Final_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## King County Real Estate\n",
    "\n",
    "### Modeling Home Price Value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project by: Mahum Abid, Kevin Atkinson, Luke Sims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project will analyze King County housing sales data to create a housing price model.\n",
    "\n",
    "We have narrowed down the features that most strongly impact housing price to:\n",
    "- Zip Code\n",
    "- Grade\n",
    "- View\n",
    "- Condition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seattle and King County represent one of the most stable and lucrative parts of the American Real Estate Market. Our client, a major real estate investment firm in the area, contracted us to thoroughly examine the factors around housing and land prices to understand better when and what to buy and sell. We came to the following conclusions: \n",
    "\n",
    "- SQ Footage of Living Space and Lot Space played a determinant role in the price\n",
    "- There are specific neighborhoods that retain their value better, especially properties with views, and quality construction and are situated by the waterfront.\n",
    "- Converting SQ Footage of Basement Space multiply the value per foot by a factor of 4. \n",
    "- Garages with Patio spaces atop are a highly preferable modification. \n",
    "- Avoid neighborhoods with Private Sewer Systems and older buildings. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium as fl\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point\n",
    "import statsmodels.api as sm\n",
    "import json\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading in data files: raw housing data and school district information\n",
    "housing_data = 'data/kc_house_data.csv'\n",
    "school_districts = 'data/school_districts.geojson'\n",
    "districts_shp = 'data/school_districts.shp'\n",
    "\n",
    "data = pd.read_csv(housing_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's explore the data to get a feel for what's here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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>greenbelt</th>\n",
       "      <th>...</th>\n",
       "      <th>sewer_system</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>sqft_garage</th>\n",
       "      <th>sqft_patio</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>address</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7399300360</td>\n",
       "      <td>5/24/2022</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1180</td>\n",
       "      <td>7140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>2102 Southeast 21st Court, Renton, Washington ...</td>\n",
       "      <td>47.461975</td>\n",
       "      <td>-122.19052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8910500230</td>\n",
       "      <td>12/13/2021</td>\n",
       "      <td>920000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2770</td>\n",
       "      <td>6703</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>1570</td>\n",
       "      <td>1570</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>11231 Greenwood Avenue North, Seattle, Washing...</td>\n",
       "      <td>47.711525</td>\n",
       "      <td>-122.35591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1180000275</td>\n",
       "      <td>9/29/2021</td>\n",
       "      <td>311000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2880</td>\n",
       "      <td>6156</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>1580</td>\n",
       "      <td>1580</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1956</td>\n",
       "      <td>0</td>\n",
       "      <td>8504 South 113th Street, Seattle, Washington 9...</td>\n",
       "      <td>47.502045</td>\n",
       "      <td>-122.22520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1604601802</td>\n",
       "      <td>12/14/2021</td>\n",
       "      <td>775000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2160</td>\n",
       "      <td>1400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>1090</td>\n",
       "      <td>1070</td>\n",
       "      <td>200</td>\n",
       "      <td>270</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>4079 Letitia Avenue South, Seattle, Washington...</td>\n",
       "      <td>47.566110</td>\n",
       "      <td>-122.29020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8562780790</td>\n",
       "      <td>8/24/2021</td>\n",
       "      <td>592500.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1120</td>\n",
       "      <td>758</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>1120</td>\n",
       "      <td>550</td>\n",
       "      <td>550</td>\n",
       "      <td>30</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>2193 Northwest Talus Drive, Issaquah, Washingt...</td>\n",
       "      <td>47.532470</td>\n",
       "      <td>-122.07188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7399300360   5/24/2022  675000.0         4        1.0         1180   \n",
       "1  8910500230  12/13/2021  920000.0         5        2.5         2770   \n",
       "2  1180000275   9/29/2021  311000.0         6        2.0         2880   \n",
       "3  1604601802  12/14/2021  775000.0         3        3.0         2160   \n",
       "4  8562780790   8/24/2021  592500.0         2        2.0         1120   \n",
       "\n",
       "   sqft_lot  floors waterfront greenbelt  ... sewer_system sqft_above  \\\n",
       "0      7140     1.0         NO        NO  ...       PUBLIC       1180   \n",
       "1      6703     1.0         NO        NO  ...       PUBLIC       1570   \n",
       "2      6156     1.0         NO        NO  ...       PUBLIC       1580   \n",
       "3      1400     2.0         NO        NO  ...       PUBLIC       1090   \n",
       "4       758     2.0         NO        NO  ...       PUBLIC       1120   \n",
       "\n",
       "  sqft_basement sqft_garage sqft_patio yr_built  yr_renovated  \\\n",
       "0             0           0         40     1969             0   \n",
       "1          1570           0        240     1950             0   \n",
       "2          1580           0          0     1956             0   \n",
       "3          1070         200        270     2010             0   \n",
       "4           550         550         30     2012             0   \n",
       "\n",
       "                                             address        lat       long  \n",
       "0  2102 Southeast 21st Court, Renton, Washington ...  47.461975 -122.19052  \n",
       "1  11231 Greenwood Avenue North, Seattle, Washing...  47.711525 -122.35591  \n",
       "2  8504 South 113th Street, Seattle, Washington 9...  47.502045 -122.22520  \n",
       "3  4079 Letitia Avenue South, Seattle, Washington...  47.566110 -122.29020  \n",
       "4  2193 Northwest Talus Drive, Issaquah, Washingt...  47.532470 -122.07188  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30155 entries, 0 to 30154\n",
      "Data columns (total 25 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             30155 non-null  int64  \n",
      " 1   date           30155 non-null  object \n",
      " 2   price          30155 non-null  float64\n",
      " 3   bedrooms       30155 non-null  int64  \n",
      " 4   bathrooms      30155 non-null  float64\n",
      " 5   sqft_living    30155 non-null  int64  \n",
      " 6   sqft_lot       30155 non-null  int64  \n",
      " 7   floors         30155 non-null  float64\n",
      " 8   waterfront     30155 non-null  object \n",
      " 9   greenbelt      30155 non-null  object \n",
      " 10  nuisance       30155 non-null  object \n",
      " 11  view           30155 non-null  object \n",
      " 12  condition      30155 non-null  object \n",
      " 13  grade          30155 non-null  object \n",
      " 14  heat_source    30123 non-null  object \n",
      " 15  sewer_system   30141 non-null  object \n",
      " 16  sqft_above     30155 non-null  int64  \n",
      " 17  sqft_basement  30155 non-null  int64  \n",
      " 18  sqft_garage    30155 non-null  int64  \n",
      " 19  sqft_patio     30155 non-null  int64  \n",
      " 20  yr_built       30155 non-null  int64  \n",
      " 21  yr_renovated   30155 non-null  int64  \n",
      " 22  address        30155 non-null  object \n",
      " 23  lat            30155 non-null  float64\n",
      " 24  long           30155 non-null  float64\n",
      "dtypes: float64(5), int64(10), object(10)\n",
      "memory usage: 5.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize': (12,12)})\n",
    "sns.heatmap(data.corr(), annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on our heatmap above, we can see that price is most strongly correlated with `sqft_living`. Using this information we can create a simple linear regression using `sqft_living` as the independent variable, and `price` as the dependent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create plot of square feet of living space vs price with regression\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(x=data[\"sqft_living\"], y=data[\"price\"])\n",
    "sns.regplot(data = data, x = 'sqft_living', y = 'price', line_kws={'ls':'--', 'color': 'r'}, ci = None)\n",
    "ax.set_xlabel('Square Feet of Living Space')\n",
    "ax.set_ylabel('Price ($millions)')\n",
    "ax.set_title('Relationship Between Square Feet of Living Space and Price')\n",
    "ax.set_xticks([(i+1)*2000 for i in range(7)])\n",
    "ax.set_xticklabels(['2,000', '4,000', '6,000', '8,000', '10,000', '12,000', '14,000'])\n",
    "ax.set_yticks([i*5000000 for i in range(7)])\n",
    "ax.set_yticklabels([i*5.0 for i in range(7)]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diving Deeper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Incorporating more features will enable us to gain a more holisitic representaion of each home than is possible using `sqft_living` alone. An extremely valuable aspect of purchasing a home is the school district in which it resides. By utilizing [GIS data](https://gis-kingcounty.opendata.arcgis.com/datasets/kingcounty::school-districts-in-king-county-schdst-area/explore?location=47.526470%2C-121.923521%2C11.00) for King County School Districts we can isolate which school district each home belongs to. We can then incorporate that information into our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[15,8])\n",
    "ax.scatter(data['long'], data['lat']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Data for further modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Data based on location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As evidenced by the plot above, due to errors in the data set, many of the data points have locations outside of King County. We'll narrow that down to King County in the following cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = data.loc[(data['long'] > -140) & (data['long'] < -121.6) & (data['lat'] > 47.1) & (data['lat'] < 47.8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "to_drop = data.loc[((data['long'] < -121.9) & (data['lat'] > 47.78)) |\n",
    "                   ((data['long'] < -122.2) & (data['lat'] < 47.2)) |\n",
    "                   ((data['long'] > -121.8) & (data['lat'] < 47.2))\n",
    "                  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = data.drop(index=to_drop.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because our anaylsis is focusing on modeling the most houses as accurately as possible, we'll drop outliers from the `price` and `sqft_lot` columns, as their standard deviations are excessively high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "bottom_025_price = sorted(data['price'])[int(len(data)*0.025)]\n",
    "top_975_price = sorted(data['price'])[int(len(data)*0.975)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bottom_025_lot = sorted(data['sqft_lot'])[int(len(data)*0.05)]\n",
    "top_975_lot = sorted(data['sqft_lot'])[int(len(data)*0.95)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.loc[(data['price'] > bottom_025_price) & (data['price'] < top_975_price)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.loc[(data['sqft_lot'] > bottom_025_lot) & (data['sqft_lot'] < top_975_lot)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Zipcode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our analysis, we're only interested in the Zip Code of the location, so we'll extract Zip Code and drop all address other info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['zipcode'] = data.address.str.split(',', expand = True)[2].str.strip().str.split(' ', expand = True)[1]\n",
    "# Because of some oddly formatted addresses, we need to manually assign the zipcode for four rows:\n",
    "warnings.filterwarnings('ignore')\n",
    "data['zipcode'].loc[(data['id'] == 139000091) | (data['id'] == 139000123) | (data['id'] == 139000125)] = 98126\n",
    "data['zipcode'].loc[data['id'] == 4083306403] = 98103\n",
    "data.drop(['address'], axis=1, inplace=True)\n",
    "data['zipcode'] = data['zipcode'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Converting date column into Datetime format, and extracting year\n",
    "data['date'] = pd.DatetimeIndex(data['date']).year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-referencing House Location and School Districts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a simple function to plot each house on the school district map\n",
    "def plotPoint(df):\n",
    "    fl.CircleMarker(location=[df['lat'], df['long']], radius=0.1, color='red').add_to(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# m = fl.Map(location=[47.51, -122.0],\n",
    "#            zoom_start=10,\n",
    "#            control_scale=True,\n",
    "#            tiles='Stamen Terrain',\n",
    "#            attr=\"http://stamen.com\")\n",
    "# fl.GeoJson(school_districts, tooltip=fl.GeoJsonTooltip(fields=['NAME'])).add_to(m)\n",
    "# data.apply(plotPoint, axis=1)\n",
    "# m.save(\"district_map.html\")\n",
    "# m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determining School District based on Latitude and Longitude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to map a school district to each home, we can use GeoPandas and determine to which region each point belongs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "district_regions = json.load(open(school_districts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create a dictionary with District ID as keys and District Name as values\n",
    "district_nums = {}\n",
    "for i in district_regions['features']:\n",
    "    district_nums[float(i['properties']['OBJECTID']-1)] = i['properties']['NAME']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing school districts shape file and assigning it to variable called district_regions\n",
    "district_regions = gpd.GeoDataFrame.from_file(districts_shp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new column called 'coords' to be used in a GeoPandas spatial join\n",
    "data['coords'] = list(zip(data['long'], data['lat']))\n",
    "data['coords'] = data['coords'].apply(Point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a GeoDataFrame called houses, with values of Point Data Type representing the location of each home\n",
    "houses = pd.DataFrame()\n",
    "houses = gpd.GeoDataFrame(data['coords'], geometry='coords', crs=district_regions.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GeoPandas Spatial Join using Home location and School District Regions\n",
    "house_in_districts = gpd.tools.sjoin(houses, district_regions, predicate=\"within\", how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Merging our new GeoDataFrame into our DataFrame, and dropping extraneous information\n",
    "house_in_districts = pd.DataFrame(house_in_districts)\n",
    "data = data.join(house_in_districts, lsuffix='_left', rsuffix='_right')\n",
    "data = data.drop(columns=['coords_left', 'coords_right'])\n",
    "data = data.rename(columns={'index_right':'district'})\n",
    "data = data.replace({\"district\": district_nums})\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving our cleaned data to a new CSV file\n",
    "data.to_csv('data/kc_house_data_clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new DataFrame to be used in our modeling process\n",
    "# Dropping columns which have proven to not influence the strength of our model\n",
    "model_data = data.drop(columns=['floors', 'sqft_basement', 'greenbelt', 'nuisance', 'lat', 'long', 'id', 'yr_built'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's explore which variables should be transformed to be more normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log of price makes for much more normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAGIbgBADAIwQ0AgEEIbgAADLLi4P7a176mxsZGSVJfX5+8Xq8qKyt18uTJRJ+hoSH5fD5VVVWpublZCwsLkqSxsTEdOnRI1dXVOnz4sGZnZ1d5GgAAbA4rCu7XXntNZ86ckSTNzc2pqalJfr9fwWBQg4OD6u3tlSQ1NDSotbVV586dk2VZ6urqkiS1t7ertrZWoVBIe/bskd/vX6PpAACQ3VIG9//93//p5MmTevTRRyVJFy5cUHFxsYqKiuRwOOT1ehUKhTQ6Oqq5uTmVlpZKknw+n0KhkObn59Xf36+qqqpF7QAAIH2OVB1aW1t19OhRXb58WZI0Pj4ul8uV2O92uxUOh5e0u1wuhcNhTU1NKTc3Vw6HY1E7zHdtPiaXa2faj7uVx9yKueiCpq9cXZfXAoD1kjS4v/Od76igoEDl5eX63ve+J0mKx+Oy2WyJPpZlyWazLdt+/eeNbt5eiV27ctN+DNbWVqdd3scDmR7Gsl5+5oBuW6f/JKyW9fpPjamoT3LUZ3nZVJukwR0MBhWJRHTgwAH97Gc/089//nONjo7Kbrcn+kQiEbndbuXn5ysSiSTaJyYm5Ha7lZeXp+npacViMdnt9kT/dE1Oziget9J+3IfJpl8gkotEpjM9hBVzuXYaNd71Rn2Soz7LM7E2W7bYlj1hTfoe9wsvvKCenh4FAgEdOXJEn/3sZ/XP//zPunjxokZGRhSLxdTT0yOPx6PCwkLl5ORoYGBAkhQIBOTxeOR0OlVWVqZgMChJ6u7ulsfjWeUpAgCwOaR8j/tmOTk5OnbsmOrr6xWNRlVRUaHq6mpJUkdHh1paWjQzM6OSkhLV1dVJktra2tTY2KjOzk4VFBToxIkTqzsLAAA2iRUHt8/nk8/nkySVl5fr7NmzS/rs3r1bp0+fXtJeWFioU6dOfYRhAgAAiW9OAwDAKAQ3AAAGIbgBADAIwQ0AgEEIbgAADEJwAwBgEIIbAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAGIbgBADAIwQ0AgEEIbgAADEJwAwBgEIIbAACDrCi4v/71r+v+++9XTU2NXnjhBUlSX1+fvF6vKisrdfLkyUTfoaEh+Xw+VVVVqbm5WQsLC5KksbExHTp0SNXV1Tp8+LBmZ2fXYDoAAGS3lMH9+uuv6z//8z919uxZffe739WpU6f01ltvqampSX6/X8FgUIODg+rt7ZUkNTQ0qLW1VefOnZNlWerq6pIktbe3q7a2VqFQSHv27JHf71/bmQEAkIVSBvenPvUpvfjii3I4HJqcnFQsFtOVK1dUXFysoqIiORwOeb1ehUIhjY6Oam5uTqWlpZIkn8+nUCik+fl59ff3q6qqalE7AABIz4qWyp1Op5599lnV1NSovLxc4+Pjcrlcif1ut1vhcHhJu8vlUjgc1tTUlHJzc+VwOBa1AwCA9DhW2vHIkSN65JFH9Oijj2p4eFg2my2xz7Is2Ww2xePxD22//vNGN2+nsmtXblr9AUlyuXZmeghpMW286436JEd9lpdNtUkZ3O+8846uXbumu+++W9u2bVNlZaVCoZDsdnuiTyQSkdvtVn5+viKRSKJ9YmJCbrdbeXl5mp6eViwWk91uT/RPx+TkjOJxK63HLCebfoFY3rX5mLY67ak7ZtBcdEHTV65K+uC4jESmMzyijYv6JEd9lmdibbZssS17wpoyuN977z09++yz+td//VdJ0iuvvKKDBw/q+PHjGhkZ0Sc+8Qn19PTo937v91RYWKicnBwNDAzovvvuUyAQkMfjkdPpVFlZmYLBoLxer7q7u+XxeFZ3lsBNtjrt8j4eyPQwknr5mQMy658TAJmWMrgrKip04cIFPfjgg7Lb7aqsrFRNTY3y8vJUX1+vaDSqiooKVVdXS5I6OjrU0tKimZkZlZSUqK6uTpLU1tamxsZGdXZ2qqCgQCdOnFjbmQEAkIVW9B53fX296uvrF7WVl5fr7NmzS/ru3r1bp0+fXtJeWFioU6dO3eIwAQCAxDenAQBgFIIbAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAGIbgBADAIwQ0AgEEIbgAADEJwAwBgEIIbAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBVhTczz33nGpqalRTU6Pjx49Lkvr6+uT1elVZWamTJ08m+g4NDcnn86mqqkrNzc1aWFiQJI2NjenQoUOqrq7W4cOHNTs7uwbTAQAgu6UM7r6+Pr366qs6c+aMuru79eMf/1g9PT1qamqS3+9XMBjU4OCgent7JUkNDQ1qbW3VuXPnZFmWurq6JEnt7e2qra1VKBTSnj175Pf713ZmAABkoZTB7XK51NjYqK1bt8rpdOrOO+/U8PCwiouLVVRUJIfDIa/Xq1AopNHRUc3Nzam0tFSS5PP5FAqFND8/r/7+flVVVS1qBwAA6UkZ3HfddVciiIeHh/X9739fNptNLpcr0cftdiscDmt8fHxRu8vlUjgc1tTUlHJzc+VwOBa1AwCA9DhW2vHtt9/Wl7/8ZT3xxBOy2+0aHh5O7LMsSzabTfF4XDabbUn79Z83unk7lV27ctPqD5jC5dr5oX/HUtQnOeqzvGyqzYqCe2BgQEeOHFFTU5Nqamr0+uuvKxKJJPZHIhG53W7l5+cvap+YmJDb7VZeXp6mp6cVi8Vkt9sT/dMxOTmjeNxK6zHLyaZfIMwXiUxL+uC4vP53LEV9kqM+yzOxNlu22JY9YU25VH758mV95StfUUdHh2pqaiRJe/fu1cWLFzUyMqJYLKaenh55PB4VFhYqJydHAwMDkqRAICCPxyOn06mysjIFg0FJUnd3tzwez2rNDwCATSPlGffzzz+vaDSqY8eOJdoOHjyoY8eOqb6+XtFoVBUVFaqurpYkdXR0qKWlRTMzMyopKVFdXZ0kqa2tTY2Njers7FRBQYFOnDixRlMCACB7pQzulpYWtbS0fOi+s2fPLmnbvXu3Tp8+vaS9sLBQp06duoUhAgCA6/jmNAAADEJwAwBgEIIbAACDrPhz3ABW37X52Ib+HPdcdEHTV65mehgAbkBwAxm01WmX9/FApoexrJefOSCzPv0KZD+WygEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIMQ3AAAGITgBgDAIAQ3AAAGIbgBADAIwQ0AgEEIbgAADEJwAwBgEIIbAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAZxZHoAADaua/MxuVw7Mz2MhA8by1x0QdNXrmZgNEBmrCi4Z2ZmdPDgQf393/+9PvGJT6ivr09PP/20otGo9u/fr6NHj0qShoaG1NzcrNnZWZWVlam9vV0Oh0NjY2NqaGjQ5OSk7rjjDnV0dGjHjh1rOjEAH91Wp13exwOZHkZSLz9zQNOZHgSwjlIulb/55pt66KGHNDw8LEmam5tTU1OT/H6/gsGgBgcH1dvbK0lqaGhQa2urzp07J8uy1NXVJUlqb29XbW2tQqGQ9uzZI7/fv3YzAgAgi6UM7q6uLrW1tcntdkuSLly4oOLiYhUVFcnhcMjr9SoUCml0dFRzc3MqLS2VJPl8PoVCIc3Pz6u/v19VVVWL2gEAQPpSLpU/9dRTi7bHx8flcrkS2263W+FweEm7y+VSOBzW1NSUcnNz5XA4FrUDAID0pX1xWjwel81mS2xbliWbzbZs+/WfN7p5eyV27cpN+zEANoeNdAFdJlGH5WVTbdIO7vz8fEUikcR2JBKR2+1e0j4xMSG32628vDxNT08rFovJbrcn+qdrcnJG8biV9uM+TDb9AgFIkQiXp7lcO6nDMkyszZYttmVPWNP+HPfevXt18eJFjYyMKBaLqaenRx6PR4WFhcrJydHAwIAkKRAIyOPxyOl0qqysTMFgUJLU3d0tj8fzEaYDAMDmlfYZd05Ojo4dO6b6+npFo1FVVFSourpaktTR0aGWlhbNzMyopKREdXV1kqS2tjY1Njaqs7NTBQUFOnHixOrOAgCATWLFwX3+/PnE38vLy3X27NklfXbv3q3Tp08vaS8sLNSpU6ducYgAAOA6vvIUAACDENwAABiE4AYAwCAENwAABiG4AQAwCMENAIBBCG4AAAxCcAMAYBCCGwAAgxDcAAAYJO3vKgeAjeTafGzD3/FvLrqg6StXMz0MZAmCG4DRtjrt8j4eyPQwknr5mQMy66aS2MhYKgcAwCAENwAABiG4AQAwCO9xA8AaW68L6G71Nbh4ziwENwCssY1+AR0Xz5mFpXIAAAxCcAMAYBCCGwAAgxDcAAAYhOAGAMAgBDcAAAbh42AAsMlxoxazENwAsMlt9M+ZS3zW/EYslQMAYBCCGwAAgxDcAAAYhPe4AQAb3ke9gG6tL75bz4vnCG4AwIa30S+gW8+L51gqBwDAIOsa3C+//LLuv/9+VVZW6qWXXlrPlwYAICus21J5OBzWyZMn9b3vfU9bt27VwYMH9elPf1q//Mu/vF5DAADAeOt2xt3X16ff+I3f0Mc+9jFt375dVVVVCoVC6/XyAABkhXU74x4fH5fL5Upsu91uXbhwYcWP37LFtqrjcf/itlV9vrXAGD+6jT4+aeOPcaOPT2KMq2Gjj0/a+GNczZxK9lw2y7KsVXulJDo7OxWNRvXYY49Jkrq6ujQ4OKgnn3xyPV4eAICssG5L5fn5+YpEIontSCQit9u9Xi8PAEBWWLfg/s3f/E299tprev/993X16lX94Ac/kMfjWa+XBwAgK6zbe9wf//jHdfToUdXV1Wl+fl5f+MIX9Ku/+qvr9fIAAGSFdXuPGwAAfHR8cxoAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQgvsWpbrT2XPPPad9+/bpwIEDOnDgwKa7G9rMzIweeOABvffee0v2DQ0NyefzqaqqSs3NzVpYWMjACDMrWX02+7Hz3HPPqaamRjU1NTp+/PiS/Zv5+ElVm81+7Hz961/X/fffr5qaGr3wwgtL9mfNsWMhbf/7v/9r7du3z5qamrJmZ2ctr9drvf3224v6fPnLX7b+67/+K0MjzKz/+Z//sR544AGrpKTEunTp0pL9NTU11n//939blmVZf/3Xf2299NJL6zzCzEpVn8187PzHf/yH9Yd/+IdWNBq1rl27ZtXV1Vk/+MEPFvXZrMfPSmqzmY+dH/3oR9bBgwet+fl56+rVq9a+ffusd955Z1GfbDl2OOO+BSu509ng4KD+4R/+QV6vV08++aSi0WiGRrv+urq61NbW9qFfaTs6Oqq5uTmVlpZKknw+36a7S1yy+kib+9hxuVxqbGzU1q1b5XQ6deedd2psbCyxfzMfP6lqI23uY+dTn/qUXnzxRTkcDk1OTioWi2n79u2J/dl07BDct+DD7nQWDocT27Ozs7r77rvV0NCgM2fO6MqVK/L7/ZkYakY89dRTKisr+9B9N9fO5XItqt1mkKw+m/3YueuuuxL/sA4PD+v73/++KioqEvs38/GTqjab/diRJKfTqWeffVY1NTUqLy/Xxz/+8cS+bDp2CO5bEI/HZbP9/1uuWZa1aHvHjh36p3/6J915551yOBx6+OGH1dvbm4mhbjiparfZcex84O2339bDDz+sJ554QrfffnuineNn+dpw7HzgyJEjeu2113T58mV1dXUl2rPp2CG4b0GqO52NjY3p9OnTiW3LsuRwrNvXwm9oN9duYmKCu8TdgGNHGhgY0B//8R/r8ccf1+/+7u8u2rfZj59ktdnsx84777yjoaEhSdK2bdtUWVmpn/70p4n92XTsENy3INWdzm677Tb93d/9nS5duiTLsvTSSy/pd37ndzI44o2jsLBQOTk5GhgYkCQFAgHuEneDzX7sXL58WV/5ylfU0dGhmpqaJfs38/GTqjab/dh577331NLSomvXrunatWt65ZVXdN999yX2Z9Oxs3n+O7aKlrvT2SOPPKIjR47onnvu0ZNPPqnDhw9rfn5ev/Zrv6YvfelLmR52Rt1Ym46ODrW0tGhmZkYlJSWqq6vL9PAyjmPnA88//7yi0aiOHTuWaDt48KDOnz+/6Y+fldRmMx87FRUVunDhgh588EHZ7XZVVlaqpqYmK//t4e5gAAAYhKVyAAAMQnADAGAQghsAAIMQ3AAAGITgBgBglSW7kdCN3n33XX3xi1/U5z//ef3Jn/yJfvazn6V8boIbAIBV9Oabb+qhhx7S8PBw0n6WZenw4cN65JFHdPbsWd199936x3/8x5TPz+e4AQBYRddvJPTEE08k2rq7u/XNb35T8XhcJSUlamtr09tvv63t27cnvgjm0Ucf1ZUrV1I+P5/jBgBgDXz2s5/Viy++qKtXr6qtrU0vvPCCcnJy9Mwzz2jbtm26/fbbdebMGblcLg0NDemTn/yk/uZv/kYf+9jHkj4vS+UAAKyhH/3oRxoZGdEf/MEf6MCBA3rllVf07rvvamFhQa+//roeeughnTlzRkVFRYu+GW85LJUDALCGYrGY9u/fr5aWFkkf3II1Fovpxz/+sYqLi3XPPfdIkh544AEdOXIk5fNxxg0AwBr69Kc/rR/+8IeanJyUZVn66le/qm9+85u699579f777+utt96SJJ0/f14lJSUpn48zbgAA1tDu3bv153/+5/qjP/ojxeNx3X333fqzP/sz5eTk6Bvf+IZaWlp09epV5efn6/jx4ymfj4vTAAAwCEvlAAAYhOAGAMAgBDcAAAYhuAEAMAjBDQCAQQhuAAAMQnADAGAQghsAAIP8P4Ehe2gVR+0LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize': (8,6)})\n",
    "model_data['price'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(model_data['price']).hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving transformed price column to y variable to be used in modeling\n",
    "y = np.log(model_data['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming `sqft_lot` with log makes it much more normally distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_data['sqft_lot'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(model_data['sqft_lot']).hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data['sqft_lot'] = np.log(model_data['sqft_lot'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummie out district column \n",
    "#### $R^2_{adj}$: 0.737"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model_data = model_data.join(pd.get_dummies(data['district'])).drop(columns=['district', 'Enumclaw'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>   0.738</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.737</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2500.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 31 Mar 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:03:07</td>     <th>  Log-Likelihood:    </th> <td> -119.06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 24933</td>      <th>  AIC:               </th> <td>   296.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 24904</td>      <th>  BIC:               </th> <td>   531.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    28</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>const</th>             <td> -260.0145</td> <td>    8.601</td> <td>  -30.232</td> <td> 0.000</td> <td> -276.872</td> <td> -243.157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>date</th>              <td>    0.1381</td> <td>    0.003</td> <td>   42.926</td> <td> 0.000</td> <td>    0.132</td> <td>    0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>          <td>   -0.0247</td> <td>    0.002</td> <td>  -11.236</td> <td> 0.000</td> <td>   -0.029</td> <td>   -0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>         <td>    0.0537</td> <td>    0.003</td> <td>   17.405</td> <td> 0.000</td> <td>    0.048</td> <td>    0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>       <td>    0.0002</td> <td> 4.68e-06</td> <td>   44.038</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_lot</th>          <td>    0.0297</td> <td>    0.003</td> <td>    9.642</td> <td> 0.000</td> <td>    0.024</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_above</th>        <td>  8.96e-05</td> <td> 4.17e-06</td> <td>   21.500</td> <td> 0.000</td> <td> 8.14e-05</td> <td> 9.78e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_garage</th>       <td>  5.73e-05</td> <td> 7.74e-06</td> <td>    7.404</td> <td> 0.000</td> <td> 4.21e-05</td> <td> 7.25e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_patio</th>        <td> 9.011e-05</td> <td> 7.78e-06</td> <td>   11.582</td> <td> 0.000</td> <td> 7.49e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_renovated</th>      <td> 3.202e-05</td> <td> 3.74e-06</td> <td>    8.569</td> <td> 0.000</td> <td> 2.47e-05</td> <td> 3.93e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode</th>           <td>-7.001e-05</td> <td> 5.79e-05</td> <td>   -1.210</td> <td> 0.226</td> <td>   -0.000</td> <td> 4.34e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Auburn</th>            <td>   -0.0064</td> <td>    0.013</td> <td>   -0.482</td> <td> 0.630</td> <td>   -0.032</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bellevue</th>          <td>    0.8709</td> <td>    0.013</td> <td>   68.465</td> <td> 0.000</td> <td>    0.846</td> <td>    0.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Federal Way</th>       <td>    0.0196</td> <td>    0.012</td> <td>    1.626</td> <td> 0.104</td> <td>   -0.004</td> <td>    0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fife</th>              <td>    0.0041</td> <td>    0.027</td> <td>    0.150</td> <td> 0.881</td> <td>   -0.049</td> <td>    0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Highline</th>          <td>    0.1953</td> <td>    0.015</td> <td>   12.920</td> <td> 0.000</td> <td>    0.166</td> <td>    0.225</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Issaquah</th>          <td>    0.6237</td> <td>    0.013</td> <td>   49.837</td> <td> 0.000</td> <td>    0.599</td> <td>    0.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kent</th>              <td>    0.0855</td> <td>    0.012</td> <td>    7.275</td> <td> 0.000</td> <td>    0.062</td> <td>    0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lake Washington</th>   <td>    0.7635</td> <td>    0.012</td> <td>   64.939</td> <td> 0.000</td> <td>    0.740</td> <td>    0.787</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mercer Island</th>     <td>    0.9613</td> <td>    0.018</td> <td>   52.082</td> <td> 0.000</td> <td>    0.925</td> <td>    0.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northshore</th>        <td>    0.5394</td> <td>    0.013</td> <td>   41.507</td> <td> 0.000</td> <td>    0.514</td> <td>    0.565</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Renton</th>            <td>    0.2720</td> <td>    0.013</td> <td>   21.549</td> <td> 0.000</td> <td>    0.247</td> <td>    0.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Riverview</th>         <td>    0.3226</td> <td>    0.017</td> <td>   19.313</td> <td> 0.000</td> <td>    0.290</td> <td>    0.355</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seattle</th>           <td>    0.6364</td> <td>    0.013</td> <td>   49.759</td> <td> 0.000</td> <td>    0.611</td> <td>    0.661</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Shoreline</th>         <td>    0.4709</td> <td>    0.015</td> <td>   30.910</td> <td> 0.000</td> <td>    0.441</td> <td>    0.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snoqualmie Valley</th> <td>    0.3994</td> <td>    0.014</td> <td>   29.147</td> <td> 0.000</td> <td>    0.373</td> <td>    0.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tahoma</th>            <td>    0.1736</td> <td>    0.014</td> <td>   12.838</td> <td> 0.000</td> <td>    0.147</td> <td>    0.200</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tukwila</th>           <td>    0.1406</td> <td>    0.025</td> <td>    5.522</td> <td> 0.000</td> <td>    0.091</td> <td>    0.191</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vashon Island</th>     <td>    0.3731</td> <td>    0.027</td> <td>   13.634</td> <td> 0.000</td> <td>    0.320</td> <td>    0.427</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1808.390</td> <th>  Durbin-Watson:     </th> <td>   1.983</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9186.574</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.130</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.962</td>  <th>  Cond. No.          </th> <td>5.48e+08</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, 5.48e+08. 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:                       0.738\n",
       "Model:                            OLS   Adj. R-squared:                  0.737\n",
       "Method:                 Least Squares   F-statistic:                     2500.\n",
       "Date:                Fri, 31 Mar 2023   Prob (F-statistic):               0.00\n",
       "Time:                        17:03:07   Log-Likelihood:                -119.06\n",
       "No. Observations:               24933   AIC:                             296.1\n",
       "Df Residuals:                   24904   BIC:                             531.7\n",
       "Df Model:                          28                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "const              -260.0145      8.601    -30.232      0.000    -276.872    -243.157\n",
       "date                  0.1381      0.003     42.926      0.000       0.132       0.144\n",
       "bedrooms             -0.0247      0.002    -11.236      0.000      -0.029      -0.020\n",
       "bathrooms             0.0537      0.003     17.405      0.000       0.048       0.060\n",
       "sqft_living           0.0002   4.68e-06     44.038      0.000       0.000       0.000\n",
       "sqft_lot              0.0297      0.003      9.642      0.000       0.024       0.036\n",
       "sqft_above          8.96e-05   4.17e-06     21.500      0.000    8.14e-05    9.78e-05\n",
       "sqft_garage         5.73e-05   7.74e-06      7.404      0.000    4.21e-05    7.25e-05\n",
       "sqft_patio         9.011e-05   7.78e-06     11.582      0.000    7.49e-05       0.000\n",
       "yr_renovated       3.202e-05   3.74e-06      8.569      0.000    2.47e-05    3.93e-05\n",
       "zipcode           -7.001e-05   5.79e-05     -1.210      0.226      -0.000    4.34e-05\n",
       "Auburn               -0.0064      0.013     -0.482      0.630      -0.032       0.019\n",
       "Bellevue              0.8709      0.013     68.465      0.000       0.846       0.896\n",
       "Federal Way           0.0196      0.012      1.626      0.104      -0.004       0.043\n",
       "Fife                  0.0041      0.027      0.150      0.881      -0.049       0.057\n",
       "Highline              0.1953      0.015     12.920      0.000       0.166       0.225\n",
       "Issaquah              0.6237      0.013     49.837      0.000       0.599       0.648\n",
       "Kent                  0.0855      0.012      7.275      0.000       0.062       0.109\n",
       "Lake Washington       0.7635      0.012     64.939      0.000       0.740       0.787\n",
       "Mercer Island         0.9613      0.018     52.082      0.000       0.925       0.997\n",
       "Northshore            0.5394      0.013     41.507      0.000       0.514       0.565\n",
       "Renton                0.2720      0.013     21.549      0.000       0.247       0.297\n",
       "Riverview             0.3226      0.017     19.313      0.000       0.290       0.355\n",
       "Seattle               0.6364      0.013     49.759      0.000       0.611       0.661\n",
       "Shoreline             0.4709      0.015     30.910      0.000       0.441       0.501\n",
       "Snoqualmie Valley     0.3994      0.014     29.147      0.000       0.373       0.426\n",
       "Tahoma                0.1736      0.014     12.838      0.000       0.147       0.200\n",
       "Tukwila               0.1406      0.025      5.522      0.000       0.091       0.191\n",
       "Vashon Island         0.3731      0.027     13.634      0.000       0.320       0.427\n",
       "==============================================================================\n",
       "Omnibus:                     1808.390   Durbin-Watson:                   1.983\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9186.574\n",
       "Skew:                          -0.130   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.962   Cond. No.                     5.48e+08\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, 5.48e+08. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(model_data.drop(columns=['price', 'waterfront', 'view', 'condition', 'grade', 'heat_source', 'sewer_system']))\n",
    "model = sm.OLS(y, X)\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummie out additional object columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummie heat source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data = model_data.join(pd.get_dummies(model_data['heat_source'], prefix='heat')).drop(columns=['heat_source', 'heat_Gas'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>   0.739</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.739</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2077.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 31 Mar 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:03:07</td>     <th>  Log-Likelihood:    </th> <td> -34.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 24933</td>      <th>  AIC:               </th> <td>   138.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 24898</td>      <th>  BIC:               </th> <td>   422.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    34</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>const</th>                  <td> -260.1095</td> <td>    8.577</td> <td>  -30.327</td> <td> 0.000</td> <td> -276.921</td> <td> -243.298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>date</th>                   <td>    0.1381</td> <td>    0.003</td> <td>   43.041</td> <td> 0.000</td> <td>    0.132</td> <td>    0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>               <td>   -0.0248</td> <td>    0.002</td> <td>  -11.295</td> <td> 0.000</td> <td>   -0.029</td> <td>   -0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>              <td>    0.0535</td> <td>    0.003</td> <td>   17.133</td> <td> 0.000</td> <td>    0.047</td> <td>    0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>            <td>    0.0002</td> <td> 4.68e-06</td> <td>   43.235</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_lot</th>               <td>    0.0346</td> <td>    0.003</td> <td>   11.163</td> <td> 0.000</td> <td>    0.029</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_above</th>             <td> 8.773e-05</td> <td> 4.16e-06</td> <td>   21.098</td> <td> 0.000</td> <td> 7.96e-05</td> <td> 9.59e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_garage</th>            <td> 4.855e-05</td> <td> 7.75e-06</td> <td>    6.261</td> <td> 0.000</td> <td> 3.33e-05</td> <td> 6.37e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_patio</th>             <td>  9.31e-05</td> <td> 7.77e-06</td> <td>   11.980</td> <td> 0.000</td> <td> 7.79e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_renovated</th>           <td> 3.243e-05</td> <td> 3.73e-06</td> <td>    8.693</td> <td> 0.000</td> <td> 2.51e-05</td> <td> 3.97e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode</th>                <td>-6.833e-05</td> <td> 5.77e-05</td> <td>   -1.184</td> <td> 0.236</td> <td>   -0.000</td> <td> 4.48e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Auburn</th>                 <td>   -0.0032</td> <td>    0.013</td> <td>   -0.242</td> <td> 0.809</td> <td>   -0.029</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bellevue</th>               <td>    0.8654</td> <td>    0.013</td> <td>   68.171</td> <td> 0.000</td> <td>    0.840</td> <td>    0.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Federal Way</th>            <td>    0.0170</td> <td>    0.012</td> <td>    1.414</td> <td> 0.158</td> <td>   -0.007</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fife</th>                   <td>    0.0114</td> <td>    0.027</td> <td>    0.422</td> <td> 0.673</td> <td>   -0.042</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Highline</th>               <td>    0.2015</td> <td>    0.015</td> <td>   13.351</td> <td> 0.000</td> <td>    0.172</td> <td>    0.231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Issaquah</th>               <td>    0.6232</td> <td>    0.012</td> <td>   49.950</td> <td> 0.000</td> <td>    0.599</td> <td>    0.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kent</th>                   <td>    0.0853</td> <td>    0.012</td> <td>    7.282</td> <td> 0.000</td> <td>    0.062</td> <td>    0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lake Washington</th>        <td>    0.7614</td> <td>    0.012</td> <td>   64.960</td> <td> 0.000</td> <td>    0.738</td> <td>    0.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mercer Island</th>          <td>    0.9589</td> <td>    0.018</td> <td>   52.087</td> <td> 0.000</td> <td>    0.923</td> <td>    0.995</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northshore</th>             <td>    0.5393</td> <td>    0.013</td> <td>   41.635</td> <td> 0.000</td> <td>    0.514</td> <td>    0.565</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Renton</th>                 <td>    0.2720</td> <td>    0.013</td> <td>   21.618</td> <td> 0.000</td> <td>    0.247</td> <td>    0.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Riverview</th>              <td>    0.3254</td> <td>    0.017</td> <td>   19.543</td> <td> 0.000</td> <td>    0.293</td> <td>    0.358</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seattle</th>                <td>    0.6420</td> <td>    0.013</td> <td>   50.229</td> <td> 0.000</td> <td>    0.617</td> <td>    0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Shoreline</th>              <td>    0.4778</td> <td>    0.015</td> <td>   31.411</td> <td> 0.000</td> <td>    0.448</td> <td>    0.508</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snoqualmie Valley</th>      <td>    0.4023</td> <td>    0.014</td> <td>   29.450</td> <td> 0.000</td> <td>    0.376</td> <td>    0.429</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tahoma</th>                 <td>    0.1746</td> <td>    0.013</td> <td>   12.952</td> <td> 0.000</td> <td>    0.148</td> <td>    0.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tukwila</th>                <td>    0.1443</td> <td>    0.025</td> <td>    5.684</td> <td> 0.000</td> <td>    0.095</td> <td>    0.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vashon Island</th>          <td>    0.3864</td> <td>    0.027</td> <td>   14.118</td> <td> 0.000</td> <td>    0.333</td> <td>    0.440</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity</th>       <td>   -0.0531</td> <td>    0.004</td> <td>  -12.259</td> <td> 0.000</td> <td>   -0.062</td> <td>   -0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity/Solar</th> <td>   -0.0425</td> <td>    0.039</td> <td>   -1.077</td> <td> 0.281</td> <td>   -0.120</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Gas/Solar</th>         <td>    0.0758</td> <td>    0.027</td> <td>    2.782</td> <td> 0.005</td> <td>    0.022</td> <td>    0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil</th>               <td>   -0.0274</td> <td>    0.006</td> <td>   -4.906</td> <td> 0.000</td> <td>   -0.038</td> <td>   -0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil/Solar</th>         <td>   -0.0090</td> <td>    0.121</td> <td>   -0.074</td> <td> 0.941</td> <td>   -0.247</td> <td>    0.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Other</th>             <td>    0.1649</td> <td>    0.086</td> <td>    1.917</td> <td> 0.055</td> <td>   -0.004</td> <td>    0.334</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1812.426</td> <th>  Durbin-Watson:     </th> <td>   1.981</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9358.894</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.117</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.992</td>  <th>  Cond. No.          </th> <td>5.48e+08</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, 5.48e+08. 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:                       0.739\n",
       "Model:                            OLS   Adj. R-squared:                  0.739\n",
       "Method:                 Least Squares   F-statistic:                     2077.\n",
       "Date:                Fri, 31 Mar 2023   Prob (F-statistic):               0.00\n",
       "Time:                        17:03:07   Log-Likelihood:                -34.167\n",
       "No. Observations:               24933   AIC:                             138.3\n",
       "Df Residuals:                   24898   BIC:                             422.7\n",
       "Df Model:                          34                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==========================================================================================\n",
       "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "const                   -260.1095      8.577    -30.327      0.000    -276.921    -243.298\n",
       "date                       0.1381      0.003     43.041      0.000       0.132       0.144\n",
       "bedrooms                  -0.0248      0.002    -11.295      0.000      -0.029      -0.020\n",
       "bathrooms                  0.0535      0.003     17.133      0.000       0.047       0.060\n",
       "sqft_living                0.0002   4.68e-06     43.235      0.000       0.000       0.000\n",
       "sqft_lot                   0.0346      0.003     11.163      0.000       0.029       0.041\n",
       "sqft_above              8.773e-05   4.16e-06     21.098      0.000    7.96e-05    9.59e-05\n",
       "sqft_garage             4.855e-05   7.75e-06      6.261      0.000    3.33e-05    6.37e-05\n",
       "sqft_patio               9.31e-05   7.77e-06     11.980      0.000    7.79e-05       0.000\n",
       "yr_renovated            3.243e-05   3.73e-06      8.693      0.000    2.51e-05    3.97e-05\n",
       "zipcode                -6.833e-05   5.77e-05     -1.184      0.236      -0.000    4.48e-05\n",
       "Auburn                    -0.0032      0.013     -0.242      0.809      -0.029       0.023\n",
       "Bellevue                   0.8654      0.013     68.171      0.000       0.840       0.890\n",
       "Federal Way                0.0170      0.012      1.414      0.158      -0.007       0.041\n",
       "Fife                       0.0114      0.027      0.422      0.673      -0.042       0.064\n",
       "Highline                   0.2015      0.015     13.351      0.000       0.172       0.231\n",
       "Issaquah                   0.6232      0.012     49.950      0.000       0.599       0.648\n",
       "Kent                       0.0853      0.012      7.282      0.000       0.062       0.108\n",
       "Lake Washington            0.7614      0.012     64.960      0.000       0.738       0.784\n",
       "Mercer Island              0.9589      0.018     52.087      0.000       0.923       0.995\n",
       "Northshore                 0.5393      0.013     41.635      0.000       0.514       0.565\n",
       "Renton                     0.2720      0.013     21.618      0.000       0.247       0.297\n",
       "Riverview                  0.3254      0.017     19.543      0.000       0.293       0.358\n",
       "Seattle                    0.6420      0.013     50.229      0.000       0.617       0.667\n",
       "Shoreline                  0.4778      0.015     31.411      0.000       0.448       0.508\n",
       "Snoqualmie Valley          0.4023      0.014     29.450      0.000       0.376       0.429\n",
       "Tahoma                     0.1746      0.013     12.952      0.000       0.148       0.201\n",
       "Tukwila                    0.1443      0.025      5.684      0.000       0.095       0.194\n",
       "Vashon Island              0.3864      0.027     14.118      0.000       0.333       0.440\n",
       "heat_Electricity          -0.0531      0.004    -12.259      0.000      -0.062      -0.045\n",
       "heat_Electricity/Solar    -0.0425      0.039     -1.077      0.281      -0.120       0.035\n",
       "heat_Gas/Solar             0.0758      0.027      2.782      0.005       0.022       0.129\n",
       "heat_Oil                  -0.0274      0.006     -4.906      0.000      -0.038      -0.016\n",
       "heat_Oil/Solar            -0.0090      0.121     -0.074      0.941      -0.247       0.229\n",
       "heat_Other                 0.1649      0.086      1.917      0.055      -0.004       0.334\n",
       "==============================================================================\n",
       "Omnibus:                     1812.426   Durbin-Watson:                   1.981\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9358.894\n",
       "Skew:                          -0.117   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.992   Cond. No.                     5.48e+08\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, 5.48e+08. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = model_data.drop(columns=['price', 'waterfront', 'view', 'condition', 'grade', 'sewer_system'])\n",
    "model = sm.OLS(y, sm.add_constant(X))\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummie waterfront"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data['waterfront'] = pd.get_dummies(model_data['waterfront'], drop_first=True).rename(columns={'YES': 'waterfront'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>   0.744</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.744</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2072.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 31 Mar 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:03:07</td>     <th>  Log-Likelihood:    </th> <td>  211.72</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 24933</td>      <th>  AIC:               </th> <td>  -351.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 24897</td>      <th>  BIC:               </th> <td>  -58.98</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    35</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>const</th>                  <td> -261.4990</td> <td>    8.493</td> <td>  -30.789</td> <td> 0.000</td> <td> -278.146</td> <td> -244.852</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>date</th>                   <td>    0.1392</td> <td>    0.003</td> <td>   43.802</td> <td> 0.000</td> <td>    0.133</td> <td>    0.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>               <td>   -0.0210</td> <td>    0.002</td> <td>   -9.608</td> <td> 0.000</td> <td>   -0.025</td> <td>   -0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>              <td>    0.0516</td> <td>    0.003</td> <td>   16.683</td> <td> 0.000</td> <td>    0.046</td> <td>    0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>            <td>    0.0002</td> <td> 4.64e-06</td> <td>   43.102</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_lot</th>               <td>    0.0268</td> <td>    0.003</td> <td>    8.677</td> <td> 0.000</td> <td>    0.021</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>             <td>    0.3335</td> <td>    0.015</td> <td>   22.270</td> <td> 0.000</td> <td>    0.304</td> <td>    0.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_above</th>             <td> 8.831e-05</td> <td> 4.12e-06</td> <td>   21.448</td> <td> 0.000</td> <td> 8.02e-05</td> <td> 9.64e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_garage</th>            <td> 6.119e-05</td> <td>  7.7e-06</td> <td>    7.948</td> <td> 0.000</td> <td> 4.61e-05</td> <td> 7.63e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_patio</th>             <td> 8.395e-05</td> <td> 7.71e-06</td> <td>   10.894</td> <td> 0.000</td> <td> 6.88e-05</td> <td> 9.91e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_renovated</th>           <td> 2.771e-05</td> <td>  3.7e-06</td> <td>    7.490</td> <td> 0.000</td> <td> 2.05e-05</td> <td>  3.5e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode</th>                <td>-7.588e-05</td> <td> 5.71e-05</td> <td>   -1.328</td> <td> 0.184</td> <td>   -0.000</td> <td> 3.61e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Auburn</th>                 <td>   -0.0039</td> <td>    0.013</td> <td>   -0.297</td> <td> 0.767</td> <td>   -0.029</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bellevue</th>               <td>    0.8691</td> <td>    0.013</td> <td>   69.139</td> <td> 0.000</td> <td>    0.844</td> <td>    0.894</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Federal Way</th>            <td>    0.0115</td> <td>    0.012</td> <td>    0.964</td> <td> 0.335</td> <td>   -0.012</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fife</th>                   <td>    0.0110</td> <td>    0.027</td> <td>    0.409</td> <td> 0.683</td> <td>   -0.042</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Highline</th>               <td>    0.2029</td> <td>    0.015</td> <td>   13.576</td> <td> 0.000</td> <td>    0.174</td> <td>    0.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Issaquah</th>               <td>    0.6240</td> <td>    0.012</td> <td>   50.513</td> <td> 0.000</td> <td>    0.600</td> <td>    0.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kent</th>                   <td>    0.0828</td> <td>    0.012</td> <td>    7.134</td> <td> 0.000</td> <td>    0.060</td> <td>    0.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lake Washington</th>        <td>    0.7638</td> <td>    0.012</td> <td>   65.805</td> <td> 0.000</td> <td>    0.741</td> <td>    0.787</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mercer Island</th>          <td>    0.9631</td> <td>    0.018</td> <td>   52.826</td> <td> 0.000</td> <td>    0.927</td> <td>    0.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northshore</th>             <td>    0.5423</td> <td>    0.013</td> <td>   42.277</td> <td> 0.000</td> <td>    0.517</td> <td>    0.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Renton</th>                 <td>    0.2721</td> <td>    0.012</td> <td>   21.834</td> <td> 0.000</td> <td>    0.248</td> <td>    0.296</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Riverview</th>              <td>    0.3179</td> <td>    0.016</td> <td>   19.274</td> <td> 0.000</td> <td>    0.286</td> <td>    0.350</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seattle</th>                <td>    0.6432</td> <td>    0.013</td> <td>   50.818</td> <td> 0.000</td> <td>    0.618</td> <td>    0.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Shoreline</th>              <td>    0.4827</td> <td>    0.015</td> <td>   32.043</td> <td> 0.000</td> <td>    0.453</td> <td>    0.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snoqualmie Valley</th>      <td>    0.3959</td> <td>    0.014</td> <td>   29.264</td> <td> 0.000</td> <td>    0.369</td> <td>    0.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tahoma</th>                 <td>    0.1658</td> <td>    0.013</td> <td>   12.410</td> <td> 0.000</td> <td>    0.140</td> <td>    0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tukwila</th>                <td>    0.1457</td> <td>    0.025</td> <td>    5.794</td> <td> 0.000</td> <td>    0.096</td> <td>    0.195</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vashon Island</th>          <td>    0.2921</td> <td>    0.027</td> <td>   10.649</td> <td> 0.000</td> <td>    0.238</td> <td>    0.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity</th>       <td>   -0.0571</td> <td>    0.004</td> <td>  -13.317</td> <td> 0.000</td> <td>   -0.066</td> <td>   -0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity/Solar</th> <td>   -0.0386</td> <td>    0.039</td> <td>   -0.989</td> <td> 0.323</td> <td>   -0.115</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Gas/Solar</th>         <td>    0.0809</td> <td>    0.027</td> <td>    2.998</td> <td> 0.003</td> <td>    0.028</td> <td>    0.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil</th>               <td>   -0.0278</td> <td>    0.006</td> <td>   -5.019</td> <td> 0.000</td> <td>   -0.039</td> <td>   -0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil/Solar</th>         <td>   -0.0039</td> <td>    0.120</td> <td>   -0.032</td> <td> 0.974</td> <td>   -0.239</td> <td>    0.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Other</th>             <td>    0.1170</td> <td>    0.085</td> <td>    1.373</td> <td> 0.170</td> <td>   -0.050</td> <td>    0.284</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1880.191</td> <th>  Durbin-Watson:     </th> <td>   1.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>8877.796</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.211</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.893</td>  <th>  Cond. No.          </th> <td>5.48e+08</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, 5.48e+08. 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:                       0.744\n",
       "Model:                            OLS   Adj. R-squared:                  0.744\n",
       "Method:                 Least Squares   F-statistic:                     2072.\n",
       "Date:                Fri, 31 Mar 2023   Prob (F-statistic):               0.00\n",
       "Time:                        17:03:07   Log-Likelihood:                 211.72\n",
       "No. Observations:               24933   AIC:                            -351.4\n",
       "Df Residuals:                   24897   BIC:                            -58.98\n",
       "Df Model:                          35                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==========================================================================================\n",
       "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "const                   -261.4990      8.493    -30.789      0.000    -278.146    -244.852\n",
       "date                       0.1392      0.003     43.802      0.000       0.133       0.145\n",
       "bedrooms                  -0.0210      0.002     -9.608      0.000      -0.025      -0.017\n",
       "bathrooms                  0.0516      0.003     16.683      0.000       0.046       0.058\n",
       "sqft_living                0.0002   4.64e-06     43.102      0.000       0.000       0.000\n",
       "sqft_lot                   0.0268      0.003      8.677      0.000       0.021       0.033\n",
       "waterfront                 0.3335      0.015     22.270      0.000       0.304       0.363\n",
       "sqft_above              8.831e-05   4.12e-06     21.448      0.000    8.02e-05    9.64e-05\n",
       "sqft_garage             6.119e-05    7.7e-06      7.948      0.000    4.61e-05    7.63e-05\n",
       "sqft_patio              8.395e-05   7.71e-06     10.894      0.000    6.88e-05    9.91e-05\n",
       "yr_renovated            2.771e-05    3.7e-06      7.490      0.000    2.05e-05     3.5e-05\n",
       "zipcode                -7.588e-05   5.71e-05     -1.328      0.184      -0.000    3.61e-05\n",
       "Auburn                    -0.0039      0.013     -0.297      0.767      -0.029       0.022\n",
       "Bellevue                   0.8691      0.013     69.139      0.000       0.844       0.894\n",
       "Federal Way                0.0115      0.012      0.964      0.335      -0.012       0.035\n",
       "Fife                       0.0110      0.027      0.409      0.683      -0.042       0.064\n",
       "Highline                   0.2029      0.015     13.576      0.000       0.174       0.232\n",
       "Issaquah                   0.6240      0.012     50.513      0.000       0.600       0.648\n",
       "Kent                       0.0828      0.012      7.134      0.000       0.060       0.106\n",
       "Lake Washington            0.7638      0.012     65.805      0.000       0.741       0.787\n",
       "Mercer Island              0.9631      0.018     52.826      0.000       0.927       0.999\n",
       "Northshore                 0.5423      0.013     42.277      0.000       0.517       0.567\n",
       "Renton                     0.2721      0.012     21.834      0.000       0.248       0.296\n",
       "Riverview                  0.3179      0.016     19.274      0.000       0.286       0.350\n",
       "Seattle                    0.6432      0.013     50.818      0.000       0.618       0.668\n",
       "Shoreline                  0.4827      0.015     32.043      0.000       0.453       0.512\n",
       "Snoqualmie Valley          0.3959      0.014     29.264      0.000       0.369       0.422\n",
       "Tahoma                     0.1658      0.013     12.410      0.000       0.140       0.192\n",
       "Tukwila                    0.1457      0.025      5.794      0.000       0.096       0.195\n",
       "Vashon Island              0.2921      0.027     10.649      0.000       0.238       0.346\n",
       "heat_Electricity          -0.0571      0.004    -13.317      0.000      -0.066      -0.049\n",
       "heat_Electricity/Solar    -0.0386      0.039     -0.989      0.323      -0.115       0.038\n",
       "heat_Gas/Solar             0.0809      0.027      2.998      0.003       0.028       0.134\n",
       "heat_Oil                  -0.0278      0.006     -5.019      0.000      -0.039      -0.017\n",
       "heat_Oil/Solar            -0.0039      0.120     -0.032      0.974      -0.239       0.232\n",
       "heat_Other                 0.1170      0.085      1.373      0.170      -0.050       0.284\n",
       "==============================================================================\n",
       "Omnibus:                     1880.191   Durbin-Watson:                   1.977\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             8877.796\n",
       "Skew:                          -0.211   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.893   Cond. No.                     5.48e+08\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, 5.48e+08. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = model_data.drop(columns=['price', 'view', 'condition', 'grade', 'sewer_system'])\n",
    "model = sm.OLS(y, sm.add_constant(X))\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummie view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "view = pd.get_dummies(model_data['view'], prefix='view').drop(columns=['view_NONE'])\n",
    "model_data = model_data.join(view).drop(columns=['view'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>   0.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1944.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 31 Mar 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:03:07</td>     <th>  Log-Likelihood:    </th> <td>  630.01</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 24933</td>      <th>  AIC:               </th> <td>  -1180.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 24893</td>      <th>  BIC:               </th> <td>  -855.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    39</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>const</th>                  <td> -252.1083</td> <td>    8.360</td> <td>  -30.156</td> <td> 0.000</td> <td> -268.495</td> <td> -235.722</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>date</th>                   <td>    0.1394</td> <td>    0.003</td> <td>   44.604</td> <td> 0.000</td> <td>    0.133</td> <td>    0.146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>               <td>   -0.0152</td> <td>    0.002</td> <td>   -7.049</td> <td> 0.000</td> <td>   -0.019</td> <td>   -0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>              <td>    0.0514</td> <td>    0.003</td> <td>   16.917</td> <td> 0.000</td> <td>    0.045</td> <td>    0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>            <td>    0.0002</td> <td> 4.65e-06</td> <td>   37.536</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_lot</th>               <td>    0.0245</td> <td>    0.003</td> <td>    8.051</td> <td> 0.000</td> <td>    0.019</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>             <td>    0.1487</td> <td>    0.017</td> <td>    9.008</td> <td> 0.000</td> <td>    0.116</td> <td>    0.181</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_above</th>             <td>    0.0001</td> <td> 4.09e-06</td> <td>   25.265</td> <td> 0.000</td> <td> 9.52e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_garage</th>            <td> 6.032e-05</td> <td> 7.57e-06</td> <td>    7.967</td> <td> 0.000</td> <td> 4.55e-05</td> <td> 7.52e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_patio</th>             <td> 5.832e-05</td> <td> 7.63e-06</td> <td>    7.638</td> <td> 0.000</td> <td> 4.34e-05</td> <td> 7.33e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_renovated</th>           <td> 2.358e-05</td> <td> 3.64e-06</td> <td>    6.477</td> <td> 0.000</td> <td> 1.64e-05</td> <td> 3.07e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode</th>                <td>   -0.0002</td> <td> 5.63e-05</td> <td>   -3.121</td> <td> 0.002</td> <td>   -0.000</td> <td>-6.53e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Auburn</th>                 <td>    0.0031</td> <td>    0.013</td> <td>    0.246</td> <td> 0.806</td> <td>   -0.022</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bellevue</th>               <td>    0.8697</td> <td>    0.012</td> <td>   70.319</td> <td> 0.000</td> <td>    0.845</td> <td>    0.894</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Federal Way</th>            <td>    0.0158</td> <td>    0.012</td> <td>    1.350</td> <td> 0.177</td> <td>   -0.007</td> <td>    0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fife</th>                   <td>    0.0257</td> <td>    0.026</td> <td>    0.976</td> <td> 0.329</td> <td>   -0.026</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Highline</th>               <td>    0.2158</td> <td>    0.015</td> <td>   14.670</td> <td> 0.000</td> <td>    0.187</td> <td>    0.245</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Issaquah</th>               <td>    0.6303</td> <td>    0.012</td> <td>   51.851</td> <td> 0.000</td> <td>    0.607</td> <td>    0.654</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kent</th>                   <td>    0.0931</td> <td>    0.011</td> <td>    8.148</td> <td> 0.000</td> <td>    0.071</td> <td>    0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lake Washington</th>        <td>    0.7766</td> <td>    0.011</td> <td>   67.942</td> <td> 0.000</td> <td>    0.754</td> <td>    0.799</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mercer Island</th>          <td>    0.9622</td> <td>    0.018</td> <td>   53.587</td> <td> 0.000</td> <td>    0.927</td> <td>    0.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northshore</th>             <td>    0.5580</td> <td>    0.013</td> <td>   44.172</td> <td> 0.000</td> <td>    0.533</td> <td>    0.583</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Renton</th>                 <td>    0.2777</td> <td>    0.012</td> <td>   22.654</td> <td> 0.000</td> <td>    0.254</td> <td>    0.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Riverview</th>              <td>    0.3288</td> <td>    0.016</td> <td>   20.265</td> <td> 0.000</td> <td>    0.297</td> <td>    0.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seattle</th>                <td>    0.6459</td> <td>    0.012</td> <td>   51.881</td> <td> 0.000</td> <td>    0.622</td> <td>    0.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Shoreline</th>              <td>    0.4976</td> <td>    0.015</td> <td>   33.546</td> <td> 0.000</td> <td>    0.469</td> <td>    0.527</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snoqualmie Valley</th>      <td>    0.4015</td> <td>    0.013</td> <td>   30.168</td> <td> 0.000</td> <td>    0.375</td> <td>    0.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tahoma</th>                 <td>    0.1762</td> <td>    0.013</td> <td>   13.405</td> <td> 0.000</td> <td>    0.150</td> <td>    0.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tukwila</th>                <td>    0.1617</td> <td>    0.025</td> <td>    6.540</td> <td> 0.000</td> <td>    0.113</td> <td>    0.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vashon Island</th>          <td>    0.2504</td> <td>    0.027</td> <td>    9.265</td> <td> 0.000</td> <td>    0.197</td> <td>    0.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity</th>       <td>   -0.0576</td> <td>    0.004</td> <td>  -13.636</td> <td> 0.000</td> <td>   -0.066</td> <td>   -0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity/Solar</th> <td>   -0.0573</td> <td>    0.038</td> <td>   -1.492</td> <td> 0.136</td> <td>   -0.133</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Gas/Solar</th>         <td>    0.0761</td> <td>    0.027</td> <td>    2.866</td> <td> 0.004</td> <td>    0.024</td> <td>    0.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil</th>               <td>   -0.0336</td> <td>    0.005</td> <td>   -6.165</td> <td> 0.000</td> <td>   -0.044</td> <td>   -0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil/Solar</th>         <td>   -0.0077</td> <td>    0.118</td> <td>   -0.065</td> <td> 0.948</td> <td>   -0.239</td> <td>    0.224</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Other</th>             <td>    0.0808</td> <td>    0.084</td> <td>    0.963</td> <td> 0.335</td> <td>   -0.084</td> <td>    0.245</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_AVERAGE</th>           <td>    0.0974</td> <td>    0.006</td> <td>   15.236</td> <td> 0.000</td> <td>    0.085</td> <td>    0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_EXCELLENT</th>         <td>    0.3226</td> <td>    0.015</td> <td>   21.867</td> <td> 0.000</td> <td>    0.294</td> <td>    0.352</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_FAIR</th>              <td>    0.1401</td> <td>    0.017</td> <td>    8.101</td> <td> 0.000</td> <td>    0.106</td> <td>    0.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_GOOD</th>              <td>    0.1630</td> <td>    0.010</td> <td>   16.684</td> <td> 0.000</td> <td>    0.144</td> <td>    0.182</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2029.709</td> <th>  Durbin-Watson:     </th> <td>   1.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9554.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.263</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.987</td>  <th>  Cond. No.          </th> <td>5.49e+08</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, 5.49e+08. 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:                       0.753\n",
       "Model:                            OLS   Adj. R-squared:                  0.752\n",
       "Method:                 Least Squares   F-statistic:                     1944.\n",
       "Date:                Fri, 31 Mar 2023   Prob (F-statistic):               0.00\n",
       "Time:                        17:03:07   Log-Likelihood:                 630.01\n",
       "No. Observations:               24933   AIC:                            -1180.\n",
       "Df Residuals:                   24893   BIC:                            -855.1\n",
       "Df Model:                          39                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==========================================================================================\n",
       "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "const                   -252.1083      8.360    -30.156      0.000    -268.495    -235.722\n",
       "date                       0.1394      0.003     44.604      0.000       0.133       0.146\n",
       "bedrooms                  -0.0152      0.002     -7.049      0.000      -0.019      -0.011\n",
       "bathrooms                  0.0514      0.003     16.917      0.000       0.045       0.057\n",
       "sqft_living                0.0002   4.65e-06     37.536      0.000       0.000       0.000\n",
       "sqft_lot                   0.0245      0.003      8.051      0.000       0.019       0.030\n",
       "waterfront                 0.1487      0.017      9.008      0.000       0.116       0.181\n",
       "sqft_above                 0.0001   4.09e-06     25.265      0.000    9.52e-05       0.000\n",
       "sqft_garage             6.032e-05   7.57e-06      7.967      0.000    4.55e-05    7.52e-05\n",
       "sqft_patio              5.832e-05   7.63e-06      7.638      0.000    4.34e-05    7.33e-05\n",
       "yr_renovated            2.358e-05   3.64e-06      6.477      0.000    1.64e-05    3.07e-05\n",
       "zipcode                   -0.0002   5.63e-05     -3.121      0.002      -0.000   -6.53e-05\n",
       "Auburn                     0.0031      0.013      0.246      0.806      -0.022       0.028\n",
       "Bellevue                   0.8697      0.012     70.319      0.000       0.845       0.894\n",
       "Federal Way                0.0158      0.012      1.350      0.177      -0.007       0.039\n",
       "Fife                       0.0257      0.026      0.976      0.329      -0.026       0.077\n",
       "Highline                   0.2158      0.015     14.670      0.000       0.187       0.245\n",
       "Issaquah                   0.6303      0.012     51.851      0.000       0.607       0.654\n",
       "Kent                       0.0931      0.011      8.148      0.000       0.071       0.115\n",
       "Lake Washington            0.7766      0.011     67.942      0.000       0.754       0.799\n",
       "Mercer Island              0.9622      0.018     53.587      0.000       0.927       0.997\n",
       "Northshore                 0.5580      0.013     44.172      0.000       0.533       0.583\n",
       "Renton                     0.2777      0.012     22.654      0.000       0.254       0.302\n",
       "Riverview                  0.3288      0.016     20.265      0.000       0.297       0.361\n",
       "Seattle                    0.6459      0.012     51.881      0.000       0.622       0.670\n",
       "Shoreline                  0.4976      0.015     33.546      0.000       0.469       0.527\n",
       "Snoqualmie Valley          0.4015      0.013     30.168      0.000       0.375       0.428\n",
       "Tahoma                     0.1762      0.013     13.405      0.000       0.150       0.202\n",
       "Tukwila                    0.1617      0.025      6.540      0.000       0.113       0.210\n",
       "Vashon Island              0.2504      0.027      9.265      0.000       0.197       0.303\n",
       "heat_Electricity          -0.0576      0.004    -13.636      0.000      -0.066      -0.049\n",
       "heat_Electricity/Solar    -0.0573      0.038     -1.492      0.136      -0.133       0.018\n",
       "heat_Gas/Solar             0.0761      0.027      2.866      0.004       0.024       0.128\n",
       "heat_Oil                  -0.0336      0.005     -6.165      0.000      -0.044      -0.023\n",
       "heat_Oil/Solar            -0.0077      0.118     -0.065      0.948      -0.239       0.224\n",
       "heat_Other                 0.0808      0.084      0.963      0.335      -0.084       0.245\n",
       "view_AVERAGE               0.0974      0.006     15.236      0.000       0.085       0.110\n",
       "view_EXCELLENT             0.3226      0.015     21.867      0.000       0.294       0.352\n",
       "view_FAIR                  0.1401      0.017      8.101      0.000       0.106       0.174\n",
       "view_GOOD                  0.1630      0.010     16.684      0.000       0.144       0.182\n",
       "==============================================================================\n",
       "Omnibus:                     2029.709   Durbin-Watson:                   1.974\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9554.660\n",
       "Skew:                          -0.263   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.987   Cond. No.                     5.49e+08\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, 5.49e+08. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = model_data.drop(columns=['price', 'condition', 'grade', 'sewer_system'])\n",
    "model = sm.OLS(y, sm.add_constant(X))\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummie sewer system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "sewer = pd.get_dummies(model_data['sewer_system'], prefix='sewer').drop(columns=['sewer_PUBLIC'])\n",
    "model_data = model_data.join(sewer).drop(columns=['sewer_system'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>   0.754</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1812.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 31 Mar 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:03:07</td>     <th>  Log-Likelihood:    </th> <td>  664.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 24933</td>      <th>  AIC:               </th> <td>  -1242.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 24890</td>      <th>  BIC:               </th> <td>  -892.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    42</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>const</th>                    <td> -252.4778</td> <td>    8.350</td> <td>  -30.238</td> <td> 0.000</td> <td> -268.844</td> <td> -236.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>date</th>                     <td>    0.1392</td> <td>    0.003</td> <td>   44.592</td> <td> 0.000</td> <td>    0.133</td> <td>    0.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>                 <td>   -0.0159</td> <td>    0.002</td> <td>   -7.373</td> <td> 0.000</td> <td>   -0.020</td> <td>   -0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>                <td>    0.0517</td> <td>    0.003</td> <td>   17.035</td> <td> 0.000</td> <td>    0.046</td> <td>    0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>              <td>    0.0002</td> <td> 4.65e-06</td> <td>   37.411</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_lot</th>                 <td>    0.0369</td> <td>    0.003</td> <td>   10.867</td> <td> 0.000</td> <td>    0.030</td> <td>    0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>               <td>    0.1552</td> <td>    0.017</td> <td>    9.405</td> <td> 0.000</td> <td>    0.123</td> <td>    0.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_above</th>               <td>    0.0001</td> <td> 4.08e-06</td> <td>   25.000</td> <td> 0.000</td> <td> 9.41e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_garage</th>              <td> 5.799e-05</td> <td> 7.57e-06</td> <td>    7.664</td> <td> 0.000</td> <td> 4.32e-05</td> <td> 7.28e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_patio</th>               <td>   6.1e-05</td> <td> 7.63e-06</td> <td>    7.993</td> <td> 0.000</td> <td>  4.6e-05</td> <td>  7.6e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_renovated</th>             <td> 2.371e-05</td> <td> 3.64e-06</td> <td>    6.519</td> <td> 0.000</td> <td> 1.66e-05</td> <td> 3.08e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zipcode</th>                  <td>   -0.0002</td> <td> 5.62e-05</td> <td>   -2.998</td> <td> 0.003</td> <td>   -0.000</td> <td>-5.84e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Auburn</th>                   <td>    0.0041</td> <td>    0.013</td> <td>    0.320</td> <td> 0.749</td> <td>   -0.021</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bellevue</th>                 <td>    0.8607</td> <td>    0.012</td> <td>   69.408</td> <td> 0.000</td> <td>    0.836</td> <td>    0.885</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Federal Way</th>              <td>    0.0147</td> <td>    0.012</td> <td>    1.259</td> <td> 0.208</td> <td>   -0.008</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fife</th>                     <td>    0.0295</td> <td>    0.026</td> <td>    1.118</td> <td> 0.263</td> <td>   -0.022</td> <td>    0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Highline</th>                 <td>    0.2121</td> <td>    0.015</td> <td>   14.429</td> <td> 0.000</td> <td>    0.183</td> <td>    0.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Issaquah</th>                 <td>    0.6350</td> <td>    0.012</td> <td>   52.247</td> <td> 0.000</td> <td>    0.611</td> <td>    0.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kent</th>                     <td>    0.0924</td> <td>    0.011</td> <td>    8.099</td> <td> 0.000</td> <td>    0.070</td> <td>    0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lake Washington</th>          <td>    0.7747</td> <td>    0.011</td> <td>   67.850</td> <td> 0.000</td> <td>    0.752</td> <td>    0.797</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mercer Island</th>            <td>    0.9516</td> <td>    0.018</td> <td>   52.928</td> <td> 0.000</td> <td>    0.916</td> <td>    0.987</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northshore</th>               <td>    0.5602</td> <td>    0.013</td> <td>   44.395</td> <td> 0.000</td> <td>    0.536</td> <td>    0.585</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Renton</th>                   <td>    0.2769</td> <td>    0.012</td> <td>   22.613</td> <td> 0.000</td> <td>    0.253</td> <td>    0.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Riverview</th>                <td>    0.3300</td> <td>    0.016</td> <td>   20.363</td> <td> 0.000</td> <td>    0.298</td> <td>    0.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seattle</th>                  <td>    0.6428</td> <td>    0.012</td> <td>   51.677</td> <td> 0.000</td> <td>    0.618</td> <td>    0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Shoreline</th>                <td>    0.4874</td> <td>    0.015</td> <td>   32.781</td> <td> 0.000</td> <td>    0.458</td> <td>    0.516</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snoqualmie Valley</th>        <td>    0.4103</td> <td>    0.013</td> <td>   30.772</td> <td> 0.000</td> <td>    0.384</td> <td>    0.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tahoma</th>                   <td>    0.1779</td> <td>    0.013</td> <td>   13.549</td> <td> 0.000</td> <td>    0.152</td> <td>    0.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tukwila</th>                  <td>    0.1587</td> <td>    0.025</td> <td>    6.423</td> <td> 0.000</td> <td>    0.110</td> <td>    0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vashon Island</th>            <td>    0.2730</td> <td>    0.027</td> <td>   10.060</td> <td> 0.000</td> <td>    0.220</td> <td>    0.326</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity</th>         <td>   -0.0537</td> <td>    0.004</td> <td>  -12.665</td> <td> 0.000</td> <td>   -0.062</td> <td>   -0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity/Solar</th>   <td>   -0.0534</td> <td>    0.038</td> <td>   -1.392</td> <td> 0.164</td> <td>   -0.129</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Gas/Solar</th>           <td>    0.0770</td> <td>    0.027</td> <td>    2.904</td> <td> 0.004</td> <td>    0.025</td> <td>    0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil</th>                 <td>   -0.0328</td> <td>    0.005</td> <td>   -6.029</td> <td> 0.000</td> <td>   -0.043</td> <td>   -0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil/Solar</th>           <td>   -0.0116</td> <td>    0.118</td> <td>   -0.099</td> <td> 0.922</td> <td>   -0.243</td> <td>    0.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Other</th>               <td>    0.0829</td> <td>    0.084</td> <td>    0.990</td> <td> 0.322</td> <td>   -0.081</td> <td>    0.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_AVERAGE</th>             <td>    0.0961</td> <td>    0.006</td> <td>   15.045</td> <td> 0.000</td> <td>    0.084</td> <td>    0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_EXCELLENT</th>           <td>    0.3175</td> <td>    0.015</td> <td>   21.528</td> <td> 0.000</td> <td>    0.289</td> <td>    0.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_FAIR</th>                <td>    0.1375</td> <td>    0.017</td> <td>    7.963</td> <td> 0.000</td> <td>    0.104</td> <td>    0.171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_GOOD</th>                <td>    0.1608</td> <td>    0.010</td> <td>   16.476</td> <td> 0.000</td> <td>    0.142</td> <td>    0.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PRIVATE</th>            <td>   -0.0478</td> <td>    0.006</td> <td>   -8.206</td> <td> 0.000</td> <td>   -0.059</td> <td>   -0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PRIVATE RESTRICTED</th> <td>    0.0317</td> <td>    0.167</td> <td>    0.190</td> <td> 0.850</td> <td>   -0.296</td> <td>    0.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PUBLIC RESTRICTED</th>  <td>   -0.1271</td> <td>    0.167</td> <td>   -0.762</td> <td> 0.446</td> <td>   -0.454</td> <td>    0.200</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2040.862</td> <th>  Durbin-Watson:     </th> <td>   1.973</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9629.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.265</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.998</td>  <th>  Cond. No.          </th> <td>5.49e+08</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, 5.49e+08. 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:                       0.754\n",
       "Model:                            OLS   Adj. R-squared:                  0.753\n",
       "Method:                 Least Squares   F-statistic:                     1812.\n",
       "Date:                Fri, 31 Mar 2023   Prob (F-statistic):               0.00\n",
       "Time:                        17:03:07   Log-Likelihood:                 664.00\n",
       "No. Observations:               24933   AIC:                            -1242.\n",
       "Df Residuals:                   24890   BIC:                            -892.7\n",
       "Df Model:                          42                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================\n",
       "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------\n",
       "const                     -252.4778      8.350    -30.238      0.000    -268.844    -236.112\n",
       "date                         0.1392      0.003     44.592      0.000       0.133       0.145\n",
       "bedrooms                    -0.0159      0.002     -7.373      0.000      -0.020      -0.012\n",
       "bathrooms                    0.0517      0.003     17.035      0.000       0.046       0.058\n",
       "sqft_living                  0.0002   4.65e-06     37.411      0.000       0.000       0.000\n",
       "sqft_lot                     0.0369      0.003     10.867      0.000       0.030       0.044\n",
       "waterfront                   0.1552      0.017      9.405      0.000       0.123       0.188\n",
       "sqft_above                   0.0001   4.08e-06     25.000      0.000    9.41e-05       0.000\n",
       "sqft_garage               5.799e-05   7.57e-06      7.664      0.000    4.32e-05    7.28e-05\n",
       "sqft_patio                  6.1e-05   7.63e-06      7.993      0.000     4.6e-05     7.6e-05\n",
       "yr_renovated              2.371e-05   3.64e-06      6.519      0.000    1.66e-05    3.08e-05\n",
       "zipcode                     -0.0002   5.62e-05     -2.998      0.003      -0.000   -5.84e-05\n",
       "Auburn                       0.0041      0.013      0.320      0.749      -0.021       0.029\n",
       "Bellevue                     0.8607      0.012     69.408      0.000       0.836       0.885\n",
       "Federal Way                  0.0147      0.012      1.259      0.208      -0.008       0.038\n",
       "Fife                         0.0295      0.026      1.118      0.263      -0.022       0.081\n",
       "Highline                     0.2121      0.015     14.429      0.000       0.183       0.241\n",
       "Issaquah                     0.6350      0.012     52.247      0.000       0.611       0.659\n",
       "Kent                         0.0924      0.011      8.099      0.000       0.070       0.115\n",
       "Lake Washington              0.7747      0.011     67.850      0.000       0.752       0.797\n",
       "Mercer Island                0.9516      0.018     52.928      0.000       0.916       0.987\n",
       "Northshore                   0.5602      0.013     44.395      0.000       0.536       0.585\n",
       "Renton                       0.2769      0.012     22.613      0.000       0.253       0.301\n",
       "Riverview                    0.3300      0.016     20.363      0.000       0.298       0.362\n",
       "Seattle                      0.6428      0.012     51.677      0.000       0.618       0.667\n",
       "Shoreline                    0.4874      0.015     32.781      0.000       0.458       0.516\n",
       "Snoqualmie Valley            0.4103      0.013     30.772      0.000       0.384       0.436\n",
       "Tahoma                       0.1779      0.013     13.549      0.000       0.152       0.204\n",
       "Tukwila                      0.1587      0.025      6.423      0.000       0.110       0.207\n",
       "Vashon Island                0.2730      0.027     10.060      0.000       0.220       0.326\n",
       "heat_Electricity            -0.0537      0.004    -12.665      0.000      -0.062      -0.045\n",
       "heat_Electricity/Solar      -0.0534      0.038     -1.392      0.164      -0.129       0.022\n",
       "heat_Gas/Solar               0.0770      0.027      2.904      0.004       0.025       0.129\n",
       "heat_Oil                    -0.0328      0.005     -6.029      0.000      -0.043      -0.022\n",
       "heat_Oil/Solar              -0.0116      0.118     -0.099      0.922      -0.243       0.220\n",
       "heat_Other                   0.0829      0.084      0.990      0.322      -0.081       0.247\n",
       "view_AVERAGE                 0.0961      0.006     15.045      0.000       0.084       0.109\n",
       "view_EXCELLENT               0.3175      0.015     21.528      0.000       0.289       0.346\n",
       "view_FAIR                    0.1375      0.017      7.963      0.000       0.104       0.171\n",
       "view_GOOD                    0.1608      0.010     16.476      0.000       0.142       0.180\n",
       "sewer_PRIVATE               -0.0478      0.006     -8.206      0.000      -0.059      -0.036\n",
       "sewer_PRIVATE RESTRICTED     0.0317      0.167      0.190      0.850      -0.296       0.359\n",
       "sewer_PUBLIC RESTRICTED     -0.1271      0.167     -0.762      0.446      -0.454       0.200\n",
       "==============================================================================\n",
       "Omnibus:                     2040.862   Durbin-Watson:                   1.973\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9629.946\n",
       "Skew:                          -0.265   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.998   Cond. No.                     5.49e+08\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, 5.49e+08. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = model_data.drop(columns=['price', 'condition', 'grade'])\n",
    "model = sm.OLS(y, sm.add_constant(X))\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummie zipcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "zipcode = pd.get_dummies(model_data['zipcode'], prefix='zip').drop(columns=['zip_98042'])\n",
    "model_data = model_data.join(zipcode).drop(columns=['zipcode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>   0.812</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.811</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   947.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 31 Mar 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:03:08</td>     <th>  Log-Likelihood:    </th> <td>  4022.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 24933</td>      <th>  AIC:               </th> <td>  -7817.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 24819</td>      <th>  BIC:               </th> <td>  -6891.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   113</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>const</th>                    <td> -270.0956</td> <td>    5.532</td> <td>  -48.825</td> <td> 0.000</td> <td> -280.938</td> <td> -259.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>date</th>                     <td>    0.1395</td> <td>    0.003</td> <td>   50.981</td> <td> 0.000</td> <td>    0.134</td> <td>    0.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>                 <td>   -0.0075</td> <td>    0.002</td> <td>   -3.946</td> <td> 0.000</td> <td>   -0.011</td> <td>   -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>                <td>    0.0494</td> <td>    0.003</td> <td>   18.472</td> <td> 0.000</td> <td>    0.044</td> <td>    0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>              <td>    0.0001</td> <td> 4.11e-06</td> <td>   35.176</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_lot</th>                 <td>    0.0684</td> <td>    0.003</td> <td>   21.871</td> <td> 0.000</td> <td>    0.062</td> <td>    0.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>               <td>    0.1449</td> <td>    0.015</td> <td>    9.951</td> <td> 0.000</td> <td>    0.116</td> <td>    0.173</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_above</th>               <td>    0.0001</td> <td> 3.63e-06</td> <td>   28.148</td> <td> 0.000</td> <td> 9.51e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_garage</th>              <td> 9.376e-05</td> <td> 6.74e-06</td> <td>   13.917</td> <td> 0.000</td> <td> 8.06e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_patio</th>               <td> 5.038e-05</td> <td> 6.71e-06</td> <td>    7.505</td> <td> 0.000</td> <td> 3.72e-05</td> <td> 6.35e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_renovated</th>             <td> 1.532e-05</td> <td> 3.19e-06</td> <td>    4.797</td> <td> 0.000</td> <td> 9.06e-06</td> <td> 2.16e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Auburn</th>                   <td>    0.1542</td> <td>    0.040</td> <td>    3.852</td> <td> 0.000</td> <td>    0.076</td> <td>    0.233</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bellevue</th>                 <td>    0.3831</td> <td>    0.041</td> <td>    9.402</td> <td> 0.000</td> <td>    0.303</td> <td>    0.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Federal Way</th>              <td>    0.1816</td> <td>    0.039</td> <td>    4.656</td> <td> 0.000</td> <td>    0.105</td> <td>    0.258</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fife</th>                     <td>    0.1745</td> <td>    0.046</td> <td>    3.810</td> <td> 0.000</td> <td>    0.085</td> <td>    0.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Highline</th>                 <td>    0.1662</td> <td>    0.039</td> <td>    4.234</td> <td> 0.000</td> <td>    0.089</td> <td>    0.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Issaquah</th>                 <td>    0.3170</td> <td>    0.038</td> <td>    8.353</td> <td> 0.000</td> <td>    0.243</td> <td>    0.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kent</th>                     <td>    0.2104</td> <td>    0.032</td> <td>    6.515</td> <td> 0.000</td> <td>    0.147</td> <td>    0.274</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lake Washington</th>          <td>    0.2543</td> <td>    0.050</td> <td>    5.122</td> <td> 0.000</td> <td>    0.157</td> <td>    0.352</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mercer Island</th>            <td>    0.5606</td> <td>    0.018</td> <td>   31.599</td> <td> 0.000</td> <td>    0.526</td> <td>    0.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northshore</th>               <td>    0.1415</td> <td>    0.052</td> <td>    2.697</td> <td> 0.007</td> <td>    0.039</td> <td>    0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Renton</th>                   <td>    0.2024</td> <td>    0.035</td> <td>    5.848</td> <td> 0.000</td> <td>    0.135</td> <td>    0.270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Riverview</th>                <td>    0.0534</td> <td>    0.056</td> <td>    0.946</td> <td> 0.344</td> <td>   -0.057</td> <td>    0.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seattle</th>                  <td>    0.3144</td> <td>    0.039</td> <td>    8.115</td> <td> 0.000</td> <td>    0.238</td> <td>    0.390</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Shoreline</th>                <td>    0.2359</td> <td>    0.041</td> <td>    5.731</td> <td> 0.000</td> <td>    0.155</td> <td>    0.317</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snoqualmie Valley</th>        <td>    0.0917</td> <td>    0.052</td> <td>    1.769</td> <td> 0.077</td> <td>   -0.010</td> <td>    0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tahoma</th>                   <td>    0.2102</td> <td>    0.029</td> <td>    7.339</td> <td> 0.000</td> <td>    0.154</td> <td>    0.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tukwila</th>                  <td>    0.1602</td> <td>    0.043</td> <td>    3.718</td> <td> 0.000</td> <td>    0.076</td> <td>    0.245</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vashon Island</th>            <td>    0.2092</td> <td>    0.020</td> <td>   10.450</td> <td> 0.000</td> <td>    0.170</td> <td>    0.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity</th>         <td>   -0.0350</td> <td>    0.004</td> <td>   -9.322</td> <td> 0.000</td> <td>   -0.042</td> <td>   -0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity/Solar</th>   <td>    0.0298</td> <td>    0.034</td> <td>    0.885</td> <td> 0.376</td> <td>   -0.036</td> <td>    0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Gas/Solar</th>           <td>    0.0737</td> <td>    0.023</td> <td>    3.172</td> <td> 0.002</td> <td>    0.028</td> <td>    0.119</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil</th>                 <td>   -0.0420</td> <td>    0.005</td> <td>   -8.730</td> <td> 0.000</td> <td>   -0.051</td> <td>   -0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil/Solar</th>           <td>    0.0462</td> <td>    0.103</td> <td>    0.447</td> <td> 0.655</td> <td>   -0.156</td> <td>    0.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Other</th>               <td>    0.1042</td> <td>    0.073</td> <td>    1.420</td> <td> 0.156</td> <td>   -0.040</td> <td>    0.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_AVERAGE</th>             <td>    0.0883</td> <td>    0.006</td> <td>   15.601</td> <td> 0.000</td> <td>    0.077</td> <td>    0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_EXCELLENT</th>           <td>    0.3204</td> <td>    0.013</td> <td>   24.624</td> <td> 0.000</td> <td>    0.295</td> <td>    0.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_FAIR</th>                <td>    0.1307</td> <td>    0.015</td> <td>    8.607</td> <td> 0.000</td> <td>    0.101</td> <td>    0.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_GOOD</th>                <td>    0.1591</td> <td>    0.009</td> <td>   18.430</td> <td> 0.000</td> <td>    0.142</td> <td>    0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PRIVATE</th>            <td>   -0.0613</td> <td>    0.005</td> <td>  -11.238</td> <td> 0.000</td> <td>   -0.072</td> <td>   -0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PRIVATE RESTRICTED</th> <td>    0.0697</td> <td>    0.146</td> <td>    0.476</td> <td> 0.634</td> <td>   -0.217</td> <td>    0.356</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PUBLIC RESTRICTED</th>  <td>    0.0823</td> <td>    0.146</td> <td>    0.563</td> <td> 0.573</td> <td>   -0.204</td> <td>    0.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98001</th>                <td>   -0.0003</td> <td>    0.024</td> <td>   -0.011</td> <td> 0.991</td> <td>   -0.048</td> <td>    0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98002</th>                <td>   -0.0247</td> <td>    0.028</td> <td>   -0.894</td> <td> 0.371</td> <td>   -0.079</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98003</th>                <td>   -0.0337</td> <td>    0.026</td> <td>   -1.316</td> <td> 0.188</td> <td>   -0.084</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98004</th>                <td>    0.9264</td> <td>    0.032</td> <td>   29.333</td> <td> 0.000</td> <td>    0.865</td> <td>    0.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98005</th>                <td>    0.6961</td> <td>    0.032</td> <td>   21.709</td> <td> 0.000</td> <td>    0.633</td> <td>    0.759</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98006</th>                <td>    0.5999</td> <td>    0.025</td> <td>   23.800</td> <td> 0.000</td> <td>    0.550</td> <td>    0.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98007</th>                <td>    0.5139</td> <td>    0.032</td> <td>   16.082</td> <td> 0.000</td> <td>    0.451</td> <td>    0.577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98008</th>                <td>    0.5560</td> <td>    0.029</td> <td>   19.214</td> <td> 0.000</td> <td>    0.499</td> <td>    0.613</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98010</th>                <td>    0.2050</td> <td>    0.030</td> <td>    6.798</td> <td> 0.000</td> <td>    0.146</td> <td>    0.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98011</th>                <td>    0.5652</td> <td>    0.045</td> <td>   12.640</td> <td> 0.000</td> <td>    0.478</td> <td>    0.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98014</th>                <td>    0.3652</td> <td>    0.050</td> <td>    7.244</td> <td> 0.000</td> <td>    0.266</td> <td>    0.464</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98019</th>                <td>    0.4365</td> <td>    0.050</td> <td>    8.762</td> <td> 0.000</td> <td>    0.339</td> <td>    0.534</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98022</th>                <td>    0.1346</td> <td>    0.035</td> <td>    3.860</td> <td> 0.000</td> <td>    0.066</td> <td>    0.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98023</th>                <td>   -0.0265</td> <td>    0.025</td> <td>   -1.070</td> <td> 0.284</td> <td>   -0.075</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98024</th>                <td>    0.5102</td> <td>    0.050</td> <td>   10.193</td> <td> 0.000</td> <td>    0.412</td> <td>    0.608</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98027</th>                <td>    0.4425</td> <td>    0.026</td> <td>   17.217</td> <td> 0.000</td> <td>    0.392</td> <td>    0.493</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98028</th>                <td>    0.5103</td> <td>    0.044</td> <td>   11.519</td> <td> 0.000</td> <td>    0.423</td> <td>    0.597</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98029</th>                <td>    0.5913</td> <td>    0.026</td> <td>   23.015</td> <td> 0.000</td> <td>    0.541</td> <td>    0.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98030</th>                <td>    0.0226</td> <td>    0.013</td> <td>    1.730</td> <td> 0.084</td> <td>   -0.003</td> <td>    0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98031</th>                <td>    0.0288</td> <td>    0.012</td> <td>    2.477</td> <td> 0.013</td> <td>    0.006</td> <td>    0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98032</th>                <td>   -0.0041</td> <td>    0.024</td> <td>   -0.167</td> <td> 0.868</td> <td>   -0.052</td> <td>    0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98033</th>                <td>    0.8604</td> <td>    0.040</td> <td>   21.362</td> <td> 0.000</td> <td>    0.781</td> <td>    0.939</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98034</th>                <td>    0.5667</td> <td>    0.040</td> <td>   14.130</td> <td> 0.000</td> <td>    0.488</td> <td>    0.645</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98038</th>                <td>    0.1257</td> <td>    0.029</td> <td>    4.326</td> <td> 0.000</td> <td>    0.069</td> <td>    0.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98039</th>                <td>    1.2335</td> <td>    0.057</td> <td>   21.648</td> <td> 0.000</td> <td>    1.122</td> <td>    1.345</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98040</th>                <td>    0.5606</td> <td>    0.018</td> <td>   31.599</td> <td> 0.000</td> <td>    0.526</td> <td>    0.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98045</th>                <td>    0.4375</td> <td>    0.043</td> <td>   10.154</td> <td> 0.000</td> <td>    0.353</td> <td>    0.522</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98047</th>                <td>   -0.0531</td> <td>    0.035</td> <td>   -1.523</td> <td> 0.128</td> <td>   -0.121</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98050</th>                <td>    0.3230</td> <td>    0.208</td> <td>    1.555</td> <td> 0.120</td> <td>   -0.084</td> <td>    0.730</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98051</th>                <td>    0.2033</td> <td>    0.063</td> <td>    3.206</td> <td> 0.001</td> <td>    0.079</td> <td>    0.328</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98052</th>                <td>    0.7059</td> <td>    0.039</td> <td>   17.906</td> <td> 0.000</td> <td>    0.629</td> <td>    0.783</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98053</th>                <td>    0.5857</td> <td>    0.040</td> <td>   14.594</td> <td> 0.000</td> <td>    0.507</td> <td>    0.664</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98055</th>                <td>    0.1031</td> <td>    0.021</td> <td>    4.966</td> <td> 0.000</td> <td>    0.062</td> <td>    0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98056</th>                <td>    0.3331</td> <td>    0.019</td> <td>   17.498</td> <td> 0.000</td> <td>    0.296</td> <td>    0.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98057</th>                <td>    0.1520</td> <td>    0.025</td> <td>    6.001</td> <td> 0.000</td> <td>    0.102</td> <td>    0.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98058</th>                <td>    0.1311</td> <td>    0.012</td> <td>   10.617</td> <td> 0.000</td> <td>    0.107</td> <td>    0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98059</th>                <td>    0.2638</td> <td>    0.021</td> <td>   12.781</td> <td> 0.000</td> <td>    0.223</td> <td>    0.304</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98065</th>                <td>    0.5267</td> <td>    0.044</td> <td>   12.032</td> <td> 0.000</td> <td>    0.441</td> <td>    0.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98070</th>                <td>    0.2092</td> <td>    0.020</td> <td>   10.450</td> <td> 0.000</td> <td>    0.170</td> <td>    0.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98072</th>                <td>    0.6215</td> <td>    0.044</td> <td>   13.978</td> <td> 0.000</td> <td>    0.534</td> <td>    0.709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98074</th>                <td>    0.6356</td> <td>    0.041</td> <td>   15.659</td> <td> 0.000</td> <td>    0.556</td> <td>    0.715</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98075</th>                <td>    0.5845</td> <td>    0.025</td> <td>   23.410</td> <td> 0.000</td> <td>    0.536</td> <td>    0.633</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98077</th>                <td>    0.5893</td> <td>    0.044</td> <td>   13.336</td> <td> 0.000</td> <td>    0.503</td> <td>    0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98092</th>                <td>    0.0260</td> <td>    0.026</td> <td>    1.003</td> <td> 0.316</td> <td>   -0.025</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98102</th>                <td>    0.7123</td> <td>    0.032</td> <td>   22.480</td> <td> 0.000</td> <td>    0.650</td> <td>    0.774</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98103</th>                <td>    0.6228</td> <td>    0.025</td> <td>   24.562</td> <td> 0.000</td> <td>    0.573</td> <td>    0.673</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98105</th>                <td>    0.6595</td> <td>    0.027</td> <td>   24.615</td> <td> 0.000</td> <td>    0.607</td> <td>    0.712</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98106</th>                <td>    0.1787</td> <td>    0.025</td> <td>    7.042</td> <td> 0.000</td> <td>    0.129</td> <td>    0.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98107</th>                <td>    0.6111</td> <td>    0.027</td> <td>   22.916</td> <td> 0.000</td> <td>    0.559</td> <td>    0.663</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98108</th>                <td>    0.2228</td> <td>    0.027</td> <td>    8.196</td> <td> 0.000</td> <td>    0.170</td> <td>    0.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98109</th>                <td>    0.7583</td> <td>    0.031</td> <td>   24.449</td> <td> 0.000</td> <td>    0.698</td> <td>    0.819</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98112</th>                <td>    0.7874</td> <td>    0.027</td> <td>   28.805</td> <td> 0.000</td> <td>    0.734</td> <td>    0.841</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98115</th>                <td>    0.5925</td> <td>    0.025</td> <td>   23.674</td> <td> 0.000</td> <td>    0.543</td> <td>    0.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98116</th>                <td>    0.5058</td> <td>    0.026</td> <td>   19.203</td> <td> 0.000</td> <td>    0.454</td> <td>    0.557</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98117</th>                <td>    0.5979</td> <td>    0.025</td> <td>   23.752</td> <td> 0.000</td> <td>    0.549</td> <td>    0.647</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98118</th>                <td>    0.2907</td> <td>    0.025</td> <td>   11.449</td> <td> 0.000</td> <td>    0.241</td> <td>    0.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98119</th>                <td>    0.7195</td> <td>    0.028</td> <td>   25.776</td> <td> 0.000</td> <td>    0.665</td> <td>    0.774</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98122</th>                <td>    0.5767</td> <td>    0.027</td> <td>   21.488</td> <td> 0.000</td> <td>    0.524</td> <td>    0.629</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98125</th>                <td>    0.3558</td> <td>    0.026</td> <td>   13.851</td> <td> 0.000</td> <td>    0.305</td> <td>    0.406</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98126</th>                <td>    0.3140</td> <td>    0.026</td> <td>   12.053</td> <td> 0.000</td> <td>    0.263</td> <td>    0.365</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98133</th>                <td>    0.3305</td> <td>    0.026</td> <td>   12.504</td> <td> 0.000</td> <td>    0.279</td> <td>    0.382</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98136</th>                <td>    0.4412</td> <td>    0.027</td> <td>   16.384</td> <td> 0.000</td> <td>    0.388</td> <td>    0.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98144</th>                <td>    0.4983</td> <td>    0.027</td> <td>   18.747</td> <td> 0.000</td> <td>    0.446</td> <td>    0.550</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98146</th>                <td>    0.2396</td> <td>    0.025</td> <td>    9.540</td> <td> 0.000</td> <td>    0.190</td> <td>    0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98148</th>                <td>    0.1526</td> <td>    0.033</td> <td>    4.647</td> <td> 0.000</td> <td>    0.088</td> <td>    0.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98155</th>                <td>    0.3936</td> <td>    0.029</td> <td>   13.577</td> <td> 0.000</td> <td>    0.337</td> <td>    0.450</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98166</th>                <td>    0.2893</td> <td>    0.027</td> <td>   10.730</td> <td> 0.000</td> <td>    0.236</td> <td>    0.342</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98168</th>                <td>    0.1232</td> <td>    0.027</td> <td>    4.599</td> <td> 0.000</td> <td>    0.071</td> <td>    0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98177</th>                <td>    0.4830</td> <td>    0.028</td> <td>   17.519</td> <td> 0.000</td> <td>    0.429</td> <td>    0.537</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98178</th>                <td>    0.1299</td> <td>    0.020</td> <td>    6.434</td> <td> 0.000</td> <td>    0.090</td> <td>    0.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98188</th>                <td>    0.1264</td> <td>    0.029</td> <td>    4.430</td> <td> 0.000</td> <td>    0.070</td> <td>    0.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98198</th>                <td>    0.0727</td> <td>    0.025</td> <td>    2.965</td> <td> 0.003</td> <td>    0.025</td> <td>    0.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98199</th>                <td>    0.6572</td> <td>    0.026</td> <td>   25.000</td> <td> 0.000</td> <td>    0.606</td> <td>    0.709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98354</th>                <td>    0.0165</td> <td>    0.057</td> <td>    0.288</td> <td> 0.773</td> <td>   -0.095</td> <td>    0.128</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3824.692</td> <th>  Durbin-Watson:     </th> <td>   1.963</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>25869.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.562</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 7.862</td>  <th>  Cond. No.          </th> <td>1.04e+16</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 smallest eigenvalue is 2.95e-21. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.812\n",
       "Model:                            OLS   Adj. R-squared:                  0.811\n",
       "Method:                 Least Squares   F-statistic:                     947.0\n",
       "Date:                Fri, 31 Mar 2023   Prob (F-statistic):               0.00\n",
       "Time:                        17:03:08   Log-Likelihood:                 4022.6\n",
       "No. Observations:               24933   AIC:                            -7817.\n",
       "Df Residuals:                   24819   BIC:                            -6891.\n",
       "Df Model:                         113                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================\n",
       "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------\n",
       "const                     -270.0956      5.532    -48.825      0.000    -280.938    -259.253\n",
       "date                         0.1395      0.003     50.981      0.000       0.134       0.145\n",
       "bedrooms                    -0.0075      0.002     -3.946      0.000      -0.011      -0.004\n",
       "bathrooms                    0.0494      0.003     18.472      0.000       0.044       0.055\n",
       "sqft_living                  0.0001   4.11e-06     35.176      0.000       0.000       0.000\n",
       "sqft_lot                     0.0684      0.003     21.871      0.000       0.062       0.075\n",
       "waterfront                   0.1449      0.015      9.951      0.000       0.116       0.173\n",
       "sqft_above                   0.0001   3.63e-06     28.148      0.000    9.51e-05       0.000\n",
       "sqft_garage               9.376e-05   6.74e-06     13.917      0.000    8.06e-05       0.000\n",
       "sqft_patio                5.038e-05   6.71e-06      7.505      0.000    3.72e-05    6.35e-05\n",
       "yr_renovated              1.532e-05   3.19e-06      4.797      0.000    9.06e-06    2.16e-05\n",
       "Auburn                       0.1542      0.040      3.852      0.000       0.076       0.233\n",
       "Bellevue                     0.3831      0.041      9.402      0.000       0.303       0.463\n",
       "Federal Way                  0.1816      0.039      4.656      0.000       0.105       0.258\n",
       "Fife                         0.1745      0.046      3.810      0.000       0.085       0.264\n",
       "Highline                     0.1662      0.039      4.234      0.000       0.089       0.243\n",
       "Issaquah                     0.3170      0.038      8.353      0.000       0.243       0.391\n",
       "Kent                         0.2104      0.032      6.515      0.000       0.147       0.274\n",
       "Lake Washington              0.2543      0.050      5.122      0.000       0.157       0.352\n",
       "Mercer Island                0.5606      0.018     31.599      0.000       0.526       0.595\n",
       "Northshore                   0.1415      0.052      2.697      0.007       0.039       0.244\n",
       "Renton                       0.2024      0.035      5.848      0.000       0.135       0.270\n",
       "Riverview                    0.0534      0.056      0.946      0.344      -0.057       0.164\n",
       "Seattle                      0.3144      0.039      8.115      0.000       0.238       0.390\n",
       "Shoreline                    0.2359      0.041      5.731      0.000       0.155       0.317\n",
       "Snoqualmie Valley            0.0917      0.052      1.769      0.077      -0.010       0.193\n",
       "Tahoma                       0.2102      0.029      7.339      0.000       0.154       0.266\n",
       "Tukwila                      0.1602      0.043      3.718      0.000       0.076       0.245\n",
       "Vashon Island                0.2092      0.020     10.450      0.000       0.170       0.248\n",
       "heat_Electricity            -0.0350      0.004     -9.322      0.000      -0.042      -0.028\n",
       "heat_Electricity/Solar       0.0298      0.034      0.885      0.376      -0.036       0.096\n",
       "heat_Gas/Solar               0.0737      0.023      3.172      0.002       0.028       0.119\n",
       "heat_Oil                    -0.0420      0.005     -8.730      0.000      -0.051      -0.033\n",
       "heat_Oil/Solar               0.0462      0.103      0.447      0.655      -0.156       0.249\n",
       "heat_Other                   0.1042      0.073      1.420      0.156      -0.040       0.248\n",
       "view_AVERAGE                 0.0883      0.006     15.601      0.000       0.077       0.099\n",
       "view_EXCELLENT               0.3204      0.013     24.624      0.000       0.295       0.346\n",
       "view_FAIR                    0.1307      0.015      8.607      0.000       0.101       0.161\n",
       "view_GOOD                    0.1591      0.009     18.430      0.000       0.142       0.176\n",
       "sewer_PRIVATE               -0.0613      0.005    -11.238      0.000      -0.072      -0.051\n",
       "sewer_PRIVATE RESTRICTED     0.0697      0.146      0.476      0.634      -0.217       0.356\n",
       "sewer_PUBLIC RESTRICTED      0.0823      0.146      0.563      0.573      -0.204       0.369\n",
       "zip_98001                   -0.0003      0.024     -0.011      0.991      -0.048       0.047\n",
       "zip_98002                   -0.0247      0.028     -0.894      0.371      -0.079       0.030\n",
       "zip_98003                   -0.0337      0.026     -1.316      0.188      -0.084       0.016\n",
       "zip_98004                    0.9264      0.032     29.333      0.000       0.865       0.988\n",
       "zip_98005                    0.6961      0.032     21.709      0.000       0.633       0.759\n",
       "zip_98006                    0.5999      0.025     23.800      0.000       0.550       0.649\n",
       "zip_98007                    0.5139      0.032     16.082      0.000       0.451       0.577\n",
       "zip_98008                    0.5560      0.029     19.214      0.000       0.499       0.613\n",
       "zip_98010                    0.2050      0.030      6.798      0.000       0.146       0.264\n",
       "zip_98011                    0.5652      0.045     12.640      0.000       0.478       0.653\n",
       "zip_98014                    0.3652      0.050      7.244      0.000       0.266       0.464\n",
       "zip_98019                    0.4365      0.050      8.762      0.000       0.339       0.534\n",
       "zip_98022                    0.1346      0.035      3.860      0.000       0.066       0.203\n",
       "zip_98023                   -0.0265      0.025     -1.070      0.284      -0.075       0.022\n",
       "zip_98024                    0.5102      0.050     10.193      0.000       0.412       0.608\n",
       "zip_98027                    0.4425      0.026     17.217      0.000       0.392       0.493\n",
       "zip_98028                    0.5103      0.044     11.519      0.000       0.423       0.597\n",
       "zip_98029                    0.5913      0.026     23.015      0.000       0.541       0.642\n",
       "zip_98030                    0.0226      0.013      1.730      0.084      -0.003       0.048\n",
       "zip_98031                    0.0288      0.012      2.477      0.013       0.006       0.052\n",
       "zip_98032                   -0.0041      0.024     -0.167      0.868      -0.052       0.044\n",
       "zip_98033                    0.8604      0.040     21.362      0.000       0.781       0.939\n",
       "zip_98034                    0.5667      0.040     14.130      0.000       0.488       0.645\n",
       "zip_98038                    0.1257      0.029      4.326      0.000       0.069       0.183\n",
       "zip_98039                    1.2335      0.057     21.648      0.000       1.122       1.345\n",
       "zip_98040                    0.5606      0.018     31.599      0.000       0.526       0.595\n",
       "zip_98045                    0.4375      0.043     10.154      0.000       0.353       0.522\n",
       "zip_98047                   -0.0531      0.035     -1.523      0.128      -0.121       0.015\n",
       "zip_98050                    0.3230      0.208      1.555      0.120      -0.084       0.730\n",
       "zip_98051                    0.2033      0.063      3.206      0.001       0.079       0.328\n",
       "zip_98052                    0.7059      0.039     17.906      0.000       0.629       0.783\n",
       "zip_98053                    0.5857      0.040     14.594      0.000       0.507       0.664\n",
       "zip_98055                    0.1031      0.021      4.966      0.000       0.062       0.144\n",
       "zip_98056                    0.3331      0.019     17.498      0.000       0.296       0.370\n",
       "zip_98057                    0.1520      0.025      6.001      0.000       0.102       0.202\n",
       "zip_98058                    0.1311      0.012     10.617      0.000       0.107       0.155\n",
       "zip_98059                    0.2638      0.021     12.781      0.000       0.223       0.304\n",
       "zip_98065                    0.5267      0.044     12.032      0.000       0.441       0.612\n",
       "zip_98070                    0.2092      0.020     10.450      0.000       0.170       0.248\n",
       "zip_98072                    0.6215      0.044     13.978      0.000       0.534       0.709\n",
       "zip_98074                    0.6356      0.041     15.659      0.000       0.556       0.715\n",
       "zip_98075                    0.5845      0.025     23.410      0.000       0.536       0.633\n",
       "zip_98077                    0.5893      0.044     13.336      0.000       0.503       0.676\n",
       "zip_98092                    0.0260      0.026      1.003      0.316      -0.025       0.077\n",
       "zip_98102                    0.7123      0.032     22.480      0.000       0.650       0.774\n",
       "zip_98103                    0.6228      0.025     24.562      0.000       0.573       0.673\n",
       "zip_98105                    0.6595      0.027     24.615      0.000       0.607       0.712\n",
       "zip_98106                    0.1787      0.025      7.042      0.000       0.129       0.228\n",
       "zip_98107                    0.6111      0.027     22.916      0.000       0.559       0.663\n",
       "zip_98108                    0.2228      0.027      8.196      0.000       0.170       0.276\n",
       "zip_98109                    0.7583      0.031     24.449      0.000       0.698       0.819\n",
       "zip_98112                    0.7874      0.027     28.805      0.000       0.734       0.841\n",
       "zip_98115                    0.5925      0.025     23.674      0.000       0.543       0.642\n",
       "zip_98116                    0.5058      0.026     19.203      0.000       0.454       0.557\n",
       "zip_98117                    0.5979      0.025     23.752      0.000       0.549       0.647\n",
       "zip_98118                    0.2907      0.025     11.449      0.000       0.241       0.340\n",
       "zip_98119                    0.7195      0.028     25.776      0.000       0.665       0.774\n",
       "zip_98122                    0.5767      0.027     21.488      0.000       0.524       0.629\n",
       "zip_98125                    0.3558      0.026     13.851      0.000       0.305       0.406\n",
       "zip_98126                    0.3140      0.026     12.053      0.000       0.263       0.365\n",
       "zip_98133                    0.3305      0.026     12.504      0.000       0.279       0.382\n",
       "zip_98136                    0.4412      0.027     16.384      0.000       0.388       0.494\n",
       "zip_98144                    0.4983      0.027     18.747      0.000       0.446       0.550\n",
       "zip_98146                    0.2396      0.025      9.540      0.000       0.190       0.289\n",
       "zip_98148                    0.1526      0.033      4.647      0.000       0.088       0.217\n",
       "zip_98155                    0.3936      0.029     13.577      0.000       0.337       0.450\n",
       "zip_98166                    0.2893      0.027     10.730      0.000       0.236       0.342\n",
       "zip_98168                    0.1232      0.027      4.599      0.000       0.071       0.176\n",
       "zip_98177                    0.4830      0.028     17.519      0.000       0.429       0.537\n",
       "zip_98178                    0.1299      0.020      6.434      0.000       0.090       0.169\n",
       "zip_98188                    0.1264      0.029      4.430      0.000       0.070       0.182\n",
       "zip_98198                    0.0727      0.025      2.965      0.003       0.025       0.121\n",
       "zip_98199                    0.6572      0.026     25.000      0.000       0.606       0.709\n",
       "zip_98354                    0.0165      0.057      0.288      0.773      -0.095       0.128\n",
       "==============================================================================\n",
       "Omnibus:                     3824.692   Durbin-Watson:                   1.963\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            25869.163\n",
       "Skew:                          -0.562   Prob(JB):                         0.00\n",
       "Kurtosis:                       7.862   Cond. No.                     1.04e+16\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.95e-21. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = model_data.drop(columns=['price', 'condition', 'grade'])\n",
    "model = sm.OLS(y, sm.add_constant(X))\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummie condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "conds = pd.get_dummies(model_data['condition'], prefix='cond').drop(columns=['cond_Average'])\n",
    "model_data = model_data.join(conds).drop(columns=['condition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>   0.816</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.815</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   942.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 31 Mar 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:03:08</td>     <th>  Log-Likelihood:    </th> <td>  4326.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 24933</td>      <th>  AIC:               </th> <td>  -8416.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 24815</td>      <th>  BIC:               </th> <td>  -7458.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   117</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>const</th>                    <td> -273.8167</td> <td>    5.468</td> <td>  -50.080</td> <td> 0.000</td> <td> -284.533</td> <td> -263.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>date</th>                     <td>    0.1414</td> <td>    0.003</td> <td>   52.267</td> <td> 0.000</td> <td>    0.136</td> <td>    0.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>                 <td>   -0.0093</td> <td>    0.002</td> <td>   -4.927</td> <td> 0.000</td> <td>   -0.013</td> <td>   -0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>                <td>    0.0473</td> <td>    0.003</td> <td>   17.882</td> <td> 0.000</td> <td>    0.042</td> <td>    0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>              <td>    0.0001</td> <td> 4.07e-06</td> <td>   34.463</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_lot</th>                 <td>    0.0620</td> <td>    0.003</td> <td>   19.849</td> <td> 0.000</td> <td>    0.056</td> <td>    0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>               <td>    0.1546</td> <td>    0.014</td> <td>   10.741</td> <td> 0.000</td> <td>    0.126</td> <td>    0.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_above</th>               <td>    0.0001</td> <td> 3.65e-06</td> <td>   31.884</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_garage</th>              <td>    0.0001</td> <td> 6.69e-06</td> <td>   15.859</td> <td> 0.000</td> <td>  9.3e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_patio</th>               <td> 4.306e-05</td> <td> 6.64e-06</td> <td>    6.484</td> <td> 0.000</td> <td>    3e-05</td> <td> 5.61e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_renovated</th>             <td> 2.401e-05</td> <td> 3.19e-06</td> <td>    7.528</td> <td> 0.000</td> <td> 1.78e-05</td> <td> 3.03e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Auburn</th>                   <td>    0.1422</td> <td>    0.040</td> <td>    3.593</td> <td> 0.000</td> <td>    0.065</td> <td>    0.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bellevue</th>                 <td>    0.3764</td> <td>    0.040</td> <td>    9.347</td> <td> 0.000</td> <td>    0.297</td> <td>    0.455</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Federal Way</th>              <td>    0.1703</td> <td>    0.039</td> <td>    4.417</td> <td> 0.000</td> <td>    0.095</td> <td>    0.246</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fife</th>                     <td>    0.1626</td> <td>    0.045</td> <td>    3.593</td> <td> 0.000</td> <td>    0.074</td> <td>    0.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Highline</th>                 <td>    0.1645</td> <td>    0.039</td> <td>    4.243</td> <td> 0.000</td> <td>    0.089</td> <td>    0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Issaquah</th>                 <td>    0.3126</td> <td>    0.037</td> <td>    8.335</td> <td> 0.000</td> <td>    0.239</td> <td>    0.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kent</th>                     <td>    0.2090</td> <td>    0.032</td> <td>    6.547</td> <td> 0.000</td> <td>    0.146</td> <td>    0.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lake Washington</th>          <td>    0.2466</td> <td>    0.049</td> <td>    5.027</td> <td> 0.000</td> <td>    0.150</td> <td>    0.343</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mercer Island</th>            <td>    0.5522</td> <td>    0.018</td> <td>   31.492</td> <td> 0.000</td> <td>    0.518</td> <td>    0.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northshore</th>               <td>    0.1320</td> <td>    0.052</td> <td>    2.546</td> <td> 0.011</td> <td>    0.030</td> <td>    0.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Renton</th>                   <td>    0.1963</td> <td>    0.034</td> <td>    5.741</td> <td> 0.000</td> <td>    0.129</td> <td>    0.263</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Riverview</th>                <td>    0.0536</td> <td>    0.056</td> <td>    0.962</td> <td> 0.336</td> <td>   -0.056</td> <td>    0.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seattle</th>                  <td>    0.3057</td> <td>    0.038</td> <td>    7.984</td> <td> 0.000</td> <td>    0.231</td> <td>    0.381</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Shoreline</th>                <td>    0.2213</td> <td>    0.041</td> <td>    5.438</td> <td> 0.000</td> <td>    0.142</td> <td>    0.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snoqualmie Valley</th>        <td>    0.0925</td> <td>    0.051</td> <td>    1.806</td> <td> 0.071</td> <td>   -0.008</td> <td>    0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tahoma</th>                   <td>    0.2087</td> <td>    0.028</td> <td>    7.376</td> <td> 0.000</td> <td>    0.153</td> <td>    0.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tukwila</th>                  <td>    0.1630</td> <td>    0.043</td> <td>    3.827</td> <td> 0.000</td> <td>    0.079</td> <td>    0.246</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vashon Island</th>            <td>    0.2159</td> <td>    0.020</td> <td>   10.915</td> <td> 0.000</td> <td>    0.177</td> <td>    0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity</th>         <td>   -0.0344</td> <td>    0.004</td> <td>   -9.288</td> <td> 0.000</td> <td>   -0.042</td> <td>   -0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity/Solar</th>   <td>    0.0105</td> <td>    0.033</td> <td>    0.317</td> <td> 0.751</td> <td>   -0.055</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Gas/Solar</th>           <td>    0.0664</td> <td>    0.023</td> <td>    2.894</td> <td> 0.004</td> <td>    0.021</td> <td>    0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil</th>                 <td>   -0.0290</td> <td>    0.005</td> <td>   -6.067</td> <td> 0.000</td> <td>   -0.038</td> <td>   -0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil/Solar</th>           <td>    0.0321</td> <td>    0.102</td> <td>    0.314</td> <td> 0.753</td> <td>   -0.168</td> <td>    0.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Other</th>               <td>    0.1334</td> <td>    0.073</td> <td>    1.839</td> <td> 0.066</td> <td>   -0.009</td> <td>    0.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_AVERAGE</th>             <td>    0.0882</td> <td>    0.006</td> <td>   15.766</td> <td> 0.000</td> <td>    0.077</td> <td>    0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_EXCELLENT</th>           <td>    0.3160</td> <td>    0.013</td> <td>   24.567</td> <td> 0.000</td> <td>    0.291</td> <td>    0.341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_FAIR</th>                <td>    0.1316</td> <td>    0.015</td> <td>    8.765</td> <td> 0.000</td> <td>    0.102</td> <td>    0.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_GOOD</th>                <td>    0.1600</td> <td>    0.009</td> <td>   18.757</td> <td> 0.000</td> <td>    0.143</td> <td>    0.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PRIVATE</th>            <td>   -0.0662</td> <td>    0.005</td> <td>  -12.276</td> <td> 0.000</td> <td>   -0.077</td> <td>   -0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PRIVATE RESTRICTED</th> <td>    0.0898</td> <td>    0.145</td> <td>    0.621</td> <td> 0.534</td> <td>   -0.193</td> <td>    0.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PUBLIC RESTRICTED</th>  <td>    0.0972</td> <td>    0.144</td> <td>    0.673</td> <td> 0.501</td> <td>   -0.186</td> <td>    0.380</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98001</th>                <td>    0.0127</td> <td>    0.024</td> <td>    0.534</td> <td> 0.593</td> <td>   -0.034</td> <td>    0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98002</th>                <td>   -0.0268</td> <td>    0.027</td> <td>   -0.982</td> <td> 0.326</td> <td>   -0.080</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98003</th>                <td>   -0.0261</td> <td>    0.025</td> <td>   -1.031</td> <td> 0.302</td> <td>   -0.076</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98004</th>                <td>    0.9381</td> <td>    0.031</td> <td>   30.060</td> <td> 0.000</td> <td>    0.877</td> <td>    0.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98005</th>                <td>    0.6907</td> <td>    0.032</td> <td>   21.802</td> <td> 0.000</td> <td>    0.629</td> <td>    0.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98006</th>                <td>    0.5987</td> <td>    0.025</td> <td>   24.044</td> <td> 0.000</td> <td>    0.550</td> <td>    0.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98007</th>                <td>    0.5187</td> <td>    0.032</td> <td>   16.428</td> <td> 0.000</td> <td>    0.457</td> <td>    0.581</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98008</th>                <td>    0.5533</td> <td>    0.029</td> <td>   19.352</td> <td> 0.000</td> <td>    0.497</td> <td>    0.609</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98010</th>                <td>    0.2088</td> <td>    0.030</td> <td>    7.004</td> <td> 0.000</td> <td>    0.150</td> <td>    0.267</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98011</th>                <td>    0.5781</td> <td>    0.044</td> <td>   13.084</td> <td> 0.000</td> <td>    0.492</td> <td>    0.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98014</th>                <td>    0.3747</td> <td>    0.050</td> <td>    7.524</td> <td> 0.000</td> <td>    0.277</td> <td>    0.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98019</th>                <td>    0.4430</td> <td>    0.049</td> <td>    9.002</td> <td> 0.000</td> <td>    0.347</td> <td>    0.540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98022</th>                <td>    0.1260</td> <td>    0.034</td> <td>    3.656</td> <td> 0.000</td> <td>    0.058</td> <td>    0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98023</th>                <td>   -0.0202</td> <td>    0.024</td> <td>   -0.825</td> <td> 0.409</td> <td>   -0.068</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98024</th>                <td>    0.5089</td> <td>    0.049</td> <td>   10.292</td> <td> 0.000</td> <td>    0.412</td> <td>    0.606</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98027</th>                <td>    0.4449</td> <td>    0.025</td> <td>   17.521</td> <td> 0.000</td> <td>    0.395</td> <td>    0.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98028</th>                <td>    0.5255</td> <td>    0.044</td> <td>   12.004</td> <td> 0.000</td> <td>    0.440</td> <td>    0.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98029</th>                <td>    0.6021</td> <td>    0.025</td> <td>   23.712</td> <td> 0.000</td> <td>    0.552</td> <td>    0.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98030</th>                <td>    0.0168</td> <td>    0.013</td> <td>    1.304</td> <td> 0.192</td> <td>   -0.008</td> <td>    0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98031</th>                <td>    0.0267</td> <td>    0.012</td> <td>    2.317</td> <td> 0.021</td> <td>    0.004</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98032</th>                <td>   -0.0002</td> <td>    0.024</td> <td>   -0.007</td> <td> 0.994</td> <td>   -0.048</td> <td>    0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98033</th>                <td>    0.8676</td> <td>    0.040</td> <td>   21.801</td> <td> 0.000</td> <td>    0.790</td> <td>    0.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98034</th>                <td>    0.5725</td> <td>    0.040</td> <td>   14.448</td> <td> 0.000</td> <td>    0.495</td> <td>    0.650</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98038</th>                <td>    0.1251</td> <td>    0.029</td> <td>    4.358</td> <td> 0.000</td> <td>    0.069</td> <td>    0.181</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98039</th>                <td>    1.2406</td> <td>    0.056</td> <td>   22.037</td> <td> 0.000</td> <td>    1.130</td> <td>    1.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98040</th>                <td>    0.5522</td> <td>    0.018</td> <td>   31.492</td> <td> 0.000</td> <td>    0.518</td> <td>    0.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98045</th>                <td>    0.4426</td> <td>    0.043</td> <td>   10.397</td> <td> 0.000</td> <td>    0.359</td> <td>    0.526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98047</th>                <td>   -0.0284</td> <td>    0.034</td> <td>   -0.824</td> <td> 0.410</td> <td>   -0.096</td> <td>    0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98050</th>                <td>    0.2631</td> <td>    0.205</td> <td>    1.282</td> <td> 0.200</td> <td>   -0.139</td> <td>    0.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98051</th>                <td>    0.1983</td> <td>    0.063</td> <td>    3.165</td> <td> 0.002</td> <td>    0.076</td> <td>    0.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98052</th>                <td>    0.7114</td> <td>    0.039</td> <td>   18.263</td> <td> 0.000</td> <td>    0.635</td> <td>    0.788</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98053</th>                <td>    0.5999</td> <td>    0.040</td> <td>   15.122</td> <td> 0.000</td> <td>    0.522</td> <td>    0.678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98055</th>                <td>    0.1111</td> <td>    0.021</td> <td>    5.419</td> <td> 0.000</td> <td>    0.071</td> <td>    0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98056</th>                <td>    0.3325</td> <td>    0.019</td> <td>   17.671</td> <td> 0.000</td> <td>    0.296</td> <td>    0.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98057</th>                <td>    0.1542</td> <td>    0.025</td> <td>    6.164</td> <td> 0.000</td> <td>    0.105</td> <td>    0.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98058</th>                <td>    0.1329</td> <td>    0.012</td> <td>   10.890</td> <td> 0.000</td> <td>    0.109</td> <td>    0.157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98059</th>                <td>    0.2671</td> <td>    0.020</td> <td>   13.097</td> <td> 0.000</td> <td>    0.227</td> <td>    0.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98065</th>                <td>    0.5305</td> <td>    0.043</td> <td>   12.266</td> <td> 0.000</td> <td>    0.446</td> <td>    0.615</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98070</th>                <td>    0.2159</td> <td>    0.020</td> <td>   10.915</td> <td> 0.000</td> <td>    0.177</td> <td>    0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98072</th>                <td>    0.6335</td> <td>    0.044</td> <td>   14.418</td> <td> 0.000</td> <td>    0.547</td> <td>    0.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98074</th>                <td>    0.6426</td> <td>    0.040</td> <td>   16.024</td> <td> 0.000</td> <td>    0.564</td> <td>    0.721</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98075</th>                <td>    0.5931</td> <td>    0.025</td> <td>   24.040</td> <td> 0.000</td> <td>    0.545</td> <td>    0.641</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98077</th>                <td>    0.6057</td> <td>    0.044</td> <td>   13.870</td> <td> 0.000</td> <td>    0.520</td> <td>    0.691</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98092</th>                <td>    0.0363</td> <td>    0.026</td> <td>    1.416</td> <td> 0.157</td> <td>   -0.014</td> <td>    0.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98102</th>                <td>    0.7147</td> <td>    0.031</td> <td>   22.828</td> <td> 0.000</td> <td>    0.653</td> <td>    0.776</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98103</th>                <td>    0.6296</td> <td>    0.025</td> <td>   25.127</td> <td> 0.000</td> <td>    0.580</td> <td>    0.679</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98105</th>                <td>    0.6659</td> <td>    0.026</td> <td>   25.151</td> <td> 0.000</td> <td>    0.614</td> <td>    0.718</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98106</th>                <td>    0.1891</td> <td>    0.025</td> <td>    7.543</td> <td> 0.000</td> <td>    0.140</td> <td>    0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98107</th>                <td>    0.6234</td> <td>    0.026</td> <td>   23.653</td> <td> 0.000</td> <td>    0.572</td> <td>    0.675</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98108</th>                <td>    0.2385</td> <td>    0.027</td> <td>    8.876</td> <td> 0.000</td> <td>    0.186</td> <td>    0.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98109</th>                <td>    0.7634</td> <td>    0.031</td> <td>   24.913</td> <td> 0.000</td> <td>    0.703</td> <td>    0.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98112</th>                <td>    0.7897</td> <td>    0.027</td> <td>   29.236</td> <td> 0.000</td> <td>    0.737</td> <td>    0.843</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98115</th>                <td>    0.6006</td> <td>    0.025</td> <td>   24.286</td> <td> 0.000</td> <td>    0.552</td> <td>    0.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98116</th>                <td>    0.5156</td> <td>    0.026</td> <td>   19.809</td> <td> 0.000</td> <td>    0.465</td> <td>    0.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98117</th>                <td>    0.6040</td> <td>    0.025</td> <td>   24.281</td> <td> 0.000</td> <td>    0.555</td> <td>    0.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98118</th>                <td>    0.3050</td> <td>    0.025</td> <td>   12.154</td> <td> 0.000</td> <td>    0.256</td> <td>    0.354</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98119</th>                <td>    0.7261</td> <td>    0.028</td> <td>   26.322</td> <td> 0.000</td> <td>    0.672</td> <td>    0.780</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98122</th>                <td>    0.5869</td> <td>    0.027</td> <td>   22.128</td> <td> 0.000</td> <td>    0.535</td> <td>    0.639</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98125</th>                <td>    0.3637</td> <td>    0.025</td> <td>   14.327</td> <td> 0.000</td> <td>    0.314</td> <td>    0.413</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98126</th>                <td>    0.3225</td> <td>    0.026</td> <td>   12.527</td> <td> 0.000</td> <td>    0.272</td> <td>    0.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98133</th>                <td>    0.3414</td> <td>    0.026</td> <td>   13.067</td> <td> 0.000</td> <td>    0.290</td> <td>    0.393</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98136</th>                <td>    0.4477</td> <td>    0.027</td> <td>   16.825</td> <td> 0.000</td> <td>    0.396</td> <td>    0.500</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98144</th>                <td>    0.5095</td> <td>    0.026</td> <td>   19.398</td> <td> 0.000</td> <td>    0.458</td> <td>    0.561</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98146</th>                <td>    0.2449</td> <td>    0.025</td> <td>    9.866</td> <td> 0.000</td> <td>    0.196</td> <td>    0.294</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98148</th>                <td>    0.1548</td> <td>    0.032</td> <td>    4.769</td> <td> 0.000</td> <td>    0.091</td> <td>    0.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98155</th>                <td>    0.4104</td> <td>    0.029</td> <td>   14.325</td> <td> 0.000</td> <td>    0.354</td> <td>    0.467</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98166</th>                <td>    0.2908</td> <td>    0.027</td> <td>   10.915</td> <td> 0.000</td> <td>    0.239</td> <td>    0.343</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98168</th>                <td>    0.1272</td> <td>    0.026</td> <td>    4.806</td> <td> 0.000</td> <td>    0.075</td> <td>    0.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98177</th>                <td>    0.4900</td> <td>    0.027</td> <td>   17.989</td> <td> 0.000</td> <td>    0.437</td> <td>    0.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98178</th>                <td>    0.1380</td> <td>    0.020</td> <td>    6.918</td> <td> 0.000</td> <td>    0.099</td> <td>    0.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98188</th>                <td>    0.1362</td> <td>    0.028</td> <td>    4.833</td> <td> 0.000</td> <td>    0.081</td> <td>    0.191</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98198</th>                <td>    0.0811</td> <td>    0.024</td> <td>    3.347</td> <td> 0.001</td> <td>    0.034</td> <td>    0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98199</th>                <td>    0.6619</td> <td>    0.026</td> <td>   25.482</td> <td> 0.000</td> <td>    0.611</td> <td>    0.713</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98354</th>                <td>    0.0280</td> <td>    0.056</td> <td>    0.496</td> <td> 0.620</td> <td>   -0.083</td> <td>    0.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Fair</th>                <td>   -0.1465</td> <td>    0.016</td> <td>   -9.294</td> <td> 0.000</td> <td>   -0.177</td> <td>   -0.116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Good</th>                <td>    0.0363</td> <td>    0.003</td> <td>   11.552</td> <td> 0.000</td> <td>    0.030</td> <td>    0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Poor</th>                <td>   -0.1639</td> <td>    0.034</td> <td>   -4.860</td> <td> 0.000</td> <td>   -0.230</td> <td>   -0.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Very Good</th>           <td>    0.0903</td> <td>    0.004</td> <td>   20.814</td> <td> 0.000</td> <td>    0.082</td> <td>    0.099</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3765.374</td> <th>  Durbin-Watson:     </th> <td>   1.958</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>28420.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.513</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 8.129</td>  <th>  Cond. No.          </th> <td>1.04e+16</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 smallest eigenvalue is 2.95e-21. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.816\n",
       "Model:                            OLS   Adj. R-squared:                  0.815\n",
       "Method:                 Least Squares   F-statistic:                     942.3\n",
       "Date:                Fri, 31 Mar 2023   Prob (F-statistic):               0.00\n",
       "Time:                        17:03:08   Log-Likelihood:                 4326.2\n",
       "No. Observations:               24933   AIC:                            -8416.\n",
       "Df Residuals:                   24815   BIC:                            -7458.\n",
       "Df Model:                         117                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================\n",
       "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------\n",
       "const                     -273.8167      5.468    -50.080      0.000    -284.533    -263.100\n",
       "date                         0.1414      0.003     52.267      0.000       0.136       0.147\n",
       "bedrooms                    -0.0093      0.002     -4.927      0.000      -0.013      -0.006\n",
       "bathrooms                    0.0473      0.003     17.882      0.000       0.042       0.052\n",
       "sqft_living                  0.0001   4.07e-06     34.463      0.000       0.000       0.000\n",
       "sqft_lot                     0.0620      0.003     19.849      0.000       0.056       0.068\n",
       "waterfront                   0.1546      0.014     10.741      0.000       0.126       0.183\n",
       "sqft_above                   0.0001   3.65e-06     31.884      0.000       0.000       0.000\n",
       "sqft_garage                  0.0001   6.69e-06     15.859      0.000     9.3e-05       0.000\n",
       "sqft_patio                4.306e-05   6.64e-06      6.484      0.000       3e-05    5.61e-05\n",
       "yr_renovated              2.401e-05   3.19e-06      7.528      0.000    1.78e-05    3.03e-05\n",
       "Auburn                       0.1422      0.040      3.593      0.000       0.065       0.220\n",
       "Bellevue                     0.3764      0.040      9.347      0.000       0.297       0.455\n",
       "Federal Way                  0.1703      0.039      4.417      0.000       0.095       0.246\n",
       "Fife                         0.1626      0.045      3.593      0.000       0.074       0.251\n",
       "Highline                     0.1645      0.039      4.243      0.000       0.089       0.240\n",
       "Issaquah                     0.3126      0.037      8.335      0.000       0.239       0.386\n",
       "Kent                         0.2090      0.032      6.547      0.000       0.146       0.272\n",
       "Lake Washington              0.2466      0.049      5.027      0.000       0.150       0.343\n",
       "Mercer Island                0.5522      0.018     31.492      0.000       0.518       0.587\n",
       "Northshore                   0.1320      0.052      2.546      0.011       0.030       0.234\n",
       "Renton                       0.1963      0.034      5.741      0.000       0.129       0.263\n",
       "Riverview                    0.0536      0.056      0.962      0.336      -0.056       0.163\n",
       "Seattle                      0.3057      0.038      7.984      0.000       0.231       0.381\n",
       "Shoreline                    0.2213      0.041      5.438      0.000       0.142       0.301\n",
       "Snoqualmie Valley            0.0925      0.051      1.806      0.071      -0.008       0.193\n",
       "Tahoma                       0.2087      0.028      7.376      0.000       0.153       0.264\n",
       "Tukwila                      0.1630      0.043      3.827      0.000       0.079       0.246\n",
       "Vashon Island                0.2159      0.020     10.915      0.000       0.177       0.255\n",
       "heat_Electricity            -0.0344      0.004     -9.288      0.000      -0.042      -0.027\n",
       "heat_Electricity/Solar       0.0105      0.033      0.317      0.751      -0.055       0.076\n",
       "heat_Gas/Solar               0.0664      0.023      2.894      0.004       0.021       0.111\n",
       "heat_Oil                    -0.0290      0.005     -6.067      0.000      -0.038      -0.020\n",
       "heat_Oil/Solar               0.0321      0.102      0.314      0.753      -0.168       0.232\n",
       "heat_Other                   0.1334      0.073      1.839      0.066      -0.009       0.276\n",
       "view_AVERAGE                 0.0882      0.006     15.766      0.000       0.077       0.099\n",
       "view_EXCELLENT               0.3160      0.013     24.567      0.000       0.291       0.341\n",
       "view_FAIR                    0.1316      0.015      8.765      0.000       0.102       0.161\n",
       "view_GOOD                    0.1600      0.009     18.757      0.000       0.143       0.177\n",
       "sewer_PRIVATE               -0.0662      0.005    -12.276      0.000      -0.077      -0.056\n",
       "sewer_PRIVATE RESTRICTED     0.0898      0.145      0.621      0.534      -0.193       0.373\n",
       "sewer_PUBLIC RESTRICTED      0.0972      0.144      0.673      0.501      -0.186       0.380\n",
       "zip_98001                    0.0127      0.024      0.534      0.593      -0.034       0.059\n",
       "zip_98002                   -0.0268      0.027     -0.982      0.326      -0.080       0.027\n",
       "zip_98003                   -0.0261      0.025     -1.031      0.302      -0.076       0.023\n",
       "zip_98004                    0.9381      0.031     30.060      0.000       0.877       0.999\n",
       "zip_98005                    0.6907      0.032     21.802      0.000       0.629       0.753\n",
       "zip_98006                    0.5987      0.025     24.044      0.000       0.550       0.648\n",
       "zip_98007                    0.5187      0.032     16.428      0.000       0.457       0.581\n",
       "zip_98008                    0.5533      0.029     19.352      0.000       0.497       0.609\n",
       "zip_98010                    0.2088      0.030      7.004      0.000       0.150       0.267\n",
       "zip_98011                    0.5781      0.044     13.084      0.000       0.492       0.665\n",
       "zip_98014                    0.3747      0.050      7.524      0.000       0.277       0.472\n",
       "zip_98019                    0.4430      0.049      9.002      0.000       0.347       0.540\n",
       "zip_98022                    0.1260      0.034      3.656      0.000       0.058       0.193\n",
       "zip_98023                   -0.0202      0.024     -0.825      0.409      -0.068       0.028\n",
       "zip_98024                    0.5089      0.049     10.292      0.000       0.412       0.606\n",
       "zip_98027                    0.4449      0.025     17.521      0.000       0.395       0.495\n",
       "zip_98028                    0.5255      0.044     12.004      0.000       0.440       0.611\n",
       "zip_98029                    0.6021      0.025     23.712      0.000       0.552       0.652\n",
       "zip_98030                    0.0168      0.013      1.304      0.192      -0.008       0.042\n",
       "zip_98031                    0.0267      0.012      2.317      0.021       0.004       0.049\n",
       "zip_98032                   -0.0002      0.024     -0.007      0.994      -0.048       0.047\n",
       "zip_98033                    0.8676      0.040     21.801      0.000       0.790       0.946\n",
       "zip_98034                    0.5725      0.040     14.448      0.000       0.495       0.650\n",
       "zip_98038                    0.1251      0.029      4.358      0.000       0.069       0.181\n",
       "zip_98039                    1.2406      0.056     22.037      0.000       1.130       1.351\n",
       "zip_98040                    0.5522      0.018     31.492      0.000       0.518       0.587\n",
       "zip_98045                    0.4426      0.043     10.397      0.000       0.359       0.526\n",
       "zip_98047                   -0.0284      0.034     -0.824      0.410      -0.096       0.039\n",
       "zip_98050                    0.2631      0.205      1.282      0.200      -0.139       0.665\n",
       "zip_98051                    0.1983      0.063      3.165      0.002       0.076       0.321\n",
       "zip_98052                    0.7114      0.039     18.263      0.000       0.635       0.788\n",
       "zip_98053                    0.5999      0.040     15.122      0.000       0.522       0.678\n",
       "zip_98055                    0.1111      0.021      5.419      0.000       0.071       0.151\n",
       "zip_98056                    0.3325      0.019     17.671      0.000       0.296       0.369\n",
       "zip_98057                    0.1542      0.025      6.164      0.000       0.105       0.203\n",
       "zip_98058                    0.1329      0.012     10.890      0.000       0.109       0.157\n",
       "zip_98059                    0.2671      0.020     13.097      0.000       0.227       0.307\n",
       "zip_98065                    0.5305      0.043     12.266      0.000       0.446       0.615\n",
       "zip_98070                    0.2159      0.020     10.915      0.000       0.177       0.255\n",
       "zip_98072                    0.6335      0.044     14.418      0.000       0.547       0.720\n",
       "zip_98074                    0.6426      0.040     16.024      0.000       0.564       0.721\n",
       "zip_98075                    0.5931      0.025     24.040      0.000       0.545       0.641\n",
       "zip_98077                    0.6057      0.044     13.870      0.000       0.520       0.691\n",
       "zip_98092                    0.0363      0.026      1.416      0.157      -0.014       0.087\n",
       "zip_98102                    0.7147      0.031     22.828      0.000       0.653       0.776\n",
       "zip_98103                    0.6296      0.025     25.127      0.000       0.580       0.679\n",
       "zip_98105                    0.6659      0.026     25.151      0.000       0.614       0.718\n",
       "zip_98106                    0.1891      0.025      7.543      0.000       0.140       0.238\n",
       "zip_98107                    0.6234      0.026     23.653      0.000       0.572       0.675\n",
       "zip_98108                    0.2385      0.027      8.876      0.000       0.186       0.291\n",
       "zip_98109                    0.7634      0.031     24.913      0.000       0.703       0.823\n",
       "zip_98112                    0.7897      0.027     29.236      0.000       0.737       0.843\n",
       "zip_98115                    0.6006      0.025     24.286      0.000       0.552       0.649\n",
       "zip_98116                    0.5156      0.026     19.809      0.000       0.465       0.567\n",
       "zip_98117                    0.6040      0.025     24.281      0.000       0.555       0.653\n",
       "zip_98118                    0.3050      0.025     12.154      0.000       0.256       0.354\n",
       "zip_98119                    0.7261      0.028     26.322      0.000       0.672       0.780\n",
       "zip_98122                    0.5869      0.027     22.128      0.000       0.535       0.639\n",
       "zip_98125                    0.3637      0.025     14.327      0.000       0.314       0.413\n",
       "zip_98126                    0.3225      0.026     12.527      0.000       0.272       0.373\n",
       "zip_98133                    0.3414      0.026     13.067      0.000       0.290       0.393\n",
       "zip_98136                    0.4477      0.027     16.825      0.000       0.396       0.500\n",
       "zip_98144                    0.5095      0.026     19.398      0.000       0.458       0.561\n",
       "zip_98146                    0.2449      0.025      9.866      0.000       0.196       0.294\n",
       "zip_98148                    0.1548      0.032      4.769      0.000       0.091       0.218\n",
       "zip_98155                    0.4104      0.029     14.325      0.000       0.354       0.467\n",
       "zip_98166                    0.2908      0.027     10.915      0.000       0.239       0.343\n",
       "zip_98168                    0.1272      0.026      4.806      0.000       0.075       0.179\n",
       "zip_98177                    0.4900      0.027     17.989      0.000       0.437       0.543\n",
       "zip_98178                    0.1380      0.020      6.918      0.000       0.099       0.177\n",
       "zip_98188                    0.1362      0.028      4.833      0.000       0.081       0.191\n",
       "zip_98198                    0.0811      0.024      3.347      0.001       0.034       0.129\n",
       "zip_98199                    0.6619      0.026     25.482      0.000       0.611       0.713\n",
       "zip_98354                    0.0280      0.056      0.496      0.620      -0.083       0.139\n",
       "cond_Fair                   -0.1465      0.016     -9.294      0.000      -0.177      -0.116\n",
       "cond_Good                    0.0363      0.003     11.552      0.000       0.030       0.042\n",
       "cond_Poor                   -0.1639      0.034     -4.860      0.000      -0.230      -0.098\n",
       "cond_Very Good               0.0903      0.004     20.814      0.000       0.082       0.099\n",
       "==============================================================================\n",
       "Omnibus:                     3765.374   Durbin-Watson:                   1.958\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            28420.928\n",
       "Skew:                          -0.513   Prob(JB):                         0.00\n",
       "Kurtosis:                       8.129   Cond. No.                     1.04e+16\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.95e-21. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = model_data.drop(columns=['price', 'grade'])\n",
    "model = sm.OLS(y, sm.add_constant(X))\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummie grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "grade_names = {'grade_' + col: 'grade_'+ ' '.join(col.split()[1:]) for col in pd.get_dummies(model_data['grade']).columns}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "grades = pd.get_dummies(model_data['grade'], prefix='grade').rename(columns=grade_names).drop(columns=['grade_Average'])\n",
    "model_data = model_data.join(grades).drop(columns=['grade'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>   0.830</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.829</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   959.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 31 Mar 2023</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:03:08</td>     <th>  Log-Likelihood:    </th>  <td>  5274.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 24933</td>      <th>  AIC:               </th> <td>-1.029e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 24806</td>      <th>  BIC:               </th>  <td>  -9263.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   126</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>const</th>                    <td> -272.9118</td> <td>    5.267</td> <td>  -51.818</td> <td> 0.000</td> <td> -283.235</td> <td> -262.589</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>date</th>                     <td>    0.1409</td> <td>    0.003</td> <td>   54.100</td> <td> 0.000</td> <td>    0.136</td> <td>    0.146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>                 <td>    0.0022</td> <td>    0.002</td> <td>    1.175</td> <td> 0.240</td> <td>   -0.001</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>                <td>    0.0353</td> <td>    0.003</td> <td>   13.690</td> <td> 0.000</td> <td>    0.030</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>              <td>    0.0001</td> <td>    4e-06</td> <td>   27.221</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_lot</th>                 <td>    0.0677</td> <td>    0.003</td> <td>   22.361</td> <td> 0.000</td> <td>    0.062</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>               <td>    0.1672</td> <td>    0.014</td> <td>   12.037</td> <td> 0.000</td> <td>    0.140</td> <td>    0.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_above</th>               <td>  7.86e-05</td> <td> 3.64e-06</td> <td>   21.570</td> <td> 0.000</td> <td> 7.15e-05</td> <td> 8.57e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_garage</th>              <td> 3.615e-05</td> <td> 6.71e-06</td> <td>    5.390</td> <td> 0.000</td> <td>  2.3e-05</td> <td> 4.93e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_patio</th>               <td> 2.156e-05</td> <td> 6.42e-06</td> <td>    3.359</td> <td> 0.001</td> <td> 8.98e-06</td> <td> 3.41e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_renovated</th>             <td> 3.054e-05</td> <td> 3.08e-06</td> <td>    9.927</td> <td> 0.000</td> <td> 2.45e-05</td> <td> 3.66e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Auburn</th>                   <td>    0.1171</td> <td>    0.038</td> <td>    3.074</td> <td> 0.002</td> <td>    0.042</td> <td>    0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bellevue</th>                 <td>    0.3992</td> <td>    0.039</td> <td>   10.289</td> <td> 0.000</td> <td>    0.323</td> <td>    0.475</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Federal Way</th>              <td>    0.1432</td> <td>    0.037</td> <td>    3.857</td> <td> 0.000</td> <td>    0.070</td> <td>    0.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fife</th>                     <td>    0.1335</td> <td>    0.044</td> <td>    3.061</td> <td> 0.002</td> <td>    0.048</td> <td>    0.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Highline</th>                 <td>    0.1445</td> <td>    0.037</td> <td>    3.870</td> <td> 0.000</td> <td>    0.071</td> <td>    0.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Issaquah</th>                 <td>    0.3203</td> <td>    0.036</td> <td>    8.863</td> <td> 0.000</td> <td>    0.249</td> <td>    0.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kent</th>                     <td>    0.1957</td> <td>    0.031</td> <td>    6.367</td> <td> 0.000</td> <td>    0.135</td> <td>    0.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lake Washington</th>          <td>    0.2786</td> <td>    0.047</td> <td>    5.880</td> <td> 0.000</td> <td>    0.186</td> <td>    0.371</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mercer Island</th>            <td>    0.5115</td> <td>    0.017</td> <td>   30.225</td> <td> 0.000</td> <td>    0.478</td> <td>    0.545</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northshore</th>               <td>    0.1733</td> <td>    0.050</td> <td>    3.467</td> <td> 0.001</td> <td>    0.075</td> <td>    0.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Renton</th>                   <td>    0.2075</td> <td>    0.033</td> <td>    6.300</td> <td> 0.000</td> <td>    0.143</td> <td>    0.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Riverview</th>                <td>    0.0443</td> <td>    0.054</td> <td>    0.823</td> <td> 0.411</td> <td>   -0.061</td> <td>    0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seattle</th>                  <td>    0.2883</td> <td>    0.037</td> <td>    7.814</td> <td> 0.000</td> <td>    0.216</td> <td>    0.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Shoreline</th>                <td>    0.2146</td> <td>    0.039</td> <td>    5.475</td> <td> 0.000</td> <td>    0.138</td> <td>    0.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snoqualmie Valley</th>        <td>    0.1101</td> <td>    0.050</td> <td>    2.216</td> <td> 0.027</td> <td>    0.013</td> <td>    0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tahoma</th>                   <td>    0.2091</td> <td>    0.027</td> <td>    7.673</td> <td> 0.000</td> <td>    0.156</td> <td>    0.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tukwila</th>                  <td>    0.1580</td> <td>    0.041</td> <td>    3.854</td> <td> 0.000</td> <td>    0.078</td> <td>    0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vashon Island</th>            <td>    0.2067</td> <td>    0.019</td> <td>   10.845</td> <td> 0.000</td> <td>    0.169</td> <td>    0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity</th>         <td>   -0.0250</td> <td>    0.004</td> <td>   -6.961</td> <td> 0.000</td> <td>   -0.032</td> <td>   -0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity/Solar</th>   <td>    0.0117</td> <td>    0.032</td> <td>    0.365</td> <td> 0.715</td> <td>   -0.051</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Gas/Solar</th>           <td>    0.0681</td> <td>    0.022</td> <td>    3.079</td> <td> 0.002</td> <td>    0.025</td> <td>    0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil</th>                 <td>   -0.0192</td> <td>    0.005</td> <td>   -4.163</td> <td> 0.000</td> <td>   -0.028</td> <td>   -0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil/Solar</th>           <td>    0.0735</td> <td>    0.098</td> <td>    0.747</td> <td> 0.455</td> <td>   -0.119</td> <td>    0.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Other</th>               <td>    0.1272</td> <td>    0.071</td> <td>    1.792</td> <td> 0.073</td> <td>   -0.012</td> <td>    0.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_AVERAGE</th>             <td>    0.0734</td> <td>    0.005</td> <td>   13.594</td> <td> 0.000</td> <td>    0.063</td> <td>    0.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_EXCELLENT</th>           <td>    0.2717</td> <td>    0.012</td> <td>   21.836</td> <td> 0.000</td> <td>    0.247</td> <td>    0.296</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_FAIR</th>                <td>    0.1077</td> <td>    0.014</td> <td>    7.445</td> <td> 0.000</td> <td>    0.079</td> <td>    0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_GOOD</th>                <td>    0.1333</td> <td>    0.008</td> <td>   16.158</td> <td> 0.000</td> <td>    0.117</td> <td>    0.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PRIVATE</th>            <td>   -0.0665</td> <td>    0.005</td> <td>  -12.805</td> <td> 0.000</td> <td>   -0.077</td> <td>   -0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PRIVATE RESTRICTED</th> <td>    0.0240</td> <td>    0.139</td> <td>    0.173</td> <td> 0.863</td> <td>   -0.249</td> <td>    0.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PUBLIC RESTRICTED</th>  <td>    0.0392</td> <td>    0.139</td> <td>    0.282</td> <td> 0.778</td> <td>   -0.233</td> <td>    0.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98001</th>                <td>    0.0307</td> <td>    0.023</td> <td>    1.334</td> <td> 0.182</td> <td>   -0.014</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98002</th>                <td>   -0.0015</td> <td>    0.026</td> <td>   -0.057</td> <td> 0.954</td> <td>   -0.053</td> <td>    0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98003</th>                <td>   -0.0239</td> <td>    0.024</td> <td>   -0.979</td> <td> 0.328</td> <td>   -0.072</td> <td>    0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98004</th>                <td>    0.8608</td> <td>    0.030</td> <td>   28.566</td> <td> 0.000</td> <td>    0.802</td> <td>    0.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98005</th>                <td>    0.5976</td> <td>    0.031</td> <td>   19.532</td> <td> 0.000</td> <td>    0.538</td> <td>    0.658</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98006</th>                <td>    0.5077</td> <td>    0.024</td> <td>   21.058</td> <td> 0.000</td> <td>    0.460</td> <td>    0.555</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98007</th>                <td>    0.4550</td> <td>    0.030</td> <td>   14.935</td> <td> 0.000</td> <td>    0.395</td> <td>    0.515</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98008</th>                <td>    0.4896</td> <td>    0.028</td> <td>   17.746</td> <td> 0.000</td> <td>    0.435</td> <td>    0.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98010</th>                <td>    0.2006</td> <td>    0.029</td> <td>    6.987</td> <td> 0.000</td> <td>    0.144</td> <td>    0.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98011</th>                <td>    0.5069</td> <td>    0.043</td> <td>   11.883</td> <td> 0.000</td> <td>    0.423</td> <td>    0.591</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98014</th>                <td>    0.3664</td> <td>    0.048</td> <td>    7.615</td> <td> 0.000</td> <td>    0.272</td> <td>    0.461</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98019</th>                <td>    0.4167</td> <td>    0.048</td> <td>    8.762</td> <td> 0.000</td> <td>    0.323</td> <td>    0.510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98022</th>                <td>    0.1216</td> <td>    0.033</td> <td>    3.664</td> <td> 0.000</td> <td>    0.057</td> <td>    0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98023</th>                <td>   -0.0255</td> <td>    0.024</td> <td>   -1.079</td> <td> 0.280</td> <td>   -0.072</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98024</th>                <td>    0.4676</td> <td>    0.048</td> <td>    9.741</td> <td> 0.000</td> <td>    0.373</td> <td>    0.562</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98027</th>                <td>    0.3894</td> <td>    0.025</td> <td>   15.884</td> <td> 0.000</td> <td>    0.341</td> <td>    0.437</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98028</th>                <td>    0.4544</td> <td>    0.042</td> <td>   10.749</td> <td> 0.000</td> <td>    0.372</td> <td>    0.537</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98029</th>                <td>    0.5209</td> <td>    0.025</td> <td>   21.221</td> <td> 0.000</td> <td>    0.473</td> <td>    0.569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98030</th>                <td>    0.0135</td> <td>    0.012</td> <td>    1.090</td> <td> 0.276</td> <td>   -0.011</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98031</th>                <td>    0.0219</td> <td>    0.011</td> <td>    1.974</td> <td> 0.048</td> <td>    0.000</td> <td>    0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98032</th>                <td>   -0.0040</td> <td>    0.023</td> <td>   -0.170</td> <td> 0.865</td> <td>   -0.050</td> <td>    0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98033</th>                <td>    0.7828</td> <td>    0.039</td> <td>   20.320</td> <td> 0.000</td> <td>    0.707</td> <td>    0.858</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98034</th>                <td>    0.5212</td> <td>    0.038</td> <td>   13.601</td> <td> 0.000</td> <td>    0.446</td> <td>    0.596</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98038</th>                <td>    0.1161</td> <td>    0.028</td> <td>    4.198</td> <td> 0.000</td> <td>    0.062</td> <td>    0.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98039</th>                <td>    1.1666</td> <td>    0.054</td> <td>   21.498</td> <td> 0.000</td> <td>    1.060</td> <td>    1.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98040</th>                <td>    0.5115</td> <td>    0.017</td> <td>   30.225</td> <td> 0.000</td> <td>    0.478</td> <td>    0.545</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98045</th>                <td>    0.3941</td> <td>    0.041</td> <td>    9.502</td> <td> 0.000</td> <td>    0.313</td> <td>    0.475</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98047</th>                <td>    0.0129</td> <td>    0.033</td> <td>    0.387</td> <td> 0.699</td> <td>   -0.052</td> <td>    0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98050</th>                <td>    0.2687</td> <td>    0.198</td> <td>    1.360</td> <td> 0.174</td> <td>   -0.119</td> <td>    0.656</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98051</th>                <td>    0.1722</td> <td>    0.060</td> <td>    2.852</td> <td> 0.004</td> <td>    0.054</td> <td>    0.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98052</th>                <td>    0.6257</td> <td>    0.038</td> <td>   16.591</td> <td> 0.000</td> <td>    0.552</td> <td>    0.700</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98053</th>                <td>    0.5481</td> <td>    0.038</td> <td>   14.261</td> <td> 0.000</td> <td>    0.473</td> <td>    0.623</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98055</th>                <td>    0.0764</td> <td>    0.020</td> <td>    3.863</td> <td> 0.000</td> <td>    0.038</td> <td>    0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98056</th>                <td>    0.2953</td> <td>    0.018</td> <td>   16.250</td> <td> 0.000</td> <td>    0.260</td> <td>    0.331</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98057</th>                <td>    0.1427</td> <td>    0.024</td> <td>    5.904</td> <td> 0.000</td> <td>    0.095</td> <td>    0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98058</th>                <td>    0.1057</td> <td>    0.012</td> <td>    8.985</td> <td> 0.000</td> <td>    0.083</td> <td>    0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98059</th>                <td>    0.2316</td> <td>    0.020</td> <td>   11.773</td> <td> 0.000</td> <td>    0.193</td> <td>    0.270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98065</th>                <td>    0.5051</td> <td>    0.042</td> <td>   12.014</td> <td> 0.000</td> <td>    0.423</td> <td>    0.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98070</th>                <td>    0.2067</td> <td>    0.019</td> <td>   10.845</td> <td> 0.000</td> <td>    0.169</td> <td>    0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98072</th>                <td>    0.5539</td> <td>    0.042</td> <td>   13.056</td> <td> 0.000</td> <td>    0.471</td> <td>    0.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98074</th>                <td>    0.5396</td> <td>    0.039</td> <td>   13.895</td> <td> 0.000</td> <td>    0.463</td> <td>    0.616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98075</th>                <td>    0.5321</td> <td>    0.024</td> <td>   22.337</td> <td> 0.000</td> <td>    0.485</td> <td>    0.579</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98077</th>                <td>    0.5030</td> <td>    0.042</td> <td>   11.933</td> <td> 0.000</td> <td>    0.420</td> <td>    0.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98092</th>                <td>    0.0192</td> <td>    0.025</td> <td>    0.779</td> <td> 0.436</td> <td>   -0.029</td> <td>    0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98102</th>                <td>    0.6401</td> <td>    0.030</td> <td>   21.189</td> <td> 0.000</td> <td>    0.581</td> <td>    0.699</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98103</th>                <td>    0.6009</td> <td>    0.024</td> <td>   24.882</td> <td> 0.000</td> <td>    0.554</td> <td>    0.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98105</th>                <td>    0.6257</td> <td>    0.026</td> <td>   24.512</td> <td> 0.000</td> <td>    0.576</td> <td>    0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98106</th>                <td>    0.1708</td> <td>    0.024</td> <td>    7.065</td> <td> 0.000</td> <td>    0.123</td> <td>    0.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98107</th>                <td>    0.5965</td> <td>    0.025</td> <td>   23.481</td> <td> 0.000</td> <td>    0.547</td> <td>    0.646</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98108</th>                <td>    0.2226</td> <td>    0.026</td> <td>    8.592</td> <td> 0.000</td> <td>    0.172</td> <td>    0.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98109</th>                <td>    0.7025</td> <td>    0.030</td> <td>   23.769</td> <td> 0.000</td> <td>    0.645</td> <td>    0.760</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98112</th>                <td>    0.7114</td> <td>    0.026</td> <td>   27.268</td> <td> 0.000</td> <td>    0.660</td> <td>    0.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98115</th>                <td>    0.5704</td> <td>    0.024</td> <td>   23.927</td> <td> 0.000</td> <td>    0.524</td> <td>    0.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98116</th>                <td>    0.4883</td> <td>    0.025</td> <td>   19.464</td> <td> 0.000</td> <td>    0.439</td> <td>    0.537</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98117</th>                <td>    0.5700</td> <td>    0.024</td> <td>   23.766</td> <td> 0.000</td> <td>    0.523</td> <td>    0.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98118</th>                <td>    0.3016</td> <td>    0.024</td> <td>   12.469</td> <td> 0.000</td> <td>    0.254</td> <td>    0.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98119</th>                <td>    0.6646</td> <td>    0.027</td> <td>   24.971</td> <td> 0.000</td> <td>    0.612</td> <td>    0.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98122</th>                <td>    0.5257</td> <td>    0.026</td> <td>   20.542</td> <td> 0.000</td> <td>    0.476</td> <td>    0.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98125</th>                <td>    0.3474</td> <td>    0.024</td> <td>   14.201</td> <td> 0.000</td> <td>    0.299</td> <td>    0.395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98126</th>                <td>    0.3141</td> <td>    0.025</td> <td>   12.657</td> <td> 0.000</td> <td>    0.265</td> <td>    0.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98133</th>                <td>    0.3226</td> <td>    0.025</td> <td>   12.816</td> <td> 0.000</td> <td>    0.273</td> <td>    0.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98136</th>                <td>    0.4231</td> <td>    0.026</td> <td>   16.498</td> <td> 0.000</td> <td>    0.373</td> <td>    0.473</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98144</th>                <td>    0.4833</td> <td>    0.025</td> <td>   19.089</td> <td> 0.000</td> <td>    0.434</td> <td>    0.533</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98146</th>                <td>    0.2445</td> <td>    0.024</td> <td>   10.218</td> <td> 0.000</td> <td>    0.198</td> <td>    0.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98148</th>                <td>    0.1660</td> <td>    0.031</td> <td>    5.310</td> <td> 0.000</td> <td>    0.105</td> <td>    0.227</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98155</th>                <td>    0.3872</td> <td>    0.028</td> <td>   14.023</td> <td> 0.000</td> <td>    0.333</td> <td>    0.441</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98166</th>                <td>    0.2785</td> <td>    0.026</td> <td>   10.846</td> <td> 0.000</td> <td>    0.228</td> <td>    0.329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98168</th>                <td>    0.1378</td> <td>    0.026</td> <td>    5.400</td> <td> 0.000</td> <td>    0.088</td> <td>    0.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98177</th>                <td>    0.4406</td> <td>    0.026</td> <td>   16.768</td> <td> 0.000</td> <td>    0.389</td> <td>    0.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98178</th>                <td>    0.1262</td> <td>    0.019</td> <td>    6.564</td> <td> 0.000</td> <td>    0.088</td> <td>    0.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98188</th>                <td>    0.1496</td> <td>    0.027</td> <td>    5.508</td> <td> 0.000</td> <td>    0.096</td> <td>    0.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98198</th>                <td>    0.0854</td> <td>    0.023</td> <td>    3.654</td> <td> 0.000</td> <td>    0.040</td> <td>    0.131</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98199</th>                <td>    0.6123</td> <td>    0.025</td> <td>   24.438</td> <td> 0.000</td> <td>    0.563</td> <td>    0.661</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98354</th>                <td>    0.0469</td> <td>    0.054</td> <td>    0.863</td> <td> 0.388</td> <td>   -0.060</td> <td>    0.153</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Fair</th>                <td>   -0.1148</td> <td>    0.015</td> <td>   -7.541</td> <td> 0.000</td> <td>   -0.145</td> <td>   -0.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Good</th>                <td>    0.0477</td> <td>    0.003</td> <td>   15.718</td> <td> 0.000</td> <td>    0.042</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Poor</th>                <td>   -0.1617</td> <td>    0.033</td> <td>   -4.898</td> <td> 0.000</td> <td>   -0.226</td> <td>   -0.097</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Very Good</th>           <td>    0.1071</td> <td>    0.004</td> <td>   25.509</td> <td> 0.000</td> <td>    0.099</td> <td>    0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Very Good</th>          <td>    0.2462</td> <td>    0.009</td> <td>   28.401</td> <td> 0.000</td> <td>    0.229</td> <td>    0.263</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Excellent</th>          <td>    0.2963</td> <td>    0.017</td> <td>   17.888</td> <td> 0.000</td> <td>    0.264</td> <td>    0.329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Luxury</th>             <td>    0.2775</td> <td>    0.040</td> <td>    6.863</td> <td> 0.000</td> <td>    0.198</td> <td>    0.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Poor</th>               <td>    0.1595</td> <td>    0.102</td> <td>    1.570</td> <td> 0.117</td> <td>   -0.040</td> <td>    0.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Low</th>                <td>    0.0832</td> <td>    0.039</td> <td>    2.134</td> <td> 0.033</td> <td>    0.007</td> <td>    0.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Fair</th>               <td>   -0.0862</td> <td>    0.012</td> <td>   -6.920</td> <td> 0.000</td> <td>   -0.111</td> <td>   -0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Low Average</th>        <td>   -0.0718</td> <td>    0.005</td> <td>  -14.946</td> <td> 0.000</td> <td>   -0.081</td> <td>   -0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Good</th>               <td>    0.1103</td> <td>    0.004</td> <td>   30.679</td> <td> 0.000</td> <td>    0.103</td> <td>    0.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Better</th>             <td>    0.2200</td> <td>    0.006</td> <td>   39.424</td> <td> 0.000</td> <td>    0.209</td> <td>    0.231</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4102.498</td> <th>  Durbin-Watson:     </th> <td>   1.963</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>36198.485</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.533</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 8.806</td>  <th>  Cond. No.          </th> <td>1.04e+16</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 smallest eigenvalue is 2.95e-21. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.830\n",
       "Model:                            OLS   Adj. R-squared:                  0.829\n",
       "Method:                 Least Squares   F-statistic:                     959.3\n",
       "Date:                Fri, 31 Mar 2023   Prob (F-statistic):               0.00\n",
       "Time:                        17:03:08   Log-Likelihood:                 5274.2\n",
       "No. Observations:               24933   AIC:                        -1.029e+04\n",
       "Df Residuals:                   24806   BIC:                            -9263.\n",
       "Df Model:                         126                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================\n",
       "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------\n",
       "const                     -272.9118      5.267    -51.818      0.000    -283.235    -262.589\n",
       "date                         0.1409      0.003     54.100      0.000       0.136       0.146\n",
       "bedrooms                     0.0022      0.002      1.175      0.240      -0.001       0.006\n",
       "bathrooms                    0.0353      0.003     13.690      0.000       0.030       0.040\n",
       "sqft_living                  0.0001      4e-06     27.221      0.000       0.000       0.000\n",
       "sqft_lot                     0.0677      0.003     22.361      0.000       0.062       0.074\n",
       "waterfront                   0.1672      0.014     12.037      0.000       0.140       0.194\n",
       "sqft_above                 7.86e-05   3.64e-06     21.570      0.000    7.15e-05    8.57e-05\n",
       "sqft_garage               3.615e-05   6.71e-06      5.390      0.000     2.3e-05    4.93e-05\n",
       "sqft_patio                2.156e-05   6.42e-06      3.359      0.001    8.98e-06    3.41e-05\n",
       "yr_renovated              3.054e-05   3.08e-06      9.927      0.000    2.45e-05    3.66e-05\n",
       "Auburn                       0.1171      0.038      3.074      0.002       0.042       0.192\n",
       "Bellevue                     0.3992      0.039     10.289      0.000       0.323       0.475\n",
       "Federal Way                  0.1432      0.037      3.857      0.000       0.070       0.216\n",
       "Fife                         0.1335      0.044      3.061      0.002       0.048       0.219\n",
       "Highline                     0.1445      0.037      3.870      0.000       0.071       0.218\n",
       "Issaquah                     0.3203      0.036      8.863      0.000       0.249       0.391\n",
       "Kent                         0.1957      0.031      6.367      0.000       0.135       0.256\n",
       "Lake Washington              0.2786      0.047      5.880      0.000       0.186       0.371\n",
       "Mercer Island                0.5115      0.017     30.225      0.000       0.478       0.545\n",
       "Northshore                   0.1733      0.050      3.467      0.001       0.075       0.271\n",
       "Renton                       0.2075      0.033      6.300      0.000       0.143       0.272\n",
       "Riverview                    0.0443      0.054      0.823      0.411      -0.061       0.150\n",
       "Seattle                      0.2883      0.037      7.814      0.000       0.216       0.361\n",
       "Shoreline                    0.2146      0.039      5.475      0.000       0.138       0.291\n",
       "Snoqualmie Valley            0.1101      0.050      2.216      0.027       0.013       0.207\n",
       "Tahoma                       0.2091      0.027      7.673      0.000       0.156       0.262\n",
       "Tukwila                      0.1580      0.041      3.854      0.000       0.078       0.238\n",
       "Vashon Island                0.2067      0.019     10.845      0.000       0.169       0.244\n",
       "heat_Electricity            -0.0250      0.004     -6.961      0.000      -0.032      -0.018\n",
       "heat_Electricity/Solar       0.0117      0.032      0.365      0.715      -0.051       0.074\n",
       "heat_Gas/Solar               0.0681      0.022      3.079      0.002       0.025       0.111\n",
       "heat_Oil                    -0.0192      0.005     -4.163      0.000      -0.028      -0.010\n",
       "heat_Oil/Solar               0.0735      0.098      0.747      0.455      -0.119       0.266\n",
       "heat_Other                   0.1272      0.071      1.792      0.073      -0.012       0.266\n",
       "view_AVERAGE                 0.0734      0.005     13.594      0.000       0.063       0.084\n",
       "view_EXCELLENT               0.2717      0.012     21.836      0.000       0.247       0.296\n",
       "view_FAIR                    0.1077      0.014      7.445      0.000       0.079       0.136\n",
       "view_GOOD                    0.1333      0.008     16.158      0.000       0.117       0.149\n",
       "sewer_PRIVATE               -0.0665      0.005    -12.805      0.000      -0.077      -0.056\n",
       "sewer_PRIVATE RESTRICTED     0.0240      0.139      0.173      0.863      -0.249       0.297\n",
       "sewer_PUBLIC RESTRICTED      0.0392      0.139      0.282      0.778      -0.233       0.312\n",
       "zip_98001                    0.0307      0.023      1.334      0.182      -0.014       0.076\n",
       "zip_98002                   -0.0015      0.026     -0.057      0.954      -0.053       0.050\n",
       "zip_98003                   -0.0239      0.024     -0.979      0.328      -0.072       0.024\n",
       "zip_98004                    0.8608      0.030     28.566      0.000       0.802       0.920\n",
       "zip_98005                    0.5976      0.031     19.532      0.000       0.538       0.658\n",
       "zip_98006                    0.5077      0.024     21.058      0.000       0.460       0.555\n",
       "zip_98007                    0.4550      0.030     14.935      0.000       0.395       0.515\n",
       "zip_98008                    0.4896      0.028     17.746      0.000       0.435       0.544\n",
       "zip_98010                    0.2006      0.029      6.987      0.000       0.144       0.257\n",
       "zip_98011                    0.5069      0.043     11.883      0.000       0.423       0.591\n",
       "zip_98014                    0.3664      0.048      7.615      0.000       0.272       0.461\n",
       "zip_98019                    0.4167      0.048      8.762      0.000       0.323       0.510\n",
       "zip_98022                    0.1216      0.033      3.664      0.000       0.057       0.187\n",
       "zip_98023                   -0.0255      0.024     -1.079      0.280      -0.072       0.021\n",
       "zip_98024                    0.4676      0.048      9.741      0.000       0.373       0.562\n",
       "zip_98027                    0.3894      0.025     15.884      0.000       0.341       0.437\n",
       "zip_98028                    0.4544      0.042     10.749      0.000       0.372       0.537\n",
       "zip_98029                    0.5209      0.025     21.221      0.000       0.473       0.569\n",
       "zip_98030                    0.0135      0.012      1.090      0.276      -0.011       0.038\n",
       "zip_98031                    0.0219      0.011      1.974      0.048       0.000       0.044\n",
       "zip_98032                   -0.0040      0.023     -0.170      0.865      -0.050       0.042\n",
       "zip_98033                    0.7828      0.039     20.320      0.000       0.707       0.858\n",
       "zip_98034                    0.5212      0.038     13.601      0.000       0.446       0.596\n",
       "zip_98038                    0.1161      0.028      4.198      0.000       0.062       0.170\n",
       "zip_98039                    1.1666      0.054     21.498      0.000       1.060       1.273\n",
       "zip_98040                    0.5115      0.017     30.225      0.000       0.478       0.545\n",
       "zip_98045                    0.3941      0.041      9.502      0.000       0.313       0.475\n",
       "zip_98047                    0.0129      0.033      0.387      0.699      -0.052       0.078\n",
       "zip_98050                    0.2687      0.198      1.360      0.174      -0.119       0.656\n",
       "zip_98051                    0.1722      0.060      2.852      0.004       0.054       0.290\n",
       "zip_98052                    0.6257      0.038     16.591      0.000       0.552       0.700\n",
       "zip_98053                    0.5481      0.038     14.261      0.000       0.473       0.623\n",
       "zip_98055                    0.0764      0.020      3.863      0.000       0.038       0.115\n",
       "zip_98056                    0.2953      0.018     16.250      0.000       0.260       0.331\n",
       "zip_98057                    0.1427      0.024      5.904      0.000       0.095       0.190\n",
       "zip_98058                    0.1057      0.012      8.985      0.000       0.083       0.129\n",
       "zip_98059                    0.2316      0.020     11.773      0.000       0.193       0.270\n",
       "zip_98065                    0.5051      0.042     12.014      0.000       0.423       0.587\n",
       "zip_98070                    0.2067      0.019     10.845      0.000       0.169       0.244\n",
       "zip_98072                    0.5539      0.042     13.056      0.000       0.471       0.637\n",
       "zip_98074                    0.5396      0.039     13.895      0.000       0.463       0.616\n",
       "zip_98075                    0.5321      0.024     22.337      0.000       0.485       0.579\n",
       "zip_98077                    0.5030      0.042     11.933      0.000       0.420       0.586\n",
       "zip_98092                    0.0192      0.025      0.779      0.436      -0.029       0.068\n",
       "zip_98102                    0.6401      0.030     21.189      0.000       0.581       0.699\n",
       "zip_98103                    0.6009      0.024     24.882      0.000       0.554       0.648\n",
       "zip_98105                    0.6257      0.026     24.512      0.000       0.576       0.676\n",
       "zip_98106                    0.1708      0.024      7.065      0.000       0.123       0.218\n",
       "zip_98107                    0.5965      0.025     23.481      0.000       0.547       0.646\n",
       "zip_98108                    0.2226      0.026      8.592      0.000       0.172       0.273\n",
       "zip_98109                    0.7025      0.030     23.769      0.000       0.645       0.760\n",
       "zip_98112                    0.7114      0.026     27.268      0.000       0.660       0.763\n",
       "zip_98115                    0.5704      0.024     23.927      0.000       0.524       0.617\n",
       "zip_98116                    0.4883      0.025     19.464      0.000       0.439       0.537\n",
       "zip_98117                    0.5700      0.024     23.766      0.000       0.523       0.617\n",
       "zip_98118                    0.3016      0.024     12.469      0.000       0.254       0.349\n",
       "zip_98119                    0.6646      0.027     24.971      0.000       0.612       0.717\n",
       "zip_98122                    0.5257      0.026     20.542      0.000       0.476       0.576\n",
       "zip_98125                    0.3474      0.024     14.201      0.000       0.299       0.395\n",
       "zip_98126                    0.3141      0.025     12.657      0.000       0.265       0.363\n",
       "zip_98133                    0.3226      0.025     12.816      0.000       0.273       0.372\n",
       "zip_98136                    0.4231      0.026     16.498      0.000       0.373       0.473\n",
       "zip_98144                    0.4833      0.025     19.089      0.000       0.434       0.533\n",
       "zip_98146                    0.2445      0.024     10.218      0.000       0.198       0.291\n",
       "zip_98148                    0.1660      0.031      5.310      0.000       0.105       0.227\n",
       "zip_98155                    0.3872      0.028     14.023      0.000       0.333       0.441\n",
       "zip_98166                    0.2785      0.026     10.846      0.000       0.228       0.329\n",
       "zip_98168                    0.1378      0.026      5.400      0.000       0.088       0.188\n",
       "zip_98177                    0.4406      0.026     16.768      0.000       0.389       0.492\n",
       "zip_98178                    0.1262      0.019      6.564      0.000       0.088       0.164\n",
       "zip_98188                    0.1496      0.027      5.508      0.000       0.096       0.203\n",
       "zip_98198                    0.0854      0.023      3.654      0.000       0.040       0.131\n",
       "zip_98199                    0.6123      0.025     24.438      0.000       0.563       0.661\n",
       "zip_98354                    0.0469      0.054      0.863      0.388      -0.060       0.153\n",
       "cond_Fair                   -0.1148      0.015     -7.541      0.000      -0.145      -0.085\n",
       "cond_Good                    0.0477      0.003     15.718      0.000       0.042       0.054\n",
       "cond_Poor                   -0.1617      0.033     -4.898      0.000      -0.226      -0.097\n",
       "cond_Very Good               0.1071      0.004     25.509      0.000       0.099       0.115\n",
       "grade_Very Good              0.2462      0.009     28.401      0.000       0.229       0.263\n",
       "grade_Excellent              0.2963      0.017     17.888      0.000       0.264       0.329\n",
       "grade_Luxury                 0.2775      0.040      6.863      0.000       0.198       0.357\n",
       "grade_Poor                   0.1595      0.102      1.570      0.117      -0.040       0.359\n",
       "grade_Low                    0.0832      0.039      2.134      0.033       0.007       0.160\n",
       "grade_Fair                  -0.0862      0.012     -6.920      0.000      -0.111      -0.062\n",
       "grade_Low Average           -0.0718      0.005    -14.946      0.000      -0.081      -0.062\n",
       "grade_Good                   0.1103      0.004     30.679      0.000       0.103       0.117\n",
       "grade_Better                 0.2200      0.006     39.424      0.000       0.209       0.231\n",
       "==============================================================================\n",
       "Omnibus:                     4102.498   Durbin-Watson:                   1.963\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            36198.485\n",
       "Skew:                          -0.533   Prob(JB):                         0.00\n",
       "Kurtosis:                       8.806   Cond. No.                     1.04e+16\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.95e-21. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = model_data.drop(columns=['price'])\n",
    "model = sm.OLS(y, sm.add_constant(X))\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummie greenbelt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_data['greenbelt'] = pd.get_dummies(model_data['greenbelt'], drop_first=True).rename(columns={'YES': 'greenbelt'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# X = sm.add_constant(model_data.drop(columns=['price', 'nuisance', 'view', 'condition', 'grade', 'sewer_system']))\n",
    "# model = sm.OLS(y, X)\n",
    "# model.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Greenbelt does not contribute to a higher $R^2$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummie nuisance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_data['nuisance'] = pd.get_dummies(model_data['nuisance'], drop_first=True).rename(columns={'YES': 'nuisance'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# X = model_data.drop(columns=['price', 'view', 'condition', 'grade', 'sewer_system'])\n",
    "# model = sm.OLS(y, sm.add_constant(X))\n",
    "# model.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuisance does not contribute to a higher $R^2$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_num = model_data[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'sqft_above', 'sqft_garage', 'sqft_patio', 'yr_renovated']]\n",
    "X_bin = model_data.drop(columns=X_num.columns)\n",
    "X_num_orig = X_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X_num)\n",
    "X_new = pd.DataFrame(data=X_scaled, columns=X_num_orig.columns, index=X_num_orig.index).join(X_bin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>   0.830</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.829</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   959.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 31 Mar 2023</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:03:09</td>     <th>  Log-Likelihood:    </th>  <td>  5274.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 24933</td>      <th>  AIC:               </th> <td>-1.029e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 24806</td>      <th>  BIC:               </th>  <td>  -9263.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   126</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>const</th>                    <td> -271.8303</td> <td>    5.267</td> <td>  -51.613</td> <td> 0.000</td> <td> -282.153</td> <td> -261.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>                 <td>    0.0021</td> <td>    0.002</td> <td>    1.175</td> <td> 0.240</td> <td>   -0.001</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>                <td>    0.0296</td> <td>    0.002</td> <td>   13.690</td> <td> 0.000</td> <td>    0.025</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>              <td>    0.0916</td> <td>    0.003</td> <td>   27.221</td> <td> 0.000</td> <td>    0.085</td> <td>    0.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_lot</th>                 <td>    0.0412</td> <td>    0.002</td> <td>   22.361</td> <td> 0.000</td> <td>    0.038</td> <td>    0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_above</th>               <td>    0.0612</td> <td>    0.003</td> <td>   21.570</td> <td> 0.000</td> <td>    0.056</td> <td>    0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_garage</th>              <td>    0.0096</td> <td>    0.002</td> <td>    5.390</td> <td> 0.000</td> <td>    0.006</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_patio</th>               <td>    0.0047</td> <td>    0.001</td> <td>    3.359</td> <td> 0.001</td> <td>    0.002</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_renovated</th>             <td>    0.0128</td> <td>    0.001</td> <td>    9.927</td> <td> 0.000</td> <td>    0.010</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>date</th>                     <td>    0.1409</td> <td>    0.003</td> <td>   54.100</td> <td> 0.000</td> <td>    0.136</td> <td>    0.146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>               <td>    0.1672</td> <td>    0.014</td> <td>   12.037</td> <td> 0.000</td> <td>    0.140</td> <td>    0.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Auburn</th>                   <td>    0.1171</td> <td>    0.038</td> <td>    3.074</td> <td> 0.002</td> <td>    0.042</td> <td>    0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bellevue</th>                 <td>    0.3992</td> <td>    0.039</td> <td>   10.289</td> <td> 0.000</td> <td>    0.323</td> <td>    0.475</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Federal Way</th>              <td>    0.1432</td> <td>    0.037</td> <td>    3.857</td> <td> 0.000</td> <td>    0.070</td> <td>    0.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fife</th>                     <td>    0.1335</td> <td>    0.044</td> <td>    3.061</td> <td> 0.002</td> <td>    0.048</td> <td>    0.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Highline</th>                 <td>    0.1445</td> <td>    0.037</td> <td>    3.870</td> <td> 0.000</td> <td>    0.071</td> <td>    0.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Issaquah</th>                 <td>    0.3203</td> <td>    0.036</td> <td>    8.863</td> <td> 0.000</td> <td>    0.249</td> <td>    0.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kent</th>                     <td>    0.1957</td> <td>    0.031</td> <td>    6.367</td> <td> 0.000</td> <td>    0.135</td> <td>    0.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lake Washington</th>          <td>    0.2786</td> <td>    0.047</td> <td>    5.880</td> <td> 0.000</td> <td>    0.186</td> <td>    0.371</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mercer Island</th>            <td>    0.5115</td> <td>    0.017</td> <td>   30.225</td> <td> 0.000</td> <td>    0.478</td> <td>    0.545</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northshore</th>               <td>    0.1733</td> <td>    0.050</td> <td>    3.467</td> <td> 0.001</td> <td>    0.075</td> <td>    0.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Renton</th>                   <td>    0.2075</td> <td>    0.033</td> <td>    6.300</td> <td> 0.000</td> <td>    0.143</td> <td>    0.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Riverview</th>                <td>    0.0443</td> <td>    0.054</td> <td>    0.823</td> <td> 0.411</td> <td>   -0.061</td> <td>    0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seattle</th>                  <td>    0.2883</td> <td>    0.037</td> <td>    7.814</td> <td> 0.000</td> <td>    0.216</td> <td>    0.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Shoreline</th>                <td>    0.2146</td> <td>    0.039</td> <td>    5.475</td> <td> 0.000</td> <td>    0.138</td> <td>    0.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snoqualmie Valley</th>        <td>    0.1101</td> <td>    0.050</td> <td>    2.216</td> <td> 0.027</td> <td>    0.013</td> <td>    0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tahoma</th>                   <td>    0.2091</td> <td>    0.027</td> <td>    7.673</td> <td> 0.000</td> <td>    0.156</td> <td>    0.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tukwila</th>                  <td>    0.1580</td> <td>    0.041</td> <td>    3.854</td> <td> 0.000</td> <td>    0.078</td> <td>    0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vashon Island</th>            <td>    0.2067</td> <td>    0.019</td> <td>   10.845</td> <td> 0.000</td> <td>    0.169</td> <td>    0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity</th>         <td>   -0.0250</td> <td>    0.004</td> <td>   -6.961</td> <td> 0.000</td> <td>   -0.032</td> <td>   -0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Electricity/Solar</th>   <td>    0.0117</td> <td>    0.032</td> <td>    0.365</td> <td> 0.715</td> <td>   -0.051</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Gas/Solar</th>           <td>    0.0681</td> <td>    0.022</td> <td>    3.079</td> <td> 0.002</td> <td>    0.025</td> <td>    0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil</th>                 <td>   -0.0192</td> <td>    0.005</td> <td>   -4.163</td> <td> 0.000</td> <td>   -0.028</td> <td>   -0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Oil/Solar</th>           <td>    0.0735</td> <td>    0.098</td> <td>    0.747</td> <td> 0.455</td> <td>   -0.119</td> <td>    0.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>heat_Other</th>               <td>    0.1272</td> <td>    0.071</td> <td>    1.792</td> <td> 0.073</td> <td>   -0.012</td> <td>    0.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_AVERAGE</th>             <td>    0.0734</td> <td>    0.005</td> <td>   13.594</td> <td> 0.000</td> <td>    0.063</td> <td>    0.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_EXCELLENT</th>           <td>    0.2717</td> <td>    0.012</td> <td>   21.836</td> <td> 0.000</td> <td>    0.247</td> <td>    0.296</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_FAIR</th>                <td>    0.1077</td> <td>    0.014</td> <td>    7.445</td> <td> 0.000</td> <td>    0.079</td> <td>    0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view_GOOD</th>                <td>    0.1333</td> <td>    0.008</td> <td>   16.158</td> <td> 0.000</td> <td>    0.117</td> <td>    0.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PRIVATE</th>            <td>   -0.0665</td> <td>    0.005</td> <td>  -12.805</td> <td> 0.000</td> <td>   -0.077</td> <td>   -0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PRIVATE RESTRICTED</th> <td>    0.0240</td> <td>    0.139</td> <td>    0.173</td> <td> 0.863</td> <td>   -0.249</td> <td>    0.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sewer_PUBLIC RESTRICTED</th>  <td>    0.0392</td> <td>    0.139</td> <td>    0.282</td> <td> 0.778</td> <td>   -0.233</td> <td>    0.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98001</th>                <td>    0.0307</td> <td>    0.023</td> <td>    1.334</td> <td> 0.182</td> <td>   -0.014</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98002</th>                <td>   -0.0015</td> <td>    0.026</td> <td>   -0.057</td> <td> 0.954</td> <td>   -0.053</td> <td>    0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98003</th>                <td>   -0.0239</td> <td>    0.024</td> <td>   -0.979</td> <td> 0.328</td> <td>   -0.072</td> <td>    0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98004</th>                <td>    0.8608</td> <td>    0.030</td> <td>   28.566</td> <td> 0.000</td> <td>    0.802</td> <td>    0.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98005</th>                <td>    0.5976</td> <td>    0.031</td> <td>   19.532</td> <td> 0.000</td> <td>    0.538</td> <td>    0.658</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98006</th>                <td>    0.5077</td> <td>    0.024</td> <td>   21.058</td> <td> 0.000</td> <td>    0.460</td> <td>    0.555</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98007</th>                <td>    0.4550</td> <td>    0.030</td> <td>   14.935</td> <td> 0.000</td> <td>    0.395</td> <td>    0.515</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98008</th>                <td>    0.4896</td> <td>    0.028</td> <td>   17.746</td> <td> 0.000</td> <td>    0.435</td> <td>    0.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98010</th>                <td>    0.2006</td> <td>    0.029</td> <td>    6.987</td> <td> 0.000</td> <td>    0.144</td> <td>    0.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98011</th>                <td>    0.5069</td> <td>    0.043</td> <td>   11.883</td> <td> 0.000</td> <td>    0.423</td> <td>    0.591</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98014</th>                <td>    0.3664</td> <td>    0.048</td> <td>    7.615</td> <td> 0.000</td> <td>    0.272</td> <td>    0.461</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98019</th>                <td>    0.4167</td> <td>    0.048</td> <td>    8.762</td> <td> 0.000</td> <td>    0.323</td> <td>    0.510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98022</th>                <td>    0.1216</td> <td>    0.033</td> <td>    3.664</td> <td> 0.000</td> <td>    0.057</td> <td>    0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98023</th>                <td>   -0.0255</td> <td>    0.024</td> <td>   -1.079</td> <td> 0.280</td> <td>   -0.072</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98024</th>                <td>    0.4676</td> <td>    0.048</td> <td>    9.741</td> <td> 0.000</td> <td>    0.373</td> <td>    0.562</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98027</th>                <td>    0.3894</td> <td>    0.025</td> <td>   15.884</td> <td> 0.000</td> <td>    0.341</td> <td>    0.437</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98028</th>                <td>    0.4544</td> <td>    0.042</td> <td>   10.749</td> <td> 0.000</td> <td>    0.372</td> <td>    0.537</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98029</th>                <td>    0.5209</td> <td>    0.025</td> <td>   21.221</td> <td> 0.000</td> <td>    0.473</td> <td>    0.569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98030</th>                <td>    0.0135</td> <td>    0.012</td> <td>    1.090</td> <td> 0.276</td> <td>   -0.011</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98031</th>                <td>    0.0219</td> <td>    0.011</td> <td>    1.974</td> <td> 0.048</td> <td>    0.000</td> <td>    0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98032</th>                <td>   -0.0040</td> <td>    0.023</td> <td>   -0.170</td> <td> 0.865</td> <td>   -0.050</td> <td>    0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98033</th>                <td>    0.7828</td> <td>    0.039</td> <td>   20.320</td> <td> 0.000</td> <td>    0.707</td> <td>    0.858</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98034</th>                <td>    0.5212</td> <td>    0.038</td> <td>   13.601</td> <td> 0.000</td> <td>    0.446</td> <td>    0.596</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98038</th>                <td>    0.1161</td> <td>    0.028</td> <td>    4.198</td> <td> 0.000</td> <td>    0.062</td> <td>    0.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98039</th>                <td>    1.1666</td> <td>    0.054</td> <td>   21.498</td> <td> 0.000</td> <td>    1.060</td> <td>    1.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98040</th>                <td>    0.5115</td> <td>    0.017</td> <td>   30.225</td> <td> 0.000</td> <td>    0.478</td> <td>    0.545</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98045</th>                <td>    0.3941</td> <td>    0.041</td> <td>    9.502</td> <td> 0.000</td> <td>    0.313</td> <td>    0.475</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98047</th>                <td>    0.0129</td> <td>    0.033</td> <td>    0.387</td> <td> 0.699</td> <td>   -0.052</td> <td>    0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98050</th>                <td>    0.2687</td> <td>    0.198</td> <td>    1.360</td> <td> 0.174</td> <td>   -0.119</td> <td>    0.656</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98051</th>                <td>    0.1722</td> <td>    0.060</td> <td>    2.852</td> <td> 0.004</td> <td>    0.054</td> <td>    0.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98052</th>                <td>    0.6257</td> <td>    0.038</td> <td>   16.591</td> <td> 0.000</td> <td>    0.552</td> <td>    0.700</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98053</th>                <td>    0.5481</td> <td>    0.038</td> <td>   14.261</td> <td> 0.000</td> <td>    0.473</td> <td>    0.623</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98055</th>                <td>    0.0764</td> <td>    0.020</td> <td>    3.863</td> <td> 0.000</td> <td>    0.038</td> <td>    0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98056</th>                <td>    0.2953</td> <td>    0.018</td> <td>   16.250</td> <td> 0.000</td> <td>    0.260</td> <td>    0.331</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98057</th>                <td>    0.1427</td> <td>    0.024</td> <td>    5.904</td> <td> 0.000</td> <td>    0.095</td> <td>    0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98058</th>                <td>    0.1057</td> <td>    0.012</td> <td>    8.985</td> <td> 0.000</td> <td>    0.083</td> <td>    0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98059</th>                <td>    0.2316</td> <td>    0.020</td> <td>   11.773</td> <td> 0.000</td> <td>    0.193</td> <td>    0.270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98065</th>                <td>    0.5051</td> <td>    0.042</td> <td>   12.014</td> <td> 0.000</td> <td>    0.423</td> <td>    0.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98070</th>                <td>    0.2067</td> <td>    0.019</td> <td>   10.845</td> <td> 0.000</td> <td>    0.169</td> <td>    0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98072</th>                <td>    0.5539</td> <td>    0.042</td> <td>   13.056</td> <td> 0.000</td> <td>    0.471</td> <td>    0.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98074</th>                <td>    0.5396</td> <td>    0.039</td> <td>   13.895</td> <td> 0.000</td> <td>    0.463</td> <td>    0.616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98075</th>                <td>    0.5321</td> <td>    0.024</td> <td>   22.337</td> <td> 0.000</td> <td>    0.485</td> <td>    0.579</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98077</th>                <td>    0.5030</td> <td>    0.042</td> <td>   11.933</td> <td> 0.000</td> <td>    0.420</td> <td>    0.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98092</th>                <td>    0.0192</td> <td>    0.025</td> <td>    0.779</td> <td> 0.436</td> <td>   -0.029</td> <td>    0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98102</th>                <td>    0.6401</td> <td>    0.030</td> <td>   21.189</td> <td> 0.000</td> <td>    0.581</td> <td>    0.699</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98103</th>                <td>    0.6009</td> <td>    0.024</td> <td>   24.882</td> <td> 0.000</td> <td>    0.554</td> <td>    0.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98105</th>                <td>    0.6257</td> <td>    0.026</td> <td>   24.512</td> <td> 0.000</td> <td>    0.576</td> <td>    0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98106</th>                <td>    0.1708</td> <td>    0.024</td> <td>    7.065</td> <td> 0.000</td> <td>    0.123</td> <td>    0.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98107</th>                <td>    0.5965</td> <td>    0.025</td> <td>   23.481</td> <td> 0.000</td> <td>    0.547</td> <td>    0.646</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98108</th>                <td>    0.2226</td> <td>    0.026</td> <td>    8.592</td> <td> 0.000</td> <td>    0.172</td> <td>    0.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98109</th>                <td>    0.7025</td> <td>    0.030</td> <td>   23.769</td> <td> 0.000</td> <td>    0.645</td> <td>    0.760</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98112</th>                <td>    0.7114</td> <td>    0.026</td> <td>   27.268</td> <td> 0.000</td> <td>    0.660</td> <td>    0.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98115</th>                <td>    0.5704</td> <td>    0.024</td> <td>   23.927</td> <td> 0.000</td> <td>    0.524</td> <td>    0.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98116</th>                <td>    0.4883</td> <td>    0.025</td> <td>   19.464</td> <td> 0.000</td> <td>    0.439</td> <td>    0.537</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98117</th>                <td>    0.5700</td> <td>    0.024</td> <td>   23.766</td> <td> 0.000</td> <td>    0.523</td> <td>    0.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98118</th>                <td>    0.3016</td> <td>    0.024</td> <td>   12.469</td> <td> 0.000</td> <td>    0.254</td> <td>    0.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98119</th>                <td>    0.6646</td> <td>    0.027</td> <td>   24.971</td> <td> 0.000</td> <td>    0.612</td> <td>    0.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98122</th>                <td>    0.5257</td> <td>    0.026</td> <td>   20.542</td> <td> 0.000</td> <td>    0.476</td> <td>    0.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98125</th>                <td>    0.3474</td> <td>    0.024</td> <td>   14.201</td> <td> 0.000</td> <td>    0.299</td> <td>    0.395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98126</th>                <td>    0.3141</td> <td>    0.025</td> <td>   12.657</td> <td> 0.000</td> <td>    0.265</td> <td>    0.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98133</th>                <td>    0.3226</td> <td>    0.025</td> <td>   12.816</td> <td> 0.000</td> <td>    0.273</td> <td>    0.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98136</th>                <td>    0.4231</td> <td>    0.026</td> <td>   16.498</td> <td> 0.000</td> <td>    0.373</td> <td>    0.473</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98144</th>                <td>    0.4833</td> <td>    0.025</td> <td>   19.089</td> <td> 0.000</td> <td>    0.434</td> <td>    0.533</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98146</th>                <td>    0.2445</td> <td>    0.024</td> <td>   10.218</td> <td> 0.000</td> <td>    0.198</td> <td>    0.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98148</th>                <td>    0.1660</td> <td>    0.031</td> <td>    5.310</td> <td> 0.000</td> <td>    0.105</td> <td>    0.227</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98155</th>                <td>    0.3872</td> <td>    0.028</td> <td>   14.023</td> <td> 0.000</td> <td>    0.333</td> <td>    0.441</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98166</th>                <td>    0.2785</td> <td>    0.026</td> <td>   10.846</td> <td> 0.000</td> <td>    0.228</td> <td>    0.329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98168</th>                <td>    0.1378</td> <td>    0.026</td> <td>    5.400</td> <td> 0.000</td> <td>    0.088</td> <td>    0.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98177</th>                <td>    0.4406</td> <td>    0.026</td> <td>   16.768</td> <td> 0.000</td> <td>    0.389</td> <td>    0.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98178</th>                <td>    0.1262</td> <td>    0.019</td> <td>    6.564</td> <td> 0.000</td> <td>    0.088</td> <td>    0.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98188</th>                <td>    0.1496</td> <td>    0.027</td> <td>    5.508</td> <td> 0.000</td> <td>    0.096</td> <td>    0.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98198</th>                <td>    0.0854</td> <td>    0.023</td> <td>    3.654</td> <td> 0.000</td> <td>    0.040</td> <td>    0.131</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98199</th>                <td>    0.6123</td> <td>    0.025</td> <td>   24.438</td> <td> 0.000</td> <td>    0.563</td> <td>    0.661</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_98354</th>                <td>    0.0469</td> <td>    0.054</td> <td>    0.863</td> <td> 0.388</td> <td>   -0.060</td> <td>    0.153</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Fair</th>                <td>   -0.1148</td> <td>    0.015</td> <td>   -7.541</td> <td> 0.000</td> <td>   -0.145</td> <td>   -0.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Good</th>                <td>    0.0477</td> <td>    0.003</td> <td>   15.718</td> <td> 0.000</td> <td>    0.042</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Poor</th>                <td>   -0.1617</td> <td>    0.033</td> <td>   -4.898</td> <td> 0.000</td> <td>   -0.226</td> <td>   -0.097</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_Very Good</th>           <td>    0.1071</td> <td>    0.004</td> <td>   25.509</td> <td> 0.000</td> <td>    0.099</td> <td>    0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Very Good</th>          <td>    0.2462</td> <td>    0.009</td> <td>   28.401</td> <td> 0.000</td> <td>    0.229</td> <td>    0.263</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Excellent</th>          <td>    0.2963</td> <td>    0.017</td> <td>   17.888</td> <td> 0.000</td> <td>    0.264</td> <td>    0.329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Luxury</th>             <td>    0.2775</td> <td>    0.040</td> <td>    6.863</td> <td> 0.000</td> <td>    0.198</td> <td>    0.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Poor</th>               <td>    0.1595</td> <td>    0.102</td> <td>    1.570</td> <td> 0.117</td> <td>   -0.040</td> <td>    0.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Low</th>                <td>    0.0832</td> <td>    0.039</td> <td>    2.134</td> <td> 0.033</td> <td>    0.007</td> <td>    0.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Fair</th>               <td>   -0.0862</td> <td>    0.012</td> <td>   -6.920</td> <td> 0.000</td> <td>   -0.111</td> <td>   -0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Low Average</th>        <td>   -0.0718</td> <td>    0.005</td> <td>  -14.946</td> <td> 0.000</td> <td>   -0.081</td> <td>   -0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Good</th>               <td>    0.1103</td> <td>    0.004</td> <td>   30.679</td> <td> 0.000</td> <td>    0.103</td> <td>    0.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_Better</th>             <td>    0.2200</td> <td>    0.006</td> <td>   39.424</td> <td> 0.000</td> <td>    0.209</td> <td>    0.231</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4102.498</td> <th>  Durbin-Watson:     </th> <td>   1.963</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>36198.485</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.533</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 8.806</td>  <th>  Cond. No.          </th> <td>1.00e+16</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 smallest eigenvalue is 1.02e-21. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.830\n",
       "Model:                            OLS   Adj. R-squared:                  0.829\n",
       "Method:                 Least Squares   F-statistic:                     959.3\n",
       "Date:                Fri, 31 Mar 2023   Prob (F-statistic):               0.00\n",
       "Time:                        17:03:09   Log-Likelihood:                 5274.2\n",
       "No. Observations:               24933   AIC:                        -1.029e+04\n",
       "Df Residuals:                   24806   BIC:                            -9263.\n",
       "Df Model:                         126                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================\n",
       "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------\n",
       "const                     -271.8303      5.267    -51.613      0.000    -282.153    -261.507\n",
       "bedrooms                     0.0021      0.002      1.175      0.240      -0.001       0.006\n",
       "bathrooms                    0.0296      0.002     13.690      0.000       0.025       0.034\n",
       "sqft_living                  0.0916      0.003     27.221      0.000       0.085       0.098\n",
       "sqft_lot                     0.0412      0.002     22.361      0.000       0.038       0.045\n",
       "sqft_above                   0.0612      0.003     21.570      0.000       0.056       0.067\n",
       "sqft_garage                  0.0096      0.002      5.390      0.000       0.006       0.013\n",
       "sqft_patio                   0.0047      0.001      3.359      0.001       0.002       0.008\n",
       "yr_renovated                 0.0128      0.001      9.927      0.000       0.010       0.015\n",
       "date                         0.1409      0.003     54.100      0.000       0.136       0.146\n",
       "waterfront                   0.1672      0.014     12.037      0.000       0.140       0.194\n",
       "Auburn                       0.1171      0.038      3.074      0.002       0.042       0.192\n",
       "Bellevue                     0.3992      0.039     10.289      0.000       0.323       0.475\n",
       "Federal Way                  0.1432      0.037      3.857      0.000       0.070       0.216\n",
       "Fife                         0.1335      0.044      3.061      0.002       0.048       0.219\n",
       "Highline                     0.1445      0.037      3.870      0.000       0.071       0.218\n",
       "Issaquah                     0.3203      0.036      8.863      0.000       0.249       0.391\n",
       "Kent                         0.1957      0.031      6.367      0.000       0.135       0.256\n",
       "Lake Washington              0.2786      0.047      5.880      0.000       0.186       0.371\n",
       "Mercer Island                0.5115      0.017     30.225      0.000       0.478       0.545\n",
       "Northshore                   0.1733      0.050      3.467      0.001       0.075       0.271\n",
       "Renton                       0.2075      0.033      6.300      0.000       0.143       0.272\n",
       "Riverview                    0.0443      0.054      0.823      0.411      -0.061       0.150\n",
       "Seattle                      0.2883      0.037      7.814      0.000       0.216       0.361\n",
       "Shoreline                    0.2146      0.039      5.475      0.000       0.138       0.291\n",
       "Snoqualmie Valley            0.1101      0.050      2.216      0.027       0.013       0.207\n",
       "Tahoma                       0.2091      0.027      7.673      0.000       0.156       0.262\n",
       "Tukwila                      0.1580      0.041      3.854      0.000       0.078       0.238\n",
       "Vashon Island                0.2067      0.019     10.845      0.000       0.169       0.244\n",
       "heat_Electricity            -0.0250      0.004     -6.961      0.000      -0.032      -0.018\n",
       "heat_Electricity/Solar       0.0117      0.032      0.365      0.715      -0.051       0.074\n",
       "heat_Gas/Solar               0.0681      0.022      3.079      0.002       0.025       0.111\n",
       "heat_Oil                    -0.0192      0.005     -4.163      0.000      -0.028      -0.010\n",
       "heat_Oil/Solar               0.0735      0.098      0.747      0.455      -0.119       0.266\n",
       "heat_Other                   0.1272      0.071      1.792      0.073      -0.012       0.266\n",
       "view_AVERAGE                 0.0734      0.005     13.594      0.000       0.063       0.084\n",
       "view_EXCELLENT               0.2717      0.012     21.836      0.000       0.247       0.296\n",
       "view_FAIR                    0.1077      0.014      7.445      0.000       0.079       0.136\n",
       "view_GOOD                    0.1333      0.008     16.158      0.000       0.117       0.149\n",
       "sewer_PRIVATE               -0.0665      0.005    -12.805      0.000      -0.077      -0.056\n",
       "sewer_PRIVATE RESTRICTED     0.0240      0.139      0.173      0.863      -0.249       0.297\n",
       "sewer_PUBLIC RESTRICTED      0.0392      0.139      0.282      0.778      -0.233       0.312\n",
       "zip_98001                    0.0307      0.023      1.334      0.182      -0.014       0.076\n",
       "zip_98002                   -0.0015      0.026     -0.057      0.954      -0.053       0.050\n",
       "zip_98003                   -0.0239      0.024     -0.979      0.328      -0.072       0.024\n",
       "zip_98004                    0.8608      0.030     28.566      0.000       0.802       0.920\n",
       "zip_98005                    0.5976      0.031     19.532      0.000       0.538       0.658\n",
       "zip_98006                    0.5077      0.024     21.058      0.000       0.460       0.555\n",
       "zip_98007                    0.4550      0.030     14.935      0.000       0.395       0.515\n",
       "zip_98008                    0.4896      0.028     17.746      0.000       0.435       0.544\n",
       "zip_98010                    0.2006      0.029      6.987      0.000       0.144       0.257\n",
       "zip_98011                    0.5069      0.043     11.883      0.000       0.423       0.591\n",
       "zip_98014                    0.3664      0.048      7.615      0.000       0.272       0.461\n",
       "zip_98019                    0.4167      0.048      8.762      0.000       0.323       0.510\n",
       "zip_98022                    0.1216      0.033      3.664      0.000       0.057       0.187\n",
       "zip_98023                   -0.0255      0.024     -1.079      0.280      -0.072       0.021\n",
       "zip_98024                    0.4676      0.048      9.741      0.000       0.373       0.562\n",
       "zip_98027                    0.3894      0.025     15.884      0.000       0.341       0.437\n",
       "zip_98028                    0.4544      0.042     10.749      0.000       0.372       0.537\n",
       "zip_98029                    0.5209      0.025     21.221      0.000       0.473       0.569\n",
       "zip_98030                    0.0135      0.012      1.090      0.276      -0.011       0.038\n",
       "zip_98031                    0.0219      0.011      1.974      0.048       0.000       0.044\n",
       "zip_98032                   -0.0040      0.023     -0.170      0.865      -0.050       0.042\n",
       "zip_98033                    0.7828      0.039     20.320      0.000       0.707       0.858\n",
       "zip_98034                    0.5212      0.038     13.601      0.000       0.446       0.596\n",
       "zip_98038                    0.1161      0.028      4.198      0.000       0.062       0.170\n",
       "zip_98039                    1.1666      0.054     21.498      0.000       1.060       1.273\n",
       "zip_98040                    0.5115      0.017     30.225      0.000       0.478       0.545\n",
       "zip_98045                    0.3941      0.041      9.502      0.000       0.313       0.475\n",
       "zip_98047                    0.0129      0.033      0.387      0.699      -0.052       0.078\n",
       "zip_98050                    0.2687      0.198      1.360      0.174      -0.119       0.656\n",
       "zip_98051                    0.1722      0.060      2.852      0.004       0.054       0.290\n",
       "zip_98052                    0.6257      0.038     16.591      0.000       0.552       0.700\n",
       "zip_98053                    0.5481      0.038     14.261      0.000       0.473       0.623\n",
       "zip_98055                    0.0764      0.020      3.863      0.000       0.038       0.115\n",
       "zip_98056                    0.2953      0.018     16.250      0.000       0.260       0.331\n",
       "zip_98057                    0.1427      0.024      5.904      0.000       0.095       0.190\n",
       "zip_98058                    0.1057      0.012      8.985      0.000       0.083       0.129\n",
       "zip_98059                    0.2316      0.020     11.773      0.000       0.193       0.270\n",
       "zip_98065                    0.5051      0.042     12.014      0.000       0.423       0.587\n",
       "zip_98070                    0.2067      0.019     10.845      0.000       0.169       0.244\n",
       "zip_98072                    0.5539      0.042     13.056      0.000       0.471       0.637\n",
       "zip_98074                    0.5396      0.039     13.895      0.000       0.463       0.616\n",
       "zip_98075                    0.5321      0.024     22.337      0.000       0.485       0.579\n",
       "zip_98077                    0.5030      0.042     11.933      0.000       0.420       0.586\n",
       "zip_98092                    0.0192      0.025      0.779      0.436      -0.029       0.068\n",
       "zip_98102                    0.6401      0.030     21.189      0.000       0.581       0.699\n",
       "zip_98103                    0.6009      0.024     24.882      0.000       0.554       0.648\n",
       "zip_98105                    0.6257      0.026     24.512      0.000       0.576       0.676\n",
       "zip_98106                    0.1708      0.024      7.065      0.000       0.123       0.218\n",
       "zip_98107                    0.5965      0.025     23.481      0.000       0.547       0.646\n",
       "zip_98108                    0.2226      0.026      8.592      0.000       0.172       0.273\n",
       "zip_98109                    0.7025      0.030     23.769      0.000       0.645       0.760\n",
       "zip_98112                    0.7114      0.026     27.268      0.000       0.660       0.763\n",
       "zip_98115                    0.5704      0.024     23.927      0.000       0.524       0.617\n",
       "zip_98116                    0.4883      0.025     19.464      0.000       0.439       0.537\n",
       "zip_98117                    0.5700      0.024     23.766      0.000       0.523       0.617\n",
       "zip_98118                    0.3016      0.024     12.469      0.000       0.254       0.349\n",
       "zip_98119                    0.6646      0.027     24.971      0.000       0.612       0.717\n",
       "zip_98122                    0.5257      0.026     20.542      0.000       0.476       0.576\n",
       "zip_98125                    0.3474      0.024     14.201      0.000       0.299       0.395\n",
       "zip_98126                    0.3141      0.025     12.657      0.000       0.265       0.363\n",
       "zip_98133                    0.3226      0.025     12.816      0.000       0.273       0.372\n",
       "zip_98136                    0.4231      0.026     16.498      0.000       0.373       0.473\n",
       "zip_98144                    0.4833      0.025     19.089      0.000       0.434       0.533\n",
       "zip_98146                    0.2445      0.024     10.218      0.000       0.198       0.291\n",
       "zip_98148                    0.1660      0.031      5.310      0.000       0.105       0.227\n",
       "zip_98155                    0.3872      0.028     14.023      0.000       0.333       0.441\n",
       "zip_98166                    0.2785      0.026     10.846      0.000       0.228       0.329\n",
       "zip_98168                    0.1378      0.026      5.400      0.000       0.088       0.188\n",
       "zip_98177                    0.4406      0.026     16.768      0.000       0.389       0.492\n",
       "zip_98178                    0.1262      0.019      6.564      0.000       0.088       0.164\n",
       "zip_98188                    0.1496      0.027      5.508      0.000       0.096       0.203\n",
       "zip_98198                    0.0854      0.023      3.654      0.000       0.040       0.131\n",
       "zip_98199                    0.6123      0.025     24.438      0.000       0.563       0.661\n",
       "zip_98354                    0.0469      0.054      0.863      0.388      -0.060       0.153\n",
       "cond_Fair                   -0.1148      0.015     -7.541      0.000      -0.145      -0.085\n",
       "cond_Good                    0.0477      0.003     15.718      0.000       0.042       0.054\n",
       "cond_Poor                   -0.1617      0.033     -4.898      0.000      -0.226      -0.097\n",
       "cond_Very Good               0.1071      0.004     25.509      0.000       0.099       0.115\n",
       "grade_Very Good              0.2462      0.009     28.401      0.000       0.229       0.263\n",
       "grade_Excellent              0.2963      0.017     17.888      0.000       0.264       0.329\n",
       "grade_Luxury                 0.2775      0.040      6.863      0.000       0.198       0.357\n",
       "grade_Poor                   0.1595      0.102      1.570      0.117      -0.040       0.359\n",
       "grade_Low                    0.0832      0.039      2.134      0.033       0.007       0.160\n",
       "grade_Fair                  -0.0862      0.012     -6.920      0.000      -0.111      -0.062\n",
       "grade_Low Average           -0.0718      0.005    -14.946      0.000      -0.081      -0.062\n",
       "grade_Good                   0.1103      0.004     30.679      0.000       0.103       0.117\n",
       "grade_Better                 0.2200      0.006     39.424      0.000       0.209       0.231\n",
       "==============================================================================\n",
       "Omnibus:                     4102.498   Durbin-Watson:                   1.963\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            36198.485\n",
       "Skew:                          -0.533   Prob(JB):                         0.00\n",
       "Kurtosis:                       8.806   Cond. No.                     1.00e+16\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.02e-21. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X_new.drop(columns=['price'])\n",
    "model_scaled = sm.OLS(y, sm.add_constant(X)).fit()\n",
    "model_scaled.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation of Coefficients\n",
    "\n",
    "An increase in `sqft_living` of 1 standard deviation $(866 ft^2)$ increases home value by $10\\%$, all other variables held equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_coef = [.5115, .3992, .3203, .2883, .2786, .2146, .2091, .2075, .2067, .1957, .1733, .1580, .1445, .1432, .1335, .1171, .1101, .0443]\n",
    "dist_coef = np.exp(dist_coef) -1\n",
    "dists =     ['Mercer Island', 'Bellevue', 'Issaquah', 'Seattle', 'Lake Washington', 'Shoreline', 'Tahoma',  'Renton', 'Vashon Island',\n",
    "             'Kent', 'Northshore', 'Tukwila', 'Highline', 'Federal Way', 'Fife', 'Auburn', 'Snoqualmie Valley', 'Riverview']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "plt.xticks(rotation=-90)\n",
    "colors = [\"b\" if dist > 0.25 else \"crimson\" for dist in dist_coef]\n",
    "ax.set_title('Impact of School District on Home Price')\n",
    "ax.set_xlabel('School Districts')\n",
    "ax.set_ylabel('Percentage Price Increase')\n",
    "ax.bar(dists, dist_coef, color=colors)\n",
    "plt.savefig('data/DistrictVsPrice.png', dpi=1000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "height = [1.166, 0.9697, 0.8608, 0.7828, 0.6401]\n",
    "x = ['Medina\\n98039', 'Bellevue\\n98004', 'Kirkland\\n98033', 'Seattle\\n98112', 'Seattle\\n98102']\n",
    "ax.bar(x, height)\n",
    "plt.savefig('data/ZipValues', dpi=1000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model_scaled.predict(sm.add_constant(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "plt.scatter(x=np.exp(y_pred), y=np.exp(y))\n",
    "sns.regplot(x=np.exp(y_pred), y=np.exp(y), line_kws={'ls':'--', 'color': 'r'}, ci = None)\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.ylim((325000, 3300000))\n",
    "ax.set_xticks([500000, 1000000, 2500000])\n",
    "ax.set_xticklabels(['$500,000', '$1,000,000', '$2,500,000'])\n",
    "ax.set_yticks([500000, 1000000, 2500000])\n",
    "ax.set_yticklabels(['$500,000', '$1,000,000', '$2,500,000'])\n",
    "ax.set_xlabel('Predicted Price')\n",
    "ax.set_ylabel('Actual Price')\n",
    "ax.set_title('Relationship Between Predicted Price and Actual Price')\n",
    "plt.savefig('data/PredictedVsActual.png', dpi=1000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_resids = y - y_pred\n",
    "data_resids.hist(bins='auto');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(data_resids, line='r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see our residuals are normally distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, we would make the following recommendations for purchases:\n",
    "\n",
    "- Average or Above Quality Views\n",
    "- Good or above Grade of Materials (ideally the higher the better to retain value).\n",
    "- High SQ Footage of Living Space or Lot Space\n",
    "- Waterfront Access\n",
    "- Converting SQ Footage of Basement Space to multiply the value per foot by a factor of 4.\n",
    "- Modifying Garages with Patio spaces atop to maxmize unused space.\n",
    "- Avoid neighborhoods with Private Sewer Systems and older buildings.\n",
    "- Priotizing acquisiton in the following Zip Codes: 98039, 98004, 98033, 98112, 98102, due to the quality of schools.\n",
    "- Using that access to school's as a selling point to families."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (learn-env)",
   "language": "python",
   "name": "learn-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}