Capstone project Xinran Wang.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# ALY6140 Capstone Project: Real Estate Sales by Town in State of Connecticut\n",
    "#### Xinran Wang 10/12/2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project description\n",
    "### in this project, I am going to introduce the data of Real Estate Sales By Town for 2011, 2012, 2013, 2014 in Connecticut.\n",
    "### The Office of Policy and Management maintains a listing of all real estate sales with a sales price of 2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For the first step, import the web dataset into Python and clean up the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import useful packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Name  SerialNbr  ListYear DateRecorded  AssessedValue  SalePrice  \\\n",
      "0  Bridgeport     110682      2011   03/08/2012       162040.0   233500.0   \n",
      "1     Ledyard     120059      2012   01/16/2013        67830.0    75000.0   \n",
      "2     Milford      12497      2012   05/31/2013       201300.0   255000.0   \n",
      "3   New Haven     110828      2011   07/30/2012        49630.0    39900.0   \n",
      "4   Colebrook      11007      2011   05/14/2012       196100.0   235000.0   \n",
      "\n",
      "   AdditionalRemarks  SalesRatio  NonUseCode ResidentialType  \\\n",
      "0                NaN    0.693961         NaN               1   \n",
      "1                0.0    0.904400         NaN               C   \n",
      "2                0.0    0.789412         NaN               1   \n",
      "3                NaN    1.243860        14.0               1   \n",
      "4                NaN    0.834468         NaN               1   \n",
      "\n",
      "   ResidentialUnits               Address  \\\n",
      "0                 1        144 HUGHES AVE   \n",
      "1                 1      1742-3G ROUTE 12   \n",
      "2                 1   51 SPARROWBUSH LANE   \n",
      "3                 1  337 GREENWICH AVENUE   \n",
      "4                 1         15 WHEELER RD   \n",
      "\n",
      "                                            Location  \n",
      "0  144 HUGHES AVE\\nBridgeport, CT\\n(41.1837144900...  \n",
      "1  1742-3G ROUTE 12\\nLedyard, CT\\n(41.47126158500...  \n",
      "2  51 SPARROWBUSH LANE\\nMilford, CT\\n(41.22758963...  \n",
      "3  337 GREENWICH AVENUE\\nNew Haven, CT\\n(41.28790...  \n",
      "4  15 WHEELER RD\\nColebrook, CT\\n(41.982079222000...  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kisekinokage/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:2785: DtypeWarning: Columns (12) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#download the dataset\n",
    "data = pd.read_csv('https://data.ct.gov/api/views/8udc-aepg/rows.csv?accessType=DOWNLOAD')\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name                   0\n",
      "ListYear               0\n",
      "AssessedValue          0\n",
      "SalePrice              0\n",
      "SalesRatio             0\n",
      "ResidentialType     6288\n",
      "ResidentialUnits       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#show the null values of all columns\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### we can find a significant number of null values in the original dataset, I will select name, list year, assessed value sale price, sales ratio, residential type and residential units as the columns used in following analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Name  ListYear  AssessedValue  SalePrice  SalesRatio ResidentialType  \\\n",
      "0  Bridgeport      2011       162040.0   233500.0    0.693961               1   \n",
      "1     Ledyard      2012        67830.0    75000.0    0.904400               C   \n",
      "2     Milford      2012       201300.0   255000.0    0.789412               1   \n",
      "3   New Haven      2011        49630.0    39900.0    1.243860               1   \n",
      "4   Colebrook      2011       196100.0   235000.0    0.834468               1   \n",
      "\n",
      "   ResidentialUnits  \n",
      "0                 1  \n",
      "1                 1  \n",
      "2                 1  \n",
      "3                 1  \n",
      "4                 1  \n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ListYear</th>\n",
       "      <th>AssessedValue</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>SalesRatio</th>\n",
       "      <th>ResidentialType</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bridgeport</td>\n",
       "      <td>2011</td>\n",
       "      <td>162040.0</td>\n",
       "      <td>233500.0</td>\n",
       "      <td>0.693961</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ledyard</td>\n",
       "      <td>2012</td>\n",
       "      <td>67830.0</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>0.904400</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Milford</td>\n",
       "      <td>2012</td>\n",
       "      <td>201300.0</td>\n",
       "      <td>255000.0</td>\n",
       "      <td>0.789412</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Haven</td>\n",
       "      <td>2011</td>\n",
       "      <td>49630.0</td>\n",
       "      <td>39900.0</td>\n",
       "      <td>1.243860</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Colebrook</td>\n",
       "      <td>2011</td>\n",
       "      <td>196100.0</td>\n",
       "      <td>235000.0</td>\n",
       "      <td>0.834468</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wethersfield</td>\n",
       "      <td>2012</td>\n",
       "      <td>128400.0</td>\n",
       "      <td>165000.0</td>\n",
       "      <td>0.778182</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Canaan</td>\n",
       "      <td>2012</td>\n",
       "      <td>857920.0</td>\n",
       "      <td>1176501.0</td>\n",
       "      <td>0.729213</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>East Hartford</td>\n",
       "      <td>2012</td>\n",
       "      <td>83800.0</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>0.698333</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Litchfield</td>\n",
       "      <td>2012</td>\n",
       "      <td>171320.0</td>\n",
       "      <td>238000.0</td>\n",
       "      <td>0.719832</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Southbury</td>\n",
       "      <td>2011</td>\n",
       "      <td>125370.0</td>\n",
       "      <td>119000.0</td>\n",
       "      <td>1.053529</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>East Lyme</td>\n",
       "      <td>2012</td>\n",
       "      <td>108920.0</td>\n",
       "      <td>148000.0</td>\n",
       "      <td>0.735946</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Windsor Locks</td>\n",
       "      <td>2012</td>\n",
       "      <td>173300.0</td>\n",
       "      <td>192000.0</td>\n",
       "      <td>0.902604</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Wethersfield</td>\n",
       "      <td>2011</td>\n",
       "      <td>365100.0</td>\n",
       "      <td>516340.0</td>\n",
       "      <td>0.707092</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Colchester</td>\n",
       "      <td>2012</td>\n",
       "      <td>171700.0</td>\n",
       "      <td>215000.0</td>\n",
       "      <td>0.798605</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Enfield</td>\n",
       "      <td>2012</td>\n",
       "      <td>116370.0</td>\n",
       "      <td>152500.0</td>\n",
       "      <td>0.763082</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Branford</td>\n",
       "      <td>2012</td>\n",
       "      <td>100500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>0.744444</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bloomfield</td>\n",
       "      <td>2012</td>\n",
       "      <td>184800.0</td>\n",
       "      <td>258000.0</td>\n",
       "      <td>0.716279</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>West Hartford</td>\n",
       "      <td>2012</td>\n",
       "      <td>116830.0</td>\n",
       "      <td>165000.0</td>\n",
       "      <td>0.708061</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Branford</td>\n",
       "      <td>2011</td>\n",
       "      <td>117700.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>0.784667</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Greenwich</td>\n",
       "      <td>2012</td>\n",
       "      <td>2828420.0</td>\n",
       "      <td>4800000.0</td>\n",
       "      <td>0.589254</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Waterbury</td>\n",
       "      <td>2011</td>\n",
       "      <td>102210.0</td>\n",
       "      <td>17293.0</td>\n",
       "      <td>5.910484</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Farmington</td>\n",
       "      <td>2011</td>\n",
       "      <td>577370.0</td>\n",
       "      <td>605000.0</td>\n",
       "      <td>0.954331</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Wallingford</td>\n",
       "      <td>2011</td>\n",
       "      <td>116600.0</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>0.804138</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Ansonia</td>\n",
       "      <td>2013</td>\n",
       "      <td>169500.0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>0.628000</td>\n",
       "      <td>Single Family</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Danbury</td>\n",
       "      <td>2011</td>\n",
       "      <td>255800.0</td>\n",
       "      <td>379990.0</td>\n",
       "      <td>0.673176</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>2011</td>\n",
       "      <td>249200.0</td>\n",
       "      <td>330000.0</td>\n",
       "      <td>0.755152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Putnam</td>\n",
       "      <td>2011</td>\n",
       "      <td>85700.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1.714000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Waterford</td>\n",
       "      <td>2011</td>\n",
       "      <td>246510.0</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>0.986040</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Bethany</td>\n",
       "      <td>2012</td>\n",
       "      <td>176000.0</td>\n",
       "      <td>255000.0</td>\n",
       "      <td>0.690196</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Bridgeport</td>\n",
       "      <td>2011</td>\n",
       "      <td>134840.0</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>1.225818</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>156514</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>157100.0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>0.698222</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156515</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>131500.0</td>\n",
       "      <td>185900.0</td>\n",
       "      <td>0.707370</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156516</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>134700.0</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>1.192035</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156517</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>167200.0</td>\n",
       "      <td>55500.0</td>\n",
       "      <td>3.012613</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156518</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>0.148485</td>\n",
       "      <td>Vacant Land</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156519</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>32100.0</td>\n",
       "      <td>24166.0</td>\n",
       "      <td>1.328313</td>\n",
       "      <td>Vacant Land</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156520</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>42600.0</td>\n",
       "      <td>32500.0</td>\n",
       "      <td>1.310769</td>\n",
       "      <td>Vacant Land</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156521</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>132100.0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>0.587111</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156522</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>119200.0</td>\n",
       "      <td>81375.0</td>\n",
       "      <td>1.464823</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156523</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>81900.0</td>\n",
       "      <td>101852.0</td>\n",
       "      <td>0.804108</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156524</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>122700.0</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>0.943846</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156525</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156526</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>138800.0</td>\n",
       "      <td>191500.0</td>\n",
       "      <td>0.724804</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156527</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>51500.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.858333</td>\n",
       "      <td>Vacant Land</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156528</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>122800.0</td>\n",
       "      <td>207500.0</td>\n",
       "      <td>0.591807</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156529</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>127700.0</td>\n",
       "      <td>132500.0</td>\n",
       "      <td>0.963774</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156530</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>263500.0</td>\n",
       "      <td>290000.0</td>\n",
       "      <td>0.908621</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156531</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>269500.0</td>\n",
       "      <td>389000.0</td>\n",
       "      <td>0.692802</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156532</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>364000.0</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>0.713725</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156533</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>195900.0</td>\n",
       "      <td>260000.0</td>\n",
       "      <td>0.753462</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156534</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>910.0</td>\n",
       "      <td>130500.0</td>\n",
       "      <td>0.006973</td>\n",
       "      <td>Vacant Land</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156535</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>266200.0</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>0.782941</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156536</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>136500.0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>0.505556</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156537</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>269500.0</td>\n",
       "      <td>392500.0</td>\n",
       "      <td>0.686624</td>\n",
       "      <td>Residential</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156538</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>196600.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>1.310667</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156539</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>163590.0</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>0.594873</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156540</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>557100.0</td>\n",
       "      <td>495000.0</td>\n",
       "      <td>1.125455</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156541</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>532000.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>1.064000</td>\n",
       "      <td>Residential</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156542</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>43400.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>2.893333</td>\n",
       "      <td>Vacant Land</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156543</th>\n",
       "      <td>Woodstock</td>\n",
       "      <td>2014</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>14500.0</td>\n",
       "      <td>3.310345</td>\n",
       "      <td>Vacant Land</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150256 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name  ListYear  AssessedValue  SalePrice  SalesRatio  \\\n",
       "0          Bridgeport      2011       162040.0   233500.0    0.693961   \n",
       "1             Ledyard      2012        67830.0    75000.0    0.904400   \n",
       "2             Milford      2012       201300.0   255000.0    0.789412   \n",
       "3           New Haven      2011        49630.0    39900.0    1.243860   \n",
       "4           Colebrook      2011       196100.0   235000.0    0.834468   \n",
       "5        Wethersfield      2012       128400.0   165000.0    0.778182   \n",
       "6          New Canaan      2012       857920.0  1176501.0    0.729213   \n",
       "7       East Hartford      2012        83800.0   120000.0    0.698333   \n",
       "8          Litchfield      2012       171320.0   238000.0    0.719832   \n",
       "9           Southbury      2011       125370.0   119000.0    1.053529   \n",
       "10          East Lyme      2012       108920.0   148000.0    0.735946   \n",
       "12      Windsor Locks      2012       173300.0   192000.0    0.902604   \n",
       "13       Wethersfield      2011       365100.0   516340.0    0.707092   \n",
       "14         Colchester      2012       171700.0   215000.0    0.798605   \n",
       "15            Enfield      2012       116370.0   152500.0    0.763082   \n",
       "16           Branford      2012       100500.0   135000.0    0.744444   \n",
       "17         Bloomfield      2012       184800.0   258000.0    0.716279   \n",
       "18      West Hartford      2012       116830.0   165000.0    0.708061   \n",
       "19           Branford      2011       117700.0   150000.0    0.784667   \n",
       "20          Greenwich      2012      2828420.0  4800000.0    0.589254   \n",
       "22          Waterbury      2011       102210.0    17293.0    5.910484   \n",
       "23         Farmington      2011       577370.0   605000.0    0.954331   \n",
       "24        Wallingford      2011       116600.0   145000.0    0.804138   \n",
       "25            Ansonia      2013       169500.0   270000.0    0.628000   \n",
       "26            Danbury      2011       255800.0   379990.0    0.673176   \n",
       "27            Clinton      2011       249200.0   330000.0    0.755152   \n",
       "28             Putnam      2011        85700.0    50000.0    1.714000   \n",
       "29          Waterford      2011       246510.0   250000.0    0.986040   \n",
       "30            Bethany      2012       176000.0   255000.0    0.690196   \n",
       "31         Bridgeport      2011       134840.0   110000.0    1.225818   \n",
       "...               ...       ...            ...        ...         ...   \n",
       "156514      Woodstock      2014       157100.0   225000.0    0.698222   \n",
       "156515      Woodstock      2014       131500.0   185900.0    0.707370   \n",
       "156516      Woodstock      2014       134700.0   113000.0    1.192035   \n",
       "156517      Woodstock      2014       167200.0    55500.0    3.012613   \n",
       "156518      Woodstock      2014         9800.0    66000.0    0.148485   \n",
       "156519      Woodstock      2014        32100.0    24166.0    1.328313   \n",
       "156520      Woodstock      2014        42600.0    32500.0    1.310769   \n",
       "156521      Woodstock      2014       132100.0   225000.0    0.587111   \n",
       "156522      Woodstock      2014       119200.0    81375.0    1.464823   \n",
       "156523      Woodstock      2014        81900.0   101852.0    0.804108   \n",
       "156524      Woodstock      2014       122700.0   130000.0    0.943846   \n",
       "156525      Woodstock      2014            0.0    15000.0    0.000000   \n",
       "156526      Woodstock      2014       138800.0   191500.0    0.724804   \n",
       "156527      Woodstock      2014        51500.0    60000.0    0.858333   \n",
       "156528      Woodstock      2014       122800.0   207500.0    0.591807   \n",
       "156529      Woodstock      2014       127700.0   132500.0    0.963774   \n",
       "156530      Woodstock      2014       263500.0   290000.0    0.908621   \n",
       "156531      Woodstock      2014       269500.0   389000.0    0.692802   \n",
       "156532      Woodstock      2014       364000.0   510000.0    0.713725   \n",
       "156533      Woodstock      2014       195900.0   260000.0    0.753462   \n",
       "156534      Woodstock      2014          910.0   130500.0    0.006973   \n",
       "156535      Woodstock      2014       266200.0   340000.0    0.782941   \n",
       "156536      Woodstock      2014       136500.0   270000.0    0.505556   \n",
       "156537      Woodstock      2014       269500.0   392500.0    0.686624   \n",
       "156538      Woodstock      2014       196600.0   150000.0    1.310667   \n",
       "156539      Woodstock      2014       163590.0   275000.0    0.594873   \n",
       "156540      Woodstock      2014       557100.0   495000.0    1.125455   \n",
       "156541      Woodstock      2014       532000.0   500000.0    1.064000   \n",
       "156542      Woodstock      2014        43400.0    15000.0    2.893333   \n",
       "156543      Woodstock      2014        48000.0    14500.0    3.310345   \n",
       "\n",
       "       ResidentialType  ResidentialUnits  \n",
       "0                    1                 1  \n",
       "1                    C                 1  \n",
       "2                    1                 1  \n",
       "3                    1                 1  \n",
       "4                    1                 1  \n",
       "5                    1                 1  \n",
       "6                    1                 1  \n",
       "7                    C                 1  \n",
       "8                    1                 1  \n",
       "9                    C                 1  \n",
       "10                   C                 1  \n",
       "12                   1                 1  \n",
       "13                   1                 1  \n",
       "14                   1                 1  \n",
       "15                   1                 1  \n",
       "16                   C                 1  \n",
       "17                   1                 1  \n",
       "18                   1                 1  \n",
       "19                   C                 1  \n",
       "20                   1                 1  \n",
       "22                   1                 1  \n",
       "23                   1                 1  \n",
       "24                   1                 1  \n",
       "25       Single Family                 1  \n",
       "26                   C                 1  \n",
       "27                   1                 1  \n",
       "28                   2                 1  \n",
       "29                   1                 1  \n",
       "30                   1                 1  \n",
       "31                   1                 1  \n",
       "...                ...               ...  \n",
       "156514     Residential                 1  \n",
       "156515     Residential                 1  \n",
       "156516     Residential                 1  \n",
       "156517     Residential                 1  \n",
       "156518     Vacant Land                 0  \n",
       "156519     Vacant Land                 0  \n",
       "156520     Vacant Land                 0  \n",
       "156521     Residential                 1  \n",
       "156522     Residential                 1  \n",
       "156523     Residential                 1  \n",
       "156524     Residential                 1  \n",
       "156525     Residential                 1  \n",
       "156526     Residential                 1  \n",
       "156527     Vacant Land                 0  \n",
       "156528     Residential                 1  \n",
       "156529     Residential                 1  \n",
       "156530     Residential                 1  \n",
       "156531     Residential                 1  \n",
       "156532     Residential                 1  \n",
       "156533     Residential                 1  \n",
       "156534     Vacant Land                 0  \n",
       "156535     Residential                 1  \n",
       "156536     Residential                 1  \n",
       "156537     Residential                 2  \n",
       "156538     Residential                 1  \n",
       "156539     Residential                 1  \n",
       "156540     Residential                 1  \n",
       "156541     Residential                 1  \n",
       "156542     Vacant Land                 0  \n",
       "156543     Vacant Land                 0  \n",
       "\n",
       "[150256 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select the columns we need\n",
    "data=data.loc[:,['Name','ListYear','AssessedValue','SalePrice',\n",
    "                   'SalesRatio','ResidentialType','ResidentialUnits']]\n",
    "\n",
    "# drop the null vaules from the using columns \n",
    "print(data.head())\n",
    "data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next, we find AssessedValue, SalePrice andSalesRatio to be the three important numerical columns, we will observe their correaltions first.\n",
    "### Since the number of observations is large, it may take several minutes to run the visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kisekinokage/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c28505f98>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#visualize the corelation among AssessedValue SalePrice and SalesRatio using Seaborn line plot\n",
    "\n",
    "#AssessedValue and SalePrice\n",
    "sns.lmplot(x=\"AssessedValue\", y=\"SalePrice\",data=data)\n",
    "#most values of SalePrice and AssessedValue center on less than 200000, they have a positive linear correlation\n",
    "\n",
    "#SalePrice and SalesRatio\n",
    "sns.lmplot(x=\"SalePrice\", y=\"SalesRatio\",data=data)\n",
    "#the value distribution are equal, they do not have a significant correlation\n",
    "\n",
    "#Assessedvalue and SalesRatio\n",
    "sns.lmplot(x=\"AssessedValue\", y=\"SalesRatio\",data=data)\n",
    "##the value distribution are equal, they have a weak positive linear correlation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### we generate the saleprice by year with the list year (2011~2014), find the mean of saleprice for each year, computing a line chart to compare the average saleprice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "price_year =  data.loc[:,['SalePrice','ListYear']]\n",
    "price = price_year.groupby('ListYear').mean()\n",
    "price['year']=['2011','2012','2013','2014']\n",
    "plt.title('sale price by year')\n",
    "plt.plot(price['year'], price['SalePrice'])\n",
    "plt.show()\n",
    "# year 2013 has the largest average saleprice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### apply the same methodology to AssessedValue and SalesRatio"
   ]
  },
  {
   "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": [
    "value_year = data.loc[:,['AssessedValue','ListYear']]\n",
    "value = value_year.groupby('ListYear').mean()\n",
    "value['year']=['2011','2012','2013','2014']\n",
    "plt.title('assessment value by year')\n",
    "plt.plot(value['year'], value['AssessedValue'])\n",
    "plt.show()\n",
    "# we observe that from year 2011 to 2014, average AssessedValue has a significant decreasing tread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "ratio_year = data.loc[:,['SalesRatio','ListYear']]\n",
    "ratio = ratio_year.groupby('ListYear').mean()\n",
    "ratio['year']=['2011','2012','2013','2014']\n",
    "plt.title('sales ratio by year')\n",
    "plt.plot(ratio['year'], ratio['SalesRatio'])\n",
    "plt.show()\n",
    "# we observe year 2011 has the largest sales ratio, where year 2014 has the least, this is the same as assessed value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Then we will use linear regression to generate the mean square to find the predicative correlation among AssessedValue, SalesRatio and SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kisekinokage/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from patsy import dmatrices\n",
    "import sklearn\n",
    "import sklearn.metrics\n",
    "from sklearn import ensemble\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse_error: 852.0572460549857\n"
     ]
    }
   ],
   "source": [
    "x=data.SalePrice\n",
    "y=data.SalesRatio\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.3)\n",
    "model = smf.OLS(y_train,x_train)\n",
    "result = model.fit()\n",
    "yhat = result.predict(x_test) \n",
    "mse_error = np.mean((yhat-y_test) ** 2)\n",
    "print('mse_error:',mse_error)\n",
    "# large mean square error indicates that sale price does not predicate sales ratio well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse_error: 607.4712278605434\n"
     ]
    }
   ],
   "source": [
    "x=data.AssessedValue\n",
    "y=data.SalesRatio\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.3)\n",
    "model = smf.OLS(y_train,x_train)\n",
    "result = model.fit()\n",
    "yhat = result.predict(x_test) \n",
    "mse_error = np.mean((yhat-y_test) ** 2)\n",
    "print('mse_error:',mse_error)\n",
    "# assessed value shows a relatively better result of predicting sales ratio compared with sale price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse_error: 1733205131295.208\n"
     ]
    }
   ],
   "source": [
    "x=data.AssessedValue\n",
    "y=data.SalePrice\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.3)\n",
    "model = smf.OLS(y_train,x_train)\n",
    "result = model.fit()\n",
    "yhat = result.predict(x_test) \n",
    "mse_error = np.mean((yhat-y_test) ** 2)\n",
    "print('mse_error:',mse_error)\n",
    "# this number indicates that assessed value does not have any predicative correlation with sale price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "## 1. From the year 2011 to 2014, assessed value has a positive linear relation with sale price, but assessed value can not predicate the trend of future sale price.\n",
    "## 2. From the year 2011 to 2014, sales ratio has no linear relation with sale price, it either shows a weak predicative relation with the sale price.\n",
    "## 3. From the year 2011 to 2014, sales ratio has a relatively weak linear relation with assessed value, but it either shows a stronger predicative relation with the sales ratio compared with sale price.\n",
    "## 4. From the year 2011 to 2014, both sales ratio and assessed value has a decreasing trend, only saleprice is unsteadily increasing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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