Project 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project 3\n",
    "\n",
    "In Project 1, we calculated information about the most expensive property, when the highest price was sold, and the average prices per WARD in Washington DC. \n",
    "In project 2, we visualized the data, and showed where the relevant data and outliers were located. In project 3, we are going to predict the correlation of number of bathrooms and bedrooms per ward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Step\n",
    "Create Data Frame from DC Properties CSV data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2698: DtypeWarning: Columns (33) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROOMS</th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>SALEDATE</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>FULLADDRESS</th>\n",
       "      <th>WARD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2003-11-25 00:00:00</td>\n",
       "      <td>1095000.0</td>\n",
       "      <td>1748 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-08-17 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1746 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-06-21 00:00:00</td>\n",
       "      <td>2100000.0</td>\n",
       "      <td>1744 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2006-07-12 00:00:00</td>\n",
       "      <td>1602000.0</td>\n",
       "      <td>1742 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1804 NEW HAMPSHIRE AVENUE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2010-02-26 00:00:00</td>\n",
       "      <td>1950000.0</td>\n",
       "      <td>1709 S STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-05-02 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1769 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-09-29 00:00:00</td>\n",
       "      <td>1050000.0</td>\n",
       "      <td>1746 1/2 T STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-05-03 00:00:00</td>\n",
       "      <td>1430000.0</td>\n",
       "      <td>1727 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2008-12-05 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1733 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1755 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2009-03-12 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1734 T STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1717 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1719 1/2 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-09-30 00:00:00</td>\n",
       "      <td>1325000.0</td>\n",
       "      <td>1721 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1723 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-11-04 00:00:00</td>\n",
       "      <td>1240000.0</td>\n",
       "      <td>1725 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2008-05-08 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1729 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-08-18 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1731 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2001-07-23 00:00:00</td>\n",
       "      <td>592250.0</td>\n",
       "      <td>1735 SWANN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-01-12 00:00:00</td>\n",
       "      <td>33232.0</td>\n",
       "      <td>1441 Q STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-04-30 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1443 Q STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2006-01-09 00:00:00</td>\n",
       "      <td>907400.0</td>\n",
       "      <td>1445 Q STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-08-21 00:00:00</td>\n",
       "      <td>1065000.0</td>\n",
       "      <td>1447 Q STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-07-25 00:00:00</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>1449 Q STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1996-04-02 00:00:00</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1451 Q STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1458 CORCORAN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2005-04-06 00:00:00</td>\n",
       "      <td>703700.0</td>\n",
       "      <td>1456 CORCORAN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-02-06 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1454 CORCORAN STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-12-11 00:00:00</td>\n",
       "      <td>1210000.0</td>\n",
       "      <td>1452 CORCORAN STREET NW</td>\n",
       "      <td>Ward 2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2007-03-08 00:00:00</td>\n",
       "      <td>1080000.0</td>\n",
       "      <td>2124 O STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-01-14 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2138 O STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-04-11 00:00:00</td>\n",
       "      <td>280745.0</td>\n",
       "      <td>2136 O STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-10-27 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2134 O STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2009-06-05 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2132 O STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2013-03-22 00:00:00</td>\n",
       "      <td>1387000.0</td>\n",
       "      <td>2130 O STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-12-09 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2128 O STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-02-19 00:00:00</td>\n",
       "      <td>1120160.0</td>\n",
       "      <td>1331 22ND STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2003-09-22 00:00:00</td>\n",
       "      <td>785000.0</td>\n",
       "      <td>2111 N STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2134 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2006-12-22 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2153 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2145 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2016-02-25 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1335 22ND STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2005-01-14 00:00:00</td>\n",
       "      <td>725000.0</td>\n",
       "      <td>2117 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2115 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-07-15 00:00:00</td>\n",
       "      <td>1050000.0</td>\n",
       "      <td>2132 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2008-05-15 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2130 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-08-04 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2128 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-02 00:00:00</td>\n",
       "      <td>499000.0</td>\n",
       "      <td>2151 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2149 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-12 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2147 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1333 22ND STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2012-08-20 00:00:00</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>2120 NEWPORT PLACE NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-04-21 00:00:00</td>\n",
       "      <td>1037250.0</td>\n",
       "      <td>2128 N STREET NW</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1719 6TH STREET NW</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1721 6TH STREET NW</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2006-11-21 00:00:00</td>\n",
       "      <td>575000.0</td>\n",
       "      <td>1707 6TH STREET NW</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-06-04 00:00:00</td>\n",
       "      <td>737500.0</td>\n",
       "      <td>1709 6TH STREET NW</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-03-25 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>517 RHODE ISLAND AVENUE NW</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1999-05-04 00:00:00</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>519 RHODE ISLAND AVENUE NW</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ROOMS  BEDRM             SALEDATE      PRICE  \\\n",
       "0        8      4  2003-11-25 00:00:00  1095000.0   \n",
       "1       11      5  2000-08-17 00:00:00        NaN   \n",
       "2        9      5  2016-06-21 00:00:00  2100000.0   \n",
       "3        8      5  2006-07-12 00:00:00  1602000.0   \n",
       "4       11      3                  NaN        NaN   \n",
       "5       10      5  2010-02-26 00:00:00  1950000.0   \n",
       "6        5      2  2011-05-02 00:00:00        NaN   \n",
       "7        8      4  2011-09-29 00:00:00  1050000.0   \n",
       "8        7      3  2018-05-03 00:00:00  1430000.0   \n",
       "9        6      2  2008-12-05 00:00:00        NaN   \n",
       "10      12      5                  NaN        NaN   \n",
       "11      16      6  2009-03-12 00:00:00        NaN   \n",
       "12       9      5                  NaN        NaN   \n",
       "13      11      5                  NaN        NaN   \n",
       "14       5      3  2011-09-30 00:00:00  1325000.0   \n",
       "15       9      4                  NaN        NaN   \n",
       "16       8      3  2010-11-04 00:00:00  1240000.0   \n",
       "17       6      3  2008-05-08 00:00:00        NaN   \n",
       "18      10      4  2016-08-18 00:00:00        NaN   \n",
       "19       9      3  2001-07-23 00:00:00   592250.0   \n",
       "20      14      5  2016-01-12 00:00:00    33232.0   \n",
       "21      12      3  2014-04-30 00:00:00        NaN   \n",
       "22       6      3  2006-01-09 00:00:00   907400.0   \n",
       "23       5      3  2012-08-21 00:00:00  1065000.0   \n",
       "24       8      3  2012-07-25 00:00:00  1100000.0   \n",
       "25       8      4  1996-04-02 00:00:00    20000.0   \n",
       "26      10      5                  NaN        NaN   \n",
       "27       9      3  2005-04-06 00:00:00   703700.0   \n",
       "28      10      4  2015-02-06 00:00:00        NaN   \n",
       "29      11      3  2013-12-11 00:00:00  1210000.0   \n",
       "..     ...    ...                  ...        ...   \n",
       "970      9      4  2007-03-08 00:00:00  1080000.0   \n",
       "971      7      3  2010-01-14 00:00:00        NaN   \n",
       "972     10      3  2014-04-11 00:00:00   280745.0   \n",
       "973      6      3  2016-10-27 00:00:00        NaN   \n",
       "974     11      5  2009-06-05 00:00:00        NaN   \n",
       "975      8      5  2013-03-22 00:00:00  1387000.0   \n",
       "976      8      4  2015-12-09 00:00:00        NaN   \n",
       "977     16      8  2016-02-19 00:00:00  1120160.0   \n",
       "978     10      4  2003-09-22 00:00:00   785000.0   \n",
       "979      8      3                  NaN        NaN   \n",
       "980      6      3  2006-12-22 00:00:00        NaN   \n",
       "981      4      2                  NaN        NaN   \n",
       "982      9      6  2016-02-25 00:00:00        NaN   \n",
       "983      5      2  2005-01-14 00:00:00   725000.0   \n",
       "984      8      3                  NaN        NaN   \n",
       "985      6      3  2015-07-15 00:00:00  1050000.0   \n",
       "986      7      3  2008-05-15 00:00:00        NaN   \n",
       "987      7      3  2011-08-04 00:00:00        NaN   \n",
       "988      4      2  2010-12-02 00:00:00   499000.0   \n",
       "989      4      2                  NaN        NaN   \n",
       "990      4      2  2017-09-12 00:00:00        NaN   \n",
       "991      8      4                  NaN        NaN   \n",
       "992      5      2  2012-08-20 00:00:00   850000.0   \n",
       "993      6      3  2017-04-21 00:00:00  1037250.0   \n",
       "994      6      3                  NaN        NaN   \n",
       "995      7      3                  NaN        NaN   \n",
       "996      7      3  2006-11-21 00:00:00   575000.0   \n",
       "997      4      2  2014-06-04 00:00:00   737500.0   \n",
       "998      5      2  2015-03-25 00:00:00        NaN   \n",
       "999      6      3  1999-05-04 00:00:00   114000.0   \n",
       "\n",
       "                      FULLADDRESS    WARD  \n",
       "0            1748 SWANN STREET NW  Ward 2  \n",
       "1            1746 SWANN STREET NW  Ward 2  \n",
       "2            1744 SWANN STREET NW  Ward 2  \n",
       "3            1742 SWANN STREET NW  Ward 2  \n",
       "4    1804 NEW HAMPSHIRE AVENUE NW  Ward 2  \n",
       "5                1709 S STREET NW  Ward 2  \n",
       "6            1769 SWANN STREET NW  Ward 2  \n",
       "7            1746 1/2 T STREET NW  Ward 2  \n",
       "8            1727 SWANN STREET NW  Ward 2  \n",
       "9            1733 SWANN STREET NW  Ward 2  \n",
       "10           1755 SWANN STREET NW  Ward 2  \n",
       "11               1734 T STREET NW  Ward 2  \n",
       "12           1717 SWANN STREET NW  Ward 2  \n",
       "13       1719 1/2 SWANN STREET NW  Ward 2  \n",
       "14           1721 SWANN STREET NW  Ward 2  \n",
       "15           1723 SWANN STREET NW  Ward 2  \n",
       "16           1725 SWANN STREET NW  Ward 2  \n",
       "17           1729 SWANN STREET NW  Ward 2  \n",
       "18           1731 SWANN STREET NW  Ward 2  \n",
       "19           1735 SWANN STREET NW  Ward 2  \n",
       "20               1441 Q STREET NW  Ward 2  \n",
       "21               1443 Q STREET NW  Ward 2  \n",
       "22               1445 Q STREET NW  Ward 2  \n",
       "23               1447 Q STREET NW  Ward 2  \n",
       "24               1449 Q STREET NW  Ward 2  \n",
       "25               1451 Q STREET NW  Ward 2  \n",
       "26        1458 CORCORAN STREET NW  Ward 2  \n",
       "27        1456 CORCORAN STREET NW  Ward 2  \n",
       "28        1454 CORCORAN STREET NW  Ward 2  \n",
       "29        1452 CORCORAN STREET NW  Ward 2  \n",
       "..                            ...     ...  \n",
       "970              2124 O STREET NW  Ward 2  \n",
       "971              2138 O STREET NW  Ward 2  \n",
       "972              2136 O STREET NW  Ward 2  \n",
       "973              2134 O STREET NW  Ward 2  \n",
       "974              2132 O STREET NW  Ward 2  \n",
       "975              2130 O STREET NW  Ward 2  \n",
       "976              2128 O STREET NW  Ward 2  \n",
       "977           1331 22ND STREET NW  Ward 2  \n",
       "978              2111 N STREET NW  Ward 2  \n",
       "979         2134 NEWPORT PLACE NW  Ward 2  \n",
       "980         2153 NEWPORT PLACE NW  Ward 2  \n",
       "981         2145 NEWPORT PLACE NW  Ward 2  \n",
       "982           1335 22ND STREET NW  Ward 2  \n",
       "983         2117 NEWPORT PLACE NW  Ward 2  \n",
       "984         2115 NEWPORT PLACE NW  Ward 2  \n",
       "985         2132 NEWPORT PLACE NW  Ward 2  \n",
       "986         2130 NEWPORT PLACE NW  Ward 2  \n",
       "987         2128 NEWPORT PLACE NW  Ward 2  \n",
       "988         2151 NEWPORT PLACE NW  Ward 2  \n",
       "989         2149 NEWPORT PLACE NW  Ward 2  \n",
       "990         2147 NEWPORT PLACE NW  Ward 2  \n",
       "991           1333 22ND STREET NW  Ward 2  \n",
       "992         2120 NEWPORT PLACE NW  Ward 2  \n",
       "993              2128 N STREET NW  Ward 2  \n",
       "994            1719 6TH STREET NW  Ward 6  \n",
       "995            1721 6TH STREET NW  Ward 6  \n",
       "996            1707 6TH STREET NW  Ward 6  \n",
       "997            1709 6TH STREET NW  Ward 6  \n",
       "998    517 RHODE ISLAND AVENUE NW  Ward 6  \n",
       "999    519 RHODE ISLAND AVENUE NW  Ward 6  \n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location = \"DC_Properties.csv\"\n",
    "df = pd.read_csv(location, \n",
    "                 usecols=[\"ROOMS\", \"BEDRM\", \"SALEDATE\", \"PRICE\", \"WARD\", \"FULLADDRESS\"])\n",
    "df\n",
    "df.dtypes \n",
    "df.head(1000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Question\n",
    "\n",
    "Which neighborhood has the most expensive property?\n",
    "\n",
    "To accomplish this step, we eliminated nulls from PRICE and FULLADDRESS columns.\n",
    "Then we sorted by PRICE and FULLADDRESS and found the max price:\n",
    "\n",
    "2327 GOOD HOPE COURT SE          25100000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2285f7b6d68>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o6Oh1TG2JKAAoOSeWl5OIAoCScwPichJRAFByu3fv7nVMbYkoACi5H/7wh72OqS0RBQAl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\nABEFAFCAiAIAKEBEAQAUIKIAAAr4f0sI6+AeCWfvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2285f7a9ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_price=df[df['FULLADDRESS'].notnull() & df['PRICE'].notnull()]\n",
    "sorted_by_price = df_price.groupby(['PRICE', 'FULLADDRESS'], sort=True) ['PRICE'].max()\n",
    "sorted_by_price\n",
    "df_price['FULLADDRESS']=df_price['FULLADDRESS'].apply(lambda x: x.split(None,1)[1])\n",
    "df_price\n",
    "# The violin plot was good in the sense on looking at the magnitude of the data, but \n",
    "# not good at displaying details of the data.\n",
    "plt.subplots(figsize=(10,6))\n",
    "sns.set(style=\"whitegrid\")\n",
    "#sns.violinplot(x='FULLADDRESS', y='PRICE', data=df_price)\n",
    "sns.boxplot(data=df_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to plot Neighborhood price information to show where the expensive homes are located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second Question\n",
    "When houses sold at the highest price?\n",
    "\n",
    "To accomplish this step, we eliminated nulls from PRICE, SALEDATE, and FULLADDRESS columns.\n",
    "Then we sorted by PRICE and SALEDATE and found the max price:\n",
    "\n",
    "2018-02-15 00:00:00    25100000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n"
     ]
    },
    {
     "data": {
      "image/png": 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6dUs1NTUvnL906ZLKy8tVWVmp1tZW7z/kJsh3333nfvDBB67ruu7Nmzfdd955J3atv7/f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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2285f9d26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_date=df[df['FULLADDRESS'].notnull() & df['PRICE'].notnull() & df['SALEDATE'].notnull()]\n",
    "sorted_by_date = df_price.groupby(['PRICE','SALEDATE'], sort=True) ['PRICE'].max()\n",
    "sorted_by_date\n",
    "df_date\n",
    "sns.lmplot(x='PRICE', y='SALEDATE', data=df_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Third Question\n",
    "What are the average prices per WARD in Washington DC?\n",
    "\n",
    "For our dataset, we grouped by WARD and PRICE and then calculated the mean for each ward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WARD\n",
       "Ward 1    5.738363e+05\n",
       "Ward 2    1.098570e+06\n",
       "Ward 3    3.077716e+06\n",
       "Ward 4    4.899224e+05\n",
       "Ward 5    3.952432e+05\n",
       "Ward 6    5.268684e+05\n",
       "Ward 7    2.339222e+05\n",
       "Ward 8    2.715224e+05\n",
       "Name: PRICE, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('WARD')['PRICE'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We are going to plot Ward price information to show where the expensive homes are located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2285d0c6748>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3hLhmAUlNjU8y5heW4+NDZ3ClqQVJH9Vh+tihmJ+ZpmhNcvLbw42JifH4uuNrCo6/Bx+I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\nUlg1ufXhHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2285d0c6940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_price=df[df['FULLADDRESS'].notnull() & df['PRICE'].notnull()]\n",
    "df_price['WARD']=df_price['WARD'].apply(lambda x: x.replace('Ward ', '') )\n",
    "df_price['WARD'] = pd.to_numeric(df_price['WARD'])\n",
    "df_price.dtypes\n",
    "#ward_price_df = df.groupby('WARD')['PRICE'].mean()\n",
    "sns.lmplot(x='WARD', y='PRICE', data=df_price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2285c0e6a90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h0aNH+TpOrvuAhRDC7hXQrcgRERG0atUKgBdeeIHz58+bbd+9ezcqlYrWrVvn6zj5e9iD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2285c8df8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show correlation of stats via heatmap\n",
    "corr = df_price.corr()\n",
    "\n",
    "sns.heatmap(corr, vmin=-1, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2285c6d0b38>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RWlpauP7660e1rN1uT8sFy7VZ+dIXzqftzQ/T1jOJubjYxTdmTeWIL8j55xZg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\nTFOYiIiIaf8ffF1J0ZtKoZgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2285c6b5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a histogram of the distribution of attack power\n",
    "sns.distplot(df_price['PRICE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROOMS</th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>SALEDATE</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>FULLADDRESS</th>\n",
       "      <th>WARD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2003-11-25 00:00:00</td>\n",
       "      <td>1095000.0</td>\n",
       "      <td>1748 SWANN STREET NW</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2006-07-12 00:00:00</td>\n",
       "      <td>1602000.0</td>\n",
       "      <td>1742 SWANN STREET NW</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-09-29 00:00:00</td>\n",
       "      <td>1050000.0</td>\n",
       "      <td>1746 1/2 T STREET NW</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-05-03 00:00:00</td>\n",
       "      <td>1430000.0</td>\n",
       "      <td>1727 SWANN STREET NW</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-09-30 00:00:00</td>\n",
       "      <td>1325000.0</td>\n",
       "      <td>1721 SWANN STREET NW</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ROOMS  BEDRM             SALEDATE      PRICE           FULLADDRESS  WARD\n",
       "0       8      4  2003-11-25 00:00:00  1095000.0  1748 SWANN STREET NW     2\n",
       "3       8      5  2006-07-12 00:00:00  1602000.0  1742 SWANN STREET NW     2\n",
       "7       8      4  2011-09-29 00:00:00  1050000.0  1746 1/2 T STREET NW     2\n",
       "8       7      3  2018-05-03 00:00:00  1430000.0  1727 SWANN STREET NW     2\n",
       "14      5      3  2011-09-30 00:00:00  1325000.0  1721 SWANN STREET NW     2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meangrade = df_price['PRICE'].mean()\n",
    "stdgrade = df_price['PRICE'].std()\n",
    "\n",
    "toprange = meangrade + stdgrade * 1.96\n",
    "botrange = meangrade - stdgrade * 1.96\n",
    "\n",
    "copydf = df_price.copy() #to not mess up the original df\n",
    "copydf = copydf.drop(copydf[copydf['PRICE'] > toprange].index)\n",
    "copydf = copydf.drop(copydf[copydf['PRICE'] < botrange].index)\n",
    "\n",
    "copydf.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2285c704ef0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CBiJO8OjaKNmPcSUj0xN176256unx6w9vH9P51k6zSwIAAJBEyECEGeyja8PdmFHJ+kzR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2285c6b5e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a histogram of the distribution of attack power\n",
    "plt.figure(figsize=(13,7))\n",
    "sns.distplot(copydf['PRICE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "As a group, we've decided to predict the houses on each ward with the highest number of bedrooms and bathrooms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_price = df_price.drop(['FULLADDRESS'], axis=1)\n",
    "df_price = df_price.drop(['SALEDATE'], axis=1)\n",
    "df_price = df_price.drop(['PRICE'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ROOMS    0\n",
       "BEDRM    0\n",
       "WARD     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_price.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract target variable\n",
    "y = df_price['WARD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy without 'WARD' column\n",
    "X = df_price.drop(['WARD'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#assign decision tree function to model variable\n",
    "model = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#develop model using training data\n",
    "#defining arguments in the model can help prevent overfitting\n",
    "model.fit(X_train, y_train)\n",
    "#df_price.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26535221054460173"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run the predictions on the test data\n",
    "y_predict = model.predict(X_test)\n",
    "#check the accuracy of model\n",
    "accuracy_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ward 1</th>\n",
       "      <th>Ward 2</th>\n",
       "      <th>Ward 3</th>\n",
       "      <th>Ward 4</th>\n",
       "      <th>Ward 5</th>\n",
       "      <th>Ward 6</th>\n",
       "      <th>Ward 7</th>\n",
       "      <th>Ward 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True Ward 1</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>299</td>\n",
       "      <td>317</td>\n",
       "      <td>67</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Ward 2</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>115</td>\n",
       "      <td>243</td>\n",
       "      <td>126</td>\n",
       "      <td>110</td>\n",
       "      <td>65</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Ward 3</th>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>371</td>\n",
       "      <td>735</td>\n",
       "      <td>332</td>\n",
       "      <td>57</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Ward 4</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>167</td>\n",
       "      <td>1054</td>\n",
       "      <td>609</td>\n",
       "      <td>56</td>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Ward 5</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>637</td>\n",
       "      <td>989</td>\n",
       "      <td>137</td>\n",
       "      <td>117</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Ward 6</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>103</td>\n",
       "      <td>660</td>\n",
       "      <td>612</td>\n",
       "      <td>326</td>\n",
       "      <td>209</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Ward 7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>444</td>\n",
       "      <td>554</td>\n",
       "      <td>226</td>\n",
       "      <td>282</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Ward 8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>284</td>\n",
       "      <td>382</td>\n",
       "      <td>71</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Ward 1  Ward 2  Ward 3  Ward 4  Ward 5  Ward 6  Ward 7  Ward 8\n",
       "True Ward 1      20       2     112     299     317      67      41       5\n",
       "True Ward 2      11       2     115     243     126     110      65       4\n",
       "True Ward 3      23       4     371     735     332      57      43       3\n",
       "True Ward 4      27       1     167    1054     609      56      99       2\n",
       "True Ward 5      17       1      89     637     989     137     117       6\n",
       "True Ward 6      13       3     103     660     612     326     209       2\n",
       "True Ward 7       7       0      37     444     554     226     282       3\n",
       "True Ward 8       9       1      24     284     382      71     135      11"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#look at true and false predictions\n",
    "pd.DataFrame(\n",
    "    confusion_matrix(y_test, y_predict),\n",
    "    columns=['Ward 1', 'Ward 2', \"Ward 3\", \"Ward 4\", \"Ward 5\", \"Ward 6\", \"Ward 7\", \"Ward 8\"],\n",
    "    index=['True Ward 1', 'True Ward 2', \"True Ward 3\", \"True Ward 4\", \"True Ward 5\", \"True Ward 6\", \"True Ward 7\", \"True Ward 8\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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