0.000    4.35e+04    1.29e+05\n",
       "zipcode[T.98039]  1.921e+06   4.78e+04     40.147      0.000    1.83e+06    2.01e+06\n",
       "zipcode[T.98040]   9.01e+05   2.57e+04     35.055      0.000    8.51e+05    9.51e+05\n",
       "zipcode[T.98042]  2.717e+04   2.22e+04      1.224      0.221   -1.63e+04    7.07e+04\n",
       "zipcode[T.98045]  1.493e+05   2.78e+04      5.378      0.000    9.49e+04    2.04e+05\n",
       "zipcode[T.98052]  3.641e+05   2.17e+04     16.764      0.000    3.21e+05    4.07e+05\n",
       "zipcode[T.98053]  3.953e+05   2.35e+04     16.805      0.000    3.49e+05    4.41e+05\n",
       "zipcode[T.98055]  2.485e+04   2.63e+04      0.944      0.345   -2.68e+04    7.65e+04\n",
       "zipcode[T.98056]  1.415e+05   2.33e+04      6.066      0.000    9.58e+04    1.87e+05\n",
       "zipcode[T.98058]  7.237e+04   2.26e+04      3.197      0.001     2.8e+04    1.17e+05\n",
       "zipcode[T.98059]  2.124e+05   2.28e+04      9.318      0.000    1.68e+05    2.57e+05\n",
       "zipcode[T.98065]  2.452e+05   2.51e+04      9.760      0.000    1.96e+05    2.94e+05\n",
       "zipcode[T.98070]  2.137e+05   3.38e+04      6.326      0.000    1.47e+05     2.8e+05\n",
       "zipcode[T.98072]  2.847e+05   2.62e+04     10.883      0.000    2.33e+05    3.36e+05\n",
       "zipcode[T.98074]  4.093e+05    2.3e+04     17.796      0.000    3.64e+05    4.54e+05\n",
       "zipcode[T.98075]   5.12e+05   2.42e+04     21.118      0.000    4.64e+05    5.59e+05\n",
       "zipcode[T.98077]  3.949e+05   2.85e+04     13.859      0.000    3.39e+05    4.51e+05\n",
       "zipcode[T.98092]  5.772e+04   2.42e+04      2.381      0.017    1.02e+04    1.05e+05\n",
       "zipcode[T.98102]  6.517e+05   3.66e+04     17.790      0.000     5.8e+05    7.23e+05\n",
       "zipcode[T.98103]  3.078e+05   2.18e+04     14.099      0.000    2.65e+05    3.51e+05\n",
       "zipcode[T.98105]  5.868e+05   2.81e+04     20.909      0.000    5.32e+05    6.42e+05\n",
       "zipcode[T.98106]  3.637e+04   2.47e+04      1.473      0.141    -1.2e+04    8.48e+04\n",
       "zipcode[T.98107]  3.015e+05   2.63e+04     11.478      0.000     2.5e+05    3.53e+05\n",
       "zipcode[T.98108]  7.408e+04   2.91e+04      2.542      0.011     1.7e+04    1.31e+05\n",
       "zipcode[T.98109]   5.94e+05   3.58e+04     16.601      0.000    5.24e+05    6.64e+05\n",
       "zipcode[T.98112]  8.375e+05   2.61e+04     32.053      0.000    7.86e+05    8.89e+05\n",
       "zipcode[T.98115]  3.317e+05   2.18e+04     15.221      0.000    2.89e+05    3.74e+05\n",
       "zipcode[T.98116]   3.33e+05   2.47e+04     13.486      0.000    2.85e+05    3.81e+05\n",
       "zipcode[T.98117]  2.882e+05   2.21e+04     13.065      0.000    2.45e+05    3.31e+05\n",
       "zipcode[T.98118]  1.298e+05   2.23e+04      5.808      0.000     8.6e+04    1.74e+05\n",
       "zipcode[T.98119]  5.893e+05   2.93e+04     20.129      0.000    5.32e+05    6.47e+05\n",
       "zipcode[T.98122]  3.372e+05   2.55e+04     13.214      0.000    2.87e+05    3.87e+05\n",
       "zipcode[T.98125]  1.932e+05   2.36e+04      8.196      0.000    1.47e+05    2.39e+05\n",
       "zipcode[T.98126]  1.431e+05   2.44e+04      5.860      0.000    9.52e+04    1.91e+05\n",
       "zipcode[T.98133]  1.044e+05   2.25e+04      4.649      0.000    6.04e+04    1.48e+05\n",
       "zipcode[T.98136]  2.839e+05   2.62e+04     10.838      0.000    2.33e+05    3.35e+05\n",
       "zipcode[T.98144]  3.394e+05   2.46e+04     13.783      0.000    2.91e+05    3.88e+05\n",
       "zipcode[T.98146]  8.175e+04   2.52e+04      3.243      0.001    3.23e+04    1.31e+05\n",
       "zipcode[T.98148] -9916.4315   4.74e+04     -0.209      0.834   -1.03e+05    8.29e+04\n",
       "zipcode[T.98155]   1.41e+05   2.31e+04      6.101      0.000    9.57e+04    1.86e+05\n",
       "zipcode[T.98166]  1.974e+05   2.64e+04      7.484      0.000    1.46e+05    2.49e+05\n",
       "zipcode[T.98168] -4.096e+04   2.62e+04     -1.566      0.117   -9.22e+04    1.03e+04\n",
       "zipcode[T.98177]  3.733e+05   2.61e+04     14.323      0.000    3.22e+05    4.24e+05\n",
       "zipcode[T.98178]  3.068e+04   2.62e+04      1.173      0.241   -2.06e+04     8.2e+04\n",
       "zipcode[T.98188]  7033.0686   3.26e+04      0.216      0.829   -5.69e+04    7.09e+04\n",
       "zipcode[T.98198]  2.318e+04    2.6e+04      0.892      0.373   -2.78e+04    7.41e+04\n",
       "zipcode[T.98199]  5.051e+05   2.47e+04     20.457      0.000    4.57e+05    5.54e+05\n",
       "==============================================================================\n",
       "Omnibus:                    17985.408   Durbin-Watson:                   2.006\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          2528120.167\n",
       "Skew:                           4.931   Prob(JB):                         0.00\n",
       "Kurtosis:                      61.412   Cond. No.                         66.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use C(zipcode) to parse all zipcodes from that column properly if stored as int. Already changed to string so no need.\n",
    "zip_model = ols(formula='price~zipcode', data = df).fit()\n",
    "zip_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:**\n",
    "- It may be better to look at the top zipcodes instead of all of them together.\n",
    "- Perhaps top 10 would perform better?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Dummy Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Waterfront** (already 0s and 1s), **zipcode**, and **grade** (high correlation to price)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dummy variables for grade, waterfront, and zipcode\n",
    "df = pd.get_dummies(df, columns = ['grade', 'waterfront', 'zipcode', 'season_sold', 'view', 'condition'], drop_first = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at Correlation of Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>year_sold</th>\n",
       "      <th>has_basement</th>\n",
       "      <th>age_built</th>\n",
       "      <th>last_reno</th>\n",
       "      <th>month_sold</th>\n",
       "      <th>was_reno</th>\n",
       "      <th>bed_sqft_ratio</th>\n",
       "      <th>bath_sqft_ratio</th>\n",
       "      <th>floors_sqft_ratio</th>\n",
       "      <th>grade_3</th>\n",
       "      <th>grade_4</th>\n",
       "      <th>grade_5</th>\n",
       "      <th>grade_6</th>\n",
       "      <th>grade_7</th>\n",
       "      <th>grade_8</th>\n",
       "      <th>grade_9</th>\n",
       "      <th>grade_10</th>\n",
       "      <th>grade_11</th>\n",
       "      <th>grade_12</th>\n",
       "      <th>grade_13</th>\n",
       "      <th>waterfront_1</th>\n",
       "      <th>zipcode_98002</th>\n",
       "      <th>zipcode_98003</th>\n",
       "      <th>zipcode_98004</th>\n",
       "      <th>zipcode_98005</th>\n",
       "      <th>zipcode_98006</th>\n",
       "      <th>zipcode_98007</th>\n",
       "      <th>zipcode_98008</th>\n",
       "      <th>zipcode_98010</th>\n",
       "      <th>zipcode_98011</th>\n",
       "      <th>zipcode_98014</th>\n",
       "      <th>zipcode_98019</th>\n",
       "      <th>zipcode_98022</th>\n",
       "      <th>zipcode_98023</th>\n",
       "      <th>zipcode_98024</th>\n",
       "      <th>...</th>\n",
       "      <th>zipcode_98056</th>\n",
       "      <th>zipcode_98058</th>\n",
       "      <th>zipcode_98059</th>\n",
       "      <th>zipcode_98065</th>\n",
       "      <th>zipcode_98070</th>\n",
       "      <th>zipcode_98072</th>\n",
       "      <th>zipcode_98074</th>\n",
       "      <th>zipcode_98075</th>\n",
       "      <th>zipcode_98077</th>\n",
       "      <th>zipcode_98092</th>\n",
       "      <th>zipcode_98102</th>\n",
       "      <th>zipcode_98103</th>\n",
       "      <th>zipcode_98105</th>\n",
       "      <th>zipcode_98106</th>\n",
       "      <th>zipcode_98107</th>\n",
       "      <th>zipcode_98108</th>\n",
       "      <th>zipcode_98109</th>\n",
       "      <th>zipcode_98112</th>\n",
       "      <th>zipcode_98115</th>\n",
       "      <th>zipcode_98116</th>\n",
       "      <th>zipcode_98117</th>\n",
       "      <th>zipcode_98118</th>\n",
       "      <th>zipcode_98119</th>\n",
       "      <th>zipcode_98122</th>\n",
       "      <th>zipcode_98125</th>\n",
       "      <th>zipcode_98126</th>\n",
       "      <th>zipcode_98133</th>\n",
       "      <th>zipcode_98136</th>\n",
       "      <th>zipcode_98144</th>\n",
       "      <th>zipcode_98146</th>\n",
       "      <th>zipcode_98148</th>\n",
       "      <th>zipcode_98155</th>\n",
       "      <th>zipcode_98166</th>\n",
       "      <th>zipcode_98168</th>\n",
       "      <th>zipcode_98177</th>\n",
       "      <th>zipcode_98178</th>\n",
       "      <th>zipcode_98188</th>\n",
       "      <th>zipcode_98198</th>\n",
       "      <th>zipcode_98199</th>\n",
       "      <th>season_sold_2</th>\n",
       "      <th>season_sold_3</th>\n",
       "      <th>season_sold_4</th>\n",
       "      <th>view_1</th>\n",
       "      <th>view_2</th>\n",
       "      <th>view_3</th>\n",
       "      <th>view_4</th>\n",
       "      <th>condition_2</th>\n",
       "      <th>condition_3</th>\n",
       "      <th>condition_4</th>\n",
       "      <th>condition_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012306</td>\n",
       "      <td>-0.010562</td>\n",
       "      <td>-0.005151</td>\n",
       "      <td>-0.008900</td>\n",
       "      <td>-0.003500</td>\n",
       "      <td>0.010599</td>\n",
       "      <td>-0.003406</td>\n",
       "      <td>-0.012104</td>\n",
       "      <td>0.007496</td>\n",
       "      <td>-0.003423</td>\n",
       "      <td>0.002470</td>\n",
       "      <td>-0.006992</td>\n",
       "      <td>-0.008961</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>-0.003019</td>\n",
       "      <td>-0.010365</td>\n",
       "      <td>-0.007544</td>\n",
       "      <td>-0.008349</td>\n",
       "      <td>0.003139</td>\n",
       "      <td>-0.003421</td>\n",
       "      <td>-0.001004</td>\n",
       "      <td>0.003391</td>\n",
       "      <td>0.014655</td>\n",
       "      <td>-0.002952</td>\n",
       "      <td>0.006610</td>\n",
       "      <td>-0.004155</td>\n",
       "      <td>-0.003335</td>\n",
       "      <td>0.004959</td>\n",
       "      <td>0.003503</td>\n",
       "      <td>-0.007257</td>\n",
       "      <td>0.006833</td>\n",
       "      <td>-0.011366</td>\n",
       "      <td>-0.004632</td>\n",
       "      <td>-0.005438</td>\n",
       "      <td>-0.008845</td>\n",
       "      <td>-0.004590</td>\n",
       "      <td>0.005155</td>\n",
       "      <td>-0.012322</td>\n",
       "      <td>0.006336</td>\n",
       "      <td>-0.003008</td>\n",
       "      <td>-0.005246</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000546</td>\n",
       "      <td>0.006028</td>\n",
       "      <td>-0.013795</td>\n",
       "      <td>-0.003682</td>\n",
       "      <td>0.004126</td>\n",
       "      <td>-0.007205</td>\n",
       "      <td>0.004073</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009209</td>\n",
       "      <td>0.005365</td>\n",
       "      <td>0.004190</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.002217</td>\n",
       "      <td>-0.004307</td>\n",
       "      <td>0.004859</td>\n",
       "      <td>0.008688</td>\n",
       "      <td>0.005512</td>\n",
       "      <td>0.004100</td>\n",
       "      <td>-0.002682</td>\n",
       "      <td>-0.005442</td>\n",
       "      <td>0.000905</td>\n",
       "      <td>-0.000714</td>\n",
       "      <td>0.007705</td>\n",
       "      <td>0.015821</td>\n",
       "      <td>-0.007265</td>\n",
       "      <td>0.005918</td>\n",
       "      <td>0.002568</td>\n",
       "      <td>0.011248</td>\n",
       "      <td>0.010876</td>\n",
       "      <td>-0.009278</td>\n",
       "      <td>-0.003248</td>\n",
       "      <td>-0.007791</td>\n",
       "      <td>0.010551</td>\n",
       "      <td>-0.005719</td>\n",
       "      <td>0.007452</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>-0.007500</td>\n",
       "      <td>0.011195</td>\n",
       "      <td>-0.021353</td>\n",
       "      <td>-0.001709</td>\n",
       "      <td>0.004273</td>\n",
       "      <td>-0.002224</td>\n",
       "      <td>-0.008480</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>-0.002428</td>\n",
       "      <td>-0.003405</td>\n",
       "      <td>0.001918</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>-0.002701</td>\n",
       "      <td>0.004347</td>\n",
       "      <td>0.004443</td>\n",
       "      <td>-0.003094</td>\n",
       "      <td>-0.001935</td>\n",
       "      <td>-0.005793</td>\n",
       "      <td>0.003457</td>\n",
       "      <td>-0.004357</td>\n",
       "      <td>0.001670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.012306</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.313775</td>\n",
       "      <td>0.524838</td>\n",
       "      <td>0.703540</td>\n",
       "      <td>0.086803</td>\n",
       "      <td>0.256985</td>\n",
       "      <td>0.607760</td>\n",
       "      <td>0.323312</td>\n",
       "      <td>0.056450</td>\n",
       "      <td>0.117770</td>\n",
       "      <td>0.302394</td>\n",
       "      <td>0.019327</td>\n",
       "      <td>0.585567</td>\n",
       "      <td>0.081423</td>\n",
       "      <td>-0.002334</td>\n",
       "      <td>0.176970</td>\n",
       "      <td>-0.056484</td>\n",
       "      <td>-0.104828</td>\n",
       "      <td>-0.004350</td>\n",
       "      <td>0.117409</td>\n",
       "      <td>-0.474124</td>\n",
       "      <td>-0.266844</td>\n",
       "      <td>-0.306713</td>\n",
       "      <td>-0.010725</td>\n",
       "      <td>-0.031562</td>\n",
       "      <td>-0.084280</td>\n",
       "      <td>-0.206989</td>\n",
       "      <td>-0.312121</td>\n",
       "      <td>0.003797</td>\n",
       "      <td>0.232428</td>\n",
       "      <td>0.332676</td>\n",
       "      <td>0.355224</td>\n",
       "      <td>0.307231</td>\n",
       "      <td>0.233989</td>\n",
       "      <td>0.275779</td>\n",
       "      <td>-0.079096</td>\n",
       "      <td>-0.076717</td>\n",
       "      <td>0.270366</td>\n",
       "      <td>0.064889</td>\n",
       "      <td>0.124801</td>\n",
       "      <td>0.012829</td>\n",
       "      <td>0.030831</td>\n",
       "      <td>-0.020349</td>\n",
       "      <td>-0.013093</td>\n",
       "      <td>-0.017358</td>\n",
       "      <td>-0.027669</td>\n",
       "      <td>-0.065326</td>\n",
       "      <td>-0.104858</td>\n",
       "      <td>0.008269</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044213</td>\n",
       "      <td>-0.075343</td>\n",
       "      <td>-0.018443</td>\n",
       "      <td>-0.004379</td>\n",
       "      <td>-0.009161</td>\n",
       "      <td>0.007813</td>\n",
       "      <td>0.058627</td>\n",
       "      <td>0.087974</td>\n",
       "      <td>0.035470</td>\n",
       "      <td>-0.069836</td>\n",
       "      <td>0.071764</td>\n",
       "      <td>0.021747</td>\n",
       "      <td>0.087568</td>\n",
       "      <td>-0.074458</td>\n",
       "      <td>0.012757</td>\n",
       "      <td>-0.046430</td>\n",
       "      <td>0.063122</td>\n",
       "      <td>0.174365</td>\n",
       "      <td>0.032511</td>\n",
       "      <td>0.024873</td>\n",
       "      <td>0.012703</td>\n",
       "      <td>-0.053841</td>\n",
       "      <td>0.082551</td>\n",
       "      <td>0.024683</td>\n",
       "      <td>-0.024139</td>\n",
       "      <td>-0.039607</td>\n",
       "      <td>-0.063628</td>\n",
       "      <td>0.007550</td>\n",
       "      <td>0.027147</td>\n",
       "      <td>-0.056960</td>\n",
       "      <td>-0.035934</td>\n",
       "      <td>-0.045335</td>\n",
       "      <td>-0.018183</td>\n",
       "      <td>-0.090086</td>\n",
       "      <td>0.034662</td>\n",
       "      <td>-0.068552</td>\n",
       "      <td>-0.053705</td>\n",
       "      <td>-0.071636</td>\n",
       "      <td>0.082508</td>\n",
       "      <td>0.033185</td>\n",
       "      <td>-0.004643</td>\n",
       "      <td>-0.011459</td>\n",
       "      <td>0.091073</td>\n",
       "      <td>0.138504</td>\n",
       "      <td>0.176706</td>\n",
       "      <td>0.317456</td>\n",
       "      <td>-0.048513</td>\n",
       "      <td>0.006550</td>\n",
       "      <td>-0.031574</td>\n",
       "      <td>0.058559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>-0.010562</td>\n",
       "      <td>0.313775</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.523016</td>\n",
       "      <td>0.588536</td>\n",
       "      <td>0.030674</td>\n",
       "      <td>0.179908</td>\n",
       "      <td>0.488420</td>\n",
       "      <td>0.307873</td>\n",
       "      <td>0.159936</td>\n",
       "      <td>0.014489</td>\n",
       "      <td>-0.003991</td>\n",
       "      <td>0.129593</td>\n",
       "      <td>0.401073</td>\n",
       "      <td>0.028038</td>\n",
       "      <td>-0.010211</td>\n",
       "      <td>0.163935</td>\n",
       "      <td>-0.160089</td>\n",
       "      <td>-0.168892</td>\n",
       "      <td>0.002985</td>\n",
       "      <td>0.014182</td>\n",
       "      <td>0.022666</td>\n",
       "      <td>-0.245144</td>\n",
       "      <td>-0.437583</td>\n",
       "      <td>-0.027963</td>\n",
       "      <td>-0.076783</td>\n",
       "      <td>-0.118132</td>\n",
       "      <td>-0.243196</td>\n",
       "      <td>-0.103991</td>\n",
       "      <td>0.072530</td>\n",
       "      <td>0.164262</td>\n",
       "      <td>0.138973</td>\n",
       "      <td>0.120530</td>\n",
       "      <td>0.062352</td>\n",
       "      <td>0.042565</td>\n",
       "      <td>-0.008668</td>\n",
       "      <td>-0.006824</td>\n",
       "      <td>0.000536</td>\n",
       "      <td>0.062804</td>\n",
       "      <td>0.046070</td>\n",
       "      <td>0.074479</td>\n",
       "      <td>0.036197</td>\n",
       "      <td>0.047027</td>\n",
       "      <td>-0.003337</td>\n",
       "      <td>0.022033</td>\n",
       "      <td>-0.030332</td>\n",
       "      <td>0.000865</td>\n",
       "      <td>-0.028711</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>-0.002174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008929</td>\n",
       "      <td>0.025046</td>\n",
       "      <td>0.054882</td>\n",
       "      <td>0.017401</td>\n",
       "      <td>-0.049345</td>\n",
       "      <td>0.007035</td>\n",
       "      <td>0.039765</td>\n",
       "      <td>0.067117</td>\n",
       "      <td>0.030244</td>\n",
       "      <td>0.015401</td>\n",
       "      <td>-0.004354</td>\n",
       "      <td>-0.062170</td>\n",
       "      <td>0.018625</td>\n",
       "      <td>-0.039997</td>\n",
       "      <td>-0.049907</td>\n",
       "      <td>-0.020437</td>\n",
       "      <td>-0.011346</td>\n",
       "      <td>0.017657</td>\n",
       "      <td>-0.030342</td>\n",
       "      <td>-0.040513</td>\n",
       "      <td>-0.062638</td>\n",
       "      <td>-0.050439</td>\n",
       "      <td>-0.007712</td>\n",
       "      <td>-0.023621</td>\n",
       "      <td>-0.023026</td>\n",
       "      <td>-0.074663</td>\n",
       "      <td>-0.035564</td>\n",
       "      <td>-0.048955</td>\n",
       "      <td>-0.009085</td>\n",
       "      <td>-0.030634</td>\n",
       "      <td>-0.011386</td>\n",
       "      <td>-0.007997</td>\n",
       "      <td>0.002041</td>\n",
       "      <td>-0.049054</td>\n",
       "      <td>-0.005014</td>\n",
       "      <td>-0.004412</td>\n",
       "      <td>0.002075</td>\n",
       "      <td>-0.022387</td>\n",
       "      <td>-0.017838</td>\n",
       "      <td>0.005659</td>\n",
       "      <td>0.000690</td>\n",
       "      <td>-0.000167</td>\n",
       "      <td>0.021789</td>\n",
       "      <td>0.044042</td>\n",
       "      <td>0.052522</td>\n",
       "      <td>0.038828</td>\n",
       "      <td>-0.052935</td>\n",
       "      <td>0.008839</td>\n",
       "      <td>-0.006531</td>\n",
       "      <td>0.017442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>-0.005151</td>\n",
       "      <td>0.524838</td>\n",
       "      <td>0.523016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.755964</td>\n",
       "      <td>0.085569</td>\n",
       "      <td>0.498886</td>\n",
       "      <td>0.687623</td>\n",
       "      <td>0.282700</td>\n",
       "      <td>0.508450</td>\n",
       "      <td>0.048088</td>\n",
       "      <td>0.023026</td>\n",
       "      <td>0.222025</td>\n",
       "      <td>0.568747</td>\n",
       "      <td>0.086658</td>\n",
       "      <td>-0.030027</td>\n",
       "      <td>0.162319</td>\n",
       "      <td>-0.508897</td>\n",
       "      <td>-0.537872</td>\n",
       "      <td>0.012926</td>\n",
       "      <td>0.047567</td>\n",
       "      <td>-0.530903</td>\n",
       "      <td>0.191606</td>\n",
       "      <td>-0.266709</td>\n",
       "      <td>-0.019040</td>\n",
       "      <td>-0.055280</td>\n",
       "      <td>-0.139537</td>\n",
       "      <td>-0.364921</td>\n",
       "      <td>-0.309755</td>\n",
       "      <td>0.187285</td>\n",
       "      <td>0.265830</td>\n",
       "      <td>0.270364</td>\n",
       "      <td>0.243163</td>\n",
       "      <td>0.164158</td>\n",
       "      <td>0.104360</td>\n",
       "      <td>0.065876</td>\n",
       "      <td>-0.035364</td>\n",
       "      <td>-0.006754</td>\n",
       "      <td>0.063586</td>\n",
       "      <td>0.035869</td>\n",
       "      <td>0.084296</td>\n",
       "      <td>-0.000857</td>\n",
       "      <td>-0.003565</td>\n",
       "      <td>-0.002375</td>\n",
       "      <td>0.019784</td>\n",
       "      <td>-0.006998</td>\n",
       "      <td>0.023250</td>\n",
       "      <td>-0.027452</td>\n",
       "      <td>-0.007807</td>\n",
       "      <td>0.009147</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011157</td>\n",
       "      <td>-0.004711</td>\n",
       "      <td>0.045443</td>\n",
       "      <td>0.064518</td>\n",
       "      <td>-0.019294</td>\n",
       "      <td>0.018547</td>\n",
       "      <td>0.075754</td>\n",
       "      <td>0.100536</td>\n",
       "      <td>0.044894</td>\n",
       "      <td>0.024672</td>\n",
       "      <td>0.014688</td>\n",
       "      <td>-0.039554</td>\n",
       "      <td>0.006210</td>\n",
       "      <td>-0.050151</td>\n",
       "      <td>-0.011141</td>\n",
       "      <td>-0.021980</td>\n",
       "      <td>0.003977</td>\n",
       "      <td>0.039066</td>\n",
       "      <td>-0.054346</td>\n",
       "      <td>-0.022520</td>\n",
       "      <td>-0.066119</td>\n",
       "      <td>-0.078810</td>\n",
       "      <td>0.012816</td>\n",
       "      <td>-0.008534</td>\n",
       "      <td>-0.049723</td>\n",
       "      <td>-0.069895</td>\n",
       "      <td>-0.074454</td>\n",
       "      <td>-0.031295</td>\n",
       "      <td>-0.002865</td>\n",
       "      <td>-0.075283</td>\n",
       "      <td>-0.025018</td>\n",
       "      <td>-0.059537</td>\n",
       "      <td>-0.025946</td>\n",
       "      <td>-0.099456</td>\n",
       "      <td>-0.008412</td>\n",
       "      <td>-0.052322</td>\n",
       "      <td>-0.024207</td>\n",
       "      <td>-0.050768</td>\n",
       "      <td>0.010911</td>\n",
       "      <td>0.014509</td>\n",
       "      <td>0.014249</td>\n",
       "      <td>-0.003946</td>\n",
       "      <td>0.036831</td>\n",
       "      <td>0.083062</td>\n",
       "      <td>0.113946</td>\n",
       "      <td>0.112392</td>\n",
       "      <td>-0.076889</td>\n",
       "      <td>0.189025</td>\n",
       "      <td>-0.165016</td>\n",
       "      <td>-0.033717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>-0.008900</td>\n",
       "      <td>0.703540</td>\n",
       "      <td>0.588536</td>\n",
       "      <td>0.755964</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166810</td>\n",
       "      <td>0.357169</td>\n",
       "      <td>0.876696</td>\n",
       "      <td>0.435529</td>\n",
       "      <td>0.319397</td>\n",
       "      <td>0.051448</td>\n",
       "      <td>0.052646</td>\n",
       "      <td>0.234453</td>\n",
       "      <td>0.755066</td>\n",
       "      <td>0.179570</td>\n",
       "      <td>-0.033606</td>\n",
       "      <td>0.203606</td>\n",
       "      <td>-0.319912</td>\n",
       "      <td>-0.343313</td>\n",
       "      <td>0.017737</td>\n",
       "      <td>0.051159</td>\n",
       "      <td>-0.689571</td>\n",
       "      <td>-0.435071</td>\n",
       "      <td>-0.548514</td>\n",
       "      <td>-0.017379</td>\n",
       "      <td>-0.054949</td>\n",
       "      <td>-0.128932</td>\n",
       "      <td>-0.311957</td>\n",
       "      <td>-0.354918</td>\n",
       "      <td>0.070028</td>\n",
       "      <td>0.318358</td>\n",
       "      <td>0.362762</td>\n",
       "      <td>0.346887</td>\n",
       "      <td>0.248618</td>\n",
       "      <td>0.153617</td>\n",
       "      <td>0.109738</td>\n",
       "      <td>-0.048220</td>\n",
       "      <td>-0.017726</td>\n",
       "      <td>0.113783</td>\n",
       "      <td>0.058706</td>\n",
       "      <td>0.128092</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>0.005764</td>\n",
       "      <td>0.006015</td>\n",
       "      <td>0.016749</td>\n",
       "      <td>0.002062</td>\n",
       "      <td>0.012361</td>\n",
       "      <td>-0.030438</td>\n",
       "      <td>-0.014144</td>\n",
       "      <td>0.018768</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007597</td>\n",
       "      <td>-0.006954</td>\n",
       "      <td>0.052684</td>\n",
       "      <td>0.062005</td>\n",
       "      <td>-0.014292</td>\n",
       "      <td>0.033239</td>\n",
       "      <td>0.088892</td>\n",
       "      <td>0.128044</td>\n",
       "      <td>0.082587</td>\n",
       "      <td>0.019379</td>\n",
       "      <td>0.012326</td>\n",
       "      <td>-0.075749</td>\n",
       "      <td>0.010088</td>\n",
       "      <td>-0.080578</td>\n",
       "      <td>-0.058582</td>\n",
       "      <td>-0.038305</td>\n",
       "      <td>-0.000166</td>\n",
       "      <td>0.055769</td>\n",
       "      <td>-0.046046</td>\n",
       "      <td>-0.031276</td>\n",
       "      <td>-0.074852</td>\n",
       "      <td>-0.071109</td>\n",
       "      <td>-0.005669</td>\n",
       "      <td>-0.039003</td>\n",
       "      <td>-0.044765</td>\n",
       "      <td>-0.073066</td>\n",
       "      <td>-0.076949</td>\n",
       "      <td>-0.041202</td>\n",
       "      <td>-0.013726</td>\n",
       "      <td>-0.057380</td>\n",
       "      <td>-0.026674</td>\n",
       "      <td>-0.048775</td>\n",
       "      <td>0.001784</td>\n",
       "      <td>-0.074257</td>\n",
       "      <td>0.015918</td>\n",
       "      <td>-0.041365</td>\n",
       "      <td>-0.025332</td>\n",
       "      <td>-0.041584</td>\n",
       "      <td>0.013689</td>\n",
       "      <td>0.011872</td>\n",
       "      <td>0.013336</td>\n",
       "      <td>0.001835</td>\n",
       "      <td>0.065190</td>\n",
       "      <td>0.123936</td>\n",
       "      <td>0.157149</td>\n",
       "      <td>0.178710</td>\n",
       "      <td>-0.065621</td>\n",
       "      <td>0.104360</td>\n",
       "      <td>-0.084267</td>\n",
       "      <td>-0.021026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view_4</th>\n",
       "      <td>-0.001935</td>\n",
       "      <td>0.317456</td>\n",
       "      <td>0.038828</td>\n",
       "      <td>0.112392</td>\n",
       "      <td>0.178710</td>\n",
       "      <td>0.022213</td>\n",
       "      <td>0.024338</td>\n",
       "      <td>0.115175</td>\n",
       "      <td>0.155491</td>\n",
       "      <td>-0.015816</td>\n",
       "      <td>0.071099</td>\n",
       "      <td>0.013310</td>\n",
       "      <td>-0.042353</td>\n",
       "      <td>0.161635</td>\n",
       "      <td>0.028620</td>\n",
       "      <td>-0.004236</td>\n",
       "      <td>0.085665</td>\n",
       "      <td>0.015747</td>\n",
       "      <td>-0.005612</td>\n",
       "      <td>0.002986</td>\n",
       "      <td>0.071317</td>\n",
       "      <td>-0.132352</td>\n",
       "      <td>-0.078986</td>\n",
       "      <td>-0.085963</td>\n",
       "      <td>-0.001355</td>\n",
       "      <td>-0.004496</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>-0.028174</td>\n",
       "      <td>-0.081569</td>\n",
       "      <td>-0.012157</td>\n",
       "      <td>0.038883</td>\n",
       "      <td>0.090750</td>\n",
       "      <td>0.083259</td>\n",
       "      <td>0.136734</td>\n",
       "      <td>0.052323</td>\n",
       "      <td>0.586345</td>\n",
       "      <td>-0.012057</td>\n",
       "      <td>-0.014430</td>\n",
       "      <td>0.013236</td>\n",
       "      <td>-0.000969</td>\n",
       "      <td>0.037900</td>\n",
       "      <td>-0.010617</td>\n",
       "      <td>0.038898</td>\n",
       "      <td>-0.008258</td>\n",
       "      <td>-0.012287</td>\n",
       "      <td>-0.009655</td>\n",
       "      <td>-0.011583</td>\n",
       "      <td>-0.008755</td>\n",
       "      <td>-0.010432</td>\n",
       "      <td>-0.007737</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007593</td>\n",
       "      <td>-0.019007</td>\n",
       "      <td>-0.018702</td>\n",
       "      <td>0.004353</td>\n",
       "      <td>0.046085</td>\n",
       "      <td>-0.014133</td>\n",
       "      <td>0.007893</td>\n",
       "      <td>0.027554</td>\n",
       "      <td>-0.012249</td>\n",
       "      <td>-0.009020</td>\n",
       "      <td>-0.001713</td>\n",
       "      <td>-0.012159</td>\n",
       "      <td>0.001632</td>\n",
       "      <td>-0.015744</td>\n",
       "      <td>-0.005555</td>\n",
       "      <td>-0.011822</td>\n",
       "      <td>-0.002183</td>\n",
       "      <td>-0.014166</td>\n",
       "      <td>-0.000754</td>\n",
       "      <td>0.044899</td>\n",
       "      <td>-0.005422</td>\n",
       "      <td>-0.001234</td>\n",
       "      <td>0.003398</td>\n",
       "      <td>-0.006727</td>\n",
       "      <td>0.006868</td>\n",
       "      <td>-0.012392</td>\n",
       "      <td>-0.019383</td>\n",
       "      <td>0.032312</td>\n",
       "      <td>0.003013</td>\n",
       "      <td>0.036075</td>\n",
       "      <td>-0.006289</td>\n",
       "      <td>0.001770</td>\n",
       "      <td>0.050100</td>\n",
       "      <td>-0.014133</td>\n",
       "      <td>0.069384</td>\n",
       "      <td>0.023753</td>\n",
       "      <td>-0.010032</td>\n",
       "      <td>0.035646</td>\n",
       "      <td>-0.015744</td>\n",
       "      <td>-0.004664</td>\n",
       "      <td>0.002566</td>\n",
       "      <td>0.003817</td>\n",
       "      <td>-0.015684</td>\n",
       "      <td>-0.027284</td>\n",
       "      <td>-0.019629</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011339</td>\n",
       "      <td>-0.021116</td>\n",
       "      <td>0.011127</td>\n",
       "      <td>0.021874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_2</th>\n",
       "      <td>-0.005793</td>\n",
       "      <td>-0.048513</td>\n",
       "      <td>-0.052935</td>\n",
       "      <td>-0.076889</td>\n",
       "      <td>-0.065621</td>\n",
       "      <td>0.041988</td>\n",
       "      <td>-0.056070</td>\n",
       "      <td>-0.059759</td>\n",
       "      <td>-0.024408</td>\n",
       "      <td>-0.067830</td>\n",
       "      <td>-0.009227</td>\n",
       "      <td>-0.019768</td>\n",
       "      <td>-0.013615</td>\n",
       "      <td>-0.051270</td>\n",
       "      <td>0.020149</td>\n",
       "      <td>0.016735</td>\n",
       "      <td>-0.010228</td>\n",
       "      <td>0.068093</td>\n",
       "      <td>0.074611</td>\n",
       "      <td>-0.012531</td>\n",
       "      <td>-0.009160</td>\n",
       "      <td>0.059697</td>\n",
       "      <td>0.012526</td>\n",
       "      <td>0.036383</td>\n",
       "      <td>-0.000968</td>\n",
       "      <td>0.069459</td>\n",
       "      <td>0.058445</td>\n",
       "      <td>0.074440</td>\n",
       "      <td>0.006904</td>\n",
       "      <td>-0.041777</td>\n",
       "      <td>-0.029498</td>\n",
       "      <td>-0.015156</td>\n",
       "      <td>-0.012343</td>\n",
       "      <td>-0.005822</td>\n",
       "      <td>-0.002271</td>\n",
       "      <td>-0.000796</td>\n",
       "      <td>-0.008618</td>\n",
       "      <td>-0.004587</td>\n",
       "      <td>-0.005043</td>\n",
       "      <td>0.006320</td>\n",
       "      <td>-0.009196</td>\n",
       "      <td>-0.007589</td>\n",
       "      <td>-0.010360</td>\n",
       "      <td>-0.005902</td>\n",
       "      <td>-0.008782</td>\n",
       "      <td>0.018596</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>-0.003235</td>\n",
       "      <td>0.011509</td>\n",
       "      <td>0.005053</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007982</td>\n",
       "      <td>0.008361</td>\n",
       "      <td>-0.004452</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.001752</td>\n",
       "      <td>-0.010102</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>-0.011675</td>\n",
       "      <td>-0.002033</td>\n",
       "      <td>0.003558</td>\n",
       "      <td>-0.006138</td>\n",
       "      <td>-0.006859</td>\n",
       "      <td>-0.002407</td>\n",
       "      <td>-0.005991</td>\n",
       "      <td>0.001740</td>\n",
       "      <td>0.019393</td>\n",
       "      <td>-0.006328</td>\n",
       "      <td>0.001537</td>\n",
       "      <td>0.005051</td>\n",
       "      <td>-0.000729</td>\n",
       "      <td>-0.002118</td>\n",
       "      <td>0.041306</td>\n",
       "      <td>-0.008393</td>\n",
       "      <td>0.000620</td>\n",
       "      <td>0.011608</td>\n",
       "      <td>-0.001210</td>\n",
       "      <td>-0.013854</td>\n",
       "      <td>0.007496</td>\n",
       "      <td>-0.000851</td>\n",
       "      <td>0.038403</td>\n",
       "      <td>0.008508</td>\n",
       "      <td>-0.003742</td>\n",
       "      <td>0.019675</td>\n",
       "      <td>0.036654</td>\n",
       "      <td>-0.010173</td>\n",
       "      <td>-0.004257</td>\n",
       "      <td>-0.007171</td>\n",
       "      <td>0.007115</td>\n",
       "      <td>-0.011253</td>\n",
       "      <td>-0.005700</td>\n",
       "      <td>-0.018716</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>-0.005928</td>\n",
       "      <td>-0.010112</td>\n",
       "      <td>-0.005517</td>\n",
       "      <td>-0.011339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.122769</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>-0.026378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_3</th>\n",
       "      <td>0.003457</td>\n",
       "      <td>0.006550</td>\n",
       "      <td>0.008839</td>\n",
       "      <td>0.189025</td>\n",
       "      <td>0.104360</td>\n",
       "      <td>-0.006863</td>\n",
       "      <td>0.312145</td>\n",
       "      <td>0.194242</td>\n",
       "      <td>-0.146822</td>\n",
       "      <td>0.388859</td>\n",
       "      <td>0.069417</td>\n",
       "      <td>0.038282</td>\n",
       "      <td>0.107630</td>\n",
       "      <td>0.115386</td>\n",
       "      <td>-0.008051</td>\n",
       "      <td>0.029739</td>\n",
       "      <td>-0.118680</td>\n",
       "      <td>-0.388361</td>\n",
       "      <td>-0.427264</td>\n",
       "      <td>-0.007292</td>\n",
       "      <td>0.068858</td>\n",
       "      <td>-0.123279</td>\n",
       "      <td>0.099584</td>\n",
       "      <td>0.155253</td>\n",
       "      <td>-0.003390</td>\n",
       "      <td>-0.021458</td>\n",
       "      <td>-0.049607</td>\n",
       "      <td>-0.091991</td>\n",
       "      <td>-0.120408</td>\n",
       "      <td>0.074051</td>\n",
       "      <td>0.103976</td>\n",
       "      <td>0.076320</td>\n",
       "      <td>0.050965</td>\n",
       "      <td>0.019168</td>\n",
       "      <td>0.008882</td>\n",
       "      <td>-0.011918</td>\n",
       "      <td>-0.055107</td>\n",
       "      <td>-0.000720</td>\n",
       "      <td>-0.028294</td>\n",
       "      <td>-0.050134</td>\n",
       "      <td>-0.060311</td>\n",
       "      <td>-0.039613</td>\n",
       "      <td>-0.053362</td>\n",
       "      <td>-0.009519</td>\n",
       "      <td>0.033884</td>\n",
       "      <td>0.022787</td>\n",
       "      <td>0.054136</td>\n",
       "      <td>-0.039554</td>\n",
       "      <td>-0.019633</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051405</td>\n",
       "      <td>-0.018390</td>\n",
       "      <td>0.007873</td>\n",
       "      <td>0.070315</td>\n",
       "      <td>-0.039192</td>\n",
       "      <td>0.013332</td>\n",
       "      <td>0.070853</td>\n",
       "      <td>0.045653</td>\n",
       "      <td>0.004593</td>\n",
       "      <td>0.016174</td>\n",
       "      <td>0.005316</td>\n",
       "      <td>-0.003611</td>\n",
       "      <td>-0.028953</td>\n",
       "      <td>0.030564</td>\n",
       "      <td>0.006759</td>\n",
       "      <td>0.004956</td>\n",
       "      <td>0.006456</td>\n",
       "      <td>-0.010339</td>\n",
       "      <td>-0.013043</td>\n",
       "      <td>-0.017723</td>\n",
       "      <td>-0.033373</td>\n",
       "      <td>0.010657</td>\n",
       "      <td>0.015881</td>\n",
       "      <td>0.016943</td>\n",
       "      <td>0.014599</td>\n",
       "      <td>-0.007510</td>\n",
       "      <td>-0.027533</td>\n",
       "      <td>0.004204</td>\n",
       "      <td>-0.009737</td>\n",
       "      <td>0.011287</td>\n",
       "      <td>0.017134</td>\n",
       "      <td>-0.004833</td>\n",
       "      <td>-0.043192</td>\n",
       "      <td>0.015521</td>\n",
       "      <td>-0.012793</td>\n",
       "      <td>0.025371</td>\n",
       "      <td>0.017033</td>\n",
       "      <td>-0.016307</td>\n",
       "      <td>-0.010825</td>\n",
       "      <td>-0.004689</td>\n",
       "      <td>-0.016073</td>\n",
       "      <td>0.010450</td>\n",
       "      <td>-0.018480</td>\n",
       "      <td>-0.018755</td>\n",
       "      <td>-0.017242</td>\n",
       "      <td>-0.021116</td>\n",
       "      <td>-0.122769</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.810849</td>\n",
       "      <td>-0.399575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_4</th>\n",
       "      <td>-0.004357</td>\n",
       "      <td>-0.031574</td>\n",
       "      <td>-0.006531</td>\n",
       "      <td>-0.165016</td>\n",
       "      <td>-0.084267</td>\n",
       "      <td>0.006601</td>\n",
       "      <td>-0.252076</td>\n",
       "      <td>-0.142895</td>\n",
       "      <td>0.092454</td>\n",
       "      <td>-0.253728</td>\n",
       "      <td>-0.052046</td>\n",
       "      <td>-0.057107</td>\n",
       "      <td>-0.060466</td>\n",
       "      <td>-0.077115</td>\n",
       "      <td>0.016937</td>\n",
       "      <td>-0.006271</td>\n",
       "      <td>0.076355</td>\n",
       "      <td>0.253612</td>\n",
       "      <td>0.282690</td>\n",
       "      <td>-0.003280</td>\n",
       "      <td>-0.051614</td>\n",
       "      <td>0.095452</td>\n",
       "      <td>-0.103244</td>\n",
       "      <td>-0.133523</td>\n",
       "      <td>-0.006395</td>\n",
       "      <td>0.012017</td>\n",
       "      <td>0.021172</td>\n",
       "      <td>0.051363</td>\n",
       "      <td>0.103626</td>\n",
       "      <td>-0.051662</td>\n",
       "      <td>-0.077903</td>\n",
       "      <td>-0.063800</td>\n",
       "      <td>-0.036513</td>\n",
       "      <td>-0.013915</td>\n",
       "      <td>-0.004559</td>\n",
       "      <td>0.007333</td>\n",
       "      <td>0.054122</td>\n",
       "      <td>0.018039</td>\n",
       "      <td>0.036346</td>\n",
       "      <td>0.046179</td>\n",
       "      <td>0.043225</td>\n",
       "      <td>0.048973</td>\n",
       "      <td>0.055608</td>\n",
       "      <td>0.011439</td>\n",
       "      <td>-0.023944</td>\n",
       "      <td>-0.016206</td>\n",
       "      <td>-0.043130</td>\n",
       "      <td>0.023596</td>\n",
       "      <td>0.040593</td>\n",
       "      <td>-0.004254</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019304</td>\n",
       "      <td>0.017348</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>-0.056401</td>\n",
       "      <td>0.035147</td>\n",
       "      <td>0.003379</td>\n",
       "      <td>-0.055020</td>\n",
       "      <td>-0.029627</td>\n",
       "      <td>0.005039</td>\n",
       "      <td>-0.000723</td>\n",
       "      <td>-0.007557</td>\n",
       "      <td>-0.020767</td>\n",
       "      <td>0.007469</td>\n",
       "      <td>-0.026184</td>\n",
       "      <td>-0.015999</td>\n",
       "      <td>-0.011943</td>\n",
       "      <td>-0.015438</td>\n",
       "      <td>-0.008792</td>\n",
       "      <td>-0.010136</td>\n",
       "      <td>0.000560</td>\n",
       "      <td>0.024208</td>\n",
       "      <td>-0.020844</td>\n",
       "      <td>-0.016976</td>\n",
       "      <td>-0.024311</td>\n",
       "      <td>-0.022280</td>\n",
       "      <td>-0.003780</td>\n",
       "      <td>0.022354</td>\n",
       "      <td>-0.007020</td>\n",
       "      <td>-0.006660</td>\n",
       "      <td>-0.018069</td>\n",
       "      <td>-0.019115</td>\n",
       "      <td>-0.009739</td>\n",
       "      <td>0.040254</td>\n",
       "      <td>-0.020383</td>\n",
       "      <td>0.016591</td>\n",
       "      <td>-0.029888</td>\n",
       "      <td>-0.017416</td>\n",
       "      <td>0.026475</td>\n",
       "      <td>-0.002650</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>0.010584</td>\n",
       "      <td>-0.010783</td>\n",
       "      <td>0.015081</td>\n",
       "      <td>0.008003</td>\n",
       "      <td>0.016364</td>\n",
       "      <td>0.011127</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>-0.810849</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.174215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_5</th>\n",
       "      <td>0.001670</td>\n",
       "      <td>0.058559</td>\n",
       "      <td>0.017442</td>\n",
       "      <td>-0.033717</td>\n",
       "      <td>-0.021026</td>\n",
       "      <td>-0.013745</td>\n",
       "      <td>-0.119822</td>\n",
       "      <td>-0.087236</td>\n",
       "      <td>0.119592</td>\n",
       "      <td>-0.244772</td>\n",
       "      <td>-0.033816</td>\n",
       "      <td>0.031544</td>\n",
       "      <td>-0.086042</td>\n",
       "      <td>-0.060466</td>\n",
       "      <td>-0.019533</td>\n",
       "      <td>-0.047080</td>\n",
       "      <td>0.092086</td>\n",
       "      <td>0.244006</td>\n",
       "      <td>0.262712</td>\n",
       "      <td>0.021802</td>\n",
       "      <td>-0.033556</td>\n",
       "      <td>0.040943</td>\n",
       "      <td>-0.012223</td>\n",
       "      <td>-0.073584</td>\n",
       "      <td>0.016779</td>\n",
       "      <td>-0.010458</td>\n",
       "      <td>0.020085</td>\n",
       "      <td>0.049418</td>\n",
       "      <td>0.044404</td>\n",
       "      <td>-0.030681</td>\n",
       "      <td>-0.045254</td>\n",
       "      <td>-0.024796</td>\n",
       "      <td>-0.025836</td>\n",
       "      <td>-0.008962</td>\n",
       "      <td>-0.007393</td>\n",
       "      <td>0.007442</td>\n",
       "      <td>0.012625</td>\n",
       "      <td>-0.025987</td>\n",
       "      <td>-0.008734</td>\n",
       "      <td>0.011794</td>\n",
       "      <td>0.037131</td>\n",
       "      <td>-0.006775</td>\n",
       "      <td>0.007797</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>-0.019712</td>\n",
       "      <td>-0.019647</td>\n",
       "      <td>-0.026946</td>\n",
       "      <td>0.033100</td>\n",
       "      <td>-0.035803</td>\n",
       "      <td>-0.010994</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062784</td>\n",
       "      <td>0.000815</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>-0.033619</td>\n",
       "      <td>0.011854</td>\n",
       "      <td>-0.025142</td>\n",
       "      <td>-0.035079</td>\n",
       "      <td>-0.027917</td>\n",
       "      <td>-0.015147</td>\n",
       "      <td>-0.027917</td>\n",
       "      <td>0.005290</td>\n",
       "      <td>0.041980</td>\n",
       "      <td>0.040317</td>\n",
       "      <td>-0.010503</td>\n",
       "      <td>0.014098</td>\n",
       "      <td>0.004749</td>\n",
       "      <td>0.016180</td>\n",
       "      <td>0.030726</td>\n",
       "      <td>0.038735</td>\n",
       "      <td>0.027811</td>\n",
       "      <td>0.019732</td>\n",
       "      <td>0.000823</td>\n",
       "      <td>0.002828</td>\n",
       "      <td>0.006331</td>\n",
       "      <td>0.007337</td>\n",
       "      <td>0.018780</td>\n",
       "      <td>0.017644</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.028979</td>\n",
       "      <td>-0.004433</td>\n",
       "      <td>-0.006023</td>\n",
       "      <td>0.026373</td>\n",
       "      <td>0.004856</td>\n",
       "      <td>-0.007734</td>\n",
       "      <td>-0.000453</td>\n",
       "      <td>0.005805</td>\n",
       "      <td>0.001035</td>\n",
       "      <td>-0.016052</td>\n",
       "      <td>0.027811</td>\n",
       "      <td>0.007986</td>\n",
       "      <td>0.017627</td>\n",
       "      <td>-0.001955</td>\n",
       "      <td>0.008963</td>\n",
       "      <td>0.024577</td>\n",
       "      <td>0.006458</td>\n",
       "      <td>0.021874</td>\n",
       "      <td>-0.026378</td>\n",
       "      <td>-0.399575</td>\n",
       "      <td>-0.174215</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Unnamed: 0     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "Unnamed: 0     1.000000 -0.012306 -0.010562  -0.005151    -0.008900 -0.003500   \n",
       "price         -0.012306  1.000000  0.313775   0.524838     0.703540  0.086803   \n",
       "bedrooms      -0.010562  0.313775  1.000000   0.523016     0.588536  0.030674   \n",
       "bathrooms     -0.005151  0.524838  0.523016   1.000000     0.755964  0.085569   \n",
       "sqft_living   -0.008900  0.703540  0.588536   0.755964     1.000000  0.166810   \n",
       "...                 ...       ...       ...        ...          ...       ...   \n",
       "view_4        -0.001935  0.317456  0.038828   0.112392     0.178710  0.022213   \n",
       "condition_2   -0.005793 -0.048513 -0.052935  -0.076889    -0.065621  0.041988   \n",
       "condition_3    0.003457  0.006550  0.008839   0.189025     0.104360 -0.006863   \n",
       "condition_4   -0.004357 -0.031574 -0.006531  -0.165016    -0.084267  0.006601   \n",
       "condition_5    0.001670  0.058559  0.017442  -0.033717    -0.021026 -0.013745   \n",
       "\n",
       "               floors  sqft_above  sqft_basement  yr_built  yr_renovated  \\\n",
       "Unnamed: 0   0.010599   -0.003406      -0.012104  0.007496     -0.003423   \n",
       "price        0.256985    0.607760       0.323312  0.056450      0.117770   \n",
       "bedrooms     0.179908    0.488420       0.307873  0.159936      0.014489   \n",
       "bathrooms    0.498886    0.687623       0.282700  0.508450      0.048088   \n",
       "sqft_living  0.357169    0.876696       0.435529  0.319397      0.051448   \n",
       "...               ...         ...            ...       ...           ...   \n",
       "view_4       0.024338    0.115175       0.155491 -0.015816      0.071099   \n",
       "condition_2 -0.056070   -0.059759      -0.024408 -0.067830     -0.009227   \n",
       "condition_3  0.312145    0.194242      -0.146822  0.388859      0.069417   \n",
       "condition_4 -0.252076   -0.142895       0.092454 -0.253728     -0.052046   \n",
       "condition_5 -0.119822   -0.087236       0.119592 -0.244772     -0.033816   \n",
       "\n",
       "                  lat      long  sqft_living15  sqft_lot15  year_sold  \\\n",
       "Unnamed: 0   0.002470 -0.006992      -0.008961    0.000463  -0.003019   \n",
       "price        0.302394  0.019327       0.585567    0.081423  -0.002334   \n",
       "bedrooms    -0.003991  0.129593       0.401073    0.028038  -0.010211   \n",
       "bathrooms    0.023026  0.222025       0.568747    0.086658  -0.030027   \n",
       "sqft_living  0.052646  0.234453       0.755066    0.179570  -0.033606   \n",
       "...               ...       ...            ...         ...        ...   \n",
       "view_4       0.013310 -0.042353       0.161635    0.028620  -0.004236   \n",
       "condition_2 -0.019768 -0.013615      -0.051270    0.020149   0.016735   \n",
       "condition_3  0.038282  0.107630       0.115386   -0.008051   0.029739   \n",
       "condition_4 -0.057107 -0.060466      -0.077115    0.016937  -0.006271   \n",
       "condition_5  0.031544 -0.086042      -0.060466   -0.019533  -0.047080   \n",
       "\n",
       "             has_basement  age_built  last_reno  month_sold  was_reno  \\\n",
       "Unnamed: 0      -0.010365  -0.007544  -0.008349    0.003139 -0.003421   \n",
       "price            0.176970  -0.056484  -0.104828   -0.004350  0.117409   \n",
       "bedrooms         0.163935  -0.160089  -0.168892    0.002985  0.014182   \n",
       "bathrooms        0.162319  -0.508897  -0.537872    0.012926  0.047567   \n",
       "sqft_living      0.203606  -0.319912  -0.343313    0.017737  0.051159   \n",
       "...                   ...        ...        ...         ...       ...   \n",
       "view_4           0.085665   0.015747  -0.005612    0.002986  0.071317   \n",
       "condition_2     -0.010228   0.068093   0.074611   -0.012531 -0.009160   \n",
       "condition_3     -0.118680  -0.388361  -0.427264   -0.007292  0.068858   \n",
       "condition_4      0.076355   0.253612   0.282690   -0.003280 -0.051614   \n",
       "condition_5      0.092086   0.244006   0.262712    0.021802 -0.033556   \n",
       "\n",
       "             bed_sqft_ratio  bath_sqft_ratio  floors_sqft_ratio   grade_3  \\\n",
       "Unnamed: 0        -0.001004         0.003391           0.014655 -0.002952   \n",
       "price             -0.474124        -0.266844          -0.306713 -0.010725   \n",
       "bedrooms           0.022666        -0.245144          -0.437583 -0.027963   \n",
       "bathrooms         -0.530903         0.191606          -0.266709 -0.019040   \n",
       "sqft_living       -0.689571        -0.435071          -0.548514 -0.017379   \n",
       "...                     ...              ...                ...       ...   \n",
       "view_4            -0.132352        -0.078986          -0.085963 -0.001355   \n",
       "condition_2        0.059697         0.012526           0.036383 -0.000968   \n",
       "condition_3       -0.123279         0.099584           0.155253 -0.003390   \n",
       "condition_4        0.095452        -0.103244          -0.133523 -0.006395   \n",
       "condition_5        0.040943        -0.012223          -0.073584  0.016779   \n",
       "\n",
       "              grade_4   grade_5   grade_6   grade_7   grade_8   grade_9  \\\n",
       "Unnamed: 0   0.006610 -0.004155 -0.003335  0.004959  0.003503 -0.007257   \n",
       "price       -0.031562 -0.084280 -0.206989 -0.312121  0.003797  0.232428   \n",
       "bedrooms    -0.076783 -0.118132 -0.243196 -0.103991  0.072530  0.164262   \n",
       "bathrooms   -0.055280 -0.139537 -0.364921 -0.309755  0.187285  0.265830   \n",
       "sqft_living -0.054949 -0.128932 -0.311957 -0.354918  0.070028  0.318358   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "view_4      -0.004496  0.000008 -0.028174 -0.081569 -0.012157  0.038883   \n",
       "condition_2  0.069459  0.058445  0.074440  0.006904 -0.041777 -0.029498   \n",
       "condition_3 -0.021458 -0.049607 -0.091991 -0.120408  0.074051  0.103976   \n",
       "condition_4  0.012017  0.021172  0.051363  0.103626 -0.051662 -0.077903   \n",
       "condition_5 -0.010458  0.020085  0.049418  0.044404 -0.030681 -0.045254   \n",
       "\n",
       "             grade_10  grade_11  grade_12  grade_13  waterfront_1  \\\n",
       "Unnamed: 0   0.006833 -0.011366 -0.004632 -0.005438     -0.008845   \n",
       "price        0.332676  0.355224  0.307231  0.233989      0.275779   \n",
       "bedrooms     0.138973  0.120530  0.062352  0.042565     -0.008668   \n",
       "bathrooms    0.270364  0.243163  0.164158  0.104360      0.065876   \n",
       "sqft_living  0.362762  0.346887  0.248618  0.153617      0.109738   \n",
       "...               ...       ...       ...       ...           ...   \n",
       "view_4       0.090750  0.083259  0.136734  0.052323      0.586345   \n",
       "condition_2 -0.015156 -0.012343 -0.005822 -0.002271     -0.000796   \n",
       "condition_3  0.076320  0.050965  0.019168  0.008882     -0.011918   \n",
       "condition_4 -0.063800 -0.036513 -0.013915 -0.004559      0.007333   \n",
       "condition_5 -0.024796 -0.025836 -0.008962 -0.007393      0.007442   \n",
       "\n",
       "             zipcode_98002  zipcode_98003  zipcode_98004  zipcode_98005  \\\n",
       "Unnamed: 0       -0.004590       0.005155      -0.012322       0.006336   \n",
       "price            -0.079096      -0.076717       0.270366       0.064889   \n",
       "bedrooms         -0.006824       0.000536       0.062804       0.046070   \n",
       "bathrooms        -0.035364      -0.006754       0.063586       0.035869   \n",
       "sqft_living      -0.048220      -0.017726       0.113783       0.058706   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4           -0.012057      -0.014430       0.013236      -0.000969   \n",
       "condition_2      -0.008618      -0.004587      -0.005043       0.006320   \n",
       "condition_3      -0.055107      -0.000720      -0.028294      -0.050134   \n",
       "condition_4       0.054122       0.018039       0.036346       0.046179   \n",
       "condition_5       0.012625      -0.025987      -0.008734       0.011794   \n",
       "\n",
       "             zipcode_98006  zipcode_98007  zipcode_98008  zipcode_98010  \\\n",
       "Unnamed: 0       -0.003008      -0.005246       0.000045       0.000546   \n",
       "price             0.124801       0.012829       0.030831      -0.020349   \n",
       "bedrooms          0.074479       0.036197       0.047027      -0.003337   \n",
       "bathrooms         0.084296      -0.000857      -0.003565      -0.002375   \n",
       "sqft_living       0.128092       0.000534       0.005764       0.006015   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4            0.037900      -0.010617       0.038898      -0.008258   \n",
       "condition_2      -0.009196      -0.007589      -0.010360      -0.005902   \n",
       "condition_3      -0.060311      -0.039613      -0.053362      -0.009519   \n",
       "condition_4       0.043225       0.048973       0.055608       0.011439   \n",
       "condition_5       0.037131      -0.006775       0.007797       0.000490   \n",
       "\n",
       "             zipcode_98011  zipcode_98014  zipcode_98019  zipcode_98022  \\\n",
       "Unnamed: 0        0.006028      -0.013795      -0.003682       0.004126   \n",
       "price            -0.013093      -0.017358      -0.027669      -0.065326   \n",
       "bedrooms          0.022033      -0.030332       0.000865      -0.028711   \n",
       "bathrooms         0.019784      -0.006998       0.023250      -0.027452   \n",
       "sqft_living       0.016749       0.002062       0.012361      -0.030438   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4           -0.012287      -0.009655      -0.011583      -0.008755   \n",
       "condition_2      -0.008782       0.018596       0.005925      -0.003235   \n",
       "condition_3       0.033884       0.022787       0.054136      -0.039554   \n",
       "condition_4      -0.023944      -0.016206      -0.043130       0.023596   \n",
       "condition_5      -0.019712      -0.019647      -0.026946       0.033100   \n",
       "\n",
       "             zipcode_98023  zipcode_98024  ...  zipcode_98056  zipcode_98058  \\\n",
       "Unnamed: 0       -0.007205       0.004073  ...       0.009209       0.005365   \n",
       "price            -0.104858       0.008269  ...      -0.044213      -0.075343   \n",
       "bedrooms          0.006252      -0.002174  ...       0.008929       0.025046   \n",
       "bathrooms        -0.007807       0.009147  ...      -0.011157      -0.004711   \n",
       "sqft_living      -0.014144       0.018768  ...      -0.007597      -0.006954   \n",
       "...                    ...            ...  ...            ...            ...   \n",
       "view_4           -0.010432      -0.007737  ...      -0.007593      -0.019007   \n",
       "condition_2       0.011509       0.005053  ...      -0.007982       0.008361   \n",
       "condition_3      -0.019633       0.007391  ...      -0.051405      -0.018390   \n",
       "condition_4       0.040593      -0.004254  ...       0.019304       0.017348   \n",
       "condition_5      -0.035803      -0.010994  ...       0.062784       0.000815   \n",
       "\n",
       "             zipcode_98059  zipcode_98065  zipcode_98070  zipcode_98072  \\\n",
       "Unnamed: 0        0.004190       0.000331       0.002217      -0.004307   \n",
       "price            -0.018443      -0.004379      -0.009161       0.007813   \n",
       "bedrooms          0.054882       0.017401      -0.049345       0.007035   \n",
       "bathrooms         0.045443       0.064518      -0.019294       0.018547   \n",
       "sqft_living       0.052684       0.062005      -0.014292       0.033239   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4           -0.018702       0.004353       0.046085      -0.014133   \n",
       "condition_2      -0.004452      -0.000010       0.001752      -0.010102   \n",
       "condition_3       0.007873       0.070315      -0.039192       0.013332   \n",
       "condition_4      -0.008541      -0.056401       0.035147       0.003379   \n",
       "condition_5       0.002227      -0.033619       0.011854      -0.025142   \n",
       "\n",
       "             zipcode_98074  zipcode_98075  zipcode_98077  zipcode_98092  \\\n",
       "Unnamed: 0        0.004859       0.008688       0.005512       0.004100   \n",
       "price             0.058627       0.087974       0.035470      -0.069836   \n",
       "bedrooms          0.039765       0.067117       0.030244       0.015401   \n",
       "bathrooms         0.075754       0.100536       0.044894       0.024672   \n",
       "sqft_living       0.088892       0.128044       0.082587       0.019379   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4            0.007893       0.027554      -0.012249      -0.009020   \n",
       "condition_2       0.000555      -0.011675      -0.002033       0.003558   \n",
       "condition_3       0.070853       0.045653       0.004593       0.016174   \n",
       "condition_4      -0.055020      -0.029627       0.005039      -0.000723   \n",
       "condition_5      -0.035079      -0.027917      -0.015147      -0.027917   \n",
       "\n",
       "             zipcode_98102  zipcode_98103  zipcode_98105  zipcode_98106  \\\n",
       "Unnamed: 0       -0.002682      -0.005442       0.000905      -0.000714   \n",
       "price             0.071764       0.021747       0.087568      -0.074458   \n",
       "bedrooms         -0.004354      -0.062170       0.018625      -0.039997   \n",
       "bathrooms         0.014688      -0.039554       0.006210      -0.050151   \n",
       "sqft_living       0.012326      -0.075749       0.010088      -0.080578   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4           -0.001713      -0.012159       0.001632      -0.015744   \n",
       "condition_2      -0.006138      -0.006859      -0.002407      -0.005991   \n",
       "condition_3       0.005316      -0.003611      -0.028953       0.030564   \n",
       "condition_4      -0.007557      -0.020767       0.007469      -0.026184   \n",
       "condition_5       0.005290       0.041980       0.040317      -0.010503   \n",
       "\n",
       "             zipcode_98107  zipcode_98108  zipcode_98109  zipcode_98112  \\\n",
       "Unnamed: 0        0.007705       0.015821      -0.007265       0.005918   \n",
       "price             0.012757      -0.046430       0.063122       0.174365   \n",
       "bedrooms         -0.049907      -0.020437      -0.011346       0.017657   \n",
       "bathrooms        -0.011141      -0.021980       0.003977       0.039066   \n",
       "sqft_living      -0.058582      -0.038305      -0.000166       0.055769   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4           -0.005555      -0.011822      -0.002183      -0.014166   \n",
       "condition_2       0.001740       0.019393      -0.006328       0.001537   \n",
       "condition_3       0.006759       0.004956       0.006456      -0.010339   \n",
       "condition_4      -0.015999      -0.011943      -0.015438      -0.008792   \n",
       "condition_5       0.014098       0.004749       0.016180       0.030726   \n",
       "\n",
       "             zipcode_98115  zipcode_98116  zipcode_98117  zipcode_98118  \\\n",
       "Unnamed: 0        0.002568       0.011248       0.010876      -0.009278   \n",
       "price             0.032511       0.024873       0.012703      -0.053841   \n",
       "bedrooms         -0.030342      -0.040513      -0.062638      -0.050439   \n",
       "bathrooms        -0.054346      -0.022520      -0.066119      -0.078810   \n",
       "sqft_living      -0.046046      -0.031276      -0.074852      -0.071109   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4           -0.000754       0.044899      -0.005422      -0.001234   \n",
       "condition_2       0.005051      -0.000729      -0.002118       0.041306   \n",
       "condition_3      -0.013043      -0.017723      -0.033373       0.010657   \n",
       "condition_4      -0.010136       0.000560       0.024208      -0.020844   \n",
       "condition_5       0.038735       0.027811       0.019732       0.000823   \n",
       "\n",
       "             zipcode_98119  zipcode_98122  zipcode_98125  zipcode_98126  \\\n",
       "Unnamed: 0       -0.003248      -0.007791       0.010551      -0.005719   \n",
       "price             0.082551       0.024683      -0.024139      -0.039607   \n",
       "bedrooms         -0.007712      -0.023621      -0.023026      -0.074663   \n",
       "bathrooms         0.012816      -0.008534      -0.049723      -0.069895   \n",
       "sqft_living      -0.005669      -0.039003      -0.044765      -0.073066   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4            0.003398      -0.006727       0.006868      -0.012392   \n",
       "condition_2      -0.008393       0.000620       0.011608      -0.001210   \n",
       "condition_3       0.015881       0.016943       0.014599      -0.007510   \n",
       "condition_4      -0.016976      -0.024311      -0.022280      -0.003780   \n",
       "condition_5       0.002828       0.006331       0.007337       0.018780   \n",
       "\n",
       "             zipcode_98133  zipcode_98136  zipcode_98144  zipcode_98146  \\\n",
       "Unnamed: 0        0.007452       0.001004      -0.007500       0.011195   \n",
       "price            -0.063628       0.007550       0.027147      -0.056960   \n",
       "bedrooms         -0.035564      -0.048955      -0.009085      -0.030634   \n",
       "bathrooms        -0.074454      -0.031295      -0.002865      -0.075283   \n",
       "sqft_living      -0.076949      -0.041202      -0.013726      -0.057380   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4           -0.019383       0.032312       0.003013       0.036075   \n",
       "condition_2      -0.013854       0.007496      -0.000851       0.038403   \n",
       "condition_3      -0.027533       0.004204      -0.009737       0.011287   \n",
       "condition_4       0.022354      -0.007020      -0.006660      -0.018069   \n",
       "condition_5       0.017644       0.000156       0.028979      -0.004433   \n",
       "\n",
       "             zipcode_98148  zipcode_98155  zipcode_98166  zipcode_98168  \\\n",
       "Unnamed: 0       -0.021353      -0.001709       0.004273      -0.002224   \n",
       "price            -0.035934      -0.045335      -0.018183      -0.090086   \n",
       "bedrooms         -0.011386      -0.007997       0.002041      -0.049054   \n",
       "bathrooms        -0.025018      -0.059537      -0.025946      -0.099456   \n",
       "sqft_living      -0.026674      -0.048775       0.001784      -0.074257   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4           -0.006289       0.001770       0.050100      -0.014133   \n",
       "condition_2       0.008508      -0.003742       0.019675       0.036654   \n",
       "condition_3       0.017134      -0.004833      -0.043192       0.015521   \n",
       "condition_4      -0.019115      -0.009739       0.040254      -0.020383   \n",
       "condition_5      -0.006023       0.026373       0.004856      -0.007734   \n",
       "\n",
       "             zipcode_98177  zipcode_98178  zipcode_98188  zipcode_98198  \\\n",
       "Unnamed: 0       -0.008480       0.001099      -0.002428      -0.003405   \n",
       "price             0.034662      -0.068552      -0.053705      -0.071636   \n",
       "bedrooms         -0.005014      -0.004412       0.002075      -0.022387   \n",
       "bathrooms        -0.008412      -0.052322      -0.024207      -0.050768   \n",
       "sqft_living       0.015918      -0.041365      -0.025332      -0.041584   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4            0.069384       0.023753      -0.010032       0.035646   \n",
       "condition_2      -0.010173      -0.004257      -0.007171       0.007115   \n",
       "condition_3      -0.012793       0.025371       0.017033      -0.016307   \n",
       "condition_4       0.016591      -0.029888      -0.017416       0.026475   \n",
       "condition_5      -0.000453       0.005805       0.001035      -0.016052   \n",
       "\n",
       "             zipcode_98199  season_sold_2  season_sold_3  season_sold_4  \\\n",
       "Unnamed: 0        0.001918       0.000594       0.002092      -0.002701   \n",
       "price             0.082508       0.033185      -0.004643      -0.011459   \n",
       "bedrooms         -0.017838       0.005659       0.000690      -0.000167   \n",
       "bathrooms         0.010911       0.014509       0.014249      -0.003946   \n",
       "sqft_living       0.013689       0.011872       0.013336       0.001835   \n",
       "...                    ...            ...            ...            ...   \n",
       "view_4           -0.015744      -0.004664       0.002566       0.003817   \n",
       "condition_2      -0.011253      -0.005700      -0.018716       0.002052   \n",
       "condition_3      -0.010825      -0.004689      -0.016073       0.010450   \n",
       "condition_4      -0.002650       0.001235       0.010584      -0.010783   \n",
       "condition_5       0.027811       0.007986       0.017627      -0.001955   \n",
       "\n",
       "               view_1    view_2    view_3    view_4  condition_2  condition_3  \\\n",
       "Unnamed: 0   0.004347  0.004443 -0.003094 -0.001935    -0.005793     0.003457   \n",
       "price        0.091073  0.138504  0.176706  0.317456    -0.048513     0.006550   \n",
       "bedrooms     0.021789  0.044042  0.052522  0.038828    -0.052935     0.008839   \n",
       "bathrooms    0.036831  0.083062  0.113946  0.112392    -0.076889     0.189025   \n",
       "sqft_living  0.065190  0.123936  0.157149  0.178710    -0.065621     0.104360   \n",
       "...               ...       ...       ...       ...          ...          ...   \n",
       "view_4      -0.015684 -0.027284 -0.019629  1.000000    -0.011339    -0.021116   \n",
       "condition_2 -0.005928 -0.010112 -0.005517 -0.011339     1.000000    -0.122769   \n",
       "condition_3 -0.018480 -0.018755 -0.017242 -0.021116    -0.122769     1.000000   \n",
       "condition_4  0.015081  0.008003  0.016364  0.011127    -0.053527    -0.810849   \n",
       "condition_5  0.008963  0.024577  0.006458  0.021874    -0.026378    -0.399575   \n",
       "\n",
       "             condition_4  condition_5  \n",
       "Unnamed: 0     -0.004357     0.001670  \n",
       "price          -0.031574     0.058559  \n",
       "bedrooms       -0.006531     0.017442  \n",
       "bathrooms      -0.165016    -0.033717  \n",
       "sqft_living    -0.084267    -0.021026  \n",
       "...                  ...          ...  \n",
       "view_4          0.011127     0.021874  \n",
       "condition_2    -0.053527    -0.026378  \n",
       "condition_3    -0.810849    -0.399575  \n",
       "condition_4     1.000000    -0.174215  \n",
       "condition_5    -0.174215     1.000000  \n",
       "\n",
       "[116 rows x 116 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create new dataframe including dummy variables to check for correlation.\n",
    "df_corr = df.corr()\n",
    "df_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at OLS Summary of Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.566</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.566</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   1881.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Mar 2021</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:29:55</td>     <th>  Log-Likelihood:    </th> <td>-2.3914e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 17290</td>      <th>  AIC:               </th>  <td>4.783e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 17277</td>      <th>  BIC:               </th>  <td>4.784e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td> 7.656e+05</td> <td> 5372.110</td> <td>  142.507</td> <td> 0.000</td> <td> 7.55e+05</td> <td> 7.76e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.A+]</th> <td> 2.853e+05</td> <td> 9831.040</td> <td>   29.019</td> <td> 0.000</td> <td> 2.66e+05</td> <td> 3.05e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.A-]</th> <td>-2.281e+05</td> <td> 6425.713</td> <td>  -35.504</td> <td> 0.000</td> <td>-2.41e+05</td> <td>-2.16e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.B]</th>  <td>-4.664e+05</td> <td> 8094.561</td> <td>  -57.615</td> <td> 0.000</td> <td>-4.82e+05</td> <td> -4.5e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.B+]</th> <td>-3.653e+05</td> <td> 6103.733</td> <td>  -59.852</td> <td> 0.000</td> <td>-3.77e+05</td> <td>-3.53e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.B-]</th> <td>-5.388e+05</td> <td> 1.85e+04</td> <td>  -29.059</td> <td> 0.000</td> <td>-5.75e+05</td> <td>-5.02e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.C]</th>  <td>-5.971e+05</td> <td> 1.74e+05</td> <td>   -3.432</td> <td> 0.001</td> <td>-9.38e+05</td> <td>-2.56e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.C+]</th> <td>-5.549e+05</td> <td> 5.27e+04</td> <td>  -10.529</td> <td> 0.000</td> <td>-6.58e+05</td> <td>-4.52e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.D]</th>  <td>-6.236e+05</td> <td> 2.46e+05</td> <td>   -2.535</td> <td> 0.011</td> <td>-1.11e+06</td> <td>-1.41e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.S]</th>  <td> 1.423e+06</td> <td> 2.96e+04</td> <td>   48.043</td> <td> 0.000</td> <td> 1.37e+06</td> <td> 1.48e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.S+]</th> <td> 3.237e+06</td> <td> 7.43e+04</td> <td>   43.546</td> <td> 0.000</td> <td> 3.09e+06</td> <td> 3.38e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_lvl[T.S-]</th> <td> 6.987e+05</td> <td> 1.48e+04</td> <td>   47.190</td> <td> 0.000</td> <td>  6.7e+05</td> <td> 7.28e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront_1</th>    <td> 8.206e+05</td> <td> 2.12e+04</td> <td>   38.647</td> <td> 0.000</td> <td> 7.79e+05</td> <td> 8.62e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11023.856</td> <th>  Durbin-Watson:     </th>  <td>   2.013</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>420653.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 2.510</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>26.637</td>   <th>  Cond. No.          </th>  <td>    149.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.566\n",
       "Model:                            OLS   Adj. R-squared:                  0.566\n",
       "Method:                 Least Squares   F-statistic:                     1881.\n",
       "Date:                Fri, 12 Mar 2021   Prob (F-statistic):               0.00\n",
       "Time:                        11:29:55   Log-Likelihood:            -2.3914e+05\n",
       "No. Observations:               17290   AIC:                         4.783e+05\n",
       "Df Residuals:                   17277   BIC:                         4.784e+05\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept        7.656e+05   5372.110    142.507      0.000    7.55e+05    7.76e+05\n",
       "grade_lvl[T.A+]  2.853e+05   9831.040     29.019      0.000    2.66e+05    3.05e+05\n",
       "grade_lvl[T.A-] -2.281e+05   6425.713    -35.504      0.000   -2.41e+05   -2.16e+05\n",
       "grade_lvl[T.B]  -4.664e+05   8094.561    -57.615      0.000   -4.82e+05    -4.5e+05\n",
       "grade_lvl[T.B+] -3.653e+05   6103.733    -59.852      0.000   -3.77e+05   -3.53e+05\n",
       "grade_lvl[T.B-] -5.388e+05   1.85e+04    -29.059      0.000   -5.75e+05   -5.02e+05\n",
       "grade_lvl[T.C]  -5.971e+05   1.74e+05     -3.432      0.001   -9.38e+05   -2.56e+05\n",
       "grade_lvl[T.C+] -5.549e+05   5.27e+04    -10.529      0.000   -6.58e+05   -4.52e+05\n",
       "grade_lvl[T.D]  -6.236e+05   2.46e+05     -2.535      0.011   -1.11e+06   -1.41e+05\n",
       "grade_lvl[T.S]   1.423e+06   2.96e+04     48.043      0.000    1.37e+06    1.48e+06\n",
       "grade_lvl[T.S+]  3.237e+06   7.43e+04     43.546      0.000    3.09e+06    3.38e+06\n",
       "grade_lvl[T.S-]  6.987e+05   1.48e+04     47.190      0.000     6.7e+05    7.28e+05\n",
       "waterfront_1     8.206e+05   2.12e+04     38.647      0.000    7.79e+05    8.62e+05\n",
       "==============================================================================\n",
       "Omnibus:                    11023.856   Durbin-Watson:                   2.013\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           420653.407\n",
       "Skew:                           2.510   Prob(JB):                         0.00\n",
       "Kurtosis:                      26.637   Cond. No.                         149.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#From our final_dummy Dataframe\n",
    "#Look at the dummy variables for waterfront and grade to see correlation against each ranking and waterfront property\n",
    "dummy_model = ols(formula='price~waterfront_1+grade_lvl', data = df).fit()\n",
    "dummy_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Takeaways:**\n",
    "- We can see that the grade levels 'S+' and 'S-' have the highest positive correlation with price.\n",
    "- Also the lower the grade the more that value will depreciate the sell value of the house.\n",
    "- Whether or not the property was renovated also increases the value of the home by about 20,000k."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-Linear Transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Transformations and Interaction Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a copy of the Dataframe with dummy variables already established"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_housing = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manual Polynomial Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['floors^2'] = df['floors'] ** 2\n",
    "df['sqft_above^2'] = df['sqft_above'] ** 2\n",
    "df['sqft_basement^2'] = df['sqft_basement'] ** 2\n",
    "df['bath_sqft_ratio^2'] = df['bath_sqft_ratio'] ** 2\n",
    "df['bed_sqft_ratio^2'] = df['bath_sqft_ratio'] ** 2\n",
    "df['floors_sqft_ratio^2'] = df['floors_sqft_ratio'] ** 2\n",
    "df['sqft_living^2'] = df['sqft_living'] ** 2\n",
    "df['sqft_living15^2'] = df['sqft_living'] ** 2\n",
    "df['sqft_lot^2'] = df['sqft_lot'] ** 2\n",
    "df['sqft_lot15^2'] = df['sqft_lot15'] ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manual Interaction Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grades 7-9 had the highest sale rates, so we'll create interaction features for those three.\n",
    "df['reno_basement'] = df['sqft_basement'] * df['was_reno']\n",
    "df['sqft_reno'] = df['sqft_living'] * df['was_reno']\n",
    "df['grade_7_sqft_living'] = df['sqft_living'] * df['grade_7']\n",
    "df['grade_8_sqft_living'] = df['sqft_living'] * df['grade_8']\n",
    "df['grade_9_sqft_living'] = df['sqft_living'] * df['grade_9']\n",
    "df['floors_grade_7'] = df['floors'] * df['grade_7']\n",
    "df['floors_grade_8'] = df['floors'] * df['grade_8']\n",
    "df['floors_grade_9'] = df['floors'] * df['grade_9']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run Summary Model on Interaction Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.171</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.171</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   447.0</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Mar 2021</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:29:56</td>     <th>  Log-Likelihood:    </th> <td>-2.4474e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 17290</td>      <th>  AIC:               </th>  <td>4.895e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 17281</td>      <th>  BIC:               </th>  <td>4.896e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>           <td>  6.14e+05</td> <td> 5512.492</td> <td>  111.377</td> <td> 0.000</td> <td> 6.03e+05</td> <td> 6.25e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>reno_basement</th>       <td>  190.3885</td> <td>   29.244</td> <td>    6.510</td> <td> 0.000</td> <td>  133.067</td> <td>  247.710</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_reno</th>           <td>  108.5717</td> <td>    8.019</td> <td>   13.539</td> <td> 0.000</td> <td>   92.854</td> <td>  124.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_7_sqft_living</th> <td>  -29.6358</td> <td>    6.149</td> <td>   -4.819</td> <td> 0.000</td> <td>  -41.689</td> <td>  -17.582</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_8_sqft_living</th> <td>   29.0889</td> <td>    5.169</td> <td>    5.627</td> <td> 0.000</td> <td>   18.957</td> <td>   39.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_9_sqft_living</th> <td>  115.7411</td> <td>    7.843</td> <td>   14.758</td> <td> 0.000</td> <td>  100.369</td> <td>  131.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_grade_7</th>      <td>-1.168e+05</td> <td> 7780.415</td> <td>  -15.018</td> <td> 0.000</td> <td>-1.32e+05</td> <td>-1.02e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_grade_8</th>      <td>-8.303e+04</td> <td> 6468.270</td> <td>  -12.837</td> <td> 0.000</td> <td>-9.57e+04</td> <td>-7.04e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_grade_9</th>      <td>-1.028e+05</td> <td> 1.21e+04</td> <td>   -8.479</td> <td> 0.000</td> <td>-1.27e+05</td> <td>-7.91e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>15577.119</td> <th>  Durbin-Watson:     </th>  <td>   2.029</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>1023031.939</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 4.103</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>39.779</td>   <th>  Cond. No.          </th>  <td>5.65e+03</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.65e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.171\n",
       "Model:                            OLS   Adj. R-squared:                  0.171\n",
       "Method:                 Least Squares   F-statistic:                     447.0\n",
       "Date:                Fri, 12 Mar 2021   Prob (F-statistic):               0.00\n",
       "Time:                        11:29:56   Log-Likelihood:            -2.4474e+05\n",
       "No. Observations:               17290   AIC:                         4.895e+05\n",
       "Df Residuals:                   17281   BIC:                         4.896e+05\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "Intercept             6.14e+05   5512.492    111.377      0.000    6.03e+05    6.25e+05\n",
       "reno_basement         190.3885     29.244      6.510      0.000     133.067     247.710\n",
       "sqft_reno             108.5717      8.019     13.539      0.000      92.854     124.290\n",
       "grade_7_sqft_living   -29.6358      6.149     -4.819      0.000     -41.689     -17.582\n",
       "grade_8_sqft_living    29.0889      5.169      5.627      0.000      18.957      39.221\n",
       "grade_9_sqft_living   115.7411      7.843     14.758      0.000     100.369     131.114\n",
       "floors_grade_7      -1.168e+05   7780.415    -15.018      0.000   -1.32e+05   -1.02e+05\n",
       "floors_grade_8      -8.303e+04   6468.270    -12.837      0.000   -9.57e+04   -7.04e+04\n",
       "floors_grade_9      -1.028e+05   1.21e+04     -8.479      0.000   -1.27e+05   -7.91e+04\n",
       "==============================================================================\n",
       "Omnibus:                    15577.119   Durbin-Watson:                   2.029\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1023031.939\n",
       "Skew:                           4.103   Prob(JB):                         0.00\n",
       "Kurtosis:                      39.779   Cond. No.                     5.65e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 5.65e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Interaction Features\n",
    "dummy_model = ols(formula='price~+reno_basement+sqft_reno+grade_7_sqft_living+grade_8_sqft_living+grade_9_sqft_living+floors_grade_7+floors_grade_8+floors_grade_9', data = df).fit()\n",
    "dummy_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Test Models against Training & Test Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns to be Excluded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **ID:** Not needed and irrelevant to analysis\n",
    "- **Price:** Our target variable\n",
    "- **Date:** Irrelevant now that we have year_sold\n",
    "- **Sqft_lot15:** Sqft_living15 for nearest neighbors has a higher impact on home price. Lot is negative corr.\n",
    "- **Last_reno:** Was_reno has better indication of making an impact on the price of the home rather then when it was renovated.\n",
    "- **Month_sold:** Low relationship to price. Season a bit stronger.\n",
    "- **Grade_lvl:** Multicolinearity with dummy variables for each grade score.\n",
    "- **Age_built_binned:** Mostly was for plotting purposes, keep yr_built instead.\n",
    "- **Zipcodes out of the top 20:** We'll look at zipcodes in the top 20 first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select Which Features to Use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'sqft_above', 'sqft_basement', 'yr_built',\n",
    "            'yr_renovated', 'lat', 'long', 'sqft_living15', 'year_sold', 'has_basement', 'age_built', 'was_reno',\n",
    "            'bed_sqft_ratio', 'bath_sqft_ratio', 'floors_sqft_ratio', 'grade_3', 'grade_4', 'grade_5', 'grade_6', 'grade_7',\n",
    "            'grade_8', 'grade_9', 'grade_10', 'grade_11', 'grade_12', 'grade_13', 'waterfront_1', 'zipcode_98052',\n",
    "            'zipcode_98115', 'zipcode_98103', 'zipcode_98038', 'zipcode_98117', 'zipcode_98034', 'zipcode_98042', \n",
    "            'zipcode_98023', 'zipcode_98118', 'zipcode_98133', 'zipcode_98006', 'zipcode_98058', 'zipcode_98059',\n",
    "            'zipcode_98074', 'zipcode_98033', 'zipcode_98155', 'zipcode_98056', 'zipcode_98027', 'zipcode_98053',\n",
    "            'zipcode_98125', 'season_sold_2', 'season_sold_3', 'season_sold_4', 'view_1', 'view_2', 'view_3', 'view_4',\n",
    "            'condition_2', 'condition_3', 'condition_4', 'condition_5', 'floors^2', 'sqft_above^2', 'sqft_basement^2',\n",
    "            'bath_sqft_ratio^2', 'bed_sqft_ratio^2', 'floors_sqft_ratio^2', 'sqft_living^2', 'sqft_living15^2', 'sqft_lot^2',\n",
    "            'sqft_lot15^2', 'reno_basement', 'sqft_reno', 'grade_7_sqft_living', 'grade_8_sqft_living', 'grade_9_sqft_living',\n",
    "            'floors_grade_7', 'floors_grade_8', 'floors_grade_9']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Features and Target Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = df[features]\n",
    "df_target = df['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train-Test Split the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random state is the number to keep the train-test split consistent\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_features, df_target, random_state = 42, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unscaled Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlr_unscaled(features, target):\n",
    "    \n",
    "    #Instantiate a linear regression object\n",
    "    lm = LinearRegression()\n",
    "    \n",
    "    #Fit the linear regression to the data\n",
    "    lm = lm.fit(X_train, y_train)\n",
    "    \n",
    "    #Use the fitted model to predict on the training set and the test set.\n",
    "    y_train_pred = lm.predict(X_train)\n",
    "    y_test_pred = lm.predict(X_test)\n",
    "    \n",
    "    #Evaluate the training and test predictions using RMSE.\n",
    "    train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "    test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "    \n",
    "    #View how far off we are in standard deviations of error\n",
    "    rmse_distance = test_rmse/target.std()\n",
    "    \n",
    "    #View how far off from the mean absolute error of the target\n",
    "    test_mae = metrics.mean_absolute_error(y_test, y_test_pred)\n",
    "    mae_distance = test_mae/target.std()\n",
    "    \n",
    "\n",
    "    return {'train_rmse': train_rmse, \n",
    "            'test_rmse' : test_rmse,\n",
    "            'std of error' : rmse_distance,\n",
    "            'dist_from_mae' : mae_distance,\n",
    "            'y_test_pred' : y_test_pred.mean()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_rmse': 182708.47485837253,\n",
       " 'test_rmse': 176485.3832983236,\n",
       " 'std of error': 0.4727468822763791,\n",
       " 'dist_from_mae': 0.2874846630962646,\n",
       " 'y_test_pred': 531702.9760468785}"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_unscaled(df_features, df_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlr_test_log_target(features, target):\n",
    "    \n",
    "    #Log the data to create a normal distribution\n",
    "    y_log = np.log(y_train)\n",
    "    \n",
    "    #Instantiate a linear regression object\n",
    "    lm_log = LinearRegression()\n",
    "    \n",
    "    #Fit the linear regression to the data\n",
    "    lm_log = lm_log.fit(X_train, y_log)\n",
    "    \n",
    "    #Use the model to predict on the training set\n",
    "    #Log_train_pred the predictions to get them on the same original scale \n",
    "    log_train_pred  = lm_log.predict(X_train)\n",
    "    y_train_pred = np.exp(log_train_pred)\n",
    "    \n",
    "    #Use fitted model to predict on the test examples\n",
    "    #Log_test_pred the predictions to get them back to original scale\n",
    "    log_test_pred = lm_log.predict(X_test)\n",
    "    y_test_pred = np.exp(log_test_pred)\n",
    " \n",
    "    #Evaluate the training and test predictions using RMSE.\n",
    "    log_train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "    log_test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "    \n",
    "    #View how far off we are in standard deviations of error\n",
    "    log_rmse_distance = log_test_rmse/target.std()\n",
    "    \n",
    "    #View how farr off from the mean absolute error of the target\n",
    "    log_test_mae = metrics.mean_absolute_error(y_test, y_test_pred)\n",
    "    log_mae_distance = log_test_mae/target.std()\n",
    "    \n",
    "    \n",
    "    return {'log_train_rmse': log_train_rmse, \n",
    "            'log_test_rmse' : log_test_rmse,\n",
    "            'std of error' : log_rmse_distance,\n",
    "            'dist_from_mae' : log_mae_distance,\n",
    "            'y_test_pred' : y_test_pred.mean()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'log_train_rmse': 178653.78757928527,\n",
       " 'log_test_rmse': 175459.2484560281,\n",
       " 'std of error': 0.4699982011197624,\n",
       " 'dist_from_mae': 0.2619551460986875,\n",
       " 'y_test_pred': 515095.8989904364}"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_test_log_target(df_features, df_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select K-Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17290, 138)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\feature_selection\\_univariate_selection.py:302: RuntimeWarning: invalid value encountered in true_divide\n",
      "  corr /= X_norms\n",
      "C:\\Users\\User\\anaconda3\\envs\\learn-env\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1932: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SelectKBest(k=70, score_func=<function f_regression at 0x000001B713D6A4C0>)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector = SelectKBest(f_regression, k=70)\n",
    "\n",
    "selector.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True, False,  True,  True,  True,  True,  True,\n",
       "        True, False,  True,  True,  True,  True, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "       False,  True,  True,  True,  True,  True,  True, False,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True, False, False,\n",
       "        True,  True,  True,  True,  True, False,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train = X_train[selected_columns]\n",
    "# X_test = X_test[selected_columns]\n",
    "selected_columns = X_train.columns[selector.get_support()]\n",
    "removed_columns = X_train.columns[~selector.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['year_sold',\n",
       " 'grade_3',\n",
       " 'grade_8',\n",
       " 'zipcode_98117',\n",
       " 'zipcode_98034',\n",
       " 'zipcode_98059',\n",
       " 'season_sold_3',\n",
       " 'season_sold_4',\n",
       " 'condition_3',\n",
       " 'floors_grade_8']"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(removed_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bedrooms',\n",
       " 'bathrooms',\n",
       " 'sqft_living',\n",
       " 'sqft_lot',\n",
       " 'floors',\n",
       " 'sqft_above',\n",
       " 'sqft_basement',\n",
       " 'yr_built',\n",
       " 'yr_renovated',\n",
       " 'lat',\n",
       " 'long',\n",
       " 'sqft_living15',\n",
       " 'has_basement',\n",
       " 'age_built',\n",
       " 'was_reno',\n",
       " 'bed_sqft_ratio',\n",
       " 'bath_sqft_ratio',\n",
       " 'floors_sqft_ratio',\n",
       " 'grade_4',\n",
       " 'grade_5',\n",
       " 'grade_6',\n",
       " 'grade_7',\n",
       " 'grade_9',\n",
       " 'grade_10',\n",
       " 'grade_11',\n",
       " 'grade_12',\n",
       " 'grade_13',\n",
       " 'waterfront_1',\n",
       " 'zipcode_98052',\n",
       " 'zipcode_98115',\n",
       " 'zipcode_98103',\n",
       " 'zipcode_98038',\n",
       " 'zipcode_98042',\n",
       " 'zipcode_98023',\n",
       " 'zipcode_98118',\n",
       " 'zipcode_98133',\n",
       " 'zipcode_98006',\n",
       " 'zipcode_98058',\n",
       " 'zipcode_98074',\n",
       " 'zipcode_98033',\n",
       " 'zipcode_98155',\n",
       " 'zipcode_98056',\n",
       " 'zipcode_98027',\n",
       " 'zipcode_98053',\n",
       " 'zipcode_98125',\n",
       " 'season_sold_2',\n",
       " 'view_1',\n",
       " 'view_2',\n",
       " 'view_3',\n",
       " 'view_4',\n",
       " 'condition_2',\n",
       " 'condition_4',\n",
       " 'condition_5',\n",
       " 'floors^2',\n",
       " 'sqft_above^2',\n",
       " 'sqft_basement^2',\n",
       " 'bath_sqft_ratio^2',\n",
       " 'bed_sqft_ratio^2',\n",
       " 'floors_sqft_ratio^2',\n",
       " 'sqft_living^2',\n",
       " 'sqft_living15^2',\n",
       " 'sqft_lot^2',\n",
       " 'sqft_lot15^2',\n",
       " 'reno_basement',\n",
       " 'sqft_reno',\n",
       " 'grade_7_sqft_living',\n",
       " 'grade_8_sqft_living',\n",
       " 'grade_9_sqft_living',\n",
       " 'floors_grade_7',\n",
       " 'floors_grade_9']"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(selected_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlr_Kbest(features, target):\n",
    "\n",
    "    #instantiate a linear regression object\n",
    "    lm_Kbest = LinearRegression()\n",
    "\n",
    "    #fit the linear regression to the data\n",
    "    lm_Kbest.fit(X_train[selected_columns], y_train)\n",
    "\n",
    "    #Use the model to predict on the training and test set\n",
    "    y_train_Kbest = lm_Kbest.predict(X_train[selected_columns])\n",
    "    y_test_pred = lm_Kbest.predict(X_test[selected_columns])\n",
    "\n",
    "    #Evaluate the training and test predictions using RMSE.\n",
    "    trainK_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_train_Kbest))\n",
    "    testK_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "    \n",
    "    #View how far off we are in standard deviations of error\n",
    "    rmseK_distance = testK_rmse/target.std()\n",
    "    \n",
    "    #View how farr off from the mean absolute error of the target\n",
    "    testK_mae = metrics.mean_absolute_error(y_test, y_test_pred)\n",
    "    maeK_distance = testK_mae/target.std()\n",
    "\n",
    "\n",
    "    return {'trainK_rmse': trainK_rmse, \n",
    "            'testK_rmse' : testK_rmse,\n",
    "            'std of error' : rmseK_distance,\n",
    "            'dist_from_mae' : maeK_distance,\n",
    "            'y_test_pred' : y_test_pred.mean()}\n",
    "#             'paired_coef' : list(zip(selected_columns, lm_Kbest.coef_))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trainK_rmse': 183594.6597452555,\n",
       " 'testK_rmse': 177080.04817774257,\n",
       " 'std of error': 0.47433979587914116,\n",
       " 'dist_from_mae': 0.2902491875505212,\n",
       " 'y_test_pred': 532140.8190055625}"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_Kbest(df_features, df_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 80 features.\n",
      "Fitting estimator with 79 features.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RFECV(cv=5, estimator=LinearRegression(), n_jobs=-1,\n",
       "      scoring='neg_mean_squared_error', verbose=1)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols = LinearRegression()\n",
    "# Create recursive feature eliminator that scores features by mean squared errors\n",
    "# Step means remove 1 feature at a time\n",
    "# CV = cross validation, and is the number of train-test splits the model runs\n",
    "# N_jobs runs separate models using separte cpu cores. Can set the number of cores to run on. (-1 runs on all available cores)\n",
    "# Verbose gives feedback to show where the model is during the process. \n",
    "selector = RFECV(estimator=ols, step=1, cv=5, scoring='neg_mean_squared_error', n_jobs=-1, verbose=1)\n",
    "\n",
    "# Fit recursive feature eliminator       \n",
    "# Can run on best K_model in place of X_train to combine the tests (selected_columns)\n",
    "selector.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_rfe = X_train.columns[selector.support_]\n",
    "removed_rfe = X_train.columns[~selector.support_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sqft_lot^2', 'sqft_lot15^2']"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(removed_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(selected_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
       "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'lat',\n",
       "       'long', 'sqft_living15', 'year_sold', 'has_basement', 'age_built',\n",
       "       'was_reno', 'bed_sqft_ratio', 'bath_sqft_ratio', 'floors_sqft_ratio',\n",
       "       'grade_3', 'grade_4', 'grade_5', 'grade_6', 'grade_7', 'grade_8',\n",
       "       'grade_9', 'grade_10', 'grade_11', 'grade_12', 'grade_13',\n",
       "       'waterfront_1', 'zipcode_98052', 'zipcode_98115', 'zipcode_98103',\n",
       "       'zipcode_98038', 'zipcode_98117', 'zipcode_98034', 'zipcode_98042',\n",
       "       'zipcode_98023', 'zipcode_98118', 'zipcode_98133', 'zipcode_98006',\n",
       "       'zipcode_98058', 'zipcode_98059', 'zipcode_98074', 'zipcode_98033',\n",
       "       'zipcode_98155', 'zipcode_98056', 'zipcode_98027', 'zipcode_98053',\n",
       "       'zipcode_98125', 'season_sold_2', 'season_sold_3', 'season_sold_4',\n",
       "       'view_1', 'view_2', 'view_3', 'view_4', 'condition_2', 'condition_3',\n",
       "       'condition_4', 'condition_5', 'floors^2', 'sqft_above^2',\n",
       "       'sqft_basement^2', 'bath_sqft_ratio^2', 'bed_sqft_ratio^2',\n",
       "       'floors_sqft_ratio^2', 'sqft_living^2', 'sqft_living15^2',\n",
       "       'reno_basement', 'sqft_reno', 'grade_7_sqft_living',\n",
       "       'grade_8_sqft_living', 'grade_9_sqft_living', 'floors_grade_7',\n",
       "       'floors_grade_8', 'floors_grade_9'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_rfe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlr_rfe(features, target):\n",
    "    \n",
    "    #instantiate a linear regression object\n",
    "    lm_rfe = LinearRegression()\n",
    "\n",
    "    #fit the linear regression to the data\n",
    "    lm_rfe = lm_rfe.fit(X_train[selected_rfe], y_train)\n",
    "    \n",
    "    #Use the model to predict on the training and test set\n",
    "    y_rfe = lm_rfe.predict(X_train[selected_rfe])\n",
    "    y_test_pred = lm_rfe.predict(X_test[selected_rfe])\n",
    "\n",
    "    #Evaluate the training and test predictions using RMSE.\n",
    "    trainRFE_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_rfe))\n",
    "    testRFE_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "    \n",
    "    #View how far off we are in standard deviations of error\n",
    "    rmseRFE_distance = testRFE_rmse/target.std()\n",
    "    \n",
    "    #View how farr off from the mean absolute error of the target\n",
    "    testRFE_mae = metrics.mean_absolute_error(y_test, y_test_pred)\n",
    "    maeRFE_distance = testRFE_mae/target.std()\n",
    "\n",
    "\n",
    "    return {'trainRFE_rmse': trainRFE_rmse, \n",
    "            'testRFE_rmse' : testRFE_rmse,\n",
    "            'std of error' : rmseRFE_distance,\n",
    "            'dist_from_mae' : maeRFE_distance,\n",
    "            'y_test_pred' : y_test_pred.mean()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trainRFE_rmse': 182508.76875996107,\n",
       " 'testRFE_rmse': 176805.0334982784,\n",
       " 'std of error': 0.47360312109130814,\n",
       " 'dist_from_mae': 0.2871823228711175,\n",
       " 'y_test_pred': 531610.3422504651}"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_rfe(df_features, df_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polynomial and Interaction Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Use the final_housing dataframe copy we created before transforming the data to use in the other models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>year_sold</th>\n",
       "      <th>has_basement</th>\n",
       "      <th>age_built</th>\n",
       "      <th>last_reno</th>\n",
       "      <th>month_sold</th>\n",
       "      <th>was_reno</th>\n",
       "      <th>bed_sqft_ratio</th>\n",
       "      <th>bath_sqft_ratio</th>\n",
       "      <th>floors_sqft_ratio</th>\n",
       "      <th>grade_lvl</th>\n",
       "      <th>age_built_binned</th>\n",
       "      <th>grade_3</th>\n",
       "      <th>grade_4</th>\n",
       "      <th>grade_5</th>\n",
       "      <th>grade_6</th>\n",
       "      <th>grade_7</th>\n",
       "      <th>grade_8</th>\n",
       "      <th>grade_9</th>\n",
       "      <th>grade_10</th>\n",
       "      <th>grade_11</th>\n",
       "      <th>grade_12</th>\n",
       "      <th>grade_13</th>\n",
       "      <th>waterfront_1</th>\n",
       "      <th>zipcode_98002</th>\n",
       "      <th>zipcode_98003</th>\n",
       "      <th>zipcode_98004</th>\n",
       "      <th>zipcode_98005</th>\n",
       "      <th>zipcode_98006</th>\n",
       "      <th>zipcode_98007</th>\n",
       "      <th>zipcode_98008</th>\n",
       "      <th>zipcode_98010</th>\n",
       "      <th>zipcode_98011</th>\n",
       "      <th>zipcode_98014</th>\n",
       "      <th>...</th>\n",
       "      <th>zipcode_98056</th>\n",
       "      <th>zipcode_98058</th>\n",
       "      <th>zipcode_98059</th>\n",
       "      <th>zipcode_98065</th>\n",
       "      <th>zipcode_98070</th>\n",
       "      <th>zipcode_98072</th>\n",
       "      <th>zipcode_98074</th>\n",
       "      <th>zipcode_98075</th>\n",
       "      <th>zipcode_98077</th>\n",
       "      <th>zipcode_98092</th>\n",
       "      <th>zipcode_98102</th>\n",
       "      <th>zipcode_98103</th>\n",
       "      <th>zipcode_98105</th>\n",
       "      <th>zipcode_98106</th>\n",
       "      <th>zipcode_98107</th>\n",
       "      <th>zipcode_98108</th>\n",
       "      <th>zipcode_98109</th>\n",
       "      <th>zipcode_98112</th>\n",
       "      <th>zipcode_98115</th>\n",
       "      <th>zipcode_98116</th>\n",
       "      <th>zipcode_98117</th>\n",
       "      <th>zipcode_98118</th>\n",
       "      <th>zipcode_98119</th>\n",
       "      <th>zipcode_98122</th>\n",
       "      <th>zipcode_98125</th>\n",
       "      <th>zipcode_98126</th>\n",
       "      <th>zipcode_98133</th>\n",
       "      <th>zipcode_98136</th>\n",
       "      <th>zipcode_98144</th>\n",
       "      <th>zipcode_98146</th>\n",
       "      <th>zipcode_98148</th>\n",
       "      <th>zipcode_98155</th>\n",
       "      <th>zipcode_98166</th>\n",
       "      <th>zipcode_98168</th>\n",
       "      <th>zipcode_98177</th>\n",
       "      <th>zipcode_98178</th>\n",
       "      <th>zipcode_98188</th>\n",
       "      <th>zipcode_98198</th>\n",
       "      <th>zipcode_98199</th>\n",
       "      <th>season_sold_2</th>\n",
       "      <th>season_sold_3</th>\n",
       "      <th>season_sold_4</th>\n",
       "      <th>view_1</th>\n",
       "      <th>view_2</th>\n",
       "      <th>view_3</th>\n",
       "      <th>view_4</th>\n",
       "      <th>condition_2</th>\n",
       "      <th>condition_3</th>\n",
       "      <th>condition_4</th>\n",
       "      <th>condition_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2591820310</td>\n",
       "      <td>2014-10-06 00:00:00+00:00</td>\n",
       "      <td>365000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2070</td>\n",
       "      <td>8893</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2070</td>\n",
       "      <td>0</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>47.4388</td>\n",
       "      <td>-122.162</td>\n",
       "      <td>2390</td>\n",
       "      <td>7700</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001932</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.000966</td>\n",
       "      <td>A-</td>\n",
       "      <td>30-40</td>\n",
       "      <td>0</td>\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>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",
       "      <td>0</td>\n",
       "      <td>...</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>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",
       "      <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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7974200820</td>\n",
       "      <td>2014-08-21 00:00:00+00:00</td>\n",
       "      <td>865000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2900</td>\n",
       "      <td>6730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>1070</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>47.6784</td>\n",
       "      <td>-122.285</td>\n",
       "      <td>2370</td>\n",
       "      <td>6283</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001724</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>A-</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\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>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",
       "      <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",
       "      <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>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>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",
       "      <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7701450110</td>\n",
       "      <td>2014-08-15 00:00:00+00:00</td>\n",
       "      <td>1038000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3770</td>\n",
       "      <td>10893</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3770</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5646</td>\n",
       "      <td>-122.129</td>\n",
       "      <td>3710</td>\n",
       "      <td>9685</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001061</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>S-</td>\n",
       "      <td>20-30</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>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>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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9522300010</td>\n",
       "      <td>2015-03-31 00:00:00+00:00</td>\n",
       "      <td>1490000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4560</td>\n",
       "      <td>14608</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4560</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>47.6995</td>\n",
       "      <td>-122.228</td>\n",
       "      <td>4050</td>\n",
       "      <td>14226</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>S</td>\n",
       "      <td>30-40</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>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>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",
       "      <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",
       "      <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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9510861140</td>\n",
       "      <td>2014-07-14 00:00:00+00:00</td>\n",
       "      <td>711000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2550</td>\n",
       "      <td>5376</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2550</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>47.6647</td>\n",
       "      <td>-122.083</td>\n",
       "      <td>2250</td>\n",
       "      <td>4050</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001176</td>\n",
       "      <td>0.000980</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>A</td>\n",
       "      <td>11-20</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>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>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>...</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          id                      date      price  bedrooms  \\\n",
       "0           0  2591820310 2014-10-06 00:00:00+00:00   365000.0         4   \n",
       "1           1  7974200820 2014-08-21 00:00:00+00:00   865000.0         5   \n",
       "2           2  7701450110 2014-08-15 00:00:00+00:00  1038000.0         4   \n",
       "3           3  9522300010 2015-03-31 00:00:00+00:00  1490000.0         3   \n",
       "4           4  9510861140 2014-07-14 00:00:00+00:00   711000.0         3   \n",
       "\n",
       "   bathrooms  sqft_living  sqft_lot  floors  sqft_above  sqft_basement  \\\n",
       "0       2.25         2070      8893     2.0        2070              0   \n",
       "1       3.00         2900      6730     1.0        1830           1070   \n",
       "2       2.50         3770     10893     2.0        3770              0   \n",
       "3       3.50         4560     14608     2.0        4560              0   \n",
       "4       2.50         2550      5376     2.0        2550              0   \n",
       "\n",
       "   yr_built  yr_renovated      lat     long  sqft_living15  sqft_lot15  \\\n",
       "0      1986             0  47.4388 -122.162           2390        7700   \n",
       "1      1977             0  47.6784 -122.285           2370        6283   \n",
       "2      1997             0  47.5646 -122.129           3710        9685   \n",
       "3      1990             0  47.6995 -122.228           4050       14226   \n",
       "4      2004             0  47.6647 -122.083           2250        4050   \n",
       "\n",
       "   year_sold  has_basement  age_built  last_reno  month_sold  was_reno  \\\n",
       "0       2014             0         34         34          10         0   \n",
       "1       2014             1         43         43           8         0   \n",
       "2       2014             0         23         23           8         0   \n",
       "3       2015             0         31         31           3         0   \n",
       "4       2014             0         16         16           7         0   \n",
       "\n",
       "   bed_sqft_ratio  bath_sqft_ratio  floors_sqft_ratio grade_lvl  \\\n",
       "0        0.001932         0.001087           0.000966        A-   \n",
       "1        0.001724         0.001034           0.000345        A-   \n",
       "2        0.001061         0.000663           0.000531        S-   \n",
       "3        0.000658         0.000768           0.000439         S   \n",
       "4        0.001176         0.000980           0.000784         A   \n",
       "\n",
       "  age_built_binned  grade_3  grade_4  grade_5  grade_6  grade_7  grade_8  \\\n",
       "0            30-40        0        0        0        0        0        1   \n",
       "1            40-50        0        0        0        0        0        1   \n",
       "2            20-30        0        0        0        0        0        0   \n",
       "3            30-40        0        0        0        0        0        0   \n",
       "4            11-20        0        0        0        0        0        0   \n",
       "\n",
       "   grade_9  grade_10  grade_11  grade_12  grade_13  waterfront_1  \\\n",
       "0        0         0         0         0         0             0   \n",
       "1        0         0         0         0         0             0   \n",
       "2        0         0         1         0         0             0   \n",
       "3        0         0         0         1         0             0   \n",
       "4        1         0         0         0         0             0   \n",
       "\n",
       "   zipcode_98002  zipcode_98003  zipcode_98004  zipcode_98005  zipcode_98006  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              1   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   zipcode_98007  zipcode_98008  zipcode_98010  zipcode_98011  zipcode_98014  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   ...  zipcode_98056  zipcode_98058  zipcode_98059  zipcode_98065  \\\n",
       "0  ...              0              1              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",
       "   zipcode_98070  zipcode_98072  zipcode_98074  zipcode_98075  zipcode_98077  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   zipcode_98092  zipcode_98102  zipcode_98103  zipcode_98105  zipcode_98106  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   zipcode_98107  zipcode_98108  zipcode_98109  zipcode_98112  zipcode_98115  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              1   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   zipcode_98116  zipcode_98117  zipcode_98118  zipcode_98119  zipcode_98122  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   zipcode_98125  zipcode_98126  zipcode_98133  zipcode_98136  zipcode_98144  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   zipcode_98146  zipcode_98148  zipcode_98155  zipcode_98166  zipcode_98168  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   zipcode_98177  zipcode_98178  zipcode_98188  zipcode_98198  zipcode_98199  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   season_sold_2  season_sold_3  season_sold_4  view_1  view_2  view_3  \\\n",
       "0              0              0              1       0       0       0   \n",
       "1              0              1              0       0       0       0   \n",
       "2              0              1              0       0       1       0   \n",
       "3              0              0              0       0       1       0   \n",
       "4              0              1              0       0       0       0   \n",
       "\n",
       "   view_4  condition_2  condition_3  condition_4  condition_5  \n",
       "0       0            0            0            1            0  \n",
       "1       0            0            0            0            1  \n",
       "2       0            0            1            0            0  \n",
       "3       0            0            1            0            0  \n",
       "4       0            0            1            0            0  \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17290, 120)\n",
      "(17290, 138)\n"
     ]
    }
   ],
   "source": [
    "#Checking to see sizes of both Dataframes\n",
    "print(final_housing.shape)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate Polynomial and Interaction features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
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       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>year_sold</th>\n",
       "      <th>has_basement</th>\n",
       "      <th>age_built</th>\n",
       "      <th>last_reno</th>\n",
       "      <th>month_sold</th>\n",
       "      <th>was_reno</th>\n",
       "      <th>bed_sqft_ratio</th>\n",
       "      <th>bath_sqft_ratio</th>\n",
       "      <th>floors_sqft_ratio</th>\n",
       "      <th>grade_3</th>\n",
       "      <th>grade_4</th>\n",
       "      <th>grade_5</th>\n",
       "      <th>grade_6</th>\n",
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       "      <th>grade_11</th>\n",
       "      <th>grade_12</th>\n",
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       "      <th>zipcode_98002</th>\n",
       "      <th>zipcode_98003</th>\n",
       "      <th>zipcode_98004</th>\n",
       "      <th>zipcode_98005</th>\n",
       "      <th>zipcode_98006</th>\n",
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       "      <th>zipcode_98027</th>\n",
       "      <th>zipcode_98028</th>\n",
       "      <th>zipcode_98029</th>\n",
       "      <th>...</th>\n",
       "      <th>season_sold_3 view_4</th>\n",
       "      <th>season_sold_3 condition_2</th>\n",
       "      <th>season_sold_3 condition_3</th>\n",
       "      <th>season_sold_3 condition_4</th>\n",
       "      <th>season_sold_3 condition_5</th>\n",
       "      <th>season_sold_4^2</th>\n",
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       "      <th>season_sold_4 view_2</th>\n",
       "      <th>season_sold_4 view_3</th>\n",
       "      <th>season_sold_4 view_4</th>\n",
       "      <th>season_sold_4 condition_2</th>\n",
       "      <th>season_sold_4 condition_3</th>\n",
       "      <th>season_sold_4 condition_4</th>\n",
       "      <th>season_sold_4 condition_5</th>\n",
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       "      <th>view_1 view_2</th>\n",
       "      <th>view_1 view_3</th>\n",
       "      <th>view_1 view_4</th>\n",
       "      <th>view_1 condition_2</th>\n",
       "      <th>view_1 condition_3</th>\n",
       "      <th>view_1 condition_4</th>\n",
       "      <th>view_1 condition_5</th>\n",
       "      <th>view_2^2</th>\n",
       "      <th>view_2 view_3</th>\n",
       "      <th>view_2 view_4</th>\n",
       "      <th>view_2 condition_2</th>\n",
       "      <th>view_2 condition_3</th>\n",
       "      <th>view_2 condition_4</th>\n",
       "      <th>view_2 condition_5</th>\n",
       "      <th>view_3^2</th>\n",
       "      <th>view_3 view_4</th>\n",
       "      <th>view_3 condition_2</th>\n",
       "      <th>view_3 condition_3</th>\n",
       "      <th>view_3 condition_4</th>\n",
       "      <th>view_3 condition_5</th>\n",
       "      <th>view_4^2</th>\n",
       "      <th>view_4 condition_2</th>\n",
       "      <th>view_4 condition_3</th>\n",
       "      <th>view_4 condition_4</th>\n",
       "      <th>view_4 condition_5</th>\n",
       "      <th>condition_2^2</th>\n",
       "      <th>condition_2 condition_3</th>\n",
       "      <th>condition_2 condition_4</th>\n",
       "      <th>condition_2 condition_5</th>\n",
       "      <th>condition_3^2</th>\n",
       "      <th>condition_3 condition_4</th>\n",
       "      <th>condition_3 condition_5</th>\n",
       "      <th>condition_4^2</th>\n",
       "      <th>condition_4 condition_5</th>\n",
       "      <th>condition_5^2</th>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.00</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1977.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>6283.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.001034</td>\n",
       "      <td>0.000345</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3770.0</td>\n",
       "      <td>10893.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3770.0</td>\n",
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       "      <td>1997.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3710.0</td>\n",
       "      <td>9685.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001061</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4560.0</td>\n",
       "      <td>14608.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4050.0</td>\n",
       "      <td>14226.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>5376.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>4050.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001176</td>\n",
       "      <td>0.000980</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 6554 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  bedrooms  bathrooms  sqft_living  sqft_lot  floors  sqft_above  \\\n",
       "0         0.0       4.0       2.25       2070.0    8893.0     2.0      2070.0   \n",
       "1         1.0       5.0       3.00       2900.0    6730.0     1.0      1830.0   \n",
       "2         2.0       4.0       2.50       3770.0   10893.0     2.0      3770.0   \n",
       "3         3.0       3.0       3.50       4560.0   14608.0     2.0      4560.0   \n",
       "4         4.0       3.0       2.50       2550.0    5376.0     2.0      2550.0   \n",
       "\n",
       "   sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
       "0            0.0    1986.0           0.0         2390.0      7700.0   \n",
       "1         1070.0    1977.0           0.0         2370.0      6283.0   \n",
       "2            0.0    1997.0           0.0         3710.0      9685.0   \n",
       "3            0.0    1990.0           0.0         4050.0     14226.0   \n",
       "4            0.0    2004.0           0.0         2250.0      4050.0   \n",
       "\n",
       "   year_sold  has_basement  age_built  last_reno  month_sold  was_reno  \\\n",
       "0     2014.0           0.0       34.0       34.0        10.0       0.0   \n",
       "1     2014.0           1.0       43.0       43.0         8.0       0.0   \n",
       "2     2014.0           0.0       23.0       23.0         8.0       0.0   \n",
       "3     2015.0           0.0       31.0       31.0         3.0       0.0   \n",
       "4     2014.0           0.0       16.0       16.0         7.0       0.0   \n",
       "\n",
       "   bed_sqft_ratio  bath_sqft_ratio  floors_sqft_ratio  grade_3  grade_4  \\\n",
       "0        0.001932         0.001087           0.000966      0.0      0.0   \n",
       "1        0.001724         0.001034           0.000345      0.0      0.0   \n",
       "2        0.001061         0.000663           0.000531      0.0      0.0   \n",
       "3        0.000658         0.000768           0.000439      0.0      0.0   \n",
       "4        0.001176         0.000980           0.000784      0.0      0.0   \n",
       "\n",
       "   grade_5  grade_6  grade_7  grade_8  grade_9  grade_10  grade_11  grade_12  \\\n",
       "0      0.0      0.0      0.0      1.0      0.0       0.0       0.0       0.0   \n",
       "1      0.0      0.0      0.0      1.0      0.0       0.0       0.0       0.0   \n",
       "2      0.0      0.0      0.0      0.0      0.0       0.0       1.0       0.0   \n",
       "3      0.0      0.0      0.0      0.0      0.0       0.0       0.0       1.0   \n",
       "4      0.0      0.0      0.0      0.0      1.0       0.0       0.0       0.0   \n",
       "\n",
       "   grade_13  waterfront_1  zipcode_98002  zipcode_98003  zipcode_98004  \\\n",
       "0       0.0           0.0            0.0            0.0            0.0   \n",
       "1       0.0           0.0            0.0            0.0            0.0   \n",
       "2       0.0           0.0            0.0            0.0            0.0   \n",
       "3       0.0           0.0            0.0            0.0            0.0   \n",
       "4       0.0           0.0            0.0            0.0            0.0   \n",
       "\n",
       "   zipcode_98005  zipcode_98006  zipcode_98007  zipcode_98008  zipcode_98010  \\\n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "1            0.0            0.0            0.0            0.0            0.0   \n",
       "2            0.0            1.0            0.0            0.0            0.0   \n",
       "3            0.0            0.0            0.0            0.0            0.0   \n",
       "4            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   zipcode_98011  zipcode_98014  zipcode_98019  zipcode_98022  zipcode_98023  \\\n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "1            0.0            0.0            0.0            0.0            0.0   \n",
       "2            0.0            0.0            0.0            0.0            0.0   \n",
       "3            0.0            0.0            0.0            0.0            0.0   \n",
       "4            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   zipcode_98024  zipcode_98027  zipcode_98028  zipcode_98029  ...  \\\n",
       "0            0.0            0.0            0.0            0.0  ...   \n",
       "1            0.0            0.0            0.0            0.0  ...   \n",
       "2            0.0            0.0            0.0            0.0  ...   \n",
       "3            0.0            0.0            0.0            0.0  ...   \n",
       "4            0.0            0.0            0.0            0.0  ...   \n",
       "\n",
       "   season_sold_3 view_4  season_sold_3 condition_2  season_sold_3 condition_3  \\\n",
       "0                   0.0                        0.0                        0.0   \n",
       "1                   0.0                        0.0                        0.0   \n",
       "2                   0.0                        0.0                        1.0   \n",
       "3                   0.0                        0.0                        0.0   \n",
       "4                   0.0                        0.0                        1.0   \n",
       "\n",
       "   season_sold_3 condition_4  season_sold_3 condition_5  season_sold_4^2  \\\n",
       "0                        0.0                        0.0              1.0   \n",
       "1                        0.0                        1.0              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",
       "   season_sold_4 view_1  season_sold_4 view_2  season_sold_4 view_3  \\\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",
       "   season_sold_4 view_4  season_sold_4 condition_2  season_sold_4 condition_3  \\\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",
       "   season_sold_4 condition_4  season_sold_4 condition_5  view_1^2  \\\n",
       "0                        1.0                        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",
       "   view_1 view_2  view_1 view_3  view_1 view_4  view_1 condition_2  \\\n",
       "0            0.0            0.0            0.0                 0.0   \n",
       "1            0.0            0.0            0.0                 0.0   \n",
       "2            0.0            0.0            0.0                 0.0   \n",
       "3            0.0            0.0            0.0                 0.0   \n",
       "4            0.0            0.0            0.0                 0.0   \n",
       "\n",
       "   view_1 condition_3  view_1 condition_4  view_1 condition_5  view_2^2  \\\n",
       "0                 0.0                 0.0                 0.0       0.0   \n",
       "1                 0.0                 0.0                 0.0       0.0   \n",
       "2                 0.0                 0.0                 0.0       1.0   \n",
       "3                 0.0                 0.0                 0.0       1.0   \n",
       "4                 0.0                 0.0                 0.0       0.0   \n",
       "\n",
       "   view_2 view_3  view_2 view_4  view_2 condition_2  view_2 condition_3  \\\n",
       "0            0.0            0.0                 0.0                 0.0   \n",
       "1            0.0            0.0                 0.0                 0.0   \n",
       "2            0.0            0.0                 0.0                 1.0   \n",
       "3            0.0            0.0                 0.0                 1.0   \n",
       "4            0.0            0.0                 0.0                 0.0   \n",
       "\n",
       "   view_2 condition_4  view_2 condition_5  view_3^2  view_3 view_4  \\\n",
       "0                 0.0                 0.0       0.0            0.0   \n",
       "1                 0.0                 0.0       0.0            0.0   \n",
       "2                 0.0                 0.0       0.0            0.0   \n",
       "3                 0.0                 0.0       0.0            0.0   \n",
       "4                 0.0                 0.0       0.0            0.0   \n",
       "\n",
       "   view_3 condition_2  view_3 condition_3  view_3 condition_4  \\\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",
       "   view_3 condition_5  view_4^2  view_4 condition_2  view_4 condition_3  \\\n",
       "0                 0.0       0.0                 0.0                 0.0   \n",
       "1                 0.0       0.0                 0.0                 0.0   \n",
       "2                 0.0       0.0                 0.0                 0.0   \n",
       "3                 0.0       0.0                 0.0                 0.0   \n",
       "4                 0.0       0.0                 0.0                 0.0   \n",
       "\n",
       "   view_4 condition_4  view_4 condition_5  condition_2^2  \\\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",
       "   condition_2 condition_3  condition_2 condition_4  condition_2 condition_5  \\\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",
       "   condition_3^2  condition_3 condition_4  condition_3 condition_5  \\\n",
       "0            0.0                      0.0                      0.0   \n",
       "1            0.0                      0.0                      0.0   \n",
       "2            1.0                      0.0                      0.0   \n",
       "3            1.0                      0.0                      0.0   \n",
       "4            1.0                      0.0                      0.0   \n",
       "\n",
       "   condition_4^2  condition_4 condition_5  condition_5^2  \n",
       "0            1.0                      0.0            0.0  \n",
       "1            0.0                      0.0            1.0  \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",
       "[5 rows x 6554 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create Polynomial and Interaction Features\n",
    "\n",
    "#Drop non-numerical and categorical values that would break code below\n",
    "final_housing.drop('price', axis=1, inplace=True)\n",
    "final_housing.drop('date', axis=1, inplace=True)\n",
    "final_housing.drop('grade_lvl', axis=1, inplace=True)\n",
    "final_housing.drop('age_built_binned', axis=1, inplace=True)\n",
    "final_housing.drop('id', axis=1, inplace=True)\n",
    "final_housing.drop('lat', axis=1, inplace=True)\n",
    "final_housing.drop('long', axis=1, inplace=True)\n",
    "\n",
    "#Polynomial and Interaction Creation\n",
    "poly_2 = PolynomialFeatures(degree=2, include_bias=False)\n",
    "poly2_data = poly_2.fit_transform(final_housing)\n",
    "poly2_columns = poly_2.get_feature_names(final_housing.columns)\n",
    "df_poly2 = pd.DataFrame(poly2_data, columns=poly2_columns)\n",
    "df_poly2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Feature and Target Variables for Polynomial and Interaction Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_features = df_poly2[poly2_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train-Test Split the df_poly_clean Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Random state is the number to keep the train-test split consistent\n",
    "X_train_poly, X_test_poly, y_train_poly, y_test_poly = train_test_split(poly_features, df_target, random_state = 42, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def mlr_poly(features, target):\n",
    "    \n",
    "    #Instantiate a linear regression object\n",
    "    lm_poly = LinearRegression()\n",
    "    \n",
    "    #Fit the linear regression to the data\n",
    "    lm_poly = lm_poly.fit(X_train_poly, y_train_poly)\n",
    "    \n",
    "    #Use the fitted model to predict on the training set and the test set.\n",
    "    y_train_pred_poly = lm_poly.predict(X_train_poly)\n",
    "    y_test_pred = lm_poly.predict(X_test_poly)\n",
    "    \n",
    "    #Evaluate the training and test predictions using RMSE.\n",
    "    train_rmse_poly = np.sqrt(metrics.mean_squared_error(y_train_poly, y_train_pred_poly))\n",
    "    test_rmse_poly = np.sqrt(metrics.mean_squared_error(y_test_poly, y_test_pred))\n",
    "    \n",
    "    #View how far off we are in standard deviations of error\n",
    "    rmse_distance_poly = test_rmse_poly/target.std()\n",
    "    \n",
    "    #View how far off from the mean absolute error of the target\n",
    "    test_mae_poly = metrics.mean_absolute_error(y_test_poly, y_test_pred)\n",
    "    mae_distance_poly = test_mae_poly/target.std()\n",
    "    \n",
    "\n",
    "    return {'train_rmse': train_rmse_poly, \n",
    "            'test_rmse' : test_rmse_poly,\n",
    "            'std of error' : rmse_distance_poly,\n",
    "            'dist_from_mae' : mae_distance_poly,\n",
    "            'y_test_pred' : y_test_pred.mean()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_rmse': 87133.72489233244,\n",
       " 'test_rmse': 76638345.20257708,\n",
       " 'std of error': 205.28917511597322,\n",
       " 'dist_from_mae': 4.372930203826928,\n",
       " 'y_test_pred': -707380.7724313632}"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_poly(poly_features, df_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polynomial w/ K-Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\envs\\learn-env\\lib\\site-packages\\sklearn\\feature_selection\\_univariate_selection.py:302: RuntimeWarning: invalid value encountered in true_divide\n",
      "  corr /= X_norms\n",
      "C:\\Users\\User\\anaconda3\\envs\\learn-env\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1932: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SelectKBest(k=700, score_func=<function f_regression at 0x000001B713D6A4C0>)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_selector = SelectKBest(f_regression, k=700)\n",
    "\n",
    "poly_selector.fit(X_train_poly, y_train_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, ..., False, False, False])"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_selector.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train = X_train[selected_columns]\n",
    "# X_test = X_test[selected_columns]\n",
    "poly_selected_columns = X_train_poly.columns[poly_selector.get_support()]\n",
    "poly_removed_columns = X_train_poly.columns[~poly_selector.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0',\n",
       " 'sqft_lot',\n",
       " 'yr_built',\n",
       " 'sqft_lot15',\n",
       " 'year_sold',\n",
       " 'age_built',\n",
       " 'month_sold',\n",
       " 'grade_3',\n",
       " 'grade_4',\n",
       " 'grade_5',\n",
       " 'grade_8',\n",
       " 'zipcode_98002',\n",
       " 'zipcode_98003',\n",
       " 'zipcode_98005',\n",
       " 'zipcode_98007',\n",
       " 'zipcode_98008',\n",
       " 'zipcode_98010',\n",
       " 'zipcode_98011',\n",
       " 'zipcode_98014',\n",
       " 'zipcode_98019',\n",
       " 'zipcode_98022',\n",
       " 'zipcode_98024',\n",
       " 'zipcode_98027',\n",
       " 'zipcode_98028',\n",
       " 'zipcode_98029',\n",
       " 'zipcode_98030',\n",
       " 'zipcode_98031',\n",
       " 'zipcode_98032',\n",
       " 'zipcode_98033',\n",
       " 'zipcode_98034',\n",
       " 'zipcode_98038',\n",
       " 'zipcode_98042',\n",
       " 'zipcode_98045',\n",
       " 'zipcode_98052',\n",
       " 'zipcode_98053',\n",
       " 'zipcode_98055',\n",
       " 'zipcode_98056',\n",
       " 'zipcode_98058',\n",
       " 'zipcode_98059',\n",
       " 'zipcode_98065',\n",
       " 'zipcode_98070',\n",
       " 'zipcode_98072',\n",
       " 'zipcode_98074',\n",
       " 'zipcode_98075',\n",
       " 'zipcode_98077',\n",
       " 'zipcode_98092',\n",
       " 'zipcode_98102',\n",
       " 'zipcode_98103',\n",
       " 'zipcode_98105',\n",
       " 'zipcode_98106',\n",
       " 'zipcode_98107',\n",
       " 'zipcode_98108',\n",
       " 'zipcode_98109',\n",
       " 'zipcode_98115',\n",
       " 'zipcode_98116',\n",
       " 'zipcode_98117',\n",
       " 'zipcode_98118',\n",
       " 'zipcode_98119',\n",
       " 'zipcode_98122',\n",
       " 'zipcode_98125',\n",
       " 'zipcode_98126',\n",
       " 'zipcode_98133',\n",
       " 'zipcode_98136',\n",
       " 'zipcode_98144',\n",
       " 'zipcode_98146',\n",
       " 'zipcode_98148',\n",
       " 'zipcode_98155',\n",
       " 'zipcode_98166',\n",
       " 'zipcode_98168',\n",
       " 'zipcode_98177',\n",
       " 'zipcode_98178',\n",
       " 'zipcode_98188',\n",
       " 'zipcode_98198',\n",
       " 'zipcode_98199',\n",
       " 'season_sold_2',\n",
       " 'season_sold_3',\n",
       " 'season_sold_4',\n",
       " 'view_1',\n",
       " 'condition_2',\n",
       " 'condition_3',\n",
       " 'condition_4',\n",
       " 'condition_5',\n",
       " 'Unnamed: 0^2',\n",
       " 'Unnamed: 0 sqft_lot',\n",
       " 'Unnamed: 0 yr_built',\n",
       " 'Unnamed: 0 yr_renovated',\n",
       " 'Unnamed: 0 sqft_lot15',\n",
       " 'Unnamed: 0 year_sold',\n",
       " 'Unnamed: 0 age_built',\n",
       " 'Unnamed: 0 last_reno',\n",
       " 'Unnamed: 0 month_sold',\n",
       " 'Unnamed: 0 was_reno',\n",
       " 'Unnamed: 0 grade_3',\n",
       " 'Unnamed: 0 grade_4',\n",
       " 'Unnamed: 0 grade_5',\n",
       " 'Unnamed: 0 grade_8',\n",
       " 'Unnamed: 0 zipcode_98002',\n",
       " 'Unnamed: 0 zipcode_98003',\n",
       " 'Unnamed: 0 zipcode_98005',\n",
       " 'Unnamed: 0 zipcode_98007',\n",
       " 'Unnamed: 0 zipcode_98008',\n",
       " 'Unnamed: 0 zipcode_98010',\n",
       " 'Unnamed: 0 zipcode_98011',\n",
       " 'Unnamed: 0 zipcode_98014',\n",
       " 'Unnamed: 0 zipcode_98019',\n",
       " 'Unnamed: 0 zipcode_98022',\n",
       " 'Unnamed: 0 zipcode_98023',\n",
       " 'Unnamed: 0 zipcode_98024',\n",
       " 'Unnamed: 0 zipcode_98027',\n",
       " 'Unnamed: 0 zipcode_98028',\n",
       " 'Unnamed: 0 zipcode_98029',\n",
       " 'Unnamed: 0 zipcode_98030',\n",
       " 'Unnamed: 0 zipcode_98031',\n",
       " 'Unnamed: 0 zipcode_98032',\n",
       " 'Unnamed: 0 zipcode_98033',\n",
       " 'Unnamed: 0 zipcode_98034',\n",
       " 'Unnamed: 0 zipcode_98038',\n",
       " 'Unnamed: 0 zipcode_98042',\n",
       " 'Unnamed: 0 zipcode_98045',\n",
       " 'Unnamed: 0 zipcode_98052',\n",
       " 'Unnamed: 0 zipcode_98053',\n",
       " 'Unnamed: 0 zipcode_98055',\n",
       " 'Unnamed: 0 zipcode_98056',\n",
       " 'Unnamed: 0 zipcode_98058',\n",
       " 'Unnamed: 0 zipcode_98059',\n",
       " 'Unnamed: 0 zipcode_98065',\n",
       " 'Unnamed: 0 zipcode_98070',\n",
       " 'Unnamed: 0 zipcode_98072',\n",
       " 'Unnamed: 0 zipcode_98074',\n",
       " 'Unnamed: 0 zipcode_98075',\n",
       " 'Unnamed: 0 zipcode_98077',\n",
       " 'Unnamed: 0 zipcode_98092',\n",
       " 'Unnamed: 0 zipcode_98102',\n",
       " 'Unnamed: 0 zipcode_98103',\n",
       " 'Unnamed: 0 zipcode_98105',\n",
       " 'Unnamed: 0 zipcode_98106',\n",
       " 'Unnamed: 0 zipcode_98107',\n",
       " 'Unnamed: 0 zipcode_98108',\n",
       " 'Unnamed: 0 zipcode_98109',\n",
       " 'Unnamed: 0 zipcode_98115',\n",
       " 'Unnamed: 0 zipcode_98116',\n",
       " 'Unnamed: 0 zipcode_98117',\n",
       " 'Unnamed: 0 zipcode_98118',\n",
       " 'Unnamed: 0 zipcode_98119',\n",
       " 'Unnamed: 0 zipcode_98122',\n",
       " 'Unnamed: 0 zipcode_98125',\n",
       " 'Unnamed: 0 zipcode_98126',\n",
       " 'Unnamed: 0 zipcode_98133',\n",
       " 'Unnamed: 0 zipcode_98136',\n",
       " 'Unnamed: 0 zipcode_98144',\n",
       " 'Unnamed: 0 zipcode_98146',\n",
       " 'Unnamed: 0 zipcode_98148',\n",
       " 'Unnamed: 0 zipcode_98155',\n",
       " 'Unnamed: 0 zipcode_98166',\n",
       " 'Unnamed: 0 zipcode_98168',\n",
       " 'Unnamed: 0 zipcode_98177',\n",
       " 'Unnamed: 0 zipcode_98178',\n",
       " 'Unnamed: 0 zipcode_98188',\n",
       " 'Unnamed: 0 zipcode_98198',\n",
       " 'Unnamed: 0 zipcode_98199',\n",
       " 'Unnamed: 0 season_sold_2',\n",
       " 'Unnamed: 0 season_sold_3',\n",
       " 'Unnamed: 0 season_sold_4',\n",
       " 'Unnamed: 0 view_1',\n",
       " 'Unnamed: 0 condition_2',\n",
       " 'Unnamed: 0 condition_3',\n",
       " 'Unnamed: 0 condition_4',\n",
       " 'Unnamed: 0 condition_5',\n",
       " 'bedrooms age_built',\n",
       " 'bedrooms last_reno',\n",
       " 'bedrooms bath_sqft_ratio',\n",
       " 'bedrooms grade_3',\n",
       " 'bedrooms grade_4',\n",
       " 'bedrooms grade_5',\n",
       " 'bedrooms grade_8',\n",
       " 'bedrooms zipcode_98002',\n",
       " 'bedrooms zipcode_98003',\n",
       " 'bedrooms zipcode_98005',\n",
       " 'bedrooms zipcode_98007',\n",
       " 'bedrooms zipcode_98008',\n",
       " 'bedrooms zipcode_98010',\n",
       " 'bedrooms zipcode_98011',\n",
       " 'bedrooms zipcode_98014',\n",
       " 'bedrooms zipcode_98019',\n",
       " 'bedrooms zipcode_98022',\n",
       " 'bedrooms zipcode_98023',\n",
       " 'bedrooms zipcode_98024',\n",
       " 'bedrooms zipcode_98027',\n",
       " 'bedrooms zipcode_98028',\n",
       " 'bedrooms zipcode_98029',\n",
       " 'bedrooms zipcode_98030',\n",
       " 'bedrooms zipcode_98031',\n",
       " 'bedrooms zipcode_98032',\n",
       " 'bedrooms zipcode_98034',\n",
       " 'bedrooms zipcode_98038',\n",
       " 'bedrooms zipcode_98042',\n",
       " 'bedrooms zipcode_98045',\n",
       " 'bedrooms zipcode_98052',\n",
       " 'bedrooms zipcode_98053',\n",
       " 'bedrooms zipcode_98055',\n",
       " 'bedrooms zipcode_98056',\n",
       " 'bedrooms zipcode_98058',\n",
       " 'bedrooms zipcode_98059',\n",
       " 'bedrooms zipcode_98065',\n",
       " 'bedrooms zipcode_98070',\n",
       " 'bedrooms zipcode_98072',\n",
       " 'bedrooms zipcode_98074',\n",
       " 'bedrooms zipcode_98075',\n",
       " 'bedrooms zipcode_98077',\n",
       " 'bedrooms zipcode_98092',\n",
       " 'bedrooms zipcode_98102',\n",
       " 'bedrooms zipcode_98103',\n",
       " 'bedrooms zipcode_98105',\n",
       " 'bedrooms zipcode_98106',\n",
       " 'bedrooms zipcode_98107',\n",
       " 'bedrooms zipcode_98108',\n",
       " 'bedrooms zipcode_98109',\n",
       " 'bedrooms zipcode_98115',\n",
       " 'bedrooms zipcode_98116',\n",
       " 'bedrooms zipcode_98117',\n",
       " 'bedrooms zipcode_98118',\n",
       " 'bedrooms zipcode_98122',\n",
       " 'bedrooms zipcode_98125',\n",
       " 'bedrooms zipcode_98126',\n",
       " 'bedrooms zipcode_98133',\n",
       " 'bedrooms zipcode_98136',\n",
       " 'bedrooms zipcode_98144',\n",
       " 'bedrooms zipcode_98146',\n",
       " 'bedrooms zipcode_98148',\n",
       " 'bedrooms zipcode_98155',\n",
       " 'bedrooms zipcode_98166',\n",
       " 'bedrooms zipcode_98168',\n",
       " 'bedrooms zipcode_98177',\n",
       " 'bedrooms zipcode_98178',\n",
       " 'bedrooms zipcode_98188',\n",
       " 'bedrooms zipcode_98198',\n",
       " 'bedrooms zipcode_98199',\n",
       " 'bedrooms season_sold_2',\n",
       " 'bedrooms season_sold_3',\n",
       " 'bedrooms season_sold_4',\n",
       " 'bedrooms condition_2',\n",
       " 'bedrooms condition_4',\n",
       " 'bedrooms condition_5',\n",
       " 'bathrooms bed_sqft_ratio',\n",
       " 'bathrooms floors_sqft_ratio',\n",
       " 'bathrooms grade_3',\n",
       " 'bathrooms grade_4',\n",
       " 'bathrooms grade_5',\n",
       " 'bathrooms grade_8',\n",
       " 'bathrooms zipcode_98002',\n",
       " 'bathrooms zipcode_98003',\n",
       " 'bathrooms zipcode_98005',\n",
       " 'bathrooms zipcode_98007',\n",
       " 'bathrooms zipcode_98008',\n",
       " 'bathrooms zipcode_98010',\n",
       " 'bathrooms zipcode_98011',\n",
       " 'bathrooms zipcode_98014',\n",
       " 'bathrooms zipcode_98019',\n",
       " 'bathrooms zipcode_98022',\n",
       " 'bathrooms zipcode_98023',\n",
       " 'bathrooms zipcode_98024',\n",
       " 'bathrooms zipcode_98027',\n",
       " 'bathrooms zipcode_98028',\n",
       " 'bathrooms zipcode_98029',\n",
       " 'bathrooms zipcode_98030',\n",
       " 'bathrooms zipcode_98031',\n",
       " 'bathrooms zipcode_98032',\n",
       " 'bathrooms zipcode_98034',\n",
       " 'bathrooms zipcode_98038',\n",
       " 'bathrooms zipcode_98042',\n",
       " 'bathrooms zipcode_98045',\n",
       " 'bathrooms zipcode_98052',\n",
       " 'bathrooms zipcode_98053',\n",
       " 'bathrooms zipcode_98055',\n",
       " 'bathrooms zipcode_98056',\n",
       " 'bathrooms zipcode_98058',\n",
       " 'bathrooms zipcode_98059',\n",
       " 'bathrooms zipcode_98065',\n",
       " 'bathrooms zipcode_98070',\n",
       " 'bathrooms zipcode_98072',\n",
       " 'bathrooms zipcode_98074',\n",
       " 'bathrooms zipcode_98075',\n",
       " 'bathrooms zipcode_98077',\n",
       " 'bathrooms zipcode_98092',\n",
       " 'bathrooms zipcode_98103',\n",
       " 'bathrooms zipcode_98106',\n",
       " 'bathrooms zipcode_98107',\n",
       " 'bathrooms zipcode_98108',\n",
       " 'bathrooms zipcode_98109',\n",
       " 'bathrooms zipcode_98115',\n",
       " 'bathrooms zipcode_98116',\n",
       " 'bathrooms zipcode_98117',\n",
       " 'bathrooms zipcode_98118',\n",
       " 'bathrooms zipcode_98119',\n",
       " 'bathrooms zipcode_98122',\n",
       " 'bathrooms zipcode_98125',\n",
       " 'bathrooms zipcode_98126',\n",
       " 'bathrooms zipcode_98133',\n",
       " 'bathrooms zipcode_98136',\n",
       " 'bathrooms zipcode_98144',\n",
       " 'bathrooms zipcode_98146',\n",
       " 'bathrooms zipcode_98148',\n",
       " 'bathrooms zipcode_98155',\n",
       " 'bathrooms zipcode_98166',\n",
       " 'bathrooms zipcode_98168',\n",
       " 'bathrooms zipcode_98177',\n",
       " 'bathrooms zipcode_98178',\n",
       " 'bathrooms zipcode_98188',\n",
       " 'bathrooms zipcode_98198',\n",
       " 'bathrooms zipcode_98199',\n",
       " 'bathrooms season_sold_3',\n",
       " 'bathrooms season_sold_4',\n",
       " 'bathrooms condition_2',\n",
       " 'bathrooms condition_4',\n",
       " 'sqft_living grade_3',\n",
       " 'sqft_living grade_4',\n",
       " 'sqft_living grade_5',\n",
       " 'sqft_living grade_8',\n",
       " 'sqft_living zipcode_98002',\n",
       " 'sqft_living zipcode_98003',\n",
       " 'sqft_living zipcode_98005',\n",
       " 'sqft_living zipcode_98007',\n",
       " 'sqft_living zipcode_98008',\n",
       " 'sqft_living zipcode_98010',\n",
       " 'sqft_living zipcode_98011',\n",
       " 'sqft_living zipcode_98014',\n",
       " 'sqft_living zipcode_98019',\n",
       " 'sqft_living zipcode_98022',\n",
       " 'sqft_living zipcode_98023',\n",
       " 'sqft_living zipcode_98024',\n",
       " 'sqft_living zipcode_98027',\n",
       " 'sqft_living zipcode_98028',\n",
       " 'sqft_living zipcode_98029',\n",
       " 'sqft_living zipcode_98030',\n",
       " 'sqft_living zipcode_98031',\n",
       " 'sqft_living zipcode_98032',\n",
       " 'sqft_living zipcode_98034',\n",
       " 'sqft_living zipcode_98038',\n",
       " 'sqft_living zipcode_98042',\n",
       " 'sqft_living zipcode_98045',\n",
       " 'sqft_living zipcode_98052',\n",
       " 'sqft_living zipcode_98053',\n",
       " 'sqft_living zipcode_98055',\n",
       " 'sqft_living zipcode_98056',\n",
       " 'sqft_living zipcode_98058',\n",
       " 'sqft_living zipcode_98059',\n",
       " 'sqft_living zipcode_98065',\n",
       " 'sqft_living zipcode_98070',\n",
       " 'sqft_living zipcode_98072',\n",
       " 'sqft_living zipcode_98074',\n",
       " 'sqft_living zipcode_98075',\n",
       " 'sqft_living zipcode_98077',\n",
       " 'sqft_living zipcode_98092',\n",
       " 'sqft_living zipcode_98103',\n",
       " 'sqft_living zipcode_98106',\n",
       " 'sqft_living zipcode_98107',\n",
       " 'sqft_living zipcode_98108',\n",
       " 'sqft_living zipcode_98109',\n",
       " 'sqft_living zipcode_98115',\n",
       " 'sqft_living zipcode_98116',\n",
       " 'sqft_living zipcode_98117',\n",
       " 'sqft_living zipcode_98118',\n",
       " 'sqft_living zipcode_98122',\n",
       " 'sqft_living zipcode_98125',\n",
       " 'sqft_living zipcode_98126',\n",
       " 'sqft_living zipcode_98133',\n",
       " 'sqft_living zipcode_98136',\n",
       " 'sqft_living zipcode_98144',\n",
       " 'sqft_living zipcode_98146',\n",
       " 'sqft_living zipcode_98148',\n",
       " 'sqft_living zipcode_98155',\n",
       " 'sqft_living zipcode_98166',\n",
       " 'sqft_living zipcode_98168',\n",
       " 'sqft_living zipcode_98177',\n",
       " 'sqft_living zipcode_98178',\n",
       " 'sqft_living zipcode_98188',\n",
       " 'sqft_living zipcode_98198',\n",
       " 'sqft_living condition_2',\n",
       " 'sqft_lot^2',\n",
       " 'sqft_lot yr_built',\n",
       " 'sqft_lot yr_renovated',\n",
       " 'sqft_lot sqft_lot15',\n",
       " 'sqft_lot year_sold',\n",
       " 'sqft_lot age_built',\n",
       " 'sqft_lot last_reno',\n",
       " 'sqft_lot month_sold',\n",
       " 'sqft_lot was_reno',\n",
       " 'sqft_lot bed_sqft_ratio',\n",
       " 'sqft_lot bath_sqft_ratio',\n",
       " 'sqft_lot floors_sqft_ratio',\n",
       " 'sqft_lot grade_3',\n",
       " 'sqft_lot grade_4',\n",
       " 'sqft_lot grade_5',\n",
       " 'sqft_lot grade_6',\n",
       " 'sqft_lot grade_7',\n",
       " 'sqft_lot grade_8',\n",
       " 'sqft_lot grade_9',\n",
       " 'sqft_lot zipcode_98002',\n",
       " 'sqft_lot zipcode_98003',\n",
       " 'sqft_lot zipcode_98005',\n",
       " 'sqft_lot zipcode_98007',\n",
       " 'sqft_lot zipcode_98008',\n",
       " 'sqft_lot zipcode_98010',\n",
       " 'sqft_lot zipcode_98011',\n",
       " 'sqft_lot zipcode_98014',\n",
       " 'sqft_lot zipcode_98019',\n",
       " 'sqft_lot zipcode_98022',\n",
       " 'sqft_lot zipcode_98023',\n",
       " 'sqft_lot zipcode_98024',\n",
       " 'sqft_lot zipcode_98027',\n",
       " 'sqft_lot zipcode_98028',\n",
       " 'sqft_lot zipcode_98029',\n",
       " 'sqft_lot zipcode_98030',\n",
       " 'sqft_lot zipcode_98031',\n",
       " 'sqft_lot zipcode_98032',\n",
       " 'sqft_lot zipcode_98034',\n",
       " 'sqft_lot zipcode_98038',\n",
       " 'sqft_lot zipcode_98042',\n",
       " 'sqft_lot zipcode_98045',\n",
       " 'sqft_lot zipcode_98052',\n",
       " 'sqft_lot zipcode_98053',\n",
       " 'sqft_lot zipcode_98055',\n",
       " 'sqft_lot zipcode_98056',\n",
       " 'sqft_lot zipcode_98058',\n",
       " 'sqft_lot zipcode_98059',\n",
       " 'sqft_lot zipcode_98065',\n",
       " 'sqft_lot zipcode_98070',\n",
       " 'sqft_lot zipcode_98072',\n",
       " 'sqft_lot zipcode_98074',\n",
       " 'sqft_lot zipcode_98075',\n",
       " 'sqft_lot zipcode_98077',\n",
       " 'sqft_lot zipcode_98092',\n",
       " 'sqft_lot zipcode_98103',\n",
       " 'sqft_lot zipcode_98106',\n",
       " 'sqft_lot zipcode_98107',\n",
       " 'sqft_lot zipcode_98108',\n",
       " 'sqft_lot zipcode_98109',\n",
       " 'sqft_lot zipcode_98115',\n",
       " 'sqft_lot zipcode_98116',\n",
       " 'sqft_lot zipcode_98117',\n",
       " 'sqft_lot zipcode_98118',\n",
       " 'sqft_lot zipcode_98122',\n",
       " 'sqft_lot zipcode_98125',\n",
       " 'sqft_lot zipcode_98126',\n",
       " 'sqft_lot zipcode_98133',\n",
       " 'sqft_lot zipcode_98136',\n",
       " 'sqft_lot zipcode_98144',\n",
       " 'sqft_lot zipcode_98146',\n",
       " 'sqft_lot zipcode_98148',\n",
       " 'sqft_lot zipcode_98155',\n",
       " 'sqft_lot zipcode_98166',\n",
       " 'sqft_lot zipcode_98168',\n",
       " 'sqft_lot zipcode_98177',\n",
       " 'sqft_lot zipcode_98178',\n",
       " 'sqft_lot zipcode_98188',\n",
       " 'sqft_lot zipcode_98198',\n",
       " 'sqft_lot season_sold_2',\n",
       " 'sqft_lot season_sold_3',\n",
       " 'sqft_lot season_sold_4',\n",
       " 'sqft_lot view_1',\n",
       " 'sqft_lot view_2',\n",
       " 'sqft_lot view_3',\n",
       " 'sqft_lot condition_2',\n",
       " 'sqft_lot condition_3',\n",
       " 'sqft_lot condition_4',\n",
       " 'sqft_lot condition_5',\n",
       " 'floors last_reno',\n",
       " 'floors bath_sqft_ratio',\n",
       " 'floors floors_sqft_ratio',\n",
       " 'floors grade_3',\n",
       " 'floors grade_4',\n",
       " 'floors grade_5',\n",
       " 'floors grade_8',\n",
       " 'floors zipcode_98002',\n",
       " 'floors zipcode_98003',\n",
       " 'floors zipcode_98005',\n",
       " 'floors zipcode_98007',\n",
       " 'floors zipcode_98008',\n",
       " 'floors zipcode_98010',\n",
       " 'floors zipcode_98011',\n",
       " 'floors zipcode_98014',\n",
       " 'floors zipcode_98019',\n",
       " 'floors zipcode_98022',\n",
       " 'floors zipcode_98023',\n",
       " 'floors zipcode_98024',\n",
       " 'floors zipcode_98027',\n",
       " 'floors zipcode_98028',\n",
       " 'floors zipcode_98029',\n",
       " 'floors zipcode_98030',\n",
       " 'floors zipcode_98031',\n",
       " 'floors zipcode_98032',\n",
       " 'floors zipcode_98034',\n",
       " 'floors zipcode_98038',\n",
       " 'floors zipcode_98042',\n",
       " 'floors zipcode_98045',\n",
       " 'floors zipcode_98052',\n",
       " 'floors zipcode_98053',\n",
       " 'floors zipcode_98055',\n",
       " 'floors zipcode_98056',\n",
       " 'floors zipcode_98058',\n",
       " 'floors zipcode_98059',\n",
       " 'floors zipcode_98065',\n",
       " 'floors zipcode_98070',\n",
       " 'floors zipcode_98072',\n",
       " 'floors zipcode_98074',\n",
       " 'floors zipcode_98075',\n",
       " 'floors zipcode_98077',\n",
       " 'floors zipcode_98092',\n",
       " 'floors zipcode_98102',\n",
       " 'floors zipcode_98103',\n",
       " 'floors zipcode_98105',\n",
       " 'floors zipcode_98106',\n",
       " 'floors zipcode_98107',\n",
       " 'floors zipcode_98108',\n",
       " 'floors zipcode_98109',\n",
       " 'floors zipcode_98115',\n",
       " 'floors zipcode_98116',\n",
       " 'floors zipcode_98117',\n",
       " 'floors zipcode_98118',\n",
       " 'floors zipcode_98119',\n",
       " 'floors zipcode_98122',\n",
       " 'floors zipcode_98125',\n",
       " 'floors zipcode_98126',\n",
       " 'floors zipcode_98133',\n",
       " 'floors zipcode_98136',\n",
       " 'floors zipcode_98144',\n",
       " 'floors zipcode_98146',\n",
       " 'floors zipcode_98148',\n",
       " 'floors zipcode_98155',\n",
       " 'floors zipcode_98166',\n",
       " 'floors zipcode_98168',\n",
       " 'floors zipcode_98177',\n",
       " 'floors zipcode_98178',\n",
       " 'floors zipcode_98188',\n",
       " 'floors zipcode_98198',\n",
       " 'floors zipcode_98199',\n",
       " 'floors season_sold_2',\n",
       " 'floors season_sold_3',\n",
       " 'floors season_sold_4',\n",
       " 'floors condition_2',\n",
       " 'floors condition_3',\n",
       " 'floors condition_4',\n",
       " 'sqft_above grade_3',\n",
       " 'sqft_above grade_4',\n",
       " 'sqft_above grade_5',\n",
       " 'sqft_above grade_8',\n",
       " 'sqft_above zipcode_98002',\n",
       " 'sqft_above zipcode_98003',\n",
       " 'sqft_above zipcode_98005',\n",
       " 'sqft_above zipcode_98007',\n",
       " 'sqft_above zipcode_98008',\n",
       " 'sqft_above zipcode_98010',\n",
       " 'sqft_above zipcode_98011',\n",
       " 'sqft_above zipcode_98014',\n",
       " 'sqft_above zipcode_98019',\n",
       " 'sqft_above zipcode_98022',\n",
       " 'sqft_above zipcode_98023',\n",
       " 'sqft_above zipcode_98024',\n",
       " 'sqft_above zipcode_98027',\n",
       " 'sqft_above zipcode_98028',\n",
       " 'sqft_above zipcode_98029',\n",
       " 'sqft_above zipcode_98030',\n",
       " 'sqft_above zipcode_98031',\n",
       " 'sqft_above zipcode_98032',\n",
       " 'sqft_above zipcode_98034',\n",
       " 'sqft_above zipcode_98038',\n",
       " 'sqft_above zipcode_98042',\n",
       " 'sqft_above zipcode_98045',\n",
       " 'sqft_above zipcode_98052',\n",
       " 'sqft_above zipcode_98053',\n",
       " 'sqft_above zipcode_98055',\n",
       " 'sqft_above zipcode_98056',\n",
       " 'sqft_above zipcode_98058',\n",
       " 'sqft_above zipcode_98059',\n",
       " 'sqft_above zipcode_98065',\n",
       " 'sqft_above zipcode_98070',\n",
       " 'sqft_above zipcode_98072',\n",
       " 'sqft_above zipcode_98074',\n",
       " 'sqft_above zipcode_98075',\n",
       " 'sqft_above zipcode_98077',\n",
       " 'sqft_above zipcode_98092',\n",
       " 'sqft_above zipcode_98103',\n",
       " 'sqft_above zipcode_98106',\n",
       " 'sqft_above zipcode_98107',\n",
       " 'sqft_above zipcode_98108',\n",
       " 'sqft_above zipcode_98109',\n",
       " 'sqft_above zipcode_98115',\n",
       " 'sqft_above zipcode_98116',\n",
       " 'sqft_above zipcode_98117',\n",
       " 'sqft_above zipcode_98118',\n",
       " 'sqft_above zipcode_98122',\n",
       " 'sqft_above zipcode_98125',\n",
       " 'sqft_above zipcode_98126',\n",
       " 'sqft_above zipcode_98133',\n",
       " 'sqft_above zipcode_98136',\n",
       " 'sqft_above zipcode_98144',\n",
       " 'sqft_above zipcode_98146',\n",
       " 'sqft_above zipcode_98148',\n",
       " 'sqft_above zipcode_98155',\n",
       " 'sqft_above zipcode_98166',\n",
       " 'sqft_above zipcode_98168',\n",
       " 'sqft_above zipcode_98177',\n",
       " 'sqft_above zipcode_98178',\n",
       " 'sqft_above zipcode_98188',\n",
       " 'sqft_above zipcode_98198',\n",
       " 'sqft_above condition_2',\n",
       " 'sqft_basement grade_3',\n",
       " 'sqft_basement grade_4',\n",
       " 'sqft_basement grade_5',\n",
       " 'sqft_basement grade_6',\n",
       " 'sqft_basement grade_7',\n",
       " 'sqft_basement grade_8',\n",
       " 'sqft_basement zipcode_98002',\n",
       " 'sqft_basement zipcode_98003',\n",
       " 'sqft_basement zipcode_98005',\n",
       " 'sqft_basement zipcode_98007',\n",
       " 'sqft_basement zipcode_98008',\n",
       " 'sqft_basement zipcode_98010',\n",
       " 'sqft_basement zipcode_98011',\n",
       " 'sqft_basement zipcode_98014',\n",
       " 'sqft_basement zipcode_98019',\n",
       " 'sqft_basement zipcode_98022',\n",
       " 'sqft_basement zipcode_98023',\n",
       " 'sqft_basement zipcode_98024',\n",
       " 'sqft_basement zipcode_98027',\n",
       " 'sqft_basement zipcode_98028',\n",
       " 'sqft_basement zipcode_98029',\n",
       " 'sqft_basement zipcode_98030',\n",
       " 'sqft_basement zipcode_98031',\n",
       " 'sqft_basement zipcode_98032',\n",
       " 'sqft_basement zipcode_98034',\n",
       " 'sqft_basement zipcode_98038',\n",
       " 'sqft_basement zipcode_98042',\n",
       " 'sqft_basement zipcode_98045',\n",
       " 'sqft_basement zipcode_98052',\n",
       " 'sqft_basement zipcode_98053',\n",
       " 'sqft_basement zipcode_98055',\n",
       " 'sqft_basement zipcode_98056',\n",
       " 'sqft_basement zipcode_98058',\n",
       " 'sqft_basement zipcode_98059',\n",
       " 'sqft_basement zipcode_98065',\n",
       " 'sqft_basement zipcode_98070',\n",
       " 'sqft_basement zipcode_98072',\n",
       " 'sqft_basement zipcode_98074',\n",
       " 'sqft_basement zipcode_98075',\n",
       " 'sqft_basement zipcode_98077',\n",
       " 'sqft_basement zipcode_98092',\n",
       " 'sqft_basement zipcode_98103',\n",
       " 'sqft_basement zipcode_98106',\n",
       " 'sqft_basement zipcode_98107',\n",
       " 'sqft_basement zipcode_98108',\n",
       " 'sqft_basement zipcode_98109',\n",
       " 'sqft_basement zipcode_98115',\n",
       " 'sqft_basement zipcode_98116',\n",
       " 'sqft_basement zipcode_98117',\n",
       " 'sqft_basement zipcode_98118',\n",
       " 'sqft_basement zipcode_98119',\n",
       " 'sqft_basement zipcode_98122',\n",
       " 'sqft_basement zipcode_98125',\n",
       " 'sqft_basement zipcode_98126',\n",
       " 'sqft_basement zipcode_98133',\n",
       " 'sqft_basement zipcode_98136',\n",
       " 'sqft_basement zipcode_98144',\n",
       " 'sqft_basement zipcode_98146',\n",
       " 'sqft_basement zipcode_98148',\n",
       " 'sqft_basement zipcode_98155',\n",
       " 'sqft_basement zipcode_98166',\n",
       " 'sqft_basement zipcode_98168',\n",
       " 'sqft_basement zipcode_98177',\n",
       " 'sqft_basement zipcode_98178',\n",
       " 'sqft_basement zipcode_98188',\n",
       " 'sqft_basement zipcode_98198',\n",
       " 'sqft_basement zipcode_98199',\n",
       " 'sqft_basement condition_2',\n",
       " 'yr_built^2',\n",
       " 'yr_built sqft_lot15',\n",
       " 'yr_built year_sold',\n",
       " 'yr_built age_built',\n",
       " 'yr_built month_sold',\n",
       " 'yr_built grade_3',\n",
       " 'yr_built grade_4',\n",
       " 'yr_built grade_5',\n",
       " 'yr_built grade_8',\n",
       " 'yr_built zipcode_98002',\n",
       " 'yr_built zipcode_98003',\n",
       " 'yr_built zipcode_98005',\n",
       " 'yr_built zipcode_98007',\n",
       " 'yr_built zipcode_98008',\n",
       " 'yr_built zipcode_98010',\n",
       " 'yr_built zipcode_98011',\n",
       " 'yr_built zipcode_98014',\n",
       " 'yr_built zipcode_98019',\n",
       " 'yr_built zipcode_98022',\n",
       " 'yr_built zipcode_98024',\n",
       " 'yr_built zipcode_98027',\n",
       " 'yr_built zipcode_98028',\n",
       " 'yr_built zipcode_98029',\n",
       " 'yr_built zipcode_98030',\n",
       " 'yr_built zipcode_98031',\n",
       " 'yr_built zipcode_98032',\n",
       " 'yr_built zipcode_98034',\n",
       " 'yr_built zipcode_98038',\n",
       " 'yr_built zipcode_98042',\n",
       " 'yr_built zipcode_98045',\n",
       " 'yr_built zipcode_98052',\n",
       " 'yr_built zipcode_98053',\n",
       " 'yr_built zipcode_98055',\n",
       " 'yr_built zipcode_98056',\n",
       " 'yr_built zipcode_98058',\n",
       " 'yr_built zipcode_98059',\n",
       " 'yr_built zipcode_98065',\n",
       " 'yr_built zipcode_98070',\n",
       " 'yr_built zipcode_98072',\n",
       " 'yr_built zipcode_98074',\n",
       " 'yr_built zipcode_98075',\n",
       " 'yr_built zipcode_98077',\n",
       " 'yr_built zipcode_98092',\n",
       " 'yr_built zipcode_98102',\n",
       " 'yr_built zipcode_98103',\n",
       " 'yr_built zipcode_98105',\n",
       " 'yr_built zipcode_98106',\n",
       " 'yr_built zipcode_98107',\n",
       " 'yr_built zipcode_98108',\n",
       " 'yr_built zipcode_98109',\n",
       " 'yr_built zipcode_98115',\n",
       " 'yr_built zipcode_98116',\n",
       " 'yr_built zipcode_98117',\n",
       " 'yr_built zipcode_98118',\n",
       " 'yr_built zipcode_98119',\n",
       " 'yr_built zipcode_98122',\n",
       " 'yr_built zipcode_98125',\n",
       " 'yr_built zipcode_98126',\n",
       " 'yr_built zipcode_98133',\n",
       " 'yr_built zipcode_98136',\n",
       " 'yr_built zipcode_98144',\n",
       " 'yr_built zipcode_98146',\n",
       " 'yr_built zipcode_98148',\n",
       " 'yr_built zipcode_98155',\n",
       " 'yr_built zipcode_98166',\n",
       " 'yr_built zipcode_98168',\n",
       " 'yr_built zipcode_98177',\n",
       " 'yr_built zipcode_98178',\n",
       " 'yr_built zipcode_98188',\n",
       " 'yr_built zipcode_98198',\n",
       " 'yr_built zipcode_98199',\n",
       " 'yr_built season_sold_2',\n",
       " 'yr_built season_sold_3',\n",
       " 'yr_built season_sold_4',\n",
       " 'yr_built view_1',\n",
       " 'yr_built condition_2',\n",
       " 'yr_built condition_3',\n",
       " 'yr_built condition_4',\n",
       " 'yr_built condition_5',\n",
       " 'yr_renovated sqft_lot15',\n",
       " 'yr_renovated last_reno',\n",
       " 'yr_renovated bed_sqft_ratio',\n",
       " 'yr_renovated bath_sqft_ratio',\n",
       " 'yr_renovated floors_sqft_ratio',\n",
       " 'yr_renovated grade_3',\n",
       " 'yr_renovated grade_4',\n",
       " 'yr_renovated grade_5',\n",
       " 'yr_renovated grade_6',\n",
       " 'yr_renovated grade_7',\n",
       " 'yr_renovated grade_8',\n",
       " 'yr_renovated grade_12',\n",
       " 'yr_renovated zipcode_98002',\n",
       " 'yr_renovated zipcode_98003',\n",
       " 'yr_renovated zipcode_98005',\n",
       " 'yr_renovated zipcode_98006',\n",
       " 'yr_renovated zipcode_98007',\n",
       " 'yr_renovated zipcode_98008',\n",
       " 'yr_renovated zipcode_98010',\n",
       " 'yr_renovated zipcode_98011',\n",
       " 'yr_renovated zipcode_98014',\n",
       " 'yr_renovated zipcode_98019',\n",
       " 'yr_renovated zipcode_98022',\n",
       " 'yr_renovated zipcode_98023',\n",
       " 'yr_renovated zipcode_98024',\n",
       " 'yr_renovated zipcode_98027',\n",
       " 'yr_renovated zipcode_98028',\n",
       " 'yr_renovated zipcode_98029',\n",
       " 'yr_renovated zipcode_98030',\n",
       " 'yr_renovated zipcode_98031',\n",
       " 'yr_renovated zipcode_98032',\n",
       " 'yr_renovated zipcode_98033',\n",
       " 'yr_renovated zipcode_98034',\n",
       " 'yr_renovated zipcode_98038',\n",
       " 'yr_renovated zipcode_98039',\n",
       " 'yr_renovated zipcode_98042',\n",
       " 'yr_renovated zipcode_98045',\n",
       " 'yr_renovated zipcode_98052',\n",
       " 'yr_renovated zipcode_98053',\n",
       " 'yr_renovated zipcode_98055',\n",
       " 'yr_renovated zipcode_98056',\n",
       " 'yr_renovated zipcode_98058',\n",
       " 'yr_renovated zipcode_98059',\n",
       " 'yr_renovated zipcode_98065',\n",
       " 'yr_renovated zipcode_98070',\n",
       " 'yr_renovated zipcode_98072',\n",
       " 'yr_renovated zipcode_98074',\n",
       " 'yr_renovated zipcode_98075',\n",
       " 'yr_renovated zipcode_98077',\n",
       " 'yr_renovated zipcode_98092',\n",
       " 'yr_renovated zipcode_98102',\n",
       " 'yr_renovated zipcode_98103',\n",
       " 'yr_renovated zipcode_98105',\n",
       " 'yr_renovated zipcode_98106',\n",
       " 'yr_renovated zipcode_98107',\n",
       " 'yr_renovated zipcode_98108',\n",
       " 'yr_renovated zipcode_98109',\n",
       " 'yr_renovated zipcode_98112',\n",
       " 'yr_renovated zipcode_98115',\n",
       " 'yr_renovated zipcode_98116',\n",
       " 'yr_renovated zipcode_98117',\n",
       " 'yr_renovated zipcode_98118',\n",
       " 'yr_renovated zipcode_98119',\n",
       " 'yr_renovated zipcode_98122',\n",
       " 'yr_renovated zipcode_98125',\n",
       " 'yr_renovated zipcode_98126',\n",
       " 'yr_renovated zipcode_98133',\n",
       " 'yr_renovated zipcode_98136',\n",
       " 'yr_renovated zipcode_98144',\n",
       " 'yr_renovated zipcode_98146',\n",
       " 'yr_renovated zipcode_98148',\n",
       " 'yr_renovated zipcode_98155',\n",
       " 'yr_renovated zipcode_98166',\n",
       " 'yr_renovated zipcode_98168',\n",
       " 'yr_renovated zipcode_98177',\n",
       " 'yr_renovated zipcode_98178',\n",
       " 'yr_renovated zipcode_98188',\n",
       " 'yr_renovated zipcode_98198',\n",
       " 'yr_renovated zipcode_98199',\n",
       " 'yr_renovated season_sold_2',\n",
       " 'yr_renovated season_sold_3',\n",
       " 'yr_renovated season_sold_4',\n",
       " 'yr_renovated view_1',\n",
       " 'yr_renovated view_2',\n",
       " 'yr_renovated condition_2',\n",
       " 'yr_renovated condition_4',\n",
       " 'yr_renovated condition_5',\n",
       " 'sqft_living15 bed_sqft_ratio',\n",
       " 'sqft_living15 floors_sqft_ratio',\n",
       " 'sqft_living15 grade_3',\n",
       " 'sqft_living15 grade_4',\n",
       " 'sqft_living15 grade_5',\n",
       " 'sqft_living15 grade_8',\n",
       " 'sqft_living15 zipcode_98002',\n",
       " 'sqft_living15 zipcode_98003',\n",
       " 'sqft_living15 zipcode_98005',\n",
       " 'sqft_living15 zipcode_98007',\n",
       " 'sqft_living15 zipcode_98008',\n",
       " 'sqft_living15 zipcode_98010',\n",
       " 'sqft_living15 zipcode_98011',\n",
       " 'sqft_living15 zipcode_98014',\n",
       " 'sqft_living15 zipcode_98019',\n",
       " 'sqft_living15 zipcode_98022',\n",
       " 'sqft_living15 zipcode_98023',\n",
       " 'sqft_living15 zipcode_98024',\n",
       " 'sqft_living15 zipcode_98027',\n",
       " 'sqft_living15 zipcode_98028',\n",
       " 'sqft_living15 zipcode_98029',\n",
       " 'sqft_living15 zipcode_98030',\n",
       " 'sqft_living15 zipcode_98031',\n",
       " 'sqft_living15 zipcode_98032',\n",
       " 'sqft_living15 zipcode_98034',\n",
       " 'sqft_living15 zipcode_98038',\n",
       " 'sqft_living15 zipcode_98042',\n",
       " 'sqft_living15 zipcode_98045',\n",
       " 'sqft_living15 zipcode_98052',\n",
       " 'sqft_living15 zipcode_98053',\n",
       " 'sqft_living15 zipcode_98055',\n",
       " 'sqft_living15 zipcode_98056',\n",
       " 'sqft_living15 zipcode_98058',\n",
       " 'sqft_living15 zipcode_98059',\n",
       " 'sqft_living15 zipcode_98065',\n",
       " 'sqft_living15 zipcode_98070',\n",
       " 'sqft_living15 zipcode_98072',\n",
       " 'sqft_living15 zipcode_98074',\n",
       " 'sqft_living15 zipcode_98075',\n",
       " 'sqft_living15 zipcode_98077',\n",
       " 'sqft_living15 zipcode_98092',\n",
       " 'sqft_living15 zipcode_98103',\n",
       " 'sqft_living15 zipcode_98106',\n",
       " 'sqft_living15 zipcode_98107',\n",
       " 'sqft_living15 zipcode_98108',\n",
       " 'sqft_living15 zipcode_98109',\n",
       " 'sqft_living15 zipcode_98115',\n",
       " 'sqft_living15 zipcode_98116',\n",
       " 'sqft_living15 zipcode_98117',\n",
       " 'sqft_living15 zipcode_98118',\n",
       " 'sqft_living15 zipcode_98119',\n",
       " 'sqft_living15 zipcode_98122',\n",
       " 'sqft_living15 zipcode_98125',\n",
       " 'sqft_living15 zipcode_98126',\n",
       " 'sqft_living15 zipcode_98133',\n",
       " 'sqft_living15 zipcode_98136',\n",
       " 'sqft_living15 zipcode_98144',\n",
       " 'sqft_living15 zipcode_98146',\n",
       " 'sqft_living15 zipcode_98148',\n",
       " 'sqft_living15 zipcode_98155',\n",
       " 'sqft_living15 zipcode_98166',\n",
       " 'sqft_living15 zipcode_98168',\n",
       " 'sqft_living15 zipcode_98177',\n",
       " 'sqft_living15 zipcode_98178',\n",
       " 'sqft_living15 zipcode_98188',\n",
       " 'sqft_living15 zipcode_98198',\n",
       " 'sqft_living15 zipcode_98199',\n",
       " 'sqft_living15 season_sold_4',\n",
       " 'sqft_living15 condition_2',\n",
       " 'sqft_living15 condition_4',\n",
       " 'sqft_lot15^2',\n",
       " 'sqft_lot15 year_sold',\n",
       " 'sqft_lot15 age_built',\n",
       " 'sqft_lot15 last_reno',\n",
       " 'sqft_lot15 month_sold',\n",
       " 'sqft_lot15 was_reno',\n",
       " 'sqft_lot15 bed_sqft_ratio',\n",
       " 'sqft_lot15 bath_sqft_ratio',\n",
       " 'sqft_lot15 floors_sqft_ratio',\n",
       " 'sqft_lot15 grade_3',\n",
       " 'sqft_lot15 grade_4',\n",
       " 'sqft_lot15 grade_5',\n",
       " 'sqft_lot15 grade_6',\n",
       " 'sqft_lot15 grade_8',\n",
       " 'sqft_lot15 grade_9',\n",
       " 'sqft_lot15 zipcode_98002',\n",
       " 'sqft_lot15 zipcode_98003',\n",
       " 'sqft_lot15 zipcode_98005',\n",
       " 'sqft_lot15 zipcode_98007',\n",
       " 'sqft_lot15 zipcode_98008',\n",
       " 'sqft_lot15 zipcode_98010',\n",
       " 'sqft_lot15 zipcode_98011',\n",
       " 'sqft_lot15 zipcode_98014',\n",
       " 'sqft_lot15 zipcode_98019',\n",
       " 'sqft_lot15 zipcode_98022',\n",
       " 'sqft_lot15 zipcode_98023',\n",
       " 'sqft_lot15 zipcode_98024',\n",
       " 'sqft_lot15 zipcode_98027',\n",
       " 'sqft_lot15 zipcode_98028',\n",
       " 'sqft_lot15 zipcode_98029',\n",
       " 'sqft_lot15 zipcode_98030',\n",
       " 'sqft_lot15 zipcode_98031',\n",
       " 'sqft_lot15 zipcode_98032',\n",
       " 'sqft_lot15 zipcode_98034',\n",
       " 'sqft_lot15 zipcode_98038',\n",
       " 'sqft_lot15 zipcode_98042',\n",
       " 'sqft_lot15 zipcode_98045',\n",
       " 'sqft_lot15 zipcode_98052',\n",
       " 'sqft_lot15 zipcode_98053',\n",
       " 'sqft_lot15 zipcode_98055',\n",
       " 'sqft_lot15 zipcode_98056',\n",
       " 'sqft_lot15 zipcode_98058',\n",
       " 'sqft_lot15 zipcode_98059',\n",
       " 'sqft_lot15 zipcode_98065',\n",
       " 'sqft_lot15 zipcode_98070',\n",
       " 'sqft_lot15 zipcode_98072',\n",
       " 'sqft_lot15 zipcode_98074',\n",
       " 'sqft_lot15 zipcode_98075',\n",
       " 'sqft_lot15 zipcode_98077',\n",
       " 'sqft_lot15 zipcode_98092',\n",
       " 'sqft_lot15 zipcode_98103',\n",
       " 'sqft_lot15 zipcode_98106',\n",
       " 'sqft_lot15 zipcode_98107',\n",
       " 'sqft_lot15 zipcode_98108',\n",
       " 'sqft_lot15 zipcode_98109',\n",
       " 'sqft_lot15 zipcode_98115',\n",
       " 'sqft_lot15 zipcode_98116',\n",
       " 'sqft_lot15 zipcode_98117',\n",
       " 'sqft_lot15 zipcode_98118',\n",
       " 'sqft_lot15 zipcode_98119',\n",
       " 'sqft_lot15 zipcode_98122',\n",
       " 'sqft_lot15 zipcode_98125',\n",
       " 'sqft_lot15 zipcode_98126',\n",
       " 'sqft_lot15 zipcode_98133',\n",
       " 'sqft_lot15 zipcode_98136',\n",
       " 'sqft_lot15 zipcode_98144',\n",
       " 'sqft_lot15 zipcode_98146',\n",
       " 'sqft_lot15 zipcode_98148',\n",
       " 'sqft_lot15 zipcode_98155',\n",
       " 'sqft_lot15 zipcode_98166',\n",
       " 'sqft_lot15 zipcode_98168',\n",
       " 'sqft_lot15 zipcode_98177',\n",
       " 'sqft_lot15 zipcode_98178',\n",
       " 'sqft_lot15 zipcode_98188',\n",
       " 'sqft_lot15 zipcode_98198',\n",
       " 'sqft_lot15 zipcode_98199',\n",
       " 'sqft_lot15 season_sold_2',\n",
       " 'sqft_lot15 season_sold_3',\n",
       " 'sqft_lot15 season_sold_4',\n",
       " 'sqft_lot15 view_1',\n",
       " 'sqft_lot15 view_2',\n",
       " 'sqft_lot15 view_3',\n",
       " 'sqft_lot15 condition_2',\n",
       " 'sqft_lot15 condition_3',\n",
       " 'sqft_lot15 condition_4',\n",
       " 'sqft_lot15 condition_5',\n",
       " 'year_sold^2',\n",
       " 'year_sold age_built',\n",
       " 'year_sold month_sold',\n",
       " 'year_sold grade_3',\n",
       " ...]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(poly_removed_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bedrooms',\n",
       " 'bathrooms',\n",
       " 'sqft_living',\n",
       " 'floors',\n",
       " 'sqft_above',\n",
       " 'sqft_basement',\n",
       " 'yr_renovated',\n",
       " 'sqft_living15',\n",
       " 'has_basement',\n",
       " 'last_reno',\n",
       " 'was_reno',\n",
       " 'bed_sqft_ratio',\n",
       " 'bath_sqft_ratio',\n",
       " 'floors_sqft_ratio',\n",
       " 'grade_6',\n",
       " 'grade_7',\n",
       " 'grade_9',\n",
       " 'grade_10',\n",
       " 'grade_11',\n",
       " 'grade_12',\n",
       " 'grade_13',\n",
       " 'waterfront_1',\n",
       " 'zipcode_98004',\n",
       " 'zipcode_98006',\n",
       " 'zipcode_98023',\n",
       " 'zipcode_98039',\n",
       " 'zipcode_98040',\n",
       " 'zipcode_98112',\n",
       " 'view_2',\n",
       " 'view_3',\n",
       " 'view_4',\n",
       " 'Unnamed: 0 bedrooms',\n",
       " 'Unnamed: 0 bathrooms',\n",
       " 'Unnamed: 0 sqft_living',\n",
       " 'Unnamed: 0 floors',\n",
       " 'Unnamed: 0 sqft_above',\n",
       " 'Unnamed: 0 sqft_basement',\n",
       " 'Unnamed: 0 sqft_living15',\n",
       " 'Unnamed: 0 has_basement',\n",
       " 'Unnamed: 0 bed_sqft_ratio',\n",
       " 'Unnamed: 0 bath_sqft_ratio',\n",
       " 'Unnamed: 0 floors_sqft_ratio',\n",
       " 'Unnamed: 0 grade_6',\n",
       " 'Unnamed: 0 grade_7',\n",
       " 'Unnamed: 0 grade_9',\n",
       " 'Unnamed: 0 grade_10',\n",
       " 'Unnamed: 0 grade_11',\n",
       " 'Unnamed: 0 grade_12',\n",
       " 'Unnamed: 0 grade_13',\n",
       " 'Unnamed: 0 waterfront_1',\n",
       " 'Unnamed: 0 zipcode_98004',\n",
       " 'Unnamed: 0 zipcode_98006',\n",
       " 'Unnamed: 0 zipcode_98039',\n",
       " 'Unnamed: 0 zipcode_98040',\n",
       " 'Unnamed: 0 zipcode_98112',\n",
       " 'Unnamed: 0 view_2',\n",
       " 'Unnamed: 0 view_3',\n",
       " 'Unnamed: 0 view_4',\n",
       " 'bedrooms^2',\n",
       " 'bedrooms bathrooms',\n",
       " 'bedrooms sqft_living',\n",
       " 'bedrooms sqft_lot',\n",
       " 'bedrooms floors',\n",
       " 'bedrooms sqft_above',\n",
       " 'bedrooms sqft_basement',\n",
       " 'bedrooms yr_built',\n",
       " 'bedrooms yr_renovated',\n",
       " 'bedrooms sqft_living15',\n",
       " 'bedrooms sqft_lot15',\n",
       " 'bedrooms year_sold',\n",
       " 'bedrooms has_basement',\n",
       " 'bedrooms month_sold',\n",
       " 'bedrooms was_reno',\n",
       " 'bedrooms bed_sqft_ratio',\n",
       " 'bedrooms floors_sqft_ratio',\n",
       " 'bedrooms grade_6',\n",
       " 'bedrooms grade_7',\n",
       " 'bedrooms grade_9',\n",
       " 'bedrooms grade_10',\n",
       " 'bedrooms grade_11',\n",
       " 'bedrooms grade_12',\n",
       " 'bedrooms grade_13',\n",
       " 'bedrooms waterfront_1',\n",
       " 'bedrooms zipcode_98004',\n",
       " 'bedrooms zipcode_98006',\n",
       " 'bedrooms zipcode_98033',\n",
       " 'bedrooms zipcode_98039',\n",
       " 'bedrooms zipcode_98040',\n",
       " 'bedrooms zipcode_98112',\n",
       " 'bedrooms zipcode_98119',\n",
       " 'bedrooms view_1',\n",
       " 'bedrooms view_2',\n",
       " 'bedrooms view_3',\n",
       " 'bedrooms view_4',\n",
       " 'bedrooms condition_3',\n",
       " 'bathrooms^2',\n",
       " 'bathrooms sqft_living',\n",
       " 'bathrooms sqft_lot',\n",
       " 'bathrooms floors',\n",
       " 'bathrooms sqft_above',\n",
       " 'bathrooms sqft_basement',\n",
       " 'bathrooms yr_built',\n",
       " 'bathrooms yr_renovated',\n",
       " 'bathrooms sqft_living15',\n",
       " 'bathrooms sqft_lot15',\n",
       " 'bathrooms year_sold',\n",
       " 'bathrooms has_basement',\n",
       " 'bathrooms age_built',\n",
       " 'bathrooms last_reno',\n",
       " 'bathrooms month_sold',\n",
       " 'bathrooms was_reno',\n",
       " 'bathrooms bath_sqft_ratio',\n",
       " 'bathrooms grade_6',\n",
       " 'bathrooms grade_7',\n",
       " 'bathrooms grade_9',\n",
       " 'bathrooms grade_10',\n",
       " 'bathrooms grade_11',\n",
       " 'bathrooms grade_12',\n",
       " 'bathrooms grade_13',\n",
       " 'bathrooms waterfront_1',\n",
       " 'bathrooms zipcode_98004',\n",
       " 'bathrooms zipcode_98006',\n",
       " 'bathrooms zipcode_98033',\n",
       " 'bathrooms zipcode_98039',\n",
       " 'bathrooms zipcode_98040',\n",
       " 'bathrooms zipcode_98102',\n",
       " 'bathrooms zipcode_98105',\n",
       " 'bathrooms zipcode_98112',\n",
       " 'bathrooms season_sold_2',\n",
       " 'bathrooms view_1',\n",
       " 'bathrooms view_2',\n",
       " 'bathrooms view_3',\n",
       " 'bathrooms view_4',\n",
       " 'bathrooms condition_3',\n",
       " 'bathrooms condition_5',\n",
       " 'sqft_living^2',\n",
       " 'sqft_living sqft_lot',\n",
       " 'sqft_living floors',\n",
       " 'sqft_living sqft_above',\n",
       " 'sqft_living sqft_basement',\n",
       " 'sqft_living yr_built',\n",
       " 'sqft_living yr_renovated',\n",
       " 'sqft_living sqft_living15',\n",
       " 'sqft_living sqft_lot15',\n",
       " 'sqft_living year_sold',\n",
       " 'sqft_living has_basement',\n",
       " 'sqft_living age_built',\n",
       " 'sqft_living last_reno',\n",
       " 'sqft_living month_sold',\n",
       " 'sqft_living was_reno',\n",
       " 'sqft_living bed_sqft_ratio',\n",
       " 'sqft_living bath_sqft_ratio',\n",
       " 'sqft_living floors_sqft_ratio',\n",
       " 'sqft_living grade_6',\n",
       " 'sqft_living grade_7',\n",
       " 'sqft_living grade_9',\n",
       " 'sqft_living grade_10',\n",
       " 'sqft_living grade_11',\n",
       " 'sqft_living grade_12',\n",
       " 'sqft_living grade_13',\n",
       " 'sqft_living waterfront_1',\n",
       " 'sqft_living zipcode_98004',\n",
       " 'sqft_living zipcode_98006',\n",
       " 'sqft_living zipcode_98033',\n",
       " 'sqft_living zipcode_98039',\n",
       " 'sqft_living zipcode_98040',\n",
       " 'sqft_living zipcode_98102',\n",
       " 'sqft_living zipcode_98105',\n",
       " 'sqft_living zipcode_98112',\n",
       " 'sqft_living zipcode_98119',\n",
       " 'sqft_living zipcode_98199',\n",
       " 'sqft_living season_sold_2',\n",
       " 'sqft_living season_sold_3',\n",
       " 'sqft_living season_sold_4',\n",
       " 'sqft_living view_1',\n",
       " 'sqft_living view_2',\n",
       " 'sqft_living view_3',\n",
       " 'sqft_living view_4',\n",
       " 'sqft_living condition_3',\n",
       " 'sqft_living condition_4',\n",
       " 'sqft_living condition_5',\n",
       " 'sqft_lot floors',\n",
       " 'sqft_lot sqft_above',\n",
       " 'sqft_lot sqft_basement',\n",
       " 'sqft_lot sqft_living15',\n",
       " 'sqft_lot has_basement',\n",
       " 'sqft_lot grade_10',\n",
       " 'sqft_lot grade_11',\n",
       " 'sqft_lot grade_12',\n",
       " 'sqft_lot grade_13',\n",
       " 'sqft_lot waterfront_1',\n",
       " 'sqft_lot zipcode_98004',\n",
       " 'sqft_lot zipcode_98006',\n",
       " 'sqft_lot zipcode_98033',\n",
       " 'sqft_lot zipcode_98039',\n",
       " 'sqft_lot zipcode_98040',\n",
       " 'sqft_lot zipcode_98102',\n",
       " 'sqft_lot zipcode_98105',\n",
       " 'sqft_lot zipcode_98112',\n",
       " 'sqft_lot zipcode_98119',\n",
       " 'sqft_lot zipcode_98199',\n",
       " 'sqft_lot view_4',\n",
       " 'floors^2',\n",
       " 'floors sqft_above',\n",
       " 'floors sqft_basement',\n",
       " 'floors yr_built',\n",
       " 'floors yr_renovated',\n",
       " 'floors sqft_living15',\n",
       " 'floors sqft_lot15',\n",
       " 'floors year_sold',\n",
       " 'floors has_basement',\n",
       " 'floors age_built',\n",
       " 'floors month_sold',\n",
       " 'floors was_reno',\n",
       " 'floors bed_sqft_ratio',\n",
       " 'floors grade_6',\n",
       " 'floors grade_7',\n",
       " 'floors grade_9',\n",
       " 'floors grade_10',\n",
       " 'floors grade_11',\n",
       " 'floors grade_12',\n",
       " 'floors grade_13',\n",
       " 'floors waterfront_1',\n",
       " 'floors zipcode_98004',\n",
       " 'floors zipcode_98006',\n",
       " 'floors zipcode_98033',\n",
       " 'floors zipcode_98039',\n",
       " 'floors zipcode_98040',\n",
       " 'floors zipcode_98112',\n",
       " 'floors view_1',\n",
       " 'floors view_2',\n",
       " 'floors view_3',\n",
       " 'floors view_4',\n",
       " 'floors condition_5',\n",
       " 'sqft_above^2',\n",
       " 'sqft_above sqft_basement',\n",
       " 'sqft_above yr_built',\n",
       " 'sqft_above yr_renovated',\n",
       " 'sqft_above sqft_living15',\n",
       " 'sqft_above sqft_lot15',\n",
       " 'sqft_above year_sold',\n",
       " 'sqft_above has_basement',\n",
       " 'sqft_above age_built',\n",
       " 'sqft_above last_reno',\n",
       " 'sqft_above month_sold',\n",
       " 'sqft_above was_reno',\n",
       " 'sqft_above bed_sqft_ratio',\n",
       " 'sqft_above bath_sqft_ratio',\n",
       " 'sqft_above floors_sqft_ratio',\n",
       " 'sqft_above grade_6',\n",
       " 'sqft_above grade_7',\n",
       " 'sqft_above grade_9',\n",
       " 'sqft_above grade_10',\n",
       " 'sqft_above grade_11',\n",
       " 'sqft_above grade_12',\n",
       " 'sqft_above grade_13',\n",
       " 'sqft_above waterfront_1',\n",
       " 'sqft_above zipcode_98004',\n",
       " 'sqft_above zipcode_98006',\n",
       " 'sqft_above zipcode_98033',\n",
       " 'sqft_above zipcode_98039',\n",
       " 'sqft_above zipcode_98040',\n",
       " 'sqft_above zipcode_98102',\n",
       " 'sqft_above zipcode_98105',\n",
       " 'sqft_above zipcode_98112',\n",
       " 'sqft_above zipcode_98119',\n",
       " 'sqft_above zipcode_98199',\n",
       " 'sqft_above season_sold_2',\n",
       " 'sqft_above season_sold_3',\n",
       " 'sqft_above season_sold_4',\n",
       " 'sqft_above view_1',\n",
       " 'sqft_above view_2',\n",
       " 'sqft_above view_3',\n",
       " 'sqft_above view_4',\n",
       " 'sqft_above condition_3',\n",
       " 'sqft_above condition_4',\n",
       " 'sqft_above condition_5',\n",
       " 'sqft_basement^2',\n",
       " 'sqft_basement yr_built',\n",
       " 'sqft_basement yr_renovated',\n",
       " 'sqft_basement sqft_living15',\n",
       " 'sqft_basement sqft_lot15',\n",
       " 'sqft_basement year_sold',\n",
       " 'sqft_basement has_basement',\n",
       " 'sqft_basement age_built',\n",
       " 'sqft_basement last_reno',\n",
       " 'sqft_basement month_sold',\n",
       " 'sqft_basement was_reno',\n",
       " 'sqft_basement bed_sqft_ratio',\n",
       " 'sqft_basement bath_sqft_ratio',\n",
       " 'sqft_basement floors_sqft_ratio',\n",
       " 'sqft_basement grade_9',\n",
       " 'sqft_basement grade_10',\n",
       " 'sqft_basement grade_11',\n",
       " 'sqft_basement grade_12',\n",
       " 'sqft_basement grade_13',\n",
       " 'sqft_basement waterfront_1',\n",
       " 'sqft_basement zipcode_98004',\n",
       " 'sqft_basement zipcode_98006',\n",
       " 'sqft_basement zipcode_98033',\n",
       " 'sqft_basement zipcode_98039',\n",
       " 'sqft_basement zipcode_98040',\n",
       " 'sqft_basement zipcode_98102',\n",
       " 'sqft_basement zipcode_98105',\n",
       " 'sqft_basement zipcode_98112',\n",
       " 'sqft_basement season_sold_2',\n",
       " 'sqft_basement season_sold_3',\n",
       " 'sqft_basement season_sold_4',\n",
       " 'sqft_basement view_1',\n",
       " 'sqft_basement view_2',\n",
       " 'sqft_basement view_3',\n",
       " 'sqft_basement view_4',\n",
       " 'sqft_basement condition_3',\n",
       " 'sqft_basement condition_4',\n",
       " 'sqft_basement condition_5',\n",
       " 'yr_built yr_renovated',\n",
       " 'yr_built sqft_living15',\n",
       " 'yr_built has_basement',\n",
       " 'yr_built last_reno',\n",
       " 'yr_built was_reno',\n",
       " 'yr_built bed_sqft_ratio',\n",
       " 'yr_built bath_sqft_ratio',\n",
       " 'yr_built floors_sqft_ratio',\n",
       " 'yr_built grade_6',\n",
       " 'yr_built grade_7',\n",
       " 'yr_built grade_9',\n",
       " 'yr_built grade_10',\n",
       " 'yr_built grade_11',\n",
       " 'yr_built grade_12',\n",
       " 'yr_built grade_13',\n",
       " 'yr_built waterfront_1',\n",
       " 'yr_built zipcode_98004',\n",
       " 'yr_built zipcode_98006',\n",
       " 'yr_built zipcode_98023',\n",
       " 'yr_built zipcode_98033',\n",
       " 'yr_built zipcode_98039',\n",
       " 'yr_built zipcode_98040',\n",
       " 'yr_built zipcode_98112',\n",
       " 'yr_built view_2',\n",
       " 'yr_built view_3',\n",
       " 'yr_built view_4',\n",
       " 'yr_renovated^2',\n",
       " 'yr_renovated sqft_living15',\n",
       " 'yr_renovated year_sold',\n",
       " 'yr_renovated has_basement',\n",
       " 'yr_renovated age_built',\n",
       " 'yr_renovated month_sold',\n",
       " 'yr_renovated was_reno',\n",
       " 'yr_renovated grade_9',\n",
       " 'yr_renovated grade_10',\n",
       " 'yr_renovated grade_11',\n",
       " 'yr_renovated grade_13',\n",
       " 'yr_renovated waterfront_1',\n",
       " 'yr_renovated zipcode_98004',\n",
       " 'yr_renovated zipcode_98040',\n",
       " 'yr_renovated view_3',\n",
       " 'yr_renovated view_4',\n",
       " 'yr_renovated condition_3',\n",
       " 'sqft_living15^2',\n",
       " 'sqft_living15 sqft_lot15',\n",
       " 'sqft_living15 year_sold',\n",
       " 'sqft_living15 has_basement',\n",
       " 'sqft_living15 age_built',\n",
       " 'sqft_living15 last_reno',\n",
       " 'sqft_living15 month_sold',\n",
       " 'sqft_living15 was_reno',\n",
       " 'sqft_living15 bath_sqft_ratio',\n",
       " 'sqft_living15 grade_6',\n",
       " 'sqft_living15 grade_7',\n",
       " 'sqft_living15 grade_9',\n",
       " 'sqft_living15 grade_10',\n",
       " 'sqft_living15 grade_11',\n",
       " 'sqft_living15 grade_12',\n",
       " 'sqft_living15 grade_13',\n",
       " 'sqft_living15 waterfront_1',\n",
       " 'sqft_living15 zipcode_98004',\n",
       " 'sqft_living15 zipcode_98006',\n",
       " 'sqft_living15 zipcode_98033',\n",
       " 'sqft_living15 zipcode_98039',\n",
       " 'sqft_living15 zipcode_98040',\n",
       " 'sqft_living15 zipcode_98102',\n",
       " 'sqft_living15 zipcode_98105',\n",
       " 'sqft_living15 zipcode_98112',\n",
       " 'sqft_living15 season_sold_2',\n",
       " 'sqft_living15 season_sold_3',\n",
       " 'sqft_living15 view_1',\n",
       " 'sqft_living15 view_2',\n",
       " 'sqft_living15 view_3',\n",
       " 'sqft_living15 view_4',\n",
       " 'sqft_living15 condition_3',\n",
       " 'sqft_living15 condition_5',\n",
       " 'sqft_lot15 has_basement',\n",
       " 'sqft_lot15 grade_7',\n",
       " 'sqft_lot15 grade_10',\n",
       " 'sqft_lot15 grade_11',\n",
       " 'sqft_lot15 grade_12',\n",
       " 'sqft_lot15 grade_13',\n",
       " 'sqft_lot15 waterfront_1',\n",
       " 'sqft_lot15 zipcode_98004',\n",
       " 'sqft_lot15 zipcode_98006',\n",
       " 'sqft_lot15 zipcode_98033',\n",
       " 'sqft_lot15 zipcode_98039',\n",
       " 'sqft_lot15 zipcode_98040',\n",
       " 'sqft_lot15 zipcode_98102',\n",
       " 'sqft_lot15 zipcode_98105',\n",
       " 'sqft_lot15 zipcode_98112',\n",
       " 'sqft_lot15 view_4',\n",
       " 'year_sold has_basement',\n",
       " 'year_sold last_reno',\n",
       " 'year_sold was_reno',\n",
       " 'year_sold bed_sqft_ratio',\n",
       " 'year_sold bath_sqft_ratio',\n",
       " 'year_sold floors_sqft_ratio',\n",
       " 'year_sold grade_6',\n",
       " 'year_sold grade_7',\n",
       " 'year_sold grade_9',\n",
       " 'year_sold grade_10',\n",
       " 'year_sold grade_11',\n",
       " 'year_sold grade_12',\n",
       " 'year_sold grade_13',\n",
       " 'year_sold waterfront_1',\n",
       " 'year_sold zipcode_98004',\n",
       " 'year_sold zipcode_98006',\n",
       " 'year_sold zipcode_98023',\n",
       " 'year_sold zipcode_98039',\n",
       " 'year_sold zipcode_98040',\n",
       " 'year_sold zipcode_98112',\n",
       " 'year_sold view_2',\n",
       " 'year_sold view_3',\n",
       " 'year_sold view_4',\n",
       " 'has_basement^2',\n",
       " 'has_basement age_built',\n",
       " 'has_basement last_reno',\n",
       " 'has_basement month_sold',\n",
       " 'has_basement was_reno',\n",
       " 'has_basement grade_7',\n",
       " 'has_basement grade_9',\n",
       " 'has_basement grade_10',\n",
       " 'has_basement grade_11',\n",
       " 'has_basement grade_12',\n",
       " 'has_basement grade_13',\n",
       " 'has_basement waterfront_1',\n",
       " 'has_basement zipcode_98004',\n",
       " 'has_basement zipcode_98006',\n",
       " 'has_basement zipcode_98039',\n",
       " 'has_basement zipcode_98040',\n",
       " 'has_basement zipcode_98112',\n",
       " 'has_basement season_sold_2',\n",
       " 'has_basement view_2',\n",
       " 'has_basement view_3',\n",
       " 'has_basement view_4',\n",
       " 'has_basement condition_5',\n",
       " 'age_built was_reno',\n",
       " 'age_built bed_sqft_ratio',\n",
       " 'age_built bath_sqft_ratio',\n",
       " 'age_built floors_sqft_ratio',\n",
       " 'age_built grade_6',\n",
       " 'age_built grade_7',\n",
       " 'age_built grade_9',\n",
       " 'age_built grade_10',\n",
       " 'age_built grade_11',\n",
       " 'age_built grade_12',\n",
       " 'age_built grade_13',\n",
       " 'age_built waterfront_1',\n",
       " 'age_built zipcode_98004',\n",
       " 'age_built zipcode_98023',\n",
       " 'age_built zipcode_98039',\n",
       " 'age_built zipcode_98040',\n",
       " 'age_built zipcode_98112',\n",
       " 'age_built view_2',\n",
       " 'age_built view_3',\n",
       " 'age_built view_4',\n",
       " 'last_reno bed_sqft_ratio',\n",
       " 'last_reno bath_sqft_ratio',\n",
       " 'last_reno floors_sqft_ratio',\n",
       " 'last_reno grade_6',\n",
       " 'last_reno grade_7',\n",
       " 'last_reno grade_9',\n",
       " 'last_reno grade_10',\n",
       " 'last_reno grade_11',\n",
       " 'last_reno grade_12',\n",
       " 'last_reno grade_13',\n",
       " 'last_reno waterfront_1',\n",
       " 'last_reno zipcode_98004',\n",
       " 'last_reno zipcode_98023',\n",
       " 'last_reno zipcode_98039',\n",
       " 'last_reno zipcode_98040',\n",
       " 'last_reno zipcode_98112',\n",
       " 'last_reno view_3',\n",
       " 'last_reno view_4',\n",
       " 'last_reno condition_3',\n",
       " 'month_sold was_reno',\n",
       " 'month_sold bed_sqft_ratio',\n",
       " 'month_sold bath_sqft_ratio',\n",
       " 'month_sold floors_sqft_ratio',\n",
       " 'month_sold grade_6',\n",
       " 'month_sold grade_7',\n",
       " 'month_sold grade_9',\n",
       " 'month_sold grade_10',\n",
       " 'month_sold grade_11',\n",
       " 'month_sold grade_12',\n",
       " 'month_sold grade_13',\n",
       " 'month_sold waterfront_1',\n",
       " 'month_sold zipcode_98004',\n",
       " 'month_sold zipcode_98006',\n",
       " 'month_sold zipcode_98039',\n",
       " 'month_sold zipcode_98040',\n",
       " 'month_sold zipcode_98112',\n",
       " 'month_sold view_2',\n",
       " 'month_sold view_3',\n",
       " 'month_sold view_4',\n",
       " 'was_reno^2',\n",
       " 'was_reno grade_9',\n",
       " 'was_reno grade_10',\n",
       " 'was_reno grade_11',\n",
       " 'was_reno grade_13',\n",
       " 'was_reno waterfront_1',\n",
       " 'was_reno zipcode_98004',\n",
       " 'was_reno zipcode_98040',\n",
       " 'was_reno view_3',\n",
       " 'was_reno view_4',\n",
       " 'was_reno condition_3',\n",
       " 'bed_sqft_ratio^2',\n",
       " 'bed_sqft_ratio bath_sqft_ratio',\n",
       " 'bed_sqft_ratio floors_sqft_ratio',\n",
       " 'bed_sqft_ratio grade_6',\n",
       " 'bed_sqft_ratio grade_7',\n",
       " 'bed_sqft_ratio grade_9',\n",
       " 'bed_sqft_ratio grade_10',\n",
       " 'bed_sqft_ratio grade_11',\n",
       " 'bed_sqft_ratio grade_12',\n",
       " 'bed_sqft_ratio grade_13',\n",
       " 'bed_sqft_ratio waterfront_1',\n",
       " 'bed_sqft_ratio zipcode_98004',\n",
       " 'bed_sqft_ratio zipcode_98023',\n",
       " 'bed_sqft_ratio zipcode_98039',\n",
       " 'bed_sqft_ratio zipcode_98040',\n",
       " 'bed_sqft_ratio zipcode_98042',\n",
       " 'bed_sqft_ratio zipcode_98112',\n",
       " 'bed_sqft_ratio view_3',\n",
       " 'bed_sqft_ratio view_4',\n",
       " 'bed_sqft_ratio condition_3',\n",
       " 'bath_sqft_ratio^2',\n",
       " 'bath_sqft_ratio floors_sqft_ratio',\n",
       " 'bath_sqft_ratio grade_6',\n",
       " 'bath_sqft_ratio grade_7',\n",
       " 'bath_sqft_ratio grade_9',\n",
       " 'bath_sqft_ratio grade_10',\n",
       " 'bath_sqft_ratio grade_11',\n",
       " 'bath_sqft_ratio grade_12',\n",
       " 'bath_sqft_ratio grade_13',\n",
       " 'bath_sqft_ratio waterfront_1',\n",
       " 'bath_sqft_ratio zipcode_98004',\n",
       " 'bath_sqft_ratio zipcode_98006',\n",
       " 'bath_sqft_ratio zipcode_98023',\n",
       " 'bath_sqft_ratio zipcode_98039',\n",
       " 'bath_sqft_ratio zipcode_98040',\n",
       " 'bath_sqft_ratio zipcode_98112',\n",
       " 'bath_sqft_ratio view_2',\n",
       " 'bath_sqft_ratio view_3',\n",
       " 'bath_sqft_ratio view_4',\n",
       " 'floors_sqft_ratio^2',\n",
       " 'floors_sqft_ratio grade_6',\n",
       " 'floors_sqft_ratio grade_7',\n",
       " 'floors_sqft_ratio grade_9',\n",
       " 'floors_sqft_ratio grade_10',\n",
       " 'floors_sqft_ratio grade_11',\n",
       " 'floors_sqft_ratio grade_12',\n",
       " 'floors_sqft_ratio grade_13',\n",
       " 'floors_sqft_ratio waterfront_1',\n",
       " 'floors_sqft_ratio zipcode_98004',\n",
       " 'floors_sqft_ratio zipcode_98023',\n",
       " 'floors_sqft_ratio zipcode_98039',\n",
       " 'floors_sqft_ratio zipcode_98040',\n",
       " 'floors_sqft_ratio zipcode_98112',\n",
       " 'floors_sqft_ratio view_3',\n",
       " 'floors_sqft_ratio view_4',\n",
       " 'floors_sqft_ratio condition_3',\n",
       " 'grade_6^2',\n",
       " 'grade_6 season_sold_2',\n",
       " 'grade_6 season_sold_3',\n",
       " 'grade_6 condition_3',\n",
       " 'grade_6 condition_4',\n",
       " 'grade_7^2',\n",
       " 'grade_7 season_sold_2',\n",
       " 'grade_7 season_sold_3',\n",
       " 'grade_7 season_sold_4',\n",
       " 'grade_7 condition_3',\n",
       " 'grade_7 condition_4',\n",
       " 'grade_9^2',\n",
       " 'grade_9 zipcode_98004',\n",
       " 'grade_9 zipcode_98112',\n",
       " 'grade_9 season_sold_2',\n",
       " 'grade_9 season_sold_3',\n",
       " 'grade_9 view_3',\n",
       " 'grade_9 condition_3',\n",
       " 'grade_9 condition_4',\n",
       " 'grade_9 condition_5',\n",
       " 'grade_10^2',\n",
       " 'grade_10 waterfront_1',\n",
       " 'grade_10 zipcode_98004',\n",
       " 'grade_10 zipcode_98033',\n",
       " 'grade_10 zipcode_98040',\n",
       " 'grade_10 zipcode_98112',\n",
       " 'grade_10 season_sold_2',\n",
       " 'grade_10 season_sold_3',\n",
       " 'grade_10 season_sold_4',\n",
       " 'grade_10 view_3',\n",
       " 'grade_10 view_4',\n",
       " 'grade_10 condition_3',\n",
       " 'grade_10 condition_4',\n",
       " 'grade_10 condition_5',\n",
       " 'grade_11^2',\n",
       " 'grade_11 waterfront_1',\n",
       " 'grade_11 zipcode_98004',\n",
       " 'grade_11 zipcode_98039',\n",
       " 'grade_11 zipcode_98040',\n",
       " 'grade_11 season_sold_2',\n",
       " 'grade_11 season_sold_3',\n",
       " 'grade_11 season_sold_4',\n",
       " 'grade_11 view_1',\n",
       " 'grade_11 view_2',\n",
       " 'grade_11 view_3',\n",
       " 'grade_11 view_4',\n",
       " 'grade_11 condition_3',\n",
       " 'grade_11 condition_4',\n",
       " 'grade_11 condition_5',\n",
       " 'grade_12^2',\n",
       " 'grade_12 waterfront_1',\n",
       " 'grade_12 zipcode_98004',\n",
       " 'grade_12 zipcode_98033',\n",
       " 'grade_12 zipcode_98039',\n",
       " 'grade_12 zipcode_98040',\n",
       " 'grade_12 season_sold_2',\n",
       " 'grade_12 season_sold_3',\n",
       " 'grade_12 season_sold_4',\n",
       " 'grade_12 view_4',\n",
       " 'grade_12 condition_3',\n",
       " 'grade_12 condition_4',\n",
       " 'grade_13^2',\n",
       " 'grade_13 zipcode_98039',\n",
       " 'grade_13 zipcode_98102',\n",
       " 'grade_13 season_sold_3',\n",
       " 'grade_13 season_sold_4',\n",
       " 'grade_13 view_3',\n",
       " 'grade_13 view_4',\n",
       " 'grade_13 condition_3',\n",
       " 'grade_13 condition_4',\n",
       " 'waterfront_1^2',\n",
       " 'waterfront_1 zipcode_98004',\n",
       " 'waterfront_1 zipcode_98008',\n",
       " 'waterfront_1 zipcode_98033',\n",
       " 'waterfront_1 zipcode_98040',\n",
       " 'waterfront_1 season_sold_2',\n",
       " 'waterfront_1 season_sold_3',\n",
       " 'waterfront_1 season_sold_4',\n",
       " 'waterfront_1 view_4',\n",
       " 'waterfront_1 condition_3',\n",
       " 'waterfront_1 condition_4',\n",
       " 'zipcode_98004^2',\n",
       " 'zipcode_98004 season_sold_2',\n",
       " 'zipcode_98004 season_sold_3',\n",
       " 'zipcode_98004 season_sold_4',\n",
       " 'zipcode_98004 view_1',\n",
       " 'zipcode_98004 view_2',\n",
       " 'zipcode_98004 view_4',\n",
       " 'zipcode_98004 condition_3',\n",
       " 'zipcode_98004 condition_4',\n",
       " 'zipcode_98006^2',\n",
       " 'zipcode_98006 condition_3',\n",
       " 'zipcode_98023^2',\n",
       " 'zipcode_98033 view_4',\n",
       " 'zipcode_98033 condition_3',\n",
       " 'zipcode_98039^2',\n",
       " 'zipcode_98039 season_sold_2',\n",
       " 'zipcode_98039 season_sold_3',\n",
       " 'zipcode_98039 season_sold_4',\n",
       " 'zipcode_98039 view_4',\n",
       " 'zipcode_98039 condition_3',\n",
       " 'zipcode_98040^2',\n",
       " 'zipcode_98040 season_sold_2',\n",
       " 'zipcode_98040 view_3',\n",
       " 'zipcode_98040 view_4',\n",
       " 'zipcode_98040 condition_3',\n",
       " 'zipcode_98040 condition_4',\n",
       " 'zipcode_98102 view_3',\n",
       " 'zipcode_98112^2',\n",
       " 'zipcode_98112 season_sold_2',\n",
       " 'zipcode_98112 condition_5',\n",
       " 'season_sold_2 view_4',\n",
       " 'season_sold_3 view_4',\n",
       " 'season_sold_4 view_4',\n",
       " 'view_2^2',\n",
       " 'view_3^2',\n",
       " 'view_3 condition_3',\n",
       " 'view_3 condition_4',\n",
       " 'view_4^2',\n",
       " 'view_4 condition_3',\n",
       " 'view_4 condition_4',\n",
       " 'view_4 condition_5']"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(poly_selected_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlr_poly_Kbest(features, target):\n",
    "\n",
    "    #instantiate a linear regression object\n",
    "    lm_poly_Kbest = LinearRegression()\n",
    "\n",
    "    #fit the linear regression to the data\n",
    "    lm_poly_Kbest.fit(X_train_poly[poly_selected_columns], y_train_poly)\n",
    "\n",
    "    #Use the model to predict on the training and test set\n",
    "    y_train_poly_Kbest = lm_poly_Kbest.predict(X_train_poly[poly_selected_columns])\n",
    "    y_test_pred = lm_poly_Kbest.predict(X_test_poly[poly_selected_columns])\n",
    "\n",
    "    #Evaluate the training and test predictions using RMSE.\n",
    "    train_poly_K_rmse = np.sqrt(metrics.mean_squared_error(y_train_poly, y_train_poly_Kbest))\n",
    "    test_poly_K_rmse = np.sqrt(metrics.mean_squared_error(y_test_poly, y_test_pred))\n",
    "    \n",
    "    #View how far off we are in standard deviations of error\n",
    "    rmse_poly_K_distance = test_poly_K_rmse/target.std()\n",
    "    \n",
    "    #View how farr off from the mean absolute error of the target\n",
    "    test_poly_K_mae = metrics.mean_absolute_error(y_test_poly, y_test_pred)\n",
    "    mae_poly_K_distance = test_poly_K_mae/target.std()\n",
    "\n",
    "\n",
    "    return {'train_poly_K_rmse': train_poly_K_rmse, \n",
    "            'test_poly_K_rmse' : test_poly_K_rmse,\n",
    "            'std of error' : rmse_poly_K_distance,\n",
    "            'dist_from_mae' : mae_poly_K_distance,\n",
    "            'y_test_pred' : y_test_pred.mean()}\n",
    "#             'paired_coef' : list(zip(poly_selected_columns, lm_poly_Kbest.coef_))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_poly_K_rmse': 135399.89423866107,\n",
       " 'test_poly_K_rmse': 191614.65370692543,\n",
       " 'std of error': 0.5132732719587065,\n",
       " 'dist_from_mae': 0.294190607510903,\n",
       " 'y_test_pred': 533506.0519358267}"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_poly_Kbest(poly_features, df_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting the Best Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_rmse': 182708.47485837253,\n",
       " 'test_rmse': 176485.3832983236,\n",
       " 'std of error': 0.4727468822763791,\n",
       " 'dist_from_mae': 0.2874846630962646,\n",
       " 'y_test_pred': 531702.9760468785}"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_unscaled(df_features, df_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'log_train_rmse': 178653.78757928527,\n",
       " 'log_test_rmse': 175459.2484560281,\n",
       " 'std of error': 0.4699982011197624,\n",
       " 'dist_from_mae': 0.2619551460986875,\n",
       " 'y_test_pred': 515095.8989904364}"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_test_log_target(df_features, df_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trainK_rmse': 183594.6597452555,\n",
       " 'testK_rmse': 177080.04817774257,\n",
       " 'std of error': 0.47433979587914116,\n",
       " 'dist_from_mae': 0.2902491875505212,\n",
       " 'y_test_pred': 532140.8190055625}"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_Kbest(df_features, df_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trainRFE_rmse': 182508.76875996107,\n",
       " 'testRFE_rmse': 176805.0334982784,\n",
       " 'std of error': 0.47360312109130814,\n",
       " 'dist_from_mae': 0.2871823228711175,\n",
       " 'y_test_pred': 531610.3422504651}"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_rfe(df_features, df_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_rmse': 87133.72489233244,\n",
       " 'test_rmse': 76638345.20257708,\n",
       " 'std of error': 205.28917511597322,\n",
       " 'dist_from_mae': 4.372930203826928,\n",
       " 'y_test_pred': -707380.7724313632}"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_poly(poly_features, df_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_poly_K_rmse': 135399.89423866107,\n",
       " 'test_poly_K_rmse': 191614.65370692543,\n",
       " 'std of error': 0.5132732719587065,\n",
       " 'dist_from_mae': 0.294190607510903,\n",
       " 'y_test_pred': 533506.0519358267}"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_poly_Kbest(poly_features, df_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Test Will Be Our Final Model\n",
    "- Lowest test_rmse score of: 175459.2484560281\n",
    "- Lowest prediction mean of: 515095.8989904364\n",
    "- Lowest std of error of: 0.4699982011197624\n",
    "- Lowest dist_from_mae of: 0.2619551460986875"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit Best Model to Entire Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_columns = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'sqft_above', 'sqft_basement', 'yr_built',\n",
    "            'yr_renovated', 'lat', 'long', 'sqft_living15', 'year_sold', 'has_basement', 'age_built', 'was_reno',\n",
    "            'bed_sqft_ratio', 'bath_sqft_ratio', 'floors_sqft_ratio', 'grade_3', 'grade_4', 'grade_5', 'grade_6', 'grade_7',\n",
    "            'grade_8', 'grade_9', 'grade_10', 'grade_11', 'grade_12', 'grade_13', 'waterfront_1', 'zipcode_98052',\n",
    "            'zipcode_98115', 'zipcode_98103', 'zipcode_98038', 'zipcode_98117', 'zipcode_98034', 'zipcode_98042', \n",
    "            'zipcode_98023', 'zipcode_98118', 'zipcode_98133', 'zipcode_98006', 'zipcode_98058', 'zipcode_98059',\n",
    "            'zipcode_98074', 'zipcode_98033', 'zipcode_98155', 'zipcode_98056', 'zipcode_98027', 'zipcode_98053',\n",
    "            'zipcode_98125', 'season_sold_2', 'season_sold_3', 'season_sold_4', 'view_1', 'view_2', 'view_3', 'view_4',\n",
    "            'condition_2', 'condition_3', 'condition_4', 'condition_5', 'floors^2', 'sqft_above^2', 'sqft_basement^2',\n",
    "            'bath_sqft_ratio^2', 'bed_sqft_ratio^2', 'floors_sqft_ratio^2', 'sqft_living^2', 'sqft_living15^2', 'sqft_lot^2',\n",
    "            'sqft_lot15^2', 'reno_basement', 'sqft_reno', 'grade_7_sqft_living', 'grade_8_sqft_living', 'grade_9_sqft_living',\n",
    "            'floors_grade_7', 'floors_grade_8', 'floors_grade_9']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_features = df[final_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Log the data to create a normal distribution\n",
    "lm_final_y_intercept = np.log(df_target)\n",
    "\n",
    "#Instantiate a linear regression object\n",
    "lm_final = LinearRegression()\n",
    "\n",
    "#Fit the linear regression to the data\n",
    "lm_final = lm_final.fit(final_features, lm_final_y_intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8035768762848867"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R^2 Score\n",
    "lm_final.score(final_features, lm_final_y_intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-239.16794474339792\n",
      "[-3.13285383e-03  6.94834395e-02 -1.22589088e-01  4.75272452e-07\n",
      "  1.55360470e-03  1.22811921e-01  1.22675207e-01  2.71753358e-02\n",
      "  4.75872194e-03  1.46689107e+00 -2.07385929e-01  8.92007601e-05\n",
      "  5.00905554e-02  6.40702410e-02  2.97634278e-02 -9.46960841e+00\n",
      " -2.53752302e+01 -3.15350701e+01 -4.17227676e+01 -4.84731381e-02\n",
      " -1.16062438e-01 -8.74715928e-02  4.05242192e-02  2.08472864e-01\n",
      "  3.31450659e-01  5.71989507e-01  5.82071441e-01  6.83943626e-01\n",
      "  8.18832024e-01  1.05647132e+00  4.41268952e-01 -5.36597328e-03\n",
      "  7.11331397e-02  7.81227745e-02  5.18115220e-02  5.67958928e-02\n",
      " -1.50275578e-01 -6.48783155e-02 -1.37056632e-01 -2.59804143e-02\n",
      " -3.04162083e-01  1.24838692e-01 -1.02735471e-01 -3.25772602e-03\n",
      " -2.30321814e-04  1.14289524e-01 -3.39537263e-01 -5.81311622e-02\n",
      "  1.13717127e-01  1.26323912e-02 -1.70978518e-01  4.48835669e-02\n",
      "  4.89279559e-02  4.73500773e-02  1.56459288e-01  1.11171181e-01\n",
      "  1.56369535e-01  2.28121983e-01  9.24141272e-02  1.97984832e-01\n",
      "  2.66764931e-01  3.43479401e-01  1.16153435e-02 -8.32500172e-09\n",
      "  3.50975628e-09 -9.29613841e-02 -9.29613841e-02 -1.07740068e-01\n",
      " -2.21704822e-07  2.16205561e-07 -5.02931030e-14  1.21014310e-14\n",
      "  2.90393375e-05  1.69261176e-05 -2.67015387e-05 -1.54918900e-05\n",
      " -2.79339392e-05  1.98511973e-02  1.72075419e-02 -1.00968626e-02]\n",
      "80\n"
     ]
    }
   ],
   "source": [
    "print(lm_final.intercept_)\n",
    "print(lm_final.coef_)\n",
    "print(len(lm_final.coef_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Accuracy of Predictive Modeling.** Our log model did the best in terms of predicting the training data in comparison to our test data. We'll take this model also to its low standard deviation of error in regards to the mean of overall housing prices. We will see that this model captures a range of 80% of all predictive housing prices.<br><br>\n",
    "\n",
    "- **Grade and Location is Crucial.** The grade of a property in relation to its location has shown to be a large determining factor of the property's selling price.<br><br>\n",
    "\n",
    "- **Tiny Details Matter.** Things such as the view a property has, its proximity to water, as well as the amount of bathrooms and whether or not they were renovated hold weight. The age of the home has also shown to skew towards older, more classic homes and newer, perhaps more technologically advanced homes with integrated smart systems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Given more insight:** I'd like to dive deeper into geospatial data to calculate and encapsulate the distribution of the homes selling for the best prices in regards to quality of rating, and neighborhood in relationship to zipcode and latitude/longitude. Traditionally and throughout history, urban areas have seen more affluent neighborhoods concentrated to areas north-by-northwest to the city limits. Meanwhile, the opposite holds true to areas of less affluence being concentrated in eastern areas.<br><br>\n",
    "- **WHY?** This was due to the direction of winds and the jetstream, and particularly during the Industrial Revolution, pollution would be carried by these prevailing south-by-southeast winds to the east, thus making them less desireable to settle and reside in. Although these issues are much less of significance in current times, city plans and neighborhoods still reflect these patterns to this day. "
   ]
  }
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