{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from statsmodels.tools.tools import add_constant  \n",
    "\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "ROOT = \"https://raw.githubusercontent.com/kirenz/modern-statistics/main/data/\"\n",
    "DATA = \"duke-forest.csv\"\n",
    "\n",
    "df = pd.read_csv(ROOT + DATA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>address</th>\n",
       "      <th>price</th>\n",
       "      <th>bed</th>\n",
       "      <th>bath</th>\n",
       "      <th>area</th>\n",
       "      <th>type</th>\n",
       "      <th>year_built</th>\n",
       "      <th>heating</th>\n",
       "      <th>cooling</th>\n",
       "      <th>parking</th>\n",
       "      <th>lot</th>\n",
       "      <th>hoa</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 Learned Pl, Durham, NC 27705</td>\n",
       "      <td>1520000</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6040</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>1972</td>\n",
       "      <td>Other, Gas</td>\n",
       "      <td>central</td>\n",
       "      <td>0 spaces</td>\n",
       "      <td>0.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.zillow.com/homedetails/1-Learned-P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1616 Pinecrest Rd, Durham, NC 27705</td>\n",
       "      <td>1030000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4475</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>1969</td>\n",
       "      <td>Forced air, Gas</td>\n",
       "      <td>central</td>\n",
       "      <td>Carport, Covered</td>\n",
       "      <td>1.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.zillow.com/homedetails/1616-Pinecr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2418 Wrightwood Ave, Durham, NC 27705</td>\n",
       "      <td>420000</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1745</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>1959</td>\n",
       "      <td>Forced air, Gas</td>\n",
       "      <td>central</td>\n",
       "      <td>Garage - Attached, Covered</td>\n",
       "      <td>0.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.zillow.com/homedetails/2418-Wright...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2527 Sevier St, Durham, NC 27705</td>\n",
       "      <td>680000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2091</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>1961</td>\n",
       "      <td>Heat pump, Other, Electric, Gas</td>\n",
       "      <td>central</td>\n",
       "      <td>Carport, Covered</td>\n",
       "      <td>0.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.zillow.com/homedetails/2527-Sevier...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2218 Myers St, Durham, NC 27707</td>\n",
       "      <td>428500</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1772</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>2020</td>\n",
       "      <td>Forced air, Gas</td>\n",
       "      <td>central</td>\n",
       "      <td>0 spaces</td>\n",
       "      <td>0.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.zillow.com/homedetails/2218-Myers-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2507 Sevier St, Durham, NC 27705</td>\n",
       "      <td>541000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2740</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>1960</td>\n",
       "      <td>Forced air, Heat pump, Gas</td>\n",
       "      <td>central</td>\n",
       "      <td>Carport, Covered</td>\n",
       "      <td>0.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.zillow.com/homedetails/2507-Sevier...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1207 Woodburn Rd, Durham, NC 27705</td>\n",
       "      <td>473000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2171</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>1955</td>\n",
       "      <td>Forced air, Electric, Gas</td>\n",
       "      <td>other</td>\n",
       "      <td>0 spaces</td>\n",
       "      <td>0.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.zillow.com/homedetails/1207-Woodbu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3008 Montgomery St, Durham, NC 27705</td>\n",
       "      <td>490000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2972</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>1984</td>\n",
       "      <td>Forced air, Electric, Gas</td>\n",
       "      <td>central</td>\n",
       "      <td>Garage - Attached, Off-street, Covered</td>\n",
       "      <td>0.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.zillow.com/homedetails/3008-Montgo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1614 Pinecrest Rd, Durham, NC 27705</td>\n",
       "      <td>815000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3904</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>1970</td>\n",
       "      <td>Forced air, Gas</td>\n",
       "      <td>other</td>\n",
       "      <td>Garage - Attached, Garage - Detached, Covered</td>\n",
       "      <td>1.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.zillow.com/homedetails/1614-Pinecr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2708 Circle Dr, Durham, NC 27705</td>\n",
       "      <td>674500</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3766</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>1955</td>\n",
       "      <td>Forced air, Electric, Gas</td>\n",
       "      <td>other</td>\n",
       "      <td>0 spaces</td>\n",
       "      <td>0.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.zillow.com/homedetails/2708-Circle...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  address    price  bed  bath  area  \\\n",
       "0          1 Learned Pl, Durham, NC 27705  1520000    3   4.0  6040   \n",
       "1     1616 Pinecrest Rd, Durham, NC 27705  1030000    5   4.0  4475   \n",
       "2   2418 Wrightwood Ave, Durham, NC 27705   420000    2   3.0  1745   \n",
       "3        2527 Sevier St, Durham, NC 27705   680000    4   3.0  2091   \n",
       "4         2218 Myers St, Durham, NC 27707   428500    4   3.0  1772   \n",
       "..                                    ...      ...  ...   ...   ...   \n",
       "93       2507 Sevier St, Durham, NC 27705   541000    4   4.0  2740   \n",
       "94     1207 Woodburn Rd, Durham, NC 27705   473000    3   3.0  2171   \n",
       "95   3008 Montgomery St, Durham, NC 27705   490000    4   4.0  2972   \n",
       "96    1614 Pinecrest Rd, Durham, NC 27705   815000    4   4.0  3904   \n",
       "97       2708 Circle Dr, Durham, NC 27705   674500    4   4.0  3766   \n",
       "\n",
       "             type  year_built                          heating  cooling  \\\n",
       "0   Single Family        1972                       Other, Gas  central   \n",
       "1   Single Family        1969                  Forced air, Gas  central   \n",
       "2   Single Family        1959                  Forced air, Gas  central   \n",
       "3   Single Family        1961  Heat pump, Other, Electric, Gas  central   \n",
       "4   Single Family        2020                  Forced air, Gas  central   \n",
       "..            ...         ...                              ...      ...   \n",
       "93  Single Family        1960       Forced air, Heat pump, Gas  central   \n",
       "94  Single Family        1955        Forced air, Electric, Gas    other   \n",
       "95  Single Family        1984        Forced air, Electric, Gas  central   \n",
       "96  Single Family        1970                  Forced air, Gas    other   \n",
       "97  Single Family        1955        Forced air, Electric, Gas    other   \n",
       "\n",
       "                                          parking   lot  hoa  \\\n",
       "0                                        0 spaces  0.97  NaN   \n",
       "1                                Carport, Covered  1.38  NaN   \n",
       "2                      Garage - Attached, Covered  0.51  NaN   \n",
       "3                                Carport, Covered  0.84  NaN   \n",
       "4                                        0 spaces  0.16  NaN   \n",
       "..                                            ...   ...  ...   \n",
       "93                               Carport, Covered  0.51  NaN   \n",
       "94                                       0 spaces  0.61  NaN   \n",
       "95         Garage - Attached, Off-street, Covered  0.65  NaN   \n",
       "96  Garage - Attached, Garage - Detached, Covered  1.47  NaN   \n",
       "97                                       0 spaces  0.73  NaN   \n",
       "\n",
       "                                                  url  \n",
       "0   https://www.zillow.com/homedetails/1-Learned-P...  \n",
       "1   https://www.zillow.com/homedetails/1616-Pinecr...  \n",
       "2   https://www.zillow.com/homedetails/2418-Wright...  \n",
       "3   https://www.zillow.com/homedetails/2527-Sevier...  \n",
       "4   https://www.zillow.com/homedetails/2218-Myers-...  \n",
       "..                                                ...  \n",
       "93  https://www.zillow.com/homedetails/2507-Sevier...  \n",
       "94  https://www.zillow.com/homedetails/1207-Woodbu...  \n",
       "95  https://www.zillow.com/homedetails/3008-Montgo...  \n",
       "96  https://www.zillow.com/homedetails/1614-Pinecr...  \n",
       "97  https://www.zillow.com/homedetails/2708-Circle...  \n",
       "\n",
       "[98 rows x 13 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 98 entries, 0 to 97\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   address     98 non-null     object \n",
      " 1   price       98 non-null     int64  \n",
      " 2   bed         98 non-null     int64  \n",
      " 3   bath        98 non-null     float64\n",
      " 4   area        98 non-null     int64  \n",
      " 5   type        98 non-null     object \n",
      " 6   year_built  98 non-null     int64  \n",
      " 7   heating     98 non-null     object \n",
      " 8   cooling     98 non-null     object \n",
      " 9   parking     98 non-null     object \n",
      " 10  lot         97 non-null     float64\n",
      " 11  hoa         1 non-null      object \n",
      " 12  url         98 non-null     object \n",
      "dtypes: float64(2), int64(4), object(7)\n",
      "memory usage: 10.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show missing values (missing values - if present - will be displayed in yellow)\n",
    "sns.heatmap(df.isnull(), \n",
    "            yticklabels=False,\n",
    "            cbar=False, \n",
    "            cmap='viridis');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "address        0\n",
      "price          0\n",
      "bed            0\n",
      "bath           0\n",
      "area           0\n",
      "type           0\n",
      "year_built     0\n",
      "heating        0\n",
      "cooling        0\n",
      "parking        0\n",
      "lot            1\n",
      "hoa           97\n",
      "url            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Data transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# drop column with too many missing values\n",
    "df = df.drop(['hoa'], axis=1)\n",
    "\n",
    "# drop remaining row with one missing value\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Drop irrelevant features\n",
    "df = df.drop(['url', 'address'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price         0\n",
      "bed           0\n",
      "bath          0\n",
      "area          0\n",
      "type          0\n",
      "year_built    0\n",
      "heating       0\n",
      "cooling       0\n",
      "parking       0\n",
      "lot           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert data types\n",
    "categorical_list = ['type', 'heating', 'cooling', 'parking']\n",
    "\n",
    "for i in categorical_list:\n",
    "    df[i] = df[i].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 97 entries, 0 to 97\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   price       97 non-null     int64   \n",
      " 1   bed         97 non-null     int64   \n",
      " 2   bath        97 non-null     float64 \n",
      " 3   area        97 non-null     int64   \n",
      " 4   type        97 non-null     category\n",
      " 5   year_built  97 non-null     int64   \n",
      " 6   heating     97 non-null     category\n",
      " 7   cooling     97 non-null     category\n",
      " 8   parking     97 non-null     category\n",
      " 9   lot         97 non-null     float64 \n",
      "dtypes: category(4), float64(2), int64(4)\n",
      "memory usage: 7.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heating</th>\n",
       "      <td>97</td>\n",
       "      <td>19</td>\n",
       "      <td>Forced air, Gas</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cooling</th>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parking</th>\n",
       "      <td>97</td>\n",
       "      <td>19</td>\n",
       "      <td>0 spaces</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count unique              top freq\n",
       "type       97      1    Single Family   97\n",
       "heating    97     19  Forced air, Gas   34\n",
       "cooling    97      2            other   52\n",
       "parking    97     19         0 spaces   42"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics for all categorical columns\n",
    "df.describe(include=['category']).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Variable `type` has zero veriation (only single family) and therefore can be exluded from the analysis and the model. \n",
    "\n",
    "- We will also exclude `heating`and `parking` to keep this example as simple as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bed</th>\n",
       "      <th>bath</th>\n",
       "      <th>area</th>\n",
       "      <th>year_built</th>\n",
       "      <th>cooling</th>\n",
       "      <th>lot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1520000</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6040</td>\n",
       "      <td>1972</td>\n",
       "      <td>central</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1030000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4475</td>\n",
       "      <td>1969</td>\n",
       "      <td>central</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>420000</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1745</td>\n",
       "      <td>1959</td>\n",
       "      <td>central</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>680000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2091</td>\n",
       "      <td>1961</td>\n",
       "      <td>central</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>428500</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1772</td>\n",
       "      <td>2020</td>\n",
       "      <td>central</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>541000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2740</td>\n",
       "      <td>1960</td>\n",
       "      <td>central</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>473000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2171</td>\n",
       "      <td>1955</td>\n",
       "      <td>other</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>490000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2972</td>\n",
       "      <td>1984</td>\n",
       "      <td>central</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>815000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3904</td>\n",
       "      <td>1970</td>\n",
       "      <td>other</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>674500</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3766</td>\n",
       "      <td>1955</td>\n",
       "      <td>other</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  bed  bath  area  year_built  cooling   lot\n",
       "0   1520000    3   4.0  6040        1972  central  0.97\n",
       "1   1030000    5   4.0  4475        1969  central  1.38\n",
       "2    420000    2   3.0  1745        1959  central  0.51\n",
       "3    680000    4   3.0  2091        1961  central  0.84\n",
       "4    428500    4   3.0  1772        2020  central  0.16\n",
       "..      ...  ...   ...   ...         ...      ...   ...\n",
       "93   541000    4   4.0  2740        1960  central  0.51\n",
       "94   473000    3   3.0  2171        1955    other  0.61\n",
       "95   490000    4   4.0  2972        1984  central  0.65\n",
       "96   815000    4   4.0  3904        1970    other  1.47\n",
       "97   674500    4   4.0  3766        1955    other  0.73\n",
       "\n",
       "[97 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['type', 'heating', 'parking'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Data splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bed</th>\n",
       "      <th>bath</th>\n",
       "      <th>area</th>\n",
       "      <th>year_built</th>\n",
       "      <th>cooling</th>\n",
       "      <th>lot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>385000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1831</td>\n",
       "      <td>1951</td>\n",
       "      <td>central</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>485000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2609</td>\n",
       "      <td>1962</td>\n",
       "      <td>other</td>\n",
       "      <td>0.52</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>420000</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1745</td>\n",
       "      <td>1959</td>\n",
       "      <td>central</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>150000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1734</td>\n",
       "      <td>1945</td>\n",
       "      <td>other</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>105000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1094</td>\n",
       "      <td>1940</td>\n",
       "      <td>other</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>815000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3904</td>\n",
       "      <td>1970</td>\n",
       "      <td>other</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>520000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2637</td>\n",
       "      <td>1968</td>\n",
       "      <td>other</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>270000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1416</td>\n",
       "      <td>1990</td>\n",
       "      <td>other</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>590000</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3323</td>\n",
       "      <td>1980</td>\n",
       "      <td>other</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>592000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2378</td>\n",
       "      <td>1960</td>\n",
       "      <td>other</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     price  bed  bath  area  year_built  cooling   lot\n",
       "26  385000    3   2.0  1831        1951  central  0.29\n",
       "85  485000    4   3.0  2609        1962    other  0.52\n",
       "2   420000    2   3.0  1745        1959  central  0.51\n",
       "55  150000    3   1.0  1734        1945    other  0.16\n",
       "69  105000    2   1.0  1094        1940    other  0.26\n",
       "..     ...  ...   ...   ...         ...      ...   ...\n",
       "96  815000    4   4.0  3904        1970    other  1.47\n",
       "70  520000    4   3.0  2637        1968    other  0.65\n",
       "20  270000    3   3.0  1416        1990    other  0.36\n",
       "92  590000    5   3.0  3323        1980    other  0.43\n",
       "73  592000    3   2.0  2378        1960    other  0.75\n",
       "\n",
       "[78 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset = df.sample(frac=0.8, random_state=0)\n",
    "test_dataset = df.drop(train_dataset.index)\n",
    "\n",
    "train_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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       "      <th>price</th>\n",
       "      <td>78.0</td>\n",
       "      <td>560762.18</td>\n",
       "      <td>243254.08</td>\n",
       "      <td>95000.00</td>\n",
       "      <td>421250.00</td>\n",
       "      <td>537500.00</td>\n",
       "      <td>650000.00</td>\n",
       "      <td>1520000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bed</th>\n",
       "      <td>78.0</td>\n",
       "      <td>3.81</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.00</td>\n",
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       "    <tr>\n",
       "      <th>bath</th>\n",
       "      <td>78.0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>78.0</td>\n",
       "      <td>2831.40</td>\n",
       "      <td>986.38</td>\n",
       "      <td>1094.00</td>\n",
       "      <td>2095.25</td>\n",
       "      <td>2745.00</td>\n",
       "      <td>3261.75</td>\n",
       "      <td>6178.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_built</th>\n",
       "      <td>78.0</td>\n",
       "      <td>1965.82</td>\n",
       "      <td>16.80</td>\n",
       "      <td>1923.00</td>\n",
       "      <td>1956.25</td>\n",
       "      <td>1961.50</td>\n",
       "      <td>1971.50</td>\n",
       "      <td>2020.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lot</th>\n",
       "      <td>78.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count       mean        std       min        25%        50%  \\\n",
       "price        78.0  560762.18  243254.08  95000.00  421250.00  537500.00   \n",
       "bed          78.0       3.81       0.74      2.00       3.00       4.00   \n",
       "bath         78.0       3.10       0.92      1.00       2.50       3.00   \n",
       "area         78.0    2831.40     986.38   1094.00    2095.25    2745.00   \n",
       "year_built   78.0    1965.82      16.80   1923.00    1956.25    1961.50   \n",
       "lot          78.0       0.59       0.23      0.15       0.45       0.56   \n",
       "\n",
       "                  75%         max  \n",
       "price       650000.00  1520000.00  \n",
       "bed              4.00        6.00  \n",
       "bath             4.00        5.00  \n",
       "area          3261.75     6178.00  \n",
       "year_built    1971.50     2020.00  \n",
       "lot              0.69        1.47  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics for all numerical columns\n",
    "round(train_dataset.describe(),2).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>bed</th>\n",
       "      <th>bath</th>\n",
       "      <th>area</th>\n",
       "      <th>year_built</th>\n",
       "      <th>lot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.446668</td>\n",
       "      <td>0.593686</td>\n",
       "      <td>0.680012</td>\n",
       "      <td>0.248102</td>\n",
       "      <td>0.537264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bed</th>\n",
       "      <td>0.446668</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.599660</td>\n",
       "      <td>0.560258</td>\n",
       "      <td>0.216696</td>\n",
       "      <td>0.248166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bath</th>\n",
       "      <td>0.593686</td>\n",
       "      <td>0.599660</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659879</td>\n",
       "      <td>0.351917</td>\n",
       "      <td>0.335490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>0.680012</td>\n",
       "      <td>0.560258</td>\n",
       "      <td>0.659879</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.165495</td>\n",
       "      <td>0.412836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_built</th>\n",
       "      <td>0.248102</td>\n",
       "      <td>0.216696</td>\n",
       "      <td>0.351917</td>\n",
       "      <td>0.165495</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.047352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lot</th>\n",
       "      <td>0.537264</td>\n",
       "      <td>0.248166</td>\n",
       "      <td>0.335490</td>\n",
       "      <td>0.412836</td>\n",
       "      <td>-0.047352</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price       bed      bath      area  year_built       lot\n",
       "price       1.000000  0.446668  0.593686  0.680012    0.248102  0.537264\n",
       "bed         0.446668  1.000000  0.599660  0.560258    0.216696  0.248166\n",
       "bath        0.593686  0.599660  1.000000  0.659879    0.351917  0.335490\n",
       "area        0.680012  0.560258  0.659879  1.000000    0.165495  0.412836\n",
       "year_built  0.248102  0.216696  0.351917  0.165495    1.000000 -0.047352\n",
       "lot         0.537264  0.248166  0.335490  0.412836   -0.047352  1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create correlation matrix for numerical variables\n",
    "corr_matrix = train_dataset.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simple heatmap\n",
    "heatmap = sns.heatmap(corr_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 792x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a pretty heatmap\n",
    "\n",
    "# Use a mask to plot only part of a matrix\n",
    "mask = np.zeros_like(corr_matrix)\n",
    "mask[np.triu_indices_from(mask)]= True\n",
    "\n",
    "# Change size\n",
    "plt.subplots(figsize=(11, 15))\n",
    "\n",
    "# Build heatmap with additional options\n",
    "heatmap = sns.heatmap(corr_matrix, \n",
    "                      mask = mask, \n",
    "                      square = True, \n",
    "                      linewidths = .5,\n",
    "                      cmap = 'coolwarm',\n",
    "                      cbar_kws = {'shrink': .6,\n",
    "                                'ticks' : [-1, -.5, 0, 0.5, 1]},\n",
    "                      vmin = -1,\n",
    "                      vmax = 1,\n",
    "                      annot = True,\n",
    "                      annot_kws = {\"size\": 10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of inspecting the correlation matrix, a better way to assess **multicollinearity** is to compute the variance inflation factor (VIF). Note that we ignore the intercept in this test.\n",
    "\n",
    "- The smallest possible value for VIF is 1, which indicates the complete absence of collinearity. \n",
    "- Typically in practice there is a small amount of collinearity among the predictors. \n",
    "- As a rule of thumb, a VIF value that exceeds 5 indicates a problematic amount of collinearity and the parameter estimates will have large standard errors because of this. \n",
    "\n",
    "Note that the function `variance_inflation_factor` expects the presence of a constant in the matrix of explanatory variables. Therefore, we use `add_constant` from statsmodels to add the required constant to the dataframe before passing its values to the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>VIF Factor</th>\n",
       "      <th>Feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.52</td>\n",
       "      <td>const</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.74</td>\n",
       "      <td>bed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.17</td>\n",
       "      <td>bath</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.14</td>\n",
       "      <td>area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.19</td>\n",
       "      <td>lot</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VIF Factor Feature\n",
       "0       28.52   const\n",
       "1        1.74     bed\n",
       "2        2.17    bath\n",
       "3        2.14    area\n",
       "4        1.19     lot"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choose features and add constant\n",
    "features = add_constant(df[['bed', 'bath', 'area', 'lot']])\n",
    "# create empty DataFrame\n",
    "vif = pd.DataFrame()\n",
    "# calculate vif\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(features.values, i) for i in range(features.shape[1])]\n",
    "# add feature names\n",
    "vif[\"Feature\"] = features.columns\n",
    "\n",
    "vif.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't have a problematic amount of collinearity in our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Modeling\n",
    "\n",
    "See separate notebooks."
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