W_EDA_King_county.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA King County - Work edition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Clear the namespace.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset -fs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Import required libraries.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import statsmodels.api as sms\n",
    "import statsmodels.formula.api as smf\n",
    "import warnings\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Load the dataset.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "kingcnt = pd.read_csv(\"data/King_County_House_prices_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kingcnt = DataFrame(kingcnt,columns=['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
    "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
    "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
    "       'lat', 'long', 'sqft_living15', 'sqft_lot15']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Check whether dataset is loaded by checking the number of rows and columns*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21597, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stakeholder briefing\n",
    "\n",
    "Question from stakeholder:\n",
    "What compromises do I need to make if I want to buy a house with mid to mid-high price?\n",
    "\n",
    "Potential compromises defined by the stakeholder are:\n",
    "* waterfront\n",
    "* neighbourhood\n",
    "* renovation\n",
    "* condition\n",
    "\n",
    "What impact do these compromises have on the price?\n",
    "\n",
    "Other wishes of the stakeholder are:\n",
    "* 2 bedrooms (one child)\n",
    "* 2 bathrooms (own bathroom, because afraid of germs)\n",
    "* no outskirts\n",
    "* no crowded neighbourhood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More expensive houses:\n",
    "1. have a waterfront view\n",
    "2. are in a good neighbourhood, meaning that:\n",
    "\n",
    "     a. the 15 nearest houses are large\n",
    "     \n",
    "     b. the 15 nearest houses have large plots\n",
    "     \n",
    "3. have been renovated\n",
    "4. are in a good condition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Check the columns that can be used for our analyses.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
       "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
       "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
       "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column name and description of the variables included in the EDA are reported the table below.\n",
    "\n",
    "column name | description\n",
    "------------|-------------\n",
    "price | price of the house\n",
    "waterfront | house has a view to the waterfront\n",
    "sqft_living15 | space of the 15 closest neighboring houses in square feet\n",
    "sqft_lot15 | space of the 15 closest neighboring plots in square feet\n",
    "yr_renovated | most recent year in which the house was renovated\n",
    "condition | overall condition of the house"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Check the info of these columns.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21597 entries, 0 to 21596\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   price          21597 non-null  float64\n",
      " 1   waterfront     19221 non-null  float64\n",
      " 2   sqft_living15  21597 non-null  int64  \n",
      " 3   sqft_lot15     21597 non-null  int64  \n",
      " 4   yr_renovated   17755 non-null  float64\n",
      " 5   condition      21597 non-null  int64  \n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 1012.5 KB\n"
     ]
    }
   ],
   "source": [
    "df_kingcnt[['price', 'waterfront', 'sqft_living15', 'sqft_lot15', 'yr_renovated', 'condition']].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data frame has 21597 rows, so the columns waterfront and yr_renovated to have some non-null values. We need to take care of these values when cleaning the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Check what is going on with these columns*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Waterfront*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  0.,  1.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt.waterfront.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2376"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(df_kingcnt.waterfront.values).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2376 instances of nan in the column waterfront."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Renovated*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0., 1991.,   nan, 2002., 2010., 1992., 2013., 1994., 1978.,\n",
       "       2005., 2003., 1984., 1954., 2014., 2011., 1983., 1945., 1990.,\n",
       "       1988., 1977., 1981., 1995., 2000., 1999., 1998., 1970., 1989.,\n",
       "       2004., 1986., 2007., 1987., 2006., 1985., 2001., 1980., 1971.,\n",
       "       1979., 1997., 1950., 1969., 1948., 2009., 2015., 1974., 2008.,\n",
       "       1968., 2012., 1963., 1951., 1962., 1953., 1993., 1996., 1955.,\n",
       "       1982., 1956., 1940., 1976., 1946., 1975., 1964., 1973., 1957.,\n",
       "       1959., 1960., 1967., 1965., 1934., 1972., 1944., 1958.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt.yr_renovated.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3842"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(df_kingcnt.yr_renovated.values).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17011"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yr0 = df_kingcnt.query('yr_renovated == 0')\n",
    "len(yr0.yr_renovated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 3842 instances of nan and 17011 instances of 0 in the column yr_renovated. Houses with 0 probably have not been renovated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Check the summary of the data of the columns of interest.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.159700e+04</td>\n",
       "      <td>19221.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>17755.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.402966e+05</td>\n",
       "      <td>0.007596</td>\n",
       "      <td>1986.620318</td>\n",
       "      <td>12758.283512</td>\n",
       "      <td>83.636778</td>\n",
       "      <td>3.409825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.673681e+05</td>\n",
       "      <td>0.086825</td>\n",
       "      <td>685.230472</td>\n",
       "      <td>27274.441950</td>\n",
       "      <td>399.946414</td>\n",
       "      <td>0.650546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.800000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.220000e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price    waterfront  sqft_living15     sqft_lot15  yr_renovated  \\\n",
       "count  2.159700e+04  19221.000000   21597.000000   21597.000000  17755.000000   \n",
       "mean   5.402966e+05      0.007596    1986.620318   12758.283512     83.636778   \n",
       "std    3.673681e+05      0.086825     685.230472   27274.441950    399.946414   \n",
       "min    7.800000e+04      0.000000     399.000000     651.000000      0.000000   \n",
       "25%    3.220000e+05      0.000000    1490.000000    5100.000000      0.000000   \n",
       "50%    4.500000e+05      0.000000    1840.000000    7620.000000      0.000000   \n",
       "75%    6.450000e+05      0.000000    2360.000000   10083.000000      0.000000   \n",
       "max    7.700000e+06      1.000000    6210.000000  871200.000000   2015.000000   \n",
       "\n",
       "          condition  \n",
       "count  21597.000000  \n",
       "mean       3.409825  \n",
       "std        0.650546  \n",
       "min        1.000000  \n",
       "25%        3.000000  \n",
       "50%        3.000000  \n",
       "75%        4.000000  \n",
       "max        5.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt[['price', 'waterfront', 'sqft_living15', 'sqft_lot15', 'yr_renovated', 'condition']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Plot the columns of interest.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3)\n",
    "\n",
    "ax[0].boxplot(df_kingcnt.price);\n",
    "ax[1].violinplot(df_kingcnt.price);\n",
    "ax[2].hist(df_kingcnt.price);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The price data is not normally distributed, which is a problem for the regression analysis. Hence, we add a column in which we apply a logarithmic transformation of the price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kingcnt['price_log'] = np.log(df_kingcnt['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>12.309982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>13.195614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>12.100712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>13.311329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>13.142166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  price_log\n",
       "0  221900.0  12.309982\n",
       "1  538000.0  13.195614\n",
       "2  180000.0  12.100712\n",
       "3  604000.0  13.311329\n",
       "4  510000.0  13.142166"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt[['price','price_log']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3)\n",
    "\n",
    "ax[0].boxplot(df_kingcnt.price_log);\n",
    "ax[1].violinplot(df_kingcnt.price_log);\n",
    "ax[2].hist(df_kingcnt.price_log);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Waterfront view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAADnCAYAAAAzUZtFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUa0lEQVR4nO3deZQlZX3G8e/b3bNP9wyeAcQtBYigoog4IrgcIseIFIlrEA0xUVGIEFAxscCjqEm0FEOCREVjQEBFlERESgHF4wKR1RhAFMVYwLAzssz0MtPLmz+qxmmaXup2972/qvc+n3PuGaf7dteDZ5771n1v1fs67z0iEqYe6wAi0j4quEjAVHCRgKngIgFTwUUCpoKLBEwFFwmYCi4SMBVcJGAquEjAVHCRgKngIgFTwUUCpoKLBEwFFwmYCi4SMBVcJGAquEjAVHCRgKngIgFTwUUCpoKLBEwFFwmYCi4SMBVcJGAquEjAVHCRgPVZB5DFFyXZUuDpwM7ATjM81gHLADfpMQ5sKR8jwKPAXcCGSY87gQ15Gj/Yuf8imS+nzQebLUqyHYF9gOeVf+4D7AUsafOhR4A7gJ8D1wHXAjfkaTzY5uNKC1TwBomSzAH7Aq8CXkxR6l0sM00xDtxCUfbrgGuAG/M0njBN1cVU8JqLkmwN8ArgUOAQ6lXoKu4Dvg1cBHw/T+MttnG6iwpeQ1GS7QG8jqLUBxLOXMkm4FKKsmd5Gj9iGyd8KnhNREnWD7wReCtFqUM3CvwA+ArwdY3s7aGCG4uSbD3wLuBwYKVxHCsbgXOAM/M0/o11mJCo4AaiJFsGvJmi2C8wjlMnHrgcOC1P48utw4RABe+gKMmWAG8HPgA8xThO3d0M/Atwbp7GY9ZhmkoF74AoyXqBtwAfBHY1jtM0vwZOztP4P62DNJEK3kbl59ZHAB8GnmGbpvF+Cvx9nsZXWgdpEhW8TaIk+zPgH4HnWGcJzMXA+/M0/pV1kCZQwRdZlGRPAj4DvMY4SsjGgbOAD+ZpfJ91mDpTwRdJeTr+DuCTwBrjON1iI3BsnsYXWAepKxV8EZRXnn0BOMg4Srf6OvCuPI03WgepGxV8AaIk6wNOpJhEW26bpuvdB7wjT+NvWwepExV8nqIk2xX4BrCfdRZ5jHOAE3Sde0EFn4coyV4JnA/sYJ1FpnUncGSexj+2DmJNBW9BOZF2EvAPaLmruhuleF/+ResgllTwisq7vc4BXmudRVpyOnBinsbj1kEsqOAVREm2J/BN4JnWWWReLgWO6Mb35TrNnEOUZDHFEkQqd3MdAlwdJdnTrYN0mgo+iyjJjqRYfWTAOIos3F7ANVGSvdw6SCep4DOIkuw44FzCWS5J4AnApVGSvcY6SKeo4NOIkuxk4AyKtcIlLEuAb0RJ9gbrIJ2ggk8RJdmHgH+yziFt1Qd8LUqyN1kHaTfNok9Slvsj1jmkY8YpZtcvtA7SLip4KUqyk4CPWeeQjhsFXpuncWYdpB1UcKA8VfuqdQ4xMwIclqfxFdZBFlvXFzxKsgMp1udeZp1FTG0CXpSn8S3WQRZTVxc8SrLdgKuBHa2zSC3cBrwwT+OHrIMslq6dRY+SbC1wCSq3bPd0itn1Xusgi6UrC14u1HAhuvxUHu9PgE9Yh1gsXVlw4LPAwdYhpLZOLC9Tbryuew8eJdlbKG77FJnNCPDSPI2vtw6yEF1V8CjJ/gi4Ed08ItXcBTwvT+MHrYPMV9ecokdJ1kNx84jKLVU9mWKN+8bqmoJTrH76MusQ0jiHR0n2OusQ89UVp+hRkj0XuA5Yap1FGuk+4NlNXHc9+BG83Iv7y6jcMn87A5+2DjEfwRccbQAoi+PN5YaSjRL0KXqUZHsDPweCuTJJTN1DcaremEtZQx/B/xmVWxbPLsBp1iFaEewIHiXZIcB3rXNIcDywb57G/2sdpIogR/DyZoFPWeeQIDkatDBIkAUHjgKebR1CgnVolGQvtQ5RRXAFL7cY+qh1Dgnex60DVBFcwSk2B9zJOoQE78VRkh1mHWIuQU2yRUm2M5ADy42jSHe4ieJmlAnrIDMJbQQ/HpVbOuc5QK3XVg9mBI+SbDVwB7CDdRbpKrcCz8zTuJZFCmkEPwqVWzpvT+CV1iFmEkTBy3u9T7DOIV3reOsAMwmi4EAMRNYhpGsdEiXZM6xDTCeUgh9rHUC6mgOOsQ4xncZPskVJtgfFRIe2+hVLDwJPztN4q3WQyUIYwf8KlVvsrQNeYx1iqhAK3hUbuUsjHGUdYKpGn6KXCzrcZJ1DpDQBPDFP4wesg2zT9BFco7fUSQ9wqHWIyZpe8NdbBxCZ4k+tA0zW2FP0KMn2BH5lnUNkik3AurrMpjd5BNfpudRRP3CQdYhtVHCRxVeb5ZUrFdw5d0WVr3VKed/386yOLzKH2rwPn7XgzrnlzrknAOucczs4555QPiLgSR1JOL0DDI8tMpenldtlmeub4/tHA++mKPMNbL9i7FFsd11UwaXuXk6xVbWpWQvuvT8dON0597fe+zM6lKkKFVzqbj/rADD3CA6A9/4M59yBFLdk9k36+rltyjWjKMmWAC/o9HFFWvR86wBQfZLtPIqNBF4CrC8fViXbB1hhdGyRqvaKkmyldYhKIzhFmZ/l63FVjE7PpQl6KD7p+W/rEFXcDDyxnUFaoIJLU5i/D686gq8DbnHOXQts2fZF773FB/rm/6eJVGT+PrxqwT/czhBVRUnmgD+yziFSkflgVOkU3Xv/I4obO/rLxy/Lr3XaE4FlBscVmY9nRklWdRBti6qz6IcD1wJ/DhwOXOOcs7gWXKO3NEkfsLN1gCo+AKz33t8P4JzbEfg+cGG7gs1ABZem2QW4y+rgVWfRe7aVu7SxhZ9dTCq4NM0ulgevOoJf6py7DDi//Psbge+0J9KsIoNjiixEvQvunHPApymuXnsJxQ0nX/Def7PN2aajEVyapt4F995759xF3vv9gP/qQKbZPM34+CKtMi141ffRVzvn1ld5onPuEOfcrc6525xzyTTfd865T5ffv9E518rFANo9VJrGct2Eyu/B/xg42jl3OzBIcZruvfePuandOddLcZ/4K4ANwHXOuYu997dMetqrgD3Kx/7A58o/qzC/eF+kRfU9RXfO7eq9/x1FKat4IXCb9/7/yp//GvBqYHLBXw2cW964crVzbq1zbhfv/T0Vfv+qijlE6sL03+xcp+jbPuc+y3t/+9THNM9/MnDnpL9vKL/W6nMeJ0qyXmDpXM8TqRnTK9nmOniPc+4U4BnOufdO/ab3/rQpX5puE8Cpt5hWec50llR4jkjdVC64c+4s4DDgfu/93tN83wGnU+yeMgT8tff+Z7P9zrlG8COAkTJk/zSPqTYAT53096cAd8/jOdPprfAckbppZQT/EnDILN+fPH/1Tor5q/kf3Ht/q3PuVOAO7/35sz23dB2wh3NuV4rL844A3jzlORcDx5Xvz/cHHqn4/rvJa7jX3hLGtq5maLDfDQ/1MzSyxg2OrGFw61q3eXQNg2Nr3eaxtQz6ATdIP0P0u+Ge1Qz3rnBb+pazdekyRpcuYXxZL+Mre/BL0ZbOAEzgNsFDlZ7rvf9xuWLxTFqev6ryOfiEc+5v2H4V22zPHXPOHQdcRjHinuW9/4Vz7pjy+2dSXAF3KHAbxWnGW+f6vSUVvI1G6Vv6EANLH/IDxUeRC1i7p4eJ8VUMD/YzPDjghkYGGBwZcINb17rBLWvZPLbWbR5fw+DEGjfoBxh0/W7YrWKkdyUjvSvc1qXL2Lp0CWPL+phY0cvECodf7Vwz7yLswW9exF830/zV/Ate+p5z7n3ABRQfkwHgvf/91Cd677/DlMtYy2Jv+98eOLbicScbncfPiIEJeno3sWpgE6sG7t72QrHAxb56GR9bxfBQ+aIxPMDgyBo3tHWN27x1BzaPrXGD42vYXL5oDLl+N9SzipGelWzpW+G2LFnG6LLiRWN8RQ9+24tGJyZtF/PfbcvzV1UL/rbyz8nF9MBuFX9+wfI0HoqSbAu6H7wrjdPb9yirBx5l9cBdi/Si0cfY6CpGhvrd8OAAg8MDbnDLGqZ70RhigEH63XDPKoZ7VxZvS/rKF43lfYwv72FipYPVzj1uMnhkYSkfo+X5q6rLJu+6gFCL6fcYXzgg4Rijb8kjrF7ziF+9BnZc8AsGwBLGRlcxPFi+aIz0u+Fffm3hv3abluevKhXcObcSeC/wNO/9O51zewB7eu8vWWjiFm1EBZcaG6VvycP0r33Y968FwPPrqj/rnDufYmfSdc65DcAplB8Pz3f+quop+tkUWxcdWP59A/ANwKLgIk3ycNUneu/fNMf3W56/qjozvbv3/pOUEwbe+2FsPgZRwaVpqn1G1iZVC77VObeC8l2Kc253Ji2f3EEquDSN6b/ZVpZNvhR4qnPuK8CLqf759WJ63MdyIjX3W8uDV51Fv9w5dwPwIopT8xO89w+2Ndn0HjA4pshCVJ5ka4eqyyZf4b3f6L3PvPeXeO8fdM5d0e5w0/iNwTFFFsL03+xc94Mvp1hkYZ1zbge2T6wNYLNSxc0GxxSZr4fyNLY40/2DuU7RjwbeTVHmG9he8EcpVm7ptN8Bm4HVBscWaZX5Geesp+je+9PLq9je573fzXu/a/nYx3v/bx3K+Ad5GnseuzqMSJ2ZF7zqJNsZzrm9gWcByyd9/dx2BZvFzRRLQ4nUXTMKXq7qchBFwb9DceP5lYBVwUWawHQGHapf6PIG4GDgXu/9W4F9sLur6yaj44q06lrrAFULPuK9nwDGnHMDwP108FbRKTSCSxNsyNPY9CIXqF7w65xza4F/p5hN/xlGr055Gt8L5BbHFmnBj6wDQPVLVfsp9gb/IcUlqwPe+xvbFaqCyykWnROpq1oUvOoIfjbFfdhnAFcApzjnTmhbqrldbnhskSp+aB0AwBW3mFZ4YrEt0XqKbYyOAYa993u1MduMoiRbCzyIllKWero7T+M5N/PohMrXogNXUewLfiuw3qrcAHkaP0wNZihFZlCL03Oofop+I7AV2Bt4LrB3eX+4JZ2mS101q+De+/d4718GvJbiBvazaWEpmja5zPj4ItPxTFk23FLVK9mOA14K7AfcDpwF/KSNuaq4luJFZq1tDJHH+EmexnfO/bTOqHqKvgI4DdjLe3+w9/4j3vsftDHXnPI0HqdGr5Qipa9aB5is6in6qd77a7z3Y+0O1KLzrAOITDJKsdpwbTR9v6/vAfdahxApfTdP41qtG9jogpen6VV2PRXphFqdnkPDC1462zqACLCJYmuhWml8wfM0vgm42jqHdL2L8jQetg4xVeMLXvq8dQDpel+wDjCdUAp+AfYX3kj3ujZP4yutQ0wniIKXp0YaxcXKadYBZhJEwUufAgatQ0jXuQO40DrETIIpeLnAvMVa7dLdTi0/rq2lYApeOhWN4tI59wJftA4xm6AKXo7in7XOIV3j1DyNR6xDzCaogpc0iksnPACcaR1iLsEVPE/jB9AoLu13cp7GQ9Yh5hJcwUunUmxSKNIO11GsiVB7QRa8HMU/aJ1DguSB4/I0nrAOUkWQBS+dQbFJg8hi+lKexo1Z8DPYgpefTb4DqO1nlNI4DwOJdYhWBFtwgDyN/wc43TqHBOOUPI3vtw7RiqALXvoQxeWEIgtxEw28UjL4gudpPAgca51DGm0UeFudL0mdSfAFB8jT+BJqfEOA1N5JeRpfbx1iPrqi4KVjKNZ0F2nFd6nx7aBzqbz5YAiiJHsBcCWwzDqLNMI9wD7ldRWN1E0jOOVp1rutc0gjTABHNrnc0GUFB8jT+EzgXOscUntpnsamu/cshq4reOkYih1TRaZzFXCKdYjF0FXvwSeLkmwP4HpgwDqL1MpvgQObdkHLTLp1BCdP498Ab6F4ryUCcD/wylDKDV1ccIA8jb8FHGedQ2phEIjzNP6tdZDF1NUFB8jT+HPAR6xziKkx4A1NvZhlNl1fcIA8jT+MVoHpZkflaXypdYh2UMG3Ow5tZNiNTs7T+BzrEO3StbPo04mSrIfiM/K/sM4iHfHxPI1Ptg7RTir4FFGS9VLs83y4dRZpq5PzNP64dYh20yn6FOUtgW+iWPJJwuOB47uh3KARfFZRkr2HYs8zvRCGYRR4e57G51kH6RQVfA5Rkr0e+DKw3DqLLMgm4PV5Gn/POkgnqeAVREl2AHAxsM46i8zLvcCh5Rp9XUWnnhXkafxT4ADgNuss0rIfAc/vxnKDCl5Znsa3UZT8MussUokHPgYcnKfxPdZhrOgUvUVRkjngBCBFK8PU1YPAX4Z6dVorVPB5ipLsuRSflz/bOos8xlXAEXkab7AOUgc6RZ+nPI1vBNbTwLWyA+WBTwIHqdzbaQRfBFGSHUax2+SO1lm61C+Ad+Vp/GPrIHWjEXwRlOuuPwc4j2Ikkc4YBN4P7KtyT08j+CKLkmx/iv3Q9rfOEriLgBPyNNa2VLNQwdugnGk/kmKm/UnGcULzO4pryS+xDtIEKngbRUm2CjgJOBFd6rpQDwH/Cpyap/GwcZbGUME7IEqyCPgoxV1qfbZpGucBiq2DPpOn8SbrME2jgndQWfS/A96GRvS53E1xJ9/n8zQesg7TVCq4gSjJdqbY0vhoYCfjOHVzO/AJ4Kw8jbdYh2k6FdxQlGTLKE7bjwf2NY5jaQK4HPgP4Ft5Go8a5wmGCl4TUZLtAxwBvBHY1ThOp9xGce3A2Xka32kdJkQqeA1FSbaeouiHA081jrPY7gEuAM7P0/ha6zChU8FrrPw8/QCKsr8K2MM20byMUNwAckX5uD5PY20X1SEqeINESbYT8JJJj32p38du48ANbC/0VXkaj9hG6l4qeINFSbYSeBFF2fcDdqd4/76yQxEeAH455XFNnsaPdOj4MgcVPEBRku0C7DbpsTsQATsA/RRbJvcz/eg/DjwKPDzNYyPwa8oy52m8sU3/CbJIVPAuVu7ksmTSYwzYnKex/lEEQgUXCZjuBxcJmAouEjAVXCRgKrhIwFRwkYCp4CIBU8FFAqaCiwRMBRcJmAouEjAVXCRgKrhIwFRwkYCp4CIBU8FFAqaCiwRMBRcJmAouEjAVXCRgKrhIwFRwkYCp4CIBU8FFAqaCiwRMBRcJmAouEjAVXCRg/w9/Zg10GLbDDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_kingcnt.waterfront.value_counts().plot(kind = \"pie\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are very few waterfront houses. Are waterfront houses more expensive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>19075.0</td>\n",
       "      <td>13.041237</td>\n",
       "      <td>0.517271</td>\n",
       "      <td>11.264464</td>\n",
       "      <td>12.676076</td>\n",
       "      <td>13.017003</td>\n",
       "      <td>13.367034</td>\n",
       "      <td>15.856731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>146.0</td>\n",
       "      <td>14.137806</td>\n",
       "      <td>0.686429</td>\n",
       "      <td>12.560244</td>\n",
       "      <td>13.626151</td>\n",
       "      <td>14.226830</td>\n",
       "      <td>14.640693</td>\n",
       "      <td>15.769956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count       mean       std        min        25%        50%  \\\n",
       "waterfront                                                                  \n",
       "0.0         19075.0  13.041237  0.517271  11.264464  12.676076  13.017003   \n",
       "1.0           146.0  14.137806  0.686429  12.560244  13.626151  14.226830   \n",
       "\n",
       "                  75%        max  \n",
       "waterfront                        \n",
       "0.0         13.367034  15.856731  \n",
       "1.0         14.640693  15.769956  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt.groupby('waterfront')['price_log'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the distribution, they are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Visualize this with a violin plot.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "landview = df_kingcnt.query('waterfront == 0')['price_log']\n",
    "waterview = df_kingcnt.query('waterfront == 1')['price_log']\n",
    "\n",
    "waterfront_price = [landview, waterview]\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "bp = ax.violinplot(waterfront_price)\n",
    "bp = ax.set_title(\"Price by waterfront view\")\n",
    "bp = ax.set_xlabel(\"View\")\n",
    "bp = ax.set_ylabel(\"Price (log)\")\n",
    "bp = ax.text(0.9,11.1, 'landview')\n",
    "bp = ax.text(1.9,11.1, 'waterview')\n",
    "\n",
    "plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Waterfront houses seem to be more expensive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neighborhood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Size of neighboring houses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2)\n",
    "\n",
    "ax[0,0].boxplot(df_kingcnt.sqft_living15);\n",
    "ax[0,1].violinplot(df_kingcnt.sqft_living15);\n",
    "ax[1,0].hist(df_kingcnt.sqft_living15);\n",
    "ax[1,1].scatter(x = df_kingcnt.sqft_living15, y = df_kingcnt.price_log);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the size of neighboring houses is acceptable. Houses with larger houses in the neighborhood seem more expensive based on the scatterplot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Land plot of neighboring houses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2)\n",
    "\n",
    "ax[0,0].boxplot(df_kingcnt.sqft_lot15);\n",
    "ax[0,1].violinplot(df_kingcnt.sqft_lot15);\n",
    "ax[1,0].hist(df_kingcnt.sqft_lot15);\n",
    "ax[1,1].scatter(x = df_kingcnt.sqft_lot15, y = df_kingcnt.price_log);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The land plot of neighboring houses is very skewed. A column with the log transformed land plot will be added to the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kingcnt['lot15_log'] = np.log(df_kingcnt['sqft_lot15'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>lot15_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5650</td>\n",
       "      <td>8.639411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7639</td>\n",
       "      <td>8.941022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8062</td>\n",
       "      <td>8.994917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5000</td>\n",
       "      <td>8.517193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7503</td>\n",
       "      <td>8.923058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sqft_lot15  lot15_log\n",
       "0        5650   8.639411\n",
       "1        7639   8.941022\n",
       "2        8062   8.994917\n",
       "3        5000   8.517193\n",
       "4        7503   8.923058"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt[['sqft_lot15', 'lot15_log']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2)\n",
    "\n",
    "ax[0,0].boxplot(df_kingcnt.lot15_log);\n",
    "ax[0,1].violinplot(df_kingcnt.lot15_log);\n",
    "ax[1,0].hist(df_kingcnt.lot15_log);\n",
    "ax[1,1].scatter(x = df_kingcnt.lot15_log, y = df_kingcnt.price_log);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the data is less skewed. Based on the scatter plot, there does not seem to be a relationship between the size of the neighboring plots and the price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renovated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Code the year of renovation as a boolean: true if it has been renovated, false if not.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kingcnt['renovated'] = df_kingcnt['yr_renovated'] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_kingcnt.renovated.value_counts().plot(kind = \"pie\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are very few renovated houses. Are renovated houses more expensive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renovated</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>20853.0</td>\n",
       "      <td>13.037616</td>\n",
       "      <td>0.519781</td>\n",
       "      <td>11.264464</td>\n",
       "      <td>12.676076</td>\n",
       "      <td>13.014778</td>\n",
       "      <td>13.36138</td>\n",
       "      <td>15.745582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>744.0</td>\n",
       "      <td>13.345169</td>\n",
       "      <td>0.620570</td>\n",
       "      <td>11.608236</td>\n",
       "      <td>12.929385</td>\n",
       "      <td>13.317102</td>\n",
       "      <td>13.71015</td>\n",
       "      <td>15.856731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count       mean       std        min        25%        50%  \\\n",
       "renovated                                                                  \n",
       "False      20853.0  13.037616  0.519781  11.264464  12.676076  13.014778   \n",
       "True         744.0  13.345169  0.620570  11.608236  12.929385  13.317102   \n",
       "\n",
       "                75%        max  \n",
       "renovated                       \n",
       "False      13.36138  15.745582  \n",
       "True       13.71015  15.856731  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt.groupby('renovated')['price_log'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the distribution they are a bit more expensive. This will be checked with a violin plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "renovated_no = df_kingcnt.query('renovated == False')['price_log']\n",
    "renovated_yes = df_kingcnt.query('renovated == True')['price_log']\n",
    "\n",
    "renovated_price = [renovated_no, renovated_yes]\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "bp = ax.violinplot(renovated_price)\n",
    "bp = ax.text(0.87,11.1, 'not renovated')\n",
    "bp = ax.text(1.9,11.1, 'renovated')\n",
    "bp = ax.set_title(\"Price by renovated\")\n",
    "bp = ax.set_xlabel(\"State\")\n",
    "bp = ax.set_ylabel(\"Price (log)\")\n",
    "\n",
    "plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Renovated houses seem more expensive than not renovated houses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_kingcnt.condition.value_counts().plot(kind = \"pie\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most houses have condition 3. Are houses in conditions 4 and 5 more expensive than those in condition 3?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>12.512814</td>\n",
       "      <td>0.675849</td>\n",
       "      <td>11.264464</td>\n",
       "      <td>12.154779</td>\n",
       "      <td>12.506177</td>\n",
       "      <td>12.977338</td>\n",
       "      <td>14.220976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>170.0</td>\n",
       "      <td>12.539268</td>\n",
       "      <td>0.540546</td>\n",
       "      <td>11.289782</td>\n",
       "      <td>12.150822</td>\n",
       "      <td>12.538967</td>\n",
       "      <td>12.896960</td>\n",
       "      <td>14.755518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14020.0</td>\n",
       "      <td>13.056700</td>\n",
       "      <td>0.516417</td>\n",
       "      <td>11.314475</td>\n",
       "      <td>12.706469</td>\n",
       "      <td>13.017558</td>\n",
       "      <td>13.369223</td>\n",
       "      <td>15.769956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5677.0</td>\n",
       "      <td>13.012227</td>\n",
       "      <td>0.526025</td>\n",
       "      <td>11.396392</td>\n",
       "      <td>12.628067</td>\n",
       "      <td>12.994530</td>\n",
       "      <td>13.345507</td>\n",
       "      <td>15.856731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1701.0</td>\n",
       "      <td>13.158327</td>\n",
       "      <td>0.560970</td>\n",
       "      <td>11.608236</td>\n",
       "      <td>12.765688</td>\n",
       "      <td>13.173056</td>\n",
       "      <td>13.493927</td>\n",
       "      <td>15.110238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count       mean       std        min        25%        50%  \\\n",
       "condition                                                                  \n",
       "1             29.0  12.512814  0.675849  11.264464  12.154779  12.506177   \n",
       "2            170.0  12.539268  0.540546  11.289782  12.150822  12.538967   \n",
       "3          14020.0  13.056700  0.516417  11.314475  12.706469  13.017558   \n",
       "4           5677.0  13.012227  0.526025  11.396392  12.628067  12.994530   \n",
       "5           1701.0  13.158327  0.560970  11.608236  12.765688  13.173056   \n",
       "\n",
       "                 75%        max  \n",
       "condition                        \n",
       "1          12.977338  14.220976  \n",
       "2          12.896960  14.755518  \n",
       "3          13.369223  15.769956  \n",
       "4          13.345507  15.856731  \n",
       "5          13.493927  15.110238  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt.groupby('condition')['price_log'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the distribution, condition does not seem to have a linear relationship with the price. Check this in the violin plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition_1 = df_kingcnt.query('condition == 1')['price_log']\n",
    "condition_2 = df_kingcnt.query('condition == 2')['price_log']\n",
    "condition_3 = df_kingcnt.query('condition == 3')['price_log']\n",
    "condition_4 = df_kingcnt.query('condition == 4')['price_log']\n",
    "condition_5 = df_kingcnt.query('condition == 5')['price_log']\n",
    "\n",
    "condition_price = [condition_1, condition_2, condition_3, condition_4, condition_5]\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "bp = ax.set_title(\"Price by condition\")\n",
    "bp = ax.set_xlabel(\"Condition\")\n",
    "bp = ax.set_ylabel(\"Price (log)\")\n",
    "bp = ax.violinplot(condition_price)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There only seems to be difference in price between the first two conditions, in which there is hardly any data, and the rest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Waterfront and year of renovation had missing values. The year of renovation does not require further action, because values above 0 were coded as renovated and the rest as False, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     False\n",
       "12    False\n",
       "23    False\n",
       "26    False\n",
       "28    False\n",
       "Name: renovated, dtype: bool"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt[pd.isnull(df_kingcnt['yr_renovated'])]['renovated'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The missing values in waterfront are replaced with 0.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kingcnt.fillna({'waterfront':0}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Waterfront\n",
    "* The standard deviations of the log transformed prices of the houses with and without waterfront are comparable.\n",
    "* Houses with waterfront are significantly more expensive than houses without waterfront\n",
    "* The impact of waterfront on the price is .0291."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "waterfront\n",
       "0.0    0.517519\n",
       "1.0    0.686429\n",
       "Name: price_log, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#standard deviations\n",
    "df_kingcnt.groupby('waterfront')['price_log'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 sum_sq       df           F         PR(>F)\n",
      "waterfront   174.513643      1.0  648.287799  6.303414e-141\n",
      "Residual    5813.193039  21595.0         NaN            NaN\n"
     ]
    }
   ],
   "source": [
    "#anova\n",
    "model_water = smf.ols('price_log~waterfront', data=df_kingcnt).fit()\n",
    "print(sms.stats.anova_lm(model_water, typ=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0291'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R-squared\n",
    "rs = smf.ols(formula='price_log~waterfront', data=df_kingcnt).fit().rsquared\n",
    "f'{rs:.3}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "sns.lmplot('waterfront', 'price_log',data=df_kingcnt);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neighbourhood\n",
    "\n",
    "#### Size of surrounding houses\n",
    "* The impact of the size of the surrounding houses is .384."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.384'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R-squared\n",
    "rs = smf.ols(formula='price_log~sqft_living15', data=df_kingcnt).fit().rsquared\n",
    "f'{rs:.3}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('sqft_living15', 'price_log',data=df_kingcnt);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Size of the surrounding plots\n",
    "* The impact of the size of the surrounding plots is .0153"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0153'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R-squared\n",
    "rs = smf.ols(formula='price_log~lot15_log', data=df_kingcnt).fit().rsquared\n",
    "f'{rs:.3}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('lot15_log', 'price_log',data=df_kingcnt);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renovation\n",
    "* The standard deviations of the log transformed prices of the renovated houses are comparable to those that were not renovated.\n",
    "* rRenovated houses are significantly more expensive than not renovated ones\n",
    "* The impact of renovation on the price is .0103."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "renovated\n",
       "False    0.519781\n",
       "True     0.620570\n",
       "Name: price_log, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#standard deviations\n",
    "df_kingcnt.groupby('renovated')['price_log'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                sum_sq       df           F        PR(>F)\n",
      "renovated    67.949685      1.0  247.877312  1.535541e-55\n",
      "Residual   5919.756997  21595.0         NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "#anova\n",
    "model_renovated = smf.ols('price_log~renovated', data=df_kingcnt).fit()\n",
    "print(sms.stats.anova_lm(model_renovated, typ=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0113'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R-squared\n",
    "rs = smf.ols(formula='price_log~renovated', data=df_kingcnt).fit().rsquared\n",
    "f'{rs:.3}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('renovated', 'price_log',data=df_kingcnt);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condition\n",
    "* The standard deviations of the log transformed prices of houses in each condition are comparable.\n",
    "* Houses with better conditions are significantly more expensive than those in worse conditions.\n",
    "* The impact of condition on the price is .00151."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "condition\n",
       "1    0.675849\n",
       "2    0.540546\n",
       "3    0.516417\n",
       "4    0.526025\n",
       "5    0.560970\n",
       "Name: price_log, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#standard deviation\n",
    "df_kingcnt.groupby('condition')['price_log'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                sum_sq       df          F        PR(>F)\n",
      "condition     9.069161      1.0  32.758053  1.057555e-08\n",
      "Residual   5978.637522  21595.0        NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "#anova\n",
    "model_condition = smf.ols('price_log~condition', data=df_kingcnt).fit()\n",
    "print(sms.stats.anova_lm(model_condition, typ=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.00151'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R-squared\n",
    "rs = smf.ols(formula='price_log~condition', data=df_kingcnt).fit().rsquared\n",
    "f'{rs:.3}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('condition', 'price_log',data=df_kingcnt);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple linear regression\n",
    "R-squared and adjusted R-squared for the full model are .437, so all variables together do not have a large impact on the price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>price_log</td>    <th>  R-squared:         </th> <td>   0.437</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.437</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3348.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 15 Oct 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:15:45</td>     <th>  Log-Likelihood:    </th> <td> -10594.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 21597</td>      <th>  AIC:               </th> <td>2.120e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 21591</td>      <th>  BIC:               </th> <td>2.125e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   12.5540</td> <td>    0.032</td> <td>  390.213</td> <td> 0.000</td> <td>   12.491</td> <td>   12.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>renovated[T.True]</th> <td>    0.3080</td> <td>    0.015</td> <td>   20.788</td> <td> 0.000</td> <td>    0.279</td> <td>    0.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>        <td>    0.7481</td> <td>    0.033</td> <td>   22.612</td> <td> 0.000</td> <td>    0.683</td> <td>    0.813</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living15</th>     <td>    0.0005</td> <td> 4.28e-06</td> <td>  121.590</td> <td> 0.000</td> <td>    0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lot15_log</th>         <td>   -0.0984</td> <td>    0.004</td> <td>  -27.291</td> <td> 0.000</td> <td>   -0.105</td> <td>   -0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condition</th>         <td>    0.0956</td> <td>    0.004</td> <td>   22.793</td> <td> 0.000</td> <td>    0.087</td> <td>    0.104</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>411.858</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> 551.274</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.245</td>  <th>  Prob(JB):          </th> <td>1.96e-120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.610</td>  <th>  Cond. No.          </th> <td>2.65e+04</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.65e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              price_log   R-squared:                       0.437\n",
       "Model:                            OLS   Adj. R-squared:                  0.437\n",
       "Method:                 Least Squares   F-statistic:                     3348.\n",
       "Date:                Thu, 15 Oct 2020   Prob (F-statistic):               0.00\n",
       "Time:                        11:15:45   Log-Likelihood:                -10594.\n",
       "No. Observations:               21597   AIC:                         2.120e+04\n",
       "Df Residuals:                   21591   BIC:                         2.125e+04\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            12.5540      0.032    390.213      0.000      12.491      12.617\n",
       "renovated[T.True]     0.3080      0.015     20.788      0.000       0.279       0.337\n",
       "waterfront            0.7481      0.033     22.612      0.000       0.683       0.813\n",
       "sqft_living15         0.0005   4.28e-06    121.590      0.000       0.001       0.001\n",
       "lot15_log            -0.0984      0.004    -27.291      0.000      -0.105      -0.091\n",
       "condition             0.0956      0.004     22.793      0.000       0.087       0.104\n",
       "==============================================================================\n",
       "Omnibus:                      411.858   Durbin-Watson:                   1.974\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              551.274\n",
       "Skew:                           0.245   Prob(JB):                    1.96e-120\n",
       "Kurtosis:                       3.610   Cond. No.                     2.65e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.65e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf.ols(formula='price_log~waterfront+sqft_living15+lot15_log+renovated+condition', data=df_kingcnt).fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It was assumed that more expensive houses:\n",
    "1. have a waterfront view - CORRECT\n",
    "2. are in a good neighbourhood, meaning that:\n",
    "\n",
    "     a. the 15 nearest houses are large - CORRECT\n",
    "     \n",
    "     b. the 15 nearest houses have large plots - INCORRECT\n",
    "     \n",
    "3. have been renovated - CORRECT\n",
    "4. are in a good condition - CORRECT\n",
    "\n",
    "However, all these factors individually and in combination do not have a large impact on the price of the house. Does this mean you don't have to compromise?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine tune"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wishes of the stakeholder were:\n",
    "* 2 bedrooms\n",
    "* 2 bathrooms\n",
    "* mid to mid high price\n",
    "* waterfront if possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_log</th>\n",
       "      <th>sqft_living15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21597.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.048211</td>\n",
       "      <td>1986.620318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.526555</td>\n",
       "      <td>685.230472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.264464</td>\n",
       "      <td>399.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.682307</td>\n",
       "      <td>1490.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.017003</td>\n",
       "      <td>1840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.377006</td>\n",
       "      <td>2360.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.856731</td>\n",
       "      <td>6210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          price_log  sqft_living15\n",
       "count  21597.000000   21597.000000\n",
       "mean      13.048211    1986.620318\n",
       "std        0.526555     685.230472\n",
       "min       11.264464     399.000000\n",
       "25%       12.682307    1490.000000\n",
       "50%       13.017003    1840.000000\n",
       "75%       13.377006    2360.000000\n",
       "max       15.856731    6210.000000"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kingcnt[['price_log','sqft_living15']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "selection = df_kingcnt.query('bedrooms == 2 and bathrooms == 2 and sqft_living15 >= 1840 and price_log > 13.017003 and price_log < 13.377006 and condition > 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 24)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
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       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
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       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.1.min.js\": \"qkRvDQVAIfzsJo40iRBbxt6sttt0hv4lh74DG7OK4MCHv4C5oohXYoHUM5W11uqS\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.1.min.js\": \"Sb7Mr06a9TNlet/GEBeKaf5xH3eb6AlCzwjtU82wNPyDrnfoiVl26qnvlKjmcAd+\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.1.min.js\": \"HaJ15vgfmcfRtB4c4YBOI4f1MUujukqInOWVqZJZZGK7Q+ivud0OKGSTn/Vm2iso\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.1.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
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       "\n",
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       "    \n",
       "  var docs_json = {\"fd04e5db-2c95-4c08-93cd-036d2b6a5c19\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1013\"}],\"center\":[{\"id\":\"1020\"},{\"id\":\"1028\"}],\"left\":[{\"id\":\"1021\"}],\"renderers\":[{\"id\":\"1044\"},{\"id\":\"1049\"}],\"title\":{\"id\":\"1003\"},\"toolbar\":{\"id\":\"1036\"},\"x_range\":{\"id\":\"1005\"},\"x_scale\":{\"id\":\"1009\"},\"y_range\":{\"id\":\"1007\"},\"y_scale\":{\"id\":\"1011\"}},\"id\":\"1002\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"data\":{\"x\":{\"__ndarray__\":\"ejw84xb6acGy7aHZgABqwU+ixK736GnB/a1Onl7oacHYUCQ/qvZpweNhuvp/52nB0is3gxPpacHqidM5UfdpwVTQNfQ9/WnBSKpros78acE1VjALGehpwSM4Da2A+2nBeyTcyULoacEpNr6/HuxpwfeaafUm6GnB5HCWiqTyacEWEkNbEftpwfGHhEzv52nBFvSKO8jkacEQ5/2YBelpwRD/XbLZ+mnB\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[21]},\"y\":{\"__ndarray__\":\"SOz0rGAUV0F6INtkNetWQbA7sulSHVdBdWHkHIQcV0Gfzw22TxBXQULH3DyoA1dBasWoguscV0EjuJfA8AxXQeOXkbsuEVdB93ceSwcaV0Hkm5kmwhxXQQA1DRR5EVdBPzP4p5QcV0Ev54zd/vpWQcjh2JFzHFdBlVq4hrsmV0Ey9gwjwBZXQdtaccX/G1dB+5U3Nh4RV0ExfohjV/9WQfI2WuJGJVdB\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[21]}},\"selected\":{\"id\":\"1056\"},\"selection_policy\":{\"id\":\"1057\"}},\"id\":\"1046\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":8},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1048\",\"type\":\"Scatter\"},{\"attributes\":{\"tile_source\":{\"id\":\"1043\"}},\"id\":\"1044\",\"type\":\"TileRenderer\"},{\"attributes\":{},\"id\":\"1007\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1056\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"1046\"}},\"id\":\"1050\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1057\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data_source\":{\"id\":\"1046\"},\"glyph\":{\"id\":\"1047\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1048\"},\"selection_glyph\":null,\"view\":{\"id\":\"1050\"}},\"id\":\"1049\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1011\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1009\",\"type\":\"LinearScale\"},{\"attributes\":{\"dimension\":\"lon\"},\"id\":\"1014\",\"type\":\"MercatorTicker\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1035\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"text\":\"Map of House Sales in King County\"},\"id\":\"1003\",\"type\":\"Title\"},{\"attributes\":{\"axis_label\":\"Long\",\"formatter\":{\"id\":\"1016\"},\"ticker\":{\"id\":\"1014\"}},\"id\":\"1013\",\"type\":\"MercatorAxis\"},{\"attributes\":{},\"id\":\"1005\",\"type\":\"DataRange1d\"},{\"attributes\":{\"axis\":{\"id\":\"1013\"},\"ticker\":null},\"id\":\"1020\",\"type\":\"Grid\"},{\"attributes\":{\"dimension\":\"lon\"},\"id\":\"1016\",\"type\":\"MercatorTickFormatter\"},{\"attributes\":{\"axis_label\":\"Lat\",\"formatter\":{\"id\":\"1024\"},\"ticker\":{\"id\":\"1022\"}},\"id\":\"1021\",\"type\":\"MercatorAxis\"},{\"attributes\":{\"dimension\":\"lat\"},\"id\":\"1024\",\"type\":\"MercatorTickFormatter\"},{\"attributes\":{\"dimension\":\"lat\"},\"id\":\"1022\",\"type\":\"MercatorTicker\"},{\"attributes\":{\"axis\":{\"id\":\"1021\"},\"dimension\":1,\"ticker\":null},\"id\":\"1028\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1030\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1034\",\"type\":\"HelpTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1029\"},{\"id\":\"1030\"},{\"id\":\"1031\"},{\"id\":\"1032\"},{\"id\":\"1033\"},{\"id\":\"1034\"}]},\"id\":\"1036\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1029\",\"type\":\"PanTool\"},{\"attributes\":{\"overlay\":{\"id\":\"1035\"}},\"id\":\"1031\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1032\",\"type\":\"SaveTool\"},{\"attributes\":{\"attribution\":\"&copy; <a href=\\\"https://www.openstreetmap.org/copyright\\\">OpenStreetMap</a> contributors\",\"url\":\"https://c.tile.openstreetmap.org/{Z}/{X}/{Y}.png\"},\"id\":\"1043\",\"type\":\"WMTSTileSource\"},{\"attributes\":{},\"id\":\"1033\",\"type\":\"ResetTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":8},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1047\",\"type\":\"Scatter\"}],\"root_ids\":[\"1002\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.1\"}};\n",
       "  var render_items = [{\"docid\":\"fd04e5db-2c95-4c08-93cd-036d2b6a5c19\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"67d80275-c909-4b8e-8755-31bbcdaeb26f\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
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       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
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     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, output_file, output_notebook, show  \n",
    "from bokeh.tile_providers import OSM, get_provider\n",
    "\n",
    "# file to save the model  \n",
    "output_file(\"kcmap.html\")  \n",
    "output_notebook()\n",
    "\n",
    "# instantiating the figure object  \n",
    "graph = figure(title = \"Map of House Sales in King County\", x_axis_type=\"mercator\", y_axis_type=\"mercator\")\n",
    "\n",
    "#tile provider\n",
    "tile_provider = get_provider(OSM)\n",
    "graph.add_tile(tile_provider)\n",
    "\n",
    "# name of the x-axis  \n",
    "graph.xaxis.axis_label = \"Long\"\n",
    "\n",
    "# name of the y-axis  \n",
    "graph.yaxis.axis_label = \"Lat\"\n",
    "\n",
    "# points to be plotted converted to mercator\n",
    "k = 6378137\n",
    "x = selection.long.apply(lambda x: x * (k * np.pi/180.0))\n",
    "y = selection.lat.apply(lambda x: np.log(np.tan((90 + x) * np.pi/360.0)) * k)\n",
    "size = 8\n",
    "\n",
    "# plotting the graph  \n",
    "graph.scatter(x, y, \n",
    "              fill_alpha=0.1,\n",
    "              size = size)  \n",
    "\n",
    "# displaying the model  \n",
    "show(graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explain\n",
    "\n",
    "Houses with a waterfront view, houses in good neighborhood with larger houses, renovated houses and houses with a good condition are generally more expensive, these characteristics do not have a large impact on the price. The availability of renovated houses with a waterfront has been sparse in the past years. However, 21 houses with 2 bedrooms, 2 bathrooms, surrounded by larger houses in good neighborhoods that have a mid to mid-high price have been available. Ten of those houses are located in less crowded neighborhoods, the other ones are located in the outskirts of the city, as you can see on the map. One of the houses has a water front, but this one is on the outskirts of the city. So, if you want the waterfront house, you have to compromise on living more on the outskirts of the city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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