Question 3 copy 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## What Is the Relationship between Housing Characteristics and Complaints?\n",
    "\n",
    "The goal of this exercise is to find the answer to the Question 3 of the problem statement: \n",
    "\n",
    "Does the Complaint Type that you identified in response to Question 1 have an obvious relationship with any particular characteristic or characteristic of the Houses?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import types\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share the notebook.\n",
    "client_cred = ibm_boto3.client(service_name='Replace with your service id',\n",
    "    ibm_api_key_id='Replace with your ibm api key id',\n",
    "    ibm_auth_endpoint=\"https://iam.ng.bluemix.net/oidc/token\",\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')\n",
    "\n",
    "body = client_cred.get_object(Bucket='Replace with your ibm bucket id',Key='BX_18v1.csv')['Body']\n",
    "# add missing __iter__ method, so pandas accepts body as file-like object\n",
    "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Datasets\n",
    "1. Read 311 complaints dataset\n",
    "2. Read Bronx(Pluto) Dataset\n",
    "3. Filter out 311 dataset to capture top borough of our top complaint(HEAT/HOT WATER)\n",
    "4. Filter out relevant columns in Bronx dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read 311 complaints and Bronx(Pluto Dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/Python36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (19,20,22,23,64,65,80) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#import the bronx borough data\n",
    "data_bx = pd.read_csv(body)\n",
    "data_bx.head()\n",
    "\n",
    "#import the 311 data\n",
    "bucket='replace with your bucket id'\n",
    "\n",
    "#Read Data\n",
    "url= 'https://data.cityofnewyork.us/resource/erm2-nwe9.csv?$limit=100000000&Agency=HPD&$select=created_date,unique_key,complaint_type,incident_zip,incident_address,street_name,address_type,city,resolution_description,borough,latitude,longitude,closed_date,location_type,status'\n",
    "nyc311= pd.read_csv(url, parse_dates= True)\n",
    "\n",
    "#Upload the file to cloud\n",
    "nyc311.to_csv('./df_raw.csv')\n",
    "client_cred.upload_file('./df_raw.csv',bucket,'nyc311.csv')\n",
    "\n",
    "\n",
    "#Read file back\n",
    "client_cred.download_file(Bucket=bucket,Key='nyc311.csv',Filename='nyc311')\n",
    "df = pd.read_csv('nyc311', parse_dates= ['created_date', 'closed_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter out our top complaint type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>created_date</th>\n",
       "      <th>unique_key</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>incident_zip</th>\n",
       "      <th>incident_address</th>\n",
       "      <th>street_name</th>\n",
       "      <th>address_type</th>\n",
       "      <th>city</th>\n",
       "      <th>resolution_description</th>\n",
       "      <th>borough</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>closed_date</th>\n",
       "      <th>location_type</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-12-06 03:21:57</td>\n",
       "      <td>45098170</td>\n",
       "      <td>PAINT/PLASTER</td>\n",
       "      <td>10467.0</td>\n",
       "      <td>3857 WHITE PLAINS ROAD</td>\n",
       "      <td>WHITE PLAINS ROAD</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>The Department of Housing Preservation and Dev...</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>40.885136</td>\n",
       "      <td>-73.861955</td>\n",
       "      <td>NaT</td>\n",
       "      <td>RESIDENTIAL BUILDING</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-12-06 03:09:33</td>\n",
       "      <td>45097981</td>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>10467.0</td>\n",
       "      <td>3857 WHITE PLAINS ROAD</td>\n",
       "      <td>WHITE PLAINS ROAD</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>The Department of Housing Preservation and Dev...</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>40.885136</td>\n",
       "      <td>-73.861955</td>\n",
       "      <td>2019-12-07 18:42:35</td>\n",
       "      <td>RESIDENTIAL BUILDING</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2019-12-06 07:47:28</td>\n",
       "      <td>45097572</td>\n",
       "      <td>PAINT/PLASTER</td>\n",
       "      <td>10460.0</td>\n",
       "      <td>642 MORRIS PARK AVENUE</td>\n",
       "      <td>MORRIS PARK AVENUE</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>The following complaint conditions are still o...</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>40.844676</td>\n",
       "      <td>-73.868309</td>\n",
       "      <td>NaT</td>\n",
       "      <td>RESIDENTIAL BUILDING</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2019-12-06 07:53:41</td>\n",
       "      <td>45096211</td>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>10456.0</td>\n",
       "      <td>256 EAST  166 STREET</td>\n",
       "      <td>EAST  166 STREET</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>The complaint you filed is a duplicate of a co...</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>40.830670</td>\n",
       "      <td>-73.916123</td>\n",
       "      <td>2019-12-07 17:50:14</td>\n",
       "      <td>RESIDENTIAL BUILDING</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>2019-12-06 08:31:33</td>\n",
       "      <td>45095837</td>\n",
       "      <td>WATER LEAK</td>\n",
       "      <td>10472.0</td>\n",
       "      <td>1218 MANOR AVENUE</td>\n",
       "      <td>MANOR AVENUE</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>The following complaint conditions are still o...</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>40.829592</td>\n",
       "      <td>-73.876535</td>\n",
       "      <td>NaT</td>\n",
       "      <td>RESIDENTIAL BUILDING</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0        created_date  unique_key  complaint_type  incident_zip  \\\n",
       "1            1 2019-12-06 03:21:57    45098170   PAINT/PLASTER       10467.0   \n",
       "4            4 2019-12-06 03:09:33    45097981  HEAT/HOT WATER       10467.0   \n",
       "6            6 2019-12-06 07:47:28    45097572   PAINT/PLASTER       10460.0   \n",
       "12          12 2019-12-06 07:53:41    45096211  HEAT/HOT WATER       10456.0   \n",
       "16          16 2019-12-06 08:31:33    45095837      WATER LEAK       10472.0   \n",
       "\n",
       "          incident_address         street_name address_type   city  \\\n",
       "1   3857 WHITE PLAINS ROAD   WHITE PLAINS ROAD      ADDRESS  BRONX   \n",
       "4   3857 WHITE PLAINS ROAD   WHITE PLAINS ROAD      ADDRESS  BRONX   \n",
       "6   642 MORRIS PARK AVENUE  MORRIS PARK AVENUE      ADDRESS  BRONX   \n",
       "12    256 EAST  166 STREET    EAST  166 STREET      ADDRESS  BRONX   \n",
       "16       1218 MANOR AVENUE        MANOR AVENUE      ADDRESS  BRONX   \n",
       "\n",
       "                               resolution_description borough   latitude  \\\n",
       "1   The Department of Housing Preservation and Dev...   BRONX  40.885136   \n",
       "4   The Department of Housing Preservation and Dev...   BRONX  40.885136   \n",
       "6   The following complaint conditions are still o...   BRONX  40.844676   \n",
       "12  The complaint you filed is a duplicate of a co...   BRONX  40.830670   \n",
       "16  The following complaint conditions are still o...   BRONX  40.829592   \n",
       "\n",
       "    longitude         closed_date         location_type  status  \n",
       "1  -73.861955                 NaT  RESIDENTIAL BUILDING    Open  \n",
       "4  -73.861955 2019-12-07 18:42:35  RESIDENTIAL BUILDING  Closed  \n",
       "6  -73.868309                 NaT  RESIDENTIAL BUILDING    Open  \n",
       "12 -73.916123 2019-12-07 17:50:14  RESIDENTIAL BUILDING  Closed  \n",
       "16 -73.876535                 NaT  RESIDENTIAL BUILDING    Open  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_h= df[(df['borough'] == 'BRONX') & (df['complaint_type'] == 'HEAT/HOT WATER')]\n",
    "data_h.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter out relevant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>BldgArea</th>\n",
       "      <th>BldgDepth</th>\n",
       "      <th>BuiltFAR</th>\n",
       "      <th>CommFAR</th>\n",
       "      <th>FacilFAR</th>\n",
       "      <th>Lot</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotDepth</th>\n",
       "      <th>NumBldgs</th>\n",
       "      <th>NumFloors</th>\n",
       "      <th>OfficeArea</th>\n",
       "      <th>ResArea</th>\n",
       "      <th>ResidFAR</th>\n",
       "      <th>RetailArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearAlter1</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>YCoord</th>\n",
       "      <th>XCoord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>122 BRUCKNER BOULEVARD</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1</td>\n",
       "      <td>15000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>232162.0</td>\n",
       "      <td>1005957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126 BRUCKNER BOULEVARD</td>\n",
       "      <td>752</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4</td>\n",
       "      <td>13770</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>1994</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>232156.0</td>\n",
       "      <td>1006076.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138 BRUCKNER BOULEVARD</td>\n",
       "      <td>39375</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>10</td>\n",
       "      <td>35000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>0</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>232036.0</td>\n",
       "      <td>1006187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144 BRUCKNER BOULEVARD</td>\n",
       "      <td>12500</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>17</td>\n",
       "      <td>2500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12500</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>2001</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>232033.0</td>\n",
       "      <td>1006299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148 BRUCKNER BOULEVARD</td>\n",
       "      <td>8595</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.58</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>18</td>\n",
       "      <td>1875</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6876</td>\n",
       "      <td>6.02</td>\n",
       "      <td>1719</td>\n",
       "      <td>1920</td>\n",
       "      <td>2009</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>232040.0</td>\n",
       "      <td>1006363.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Address  BldgArea  BldgDepth  BuiltFAR  CommFAR  FacilFAR  \\\n",
       "0  122 BRUCKNER BOULEVARD         0        0.0      0.00      5.0       6.5   \n",
       "1  126 BRUCKNER BOULEVARD       752       16.0      0.05      5.0       6.5   \n",
       "2  138 BRUCKNER BOULEVARD     39375      200.0      1.13      5.0       6.5   \n",
       "3  144 BRUCKNER BOULEVARD     12500       85.0      5.00      5.0       6.5   \n",
       "4  148 BRUCKNER BOULEVARD      8595       70.0      4.58      5.0       6.5   \n",
       "\n",
       "   Lot  LotArea  LotDepth  NumBldgs  NumFloors  OfficeArea  ResArea  ResidFAR  \\\n",
       "0    1    15000     200.0         1        0.0           0        0      6.02   \n",
       "1    4    13770     100.0         2        1.0         272        0      6.02   \n",
       "2   10    35000     200.0         1        2.0           0        0      6.02   \n",
       "3   17     2500     100.0         1        5.0           0    12500      6.02   \n",
       "4   18     1875      75.0         1        5.0           0     6876      6.02   \n",
       "\n",
       "   RetailArea  YearBuilt  YearAlter1  ZipCode    YCoord     XCoord  \n",
       "0           0          0           0  10454.0  232162.0  1005957.0  \n",
       "1           0       1931        1994  10454.0  232156.0  1006076.0  \n",
       "2           0       1931           0  10454.0  232036.0  1006187.0  \n",
       "3           0       1931        2001  10454.0  232033.0  1006299.0  \n",
       "4        1719       1920        2009  10454.0  232040.0  1006363.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Glimpse of the Bronx data\n",
    "data_bx.head()\n",
    "data_bx.columns\n",
    "\n",
    "#Let's subset the data and keep variables necessary for our analysis\n",
    "bronx= data_bx[['Address', 'BldgArea', 'BldgDepth', 'BuiltFAR', 'CommFAR', 'FacilFAR', 'Lot', 'LotArea', 'LotDepth', \n",
    "'NumBldgs', 'NumFloors', 'OfficeArea', 'ResArea', 'ResidFAR', 'RetailArea', 'YearBuilt', 'YearAlter1', 'ZipCode', \n",
    "'YCoord', 'XCoord']]\n",
    "\n",
    "bronx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(89854, 20)\n",
      "Address        object\n",
      "BldgArea        int64\n",
      "BldgDepth     float64\n",
      "BuiltFAR      float64\n",
      "CommFAR       float64\n",
      "FacilFAR      float64\n",
      "Lot             int64\n",
      "LotArea         int64\n",
      "LotDepth      float64\n",
      "NumBldgs        int64\n",
      "NumFloors     float64\n",
      "OfficeArea      int64\n",
      "ResArea         int64\n",
      "ResidFAR      float64\n",
      "RetailArea      int64\n",
      "YearBuilt       int64\n",
      "YearAlter1      int64\n",
      "ZipCode       float64\n",
      "YCoord        float64\n",
      "XCoord        float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Shape of the data\n",
    "print(bronx.shape)\n",
    "\n",
    "#Bronx data types\n",
    "print(bronx.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before proceeding to handling the missing values in our Bronx borough dataset, let's create a dataset by merging our 311 dataset and the Bronx borough dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Address', 'BldgArea', 'BldgDepth', 'BuiltFAR', 'CommFAR', 'FacilFAR',\n",
       "       'Lot', 'LotArea', 'LotDepth', 'NumBldgs', 'NumFloors', 'OfficeArea',\n",
       "       'ResArea', 'ResidFAR', 'RetailArea', 'YearBuilt', 'YearAlter1',\n",
       "       'ZipCode', 'YCoord', 'XCoord', 'incident_address', 'status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Subset our 311 for hot/heat water dataset with the columns we would like to merge\n",
    "heat_m=data_h[['incident_address', 'status']]\n",
    "\n",
    "#Let's merge both datasets using incident address from 311 data and Address from Bronx borough\n",
    "total_merge= bronx.merge(heat_m, left_on='Address', right_on='incident_address', how='left')\n",
    "total_merge.head()\n",
    "\n",
    "total_merge.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>BldgArea</th>\n",
       "      <th>BldgDepth</th>\n",
       "      <th>BuiltFAR</th>\n",
       "      <th>CommFAR</th>\n",
       "      <th>FacilFAR</th>\n",
       "      <th>Lot</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotDepth</th>\n",
       "      <th>NumBldgs</th>\n",
       "      <th>...</th>\n",
       "      <th>ResArea</th>\n",
       "      <th>ResidFAR</th>\n",
       "      <th>RetailArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearAlter1</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>YCoord</th>\n",
       "      <th>XCoord</th>\n",
       "      <th>incident_address</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>122 BRUCKNER BOULEVARD</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1</td>\n",
       "      <td>15000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>232162.0</td>\n",
       "      <td>1005957.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126 BRUCKNER BOULEVARD</td>\n",
       "      <td>752</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4</td>\n",
       "      <td>13770</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>1994</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>232156.0</td>\n",
       "      <td>1006076.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138 BRUCKNER BOULEVARD</td>\n",
       "      <td>39375</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>10</td>\n",
       "      <td>35000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>0</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>232036.0</td>\n",
       "      <td>1006187.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144 BRUCKNER BOULEVARD</td>\n",
       "      <td>12500</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>17</td>\n",
       "      <td>2500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>12500</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>2001</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>232033.0</td>\n",
       "      <td>1006299.0</td>\n",
       "      <td>144 BRUCKNER BOULEVARD</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>144 BRUCKNER BOULEVARD</td>\n",
       "      <td>12500</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>17</td>\n",
       "      <td>2500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>12500</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>2001</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>232033.0</td>\n",
       "      <td>1006299.0</td>\n",
       "      <td>144 BRUCKNER BOULEVARD</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Address  BldgArea  BldgDepth  BuiltFAR  CommFAR  FacilFAR  \\\n",
       "0  122 BRUCKNER BOULEVARD         0        0.0      0.00      5.0       6.5   \n",
       "1  126 BRUCKNER BOULEVARD       752       16.0      0.05      5.0       6.5   \n",
       "2  138 BRUCKNER BOULEVARD     39375      200.0      1.13      5.0       6.5   \n",
       "3  144 BRUCKNER BOULEVARD     12500       85.0      5.00      5.0       6.5   \n",
       "4  144 BRUCKNER BOULEVARD     12500       85.0      5.00      5.0       6.5   \n",
       "\n",
       "   Lot  LotArea  LotDepth  NumBldgs  ...  ResArea  ResidFAR  RetailArea  \\\n",
       "0    1    15000     200.0         1  ...        0      6.02           0   \n",
       "1    4    13770     100.0         2  ...        0      6.02           0   \n",
       "2   10    35000     200.0         1  ...        0      6.02           0   \n",
       "3   17     2500     100.0         1  ...    12500      6.02           0   \n",
       "4   17     2500     100.0         1  ...    12500      6.02           0   \n",
       "\n",
       "   YearBuilt  YearAlter1  ZipCode    YCoord     XCoord  \\\n",
       "0          0           0  10454.0  232162.0  1005957.0   \n",
       "1       1931        1994  10454.0  232156.0  1006076.0   \n",
       "2       1931           0  10454.0  232036.0  1006187.0   \n",
       "3       1931        2001  10454.0  232033.0  1006299.0   \n",
       "4       1931        2001  10454.0  232033.0  1006299.0   \n",
       "\n",
       "         incident_address  status  \n",
       "0                     NaN     NaN  \n",
       "1                     NaN     NaN  \n",
       "2                     NaN     NaN  \n",
       "3  144 BRUCKNER BOULEVARD  Closed  \n",
       "4  144 BRUCKNER BOULEVARD  Closed  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Status into categorical values\n",
    "\n",
    "We would like to check the correlation between variables in the complaint data and the building characteristics, so we need to identify whether a building or a charaterictic from the building had previously open an issue or not.\n",
    "So we will encode status variable with the following logic.\n",
    "If a building has already reported an issue we categorize as 1 and if no issue reported we use 0.\n",
    "NaN rows for the status column will be filled with 0 as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Map status column with new encoded variables\n",
    "dict1= {'Closed': 1, 'Open': 1}\n",
    "total_merge['status']= total_merge['status'].map(dict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, let's fill missing values with 0\n",
    "total_merge['status'] = total_merge['status'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1.])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_merge['status'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation among variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. Pearson Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BldgArea</th>\n",
       "      <th>BldgDepth</th>\n",
       "      <th>BuiltFAR</th>\n",
       "      <th>CommFAR</th>\n",
       "      <th>FacilFAR</th>\n",
       "      <th>Lot</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotDepth</th>\n",
       "      <th>NumBldgs</th>\n",
       "      <th>NumFloors</th>\n",
       "      <th>OfficeArea</th>\n",
       "      <th>ResArea</th>\n",
       "      <th>ResidFAR</th>\n",
       "      <th>RetailArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearAlter1</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>YCoord</th>\n",
       "      <th>XCoord</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BldgArea</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420954</td>\n",
       "      <td>0.467296</td>\n",
       "      <td>-0.002259</td>\n",
       "      <td>0.261559</td>\n",
       "      <td>0.141840</td>\n",
       "      <td>0.189726</td>\n",
       "      <td>0.474359</td>\n",
       "      <td>0.458352</td>\n",
       "      <td>0.613144</td>\n",
       "      <td>0.151697</td>\n",
       "      <td>0.964210</td>\n",
       "      <td>0.239538</td>\n",
       "      <td>0.171725</td>\n",
       "      <td>0.091432</td>\n",
       "      <td>0.124494</td>\n",
       "      <td>-0.053930</td>\n",
       "      <td>-0.049248</td>\n",
       "      <td>-0.166252</td>\n",
       "      <td>0.283481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgDepth</th>\n",
       "      <td>0.420954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269040</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>0.384031</td>\n",
       "      <td>-0.068287</td>\n",
       "      <td>0.050503</td>\n",
       "      <td>0.400251</td>\n",
       "      <td>0.020461</td>\n",
       "      <td>0.418054</td>\n",
       "      <td>0.084222</td>\n",
       "      <td>0.412571</td>\n",
       "      <td>0.368987</td>\n",
       "      <td>0.158329</td>\n",
       "      <td>0.268156</td>\n",
       "      <td>0.180959</td>\n",
       "      <td>-0.168658</td>\n",
       "      <td>-0.044898</td>\n",
       "      <td>-0.288318</td>\n",
       "      <td>0.466762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BuiltFAR</th>\n",
       "      <td>0.467296</td>\n",
       "      <td>0.269040</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005047</td>\n",
       "      <td>0.332186</td>\n",
       "      <td>0.121029</td>\n",
       "      <td>-0.001631</td>\n",
       "      <td>0.003594</td>\n",
       "      <td>0.230408</td>\n",
       "      <td>0.401733</td>\n",
       "      <td>0.002306</td>\n",
       "      <td>0.486820</td>\n",
       "      <td>0.315153</td>\n",
       "      <td>0.147576</td>\n",
       "      <td>0.138881</td>\n",
       "      <td>0.119637</td>\n",
       "      <td>-0.143809</td>\n",
       "      <td>-0.040728</td>\n",
       "      <td>-0.256484</td>\n",
       "      <td>0.399661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CommFAR</th>\n",
       "      <td>-0.002259</td>\n",
       "      <td>0.005368</td>\n",
       "      <td>-0.005047</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.118166</td>\n",
       "      <td>0.040743</td>\n",
       "      <td>0.002151</td>\n",
       "      <td>0.015773</td>\n",
       "      <td>-0.000130</td>\n",
       "      <td>-0.030443</td>\n",
       "      <td>0.024142</td>\n",
       "      <td>-0.019178</td>\n",
       "      <td>0.010143</td>\n",
       "      <td>0.101412</td>\n",
       "      <td>-0.074047</td>\n",
       "      <td>0.064944</td>\n",
       "      <td>-0.037673</td>\n",
       "      <td>-0.100308</td>\n",
       "      <td>-0.066408</td>\n",
       "      <td>-0.036038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FacilFAR</th>\n",
       "      <td>0.261559</td>\n",
       "      <td>0.384031</td>\n",
       "      <td>0.332186</td>\n",
       "      <td>0.118166</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010457</td>\n",
       "      <td>0.002977</td>\n",
       "      <td>0.062927</td>\n",
       "      <td>-0.054139</td>\n",
       "      <td>0.415523</td>\n",
       "      <td>0.020931</td>\n",
       "      <td>0.271394</td>\n",
       "      <td>0.869651</td>\n",
       "      <td>0.101490</td>\n",
       "      <td>0.093749</td>\n",
       "      <td>0.162768</td>\n",
       "      <td>-0.422241</td>\n",
       "      <td>-0.238352</td>\n",
       "      <td>-0.646901</td>\n",
       "      <td>0.488815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot</th>\n",
       "      <td>0.141840</td>\n",
       "      <td>-0.068287</td>\n",
       "      <td>0.121029</td>\n",
       "      <td>0.040743</td>\n",
       "      <td>0.010457</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023559</td>\n",
       "      <td>0.024790</td>\n",
       "      <td>0.112471</td>\n",
       "      <td>0.059296</td>\n",
       "      <td>0.009225</td>\n",
       "      <td>0.128652</td>\n",
       "      <td>-0.009644</td>\n",
       "      <td>0.203871</td>\n",
       "      <td>-0.006250</td>\n",
       "      <td>-0.045910</td>\n",
       "      <td>0.007805</td>\n",
       "      <td>-0.006905</td>\n",
       "      <td>-0.033563</td>\n",
       "      <td>-0.003995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>0.189726</td>\n",
       "      <td>0.050503</td>\n",
       "      <td>-0.001631</td>\n",
       "      <td>0.002151</td>\n",
       "      <td>0.002977</td>\n",
       "      <td>0.023559</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.283673</td>\n",
       "      <td>0.306527</td>\n",
       "      <td>0.070189</td>\n",
       "      <td>0.100103</td>\n",
       "      <td>0.141115</td>\n",
       "      <td>0.001820</td>\n",
       "      <td>0.020523</td>\n",
       "      <td>-0.001387</td>\n",
       "      <td>0.018618</td>\n",
       "      <td>0.045404</td>\n",
       "      <td>-0.003715</td>\n",
       "      <td>-0.000617</td>\n",
       "      <td>0.012432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotDepth</th>\n",
       "      <td>0.474359</td>\n",
       "      <td>0.400251</td>\n",
       "      <td>0.003594</td>\n",
       "      <td>0.015773</td>\n",
       "      <td>0.062927</td>\n",
       "      <td>0.024790</td>\n",
       "      <td>0.283673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195047</td>\n",
       "      <td>0.308456</td>\n",
       "      <td>0.124004</td>\n",
       "      <td>0.451343</td>\n",
       "      <td>0.049696</td>\n",
       "      <td>0.089238</td>\n",
       "      <td>-0.009803</td>\n",
       "      <td>0.031665</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>-0.034352</td>\n",
       "      <td>-0.035156</td>\n",
       "      <td>0.075069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBldgs</th>\n",
       "      <td>0.458352</td>\n",
       "      <td>0.020461</td>\n",
       "      <td>0.230408</td>\n",
       "      <td>-0.000130</td>\n",
       "      <td>-0.054139</td>\n",
       "      <td>0.112471</td>\n",
       "      <td>0.306527</td>\n",
       "      <td>0.195047</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038240</td>\n",
       "      <td>0.084871</td>\n",
       "      <td>0.415897</td>\n",
       "      <td>-0.057370</td>\n",
       "      <td>0.037194</td>\n",
       "      <td>0.117487</td>\n",
       "      <td>0.010182</td>\n",
       "      <td>0.091028</td>\n",
       "      <td>0.010203</td>\n",
       "      <td>0.064768</td>\n",
       "      <td>-0.025571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumFloors</th>\n",
       "      <td>0.613144</td>\n",
       "      <td>0.418054</td>\n",
       "      <td>0.401733</td>\n",
       "      <td>-0.030443</td>\n",
       "      <td>0.415523</td>\n",
       "      <td>0.059296</td>\n",
       "      <td>0.070189</td>\n",
       "      <td>0.308456</td>\n",
       "      <td>0.038240</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019226</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.368135</td>\n",
       "      <td>0.076624</td>\n",
       "      <td>0.262905</td>\n",
       "      <td>0.155947</td>\n",
       "      <td>-0.173533</td>\n",
       "      <td>-0.064215</td>\n",
       "      <td>-0.318492</td>\n",
       "      <td>0.535236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OfficeArea</th>\n",
       "      <td>0.151697</td>\n",
       "      <td>0.084222</td>\n",
       "      <td>0.002306</td>\n",
       "      <td>0.024142</td>\n",
       "      <td>0.020931</td>\n",
       "      <td>0.009225</td>\n",
       "      <td>0.100103</td>\n",
       "      <td>0.124004</td>\n",
       "      <td>0.084871</td>\n",
       "      <td>0.019226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033761</td>\n",
       "      <td>0.018946</td>\n",
       "      <td>0.043236</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.016018</td>\n",
       "      <td>-0.021648</td>\n",
       "      <td>-0.014924</td>\n",
       "      <td>-0.017379</td>\n",
       "      <td>-0.008947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ResArea</th>\n",
       "      <td>0.964210</td>\n",
       "      <td>0.412571</td>\n",
       "      <td>0.486820</td>\n",
       "      <td>-0.019178</td>\n",
       "      <td>0.271394</td>\n",
       "      <td>0.128652</td>\n",
       "      <td>0.141115</td>\n",
       "      <td>0.451343</td>\n",
       "      <td>0.415897</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.033761</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.253004</td>\n",
       "      <td>0.119732</td>\n",
       "      <td>0.092406</td>\n",
       "      <td>0.126760</td>\n",
       "      <td>-0.081135</td>\n",
       "      <td>-0.046344</td>\n",
       "      <td>-0.171421</td>\n",
       "      <td>0.302835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ResidFAR</th>\n",
       "      <td>0.239538</td>\n",
       "      <td>0.368987</td>\n",
       "      <td>0.315153</td>\n",
       "      <td>0.010143</td>\n",
       "      <td>0.869651</td>\n",
       "      <td>-0.009644</td>\n",
       "      <td>0.001820</td>\n",
       "      <td>0.049696</td>\n",
       "      <td>-0.057370</td>\n",
       "      <td>0.368135</td>\n",
       "      <td>0.018946</td>\n",
       "      <td>0.253004</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081430</td>\n",
       "      <td>0.095207</td>\n",
       "      <td>0.134544</td>\n",
       "      <td>-0.473184</td>\n",
       "      <td>-0.121195</td>\n",
       "      <td>-0.603000</td>\n",
       "      <td>0.449348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RetailArea</th>\n",
       "      <td>0.171725</td>\n",
       "      <td>0.158329</td>\n",
       "      <td>0.147576</td>\n",
       "      <td>0.101412</td>\n",
       "      <td>0.101490</td>\n",
       "      <td>0.203871</td>\n",
       "      <td>0.020523</td>\n",
       "      <td>0.089238</td>\n",
       "      <td>0.037194</td>\n",
       "      <td>0.076624</td>\n",
       "      <td>0.043236</td>\n",
       "      <td>0.119732</td>\n",
       "      <td>0.081430</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022864</td>\n",
       "      <td>0.034511</td>\n",
       "      <td>-0.057288</td>\n",
       "      <td>-0.056732</td>\n",
       "      <td>-0.057701</td>\n",
       "      <td>0.052521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>0.091432</td>\n",
       "      <td>0.268156</td>\n",
       "      <td>0.138881</td>\n",
       "      <td>-0.074047</td>\n",
       "      <td>0.093749</td>\n",
       "      <td>-0.006250</td>\n",
       "      <td>-0.001387</td>\n",
       "      <td>-0.009803</td>\n",
       "      <td>0.117487</td>\n",
       "      <td>0.262905</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.092406</td>\n",
       "      <td>0.095207</td>\n",
       "      <td>0.022864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056667</td>\n",
       "      <td>-0.009836</td>\n",
       "      <td>0.028113</td>\n",
       "      <td>0.019704</td>\n",
       "      <td>0.223112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearAlter1</th>\n",
       "      <td>0.124494</td>\n",
       "      <td>0.180959</td>\n",
       "      <td>0.119637</td>\n",
       "      <td>0.064944</td>\n",
       "      <td>0.162768</td>\n",
       "      <td>-0.045910</td>\n",
       "      <td>0.018618</td>\n",
       "      <td>0.031665</td>\n",
       "      <td>0.010182</td>\n",
       "      <td>0.155947</td>\n",
       "      <td>0.016018</td>\n",
       "      <td>0.126760</td>\n",
       "      <td>0.134544</td>\n",
       "      <td>0.034511</td>\n",
       "      <td>0.056667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.163420</td>\n",
       "      <td>-0.155608</td>\n",
       "      <td>-0.203246</td>\n",
       "      <td>0.184019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZipCode</th>\n",
       "      <td>-0.053930</td>\n",
       "      <td>-0.168658</td>\n",
       "      <td>-0.143809</td>\n",
       "      <td>-0.037673</td>\n",
       "      <td>-0.422241</td>\n",
       "      <td>0.007805</td>\n",
       "      <td>0.045404</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>0.091028</td>\n",
       "      <td>-0.173533</td>\n",
       "      <td>-0.021648</td>\n",
       "      <td>-0.081135</td>\n",
       "      <td>-0.473184</td>\n",
       "      <td>-0.057288</td>\n",
       "      <td>-0.009836</td>\n",
       "      <td>-0.163420</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.305120</td>\n",
       "      <td>0.590009</td>\n",
       "      <td>-0.216445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YCoord</th>\n",
       "      <td>-0.049248</td>\n",
       "      <td>-0.044898</td>\n",
       "      <td>-0.040728</td>\n",
       "      <td>-0.100308</td>\n",
       "      <td>-0.238352</td>\n",
       "      <td>-0.006905</td>\n",
       "      <td>-0.003715</td>\n",
       "      <td>-0.034352</td>\n",
       "      <td>0.010203</td>\n",
       "      <td>-0.064215</td>\n",
       "      <td>-0.014924</td>\n",
       "      <td>-0.046344</td>\n",
       "      <td>-0.121195</td>\n",
       "      <td>-0.056732</td>\n",
       "      <td>0.028113</td>\n",
       "      <td>-0.155608</td>\n",
       "      <td>0.305120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.212907</td>\n",
       "      <td>-0.039812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XCoord</th>\n",
       "      <td>-0.166252</td>\n",
       "      <td>-0.288318</td>\n",
       "      <td>-0.256484</td>\n",
       "      <td>-0.066408</td>\n",
       "      <td>-0.646901</td>\n",
       "      <td>-0.033563</td>\n",
       "      <td>-0.000617</td>\n",
       "      <td>-0.035156</td>\n",
       "      <td>0.064768</td>\n",
       "      <td>-0.318492</td>\n",
       "      <td>-0.017379</td>\n",
       "      <td>-0.171421</td>\n",
       "      <td>-0.603000</td>\n",
       "      <td>-0.057701</td>\n",
       "      <td>0.019704</td>\n",
       "      <td>-0.203246</td>\n",
       "      <td>0.590009</td>\n",
       "      <td>0.212907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.413136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>0.283481</td>\n",
       "      <td>0.466762</td>\n",
       "      <td>0.399661</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.488815</td>\n",
       "      <td>-0.003995</td>\n",
       "      <td>0.012432</td>\n",
       "      <td>0.075069</td>\n",
       "      <td>-0.025571</td>\n",
       "      <td>0.535236</td>\n",
       "      <td>-0.008947</td>\n",
       "      <td>0.302835</td>\n",
       "      <td>0.449348</td>\n",
       "      <td>0.052521</td>\n",
       "      <td>0.223112</td>\n",
       "      <td>0.184019</td>\n",
       "      <td>-0.216445</td>\n",
       "      <td>-0.039812</td>\n",
       "      <td>-0.413136</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BldgArea  BldgDepth  BuiltFAR   CommFAR  FacilFAR       Lot  \\\n",
       "BldgArea    1.000000   0.420954  0.467296 -0.002259  0.261559  0.141840   \n",
       "BldgDepth   0.420954   1.000000  0.269040  0.005368  0.384031 -0.068287   \n",
       "BuiltFAR    0.467296   0.269040  1.000000 -0.005047  0.332186  0.121029   \n",
       "CommFAR    -0.002259   0.005368 -0.005047  1.000000  0.118166  0.040743   \n",
       "FacilFAR    0.261559   0.384031  0.332186  0.118166  1.000000  0.010457   \n",
       "Lot         0.141840  -0.068287  0.121029  0.040743  0.010457  1.000000   \n",
       "LotArea     0.189726   0.050503 -0.001631  0.002151  0.002977  0.023559   \n",
       "LotDepth    0.474359   0.400251  0.003594  0.015773  0.062927  0.024790   \n",
       "NumBldgs    0.458352   0.020461  0.230408 -0.000130 -0.054139  0.112471   \n",
       "NumFloors   0.613144   0.418054  0.401733 -0.030443  0.415523  0.059296   \n",
       "OfficeArea  0.151697   0.084222  0.002306  0.024142  0.020931  0.009225   \n",
       "ResArea     0.964210   0.412571  0.486820 -0.019178  0.271394  0.128652   \n",
       "ResidFAR    0.239538   0.368987  0.315153  0.010143  0.869651 -0.009644   \n",
       "RetailArea  0.171725   0.158329  0.147576  0.101412  0.101490  0.203871   \n",
       "YearBuilt   0.091432   0.268156  0.138881 -0.074047  0.093749 -0.006250   \n",
       "YearAlter1  0.124494   0.180959  0.119637  0.064944  0.162768 -0.045910   \n",
       "ZipCode    -0.053930  -0.168658 -0.143809 -0.037673 -0.422241  0.007805   \n",
       "YCoord     -0.049248  -0.044898 -0.040728 -0.100308 -0.238352 -0.006905   \n",
       "XCoord     -0.166252  -0.288318 -0.256484 -0.066408 -0.646901 -0.033563   \n",
       "status      0.283481   0.466762  0.399661 -0.036038  0.488815 -0.003995   \n",
       "\n",
       "             LotArea  LotDepth  NumBldgs  NumFloors  OfficeArea   ResArea  \\\n",
       "BldgArea    0.189726  0.474359  0.458352   0.613144    0.151697  0.964210   \n",
       "BldgDepth   0.050503  0.400251  0.020461   0.418054    0.084222  0.412571   \n",
       "BuiltFAR   -0.001631  0.003594  0.230408   0.401733    0.002306  0.486820   \n",
       "CommFAR     0.002151  0.015773 -0.000130  -0.030443    0.024142 -0.019178   \n",
       "FacilFAR    0.002977  0.062927 -0.054139   0.415523    0.020931  0.271394   \n",
       "Lot         0.023559  0.024790  0.112471   0.059296    0.009225  0.128652   \n",
       "LotArea     1.000000  0.283673  0.306527   0.070189    0.100103  0.141115   \n",
       "LotDepth    0.283673  1.000000  0.195047   0.308456    0.124004  0.451343   \n",
       "NumBldgs    0.306527  0.195047  1.000000   0.038240    0.084871  0.415897   \n",
       "NumFloors   0.070189  0.308456  0.038240   1.000000    0.019226  0.616226   \n",
       "OfficeArea  0.100103  0.124004  0.084871   0.019226    1.000000  0.033761   \n",
       "ResArea     0.141115  0.451343  0.415897   0.616226    0.033761  1.000000   \n",
       "ResidFAR    0.001820  0.049696 -0.057370   0.368135    0.018946  0.253004   \n",
       "RetailArea  0.020523  0.089238  0.037194   0.076624    0.043236  0.119732   \n",
       "YearBuilt  -0.001387 -0.009803  0.117487   0.262905    0.000829  0.092406   \n",
       "YearAlter1  0.018618  0.031665  0.010182   0.155947    0.016018  0.126760   \n",
       "ZipCode     0.045404  0.005115  0.091028  -0.173533   -0.021648 -0.081135   \n",
       "YCoord     -0.003715 -0.034352  0.010203  -0.064215   -0.014924 -0.046344   \n",
       "XCoord     -0.000617 -0.035156  0.064768  -0.318492   -0.017379 -0.171421   \n",
       "status      0.012432  0.075069 -0.025571   0.535236   -0.008947  0.302835   \n",
       "\n",
       "            ResidFAR  RetailArea  YearBuilt  YearAlter1   ZipCode    YCoord  \\\n",
       "BldgArea    0.239538    0.171725   0.091432    0.124494 -0.053930 -0.049248   \n",
       "BldgDepth   0.368987    0.158329   0.268156    0.180959 -0.168658 -0.044898   \n",
       "BuiltFAR    0.315153    0.147576   0.138881    0.119637 -0.143809 -0.040728   \n",
       "CommFAR     0.010143    0.101412  -0.074047    0.064944 -0.037673 -0.100308   \n",
       "FacilFAR    0.869651    0.101490   0.093749    0.162768 -0.422241 -0.238352   \n",
       "Lot        -0.009644    0.203871  -0.006250   -0.045910  0.007805 -0.006905   \n",
       "LotArea     0.001820    0.020523  -0.001387    0.018618  0.045404 -0.003715   \n",
       "LotDepth    0.049696    0.089238  -0.009803    0.031665  0.005115 -0.034352   \n",
       "NumBldgs   -0.057370    0.037194   0.117487    0.010182  0.091028  0.010203   \n",
       "NumFloors   0.368135    0.076624   0.262905    0.155947 -0.173533 -0.064215   \n",
       "OfficeArea  0.018946    0.043236   0.000829    0.016018 -0.021648 -0.014924   \n",
       "ResArea     0.253004    0.119732   0.092406    0.126760 -0.081135 -0.046344   \n",
       "ResidFAR    1.000000    0.081430   0.095207    0.134544 -0.473184 -0.121195   \n",
       "RetailArea  0.081430    1.000000   0.022864    0.034511 -0.057288 -0.056732   \n",
       "YearBuilt   0.095207    0.022864   1.000000    0.056667 -0.009836  0.028113   \n",
       "YearAlter1  0.134544    0.034511   0.056667    1.000000 -0.163420 -0.155608   \n",
       "ZipCode    -0.473184   -0.057288  -0.009836   -0.163420  1.000000  0.305120   \n",
       "YCoord     -0.121195   -0.056732   0.028113   -0.155608  0.305120  1.000000   \n",
       "XCoord     -0.603000   -0.057701   0.019704   -0.203246  0.590009  0.212907   \n",
       "status      0.449348    0.052521   0.223112    0.184019 -0.216445 -0.039812   \n",
       "\n",
       "              XCoord    status  \n",
       "BldgArea   -0.166252  0.283481  \n",
       "BldgDepth  -0.288318  0.466762  \n",
       "BuiltFAR   -0.256484  0.399661  \n",
       "CommFAR    -0.066408 -0.036038  \n",
       "FacilFAR   -0.646901  0.488815  \n",
       "Lot        -0.033563 -0.003995  \n",
       "LotArea    -0.000617  0.012432  \n",
       "LotDepth   -0.035156  0.075069  \n",
       "NumBldgs    0.064768 -0.025571  \n",
       "NumFloors  -0.318492  0.535236  \n",
       "OfficeArea -0.017379 -0.008947  \n",
       "ResArea    -0.171421  0.302835  \n",
       "ResidFAR   -0.603000  0.449348  \n",
       "RetailArea -0.057701  0.052521  \n",
       "YearBuilt   0.019704  0.223112  \n",
       "YearAlter1 -0.203246  0.184019  \n",
       "ZipCode     0.590009 -0.216445  \n",
       "YCoord      0.212907 -0.039812  \n",
       "XCoord      1.000000 -0.413136  \n",
       "status     -0.413136  1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_merge.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor = total_merge.corr()\n",
    "\n",
    "#plot correlation matrix\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### From the correlation matrix, we observe that FacilFAR, NumFloors, ResArea, ResidFAR, YearBuilt, BuiltFAR, BldDepth and BldArea have the strongest relation with our top complaint."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare our dataset for feature selection algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's prepare our dataset for feature selection and remove features that are negatively correlated to HEAT/HOT WATER complaint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_col=['status', \"ZipCode\", 'CommFAR', 'Lot', 'OfficeArea', \n",
    "          'incident_address', 'Address', 'NumBldgs', 'YCoord', 'XCoord']\n",
    "y= total_merge['status']\n",
    "x= total_merge.drop(drop_col, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BldgArea</th>\n",
       "      <th>BldgDepth</th>\n",
       "      <th>BuiltFAR</th>\n",
       "      <th>FacilFAR</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotDepth</th>\n",
       "      <th>NumFloors</th>\n",
       "      <th>ResArea</th>\n",
       "      <th>ResidFAR</th>\n",
       "      <th>RetailArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearAlter1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.5</td>\n",
       "      <td>15000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>752</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>6.5</td>\n",
       "      <td>13770</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39375</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.13</td>\n",
       "      <td>6.5</td>\n",
       "      <td>35000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12500</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12500</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12500</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12500</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BldgArea  BldgDepth  BuiltFAR  FacilFAR  LotArea  LotDepth  NumFloors  \\\n",
       "0         0        0.0      0.00       6.5    15000     200.0        0.0   \n",
       "1       752       16.0      0.05       6.5    13770     100.0        1.0   \n",
       "2     39375      200.0      1.13       6.5    35000     200.0        2.0   \n",
       "3     12500       85.0      5.00       6.5     2500     100.0        5.0   \n",
       "4     12500       85.0      5.00       6.5     2500     100.0        5.0   \n",
       "\n",
       "   ResArea  ResidFAR  RetailArea  YearBuilt  YearAlter1  \n",
       "0        0      6.02           0          0           0  \n",
       "1        0      6.02           0       1931        1994  \n",
       "2        0      6.02           0       1931           0  \n",
       "3    12500      6.02           0       1931        2001  \n",
       "4    12500      6.02           0       1931        2001  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fill NaN values in our dataset \n",
    "x= x.fillna(0)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of affected building: 298329\n",
      "Number of building with no issue: 76203\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAEKCAYAAADEovgeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFT5JREFUeJzt3X+sZ3Wd3/HnyxlRd5UfwmhxBjpE5w9Rd0e8xdmaJiobGNi0gxvYDEmXiSWdrYHumtpG3CbioqQSdcmyVRo2zDLQrUhwLdPtWHaCtGa7ilwUgYFSbtHCCIGBGRGXLHbw3T++n6tfhu+9853rfO539s7zkZx8z3mfz/mcz0lueHHO9zPnm6pCkqSeXjHpAUiSlj7DRpLUnWEjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7gwbSVJ3ho0kqbvlkx7A4eKEE06o1atXT3oYkvR3yt133/10Va04UDvDplm9ejXT09OTHoYk/Z2S5P+O087HaJKk7gwbSVJ3ho0kqTvDRpLUXbewSfLqJN9K8t0kO5P8QaufkuTOJA8n+VKSo1r9VW17pu1fPdTXx1r9oSRnDdXXt9pMkkuH6iPPIUmajJ53Ni8A76+qXwXWAuuTrAOuBK6qqjXAXuCi1v4iYG9VvQW4qrUjyanARuBtwHrgC0mWJVkGfB44GzgVuKC1ZZ5zSJImoFvY1MCP2+Yr21LA+4FbWn0rcG5b39C2afvPSJJWv6mqXqiq7wEzwOltmamqR6rqJ8BNwIZ2zFznkCRNQNfvbNodyD3AU8AO4P8AP6yqfa3JLmBlW18JPAbQ9j8LHD9c3++YuerHz3MOSdIEdA2bqnqxqtYCqxjcibx1VLP2mTn2Har6yyTZnGQ6yfTu3btHNZEkHQKL8gaBqvphkv8OrAOOTbK83XmsAh5vzXYBJwG7kiwHjgH2DNVnDR8zqv70POfYf1zXAtcCTE1NjQwkaSl49PJ3THoIOgyd/PH7Fu1cPWejrUhybFt/DfDrwIPAHcB5rdkm4Na2vq1t0/Z/raqq1Te22WqnAGuAbwF3AWvazLOjGEwi2NaOmesckqQJ6HlncyKwtc0aewVwc1X9RZIHgJuSfAr4DnBda38dcGOSGQZ3NBsBqmpnkpuBB4B9wMVV9SJAkkuA24BlwJaq2tn6+ugc55AkTUC3sKmqe4F3jqg/wuD7m/3rfwucP0dfVwBXjKhvB7aPew5J0mT4BgFJUneGjSSpO8NGktSdYSNJ6s6wkSR1Z9hIkrozbCRJ3Rk2kqTuDBtJUneGjSSpO8NGktSdYSNJ6s6wkSR1Z9hIkrozbCRJ3Rk2kqTuDBtJUneGjSSpO8NGktSdYSNJ6s6wkSR1Z9hIkrozbCRJ3Rk2kqTuDBtJUneGjSSpu25hk+SkJHckeTDJziS/1+qfSPKDJPe05ZyhYz6WZCbJQ0nOGqqvb7WZJJcO1U9JcmeSh5N8KclRrf6qtj3T9q/udZ2SpAPreWezD/hIVb0VWAdcnOTUtu+qqlrblu0Abd9G4G3AeuALSZYlWQZ8HjgbOBW4YKifK1tfa4C9wEWtfhGwt6reAlzV2kmSJqRb2FTVE1X17bb+HPAgsHKeQzYAN1XVC1X1PWAGOL0tM1X1SFX9BLgJ2JAkwPuBW9rxW4Fzh/ra2tZvAc5o7SVJE7Ao39m0x1jvBO5spUuS3JtkS5LjWm0l8NjQYbtaba768cAPq2rffvWX9NX2P9va7z+uzUmmk0zv3r37F7pGSdLcuodNktcCXwY+XFU/Aq4B3gysBZ4APjfbdMThtYD6fH29tFB1bVVNVdXUihUr5r0OSdLCdQ2bJK9kEDR/VlV/DlBVT1bVi1X1U+BPGDwmg8GdyUlDh68CHp+n/jRwbJLl+9Vf0lfbfwyw59BenSRpXD1nowW4Dniwqv5wqH7iULMPAPe39W3AxjaT7BRgDfAt4C5gTZt5dhSDSQTbqqqAO4Dz2vGbgFuH+trU1s8DvtbaS5ImYPmBmyzYe4DfBu5Lck+r/T6D2WRrGTzW+j7wOwBVtTPJzcADDGayXVxVLwIkuQS4DVgGbKmqna2/jwI3JfkU8B0G4Ub7vDHJDIM7mo0dr1OSdADdwqaq/orR351sn+eYK4ArRtS3jzquqh7h54/hhut/C5x/MOOVJPXjGwQkSd0ZNpKk7gwbSVJ3ho0kqTvDRpLUnWEjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7gwbSVJ3ho0kqTvDRpLUnWEjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7gwbSVJ3ho0kqTvDRpLUnWEjSerOsJEkdWfYSJK6M2wkSd11C5skJyW5I8mDSXYm+b1Wf32SHUkebp/HtXqSXJ1kJsm9SU4b6mtTa/9wkk1D9Xclua8dc3WSzHcOSdJk9Lyz2Qd8pKreCqwDLk5yKnApcHtVrQFub9sAZwNr2rIZuAYGwQFcBrwbOB24bCg8rmltZ49b3+pznUOSNAHdwqaqnqiqb7f154AHgZXABmBra7YVOLetbwBuqIFvAscmORE4C9hRVXuqai+wA1jf9h1dVd+oqgJu2K+vUeeQJE3Aonxnk2Q18E7gTuCNVfUEDAIJeENrthJ4bOiwXa02X33XiDrznEOSNAHdwybJa4EvAx+uqh/N13RErRZQP5ixbU4ynWR69+7dB3OoJOkgdA2bJK9kEDR/VlV/3spPtkdgtM+nWn0XcNLQ4auAxw9QXzWiPt85XqKqrq2qqaqaWrFixcIuUpJ0QD1nowW4Dniwqv5waNc2YHZG2Sbg1qH6hW1W2jrg2fYI7DbgzCTHtYkBZwK3tX3PJVnXznXhfn2NOockaQKWd+z7PcBvA/cluafVfh/4NHBzkouAR4Hz277twDnADPA88EGAqtqT5JPAXa3d5VW1p61/CLgeeA3w1bYwzzkkSRPQLWyq6q8Y/b0KwBkj2hdw8Rx9bQG2jKhPA28fUX9m1DkkSZPhGwQkSd0ZNpKk7gwbSVJ3ho0kqTvDRpLUnWEjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7gwbSVJ3ho0kqTvDRpLU3Vhhk+T2cWqSJI0y708MJHk18EvACe2Hy2Z/MuBo4E2dxyZJWiIO9Hs2vwN8mEGw3M3Pw+ZHwOc7jkuStITMGzZV9UfAHyX5l1X1x4s0JknSEjPWL3VW1R8n+YfA6uFjquqGTuOSJC0hY4VNkhuBNwP3AC+2cgGGjSTpgMYKG2AKOLWqqudgJElL07j/zuZ+4O/1HIgkaeka987mBOCBJN8CXpgtVtU/6TIqSdKSMm7YfKLnICRJS9u4s9H+R++BSJKWrnFnoz3HYPYZwFHAK4G/qaqjew1MkrR0jHtn87rh7STnAqd3GZEkaclZ0Fufq+o/A++fr02SLUmeSnL/UO0TSX6Q5J62nDO072NJZpI8lOSsofr6VptJculQ/ZQkdyZ5OMmXkhzV6q9q2zNt/+qFXKMk6dAZ963Pvzm0nJfk0/z8sdpcrgfWj6hfVVVr27K99X8qsBF4WzvmC0mWJVnG4B1sZwOnAhe0tgBXtr7WAHuBi1r9ImBvVb0FuKq1kyRN0Lh3Nv94aDkLeA7YMN8BVfV1YM+Y/W8AbqqqF6rqe8AMg8d0pwMzVfVIVf0EuAnYkCQM7qxuacdvBc4d6mtrW78FOKO1lyRNyLjf2XzwEJ7zkiQXAtPAR6pqL7AS+OZQm12tBvDYfvV3A8cDP6yqfSPar5w9pqr2JXm2tX/6EF6DJOkgjPsYbVWSr7TvYJ5M8uUkqxZwvmsYvGNtLfAE8LnZU4xoWwuoz9fXyyTZnGQ6yfTu3bvnG7ck6Rcw7mO0PwW2Mfhdm5XAf2m1g1JVT1bVi1X1U+BP+PmMtl3ASUNNVwGPz1N/Gjg2yfL96i/pq+0/hjke51XVtVU1VVVTK1asONjLkSSNadywWVFVf1pV+9pyPXDQ/3VOcuLQ5gcYvHMNBkG2sc0kOwVYA3wLuAtY02aeHcVgEsG29kLQO4Dz2vGbgFuH+trU1s8DvuYLRCVpssZ9Xc3TSf4p8MW2fQHwzHwHJPki8F4GPym9C7gMeG+StQwea32fwS+BUlU7k9wMPADsAy6uqhdbP5cAtwHLgC1VtbOd4qPATUk+BXwHuK7VrwNuTDLD4I5m45jXKEnqJOP8T3+Sk4F/D/wag6D4a+B3q+rRvsNbPFNTUzU9PT3pYUhdPHr5OyY9BB2GTv74fb9wH0nurqqpA7Ub987mk8CmNnOMJK8HPgv8s4UPUZJ0pBj3O5tfmQ0agKraA7yzz5AkSUvNuGHziiTHzW60O5tx74okSUe4cQPjc8BfJ7mFwXc2vwVc0W1UkqQlZdw3CNyQZJrBK2IC/GZVPdB1ZJKkJWPsR2EtXAwYSdJBW9BPDEiSdDAMG0lSd4aNJKk7w0aS1J1hI0nqzrCRJHVn2EiSujNsJEndGTaSpO4MG0lSd4aNJKk7w0aS1J1hI0nqzrCRJHVn2EiSujNsJEndGTaSpO4MG0lSd4aNJKk7w0aS1F23sEmyJclTSe4fqr0+yY4kD7fP41o9Sa5OMpPk3iSnDR2zqbV/OMmmofq7ktzXjrk6SeY7hyRpcnre2VwPrN+vdilwe1WtAW5v2wBnA2vashm4BgbBAVwGvBs4HbhsKDyuaW1nj1t/gHNIkiakW9hU1deBPfuVNwBb2/pW4Nyh+g018E3g2CQnAmcBO6pqT1XtBXYA69u+o6vqG1VVwA379TXqHJKkCVns72zeWFVPALTPN7T6SuCxoXa7Wm2++q4R9fnO8TJJNieZTjK9e/fuBV+UJGl+h8sEgYyo1QLqB6Wqrq2qqaqaWrFixcEeLkka02KHzZPtERjt86lW3wWcNNRuFfD4AeqrRtTnO4ckaUIWO2y2AbMzyjYBtw7VL2yz0tYBz7ZHYLcBZyY5rk0MOBO4re17Lsm6Ngvtwv36GnUOSdKELO/VcZIvAu8FTkiyi8Gssk8DNye5CHgUOL813w6cA8wAzwMfBKiqPUk+CdzV2l1eVbOTDj7EYMbba4CvtoV5ziFJmpBuYVNVF8yx64wRbQu4eI5+tgBbRtSngbePqD8z6hySpMk5XCYISJKWMMNGktSdYSNJ6s6wkSR1Z9hIkrozbCRJ3Rk2kqTuDBtJUneGjSSpO8NGktSdYSNJ6q7bu9GORO/6NzdMegg6DN39mQsnPQRp4ryzkSR1Z9hIkrozbCRJ3Rk2kqTuDBtJUneGjSSpO8NGktSdYSNJ6s6wkSR1Z9hIkrozbCRJ3Rk2kqTuDBtJUncTCZsk309yX5J7kky32uuT7EjycPs8rtWT5OokM0nuTXLaUD+bWvuHk2waqr+r9T/Tjs3iX6UkadYk72zeV1Vrq2qqbV8K3F5Va4Db2zbA2cCatmwGroFBOAGXAe8GTgcumw2o1mbz0HHr+1+OJGkuh9NjtA3A1ra+FTh3qH5DDXwTODbJicBZwI6q2lNVe4EdwPq27+iq+kZVFXDDUF+SpAmYVNgU8JdJ7k6yudXeWFVPALTPN7T6SuCxoWN3tdp89V0j6pKkCZnUL3W+p6oeT/IGYEeS/zVP21Hft9QC6i/veBB0mwFOPvnk+UcsSVqwidzZVNXj7fMp4CsMvnN5sj0Co30+1ZrvAk4aOnwV8PgB6qtG1EeN49qqmqqqqRUrVvyilyVJmsOih02SX07yutl14EzgfmAbMDujbBNwa1vfBlzYZqWtA55tj9luA85MclybGHAmcFvb91ySdW0W2oVDfUmSJmASj9HeCHylzUZeDvynqvpvSe4Cbk5yEfAocH5rvx04B5gBngc+CFBVe5J8Erirtbu8qva09Q8B1wOvAb7aFknShCx62FTVI8Cvjqg/A5wxol7AxXP0tQXYMqI+Dbz9Fx6sJOmQOJymPkuSlijDRpLUnWEjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7gwbSVJ3ho0kqTvDRpLUnWEjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7gwbSVJ3ho0kqTvDRpLUnWEjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7gwbSVJ3ho0kqTvDRpLU3ZINmyTrkzyUZCbJpZMejyQdyZZk2CRZBnweOBs4FbggyamTHZUkHbmWZNgApwMzVfVIVf0EuAnYMOExSdIRa6mGzUrgsaHtXa0mSZqA5ZMeQCcZUauXNUo2A5vb5o+TPNR1VEeWE4CnJz2Iw0E+u2nSQ9BL+bc567JR/6k8aH9/nEZLNWx2AScNba8CHt+/UVVdC1y7WIM6kiSZrqqpSY9D2p9/m5OxVB+j3QWsSXJKkqOAjcC2CY9Jko5YS/LOpqr2JbkEuA1YBmypqp0THpYkHbGWZNgAVNV2YPukx3EE8/GkDlf+bU5Aql72vbkkSYfUUv3ORpJ0GDFstGAHeiVQklcl+VLbf2eS1Ys/Sh2JkmxJ8lSS++fYnyRXt7/Ne5OctthjPNIYNlqQMV8JdBGwt6reAlwFXLm4o9QR7Hpg/Tz7zwbWtGUzcM0ijOmIZthoocZ5JdAGYGtbvwU4I8kh+Vdk0nyq6uvAnnmabABuqIFvAscmOXFxRndkMmy0UOO8EuhnbapqH/AscPyijE6an6+0WmSGjRZqnFcCjfXaIGkC/NtcZIaNFmqcVwL9rE2S5cAxzP9oQ1osY73SSoeOYaOFGueVQNuA2bdQngd8rfyHXTo8bAMubLPS1gHPVtUTkx7UUrZk3yCgvuZ6JVCSy4HpqtoGXAfcmGSGwR3NxsmNWEeSJF8E3guckGQXcBnwSoCq+g8M3i5yDjADPA98cDIjPXL4BgFJUnc+RpMkdWfYSJK6M2wkSd0ZNpKk7gwbSVJ3ho00QUk+nOSXDlU76XDl1GdpgpJ8H5iqqqcPRTvpcOWdjbRIkvxykv+a5LtJ7k9yGfAm4I4kd7Q21ySZTrIzyR+02u+OaPfjoX7PS3J9Wz+/9f3dJF9f5EuU5uQbBKTFsx54vKp+AyDJMQz+5fr7hu5Y/m1V7Wm/F3R7kl+pqquT/Kv92s3l48BZVfWDJMf2uhDpYHlnIy2e+4BfT3Jlkn9UVc+OaPNbSb4NfAd4G4MfpjsY/xO4Psk/Z/AaIemw4J2NtEiq6n8neReDd3L9uyR/Obw/ySnAvwb+QVXtbY/GXj1Xd0PrP2tTVf8iybuB3wDuSbK2qp45lNchLYR3NtIiSfIm4Pmq+o/AZ4HTgOeA17UmRwN/Azyb5I0Mfrp41nA7gCeTvDXJK4APDJ3jzVV1Z1V9HHial75GX5oY72ykxfMO4DNJfgr8P+BDwK8BX03yRFW9L8l3gJ3AIwweic26drgdcCnwFwx+bfJ+4LWt3WeSrGHw42C3A99dhOuSDsipz5Kk7nyMJknqzrCRJHVn2EiSujNsJEndGTaSpO4MG0lSd4aNJKk7w0aS1N3/ByEpdng1SVHZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's plot the number of buildings with and without issues \n",
    "ax=sns.countplot(y, label='Count')\n",
    "B, M = y.value_counts()\n",
    "print('Number of affected building:', B)\n",
    "print('Number of building with no issue:', M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection with SelectKBest method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "#k=6, k is the number of best features\n",
    "\n",
    "select_feature = SelectKBest(chi2, k=6).fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BldgArea : 2276550206.097799\n",
      "BldgDepth : 1124940.8638652652\n",
      "BuiltFAR : 110502.93405299366\n",
      "FacilFAR : 39342.09120624046\n",
      "LotArea : 46483564.08117056\n",
      "LotDepth : 43983.04971698611\n",
      "NumFloors : 106854.62993149101\n",
      "ResArea : 2441283096.9417844\n",
      "ResidFAR : 49518.168126747165\n",
      "RetailArea : 10898557.056113463\n",
      "YearBuilt : 470733.78143052815\n",
      "YearAlter1 : 13741562.988742499\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, len(select_feature.scores_)):\n",
    "    print(x_train.columns[i], ':' ,select_feature.scores_[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.05217391 0.07826087 0.0768116  0.02898551 0.0884058  0.06376812\n",
      " 0.07246377 0.2289855  0.06376812 0.00869565 0.20434782 0.03333334]\n"
     ]
    },
    {
     "data": {
      "image/png": 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vTfL07vfuZazgC91Dhh95swP4mwnWMpIkW4HfAS6uqq/3HddUCHQXTb71CIy7gRur6uBkqxrJOcBrGPzVfFf384uTLkrf9kbg+iSfAV4E/MGE6zlm3RHNTcCdwGcZvHesqLttk3wI+CRwVpLZJJcBVwPnJfkCcF63fsJ7irm8E/h+YF/3XvCuXvvyjmFJaldTRwKSpCczBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJatj/AfFu4CLkhogKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import loadtxt\n",
    "from xgboost import XGBClassifier\n",
    "from matplotlib import pyplot\n",
    "\n",
    "#Fit the model\n",
    "xgb=XGBClassifier()\n",
    "xgb.fit(x_train, y_train)\n",
    "\n",
    "#Feature importance\n",
    "print(xgb.feature_importances_)\n",
    "\n",
    "#Visualize\n",
    "pyplot.bar(range(len(xgb.feature_importances_)), xgb.feature_importances_)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BldgArea : 0.052173913\n",
      "BldgDepth : 0.07826087\n",
      "BuiltFAR : 0.0768116\n",
      "FacilFAR : 0.028985508\n",
      "LotArea : 0.088405795\n",
      "LotDepth : 0.06376812\n",
      "NumFloors : 0.072463766\n",
      "ResArea : 0.2289855\n",
      "ResidFAR : 0.06376812\n",
      "RetailArea : 0.008695652\n",
      "YearBuilt : 0.20434782\n",
      "YearAlter1 : 0.033333335\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, len(xgb.feature_importances_)):\n",
    "    print(x_train.columns[i], ':', xgb.feature_importances_[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between complaint and building characteristics\n",
    "\n",
    "1. With Pearson-correlation, the following are the most pertinant features\n",
    "     - FacilFAR\n",
    "     - NumFloors\n",
    "     - ResidFAR\n",
    "     - YearBuilt \n",
    "     - BuiltFAR \n",
    "     - BldDepth \n",
    "     - BldArea\n",
    "     - ResArea\n",
    " 2. With SelectKBest\n",
    "     - BldArea\n",
    "     - LotArea\n",
    "     - YearAlter1\n",
    "     - ResArea\n",
    "     - BuiltFAR\n",
    "     - BldDepth\n",
    " 3. With XGBoost\n",
    "     - YearBuilt\n",
    "     - ResArea\n",
    "     - LotArea\n",
    "     - BldgArea \n",
    "     - BldgDepth \n",
    "     - BuiltFAR\n",
    "     \n",
    " Overall, we can conclude that with these three techniques, BldArea, BuildDepth, ResArea, YearBuilt, LotArea are the features that stood out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Upload the merged file to cloud\n",
    "total_merge.to_csv('./df_merge.csv')\n",
    "client_cred.upload_file('./df_merge.csv',bucket,'total_merge.csv')"
   ]
  }
 ],
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