Multiple_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1\n",
    "#### Section a)\n",
    "##### Preprocessing and data analysis\n",
    "Read the training data from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1994, 100)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable = pd.read_csv(\"C:/Users/USER/Desktop/DSC/DSC_478 Programming to Machine Learning A/Assignment/Assignment_3/Problem_1/communities.csv\", na_values=[\"?\"])\n",
    "vstable.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describing the columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Column list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state', 'communityname', 'population', 'householdsize', 'racepctblack',\n",
       "       'racePctWhite', 'racePctAsian', 'racePctHisp', 'agePct12t21',\n",
       "       'agePct12t29', 'agePct16t24', 'agePct65up', 'numbUrban', 'pctUrban',\n",
       "       'medIncome', 'pctWWage', 'pctWFarmSelf', 'pctWInvInc', 'pctWSocSec',\n",
       "       'pctWPubAsst', 'pctWRetire', 'medFamInc', 'perCapInc', 'whitePerCap',\n",
       "       'blackPerCap', 'indianPerCap', 'AsianPerCap', 'OtherPerCap',\n",
       "       'HispPerCap', 'NumUnderPov', 'PctPopUnderPov', 'PctLess9thGrade',\n",
       "       'PctNotHSGrad', 'PctBSorMore', 'PctUnemployed', 'PctEmploy',\n",
       "       'PctEmplManu', 'PctEmplProfServ', 'MalePctDivorce', 'MalePctNevMarr',\n",
       "       'FemalePctDiv', 'TotalPctDiv', 'PersPerFam', 'PctFam2Par',\n",
       "       'PctKids2Par', 'PctYoungKids2Par', 'PctTeen2Par', 'PctWorkMomYoungKids',\n",
       "       'PctWorkMom', 'NumIlleg', 'PctIlleg', 'NumImmig', 'PctImmigRecent',\n",
       "       'PctImmigRec5', 'PctImmigRec8', 'PctImmigRec10', 'PctRecentImmig',\n",
       "       'PctRecImmig5', 'PctRecImmig8', 'PctRecImmig10', 'PctSpeakEnglOnly',\n",
       "       'PctNotSpeakEnglWell', 'PctLargHouseFam', 'PctLargHouseOccup',\n",
       "       'PersPerOccupHous', 'PersPerOwnOccHous', 'PersPerRentOccHous',\n",
       "       'PctPersOwnOccup', 'PctPersDenseHous', 'PctHousLess3BR', 'MedNumBR',\n",
       "       'HousVacant', 'PctHousOccup', 'PctHousOwnOcc', 'PctVacantBoarded',\n",
       "       'PctVacMore6Mos', 'MedYrHousBuilt', 'PctHousNoPhone', 'PctWOFullPlumb',\n",
       "       'OwnOccLowQuart', 'OwnOccMedVal', 'OwnOccHiQuart', 'RentLowQ',\n",
       "       'RentMedian', 'RentHighQ', 'MedRent', 'MedRentPctHousInc',\n",
       "       'MedOwnCostPctInc', 'MedOwnCostPctIncNoMtg', 'NumInShelters',\n",
       "       'NumStreet', 'PctForeignBorn', 'PctBornSameState', 'PctSameHouse85',\n",
       "       'PctSameCity85', 'PctSameState85', 'LandArea', 'PopDens',\n",
       "       'PctUsePubTrans', 'ViolentCrimesPerPop'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for unknown values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>state</th>\n",
       "      <th>communityname</th>\n",
       "      <th>population</th>\n",
       "      <th>householdsize</th>\n",
       "      <th>racepctblack</th>\n",
       "      <th>racePctWhite</th>\n",
       "      <th>racePctAsian</th>\n",
       "      <th>racePctHisp</th>\n",
       "      <th>agePct12t21</th>\n",
       "      <th>agePct12t29</th>\n",
       "      <th>agePct16t24</th>\n",
       "      <th>agePct65up</th>\n",
       "      <th>numbUrban</th>\n",
       "      <th>pctUrban</th>\n",
       "      <th>medIncome</th>\n",
       "      <th>pctWWage</th>\n",
       "      <th>pctWFarmSelf</th>\n",
       "      <th>pctWInvInc</th>\n",
       "      <th>pctWSocSec</th>\n",
       "      <th>pctWPubAsst</th>\n",
       "      <th>pctWRetire</th>\n",
       "      <th>medFamInc</th>\n",
       "      <th>perCapInc</th>\n",
       "      <th>whitePerCap</th>\n",
       "      <th>blackPerCap</th>\n",
       "      <th>indianPerCap</th>\n",
       "      <th>AsianPerCap</th>\n",
       "      <th>OtherPerCap</th>\n",
       "      <th>HispPerCap</th>\n",
       "      <th>NumUnderPov</th>\n",
       "      <th>PctPopUnderPov</th>\n",
       "      <th>PctLess9thGrade</th>\n",
       "      <th>PctNotHSGrad</th>\n",
       "      <th>PctBSorMore</th>\n",
       "      <th>PctUnemployed</th>\n",
       "      <th>PctEmploy</th>\n",
       "      <th>PctEmplManu</th>\n",
       "      <th>PctEmplProfServ</th>\n",
       "      <th>MalePctDivorce</th>\n",
       "      <th>MalePctNevMarr</th>\n",
       "      <th>FemalePctDiv</th>\n",
       "      <th>TotalPctDiv</th>\n",
       "      <th>PersPerFam</th>\n",
       "      <th>PctFam2Par</th>\n",
       "      <th>PctKids2Par</th>\n",
       "      <th>PctYoungKids2Par</th>\n",
       "      <th>PctTeen2Par</th>\n",
       "      <th>PctWorkMomYoungKids</th>\n",
       "      <th>PctWorkMom</th>\n",
       "      <th>NumIlleg</th>\n",
       "      <th>PctIlleg</th>\n",
       "      <th>NumImmig</th>\n",
       "      <th>PctImmigRecent</th>\n",
       "      <th>PctImmigRec5</th>\n",
       "      <th>PctImmigRec8</th>\n",
       "      <th>PctImmigRec10</th>\n",
       "      <th>PctRecentImmig</th>\n",
       "      <th>PctRecImmig5</th>\n",
       "      <th>PctRecImmig8</th>\n",
       "      <th>PctRecImmig10</th>\n",
       "      <th>PctSpeakEnglOnly</th>\n",
       "      <th>PctNotSpeakEnglWell</th>\n",
       "      <th>PctLargHouseFam</th>\n",
       "      <th>PctLargHouseOccup</th>\n",
       "      <th>PersPerOccupHous</th>\n",
       "      <th>PersPerOwnOccHous</th>\n",
       "      <th>PersPerRentOccHous</th>\n",
       "      <th>PctPersOwnOccup</th>\n",
       "      <th>PctPersDenseHous</th>\n",
       "      <th>PctHousLess3BR</th>\n",
       "      <th>MedNumBR</th>\n",
       "      <th>HousVacant</th>\n",
       "      <th>PctHousOccup</th>\n",
       "      <th>PctHousOwnOcc</th>\n",
       "      <th>PctVacantBoarded</th>\n",
       "      <th>PctVacMore6Mos</th>\n",
       "      <th>MedYrHousBuilt</th>\n",
       "      <th>PctHousNoPhone</th>\n",
       "      <th>PctWOFullPlumb</th>\n",
       "      <th>OwnOccLowQuart</th>\n",
       "      <th>OwnOccMedVal</th>\n",
       "      <th>OwnOccHiQuart</th>\n",
       "      <th>RentLowQ</th>\n",
       "      <th>RentMedian</th>\n",
       "      <th>RentHighQ</th>\n",
       "      <th>MedRent</th>\n",
       "      <th>MedRentPctHousInc</th>\n",
       "      <th>MedOwnCostPctInc</th>\n",
       "      <th>MedOwnCostPctIncNoMtg</th>\n",
       "      <th>NumInShelters</th>\n",
       "      <th>NumStreet</th>\n",
       "      <th>PctForeignBorn</th>\n",
       "      <th>PctBornSameState</th>\n",
       "      <th>PctSameHouse85</th>\n",
       "      <th>PctSameCity85</th>\n",
       "      <th>PctSameState85</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>PopDens</th>\n",
       "      <th>PctUsePubTrans</th>\n",
       "      <th>ViolentCrimesPerPop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Auburncity</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.683551</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057593</td>\n",
       "      <td>0.463395</td>\n",
       "      <td>0.179629</td>\n",
       "      <td>0.753716</td>\n",
       "      <td>0.153681</td>\n",
       "      <td>0.144022</td>\n",
       "      <td>0.424218</td>\n",
       "      <td>0.493867</td>\n",
       "      <td>0.336264</td>\n",
       "      <td>0.423164</td>\n",
       "      <td>0.064072</td>\n",
       "      <td>0.696269</td>\n",
       "      <td>0.361123</td>\n",
       "      <td>0.558154</td>\n",
       "      <td>0.291570</td>\n",
       "      <td>0.495687</td>\n",
       "      <td>0.471133</td>\n",
       "      <td>0.317778</td>\n",
       "      <td>0.479248</td>\n",
       "      <td>0.375677</td>\n",
       "      <td>0.350251</td>\n",
       "      <td>0.368049</td>\n",
       "      <td>0.291098</td>\n",
       "      <td>0.203506</td>\n",
       "      <td>0.322357</td>\n",
       "      <td>0.284742</td>\n",
       "      <td>0.386279</td>\n",
       "      <td>0.055507</td>\n",
       "      <td>0.303024</td>\n",
       "      <td>0.315807</td>\n",
       "      <td>0.383330</td>\n",
       "      <td>0.361675</td>\n",
       "      <td>0.363531</td>\n",
       "      <td>0.501073</td>\n",
       "      <td>0.396384</td>\n",
       "      <td>0.440597</td>\n",
       "      <td>0.461244</td>\n",
       "      <td>0.434453</td>\n",
       "      <td>0.487568</td>\n",
       "      <td>0.494273</td>\n",
       "      <td>0.487748</td>\n",
       "      <td>0.610918</td>\n",
       "      <td>0.620657</td>\n",
       "      <td>0.664032</td>\n",
       "      <td>0.582884</td>\n",
       "      <td>0.501449</td>\n",
       "      <td>0.526690</td>\n",
       "      <td>0.036294</td>\n",
       "      <td>0.249995</td>\n",
       "      <td>0.030060</td>\n",
       "      <td>0.320211</td>\n",
       "      <td>0.360622</td>\n",
       "      <td>0.399077</td>\n",
       "      <td>0.427879</td>\n",
       "      <td>0.181364</td>\n",
       "      <td>0.182126</td>\n",
       "      <td>0.184774</td>\n",
       "      <td>0.182879</td>\n",
       "      <td>0.785903</td>\n",
       "      <td>0.150587</td>\n",
       "      <td>0.267608</td>\n",
       "      <td>0.251891</td>\n",
       "      <td>0.462101</td>\n",
       "      <td>0.494428</td>\n",
       "      <td>0.404097</td>\n",
       "      <td>0.562598</td>\n",
       "      <td>0.186264</td>\n",
       "      <td>0.495186</td>\n",
       "      <td>0.314694</td>\n",
       "      <td>0.076815</td>\n",
       "      <td>0.719549</td>\n",
       "      <td>0.548686</td>\n",
       "      <td>0.204529</td>\n",
       "      <td>0.433335</td>\n",
       "      <td>0.494178</td>\n",
       "      <td>0.264478</td>\n",
       "      <td>0.243059</td>\n",
       "      <td>0.264689</td>\n",
       "      <td>0.263490</td>\n",
       "      <td>0.268942</td>\n",
       "      <td>0.346379</td>\n",
       "      <td>0.372457</td>\n",
       "      <td>0.422964</td>\n",
       "      <td>0.384102</td>\n",
       "      <td>0.490125</td>\n",
       "      <td>0.449754</td>\n",
       "      <td>0.403816</td>\n",
       "      <td>0.029438</td>\n",
       "      <td>0.022778</td>\n",
       "      <td>0.215552</td>\n",
       "      <td>0.608892</td>\n",
       "      <td>0.535050</td>\n",
       "      <td>0.626424</td>\n",
       "      <td>0.651530</td>\n",
       "      <td>0.065231</td>\n",
       "      <td>0.232854</td>\n",
       "      <td>0.161685</td>\n",
       "      <td>0.237979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.397553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126906</td>\n",
       "      <td>0.163717</td>\n",
       "      <td>0.253442</td>\n",
       "      <td>0.244039</td>\n",
       "      <td>0.208877</td>\n",
       "      <td>0.232492</td>\n",
       "      <td>0.155196</td>\n",
       "      <td>0.143564</td>\n",
       "      <td>0.166505</td>\n",
       "      <td>0.179185</td>\n",
       "      <td>0.128256</td>\n",
       "      <td>0.444811</td>\n",
       "      <td>0.209362</td>\n",
       "      <td>0.182913</td>\n",
       "      <td>0.204108</td>\n",
       "      <td>0.178071</td>\n",
       "      <td>0.173619</td>\n",
       "      <td>0.222137</td>\n",
       "      <td>0.167564</td>\n",
       "      <td>0.198257</td>\n",
       "      <td>0.191109</td>\n",
       "      <td>0.186804</td>\n",
       "      <td>0.171593</td>\n",
       "      <td>0.164775</td>\n",
       "      <td>0.195411</td>\n",
       "      <td>0.191008</td>\n",
       "      <td>0.183081</td>\n",
       "      <td>0.127941</td>\n",
       "      <td>0.228474</td>\n",
       "      <td>0.213360</td>\n",
       "      <td>0.202508</td>\n",
       "      <td>0.209193</td>\n",
       "      <td>0.202171</td>\n",
       "      <td>0.174036</td>\n",
       "      <td>0.202386</td>\n",
       "      <td>0.175457</td>\n",
       "      <td>0.182460</td>\n",
       "      <td>0.175437</td>\n",
       "      <td>0.175170</td>\n",
       "      <td>0.183607</td>\n",
       "      <td>0.154594</td>\n",
       "      <td>0.201976</td>\n",
       "      <td>0.206353</td>\n",
       "      <td>0.218749</td>\n",
       "      <td>0.191507</td>\n",
       "      <td>0.168612</td>\n",
       "      <td>0.175241</td>\n",
       "      <td>0.108671</td>\n",
       "      <td>0.229946</td>\n",
       "      <td>0.087189</td>\n",
       "      <td>0.219088</td>\n",
       "      <td>0.210924</td>\n",
       "      <td>0.201498</td>\n",
       "      <td>0.194970</td>\n",
       "      <td>0.235792</td>\n",
       "      <td>0.236333</td>\n",
       "      <td>0.236739</td>\n",
       "      <td>0.234822</td>\n",
       "      <td>0.226869</td>\n",
       "      <td>0.219716</td>\n",
       "      <td>0.196567</td>\n",
       "      <td>0.190709</td>\n",
       "      <td>0.169551</td>\n",
       "      <td>0.157924</td>\n",
       "      <td>0.189301</td>\n",
       "      <td>0.197087</td>\n",
       "      <td>0.209956</td>\n",
       "      <td>0.172508</td>\n",
       "      <td>0.255182</td>\n",
       "      <td>0.150465</td>\n",
       "      <td>0.194024</td>\n",
       "      <td>0.185204</td>\n",
       "      <td>0.217770</td>\n",
       "      <td>0.188986</td>\n",
       "      <td>0.232467</td>\n",
       "      <td>0.242847</td>\n",
       "      <td>0.206295</td>\n",
       "      <td>0.224425</td>\n",
       "      <td>0.231542</td>\n",
       "      <td>0.235252</td>\n",
       "      <td>0.219323</td>\n",
       "      <td>0.209278</td>\n",
       "      <td>0.248286</td>\n",
       "      <td>0.213404</td>\n",
       "      <td>0.169500</td>\n",
       "      <td>0.187274</td>\n",
       "      <td>0.192593</td>\n",
       "      <td>0.102607</td>\n",
       "      <td>0.100400</td>\n",
       "      <td>0.231134</td>\n",
       "      <td>0.204329</td>\n",
       "      <td>0.181352</td>\n",
       "      <td>0.200521</td>\n",
       "      <td>0.198221</td>\n",
       "      <td>0.109459</td>\n",
       "      <td>0.203092</td>\n",
       "      <td>0.229055</td>\n",
       "      <td>0.232985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.142500</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.172500</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.185000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.627500</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.777500</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              state communityname   population  householdsize  racepctblack  \\\n",
       "count   1994.000000          1994  1994.000000    1994.000000   1994.000000   \n",
       "unique          NaN          1828          NaN            NaN           NaN   \n",
       "top             NaN    Auburncity          NaN            NaN           NaN   \n",
       "freq            NaN             5          NaN            NaN           NaN   \n",
       "mean      28.683551           NaN     0.057593       0.463395      0.179629   \n",
       "std       16.397553           NaN     0.126906       0.163717      0.253442   \n",
       "min        1.000000           NaN     0.000000       0.000000      0.000000   \n",
       "25%       12.000000           NaN     0.010000       0.350000      0.020000   \n",
       "50%       34.000000           NaN     0.020000       0.440000      0.060000   \n",
       "75%       42.000000           NaN     0.050000       0.540000      0.230000   \n",
       "max       56.000000           NaN     1.000000       1.000000      1.000000   \n",
       "\n",
       "        racePctWhite  racePctAsian  racePctHisp  agePct12t21  agePct12t29  \\\n",
       "count    1994.000000   1994.000000  1994.000000  1994.000000  1994.000000   \n",
       "unique           NaN           NaN          NaN          NaN          NaN   \n",
       "top              NaN           NaN          NaN          NaN          NaN   \n",
       "freq             NaN           NaN          NaN          NaN          NaN   \n",
       "mean        0.753716      0.153681     0.144022     0.424218     0.493867   \n",
       "std         0.244039      0.208877     0.232492     0.155196     0.143564   \n",
       "min         0.000000      0.000000     0.000000     0.000000     0.000000   \n",
       "25%         0.630000      0.040000     0.010000     0.340000     0.410000   \n",
       "50%         0.850000      0.070000     0.040000     0.400000     0.480000   \n",
       "75%         0.940000      0.170000     0.160000     0.470000     0.540000   \n",
       "max         1.000000      1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        agePct16t24   agePct65up    numbUrban     pctUrban    medIncome  \\\n",
       "count   1994.000000  1994.000000  1994.000000  1994.000000  1994.000000   \n",
       "unique          NaN          NaN          NaN          NaN          NaN   \n",
       "top             NaN          NaN          NaN          NaN          NaN   \n",
       "freq            NaN          NaN          NaN          NaN          NaN   \n",
       "mean       0.336264     0.423164     0.064072     0.696269     0.361123   \n",
       "std        0.166505     0.179185     0.128256     0.444811     0.209362   \n",
       "min        0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%        0.250000     0.300000     0.000000     0.000000     0.200000   \n",
       "50%        0.290000     0.420000     0.030000     1.000000     0.320000   \n",
       "75%        0.360000     0.530000     0.070000     1.000000     0.490000   \n",
       "max        1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "           pctWWage  pctWFarmSelf   pctWInvInc   pctWSocSec  pctWPubAsst  \\\n",
       "count   1994.000000   1994.000000  1994.000000  1994.000000  1994.000000   \n",
       "unique          NaN           NaN          NaN          NaN          NaN   \n",
       "top             NaN           NaN          NaN          NaN          NaN   \n",
       "freq            NaN           NaN          NaN          NaN          NaN   \n",
       "mean       0.558154      0.291570     0.495687     0.471133     0.317778   \n",
       "std        0.182913      0.204108     0.178071     0.173619     0.222137   \n",
       "min        0.000000      0.000000     0.000000     0.000000     0.000000   \n",
       "25%        0.440000      0.160000     0.370000     0.350000     0.142500   \n",
       "50%        0.560000      0.230000     0.480000     0.475000     0.260000   \n",
       "75%        0.690000      0.370000     0.620000     0.580000     0.440000   \n",
       "max        1.000000      1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "         pctWRetire    medFamInc    perCapInc  whitePerCap  blackPerCap  \\\n",
       "count   1994.000000  1994.000000  1994.000000  1994.000000  1994.000000   \n",
       "unique          NaN          NaN          NaN          NaN          NaN   \n",
       "top             NaN          NaN          NaN          NaN          NaN   \n",
       "freq            NaN          NaN          NaN          NaN          NaN   \n",
       "mean       0.479248     0.375677     0.350251     0.368049     0.291098   \n",
       "std        0.167564     0.198257     0.191109     0.186804     0.171593   \n",
       "min        0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%        0.360000     0.230000     0.220000     0.240000     0.172500   \n",
       "50%        0.470000     0.330000     0.300000     0.320000     0.250000   \n",
       "75%        0.580000     0.480000     0.430000     0.440000     0.380000   \n",
       "max        1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        indianPerCap  AsianPerCap  OtherPerCap   HispPerCap  NumUnderPov  \\\n",
       "count    1994.000000  1994.000000  1993.000000  1994.000000  1994.000000   \n",
       "unique           NaN          NaN          NaN          NaN          NaN   \n",
       "top              NaN          NaN          NaN          NaN          NaN   \n",
       "freq             NaN          NaN          NaN          NaN          NaN   \n",
       "mean        0.203506     0.322357     0.284742     0.386279     0.055507   \n",
       "std         0.164775     0.195411     0.191008     0.183081     0.127941   \n",
       "min         0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%         0.110000     0.190000     0.170000     0.260000     0.010000   \n",
       "50%         0.170000     0.280000     0.250000     0.345000     0.020000   \n",
       "75%         0.250000     0.400000     0.360000     0.480000     0.050000   \n",
       "max         1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        PctPopUnderPov  PctLess9thGrade  PctNotHSGrad  PctBSorMore  \\\n",
       "count      1994.000000      1994.000000   1994.000000  1994.000000   \n",
       "unique             NaN              NaN           NaN          NaN   \n",
       "top                NaN              NaN           NaN          NaN   \n",
       "freq               NaN              NaN           NaN          NaN   \n",
       "mean          0.303024         0.315807      0.383330     0.361675   \n",
       "std           0.228474         0.213360      0.202508     0.209193   \n",
       "min           0.000000         0.000000      0.000000     0.000000   \n",
       "25%           0.110000         0.160000      0.230000     0.210000   \n",
       "50%           0.250000         0.270000      0.360000     0.310000   \n",
       "75%           0.450000         0.420000      0.510000     0.460000   \n",
       "max           1.000000         1.000000      1.000000     1.000000   \n",
       "\n",
       "        PctUnemployed    PctEmploy  PctEmplManu  PctEmplProfServ  \\\n",
       "count     1994.000000  1994.000000  1994.000000      1994.000000   \n",
       "unique            NaN          NaN          NaN              NaN   \n",
       "top               NaN          NaN          NaN              NaN   \n",
       "freq              NaN          NaN          NaN              NaN   \n",
       "mean         0.363531     0.501073     0.396384         0.440597   \n",
       "std          0.202171     0.174036     0.202386         0.175457   \n",
       "min          0.000000     0.000000     0.000000         0.000000   \n",
       "25%          0.220000     0.380000     0.250000         0.320000   \n",
       "50%          0.320000     0.510000     0.370000         0.410000   \n",
       "75%          0.480000     0.627500     0.520000         0.530000   \n",
       "max          1.000000     1.000000     1.000000         1.000000   \n",
       "\n",
       "        MalePctDivorce  MalePctNevMarr  FemalePctDiv  TotalPctDiv  \\\n",
       "count      1994.000000     1994.000000   1994.000000  1994.000000   \n",
       "unique             NaN             NaN           NaN          NaN   \n",
       "top                NaN             NaN           NaN          NaN   \n",
       "freq               NaN             NaN           NaN          NaN   \n",
       "mean          0.461244        0.434453      0.487568     0.494273   \n",
       "std           0.182460        0.175437      0.175170     0.183607   \n",
       "min           0.000000        0.000000      0.000000     0.000000   \n",
       "25%           0.330000        0.310000      0.360000     0.360000   \n",
       "50%           0.470000        0.400000      0.500000     0.500000   \n",
       "75%           0.590000        0.500000      0.620000     0.630000   \n",
       "max           1.000000        1.000000      1.000000     1.000000   \n",
       "\n",
       "         PersPerFam   PctFam2Par  PctKids2Par  PctYoungKids2Par  PctTeen2Par  \\\n",
       "count   1994.000000  1994.000000  1994.000000       1994.000000  1994.000000   \n",
       "unique          NaN          NaN          NaN               NaN          NaN   \n",
       "top             NaN          NaN          NaN               NaN          NaN   \n",
       "freq            NaN          NaN          NaN               NaN          NaN   \n",
       "mean       0.487748     0.610918     0.620657          0.664032     0.582884   \n",
       "std        0.154594     0.201976     0.206353          0.218749     0.191507   \n",
       "min        0.000000     0.000000     0.000000          0.000000     0.000000   \n",
       "25%        0.400000     0.490000     0.490000          0.530000     0.480000   \n",
       "50%        0.470000     0.630000     0.640000          0.700000     0.610000   \n",
       "75%        0.560000     0.760000     0.780000          0.840000     0.720000   \n",
       "max        1.000000     1.000000     1.000000          1.000000     1.000000   \n",
       "\n",
       "        PctWorkMomYoungKids   PctWorkMom     NumIlleg     PctIlleg  \\\n",
       "count           1994.000000  1994.000000  1994.000000  1994.000000   \n",
       "unique                  NaN          NaN          NaN          NaN   \n",
       "top                     NaN          NaN          NaN          NaN   \n",
       "freq                    NaN          NaN          NaN          NaN   \n",
       "mean               0.501449     0.526690     0.036294     0.249995   \n",
       "std                0.168612     0.175241     0.108671     0.229946   \n",
       "min                0.000000     0.000000     0.000000     0.000000   \n",
       "25%                0.390000     0.420000     0.000000     0.090000   \n",
       "50%                0.510000     0.540000     0.010000     0.170000   \n",
       "75%                0.620000     0.650000     0.020000     0.320000   \n",
       "max                1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "           NumImmig  PctImmigRecent  PctImmigRec5  PctImmigRec8  \\\n",
       "count   1994.000000     1994.000000   1994.000000   1994.000000   \n",
       "unique          NaN             NaN           NaN           NaN   \n",
       "top             NaN             NaN           NaN           NaN   \n",
       "freq            NaN             NaN           NaN           NaN   \n",
       "mean       0.030060        0.320211      0.360622      0.399077   \n",
       "std        0.087189        0.219088      0.210924      0.201498   \n",
       "min        0.000000        0.000000      0.000000      0.000000   \n",
       "25%        0.000000        0.160000      0.200000      0.250000   \n",
       "50%        0.010000        0.290000      0.340000      0.390000   \n",
       "75%        0.020000        0.430000      0.480000      0.530000   \n",
       "max        1.000000        1.000000      1.000000      1.000000   \n",
       "\n",
       "        PctImmigRec10  PctRecentImmig  PctRecImmig5  PctRecImmig8  \\\n",
       "count     1994.000000     1994.000000   1994.000000   1994.000000   \n",
       "unique            NaN             NaN           NaN           NaN   \n",
       "top               NaN             NaN           NaN           NaN   \n",
       "freq              NaN             NaN           NaN           NaN   \n",
       "mean         0.427879        0.181364      0.182126      0.184774   \n",
       "std          0.194970        0.235792      0.236333      0.236739   \n",
       "min          0.000000        0.000000      0.000000      0.000000   \n",
       "25%          0.280000        0.030000      0.030000      0.030000   \n",
       "50%          0.430000        0.090000      0.080000      0.090000   \n",
       "75%          0.560000        0.230000      0.230000      0.230000   \n",
       "max          1.000000        1.000000      1.000000      1.000000   \n",
       "\n",
       "        PctRecImmig10  PctSpeakEnglOnly  PctNotSpeakEnglWell  PctLargHouseFam  \\\n",
       "count     1994.000000       1994.000000          1994.000000      1994.000000   \n",
       "unique            NaN               NaN                  NaN              NaN   \n",
       "top               NaN               NaN                  NaN              NaN   \n",
       "freq              NaN               NaN                  NaN              NaN   \n",
       "mean         0.182879          0.785903             0.150587         0.267608   \n",
       "std          0.234822          0.226869             0.219716         0.196567   \n",
       "min          0.000000          0.000000             0.000000         0.000000   \n",
       "25%          0.030000          0.730000             0.030000         0.150000   \n",
       "50%          0.090000          0.870000             0.060000         0.200000   \n",
       "75%          0.230000          0.940000             0.160000         0.310000   \n",
       "max          1.000000          1.000000             1.000000         1.000000   \n",
       "\n",
       "        PctLargHouseOccup  PersPerOccupHous  PersPerOwnOccHous  \\\n",
       "count         1994.000000       1994.000000        1994.000000   \n",
       "unique                NaN               NaN                NaN   \n",
       "top                   NaN               NaN                NaN   \n",
       "freq                  NaN               NaN                NaN   \n",
       "mean             0.251891          0.462101           0.494428   \n",
       "std              0.190709          0.169551           0.157924   \n",
       "min              0.000000          0.000000           0.000000   \n",
       "25%              0.140000          0.340000           0.390000   \n",
       "50%              0.190000          0.440000           0.480000   \n",
       "75%              0.290000          0.550000           0.580000   \n",
       "max              1.000000          1.000000           1.000000   \n",
       "\n",
       "        PersPerRentOccHous  PctPersOwnOccup  PctPersDenseHous  PctHousLess3BR  \\\n",
       "count          1994.000000      1994.000000       1994.000000     1994.000000   \n",
       "unique                 NaN              NaN               NaN             NaN   \n",
       "top                    NaN              NaN               NaN             NaN   \n",
       "freq                   NaN              NaN               NaN             NaN   \n",
       "mean              0.404097         0.562598          0.186264        0.495186   \n",
       "std               0.189301         0.197087          0.209956        0.172508   \n",
       "min               0.000000         0.000000          0.000000        0.000000   \n",
       "25%               0.270000         0.440000          0.060000        0.400000   \n",
       "50%               0.360000         0.560000          0.110000        0.510000   \n",
       "75%               0.490000         0.700000          0.220000        0.600000   \n",
       "max               1.000000         1.000000          1.000000        1.000000   \n",
       "\n",
       "           MedNumBR   HousVacant  PctHousOccup  PctHousOwnOcc  \\\n",
       "count   1994.000000  1994.000000   1994.000000    1994.000000   \n",
       "unique          NaN          NaN           NaN            NaN   \n",
       "top             NaN          NaN           NaN            NaN   \n",
       "freq            NaN          NaN           NaN            NaN   \n",
       "mean       0.314694     0.076815      0.719549       0.548686   \n",
       "std        0.255182     0.150465      0.194024       0.185204   \n",
       "min        0.000000     0.000000      0.000000       0.000000   \n",
       "25%        0.000000     0.010000      0.630000       0.430000   \n",
       "50%        0.500000     0.030000      0.770000       0.540000   \n",
       "75%        0.500000     0.070000      0.860000       0.670000   \n",
       "max        1.000000     1.000000      1.000000       1.000000   \n",
       "\n",
       "        PctVacantBoarded  PctVacMore6Mos  MedYrHousBuilt  PctHousNoPhone  \\\n",
       "count        1994.000000     1994.000000     1994.000000     1994.000000   \n",
       "unique               NaN             NaN             NaN             NaN   \n",
       "top                  NaN             NaN             NaN             NaN   \n",
       "freq                 NaN             NaN             NaN             NaN   \n",
       "mean            0.204529        0.433335        0.494178        0.264478   \n",
       "std             0.217770        0.188986        0.232467        0.242847   \n",
       "min             0.000000        0.000000        0.000000        0.000000   \n",
       "25%             0.060000        0.290000        0.350000        0.060000   \n",
       "50%             0.130000        0.420000        0.520000        0.185000   \n",
       "75%             0.270000        0.560000        0.670000        0.420000   \n",
       "max             1.000000        1.000000        1.000000        1.000000   \n",
       "\n",
       "        PctWOFullPlumb  OwnOccLowQuart  OwnOccMedVal  OwnOccHiQuart  \\\n",
       "count      1994.000000     1994.000000   1994.000000    1994.000000   \n",
       "unique             NaN             NaN           NaN            NaN   \n",
       "top                NaN             NaN           NaN            NaN   \n",
       "freq               NaN             NaN           NaN            NaN   \n",
       "mean          0.243059        0.264689      0.263490       0.268942   \n",
       "std           0.206295        0.224425      0.231542       0.235252   \n",
       "min           0.000000        0.000000      0.000000       0.000000   \n",
       "25%           0.100000        0.090000      0.090000       0.090000   \n",
       "50%           0.190000        0.180000      0.170000       0.180000   \n",
       "75%           0.330000        0.400000      0.390000       0.380000   \n",
       "max           1.000000        1.000000      1.000000       1.000000   \n",
       "\n",
       "           RentLowQ   RentMedian    RentHighQ      MedRent  MedRentPctHousInc  \\\n",
       "count   1994.000000  1994.000000  1994.000000  1994.000000        1994.000000   \n",
       "unique          NaN          NaN          NaN          NaN                NaN   \n",
       "top             NaN          NaN          NaN          NaN                NaN   \n",
       "freq            NaN          NaN          NaN          NaN                NaN   \n",
       "mean       0.346379     0.372457     0.422964     0.384102           0.490125   \n",
       "std        0.219323     0.209278     0.248286     0.213404           0.169500   \n",
       "min        0.000000     0.000000     0.000000     0.000000           0.000000   \n",
       "25%        0.170000     0.200000     0.220000     0.210000           0.370000   \n",
       "50%        0.310000     0.330000     0.370000     0.340000           0.480000   \n",
       "75%        0.490000     0.520000     0.590000     0.530000           0.590000   \n",
       "max        1.000000     1.000000     1.000000     1.000000           1.000000   \n",
       "\n",
       "        MedOwnCostPctInc  MedOwnCostPctIncNoMtg  NumInShelters    NumStreet  \\\n",
       "count        1994.000000            1994.000000    1994.000000  1994.000000   \n",
       "unique               NaN                    NaN            NaN          NaN   \n",
       "top                  NaN                    NaN            NaN          NaN   \n",
       "freq                 NaN                    NaN            NaN          NaN   \n",
       "mean            0.449754               0.403816       0.029438     0.022778   \n",
       "std             0.187274               0.192593       0.102607     0.100400   \n",
       "min             0.000000               0.000000       0.000000     0.000000   \n",
       "25%             0.320000               0.250000       0.000000     0.000000   \n",
       "50%             0.450000               0.370000       0.000000     0.000000   \n",
       "75%             0.580000               0.510000       0.010000     0.000000   \n",
       "max             1.000000               1.000000       1.000000     1.000000   \n",
       "\n",
       "        PctForeignBorn  PctBornSameState  PctSameHouse85  PctSameCity85  \\\n",
       "count      1994.000000       1994.000000     1994.000000    1994.000000   \n",
       "unique             NaN               NaN             NaN            NaN   \n",
       "top                NaN               NaN             NaN            NaN   \n",
       "freq               NaN               NaN             NaN            NaN   \n",
       "mean          0.215552          0.608892        0.535050       0.626424   \n",
       "std           0.231134          0.204329        0.181352       0.200521   \n",
       "min           0.000000          0.000000        0.000000       0.000000   \n",
       "25%           0.060000          0.470000        0.420000       0.520000   \n",
       "50%           0.130000          0.630000        0.540000       0.670000   \n",
       "75%           0.280000          0.777500        0.660000       0.770000   \n",
       "max           1.000000          1.000000        1.000000       1.000000   \n",
       "\n",
       "        PctSameState85     LandArea      PopDens  PctUsePubTrans  \\\n",
       "count      1994.000000  1994.000000  1994.000000     1994.000000   \n",
       "unique             NaN          NaN          NaN             NaN   \n",
       "top                NaN          NaN          NaN             NaN   \n",
       "freq               NaN          NaN          NaN             NaN   \n",
       "mean          0.651530     0.065231     0.232854        0.161685   \n",
       "std           0.198221     0.109459     0.203092        0.229055   \n",
       "min           0.000000     0.000000     0.000000        0.000000   \n",
       "25%           0.560000     0.020000     0.100000        0.020000   \n",
       "50%           0.700000     0.040000     0.170000        0.070000   \n",
       "75%           0.790000     0.070000     0.280000        0.190000   \n",
       "max           1.000000     1.000000     1.000000        1.000000   \n",
       "\n",
       "        ViolentCrimesPerPop  \n",
       "count           1994.000000  \n",
       "unique                  NaN  \n",
       "top                     NaN  \n",
       "freq                    NaN  \n",
       "mean               0.237979  \n",
       "std                0.232985  \n",
       "min                0.000000  \n",
       "25%                0.070000  \n",
       "50%                0.150000  \n",
       "75%                0.330000  \n",
       "max                1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = None\n",
    "vstable.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>state</th>\n",
       "      <th>communityname</th>\n",
       "      <th>population</th>\n",
       "      <th>householdsize</th>\n",
       "      <th>racepctblack</th>\n",
       "      <th>racePctWhite</th>\n",
       "      <th>racePctAsian</th>\n",
       "      <th>racePctHisp</th>\n",
       "      <th>agePct12t21</th>\n",
       "      <th>agePct12t29</th>\n",
       "      <th>agePct16t24</th>\n",
       "      <th>agePct65up</th>\n",
       "      <th>numbUrban</th>\n",
       "      <th>pctUrban</th>\n",
       "      <th>medIncome</th>\n",
       "      <th>pctWWage</th>\n",
       "      <th>pctWFarmSelf</th>\n",
       "      <th>pctWInvInc</th>\n",
       "      <th>pctWSocSec</th>\n",
       "      <th>pctWPubAsst</th>\n",
       "      <th>pctWRetire</th>\n",
       "      <th>medFamInc</th>\n",
       "      <th>perCapInc</th>\n",
       "      <th>whitePerCap</th>\n",
       "      <th>blackPerCap</th>\n",
       "      <th>indianPerCap</th>\n",
       "      <th>AsianPerCap</th>\n",
       "      <th>OtherPerCap</th>\n",
       "      <th>HispPerCap</th>\n",
       "      <th>NumUnderPov</th>\n",
       "      <th>PctPopUnderPov</th>\n",
       "      <th>PctLess9thGrade</th>\n",
       "      <th>PctNotHSGrad</th>\n",
       "      <th>PctBSorMore</th>\n",
       "      <th>PctUnemployed</th>\n",
       "      <th>PctEmploy</th>\n",
       "      <th>PctEmplManu</th>\n",
       "      <th>PctEmplProfServ</th>\n",
       "      <th>MalePctDivorce</th>\n",
       "      <th>MalePctNevMarr</th>\n",
       "      <th>FemalePctDiv</th>\n",
       "      <th>TotalPctDiv</th>\n",
       "      <th>PersPerFam</th>\n",
       "      <th>PctFam2Par</th>\n",
       "      <th>PctKids2Par</th>\n",
       "      <th>PctYoungKids2Par</th>\n",
       "      <th>PctTeen2Par</th>\n",
       "      <th>PctWorkMomYoungKids</th>\n",
       "      <th>PctWorkMom</th>\n",
       "      <th>NumIlleg</th>\n",
       "      <th>PctIlleg</th>\n",
       "      <th>NumImmig</th>\n",
       "      <th>PctImmigRecent</th>\n",
       "      <th>PctImmigRec5</th>\n",
       "      <th>PctImmigRec8</th>\n",
       "      <th>PctImmigRec10</th>\n",
       "      <th>PctRecentImmig</th>\n",
       "      <th>PctRecImmig5</th>\n",
       "      <th>PctRecImmig8</th>\n",
       "      <th>PctRecImmig10</th>\n",
       "      <th>PctSpeakEnglOnly</th>\n",
       "      <th>PctNotSpeakEnglWell</th>\n",
       "      <th>PctLargHouseFam</th>\n",
       "      <th>PctLargHouseOccup</th>\n",
       "      <th>PersPerOccupHous</th>\n",
       "      <th>PersPerOwnOccHous</th>\n",
       "      <th>PersPerRentOccHous</th>\n",
       "      <th>PctPersOwnOccup</th>\n",
       "      <th>PctPersDenseHous</th>\n",
       "      <th>PctHousLess3BR</th>\n",
       "      <th>MedNumBR</th>\n",
       "      <th>HousVacant</th>\n",
       "      <th>PctHousOccup</th>\n",
       "      <th>PctHousOwnOcc</th>\n",
       "      <th>PctVacantBoarded</th>\n",
       "      <th>PctVacMore6Mos</th>\n",
       "      <th>MedYrHousBuilt</th>\n",
       "      <th>PctHousNoPhone</th>\n",
       "      <th>PctWOFullPlumb</th>\n",
       "      <th>OwnOccLowQuart</th>\n",
       "      <th>OwnOccMedVal</th>\n",
       "      <th>OwnOccHiQuart</th>\n",
       "      <th>RentLowQ</th>\n",
       "      <th>RentMedian</th>\n",
       "      <th>RentHighQ</th>\n",
       "      <th>MedRent</th>\n",
       "      <th>MedRentPctHousInc</th>\n",
       "      <th>MedOwnCostPctInc</th>\n",
       "      <th>MedOwnCostPctIncNoMtg</th>\n",
       "      <th>NumInShelters</th>\n",
       "      <th>NumStreet</th>\n",
       "      <th>PctForeignBorn</th>\n",
       "      <th>PctBornSameState</th>\n",
       "      <th>PctSameHouse85</th>\n",
       "      <th>PctSameCity85</th>\n",
       "      <th>PctSameState85</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>PopDens</th>\n",
       "      <th>PctUsePubTrans</th>\n",
       "      <th>ViolentCrimesPerPop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>28</td>\n",
       "      <td>Natchezcity</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state communityname  population  householdsize  racepctblack  \\\n",
       "130     28   Natchezcity        0.02           0.38          0.98   \n",
       "\n",
       "     racePctWhite  racePctAsian  racePctHisp  agePct12t21  agePct12t29  \\\n",
       "130          0.22          0.01         0.01         0.44          0.4   \n",
       "\n",
       "     agePct16t24  agePct65up  numbUrban  pctUrban  medIncome  pctWWage  \\\n",
       "130         0.27        0.58        0.0       0.0       0.09      0.24   \n",
       "\n",
       "     pctWFarmSelf  pctWInvInc  pctWSocSec  pctWPubAsst  pctWRetire  medFamInc  \\\n",
       "130          0.35        0.31        0.65         0.78        0.49       0.12   \n",
       "\n",
       "     perCapInc  whitePerCap  blackPerCap  indianPerCap  AsianPerCap  \\\n",
       "130       0.18         0.32         0.15          0.08         0.37   \n",
       "\n",
       "     OtherPerCap  HispPerCap  NumUnderPov  PctPopUnderPov  PctLess9thGrade  \\\n",
       "130          NaN        0.63         0.05            0.85             0.54   \n",
       "\n",
       "     PctNotHSGrad  PctBSorMore  PctUnemployed  PctEmploy  PctEmplManu  \\\n",
       "130          0.54         0.29           0.86       0.19         0.31   \n",
       "\n",
       "     PctEmplProfServ  MalePctDivorce  MalePctNevMarr  FemalePctDiv  \\\n",
       "130             0.53            0.62            0.44          0.62   \n",
       "\n",
       "     TotalPctDiv  PersPerFam  PctFam2Par  PctKids2Par  PctYoungKids2Par  \\\n",
       "130         0.65        0.49         0.2          0.2              0.13   \n",
       "\n",
       "     PctTeen2Par  PctWorkMomYoungKids  PctWorkMom  NumIlleg  PctIlleg  \\\n",
       "130         0.19                 0.59         0.5      0.04      0.85   \n",
       "\n",
       "     NumImmig  PctImmigRecent  PctImmigRec5  PctImmigRec8  PctImmigRec10  \\\n",
       "130       0.0            0.22          0.16          0.13           0.11   \n",
       "\n",
       "     PctRecentImmig  PctRecImmig5  PctRecImmig8  PctRecImmig10  \\\n",
       "130            0.01           0.0           0.0            0.0   \n",
       "\n",
       "     PctSpeakEnglOnly  PctNotSpeakEnglWell  PctLargHouseFam  \\\n",
       "130              0.98                 0.02             0.28   \n",
       "\n",
       "     PctLargHouseOccup  PersPerOccupHous  PersPerOwnOccHous  \\\n",
       "130               0.24               0.4               0.36   \n",
       "\n",
       "     PersPerRentOccHous  PctPersOwnOccup  PctPersDenseHous  PctHousLess3BR  \\\n",
       "130                0.47             0.52              0.17            0.46   \n",
       "\n",
       "     MedNumBR  HousVacant  PctHousOccup  PctHousOwnOcc  PctVacantBoarded  \\\n",
       "130       0.5        0.05          0.52           0.55              0.31   \n",
       "\n",
       "     PctVacMore6Mos  MedYrHousBuilt  PctHousNoPhone  PctWOFullPlumb  \\\n",
       "130            0.68            0.38            0.51            0.55   \n",
       "\n",
       "     OwnOccLowQuart  OwnOccMedVal  OwnOccHiQuart  RentLowQ  RentMedian  \\\n",
       "130            0.06          0.06           0.08       0.0        0.06   \n",
       "\n",
       "     RentHighQ  MedRent  MedRentPctHousInc  MedOwnCostPctInc  \\\n",
       "130       0.11     0.11                0.6              0.44   \n",
       "\n",
       "     MedOwnCostPctIncNoMtg  NumInShelters  NumStreet  PctForeignBorn  \\\n",
       "130                   0.76            0.0        0.0            0.01   \n",
       "\n",
       "     PctBornSameState  PctSameHouse85  PctSameCity85  PctSameState85  \\\n",
       "130              0.84             0.7           0.83            0.77   \n",
       "\n",
       "     LandArea  PopDens  PctUsePubTrans  ViolentCrimesPerPop  \n",
       "130      0.04     0.12            0.05                 0.23  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable[vstable.OtherPerCap.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vstable.drop(vstable[vstable.OtherPerCap.isnull()].index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data\n"
     ]
    },
    {
     "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>state</th>\n",
       "      <th>communityname</th>\n",
       "      <th>population</th>\n",
       "      <th>householdsize</th>\n",
       "      <th>racepctblack</th>\n",
       "      <th>racePctWhite</th>\n",
       "      <th>racePctAsian</th>\n",
       "      <th>racePctHisp</th>\n",
       "      <th>agePct12t21</th>\n",
       "      <th>agePct12t29</th>\n",
       "      <th>agePct16t24</th>\n",
       "      <th>agePct65up</th>\n",
       "      <th>numbUrban</th>\n",
       "      <th>pctUrban</th>\n",
       "      <th>medIncome</th>\n",
       "      <th>pctWWage</th>\n",
       "      <th>pctWFarmSelf</th>\n",
       "      <th>pctWInvInc</th>\n",
       "      <th>pctWSocSec</th>\n",
       "      <th>pctWPubAsst</th>\n",
       "      <th>pctWRetire</th>\n",
       "      <th>medFamInc</th>\n",
       "      <th>perCapInc</th>\n",
       "      <th>whitePerCap</th>\n",
       "      <th>blackPerCap</th>\n",
       "      <th>indianPerCap</th>\n",
       "      <th>AsianPerCap</th>\n",
       "      <th>OtherPerCap</th>\n",
       "      <th>HispPerCap</th>\n",
       "      <th>NumUnderPov</th>\n",
       "      <th>PctPopUnderPov</th>\n",
       "      <th>PctLess9thGrade</th>\n",
       "      <th>PctNotHSGrad</th>\n",
       "      <th>PctBSorMore</th>\n",
       "      <th>PctUnemployed</th>\n",
       "      <th>PctEmploy</th>\n",
       "      <th>PctEmplManu</th>\n",
       "      <th>PctEmplProfServ</th>\n",
       "      <th>MalePctDivorce</th>\n",
       "      <th>MalePctNevMarr</th>\n",
       "      <th>FemalePctDiv</th>\n",
       "      <th>TotalPctDiv</th>\n",
       "      <th>PersPerFam</th>\n",
       "      <th>PctFam2Par</th>\n",
       "      <th>PctKids2Par</th>\n",
       "      <th>PctYoungKids2Par</th>\n",
       "      <th>PctTeen2Par</th>\n",
       "      <th>PctWorkMomYoungKids</th>\n",
       "      <th>PctWorkMom</th>\n",
       "      <th>NumIlleg</th>\n",
       "      <th>PctIlleg</th>\n",
       "      <th>NumImmig</th>\n",
       "      <th>PctImmigRecent</th>\n",
       "      <th>PctImmigRec5</th>\n",
       "      <th>PctImmigRec8</th>\n",
       "      <th>PctImmigRec10</th>\n",
       "      <th>PctRecentImmig</th>\n",
       "      <th>PctRecImmig5</th>\n",
       "      <th>PctRecImmig8</th>\n",
       "      <th>PctRecImmig10</th>\n",
       "      <th>PctSpeakEnglOnly</th>\n",
       "      <th>PctNotSpeakEnglWell</th>\n",
       "      <th>PctLargHouseFam</th>\n",
       "      <th>PctLargHouseOccup</th>\n",
       "      <th>PersPerOccupHous</th>\n",
       "      <th>PersPerOwnOccHous</th>\n",
       "      <th>PersPerRentOccHous</th>\n",
       "      <th>PctPersOwnOccup</th>\n",
       "      <th>PctPersDenseHous</th>\n",
       "      <th>PctHousLess3BR</th>\n",
       "      <th>MedNumBR</th>\n",
       "      <th>HousVacant</th>\n",
       "      <th>PctHousOccup</th>\n",
       "      <th>PctHousOwnOcc</th>\n",
       "      <th>PctVacantBoarded</th>\n",
       "      <th>PctVacMore6Mos</th>\n",
       "      <th>MedYrHousBuilt</th>\n",
       "      <th>PctHousNoPhone</th>\n",
       "      <th>PctWOFullPlumb</th>\n",
       "      <th>OwnOccLowQuart</th>\n",
       "      <th>OwnOccMedVal</th>\n",
       "      <th>OwnOccHiQuart</th>\n",
       "      <th>RentLowQ</th>\n",
       "      <th>RentMedian</th>\n",
       "      <th>RentHighQ</th>\n",
       "      <th>MedRent</th>\n",
       "      <th>MedRentPctHousInc</th>\n",
       "      <th>MedOwnCostPctInc</th>\n",
       "      <th>MedOwnCostPctIncNoMtg</th>\n",
       "      <th>NumInShelters</th>\n",
       "      <th>NumStreet</th>\n",
       "      <th>PctForeignBorn</th>\n",
       "      <th>PctBornSameState</th>\n",
       "      <th>PctSameHouse85</th>\n",
       "      <th>PctSameCity85</th>\n",
       "      <th>PctSameState85</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>PopDens</th>\n",
       "      <th>PctUsePubTrans</th>\n",
       "      <th>ViolentCrimesPerPop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>Lakewoodcity</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>Tukwilacity</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>Aberdeentown</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>Willingborotownship</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>Bethlehemtownship</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state        communityname  population  householdsize  racepctblack  \\\n",
       "0      8         Lakewoodcity        0.19           0.33          0.02   \n",
       "1     53          Tukwilacity        0.00           0.16          0.12   \n",
       "2     24         Aberdeentown        0.00           0.42          0.49   \n",
       "3     34  Willingborotownship        0.04           0.77          1.00   \n",
       "4     42    Bethlehemtownship        0.01           0.55          0.02   \n",
       "\n",
       "   racePctWhite  racePctAsian  racePctHisp  agePct12t21  agePct12t29  \\\n",
       "0          0.90          0.12         0.17         0.34         0.47   \n",
       "1          0.74          0.45         0.07         0.26         0.59   \n",
       "2          0.56          0.17         0.04         0.39         0.47   \n",
       "3          0.08          0.12         0.10         0.51         0.50   \n",
       "4          0.95          0.09         0.05         0.38         0.38   \n",
       "\n",
       "   agePct16t24  agePct65up  numbUrban  pctUrban  medIncome  pctWWage  \\\n",
       "0         0.29        0.32       0.20       1.0       0.37      0.72   \n",
       "1         0.35        0.27       0.02       1.0       0.31      0.72   \n",
       "2         0.28        0.32       0.00       0.0       0.30      0.58   \n",
       "3         0.34        0.21       0.06       1.0       0.58      0.89   \n",
       "4         0.23        0.36       0.02       0.9       0.50      0.72   \n",
       "\n",
       "   pctWFarmSelf  pctWInvInc  pctWSocSec  pctWPubAsst  pctWRetire  medFamInc  \\\n",
       "0          0.34        0.60        0.29         0.15        0.43       0.39   \n",
       "1          0.11        0.45        0.25         0.29        0.39       0.29   \n",
       "2          0.19        0.39        0.38         0.40        0.84       0.28   \n",
       "3          0.21        0.43        0.36         0.20        0.82       0.51   \n",
       "4          0.16        0.68        0.44         0.11        0.71       0.46   \n",
       "\n",
       "   perCapInc  whitePerCap  blackPerCap  indianPerCap  AsianPerCap  \\\n",
       "0       0.40         0.39         0.32          0.27         0.27   \n",
       "1       0.37         0.38         0.33          0.16         0.30   \n",
       "2       0.27         0.29         0.27          0.07         0.29   \n",
       "3       0.36         0.40         0.39          0.16         0.25   \n",
       "4       0.43         0.41         0.28          0.00         0.74   \n",
       "\n",
       "   OtherPerCap  HispPerCap  NumUnderPov  PctPopUnderPov  PctLess9thGrade  \\\n",
       "0         0.36        0.41         0.08            0.19             0.10   \n",
       "1         0.22        0.35         0.01            0.24             0.14   \n",
       "2         0.28        0.39         0.01            0.27             0.27   \n",
       "3         0.36        0.44         0.01            0.10             0.09   \n",
       "4         0.51        0.48         0.00            0.06             0.25   \n",
       "\n",
       "   PctNotHSGrad  PctBSorMore  PctUnemployed  PctEmploy  PctEmplManu  \\\n",
       "0          0.18         0.48           0.27       0.68         0.23   \n",
       "1          0.24         0.30           0.27       0.73         0.57   \n",
       "2          0.43         0.19           0.36       0.58         0.32   \n",
       "3          0.25         0.31           0.33       0.71         0.36   \n",
       "4          0.30         0.33           0.12       0.65         0.67   \n",
       "\n",
       "   PctEmplProfServ  MalePctDivorce  MalePctNevMarr  FemalePctDiv  TotalPctDiv  \\\n",
       "0             0.41            0.68            0.40          0.75         0.75   \n",
       "1             0.15            1.00            0.63          0.91         1.00   \n",
       "2             0.29            0.63            0.41          0.71         0.70   \n",
       "3             0.45            0.34            0.45          0.49         0.44   \n",
       "4             0.38            0.22            0.27          0.20         0.21   \n",
       "\n",
       "   PersPerFam  PctFam2Par  PctKids2Par  PctYoungKids2Par  PctTeen2Par  \\\n",
       "0        0.35        0.55         0.59              0.61         0.56   \n",
       "1        0.29        0.43         0.47              0.60         0.39   \n",
       "2        0.45        0.42         0.44              0.43         0.43   \n",
       "3        0.75        0.65         0.54              0.83         0.65   \n",
       "4        0.51        0.91         0.91              0.89         0.85   \n",
       "\n",
       "   PctWorkMomYoungKids  PctWorkMom  NumIlleg  PctIlleg  NumImmig  \\\n",
       "0                 0.74        0.76      0.04      0.14      0.03   \n",
       "1                 0.46        0.53      0.00      0.24      0.01   \n",
       "2                 0.71        0.67      0.01      0.46      0.00   \n",
       "3                 0.85        0.86      0.03      0.33      0.02   \n",
       "4                 0.40        0.60      0.00      0.06      0.00   \n",
       "\n",
       "   PctImmigRecent  PctImmigRec5  PctImmigRec8  PctImmigRec10  PctRecentImmig  \\\n",
       "0            0.24          0.27          0.37           0.39            0.07   \n",
       "1            0.52          0.62          0.64           0.63            0.25   \n",
       "2            0.07          0.06          0.15           0.19            0.02   \n",
       "3            0.11          0.20          0.30           0.31            0.05   \n",
       "4            0.03          0.07          0.20           0.27            0.01   \n",
       "\n",
       "   PctRecImmig5  PctRecImmig8  PctRecImmig10  PctSpeakEnglOnly  \\\n",
       "0          0.07          0.08           0.08              0.89   \n",
       "1          0.27          0.25           0.23              0.84   \n",
       "2          0.02          0.04           0.05              0.88   \n",
       "3          0.08          0.11           0.11              0.81   \n",
       "4          0.02          0.04           0.05              0.88   \n",
       "\n",
       "   PctNotSpeakEnglWell  PctLargHouseFam  PctLargHouseOccup  PersPerOccupHous  \\\n",
       "0                 0.06             0.14               0.13              0.33   \n",
       "1                 0.10             0.16               0.10              0.17   \n",
       "2                 0.04             0.20               0.20              0.46   \n",
       "3                 0.08             0.56               0.62              0.85   \n",
       "4                 0.05             0.16               0.19              0.59   \n",
       "\n",
       "   PersPerOwnOccHous  PersPerRentOccHous  PctPersOwnOccup  PctPersDenseHous  \\\n",
       "0               0.39                0.28             0.55              0.09   \n",
       "1               0.29                0.17             0.26              0.20   \n",
       "2               0.52                0.43             0.42              0.15   \n",
       "3               0.77                1.00             0.94              0.12   \n",
       "4               0.60                0.37             0.89              0.02   \n",
       "\n",
       "   PctHousLess3BR  MedNumBR  HousVacant  PctHousOccup  PctHousOwnOcc  \\\n",
       "0            0.51       0.5        0.21          0.71           0.52   \n",
       "1            0.82       0.0        0.02          0.79           0.24   \n",
       "2            0.51       0.5        0.01          0.86           0.41   \n",
       "3            0.01       0.5        0.01          0.97           0.96   \n",
       "4            0.19       0.5        0.01          0.89           0.87   \n",
       "\n",
       "   PctVacantBoarded  PctVacMore6Mos  MedYrHousBuilt  PctHousNoPhone  \\\n",
       "0              0.05            0.26            0.65            0.14   \n",
       "1              0.02            0.25            0.65            0.16   \n",
       "2              0.29            0.30            0.52            0.47   \n",
       "3              0.60            0.47            0.52            0.11   \n",
       "4              0.04            0.55            0.73            0.05   \n",
       "\n",
       "   PctWOFullPlumb  OwnOccLowQuart  OwnOccMedVal  OwnOccHiQuart  RentLowQ  \\\n",
       "0            0.06            0.22          0.19           0.18      0.36   \n",
       "1            0.00            0.21          0.20           0.21      0.42   \n",
       "2            0.45            0.18          0.17           0.16      0.27   \n",
       "3            0.11            0.24          0.21           0.19      0.75   \n",
       "4            0.14            0.31          0.31           0.30      0.40   \n",
       "\n",
       "   RentMedian  RentHighQ  MedRent  MedRentPctHousInc  MedOwnCostPctInc  \\\n",
       "0        0.35       0.38     0.34               0.38              0.46   \n",
       "1        0.38       0.40     0.37               0.29              0.32   \n",
       "2        0.29       0.27     0.31               0.48              0.39   \n",
       "3        0.70       0.77     0.89               0.63              0.51   \n",
       "4        0.36       0.38     0.38               0.22              0.51   \n",
       "\n",
       "   MedOwnCostPctIncNoMtg  NumInShelters  NumStreet  PctForeignBorn  \\\n",
       "0                   0.25           0.04        0.0            0.12   \n",
       "1                   0.18           0.00        0.0            0.21   \n",
       "2                   0.28           0.00        0.0            0.14   \n",
       "3                   0.47           0.00        0.0            0.19   \n",
       "4                   0.21           0.00        0.0            0.11   \n",
       "\n",
       "   PctBornSameState  PctSameHouse85  PctSameCity85  PctSameState85  LandArea  \\\n",
       "0              0.42            0.50           0.51            0.64      0.12   \n",
       "1              0.50            0.34           0.60            0.52      0.02   \n",
       "2              0.49            0.54           0.67            0.56      0.01   \n",
       "3              0.30            0.73           0.64            0.65      0.02   \n",
       "4              0.72            0.64           0.61            0.53      0.04   \n",
       "\n",
       "   PopDens  PctUsePubTrans  ViolentCrimesPerPop  \n",
       "0     0.26            0.20                 0.20  \n",
       "1     0.12            0.45                 0.67  \n",
       "2     0.21            0.02                 0.43  \n",
       "3     0.39            0.28                 0.12  \n",
       "4     0.09            0.02                 0.03  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs = vstable.copy()\n",
    "print('Original data')\n",
    "vstable.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing state and communityname columns from the dataset. Also describing all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>population</th>\n",
       "      <th>householdsize</th>\n",
       "      <th>racepctblack</th>\n",
       "      <th>racePctWhite</th>\n",
       "      <th>racePctAsian</th>\n",
       "      <th>racePctHisp</th>\n",
       "      <th>agePct12t21</th>\n",
       "      <th>agePct12t29</th>\n",
       "      <th>agePct16t24</th>\n",
       "      <th>agePct65up</th>\n",
       "      <th>numbUrban</th>\n",
       "      <th>pctUrban</th>\n",
       "      <th>medIncome</th>\n",
       "      <th>pctWWage</th>\n",
       "      <th>pctWFarmSelf</th>\n",
       "      <th>pctWInvInc</th>\n",
       "      <th>pctWSocSec</th>\n",
       "      <th>pctWPubAsst</th>\n",
       "      <th>pctWRetire</th>\n",
       "      <th>medFamInc</th>\n",
       "      <th>perCapInc</th>\n",
       "      <th>whitePerCap</th>\n",
       "      <th>blackPerCap</th>\n",
       "      <th>indianPerCap</th>\n",
       "      <th>AsianPerCap</th>\n",
       "      <th>OtherPerCap</th>\n",
       "      <th>HispPerCap</th>\n",
       "      <th>NumUnderPov</th>\n",
       "      <th>PctPopUnderPov</th>\n",
       "      <th>PctLess9thGrade</th>\n",
       "      <th>PctNotHSGrad</th>\n",
       "      <th>PctBSorMore</th>\n",
       "      <th>PctUnemployed</th>\n",
       "      <th>PctEmploy</th>\n",
       "      <th>PctEmplManu</th>\n",
       "      <th>PctEmplProfServ</th>\n",
       "      <th>MalePctDivorce</th>\n",
       "      <th>MalePctNevMarr</th>\n",
       "      <th>FemalePctDiv</th>\n",
       "      <th>TotalPctDiv</th>\n",
       "      <th>PersPerFam</th>\n",
       "      <th>PctFam2Par</th>\n",
       "      <th>PctKids2Par</th>\n",
       "      <th>PctYoungKids2Par</th>\n",
       "      <th>PctTeen2Par</th>\n",
       "      <th>PctWorkMomYoungKids</th>\n",
       "      <th>PctWorkMom</th>\n",
       "      <th>NumIlleg</th>\n",
       "      <th>PctIlleg</th>\n",
       "      <th>NumImmig</th>\n",
       "      <th>PctImmigRecent</th>\n",
       "      <th>PctImmigRec5</th>\n",
       "      <th>PctImmigRec8</th>\n",
       "      <th>PctImmigRec10</th>\n",
       "      <th>PctRecentImmig</th>\n",
       "      <th>PctRecImmig5</th>\n",
       "      <th>PctRecImmig8</th>\n",
       "      <th>PctRecImmig10</th>\n",
       "      <th>PctSpeakEnglOnly</th>\n",
       "      <th>PctNotSpeakEnglWell</th>\n",
       "      <th>PctLargHouseFam</th>\n",
       "      <th>PctLargHouseOccup</th>\n",
       "      <th>PersPerOccupHous</th>\n",
       "      <th>PersPerOwnOccHous</th>\n",
       "      <th>PersPerRentOccHous</th>\n",
       "      <th>PctPersOwnOccup</th>\n",
       "      <th>PctPersDenseHous</th>\n",
       "      <th>PctHousLess3BR</th>\n",
       "      <th>MedNumBR</th>\n",
       "      <th>HousVacant</th>\n",
       "      <th>PctHousOccup</th>\n",
       "      <th>PctHousOwnOcc</th>\n",
       "      <th>PctVacantBoarded</th>\n",
       "      <th>PctVacMore6Mos</th>\n",
       "      <th>MedYrHousBuilt</th>\n",
       "      <th>PctHousNoPhone</th>\n",
       "      <th>PctWOFullPlumb</th>\n",
       "      <th>OwnOccLowQuart</th>\n",
       "      <th>OwnOccMedVal</th>\n",
       "      <th>OwnOccHiQuart</th>\n",
       "      <th>RentLowQ</th>\n",
       "      <th>RentMedian</th>\n",
       "      <th>RentHighQ</th>\n",
       "      <th>MedRent</th>\n",
       "      <th>MedRentPctHousInc</th>\n",
       "      <th>MedOwnCostPctInc</th>\n",
       "      <th>MedOwnCostPctIncNoMtg</th>\n",
       "      <th>NumInShelters</th>\n",
       "      <th>NumStreet</th>\n",
       "      <th>PctForeignBorn</th>\n",
       "      <th>PctBornSameState</th>\n",
       "      <th>PctSameHouse85</th>\n",
       "      <th>PctSameCity85</th>\n",
       "      <th>PctSameState85</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>PopDens</th>\n",
       "      <th>PctUsePubTrans</th>\n",
       "      <th>ViolentCrimesPerPop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.057612</td>\n",
       "      <td>0.463437</td>\n",
       "      <td>0.179227</td>\n",
       "      <td>0.753984</td>\n",
       "      <td>0.153753</td>\n",
       "      <td>0.144089</td>\n",
       "      <td>0.424210</td>\n",
       "      <td>0.493914</td>\n",
       "      <td>0.336297</td>\n",
       "      <td>0.423086</td>\n",
       "      <td>0.064104</td>\n",
       "      <td>0.696618</td>\n",
       "      <td>0.361259</td>\n",
       "      <td>0.558314</td>\n",
       "      <td>0.291540</td>\n",
       "      <td>0.495780</td>\n",
       "      <td>0.471044</td>\n",
       "      <td>0.317546</td>\n",
       "      <td>0.479242</td>\n",
       "      <td>0.375805</td>\n",
       "      <td>0.350336</td>\n",
       "      <td>0.368073</td>\n",
       "      <td>0.291169</td>\n",
       "      <td>0.203567</td>\n",
       "      <td>0.322333</td>\n",
       "      <td>0.284742</td>\n",
       "      <td>0.386157</td>\n",
       "      <td>0.055509</td>\n",
       "      <td>0.302750</td>\n",
       "      <td>0.315695</td>\n",
       "      <td>0.383251</td>\n",
       "      <td>0.361711</td>\n",
       "      <td>0.363281</td>\n",
       "      <td>0.501229</td>\n",
       "      <td>0.396427</td>\n",
       "      <td>0.440552</td>\n",
       "      <td>0.461164</td>\n",
       "      <td>0.434451</td>\n",
       "      <td>0.487501</td>\n",
       "      <td>0.494195</td>\n",
       "      <td>0.487747</td>\n",
       "      <td>0.611124</td>\n",
       "      <td>0.620868</td>\n",
       "      <td>0.664300</td>\n",
       "      <td>0.583081</td>\n",
       "      <td>0.501405</td>\n",
       "      <td>0.526703</td>\n",
       "      <td>0.036292</td>\n",
       "      <td>0.249694</td>\n",
       "      <td>0.030075</td>\n",
       "      <td>0.320261</td>\n",
       "      <td>0.360723</td>\n",
       "      <td>0.399212</td>\n",
       "      <td>0.428038</td>\n",
       "      <td>0.181450</td>\n",
       "      <td>0.182218</td>\n",
       "      <td>0.184867</td>\n",
       "      <td>0.182970</td>\n",
       "      <td>0.785805</td>\n",
       "      <td>0.150652</td>\n",
       "      <td>0.267602</td>\n",
       "      <td>0.251897</td>\n",
       "      <td>0.462132</td>\n",
       "      <td>0.494496</td>\n",
       "      <td>0.404064</td>\n",
       "      <td>0.562619</td>\n",
       "      <td>0.186272</td>\n",
       "      <td>0.495203</td>\n",
       "      <td>0.314601</td>\n",
       "      <td>0.076829</td>\n",
       "      <td>0.719649</td>\n",
       "      <td>0.548685</td>\n",
       "      <td>0.204476</td>\n",
       "      <td>0.433211</td>\n",
       "      <td>0.494235</td>\n",
       "      <td>0.264355</td>\n",
       "      <td>0.242905</td>\n",
       "      <td>0.264792</td>\n",
       "      <td>0.263593</td>\n",
       "      <td>0.269037</td>\n",
       "      <td>0.346553</td>\n",
       "      <td>0.372614</td>\n",
       "      <td>0.423121</td>\n",
       "      <td>0.384240</td>\n",
       "      <td>0.490070</td>\n",
       "      <td>0.449759</td>\n",
       "      <td>0.403638</td>\n",
       "      <td>0.029453</td>\n",
       "      <td>0.022790</td>\n",
       "      <td>0.215655</td>\n",
       "      <td>0.608776</td>\n",
       "      <td>0.534967</td>\n",
       "      <td>0.626322</td>\n",
       "      <td>0.651470</td>\n",
       "      <td>0.065243</td>\n",
       "      <td>0.232910</td>\n",
       "      <td>0.161741</td>\n",
       "      <td>0.237983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.126935</td>\n",
       "      <td>0.163747</td>\n",
       "      <td>0.252870</td>\n",
       "      <td>0.243807</td>\n",
       "      <td>0.208905</td>\n",
       "      <td>0.232531</td>\n",
       "      <td>0.155234</td>\n",
       "      <td>0.143584</td>\n",
       "      <td>0.166540</td>\n",
       "      <td>0.179196</td>\n",
       "      <td>0.128280</td>\n",
       "      <td>0.444648</td>\n",
       "      <td>0.209327</td>\n",
       "      <td>0.182820</td>\n",
       "      <td>0.204155</td>\n",
       "      <td>0.178067</td>\n",
       "      <td>0.173616</td>\n",
       "      <td>0.221951</td>\n",
       "      <td>0.167606</td>\n",
       "      <td>0.198224</td>\n",
       "      <td>0.191118</td>\n",
       "      <td>0.186848</td>\n",
       "      <td>0.171607</td>\n",
       "      <td>0.164793</td>\n",
       "      <td>0.195457</td>\n",
       "      <td>0.191008</td>\n",
       "      <td>0.183045</td>\n",
       "      <td>0.127973</td>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.213354</td>\n",
       "      <td>0.202528</td>\n",
       "      <td>0.209239</td>\n",
       "      <td>0.201916</td>\n",
       "      <td>0.173940</td>\n",
       "      <td>0.202427</td>\n",
       "      <td>0.175490</td>\n",
       "      <td>0.182471</td>\n",
       "      <td>0.175481</td>\n",
       "      <td>0.175189</td>\n",
       "      <td>0.183620</td>\n",
       "      <td>0.154633</td>\n",
       "      <td>0.201817</td>\n",
       "      <td>0.206190</td>\n",
       "      <td>0.218477</td>\n",
       "      <td>0.191353</td>\n",
       "      <td>0.168642</td>\n",
       "      <td>0.175284</td>\n",
       "      <td>0.108698</td>\n",
       "      <td>0.229610</td>\n",
       "      <td>0.087209</td>\n",
       "      <td>0.219132</td>\n",
       "      <td>0.210929</td>\n",
       "      <td>0.201458</td>\n",
       "      <td>0.194889</td>\n",
       "      <td>0.235819</td>\n",
       "      <td>0.236357</td>\n",
       "      <td>0.236762</td>\n",
       "      <td>0.234846</td>\n",
       "      <td>0.226884</td>\n",
       "      <td>0.219752</td>\n",
       "      <td>0.196616</td>\n",
       "      <td>0.190756</td>\n",
       "      <td>0.169588</td>\n",
       "      <td>0.157935</td>\n",
       "      <td>0.189343</td>\n",
       "      <td>0.197134</td>\n",
       "      <td>0.210009</td>\n",
       "      <td>0.172550</td>\n",
       "      <td>0.255212</td>\n",
       "      <td>0.150501</td>\n",
       "      <td>0.194021</td>\n",
       "      <td>0.185251</td>\n",
       "      <td>0.217812</td>\n",
       "      <td>0.188952</td>\n",
       "      <td>0.232511</td>\n",
       "      <td>0.242846</td>\n",
       "      <td>0.206232</td>\n",
       "      <td>0.224434</td>\n",
       "      <td>0.231555</td>\n",
       "      <td>0.235273</td>\n",
       "      <td>0.219240</td>\n",
       "      <td>0.209213</td>\n",
       "      <td>0.248249</td>\n",
       "      <td>0.213369</td>\n",
       "      <td>0.169524</td>\n",
       "      <td>0.187321</td>\n",
       "      <td>0.192476</td>\n",
       "      <td>0.102630</td>\n",
       "      <td>0.100424</td>\n",
       "      <td>0.231146</td>\n",
       "      <td>0.204314</td>\n",
       "      <td>0.181360</td>\n",
       "      <td>0.200520</td>\n",
       "      <td>0.198253</td>\n",
       "      <td>0.109485</td>\n",
       "      <td>0.203127</td>\n",
       "      <td>0.229099</td>\n",
       "      <td>0.233043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        population  householdsize  racepctblack  racePctWhite  racePctAsian  \\\n",
       "count  1993.000000    1993.000000   1993.000000   1993.000000   1993.000000   \n",
       "mean      0.057612       0.463437      0.179227      0.753984      0.153753   \n",
       "std       0.126935       0.163747      0.252870      0.243807      0.208905   \n",
       "min       0.000000       0.000000      0.000000      0.000000      0.000000   \n",
       "25%       0.010000       0.350000      0.020000      0.630000      0.040000   \n",
       "50%       0.020000       0.440000      0.060000      0.850000      0.070000   \n",
       "75%       0.050000       0.540000      0.230000      0.940000      0.170000   \n",
       "max       1.000000       1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       racePctHisp  agePct12t21  agePct12t29  agePct16t24   agePct65up  \\\n",
       "count  1993.000000  1993.000000  1993.000000  1993.000000  1993.000000   \n",
       "mean      0.144089     0.424210     0.493914     0.336297     0.423086   \n",
       "std       0.232531     0.155234     0.143584     0.166540     0.179196   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.010000     0.340000     0.410000     0.250000     0.300000   \n",
       "50%       0.040000     0.400000     0.480000     0.290000     0.420000   \n",
       "75%       0.160000     0.470000     0.540000     0.360000     0.530000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "         numbUrban     pctUrban    medIncome     pctWWage  pctWFarmSelf  \\\n",
       "count  1993.000000  1993.000000  1993.000000  1993.000000   1993.000000   \n",
       "mean      0.064104     0.696618     0.361259     0.558314      0.291540   \n",
       "std       0.128280     0.444648     0.209327     0.182820      0.204155   \n",
       "min       0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "25%       0.000000     0.000000     0.200000     0.440000      0.160000   \n",
       "50%       0.030000     1.000000     0.320000     0.560000      0.230000   \n",
       "75%       0.070000     1.000000     0.490000     0.690000      0.370000   \n",
       "max       1.000000     1.000000     1.000000     1.000000      1.000000   \n",
       "\n",
       "        pctWInvInc   pctWSocSec  pctWPubAsst   pctWRetire    medFamInc  \\\n",
       "count  1993.000000  1993.000000  1993.000000  1993.000000  1993.000000   \n",
       "mean      0.495780     0.471044     0.317546     0.479242     0.375805   \n",
       "std       0.178067     0.173616     0.221951     0.167606     0.198224   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.370000     0.350000     0.140000     0.360000     0.230000   \n",
       "50%       0.480000     0.470000     0.260000     0.470000     0.330000   \n",
       "75%       0.620000     0.580000     0.440000     0.580000     0.480000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "         perCapInc  whitePerCap  blackPerCap  indianPerCap  AsianPerCap  \\\n",
       "count  1993.000000  1993.000000  1993.000000   1993.000000  1993.000000   \n",
       "mean      0.350336     0.368073     0.291169      0.203567     0.322333   \n",
       "std       0.191118     0.186848     0.171607      0.164793     0.195457   \n",
       "min       0.000000     0.000000     0.000000      0.000000     0.000000   \n",
       "25%       0.220000     0.240000     0.180000      0.110000     0.190000   \n",
       "50%       0.300000     0.320000     0.250000      0.170000     0.280000   \n",
       "75%       0.430000     0.440000     0.380000      0.250000     0.400000   \n",
       "max       1.000000     1.000000     1.000000      1.000000     1.000000   \n",
       "\n",
       "       OtherPerCap   HispPerCap  NumUnderPov  PctPopUnderPov  PctLess9thGrade  \\\n",
       "count  1993.000000  1993.000000  1993.000000     1993.000000      1993.000000   \n",
       "mean      0.284742     0.386157     0.055509        0.302750         0.315695   \n",
       "std       0.191008     0.183045     0.127973        0.228202         0.213354   \n",
       "min       0.000000     0.000000     0.000000        0.000000         0.000000   \n",
       "25%       0.170000     0.260000     0.010000        0.110000         0.160000   \n",
       "50%       0.250000     0.340000     0.020000        0.250000         0.270000   \n",
       "75%       0.360000     0.480000     0.050000        0.450000         0.420000   \n",
       "max       1.000000     1.000000     1.000000        1.000000         1.000000   \n",
       "\n",
       "       PctNotHSGrad  PctBSorMore  PctUnemployed    PctEmploy  PctEmplManu  \\\n",
       "count   1993.000000  1993.000000    1993.000000  1993.000000  1993.000000   \n",
       "mean       0.383251     0.361711       0.363281     0.501229     0.396427   \n",
       "std        0.202528     0.209239       0.201916     0.173940     0.202427   \n",
       "min        0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "25%        0.230000     0.210000       0.220000     0.380000     0.250000   \n",
       "50%        0.360000     0.310000       0.320000     0.510000     0.370000   \n",
       "75%        0.510000     0.460000       0.480000     0.630000     0.520000   \n",
       "max        1.000000     1.000000       1.000000     1.000000     1.000000   \n",
       "\n",
       "       PctEmplProfServ  MalePctDivorce  MalePctNevMarr  FemalePctDiv  \\\n",
       "count      1993.000000     1993.000000     1993.000000   1993.000000   \n",
       "mean          0.440552        0.461164        0.434451      0.487501   \n",
       "std           0.175490        0.182471        0.175481      0.175189   \n",
       "min           0.000000        0.000000        0.000000      0.000000   \n",
       "25%           0.320000        0.330000        0.310000      0.360000   \n",
       "50%           0.410000        0.470000        0.400000      0.500000   \n",
       "75%           0.530000        0.590000        0.500000      0.620000   \n",
       "max           1.000000        1.000000        1.000000      1.000000   \n",
       "\n",
       "       TotalPctDiv   PersPerFam   PctFam2Par  PctKids2Par  PctYoungKids2Par  \\\n",
       "count  1993.000000  1993.000000  1993.000000  1993.000000       1993.000000   \n",
       "mean      0.494195     0.487747     0.611124     0.620868          0.664300   \n",
       "std       0.183620     0.154633     0.201817     0.206190          0.218477   \n",
       "min       0.000000     0.000000     0.000000     0.000000          0.000000   \n",
       "25%       0.360000     0.400000     0.490000     0.490000          0.530000   \n",
       "50%       0.500000     0.470000     0.630000     0.640000          0.700000   \n",
       "75%       0.630000     0.560000     0.760000     0.780000          0.840000   \n",
       "max       1.000000     1.000000     1.000000     1.000000          1.000000   \n",
       "\n",
       "       PctTeen2Par  PctWorkMomYoungKids   PctWorkMom     NumIlleg  \\\n",
       "count  1993.000000          1993.000000  1993.000000  1993.000000   \n",
       "mean      0.583081             0.501405     0.526703     0.036292   \n",
       "std       0.191353             0.168642     0.175284     0.108698   \n",
       "min       0.000000             0.000000     0.000000     0.000000   \n",
       "25%       0.480000             0.390000     0.420000     0.000000   \n",
       "50%       0.610000             0.510000     0.540000     0.010000   \n",
       "75%       0.720000             0.620000     0.650000     0.020000   \n",
       "max       1.000000             1.000000     1.000000     1.000000   \n",
       "\n",
       "          PctIlleg     NumImmig  PctImmigRecent  PctImmigRec5  PctImmigRec8  \\\n",
       "count  1993.000000  1993.000000     1993.000000   1993.000000   1993.000000   \n",
       "mean      0.249694     0.030075        0.320261      0.360723      0.399212   \n",
       "std       0.229610     0.087209        0.219132      0.210929      0.201458   \n",
       "min       0.000000     0.000000        0.000000      0.000000      0.000000   \n",
       "25%       0.090000     0.000000        0.160000      0.200000      0.250000   \n",
       "50%       0.170000     0.010000        0.290000      0.340000      0.390000   \n",
       "75%       0.320000     0.020000        0.430000      0.480000      0.530000   \n",
       "max       1.000000     1.000000        1.000000      1.000000      1.000000   \n",
       "\n",
       "       PctImmigRec10  PctRecentImmig  PctRecImmig5  PctRecImmig8  \\\n",
       "count    1993.000000     1993.000000   1993.000000   1993.000000   \n",
       "mean        0.428038        0.181450      0.182218      0.184867   \n",
       "std         0.194889        0.235819      0.236357      0.236762   \n",
       "min         0.000000        0.000000      0.000000      0.000000   \n",
       "25%         0.280000        0.030000      0.030000      0.030000   \n",
       "50%         0.430000        0.090000      0.080000      0.090000   \n",
       "75%         0.560000        0.230000      0.230000      0.230000   \n",
       "max         1.000000        1.000000      1.000000      1.000000   \n",
       "\n",
       "       PctRecImmig10  PctSpeakEnglOnly  PctNotSpeakEnglWell  PctLargHouseFam  \\\n",
       "count    1993.000000       1993.000000          1993.000000      1993.000000   \n",
       "mean        0.182970          0.785805             0.150652         0.267602   \n",
       "std         0.234846          0.226884             0.219752         0.196616   \n",
       "min         0.000000          0.000000             0.000000         0.000000   \n",
       "25%         0.030000          0.730000             0.030000         0.150000   \n",
       "50%         0.090000          0.870000             0.060000         0.200000   \n",
       "75%         0.230000          0.940000             0.160000         0.310000   \n",
       "max         1.000000          1.000000             1.000000         1.000000   \n",
       "\n",
       "       PctLargHouseOccup  PersPerOccupHous  PersPerOwnOccHous  \\\n",
       "count        1993.000000       1993.000000        1993.000000   \n",
       "mean            0.251897          0.462132           0.494496   \n",
       "std             0.190756          0.169588           0.157935   \n",
       "min             0.000000          0.000000           0.000000   \n",
       "25%             0.140000          0.340000           0.390000   \n",
       "50%             0.190000          0.440000           0.480000   \n",
       "75%             0.290000          0.550000           0.580000   \n",
       "max             1.000000          1.000000           1.000000   \n",
       "\n",
       "       PersPerRentOccHous  PctPersOwnOccup  PctPersDenseHous  PctHousLess3BR  \\\n",
       "count         1993.000000      1993.000000       1993.000000     1993.000000   \n",
       "mean             0.404064         0.562619          0.186272        0.495203   \n",
       "std              0.189343         0.197134          0.210009        0.172550   \n",
       "min              0.000000         0.000000          0.000000        0.000000   \n",
       "25%              0.270000         0.440000          0.060000        0.400000   \n",
       "50%              0.360000         0.560000          0.110000        0.510000   \n",
       "75%              0.490000         0.700000          0.220000        0.600000   \n",
       "max              1.000000         1.000000          1.000000        1.000000   \n",
       "\n",
       "          MedNumBR   HousVacant  PctHousOccup  PctHousOwnOcc  \\\n",
       "count  1993.000000  1993.000000   1993.000000    1993.000000   \n",
       "mean      0.314601     0.076829      0.719649       0.548685   \n",
       "std       0.255212     0.150501      0.194021       0.185251   \n",
       "min       0.000000     0.000000      0.000000       0.000000   \n",
       "25%       0.000000     0.010000      0.630000       0.430000   \n",
       "50%       0.500000     0.030000      0.770000       0.540000   \n",
       "75%       0.500000     0.070000      0.860000       0.670000   \n",
       "max       1.000000     1.000000      1.000000       1.000000   \n",
       "\n",
       "       PctVacantBoarded  PctVacMore6Mos  MedYrHousBuilt  PctHousNoPhone  \\\n",
       "count       1993.000000     1993.000000     1993.000000     1993.000000   \n",
       "mean           0.204476        0.433211        0.494235        0.264355   \n",
       "std            0.217812        0.188952        0.232511        0.242846   \n",
       "min            0.000000        0.000000        0.000000        0.000000   \n",
       "25%            0.060000        0.290000        0.350000        0.060000   \n",
       "50%            0.130000        0.420000        0.520000        0.180000   \n",
       "75%            0.270000        0.560000        0.670000        0.420000   \n",
       "max            1.000000        1.000000        1.000000        1.000000   \n",
       "\n",
       "       PctWOFullPlumb  OwnOccLowQuart  OwnOccMedVal  OwnOccHiQuart  \\\n",
       "count     1993.000000     1993.000000   1993.000000    1993.000000   \n",
       "mean         0.242905        0.264792      0.263593       0.269037   \n",
       "std          0.206232        0.224434      0.231555       0.235273   \n",
       "min          0.000000        0.000000      0.000000       0.000000   \n",
       "25%          0.100000        0.090000      0.090000       0.090000   \n",
       "50%          0.190000        0.180000      0.170000       0.180000   \n",
       "75%          0.330000        0.400000      0.390000       0.380000   \n",
       "max          1.000000        1.000000      1.000000       1.000000   \n",
       "\n",
       "          RentLowQ   RentMedian    RentHighQ      MedRent  MedRentPctHousInc  \\\n",
       "count  1993.000000  1993.000000  1993.000000  1993.000000        1993.000000   \n",
       "mean      0.346553     0.372614     0.423121     0.384240           0.490070   \n",
       "std       0.219240     0.209213     0.248249     0.213369           0.169524   \n",
       "min       0.000000     0.000000     0.000000     0.000000           0.000000   \n",
       "25%       0.170000     0.200000     0.220000     0.210000           0.370000   \n",
       "50%       0.310000     0.330000     0.370000     0.340000           0.480000   \n",
       "75%       0.490000     0.520000     0.590000     0.530000           0.590000   \n",
       "max       1.000000     1.000000     1.000000     1.000000           1.000000   \n",
       "\n",
       "       MedOwnCostPctInc  MedOwnCostPctIncNoMtg  NumInShelters    NumStreet  \\\n",
       "count       1993.000000            1993.000000    1993.000000  1993.000000   \n",
       "mean           0.449759               0.403638       0.029453     0.022790   \n",
       "std            0.187321               0.192476       0.102630     0.100424   \n",
       "min            0.000000               0.000000       0.000000     0.000000   \n",
       "25%            0.320000               0.250000       0.000000     0.000000   \n",
       "50%            0.450000               0.370000       0.000000     0.000000   \n",
       "75%            0.580000               0.510000       0.010000     0.000000   \n",
       "max            1.000000               1.000000       1.000000     1.000000   \n",
       "\n",
       "       PctForeignBorn  PctBornSameState  PctSameHouse85  PctSameCity85  \\\n",
       "count     1993.000000       1993.000000     1993.000000    1993.000000   \n",
       "mean         0.215655          0.608776        0.534967       0.626322   \n",
       "std          0.231146          0.204314        0.181360       0.200520   \n",
       "min          0.000000          0.000000        0.000000       0.000000   \n",
       "25%          0.060000          0.470000        0.420000       0.520000   \n",
       "50%          0.130000          0.630000        0.540000       0.670000   \n",
       "75%          0.280000          0.770000        0.660000       0.770000   \n",
       "max          1.000000          1.000000        1.000000       1.000000   \n",
       "\n",
       "       PctSameState85     LandArea      PopDens  PctUsePubTrans  \\\n",
       "count     1993.000000  1993.000000  1993.000000     1993.000000   \n",
       "mean         0.651470     0.065243     0.232910        0.161741   \n",
       "std          0.198253     0.109485     0.203127        0.229099   \n",
       "min          0.000000     0.000000     0.000000        0.000000   \n",
       "25%          0.560000     0.020000     0.100000        0.020000   \n",
       "50%          0.700000     0.040000     0.170000        0.070000   \n",
       "75%          0.790000     0.070000     0.280000        0.190000   \n",
       "max          1.000000     1.000000     1.000000        1.000000   \n",
       "\n",
       "       ViolentCrimesPerPop  \n",
       "count          1993.000000  \n",
       "mean              0.237983  \n",
       "std               0.233043  \n",
       "min               0.000000  \n",
       "25%               0.070000  \n",
       "50%               0.150000  \n",
       "75%               0.330000  \n",
       "max               1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del vs['state']\n",
    "del vs['communityname']\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = None\n",
    "vs.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating the target column and selecting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>population</th>\n",
       "      <th>householdsize</th>\n",
       "      <th>racepctblack</th>\n",
       "      <th>racePctWhite</th>\n",
       "      <th>racePctAsian</th>\n",
       "      <th>racePctHisp</th>\n",
       "      <th>agePct12t21</th>\n",
       "      <th>agePct12t29</th>\n",
       "      <th>agePct16t24</th>\n",
       "      <th>agePct65up</th>\n",
       "      <th>numbUrban</th>\n",
       "      <th>pctUrban</th>\n",
       "      <th>medIncome</th>\n",
       "      <th>pctWWage</th>\n",
       "      <th>pctWFarmSelf</th>\n",
       "      <th>pctWInvInc</th>\n",
       "      <th>pctWSocSec</th>\n",
       "      <th>pctWPubAsst</th>\n",
       "      <th>pctWRetire</th>\n",
       "      <th>medFamInc</th>\n",
       "      <th>perCapInc</th>\n",
       "      <th>whitePerCap</th>\n",
       "      <th>blackPerCap</th>\n",
       "      <th>indianPerCap</th>\n",
       "      <th>AsianPerCap</th>\n",
       "      <th>OtherPerCap</th>\n",
       "      <th>HispPerCap</th>\n",
       "      <th>NumUnderPov</th>\n",
       "      <th>PctPopUnderPov</th>\n",
       "      <th>PctLess9thGrade</th>\n",
       "      <th>PctNotHSGrad</th>\n",
       "      <th>PctBSorMore</th>\n",
       "      <th>PctUnemployed</th>\n",
       "      <th>PctEmploy</th>\n",
       "      <th>PctEmplManu</th>\n",
       "      <th>PctEmplProfServ</th>\n",
       "      <th>MalePctDivorce</th>\n",
       "      <th>MalePctNevMarr</th>\n",
       "      <th>FemalePctDiv</th>\n",
       "      <th>TotalPctDiv</th>\n",
       "      <th>PersPerFam</th>\n",
       "      <th>PctFam2Par</th>\n",
       "      <th>PctKids2Par</th>\n",
       "      <th>PctYoungKids2Par</th>\n",
       "      <th>PctTeen2Par</th>\n",
       "      <th>PctWorkMomYoungKids</th>\n",
       "      <th>PctWorkMom</th>\n",
       "      <th>NumIlleg</th>\n",
       "      <th>PctIlleg</th>\n",
       "      <th>NumImmig</th>\n",
       "      <th>PctImmigRecent</th>\n",
       "      <th>PctImmigRec5</th>\n",
       "      <th>PctImmigRec8</th>\n",
       "      <th>PctImmigRec10</th>\n",
       "      <th>PctRecentImmig</th>\n",
       "      <th>PctRecImmig5</th>\n",
       "      <th>PctRecImmig8</th>\n",
       "      <th>PctRecImmig10</th>\n",
       "      <th>PctSpeakEnglOnly</th>\n",
       "      <th>PctNotSpeakEnglWell</th>\n",
       "      <th>PctLargHouseFam</th>\n",
       "      <th>PctLargHouseOccup</th>\n",
       "      <th>PersPerOccupHous</th>\n",
       "      <th>PersPerOwnOccHous</th>\n",
       "      <th>PersPerRentOccHous</th>\n",
       "      <th>PctPersOwnOccup</th>\n",
       "      <th>PctPersDenseHous</th>\n",
       "      <th>PctHousLess3BR</th>\n",
       "      <th>MedNumBR</th>\n",
       "      <th>HousVacant</th>\n",
       "      <th>PctHousOccup</th>\n",
       "      <th>PctHousOwnOcc</th>\n",
       "      <th>PctVacantBoarded</th>\n",
       "      <th>PctVacMore6Mos</th>\n",
       "      <th>MedYrHousBuilt</th>\n",
       "      <th>PctHousNoPhone</th>\n",
       "      <th>PctWOFullPlumb</th>\n",
       "      <th>OwnOccLowQuart</th>\n",
       "      <th>OwnOccMedVal</th>\n",
       "      <th>OwnOccHiQuart</th>\n",
       "      <th>RentLowQ</th>\n",
       "      <th>RentMedian</th>\n",
       "      <th>RentHighQ</th>\n",
       "      <th>MedRent</th>\n",
       "      <th>MedRentPctHousInc</th>\n",
       "      <th>MedOwnCostPctInc</th>\n",
       "      <th>MedOwnCostPctIncNoMtg</th>\n",
       "      <th>NumInShelters</th>\n",
       "      <th>NumStreet</th>\n",
       "      <th>PctForeignBorn</th>\n",
       "      <th>PctBornSameState</th>\n",
       "      <th>PctSameHouse85</th>\n",
       "      <th>PctSameCity85</th>\n",
       "      <th>PctSameState85</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>PopDens</th>\n",
       "      <th>PctUsePubTrans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.19</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population  householdsize  racepctblack  racePctWhite  racePctAsian  \\\n",
       "0        0.19           0.33          0.02          0.90          0.12   \n",
       "1        0.00           0.16          0.12          0.74          0.45   \n",
       "2        0.00           0.42          0.49          0.56          0.17   \n",
       "3        0.04           0.77          1.00          0.08          0.12   \n",
       "4        0.01           0.55          0.02          0.95          0.09   \n",
       "\n",
       "   racePctHisp  agePct12t21  agePct12t29  agePct16t24  agePct65up  numbUrban  \\\n",
       "0         0.17         0.34         0.47         0.29        0.32       0.20   \n",
       "1         0.07         0.26         0.59         0.35        0.27       0.02   \n",
       "2         0.04         0.39         0.47         0.28        0.32       0.00   \n",
       "3         0.10         0.51         0.50         0.34        0.21       0.06   \n",
       "4         0.05         0.38         0.38         0.23        0.36       0.02   \n",
       "\n",
       "   pctUrban  medIncome  pctWWage  pctWFarmSelf  pctWInvInc  pctWSocSec  \\\n",
       "0       1.0       0.37      0.72          0.34        0.60        0.29   \n",
       "1       1.0       0.31      0.72          0.11        0.45        0.25   \n",
       "2       0.0       0.30      0.58          0.19        0.39        0.38   \n",
       "3       1.0       0.58      0.89          0.21        0.43        0.36   \n",
       "4       0.9       0.50      0.72          0.16        0.68        0.44   \n",
       "\n",
       "   pctWPubAsst  pctWRetire  medFamInc  perCapInc  whitePerCap  blackPerCap  \\\n",
       "0         0.15        0.43       0.39       0.40         0.39         0.32   \n",
       "1         0.29        0.39       0.29       0.37         0.38         0.33   \n",
       "2         0.40        0.84       0.28       0.27         0.29         0.27   \n",
       "3         0.20        0.82       0.51       0.36         0.40         0.39   \n",
       "4         0.11        0.71       0.46       0.43         0.41         0.28   \n",
       "\n",
       "   indianPerCap  AsianPerCap  OtherPerCap  HispPerCap  NumUnderPov  \\\n",
       "0          0.27         0.27         0.36        0.41         0.08   \n",
       "1          0.16         0.30         0.22        0.35         0.01   \n",
       "2          0.07         0.29         0.28        0.39         0.01   \n",
       "3          0.16         0.25         0.36        0.44         0.01   \n",
       "4          0.00         0.74         0.51        0.48         0.00   \n",
       "\n",
       "   PctPopUnderPov  PctLess9thGrade  PctNotHSGrad  PctBSorMore  PctUnemployed  \\\n",
       "0            0.19             0.10          0.18         0.48           0.27   \n",
       "1            0.24             0.14          0.24         0.30           0.27   \n",
       "2            0.27             0.27          0.43         0.19           0.36   \n",
       "3            0.10             0.09          0.25         0.31           0.33   \n",
       "4            0.06             0.25          0.30         0.33           0.12   \n",
       "\n",
       "   PctEmploy  PctEmplManu  PctEmplProfServ  MalePctDivorce  MalePctNevMarr  \\\n",
       "0       0.68         0.23             0.41            0.68            0.40   \n",
       "1       0.73         0.57             0.15            1.00            0.63   \n",
       "2       0.58         0.32             0.29            0.63            0.41   \n",
       "3       0.71         0.36             0.45            0.34            0.45   \n",
       "4       0.65         0.67             0.38            0.22            0.27   \n",
       "\n",
       "   FemalePctDiv  TotalPctDiv  PersPerFam  PctFam2Par  PctKids2Par  \\\n",
       "0          0.75         0.75        0.35        0.55         0.59   \n",
       "1          0.91         1.00        0.29        0.43         0.47   \n",
       "2          0.71         0.70        0.45        0.42         0.44   \n",
       "3          0.49         0.44        0.75        0.65         0.54   \n",
       "4          0.20         0.21        0.51        0.91         0.91   \n",
       "\n",
       "   PctYoungKids2Par  PctTeen2Par  PctWorkMomYoungKids  PctWorkMom  NumIlleg  \\\n",
       "0              0.61         0.56                 0.74        0.76      0.04   \n",
       "1              0.60         0.39                 0.46        0.53      0.00   \n",
       "2              0.43         0.43                 0.71        0.67      0.01   \n",
       "3              0.83         0.65                 0.85        0.86      0.03   \n",
       "4              0.89         0.85                 0.40        0.60      0.00   \n",
       "\n",
       "   PctIlleg  NumImmig  PctImmigRecent  PctImmigRec5  PctImmigRec8  \\\n",
       "0      0.14      0.03            0.24          0.27          0.37   \n",
       "1      0.24      0.01            0.52          0.62          0.64   \n",
       "2      0.46      0.00            0.07          0.06          0.15   \n",
       "3      0.33      0.02            0.11          0.20          0.30   \n",
       "4      0.06      0.00            0.03          0.07          0.20   \n",
       "\n",
       "   PctImmigRec10  PctRecentImmig  PctRecImmig5  PctRecImmig8  PctRecImmig10  \\\n",
       "0           0.39            0.07          0.07          0.08           0.08   \n",
       "1           0.63            0.25          0.27          0.25           0.23   \n",
       "2           0.19            0.02          0.02          0.04           0.05   \n",
       "3           0.31            0.05          0.08          0.11           0.11   \n",
       "4           0.27            0.01          0.02          0.04           0.05   \n",
       "\n",
       "   PctSpeakEnglOnly  PctNotSpeakEnglWell  PctLargHouseFam  PctLargHouseOccup  \\\n",
       "0              0.89                 0.06             0.14               0.13   \n",
       "1              0.84                 0.10             0.16               0.10   \n",
       "2              0.88                 0.04             0.20               0.20   \n",
       "3              0.81                 0.08             0.56               0.62   \n",
       "4              0.88                 0.05             0.16               0.19   \n",
       "\n",
       "   PersPerOccupHous  PersPerOwnOccHous  PersPerRentOccHous  PctPersOwnOccup  \\\n",
       "0              0.33               0.39                0.28             0.55   \n",
       "1              0.17               0.29                0.17             0.26   \n",
       "2              0.46               0.52                0.43             0.42   \n",
       "3              0.85               0.77                1.00             0.94   \n",
       "4              0.59               0.60                0.37             0.89   \n",
       "\n",
       "   PctPersDenseHous  PctHousLess3BR  MedNumBR  HousVacant  PctHousOccup  \\\n",
       "0              0.09            0.51       0.5        0.21          0.71   \n",
       "1              0.20            0.82       0.0        0.02          0.79   \n",
       "2              0.15            0.51       0.5        0.01          0.86   \n",
       "3              0.12            0.01       0.5        0.01          0.97   \n",
       "4              0.02            0.19       0.5        0.01          0.89   \n",
       "\n",
       "   PctHousOwnOcc  PctVacantBoarded  PctVacMore6Mos  MedYrHousBuilt  \\\n",
       "0           0.52              0.05            0.26            0.65   \n",
       "1           0.24              0.02            0.25            0.65   \n",
       "2           0.41              0.29            0.30            0.52   \n",
       "3           0.96              0.60            0.47            0.52   \n",
       "4           0.87              0.04            0.55            0.73   \n",
       "\n",
       "   PctHousNoPhone  PctWOFullPlumb  OwnOccLowQuart  OwnOccMedVal  \\\n",
       "0            0.14            0.06            0.22          0.19   \n",
       "1            0.16            0.00            0.21          0.20   \n",
       "2            0.47            0.45            0.18          0.17   \n",
       "3            0.11            0.11            0.24          0.21   \n",
       "4            0.05            0.14            0.31          0.31   \n",
       "\n",
       "   OwnOccHiQuart  RentLowQ  RentMedian  RentHighQ  MedRent  MedRentPctHousInc  \\\n",
       "0           0.18      0.36        0.35       0.38     0.34               0.38   \n",
       "1           0.21      0.42        0.38       0.40     0.37               0.29   \n",
       "2           0.16      0.27        0.29       0.27     0.31               0.48   \n",
       "3           0.19      0.75        0.70       0.77     0.89               0.63   \n",
       "4           0.30      0.40        0.36       0.38     0.38               0.22   \n",
       "\n",
       "   MedOwnCostPctInc  MedOwnCostPctIncNoMtg  NumInShelters  NumStreet  \\\n",
       "0              0.46                   0.25           0.04        0.0   \n",
       "1              0.32                   0.18           0.00        0.0   \n",
       "2              0.39                   0.28           0.00        0.0   \n",
       "3              0.51                   0.47           0.00        0.0   \n",
       "4              0.51                   0.21           0.00        0.0   \n",
       "\n",
       "   PctForeignBorn  PctBornSameState  PctSameHouse85  PctSameCity85  \\\n",
       "0            0.12              0.42            0.50           0.51   \n",
       "1            0.21              0.50            0.34           0.60   \n",
       "2            0.14              0.49            0.54           0.67   \n",
       "3            0.19              0.30            0.73           0.64   \n",
       "4            0.11              0.72            0.64           0.61   \n",
       "\n",
       "   PctSameState85  LandArea  PopDens  PctUsePubTrans  \n",
       "0            0.64      0.12     0.26            0.20  \n",
       "1            0.52      0.02     0.12            0.45  \n",
       "2            0.56      0.01     0.21            0.02  \n",
       "3            0.65      0.02     0.39            0.28  \n",
       "4            0.53      0.04     0.09            0.02  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = vs['ViolentCrimesPerPop']\n",
    "del vs['ViolentCrimesPerPop']\n",
    "vs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Target column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.20\n",
       "1    0.67\n",
       "2    0.43\n",
       "3    0.12\n",
       "4    0.03\n",
       "Name: ViolentCrimesPerPop, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(vs,y, test_size=0.2, random_state=22)\n",
    "X_col_train = np.array(X_train)\n",
    "y_col_train = np.array(y_train)\n",
    "X_col_test = np.array(X_test)\n",
    "y_col_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11, 0.17, 0.08, ..., 0.1 , 0.27, 0.26])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_col_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The communities dataset has been cleaned. All the rows with null column values have been discarded.\n",
    "The above cell splits the dataset into training and testing set in the ratio of 80:20%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Section b)\n",
    "##### Closed form solution implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell performs standard linear regression on data using the closed form solution implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standRegres(xArr,yArr):\n",
    "    xMat = np.mat(xArr); yMat = np.mat(yArr).T\n",
    "    xTx = xMat.T*xMat\n",
    "    if np.linalg.det(xTx) == 0.0:\n",
    "        print(\"This matrix is singular, cannot do inverse\")\n",
    "        return\n",
    "    ws = xTx.I * (xMat.T*yMat)\n",
    "    return ws"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to perform the multiple regression, we need to add a column of 1s as the coefficient for x0. Passing the values to closed form model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In order to do multiple regression we need to add a column of 1s as the coefficient for x0\n",
    "np.set_printoptions(precision=6, linewidth=120, suppress=True, edgeitems=7)\n",
    "X_train = np.array(X_train)\n",
    "X_train = np.array([np.concatenate((v,[1])) for v in X_train])\n",
    "y_train = np.array(y_train)\n",
    "X_test = np.array(X_test)\n",
    "X_test = np.array([np.concatenate((v,[1])) for v in X_test])\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = standRegres(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are the regression coefficients (weights) obtained from standard regression (closed form)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.382129]\n",
      " [-0.022894]\n",
      " [ 0.168181]\n",
      " [-0.042557]\n",
      " [-0.002137]\n",
      " [ 0.054353]\n",
      " [ 0.162162]\n",
      " [-0.300343]\n",
      " [-0.178657]\n",
      " [ 0.084349]\n",
      " [-0.541777]\n",
      " [ 0.051998]\n",
      " [-0.240754]\n",
      " [-0.176707]\n",
      " [ 0.042305]\n",
      " [-0.191949]\n",
      " [ 0.023463]\n",
      " [ 0.026503]\n",
      " [-0.099239]\n",
      " [ 0.287361]\n",
      " [ 0.095956]\n",
      " [-0.298378]\n",
      " [-0.0378  ]\n",
      " [-0.039972]\n",
      " [ 0.013088]\n",
      " [ 0.033307]\n",
      " [ 0.050984]\n",
      " [ 0.094446]\n",
      " [-0.178169]\n",
      " [-0.039986]\n",
      " [-0.024148]\n",
      " [ 0.078362]\n",
      " [ 0.011761]\n",
      " [ 0.209927]\n",
      " [-0.043055]\n",
      " [-0.005024]\n",
      " [ 0.352939]\n",
      " [ 0.253928]\n",
      " [ 0.046941]\n",
      " [-0.321893]\n",
      " [-0.20102 ]\n",
      " [ 0.129014]\n",
      " [-0.368381]\n",
      " [-0.05744 ]\n",
      " [-0.024897]\n",
      " [ 0.055847]\n",
      " [-0.170708]\n",
      " [-0.021001]\n",
      " [ 0.146187]\n",
      " [-0.14092 ]\n",
      " [-0.019618]\n",
      " [ 0.047018]\n",
      " [ 0.008173]\n",
      " [-0.015706]\n",
      " [ 0.054448]\n",
      " [-0.29651 ]\n",
      " [ 0.231036]\n",
      " [ 0.100547]\n",
      " [-0.060704]\n",
      " [-0.17075 ]\n",
      " [ 0.12514 ]\n",
      " [-0.257355]\n",
      " [ 0.759019]\n",
      " [-0.149058]\n",
      " [-0.220475]\n",
      " [-0.502158]\n",
      " [ 0.143386]\n",
      " [ 0.115691]\n",
      " [ 0.034876]\n",
      " [ 0.190921]\n",
      " [-0.026687]\n",
      " [ 0.372279]\n",
      " [ 0.036813]\n",
      " [-0.078869]\n",
      " [-0.020748]\n",
      " [ 0.007219]\n",
      " [-0.025454]\n",
      " [-0.448813]\n",
      " [ 0.424696]\n",
      " [-0.038087]\n",
      " [-0.243602]\n",
      " [-0.057587]\n",
      " [-0.067387]\n",
      " [ 0.428356]\n",
      " [ 0.05101 ]\n",
      " [-0.051101]\n",
      " [-0.077348]\n",
      " [ 0.089951]\n",
      " [ 0.150856]\n",
      " [ 0.035142]\n",
      " [ 0.028346]\n",
      " [ 0.029098]\n",
      " [ 0.016814]\n",
      " [-0.005748]\n",
      " [ 0.042383]\n",
      " [ 0.009381]\n",
      " [-0.060689]\n",
      " [ 0.609318]]\n"
     ]
    }
   ],
   "source": [
    "print(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.073617]\n",
      " [ 0.298369]\n",
      " [ 0.187501]\n",
      " [ 0.114976]\n",
      " [ 0.206547]\n",
      " [-0.003101]\n",
      " [ 0.012266]\n",
      " ...\n",
      " [-0.021372]\n",
      " [ 0.199072]\n",
      " [ 0.017742]\n",
      " [ 0.411951]\n",
      " [ 0.153954]\n",
      " [ 0.230208]\n",
      " [ 0.404918]]\n"
     ]
    }
   ],
   "source": [
    "xMat=np.mat(X_train)\n",
    "yMat=np.mat(y_train)\n",
    "yHat = xMat*w\n",
    "print(yHat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following are the predicted values of Y obtained using coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.036383 0.128369 0.107501 0.074976 0.016547 0.023101 0.047734 0.006578 0.01555  0.066758]\n"
     ]
    }
   ],
   "source": [
    "# Now we can constuct an array of errors\n",
    "err = abs(yHat.A.ravel()-yMat.A.ravel())\n",
    "\n",
    "# Let's see the error on the first 10 predictions\n",
    "print(err[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find the error by subtracting the difference between the actual target value and the predicted target value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on Training Data:  0.12925573962072312\n"
     ]
    }
   ],
   "source": [
    "# Dot product of error vector with itself gives us the sum of squared errors\n",
    "total_error = np.dot(err,err)\n",
    "\n",
    "# Finally compute RMSE\n",
    "rmse_train = np.sqrt(total_error/len(yHat))\n",
    "print(\"RMSE on Training Data: \", rmse_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predicted against actual (in the training data)\n",
    "%matplotlib inline\n",
    "pl.plot(yHat.A.ravel(), yMat.A.ravel(),'ro', markersize=5)\n",
    "pl.plot([0,1.2],[0,1.2], 'g-')\n",
    "pl.xlabel('Predicted')\n",
    "pl.ylabel('Actual')\n",
    "pl.title('Actual vs. Predicted')\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell shows the plot between actual target value and the predicted target value. We see most of the points fall on the line or are near the ideal line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following function can be used to plot the model coefficients\n",
    "%matplotlib inline\n",
    "def plot_coefficients(model, n_features, feature_names):\n",
    "    pl.barh(range(n_features), model.coef_[:-1], align='center')\n",
    "    pl.yticks(np.arange(n_features), feature_names)\n",
    "    pl.xlabel(\"Coefficient Value\")\n",
    "    pl.ylabel(\"Feature\")\n",
    "    pl.ylim(-1, n_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 97.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.rcParams['figure.figsize'] = [10, 25]\n",
    "pl.barh(range(len(w)-1), w.A.ravel()[:-1], align='center')\n",
    "pl.yticks(np.arange(len(w)-1), vs.columns)\n",
    "pl.xlabel(\"Coefficient Value\")\n",
    "pl.ylabel(\"Feature\")\n",
    "pl.title('Feature vs. Coefficient Value')\n",
    "pl.ylim(-1, len(w)-1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the weights associated with each variable. We see the following variables PctHousOwnOcc, numbUrban and PersPerOwnOccHous has the highest weights. PersPerOwnOccHous and numbUrban variables has the maximum negative weights while PctHousOwnOcc variable has the maximum positive levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_validate_closed_form(model, x, y, n, verbose=False):\n",
    "    # model: regression model to be trained\n",
    "    # X: the data matrix\n",
    "    # y: the target variable array\n",
    "    # n: the number of fold for x-validation\n",
    "    # Returns mean RMSE across all folds\n",
    "    \n",
    "    from sklearn.model_selection import KFold\n",
    "    kf = KFold(n_splits=n, random_state=22)\n",
    "    xval_err = 0\n",
    "    f = 1\n",
    "    for train,test in kf.split(x):\n",
    "        w = standRegres(x[train],y[train])\n",
    "        #print(w)\n",
    "        p = np.mat(x[test]) * w\n",
    "        e = p.A.ravel()-y[test]\n",
    "        rmse = np.sqrt(np.dot(e,e)/len(x[test]))\n",
    "        if verbose:\n",
    "            print(\"Fold %2d RMSE: %.4f\" % (f, rmse))\n",
    "        xval_err += rmse\n",
    "        f += 1\n",
    "    return xval_err/n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold  1 RMSE: 0.1455\n",
      "Fold  2 RMSE: 0.1431\n",
      "Fold  3 RMSE: 0.1570\n",
      "Fold  4 RMSE: 0.1408\n",
      "Fold  5 RMSE: 0.1309\n",
      "Fold  6 RMSE: 0.1236\n",
      "Fold  7 RMSE: 0.1270\n",
      "Fold  8 RMSE: 0.1243\n",
      "Fold  9 RMSE: 0.1302\n",
      "Fold 10 RMSE: 0.1367\n"
     ]
    }
   ],
   "source": [
    "x_closed_form = np.array([np.concatenate((v,[1])) for v in np.array(vs)])\n",
    "y_closed_form = np.array(y)\n",
    "rmse_10cv = cross_validate_closed_form(w, x_closed_form, y_closed_form, 10, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: Linear Regression\n",
      "RMSE on training: 0.1293\n",
      "RMSE on 10-fold CV: 0.1359\n"
     ]
    }
   ],
   "source": [
    "method_name = 'Linear Regression'\n",
    "print('Method: %s' %method_name)\n",
    "print('RMSE on training: %.4f' %rmse_train)\n",
    "print('RMSE on 10-fold CV: %.4f' %rmse_10cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Section c)\n",
    "##### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet, SGDRegressor\n",
    "from sklearn import feature_selection\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculates the error associated with the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure_performance(X, y, clf):\n",
    "    y_pred = clf.predict(X)\n",
    "    MAE = metrics.mean_absolute_error(y, y_pred)\n",
    "    return MAE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Returns the optimal features for the dataset and the weights associated with best features. Also plots the validation accuracy along with percentage of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def best_feature(X, y, clf,vs_column, X_test, y_test, k):\n",
    "    percentiles = range(1, 100, 5)\n",
    "    results = []\n",
    "    print(\"Average score for each percentile value\\n\")\n",
    "    for i in range(1, 100, 5):\n",
    "        fs = feature_selection.SelectPercentile(feature_selection.f_regression, percentile=i)\n",
    "        X_train_fs = fs.fit_transform(X, y)\n",
    "        scores = (cross_val_score(clf, X_train_fs, y, cv=k, scoring='neg_mean_absolute_error'))\n",
    "        print(\"%2d  %0.4f\" % (i, abs(scores.mean())))\n",
    "        results = np.append(results, scores.mean())\n",
    "    optimal_percentile_ind = np.where(results == results.max())[0][0]\n",
    "    print(\"\\nOptimal percentile of features:{0}\".format(percentiles[optimal_percentile_ind]), \"\\n\")\n",
    "    optimal_num_features = int(percentiles[optimal_percentile_ind]*len(vs_column.columns)/100)\n",
    "    print(\"Optimal number of features:{0}\".format(optimal_num_features), \"\\n\")\n",
    "    pl.figure(figsize=(10,6))\n",
    "    ax = plt.subplot()\n",
    "    ax = plt.subplot(111)\n",
    "    pl.xlabel(\"Percentage of features selected\")\n",
    "    pl.ylabel(\"Negative mean absolute error\")\n",
    "    pl.title(\"Percentage of features selected vs. Negative mean absolute error\")\n",
    "    pl.plot(percentiles,abs(results))\n",
    "    fs_variables = feature_selection.SelectPercentile(feature_selection.f_regression, optimal_num_features)\n",
    "    x_features = fs_variables.fit_transform(X, y)\n",
    "    columns = np.asarray(vs_column.columns)\n",
    "    support = np.asarray(fs_variables.get_support())\n",
    "    columns_with_support = columns[support]\n",
    "    print(\"Columns with most important features\\n\")\n",
    "    print(columns_with_support,\"\\n\")\n",
    "    print(\"Columns with most important features along with weights\\n\")\n",
    "    for i in range(len(vs_column.columns)):\n",
    "        if fs_variables.get_support()[i]:\n",
    "            print(\"%10s  %3.2f\" % (vs_column.columns.values[i], fs_variables.scores_[i]))\n",
    "    fs = feature_selection.SelectKBest(feature_selection.f_regression, optimal_num_features)\n",
    "    Xtrain_fs = fs.fit_transform(X, y)\n",
    "    clf.fit(Xtrain_fs, y)\n",
    "    X_fs = fs.transform(X)\n",
    "    mae = measure_performance(X_fs, y, clf)\n",
    "    print('\\nEvaluate performance with the new feature set on train data')\n",
    "    print(\"Mean Absolute Error for train dataset: \", mae)\n",
    "    X_train_fs = fs.fit_transform(X, y)\n",
    "    clf.fit(X_train_fs, y)\n",
    "    X_test_fs = fs.transform(X_test)\n",
    "    mae = measure_performance(X_test_fs, y_test, clf)\n",
    "    print('\\nEvaluate performance with the new feature set on test data')\n",
    "    print(\"Mean Absolute Error for test dataset: \", mae)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Following is the mean absolute error on training data.\n",
      "Mean Absolute Error:  0.09050725515087388\n"
     ]
    }
   ],
   "source": [
    "linreg = LinearRegression()\n",
    "linreg.fit(X_train,y_train)\n",
    "mae = measure_performance(X_train, y_train, linreg)\n",
    "print('Following is the mean absolute error on training data.')\n",
    "print(\"Mean Absolute Error: \", mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average score for each percentile value\n",
      "\n",
      " 1  0.1109\n",
      " 6  0.1010\n",
      "11  0.1006\n",
      "16  0.0985\n",
      "21  0.0980\n",
      "26  0.0967\n",
      "31  0.0973\n",
      "36  0.0967\n",
      "41  0.0968\n",
      "46  0.0972\n",
      "51  0.0971\n",
      "56  0.0971\n",
      "61  0.0979\n",
      "66  0.0989\n",
      "71  0.0990\n",
      "76  0.0990\n",
      "81  0.0988\n",
      "86  0.0989\n",
      "91  0.0982\n",
      "96  0.0982\n",
      "\n",
      "Optimal percentile of features:36 \n",
      "\n",
      "Optimal number of features:34 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with most important features\n",
      "\n",
      "['population' 'racepctblack' 'racePctWhite' 'numbUrban' 'medIncome' 'pctWInvInc' 'pctWPubAsst' 'medFamInc'\n",
      " 'NumUnderPov' 'PctPopUnderPov' 'PctLess9thGrade' 'PctNotHSGrad' 'PctUnemployed' 'MalePctDivorce' 'FemalePctDiv'\n",
      " 'TotalPctDiv' 'PctFam2Par' 'PctKids2Par' 'PctYoungKids2Par' 'PctTeen2Par' 'NumIlleg' 'PctIlleg' 'PctLargHouseFam'\n",
      " 'PctPersOwnOccup' 'PctPersDenseHous' 'PctHousLess3BR' 'HousVacant' 'PctHousOwnOcc' 'PctVacantBoarded' 'PctHousNoPhone'\n",
      " 'PctWOFullPlumb' 'NumInShelters' 'NumStreet'] \n",
      "\n",
      "Columns with most important features along with weights\n",
      "\n",
      "population  273.17\n",
      "racepctblack  1012.66\n",
      "racePctWhite  1370.88\n",
      " numbUrban  265.88\n",
      " medIncome  318.98\n",
      "pctWInvInc  777.69\n",
      "pctWPubAsst  794.97\n",
      " medFamInc  352.27\n",
      "NumUnderPov  436.89\n",
      "PctPopUnderPov  593.71\n",
      "PctLess9thGrade  313.68\n",
      "PctNotHSGrad  460.90\n",
      "PctUnemployed  528.58\n",
      "MalePctDivorce  589.04\n",
      "FemalePctDiv  702.30\n",
      "TotalPctDiv  684.80\n",
      "PctFam2Par  1531.55\n",
      "PctKids2Par  1856.52\n",
      "PctYoungKids2Par  1249.47\n",
      "PctTeen2Par  1210.77\n",
      "  NumIlleg  505.59\n",
      "  PctIlleg  1895.96\n",
      "PctLargHouseFam  273.54\n",
      "PctPersOwnOccup  598.82\n",
      "PctPersDenseHous  413.14\n",
      "PctHousLess3BR  423.94\n",
      "HousVacant  360.60\n",
      "PctHousOwnOcc  438.06\n",
      "PctVacantBoarded  499.62\n",
      "PctHousNoPhone  488.75\n",
      "PctWOFullPlumb  258.48\n",
      "NumInShelters  293.19\n",
      " NumStreet  224.16\n",
      "\n",
      "Evaluate performance with the new feature set on train data\n",
      "Mean Absolute Error for train dataset:  0.09372735593272716\n",
      "\n",
      "Evaluate performance with the new feature set on test data\n",
      "Mean Absolute Error for test dataset:  0.0940224709869767\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linreg = LinearRegression()\n",
    "best_feature(X_col_train, y_col_train, linreg, vs, X_col_test, y_col_test, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph shows us the mean absolute error returned from number of features. We see the best feature is when the number of features are 34. The mean aboslute error is negative.\n",
    "\n",
    "As you can see in the graph, the best value is around negative mean absolute error - 0.097\n",
    "\n",
    "The output also shows us that some of the features have greater influence on the data based on the weights associated with the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Section d)\n",
    "##### Ridge Regression and Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "def calc_params(X, y, clf, param_values, param_name, K):\n",
    "    \n",
    "    # Convert input to Numpy arrays\n",
    "    X = np.array(X)\n",
    "    y = np.array(y)\n",
    "\n",
    "    # initialize training and testing score arrays with zeros\n",
    "    train_scores = np.zeros(len(param_values))\n",
    "    test_scores = np.zeros(len(param_values))\n",
    "    alpha = np.zeros(len(param_values))\n",
    "    \n",
    "    # iterate over the different parameter values\n",
    "    for i, param_value in enumerate(param_values):\n",
    "        print(param_name, ' = ', param_value)\n",
    "        \n",
    "        # set classifier parameters\n",
    "        clf.set_params(**{param_name:param_value})\n",
    "        \n",
    "        # initialize the K scores obtained for each fold\n",
    "        k_train_scores = np.zeros(K)\n",
    "        k_test_scores = np.zeros(K)\n",
    "        \n",
    "        # create KFold cross validation\n",
    "        cv = KFold(n_splits=K, shuffle=True, random_state=0)\n",
    "        \n",
    "        # iterate over the K folds\n",
    "        j = 0\n",
    "        for train, test in cv.split(X):\n",
    "            # fit the classifier in the corresponding fold\n",
    "            # and obtain the corresponding accuracy scores on train and test sets\n",
    "            clf.fit(X[train], y[train])\n",
    "            k_train_scores[j] = measure_performance(X[train], y[train], clf)\n",
    "            k_test_scores[j] = measure_performance(X[test], y[test], clf)\n",
    "            j += 1\n",
    "            \n",
    "        # store the mean of the K fold scores\n",
    "        train_scores[i] = np.mean(k_train_scores)\n",
    "        test_scores[i] = np.mean(k_test_scores)\n",
    "        alpha[i]=float(param_value)\n",
    "       \n",
    "    # plot the training and testing scores in a log scale\n",
    "    plt.figure(figsize=(10,6))\n",
    "    plt.plot(param_values, train_scores, label='Train', alpha=0.4, lw=2, c='b')\n",
    "    plt.plot(param_values, test_scores, label='X-Val', alpha=0.4, lw=2, c='g')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlabel(param_name + \" values\")\n",
    "    plt.ylabel(\"Mean absolute error\")\n",
    "    pl.title(\"Mean absolute error vs. \" + param_name + \" values\")\n",
    "\n",
    "    # return the training and testing scores on each parameter value\n",
    "    return alpha, train_scores, test_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Cross Validation \n",
    "Function to perform cross validation on dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_validate(model, X, y, n, verbose=False):\n",
    "    # model: regression model to be trained\n",
    "    # X: the data matrix\n",
    "    # y: the target variable array\n",
    "    # n: the number of fold for x-validation\n",
    "    # Returns mean RMSE across all folds\n",
    "    \n",
    "    from sklearn.model_selection import KFold\n",
    "    kf = KFold(n_splits=n, random_state=22)\n",
    "    xval_err = 0\n",
    "    f = 1\n",
    "    for train,test in kf.split(X):\n",
    "        model.fit(X[train],y[train])\n",
    "        p = model.predict(X[test])\n",
    "        e = p-y[test]\n",
    "        rmse = np.sqrt(np.dot(e,e)/len(X[test]))\n",
    "        if verbose:\n",
    "            print(\"Fold %2d RMSE: %.4f\" % (f, rmse))\n",
    "        xval_err += rmse\n",
    "        f += 1\n",
    "    return xval_err/n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Grid search to find best parameters\n",
    "Using grid search function to find optimal alpha value for Regression method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "def best_value(alpha, method, k, X_train, y_train):  \n",
    "    alpha = np.linspace(.01,20,50)\n",
    "    parameters = {'alpha':alpha}\n",
    "    gs = GridSearchCV(method, parameters,  cv=5)\n",
    "    gs.fit(X_train, y_train)\n",
    "    print(\"Best estimator:\",gs.best_estimator_)\n",
    "    print(\"Best parameters:\", gs.best_params_)\n",
    "    print(\"Best score:\", gs.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet, SGDRegressor\n",
    "def regression_func(alpha_input, func_input, X_train, y_train, k):\n",
    "    t_rmse = np.array([])\n",
    "    cv_rmse = np.array([])\n",
    "    if func_input == 'lasso':\n",
    "        print('\\nLasso Regression Technique')\n",
    "        method = Lasso(alpha=alpha_input)\n",
    "    elif func_input == 'ridge':\n",
    "        print('\\nRidge Regression Technique')\n",
    "        method = Ridge(alpha=alpha_input)\n",
    "    print('alpha\\t RMSE_train\\t RMSE_10cv\\n')\n",
    "    for a in alpha:\n",
    "        if func_input == 'lasso':\n",
    "            met = Lasso(alpha=a)\n",
    "        elif func_input == 'ridge':\n",
    "            met = Ridge(alpha=a)\n",
    "        met.fit(X_train,y_train)\n",
    "        p = met.predict(X_train)\n",
    "        e = p-y_train\n",
    "        total_error = np.dot(e,e)\n",
    "        rmse_train = np.sqrt(total_error/len(p))\n",
    "\n",
    "                # computing the RMSE for x-validation\n",
    "        rmse_10cv = cross_validate(met, X_train, y_train, k)\n",
    "        t_rmse = np.append(t_rmse, [rmse_train])\n",
    "        cv_rmse = np.append(cv_rmse, [rmse_10cv])\n",
    "        print('{:.3f}\\t {:.4f}\\t\\t {:.4f}'.format(a,rmse_train,rmse_10cv))\n",
    "    fig = pl.figure(figsize=(10,6))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.plot(alpha, t_rmse, label='RMSE-Train')\n",
    "    ax.plot(alpha, cv_rmse, label='RMSE_XVal')\n",
    "    pl.legend( ('RMSE-Train', 'RMSE_XVal') )\n",
    "    pl.ylabel('RMSE')\n",
    "    pl.xlabel('Alpha')\n",
    "    pl.title('Alpha vs RMSE')\n",
    "    pl.show() \n",
    "    best_value(alpha_input, met, k, X_train, y_train)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaluating model\n",
    "Function to evaluate the RMSE value on the training set and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model, xtrain, ytrain, xtest, ytest):\n",
    "    model.fit(xtrain,ytrain)\n",
    "    p = model.predict(xtrain)\n",
    "    e = p-ytrain\n",
    "    total_train_error = np.dot(e,e)\n",
    "    rmse_train = np.sqrt(total_train_error/len(p))\n",
    "    \n",
    "    ptest = model.predict(xtest)\n",
    "    etest = ptest-ytest\n",
    "    total_train_error = np.dot(etest,etest)\n",
    "    rmse_test = np.sqrt(total_train_error/len(ptest))\n",
    "    print('Method: %s' %model)\n",
    "    print('RMSE on train set: %.4f' %rmse_train)\n",
    "    print('RMSE on test set: %.4f' %rmse_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calling grid search function\n",
    "Passing the values and calling the function to find the best parameters. The process uses grid search method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ridge Regression Technique\n",
      "alpha\t RMSE_train\t RMSE_10cv\n",
      "\n",
      "0.010\t 0.1293\t\t 0.1379\n",
      "0.418\t 0.1299\t\t 0.1369\n",
      "0.826\t 0.1303\t\t 0.1368\n",
      "1.234\t 0.1307\t\t 0.1367\n",
      "1.642\t 0.1309\t\t 0.1367\n",
      "2.050\t 0.1312\t\t 0.1367\n",
      "2.458\t 0.1314\t\t 0.1367\n",
      "2.866\t 0.1316\t\t 0.1368\n",
      "3.274\t 0.1318\t\t 0.1368\n",
      "3.682\t 0.1320\t\t 0.1368\n",
      "4.090\t 0.1321\t\t 0.1369\n",
      "4.498\t 0.1323\t\t 0.1369\n",
      "4.906\t 0.1324\t\t 0.1369\n",
      "5.313\t 0.1326\t\t 0.1370\n",
      "5.721\t 0.1327\t\t 0.1370\n",
      "6.129\t 0.1328\t\t 0.1370\n",
      "6.537\t 0.1329\t\t 0.1371\n",
      "6.945\t 0.1330\t\t 0.1371\n",
      "7.353\t 0.1331\t\t 0.1371\n",
      "7.761\t 0.1332\t\t 0.1372\n",
      "8.169\t 0.1333\t\t 0.1372\n",
      "8.577\t 0.1334\t\t 0.1372\n",
      "8.985\t 0.1334\t\t 0.1373\n",
      "9.393\t 0.1335\t\t 0.1373\n",
      "9.801\t 0.1336\t\t 0.1373\n",
      "10.209\t 0.1337\t\t 0.1373\n",
      "10.617\t 0.1337\t\t 0.1374\n",
      "11.025\t 0.1338\t\t 0.1374\n",
      "11.433\t 0.1339\t\t 0.1374\n",
      "11.841\t 0.1339\t\t 0.1375\n",
      "12.249\t 0.1340\t\t 0.1375\n",
      "12.657\t 0.1341\t\t 0.1375\n",
      "13.065\t 0.1341\t\t 0.1375\n",
      "13.473\t 0.1342\t\t 0.1376\n",
      "13.881\t 0.1342\t\t 0.1376\n",
      "14.289\t 0.1343\t\t 0.1376\n",
      "14.697\t 0.1343\t\t 0.1376\n",
      "15.104\t 0.1344\t\t 0.1377\n",
      "15.512\t 0.1345\t\t 0.1377\n",
      "15.920\t 0.1345\t\t 0.1377\n",
      "16.328\t 0.1346\t\t 0.1377\n",
      "16.736\t 0.1346\t\t 0.1378\n",
      "17.144\t 0.1346\t\t 0.1378\n",
      "17.552\t 0.1347\t\t 0.1378\n",
      "17.960\t 0.1347\t\t 0.1378\n",
      "18.368\t 0.1348\t\t 0.1379\n",
      "18.776\t 0.1348\t\t 0.1379\n",
      "19.184\t 0.1349\t\t 0.1379\n",
      "19.592\t 0.1349\t\t 0.1379\n",
      "20.000\t 0.1350\t\t 0.1379\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator: Ridge(alpha=2.0497959183673466, copy_X=True, fit_intercept=True, max_iter=None,\n",
      "      normalize=False, random_state=None, solver='auto', tol=0.001)\n",
      "Best parameters: {'alpha': 2.0497959183673466}\n",
      "Best score: 0.6486575816047784\n",
      "\n",
      "Lasso Regression Technique\n",
      "alpha\t RMSE_train\t RMSE_10cv\n",
      "\n",
      "0.001\t 0.1364\t\t 0.1385\n",
      "0.002\t 0.1385\t\t 0.1402\n",
      "0.002\t 0.1400\t\t 0.1415\n",
      "0.003\t 0.1412\t\t 0.1425\n",
      "0.004\t 0.1425\t\t 0.1437\n",
      "0.005\t 0.1440\t\t 0.1451\n",
      "0.005\t 0.1457\t\t 0.1466\n",
      "0.006\t 0.1475\t\t 0.1483\n",
      "0.007\t 0.1493\t\t 0.1497\n",
      "0.007\t 0.1506\t\t 0.1510\n",
      "0.008\t 0.1518\t\t 0.1522\n",
      "0.009\t 0.1531\t\t 0.1534\n",
      "0.010\t 0.1542\t\t 0.1546\n",
      "0.010\t 0.1553\t\t 0.1557\n",
      "0.011\t 0.1566\t\t 0.1570\n",
      "0.012\t 0.1578\t\t 0.1583\n",
      "0.012\t 0.1592\t\t 0.1596\n",
      "0.013\t 0.1606\t\t 0.1611\n",
      "0.014\t 0.1621\t\t 0.1626\n",
      "0.014\t 0.1637\t\t 0.1641\n",
      "0.015\t 0.1653\t\t 0.1658\n",
      "0.016\t 0.1670\t\t 0.1675\n",
      "0.017\t 0.1687\t\t 0.1692\n",
      "0.017\t 0.1705\t\t 0.1710\n",
      "0.018\t 0.1724\t\t 0.1728\n",
      "0.019\t 0.1743\t\t 0.1746\n",
      "0.019\t 0.1762\t\t 0.1764\n",
      "0.020\t 0.1782\t\t 0.1782\n",
      "0.021\t 0.1799\t\t 0.1801\n",
      "0.022\t 0.1816\t\t 0.1819\n",
      "0.022\t 0.1834\t\t 0.1836\n",
      "0.023\t 0.1852\t\t 0.1854\n",
      "0.024\t 0.1870\t\t 0.1873\n",
      "0.024\t 0.1889\t\t 0.1891\n",
      "0.025\t 0.1908\t\t 0.1910\n",
      "0.026\t 0.1927\t\t 0.1930\n",
      "0.027\t 0.1947\t\t 0.1950\n",
      "0.027\t 0.1967\t\t 0.1970\n",
      "0.028\t 0.1987\t\t 0.1990\n",
      "0.029\t 0.2008\t\t 0.2011\n",
      "0.029\t 0.2029\t\t 0.2032\n",
      "0.030\t 0.2050\t\t 0.2053\n",
      "0.031\t 0.2072\t\t 0.2075\n",
      "0.032\t 0.2094\t\t 0.2097\n",
      "0.032\t 0.2116\t\t 0.2118\n",
      "0.033\t 0.2138\t\t 0.2140\n",
      "0.034\t 0.2161\t\t 0.2162\n",
      "0.034\t 0.2184\t\t 0.2185\n",
      "0.035\t 0.2207\t\t 0.2207\n",
      "0.036\t 0.2230\t\t 0.2229\n",
      "0.037\t 0.2252\t\t 0.2252\n",
      "0.037\t 0.2275\t\t 0.2275\n",
      "0.038\t 0.2298\t\t 0.2297\n",
      "0.039\t 0.2321\t\t 0.2312\n",
      "0.039\t 0.2324\t\t 0.2320\n",
      "0.040\t 0.2324\t\t 0.2323\n",
      "0.041\t 0.2324\t\t 0.2323\n",
      "0.041\t 0.2324\t\t 0.2323\n",
      "0.042\t 0.2324\t\t 0.2323\n",
      "0.043\t 0.2324\t\t 0.2323\n",
      "0.044\t 0.2324\t\t 0.2323\n",
      "0.044\t 0.2324\t\t 0.2323\n",
      "0.045\t 0.2324\t\t 0.2323\n",
      "0.046\t 0.2324\t\t 0.2323\n",
      "0.046\t 0.2324\t\t 0.2323\n",
      "0.047\t 0.2324\t\t 0.2323\n",
      "0.048\t 0.2324\t\t 0.2323\n",
      "0.049\t 0.2324\t\t 0.2323\n",
      "0.049\t 0.2324\t\t 0.2323\n",
      "0.050\t 0.2324\t\t 0.2323\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator: Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,\n",
      "      normalize=False, positive=False, precompute=False, random_state=None,\n",
      "      selection='cyclic', tol=0.0001, warm_start=False)\n",
      "Best parameters: {'alpha': 0.01}\n",
      "Best score: 0.5496260187792757\n"
     ]
    }
   ],
   "source": [
    "alpha = np.linspace(.01,20,50)\n",
    "b = regression_func(alpha, 'ridge', X_train, y_train, 5)\n",
    "alpha = np.linspace(0.001,0.05,70) \n",
    "c = regression_func(alpha, 'lasso', X_train, y_train, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see in the above cell, the graph shows us the RMSE value plotted against alpha. The function also returns the best parameter to be used with each model\n",
    "\n",
    "Passing the best parameters returned from grid search on the Linear regression model to find mean absolute error and RMSE on cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  0.11053859185587259\n",
      "Fold  1 RMSE: 0.1605\n",
      "Fold  2 RMSE: 0.1389\n",
      "Fold  3 RMSE: 0.1721\n",
      "Fold  4 RMSE: 0.1542\n",
      "Fold  5 RMSE: 0.1511\n",
      "RMSE on 5-fold CV: 0.1554\n"
     ]
    }
   ],
   "source": [
    "lassoreg = Lasso(alpha=0.01)\n",
    "lassoreg.fit(X_train,y_train)\n",
    "mae = measure_performance(X_test, y_test, lassoreg)\n",
    "print(\"Mean Absolute Error: \", mae)\n",
    "rmse_5cv = cross_validate(lassoreg, X_train, y_train, 5, verbose=True)\n",
    "print('RMSE on 5-fold CV: %.4f' %rmse_5cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell returns the RMSE on cross validation using the lasso regression and best parameter returned from grid search.\n",
    "\n",
    "Passing the best parameters returned from grid search on the Ridge regression model to find mean absolute error and RMSE on cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  0.0940875744823813\n",
      "Fold  1 RMSE: 0.1410\n",
      "Fold  2 RMSE: 0.1173\n",
      "Fold  3 RMSE: 0.1449\n",
      "Fold  4 RMSE: 0.1462\n",
      "Fold  5 RMSE: 0.1342\n",
      "RMSE on 5-fold CV: 0.1367\n"
     ]
    }
   ],
   "source": [
    "ridgereg = Ridge(alpha=2.049)\n",
    "ridgereg.fit(X_train,y_train)\n",
    "mae = measure_performance(X_test, y_test, ridgereg)\n",
    "print(\"Mean Absolute Error: \", mae)\n",
    "rmse_5cv = cross_validate(ridgereg, X_train, y_train, 5, verbose=True)\n",
    "print('RMSE on 5-fold CV: %.4f' %rmse_5cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell returns the RMSE on cross validation using the ridge regression and best parameter returned from grid search.\n",
    "##### Calling calc parameter function\n",
    "Using the calc parameter function to pass the lasso model and calculate the test error and train error on different alpha values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha  =  0.001\n",
      "alpha  =  0.0017101449275362319\n",
      "alpha  =  0.0024202898550724637\n",
      "alpha  =  0.0031304347826086954\n",
      "alpha  =  0.0038405797101449275\n",
      "alpha  =  0.0045507246376811595\n",
      "alpha  =  0.005260869565217391\n",
      "alpha  =  0.005971014492753623\n",
      "alpha  =  0.006681159420289855\n",
      "alpha  =  0.007391304347826087\n",
      "alpha  =  0.00810144927536232\n",
      "alpha  =  0.008811594202898551\n",
      "alpha  =  0.009521739130434782\n",
      "alpha  =  0.010231884057971014\n",
      "alpha  =  0.010942028985507245\n",
      "alpha  =  0.01165217391304348\n",
      "alpha  =  0.01236231884057971\n",
      "alpha  =  0.013072463768115942\n",
      "alpha  =  0.013782608695652173\n",
      "alpha  =  0.014492753623188404\n",
      "alpha  =  0.015202898550724639\n",
      "alpha  =  0.01591304347826087\n",
      "alpha  =  0.0166231884057971\n",
      "alpha  =  0.017333333333333333\n",
      "alpha  =  0.018043478260869564\n",
      "alpha  =  0.0187536231884058\n",
      "alpha  =  0.01946376811594203\n",
      "alpha  =  0.02017391304347826\n",
      "alpha  =  0.020884057971014492\n",
      "alpha  =  0.021594202898550723\n",
      "alpha  =  0.022304347826086958\n",
      "alpha  =  0.02301449275362319\n",
      "alpha  =  0.02372463768115942\n",
      "alpha  =  0.02443478260869565\n",
      "alpha  =  0.025144927536231883\n",
      "alpha  =  0.025855072463768117\n",
      "alpha  =  0.02656521739130435\n",
      "alpha  =  0.02727536231884058\n",
      "alpha  =  0.02798550724637681\n",
      "alpha  =  0.028695652173913042\n",
      "alpha  =  0.029405797101449277\n",
      "alpha  =  0.030115942028985508\n",
      "alpha  =  0.03082608695652174\n",
      "alpha  =  0.03153623188405797\n",
      "alpha  =  0.0322463768115942\n",
      "alpha  =  0.03295652173913043\n",
      "alpha  =  0.033666666666666664\n",
      "alpha  =  0.034376811594202895\n",
      "alpha  =  0.03508695652173913\n",
      "alpha  =  0.035797101449275365\n",
      "alpha  =  0.036507246376811596\n",
      "alpha  =  0.03721739130434783\n",
      "alpha  =  0.03792753623188406\n",
      "alpha  =  0.03863768115942029\n",
      "alpha  =  0.03934782608695652\n",
      "alpha  =  0.04005797101449275\n",
      "alpha  =  0.04076811594202898\n",
      "alpha  =  0.041478260869565214\n",
      "alpha  =  0.042188405797101446\n",
      "alpha  =  0.042898550724637684\n",
      "alpha  =  0.043608695652173915\n",
      "alpha  =  0.044318840579710146\n",
      "alpha  =  0.04502898550724638\n",
      "alpha  =  0.04573913043478261\n",
      "alpha  =  0.04644927536231884\n",
      "alpha  =  0.04715942028985507\n",
      "alpha  =  0.0478695652173913\n",
      "alpha  =  0.04857971014492753\n",
      "alpha  =  0.049289855072463765\n",
      "alpha  =  0.05\n",
      "\n",
      "Lasso - best alpha value: [0.001]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAGDCAYAAABuj7cYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABPlElEQVR4nO3deXzc113v/9dH+2ZJluRNtmV532LHi2zHa7zHIaShEEjSC20KP0Lb2w0ol0C5kJYCl94CBcq9EJZutyUtKSlJ28T7GseL5H1fJNmWJVvWZu3SLOf3x3fsKIrsjG2NZiS9n4+HHp6Z76LP6Kvl7XPO9xxzziEiIiIisSEu2gWIiIiIyLsUzkRERERiiMKZiIiISAxROBMRERGJIQpnIiIiIjFE4UxEREQkhiiciUiPzGyHmf1/vXzOl8zs//XmOeW9zKzczNb29r6RYmYrzawimjWIxBqFM5E+EvpD2Glmed1eP2xmzswKo1RaTFKQE5HBSuFMpG+VAc/demJms4C06JUjAGaWEM5r93oOEZH7oXAm0re+C3y0y/OPAd/puoOZJZvZ18zsspldN7N/NLPU0LahZvYTM7thZvWhx2O6HLvDzP7UzN42syYz29S9pa7Lvnc9V8hEMztgZo1m9l9mlhM6NsXM/p+Z1ZpZg5kdNLMRoW35Zva6mdWZ2QUz+807fP73dWfd6mYzsw3AHwLPmFmzmR0Nbc8ys381syozu2pmXzGz+DucP87MXjSzi6E6f9il/sJQa+VvmNllYJuZPR/6uv2NmdUCL4U+33dCX6NLZvZHZhYXOsf79u/2+fPNrO3W5wy9NtfMasws0cwmmdlOM7sZeu0HPb2PHt7XRDPbFnpPNWb2PTPLvsO+L5nZq2b2g9D3wyEze7jbbnPM7Fiojh+YWUro2HC+P259nt83s1e7vfa3ZvZ3occfN7PToRpKzey37vL+nJlN6vL8W2b2lS7Pf97MjoS+7/aa2exudVwNfZ6zZrbmzl9JkdilcCbSt/YBmWY2PRQqngW6d939L2AKMAeYBIwG/ji0LQ74JjAOKADagG90O/4jwMeB4UAS8IU71BLOuT4K/DowCvADfxd6/WNAFjAWyAU+EToe4BWgAsgHngb+3MxW36GGHjnn3gL+HPiBcy7DOXcrUHwrVMckYC6wHrjTuLjPAL8APBqqpR74h277PApMBx4LPV8ElAIjgD8D/j70PieE9v0o3teWO+zf9T1UAu8Av9Tl5Y8ArzrnfMCfApuAocCY0OcKhwF/EXpP0/GuwUt32f8p4D+AHOD7wI/NLLHL9l8BNgDjgdnA86HXw/n+uOUV4OfMbAhA6Hv7V0KfD6Aa+HkgE+/r9zdmNi+cN9uVmc0F/g34Lbzvu38CXjfvPzRTgU8DC5xzQ/Cuafm9fg6RWKBwJtL3brWerQNOA1dvbTAzA14Afts5V+eca8ILKc8COOdqnXM/cs61hrb9GV5o6Oqbzrlzzrk24Id4Ie99wjzXd51zJ5xzLcD/BH4l9IfXh/fHcZJzLuCcK3HONZrZWGAp8PvOuXbn3BHgX3hva+F9CbXM/Rzweedci3OuGvgbQl+bHnwC+KJzrsI514EXYJ6293Y/vhQ6161gWemc+3vnnB/oDJ37D5xzTc65cuCvgF/rcvzt/buco6vvE+rGDl3bZ3k3sPjwgk9+6Gu1J5yvg3PugnNus3Ouwzl3A/hr3n/duipxzt0KhH8NpACPdNn+d865SudcHfAGoe+XML8/btV0CTgEfDj00mqg1Tm3L7T9p865i86zEy+ULg/n/XbzAvBPzrn9oe+7bwMdofcTAJKBGWaW6Jwrd85dvI/PIRJ1Cmcife+7eC0oz9OtSxMYhjcGrSTUbdMAvBV6HTNLM7N/CnWxNQK7gOxuXXvXujxuBTJ6KiLMc13p8vgSkAjkhd7DRuAVM6s0s6+GWmPygVuhsutxo+/+JQnLuNDnr+rytfknvBbCO+3/Wpd9T+P9AR/RZZ8r3Y7p+jwv9PkudXmt+3vpfnx3PwIWm9koYAUQBHaHtv0PvFawA2Z20sx+/QPOBXgh1cxeCXXfNeK1vPbYdd29RudckHdbNW/p8fslzO+Prm4HUbzv71shFDN73Mz2mdfV3YAXsu9W852MA3731jUNnWssXsC9AHweL4RXh75G+Xc8k0gMUzgT6WOhVoYyvD9Q/9ltcw1e99FM51x26CPLOXcrYP0uMBVY5JzLxPuDD94f+XsVzrnGdnlcgNfaU+Oc8znnvuScmwEsweuy+ihQCeTc6t7qctxV3q+FLjdDhP7oD+uy3XXb/wpeK0lel69NpnNu5h3e3xXg8S77ZjvnUpxzXWvp/jm6Pq/h3datO72X7se/92TO1eO1Ej2DF1hecc650LZrzrnfdM7l43XT/Z+uY63u4s9Dn3dW6Lr9Kne//revoXnj5cbgXacPcq/fa/8BrAyNS/swoXBmZsl4IfVrwAjnXDbws7ucp5X33iQzssvjK8Cfdbumac65fwdwzn3fObcM75o54C/DeJ8iMUfhTCQ6fgNYHeouvC3UsvHPeGNyhgOY2WgzuzUmagheeGswb6D5nzxADeGc61fNbIaZpQFfxhsvFTCzVWY2KxSoGvFCTNA5dwXYC/yFeTcNzA69156mxDgHpJjZE6FWtz/C65a65TpQGAoUOOeq8ILOX5lZpnkD/iea2Z269P4R+DMzGwdgZsPM7KlwvzjOuQBet/CfmdmQ0Hl+5w7v5W6+jxdcn+a9rUm/3GWAfT1emAiGcb4hQDNw08xGA7/3AfvPN7NfDHXnfh4v4O4L8/OE/b0W6mLdgTdOrcw5dzq0KQnvut4A/Gb2ON5YwTs5AnzEzOLNuzGk6/X9Z+ATZrbIPOmh758hZjbVzFaHwmB7qPZwvp4iMUfhTCQKQuNviu+w+feBC8C+UHfSFrwWDICvA6l4rTr78Lo871c45/ou3iD8a3hjlT4ben0k8CpeMDsN7AztC17XViFe68xrwJ8457Z0P7Fz7ibwKbwxaVfxWtK63r35H6F/a83sUOjxR/H+2J/CCzSv4t2s0JO/BV4HNplZU+g9LrrDvnfymVBdpcAevHD1b/d4jteBycA159zRLq8vAPabWXNon88550oBQt2c/+0O5/sSMA+4CfyU97e+dvdfeC139Xjj5X4xNP7sg3yde/9e+z6wli4hNNTF/Vm8oFuP14L4+l3O8TngSaAB+G/Aj7ucqxj4TbwbE+rxfk6eD21OxruZpgbv+3U48Adh1CwScyzUwi4iIgOMmb2Ed9PGr0a7FhEJn1rORERERGKIwpmIiIhIDFG3poiIiEgMUcuZiIiISAxROBMRERGJIQkfvEv/kJeX5woLC6NdhoiIiMgHKikpqXHODetp24AJZ4WFhRQX32naKBEREZHYYWaX7rRN3ZoiIiIiMSSi4czMNpjZWTO7YGYv9rB9hZkdMjO/mT3dbdtXQ7NknzazvzOz+1k7UERERKRfiVg4C6259w/A48AM4Dkzm9Ftt8t4S298v9uxS4ClwGzgIbxlTu60fp6IiIjIgBHJMWcLgQtd1op7BXgKb008AJxz5aFt3RendXjr+CUBBiTiLYIsIiIi/ZzP56OiooL29vZolxJxKSkpjBkzhsTExLCPiWQ4Gw1c6fK8gjAXHXbOvWNm24EqvHD2Defc6e77mdkLwAsABQUFD1ywiIiIRF5FRQVDhgyhsLCQgTxqyTlHbW0tFRUVjB8/PuzjYvKGADObBEwHxuCFvNVmtrz7fs65l51zRc65omHDerwbVURERGJMe3s7ubm5AzqYAZgZubm599xCGMlwdhUY2+X5mNBr4fgwsM851+ycawbeBBb3cn0iIiISJQM9mN1yP+8zkuHsIDDZzMabWRLwLPB6mMdeBh41swQzS8S7GeB93ZoiIiIi96q2tpY5c+YwZ84cRo4cyejRo28/7+zsvOuxxcXFfPazn41ofREbc+ac85vZp4GNQDzwb865k2b2ZaDYOfe6mS0AXgOGAk+a2ZecczOBV4HVwHG8mwPecs69EalaRUREZPDIzc3lyJEjALz00ktkZGTwhS984fZ2v99PQkLPEamoqIiioqKI1hfRFQKccz8DftbttT/u8vggXndn9+MCwG9FsjYRERGRW55//nlSUlI4fPgwS5cu5dlnn+Vzn/sc7e3tpKam8s1vfpOpU6eyY8cOvva1r/GTn/yEl156icuXL1NaWsrly5f5/Oc/3yutagNm+SYRERHpf15+OTLnfeGFez+moqKCvXv3Eh8fT2NjI7t37yYhIYEtW7bwh3/4h/zoRz963zFnzpxh+/btNDU1MXXqVD75yU/e07QZPVE4ExER6SeCLkhTRxPt/nZ8QR/+oB9fwIcv6MMX8NHW6cMXCES7zA+U4U+mqaMZgHZfZKJIU4c/rP06/J0k+jvwBXz8/C88Sau/DfxQ31DPxz72Mc6fP4+Z4fP5ejz+iSeeIDk5meTkZIYPH87169cZM+Z9nYL3ROFMREQkhviDfpo6mrjZcZPGjkYaOxq50XSTqtpGqm8209Li8PnA7wd/AAJ+73EgAEEX7erD81uL53CjoQ2An3/6A3a+Tzcawtuvtd0H8T7aOwMESLxd10v/849ZtWoVr732GuXl5axcubLH45OTk28/jo+Px+8PLxTejcKZiIhIH/MFfNS3198OX7c+6lsbqbnZSksLtLRCa+jfWzcQGkayZZBkacSTSIolEkcC8ZZIfHwiiXGJJCbER/fNhSHBjJS49GiXAUCCJZFoScRbAolxybfrarzZyOjRowH41re+1bc19elnExERGYTafG1ca752++NGaw2tre69IawF2tohjjiSbQiplklaXBY5lklGWib5uZnk5w1hWG48GRmQlASJid6/tz7iYz+XAXD69Gnyh6VFuwwAhqQnkpGRRFpKAjmZybfr+v3f/x987GMf4ytf+QpPPPFEn9ZkzvWTNtAPUFRU5IqLi6NdhoiICB3+Di7fvExVcxVVTVXc7LiJzwf19VBXBw31RlIgh5S4TFItkxTLJCUuk7S4TEYMzSA318jJgaFDIScHhgyBgTRn6+nTp5k+fXq0y+gzPb1fMytxzvU4J4dazkRERHpBu7+dSw2XKK0v5WrTVQLBIC0toTBWlwAtw8mKH8WIuJFMThxO5tBEcnO98HXrIyur/7R+SeQonImIiNynNl8b5Q3llDWUcbXxKg5HWxtcv2501OST7i8gO24kY+LySEiLIz8fxo71PrKzo129xCqFMxERkXtU11bH4arDlNaX4nAEAlBXZ/hqxxB3cwKj4wtJtBQyhkJBgRfG8vO9MWIiH0ThTEREJEzVLdUcqjrE5ZuXAWhtiSNQP5bO6vFkB8eRaCkkpMCECTBtGowcGeWCpV9SOBMREfkAVxuvcvjaYSqbKgFoa0nAVzWNpPrZpMVlADBsuBfIJk707pwUuV8KZyIiIj1wznHp5iWOXDtCdUs1AIHOJFz1TOKvPUSqpZKYDFOneh+5uVEuWAaMuGgXICIiEkuCLsiFugu8eupVNl3cRHVLNXHBFBKrF2AnPkLC9QWkxKcyaxY89xwsWaJg1t9cuXKF8ePHU1dXB0B9fT3jx4+nvLwcgG9/+9s899xz7zmmpqaGYcOG0dHR0eM5v/Wtb/HpT3+6V+pTy5mIiAgQCAY4V3uOI9eO0NTZBEByXDopDQ/TVDYNnz+BOGDKFCgqgoyM6NYr92/s2LF88pOf5MUXX+Tll1/mxRdf5IUXXqCwsBCAD3/4w/zu7/4ura2tpKV5k9K++uqrPPnkk+9ZrilSFM5ERGRQ8wV8nK45zbHrx2j1tQIwJDGLjJY51J6bzM0Or5OpoAAWLvTmI5P+77d/+7eZP38+X//619mzZw/f+MY3bm/LzMzk0Ucf5Y033uCZZ54B4JVXXuGLX/wib7zxBl/5ylfo7OwkNzeX733ve4wYMaJXa1M4ExGRQetc7Tn2Veyj3d8OQE5qLkPb53Lt+HiqWrwp+UeMgEWLdOdlpLxc8nJEzvvC/Bfuuj0xMZH//b//Nxs2bGDTpk0kdpvn5LnnnuN73/sezzzzDJWVlZw7d47Vq1fT2NjIvn37MDP+5V/+ha9+9av81V/9Va/WrnAmIiKDTlNHE7sv76aisQKAEekjGB6cR8XJsVys9/bJyYEFC2DcuCgWKhH15ptvMmrUKE6cOMG6deves+2JJ57gU5/6FI2Njfzwhz/kl37pl4iPj6eiooJnnnmGqqoqOjs7GT9+fK/XpXAmIiKDhnOOE9UnOFh5EH/QT3J8MpNSllBzdjLHr3v7ZGR4oWzSpIG1nmWs+qAWrkg5cuQImzdvZt++fSxbtoynn36aJ598EoBPfOITfOITn2DDhg289tprvPLKK/z1X/81AJ/5zGf4nd/5HT70oQ+xY8cOXnrppV6vTeFMREQGhfq2enZe2nl7WoxxQyYSX7mEk+dSAUhJgXnzYPp0rW850Dnn+OQnP8nXv/51CgoK+L3f+z1efPFFjhw58p79nnvuOV588UUaGxtZvHgxADdv3mT06NGAd1dnJGgqDRERGdCCLkhJZQk/Ov0jqluqSU9MZ17WY9QfWkPpuVTi4mD+fG9ajIceUjAbDP75n/+ZgoKC212Zn/rUpzh9+jQ7d+58z37r1q2jsrKSZ555Bgs1o7700kv88i//MvPnzycvLy8i9ZlzLiIn7mtFRUWuuLg42mWIiEgMafO1seniJq63eH2WU3Onk3h9ESePJeGcN65s9WrdgdnXTp8+zfTp06NdRp/p6f2aWYlzrqin/dWtKSIiA1Jtay0bL26kubPZay3LWc3pA6OoqfG2z5njtZippUxijcKZiIgMOOUN5Wwr24Y/6GdY2nDy29azd2MagQAMGQKrVmlqDIldCmciIjKgHLl2hANXDwBQmDkJ/8VHOXrZax6bNg0WL4ZuU1qJxBSFMxERGRACwQC7Lu3ifN15AGblLKTy0BxqayE5GVau1JxlscQ5d3uQ/UB2P2P7Fc5ERKTfa/W13l6kPCEugTlZqzn1diGtrZCVBRs2eP9KbEhJSaG2tpbc3NwBHdCcc9TW1pKSknJPxymciYhIv1bfVs+bF96kubOZjKQMpic+xuEdufj9MGoUrF/vtZxJ7BgzZgwVFRXcuHEj2qVEXEpKCmPGjLmnYxTORESk37rWfI23LrxFZ6CT4enDGdX6GAff8SaVnTIFVqyAOM3oGXMSExMjsuzRQKFwJiIi/VJZfRnbyrYRcAEKMgtJurqao+e8P2sLF3pTZYj0RwpnIiLS75ysPsnbV94GYHL2DJpPLeVylZGQ4E2ToUYZ6c8UzkREpN9wznGw8iBHrh0B4KGcBVSWzKWuDtLS4LHHYNiw6NYo8qAUzkREpF8IuiA7y3dyvu48hjE351HOvTOF5mbIzobHH/cmmBXp7xTOREQk5nUGOtlSuoWKxgpvqozMdRzfNZaODhg+3Jsq4x5nKxCJWQpnIiIS01p9rbx5/k1q22pJTUhlZvLjHN6RRyDgTSq7Zg0k6K+ZDCD6dhYRkZjV0N7Az87/jObOZrKSs5gQfJyS3Zk45y3FtGyZpsqQgUfhTEREYtK15mtsvLCRjkAHw9OHM+zmBg4f9foui4pg3rwoFygSIQpnIiISc7rPYZZYsZqT5xMwg+XLvVYzkYFK4UxERGLKieoT7L2yF4ApQ2fQemYpFyu8OczWrNHi5TLwKZyJiEhMcM6x/+p+jl0/BsDs3IVUHppDTY13J+aGDd6dmSIDncKZiIhEXSAYYEf5Di7WXyTO4pg79FHOvTOZpibIzISf+znvX5HBQOFMRESiqsPfwaaLm6hqriIxLpG5mes5tns07e3ebP8bNkBqarSrFOk7CmciIhI1zZ3NvHn+Terb60lLTGNm0uOU7MglEICCAli7VnOYyeCjb3kREYmK2tZa3rzwJq2+VoamDKXQ/zgHd2UAMH06LF2qOcxkcFI4ExGRPlfRWMHmi5vxBX2MyshnaN16Dh9PAjSHmYjCmYiI9KlztefYdWkXQRekMGsilK/kVGk8cXGwYgVMmRLtCkWiS+FMRET6zKGqQxRXFgMwPedh6o8v5No1IzER1q2DMWOiXKBIDFA4ExGRiAu6IHsu7+FMzRkA5uQspfzgTBoaID0dHn8ccnKiW6NIrFA4ExGRiOoMdLKldAsVjRXEWzxzs9dw6u1CWlu9QPb4415AExGPwpmIiERMS2cLb114i9q2WlISUpiVuoHDO4bj90N+PqxfD0lJ0a5SJLYonImISETUtdXx5vk3afG1kJWcxYTg4xTvzMQ5mDwZHn1UU2WI9EThTEREet3VxqtsLt1MZ6CT4ekjyKl7jMMnUgCYOxcWLIhygSIxTOFMRER6VdepMsZlToDylZwpSyAuDpYvh6lTo12hSGxTOBMRkV5TUllCSVUJAFOzZ1N/fBHV1UZSkrcUk6bKEPlgCmciIvLAgi7Irku7OFd7DsOYNXQJ5Qdm0tgIGRne4uWaKkMkPApnIiLyQDr8HWwu3UxlUyUJcQnMzljLqT0FtLdDXp4XzNLSol2lSP+hcCYiIvetqaOJNy+8SUN7A2mJaUxL2MDRXXkEAlBQAGvWQGJitKsU6V8UzkRE5L5Ut1Sz8cJG2vxt5KTmMLptA4f2ZQAwYwYsWaKpMkTuh8KZiIjcs7L6MraVbSPgAuRnjCH9+lqOn/Fmk120CB5+OMoFivRjCmciInJPjl0/xr6KfQBMyp5G+7llnL8SR3w8rFoFEyZEuUCRfk7hTEREwhJ0QfZe2cupG6cAmJWzkKrDc6ipgZQUbymmkSOjXKTIAKBwJiIiH8gX8LG1bCuXb14mzuKYO3QVZ9+ZSHMzZGZ6i5dnZUW7SpGBIaJDNc1sg5mdNbMLZvZiD9tXmNkhM/Ob2dPdthWY2SYzO21mp8ysMJK1iohIz1p9rbxx7g0u37xMcnwyRZk/z/GdXjAbPhx+4RcUzER6U8RazswsHvgHYB1QARw0s9edc6e67HYZeB74Qg+n+A7wZ865zWaWAQQjVauIiPSsrq2Oty68RXNnM5nJmUzmcYp3ZBEMwvjx3hizBPXBiPSqSP5ILQQuOOdKAczsFeAp4HY4c86Vh7a9J3iZ2QwgwTm3ObRfcwTrFBGRHlQ0VrD54mZ8QR8j0kcwrPExSo54i5fPnu3dlWkW5SJFBqBIhrPRwJUuzyuARWEeOwVoMLP/BMYDW4AXnXOB3i1RRER6cqbmDLsv7cbhKMyaQNzlVZy4EI+ZN3/ZzJnRrlBk4IrVxugEYDkwF6/r8wd43Z//2nUnM3sBeAGgoKCgbysUERmAnHMcrDzIkWtHAJiRM4f6EwuoqjISErzFy/XrViSyInlDwFVgbJfnY0KvhaMCOOKcK3XO+YEfA/O67+Sce9k5V+ScKxo2bNiD1isiMqj5g362lm3lyLUjGMa83OVUHlxIVZWRlgYf+pCCmUhfiGTL2UFgspmNxwtlzwIfuYdjs81smHPuBrAaKI5MmSIi0uZrY+PFjVS3VJMUn8TcrLUc2z2GtjYYOtSbKiMjI9pVigwOEWs5C7V4fRrYCJwGfuicO2lmXzazDwGY2QIzqwB+GfgnMzsZOjaAdwfnVjM7Dhjwz5GqVURkMKtvq+fHZ35MdUs1GUkZzEn9ECXbvWCWnw9PPaVgJtKXzDkX7Rp6RVFRkSsuVuOaiMi9uNp4lc2lm+kMdDIsbRij2x/jyME0AKZMgRUrtHi5SCSYWYlzrqinbbF6Q4CIiETY2Zqz7L68m6ALUpBZSErVao6c8f4sLFgAc+dGuUCRQUrhTERkkOl+R+b0nNk0nV7EuQojPh5WroSJE6NaosigpnAmIjKI+IN+tpVto7yhHMOYm7eM8oPTqavzFi9/7DEYMSLaVYoMbgpnIiKDRKuvlbcuvEVNaw1J8UnMz17H0d2jaW2F7GzYsMFbxFxEokvhTERkEKhprWHjhY20+FrITM5kRtIGDm7Lxu/37shctw6Sk6NdpYiAwpmIyIBX3lDOtrJt+IN+RmaMZFTrevbt9dbI1B2ZIrFH4UxEZAA7eu0o+6/uB2DS0CnYleUcPhsPwMKFMGdOFIsTkR4pnImIDEBBF2T3pd2crT0LwJxhC6k+NofKSoiPh1WrYMKEKBcpIj1SOBMRGWDa/e1suriJa83XSIhLoChvFaf3jufmTUhL8+7I1HLEIrFL4UxEZACpa6tj44WNNHU2kZ6YzsNDHqNkWx4dHZCb6wUzLcUkEtsUzkREBohLDZfYVrYNX9DHsLRhTHCPsW9rGsEgFBTAmjWQmBjtKkXkgyiciYgMAF0H/k/Inkhq9aPsP+79ip81Cx55BMyiWaGIhEvhTESkHwsEA+y+vJtztecAmDO8iLqT8zh52ZseY+lSmD49ykWKyD1ROBMR6afafG1suriJ6y3XSYhLYMGwVZzdN566Om9C2XXrvAlmRaR/UTgTEemHus74n56YzpzQwP/2dm8ppsceg6ysaFcpIvdD4UxEpJ8prS9lR/kO/EE/I9JHUBhcxzuhgf9jxsDatZCUFO0qReR+KZyJiPQTzjlKqko4VHUIgMk5U0ipXs7+o96M/zNnwuLFWopJpL9TOBMR6Qd8AR/by7dT3lCOYcwf8QjVJ2Zx/rJ3F+bSpTBjRrSrFJHeoHAmIhLjmjqa2HhxI3VtdSTFJ7Fo2FpO7B1Dfb038H/tWhg9OtpVikhvUTgTEYlhlU2VbCndQru/neyUbGanP8b+LVl0dHgD/zdsgMzMaFcpIr1J4UxEJEadqD7BO1feweEYmzmWUe1r2L05Cee8Gf9Xr9bAf5GBSOFMRCTGBIIB9lzew9naswDMHj6HzrIFHDjjTfE/Zw4sWKAZ/0UGKoUzEZEY0uprZdPFTVS3VJMQl8CikSsoLZ5EVRXEx8Ojj8KkSdGuUkQiSeFMRCRGVLdUs+niJlp9rWQkZVA0dD3F2/Noboa0NG9i2WHDol2liESawpmISAw4W3OW3Zd3E3RBRmWMYqKtY8+mFPx+GD4c1q/3ApqIDHwKZyIiURR0QfZV7ONE9QkAZuTNJL5qMXuOeTPJTp0Ky5Z5XZoiMjgonImIREmbr40tpVuoaq4izuJYOHIZV49O48oVb7D/4sXw0EPRrlJE+prCmYhIFNS01rDp4iaaO5tJS0xjQe46Du8aQWMjpKR4E8vm50e7ShGJBoUzEZE+dr72PLsu7SLgAoxIH8GUhHXs3ZSGzwe5ud74siFDol2liESLwpmISB8JuiD7K/ZzvPo4AFNzp5FyYym7D3sDyiZO9KbKSNBvZpFBTb8CRET6QLu/nS2lW6hsqiTO4lgwcilVx6ZzNrRw+YIF3uSyIiIKZyIiEdZ1fFlqQioLctdxdPdIbt70Fi5fswbGjIl2lSISK+4azswsHvisc+5v+qgeEZEBpev4suHpw5kSv453Nqfj80FOjje+TAuXi0hXdw1nzrmAmT0HKJyJiNyD7vOX3Rpftic0vmzCBFi5UuPLROT9wvm18LaZfQP4AdBy60Xn3KGIVSUi0o91n7+s+/iyhQvh4YejXaWIxKpwwtmc0L9f7vKaA1b3ejUiIv3cjZYbbLq4iRZfC2mJaRTlrONIaP4yjS8TkXB8YDhzzq3qi0JERPq7szVn2XN5z+35yyaaN3+Z36/5y0QkfB8YzswsC/gTYEXopZ3Al51zNyNZmIhIfxEIBnin4h1O3TgFwLTcGSRULWHvcW99zMmTYflyjS8TkfCE86vi34ATwK+Env8a8E3gFyNVlIhIf9HS2cLm0s1Ut1QTb/EsGLGMy4enUlkJcXHwyCNaH1NE7k044Wyic+6Xujz/kpkdiVA9IiL9RlVTFVtKt9DmbyMjKYO5Wes4vHMYzc2Qmgrr1sHIkdGuUkT6m3DCWZuZLXPO7QEws6VAW2TLEhGJbceuH2N/xX4cjtFDRjPGt4a9m1MIBGDECC+YpaVFu0oR6Y/CCWefAL4TGnsGUA98LHIliYjELl/Ax65Lu7hYfxGAh/Lm0Fm2gP3nDIAZM2DJEq9LU0TkfoSzQsCvOeceNrNMAOdcY59UJiISY26232TTxU3Ut9eTGJdI0bCVnD8wnpoaiI/3Bv1PmRLtKkWkvwtnhYBloccKZSIyaJU3lLOjfAedgU6yU7KZkbyekq3ZdHR4yy+tW+dNlyEi8qDC6dY8bGavA//Be1cI+M+IVSUiEiOCLsjBqwc5ev0oAIVZ48mqX8netxMBKCiAVau8CWZFRHpDOOEsBajlvSsCOEDhTEQGtDZfG1vLtlLZVIlhzB2+iBsnZnP0ire9qAjmzvWWZBIR6S3hjDmrdc59oY/qERGJCdear7GldAutvlbSEtOYm7WWY3tG0tSkZZhEJLLCGXO2tK+KERGJBSeqT7CvYh9BF2RUxihGd67hnc1pBIMwbJg3viwjI9pVishAFU635hGNORORwaD7NBkz8mbTcXEhxRe8eTFmzIDFi707M0VEIkVjzkREgLq2OraUbqGhvYHEuETm5azk3IHx1Nd7a2KuWAGTJkW7ShEZDD4wnDnnPt4XhYiIRMv52vPsvrwbf9BPTmoOk+LWcmhbNj4fZGd73ZhDh0a7ShEZLD4wnJnZFOD/AiOccw+Z2WzgQ865r0S8OhGRCAoEA7x95W3O1JwBYNLQKSRULuPAKe9X48SJXotZYmI0qxSRwSacBUb+GfgDwAfgnDsGPBvJokREIq2xo5H/OvtfnKk5Q7zFMz9vBY3HVnLmVAJxcd4STGvWKJiJSN8LZ8xZmnPugL13Ih9/hOoREYm4rrP9ZyZnMi1pLUd35NHR4d2FuXYtDB8e7SpFZLAKJ5zVmNlEvJsAMLOngaqIViUiEgFBF+TA1QMcu34MgILMQobUruTAsSTveQGsXAkpKVEsUkQGvXDC2X8HXgammdlVoAz4bxGtSkSklzV3NrO1dCvXW65jGLNzF1F9fDYnq7wZ/hcsgIcf1mz/IhJ94dytWQqsNbN0IM451xT5skREes+Vm1fYXr6ddn876YnpzEhdw4ndI2lrg7Q0b2zZqFHRrlJExBNOyxkAzrmWD95LRCR2BF2Q4spijlw7AsCYIWMZenMVB/d5/ZajR8Pq1ZCaGsUiRUS6CTuciYj0J62+VraWbqWquQrDeChnATUnHuZ4lddvOW8ezJ+vbkwRiT0KZyIy4FxtvMrWsq20+9tJS0xjespqTu3Jp63NayVbvdprNRMRiUXhTEKbBvwuUOCc+00zmwxMdc79JOLViYjcA+ccJVUlHKo6BEB+xmiyG1ZTss/rt8zP94JZWlo0qxQRubtwWs6+CZQAi0PPr+Itgq5wJiIxo9XXyraybVQ2VQIwc+h86k7O41SoG3P+fK8rU92YIhLrwlkhYKJz7qu8u0JAKxDWrzcz22BmZ83sgpm92MP2FWZ2yMz8ofnTum/PNLMKM/tGOJ9PRAanq41X+dGpH1HZVElqQirzhjxB6dvzqaoyUlPhiSc0vkxE+o9wWs46zSyVdyehnQh0fNBBZhYP/AOwDqgADprZ6865U112uww8D3zhDqf5U2BXGDWKyCDknONQ1SFKqkoAGJmez9CG1Rza7/VbqhtTRPqjcMLZS8BbwFgz+x6wFPh4GMctBC6E5knDzF4BngJuhzPnXHloW7D7wWY2HxgR+txFYXw+ERlEundjTs+eT92JeZy+bph5XZjqxhSR/iicSWg3mVkJ8Ahed+bnnHM1YZx7NHCly/MKYFE4RZlZHPBXwK8Ca++y3wvACwAFBQXhnFpEBoDKpkq2lW2j1ddKakIqUxJXc2bPaDo6NKmsiPR/4dytudU5twb4aQ+vRcqngJ855yrsLv/tdc69jLe0FEVFRS6C9YhIDHDOceTaEYori3E4RqTnM+TGao6e9vottTamiAwEdwxnZpYCpAF5ZjaUd28CyMRrFfsgV4GxXZ6PCb0WjsXAcjP7FJABJJlZs3PufTcViMjg0O5vZ3vZdq40eg3yU4bMo/b4fC7UGnFxsHAhzJqlbkwR6f/u1nL2W8DngXzgUJfXG4Fw7p48CEw2s/F4oexZ4CPhFOWcu72wupk9DxQpmIkMXtebr7O1bCvNnc2kJKQwIW4V598ei88HQ4Z43ZjDh0e7ShGR3nHHcOac+1vgb83sM865v7/XEzvn/Gb2aWAjEA/8m3PupJl9GSh2zr1uZguA14ChwJNm9iXn3Mz7eysiMhAdv36c/Vf3E3RBhqWOIL16DafOZQAwYQKsWAFJSVEuUkSkF5lzdx+qZWYf7el159x3IlLRfSoqKnLFxcXRLkNEeklnoJOd5TspaygDYEL6LBpOLqKuNo64OFiyBGbMiHKRIiL3ycxKnHM9zkYRzlQaC7o8TgHW4HVzxlQ4E5GBo6a1hi2lW2jsaCQpPolJCSu5sK+Qzk6vG3PdOsjLi3aVIiKREc5UGp/p+tzMsoFXIlWQiAxup26cYu+VvQRdkJyUPLLr1nLqdCYAhYXe3ZjqxhSRgSyclrPuWoDxvV2IiAxunYFOdl/azcX6iwAUps+g6fRiSm/EExcHixZ5d2OKiAx04cxz9gahpZvw1uKcAfwwkkWJyOBS21rLltIt3Oy4SWJcIhMTVlC2fyIdHZCRAWvX6m5MERk8wmk5+1qXx37gknOuIkL1iMggc6bmDG9ffpuAC5CdnEN23VrOnMkGNKmsiAxO4Yw529kXhYjI4OIL+Nh9eTcX6i4AUJA2jZYzSyi/kaBJZUVkULvbCgFNvNud+Z5NgHPOZUasKhEZ0LrejZkQl8D4+OVc2j+Zzk51Y4qI3G0S2iF9WYiIDA4nqk+wr2IfQRckOzmHrNq1nD+bDcC4cV43ZnJyVEsUEYmqsO7WNLOHgeWhp7ucc8ciV5KIDETt/nZ2lu/k0s1LAIxLm0HjqUe4VJuguzFFRLoI527NzwG/Cfxn6KXvmdnL97Okk4gMTlVNVWwr20aLr4Wk+CQK7VHK9o3X2pgiIj0Ip+XsN4BFzrkWADP7S+AdQOFMRO7KOcfha4cpqSzB4chLGUHa9TWcu+CtjTl+PDz6qCaVFRHpKpxwZkCgy/NA6DURkTtq7mxmW9k2rjVfA2Bi+lxqjs3nckMc8fGweLHWxhQR6Uk44eybwH4zew0vlD0F/GtEqxKRfq20vpRdl3bRGegkLTGNsf5VXNw7Gr8fsrO9bszc3GhXKSISm8KZ5+yvzWwHsCz00sedc4cjWpWI9Eu+gI+9V/ZytvYsAPnp40ioeJSzpd4sslOmwLJlkHA/C8eJiAwS4dwQMBE46Zw7ZGargOVmVuaca4h4dSLSb9S01rC1dCs3O24Sb/FMSVvM1SMzaGz0wtjy5TB5crSrFBGJfeH8//VHQJGZTQL+EXgd+D7wc5EsTET6B+ccx64f42DlQYIuyNCUHEa0rubsnhyCQcjL87oxs7KiXamISP8QTjgLOuf8ZvaLwDecc39vZurWFBHa/e1sK9tGRaO33O6kzIdoObeQM1e9Xy2zZnnLMMXHR7NKEZH+JZxw5jOz54CPAk+GXkuMXEki0h9cb77OltIttPhaSElIYUrSSs7vK6CtzVuofOVKb+FyERG5N+GEs48DnwD+zDlXZmbjge9GtiwRiWXHrx9n/9X9BF2QYWkjyK5dy7FT6QDk58Pq1ZCWFuUiRUT6qXDu1jxlZl8AppnZLOCsc+4vI1+aiMSazkAnO8t3UtZQBsDEIbO4eXIR52/EYQZFRTBnDphmQhQRuW/h3K35BN6NABfx5jkbb2a/5Zx7M9LFiUjsqG2tZXPpZho7GkmKT2JK8qNc2Dee9nbIyPAG/Y8YEe0qRUT6v3C6Nf8KWOWcuwC3p9b4KaBwJjJInK05y57Lewi4ADmpuQxvWseJA5kAjBnjdWOmpES5SBGRASKccNZ0K5iFlAJNEapHRGJI0AXZe2Uvp26cAmBC5jQ6LizhzBXvV8f8+TBvnroxRUR60x3DWWjqDIBiM/sZ8EPAAb8MHOyD2kQkilp9rWy+uJnrLdeJt3hmDllGWfFUmpogOdlrLRs7NtpViogMPHdrOXuyy+PrwKOhxzeA1IhVJCJRd735OptLN9PqayU9MZ0Jbj0ndw0jEPAmlV23DoYMiXaVIiID0x3DmXPu431ZiIjEhtM3TvP2lbcJuiAj0keRcX0tx896/x+bPh2WLNGksiIikRTO3ZopwG8AM4HbQ36dc78ewbpEpI8FggHevvI2Z2rOADA56yEaTz3CxWtxxMV5a2NOnRrlIkVEBoG4MPb5LjASeAzYCYxBNwSIDCitvlbeOPcGZ2rOEG/xPJy1kqoDS7h+LY60NPjQhxTMRET6Sjh3a05yzv2ymT3lnPu2mX0f2B3pwkSkb9xoucHGixtp9bWSkZTBJFvPiV15+P0wfDisX6/Z/kVE+lJYa2uG/m0ws4eAa8DwyJUkIn3lQt0FdpbvJOACjEgfSXbdOo6c8MaXTZ0Ky5ZpfJmISF8LJ5y9bGZDgT8CXgcygP8Z0apEJKKccxRXFnP42mEAJmZNo/3cMs5WeMswLVkCM2dGuUgRkUEqnLU1/yX0cBcwIbLliEik+QI+tpdvp7yhHMOYmbWYKyUPcfOmN8v/2rXe4uUiIhId4bScicgA0dTRxMaLG6lrqyMpPomZqWs5uWcMnZ2QkwOPPab5y0REok3hTGSQuNZ8jU0XN9Hubyc7JZsxHY9xZFcWzkFhIaxaBYmJ0a5SREQUzkQGga4Ty47KGENq5VpOnE8CvLUx58/X+pgiIrEirHBmZkuAwq77O+e+E6GaRKSXdF+4fErWLOpPLKK0Oo6EBFi5EiZoJKmISEwJZ4WA7wITgSNAIPSyAxTORGJYu7+dzRc3U9VcRZzFMStzBRcPTKG5GdLTvfFleXnRrlJERLoLp+WsCJjhnHORLkZEekdtay0bL26kubOZtMQ0Jset5+Su4fj9MGKEN7Fsamq0qxQRkZ6EE85O4C3fVBXhWkSkF5TVl7G9fDv+oJ+8tGHk1j/G0RPeFP+aWFZEJPaFE87ygFNmdgDouPWic+5DEatKRO6Zc46SqhIOVR0CYNyQyXSeX8HZq/HExcHixZpYVkSkPwgnnL0U6SJE5MF0BjrZXradSzcvYRjThizi6qHZNDZ6E8uuWwejRkW7ShERCUc4KwTs7ItCROT+NLQ3sOniJhraG0iOT2ZywhrO7BmD3+8N+F+/HjIyol2liIiEK5y7NR8B/h6YDiQB8UCLcy4zwrWJyAcobyhne9l2fEEfQ1NyyLu5nhMnvB/NSZNgxQpI0GyGIiL9Sji/tr8BPAv8B96dmx8FpkSyKBG5u+7jy8ZkTCBwYSXnryZgBosWwezZUS5SRETuS1j/p3bOXTCzeOdcAPimmR0G/iCypYlITzoDnWwr28blm5cxjCkZC6k89DBNTd74sjVrYPToaFcpIiL3K5xw1mpmScARM/sq3pQacZEtS0R6Ut9Wz6aLm7jZcZPk+GQm2BrO7RlDIKDxZSIiA0U44ezX8MLYp4HfBsYCvxTJokTk/UrrS9lRvgN/0E92cg7Ztes5fdYbX6b5y0REBo5w7ta8ZGapwCjn3Jf6oCYR6SLoghy4eoBj148BMCZtEu1nV1BenUBcHCxdCtOnR7lIERHpNeHcrfkk8DW8OzXHm9kc4MuahFYk8lp9rWwt3Xp7fczJKYu5UjKT1lZvfcx162D48GhXKSIivSncSWgXAjsAnHNHzGx8BGsSEeBa8zW2lG6h1ddKWmIao9vXce7tETgH+fnewH+tjykiMvCEE858zrmbZtb1NS2CLhJBJ6pPsK9iH0EXJC9lFEkVazh/yVsf8+GHYcECiNNtOSIiA1I44eykmX0EiDezycBngb2RLUtkcPIFfOy+vJsLdRcAKEydTd3xhdTcjCMxEVauhPFqtxYRGdDCCWefAb6It+j5vwMbgT+NZFEig1FDewObL26mvr2ehLgECllJ+b4J+P2Qk+ONL8vKinaVIiISaeHcrdmKF86+GPlyRAani3UX2XlpJ/6gn6zkoWTVrOPCuWwAJk+G5cu1DJOIyGBxx1/3Zvb63Q7U3ZoiDy7oguyr2MeJ6hMAjE6dRNvZ5Vy+kUhcHCxZAjNmRLlIERHpU3f7v/hi4ApeV+Z+wO6yr4jco+bOZraUbqG6pZo4i2NC4hIqimfQ3u7N8r92rabJEBEZjO4WzkYC64DngI8APwX+3Tl3si8KExnIKhor2Fa2jXZ/O+mJGQxrWsuFYi+JjR0Lq1Z562SKiMjgc8dwFlrk/C3gLTNLxgtpO8zsS865b/RVgSIDiXOOkqoSDlUdAmBEyliCpasor/KSWFERzJ0LpnZqEZFB665DjEOh7Am8YFYI/B3wWuTLEhl4Wn2tbCvbRmVTJQDjk4u4fngura1GSoo3qezo0VEuUkREou5uNwR8B3gI+BnwJefciT6rSmSAudp4lW1l22jzt5GakEp+xxrK9uUTDMLIkV4wS0+PdpUiIhIL7tZy9qtAC/A54LNdVggwwDnnMiNcm0i/55zjUNUhSqpKABiWkk/85dVcvOLN9j97NixcqNn+RUTkXXf8k+Cci3PODQl9ZHb5GBJuMDOzDWZ21swumNmLPWxfYWaHzMxvZk93eX2Omb1jZifN7JiZPXN/b08ketp8bfzs/M9uB7PxKfNoO/oE166kkZTkTSr7yCMKZiIi8l4Rm9bSzOKBf8C747MCOGhmrzvnTnXZ7TLwPPCFboe3Ah91zp03s3ygxMw2OucaIlWvSG+qbKpkW9k2Wn2tXjdm52rK3hlNMAh5ed40GZlqexYRkR5Ecs7xhcAF51wpgJm9AjwF3A5nzrny0LZg1wOdc+e6PK40s2pgGNAQwXpFHlj3bsxbi5ZfDC1aPnOm11oWHx/NKkVEJJZFMpyNxpvE9pYKYNG9nsTMFgJJwMUetr0AvABQUFBwf1WK9JLud2NOSJ1H9dF51DR5i5Y/+ihMmBDlIkVEJObF9Gp9ZjYK+C7wMedcsPt259zLwMsARUVFro/LE7ntys0rbC/fTru/nbTENEb7VlH6zmgCAcjN9boxtWi5iIiEI5Lh7CowtsvzMaHXwmJmmXirEnzRObevl2sT6RVBF6S4spgj144AMCJ1DPGXV3H+cioA06d762OqG1NERMIVyXB2EJhsZuPxQtmzeMtAfSAzS8Kb7PY7zrlXI1eiyP1r7mxma+lWrrdcxzAmpi7g2pGHaW42EhNhxQqYODHaVYqISH8TsXDmnPOb2aeBjUA88G/OuZNm9mWg2Dn3upktwAthQ4EnQ0tDzQR+BVgB5JrZ86FTPu+cOxKpekXuRWl9Kbsu7aIz0ElaYjr57Wso3TtSd2OKiMgDM+cGxlCtoqIiV1xcHO0yZIDzB/3svbKXMzVnABiZWgDlK7lW4a2NOWuWN6msujFFRORuzKzEOVfU07aYviFAJJbUttaytWwrDe0NxFs8k1IeoeLwTFpaIDnZuxuzsDDaVYqISH+ncCYShhPVJ9hfsZ+AC5CZlM3w5rWcK8nBORgxwlsbMyMj2lWKiMhAoHAmchft/nZ2lO/g8s3LAIxLn07r2cVcuO796MyZA0VFWoJJRER6j8KZyB1UNFawo3wHrb5WkuKTGG+PUrZvPJ2dkJYGq1dDfn60qxQRkYFG4UykG3/Qz4GrBzhRfQKAYakjSb22mrMXvH7LceO88WUpKdGsUkREBiqFM5Eualpr2F62nfr2euIsjolp86k+OocbN434eG9dzJkzo12liIgMZApnIngLlh+9fpTiymKCLkhWcjYj21Zzfk8ewSBkZ3tzl+XkRLtSEREZ6BTOZNBr7mxme9l2qpqrABifMZPWc4s4W+X9eEyfDosXQ4J+WkREpA/oz40Maudqz7H3yl46A52kJqQyjkcpfaeAzk5vTNmjj3pjzERERPqKwpkMSq2+VnZd2nV7ioz89EISKlZwptQb5T9unLc2ZmpqNKsUEZHBSOFMBp3ztefZe2UvHYEOkuKTmJi0hMslU2hp8boulyyBadOiXaWIiAxWCmcyaLT6WtlzeQ/lDeUA5KePJe3GCk6fTgdg+HBYtQqysqJYpIiIDHoKZzIoXKi7wNuX377dWjY5dTEVR6ZSeRPMYN48mDtXM/2LiEj0KZzJgNa9tWxUxhgyalZw6mAGzsHQoV5rWV5edOsUERG5ReFMBiTnHKdrTnPg6gE6A50kxiUyNX0xV49Oo6reay2bMwfmz4f4+GhXKyIi8i6FMxlwGtob2HVpF9earwEwNnMc6bVLOVnstZZlZXmtZcOHR7lQERGRHiicyYARdEGOXDvCoapDBF2Q1IRUpqUv5dKRCVyp8/aZNQsWLNCEsiIiErv0J0oGhOvN19l1aRf17fUATB46jYTrizhyIBnnIDPTm1B21KgoFyoiIvIBFM6kX2v3t3Pg6gHO1JwBICs5i8nJyzl3IJ/GRm9s2ezZUFSk1jIREekf9OdK+iXnHGdrz3Lg6gHa/e3EWRzTc2bTeWkexWe9b+ucHG+Wf40tExGR/kThTPqdmtYa9lzeQ3VLNQD5Q/IpYBnH9mTT2urNVTZvnnc3puYtExGR/kbhTPqNzkAnB68e5NSNUzgcaYlpPJy7mGunJrKvzNtn+HBvbNnQodGtVURE5H4pnEnMc85xvu48+yv20+ZvwzBmDptFWsN8ijcl4fN548kWLoSZM71xZiIiIv2VwpnEtOqWavZe2Xu7C3NkxkimpS3jxMEcamq8fQoLvcXKMzKiV6eIiEhvUTiTmNTqa+XA1QOcqz0HQFpiGnOHL6Lh4mR2nPT2yciApUth3LgoFioiItLLFM4kpgRdkOPXj3Oo6hC+oI84i2P2iNlkts6leEsira1et+WsWd7SS4mJ0a5YRESkdymcScy4fPMy71x5h5sdNwEYlzWO6UMWc+xgJkcqvX2GD4flyyE3N4qFioiIRJDCmURdQ3sD71x5hyuNVwDITslmwcglVF8Yw6YdEAxCcrI34H/aNA34FxGRgU3hTKKmM9BJSWUJJ2+cJOiCJMUnMW/UPNKaH2LvW3G0tHj7TZvmBbOUlOjWKyIi0hcUzqTPOec4U3OGg5UHafe3AzA9bzqT0os4fCCVigpvv7w8WLZMM/yLiMjgonAmfaqyqZJ3rrxDbVstAKMyRjF/xGIun8njpyfe7cJcsACmT1cXpoiIDD4KZ9InWn2t7KvYx4W6CwBkJGWwaPQjBGomsPUNaGvz9ps6FRYtUhemiIgMXgpnElHOOU7dOMXByoN0BjqJt3jmjprLSJvNgV0JVHtzyzJihDeR7LBh0a1XREQk2hTOJGJqWmvYfWk3N1pvAFCQVcDcvKWcPjKEYm9uWdLSvJaySZPUhSkiIgIKZxIB3RcoT09MZ/GYJbRWjudnr4HPB3FxMHs2zJ2riWRFRES6UjiTXnWh7gL7KvbR6mvFMGaPmM24xPns25nIDa8BjYICWLwYsrKiW6uIiEgsUjiTXtHQ3sCey3uobPKm8h+RPoJH8pdz8UQOPzkJznlrYS5Z4i1ULiIiIj1TOJMH4g/6OVR1iGPXjxF0QVISUlg4eiHJTVPZ8obR0uKNJZs9W2thioiIhEPhTO5beUM5e6/spbmzGfAmkp2etYCS/SlcuuTtM2yYtxZmXl4UCxUREelHFM7knjV2NPL25bdvr4WZl5bHkjHLuF46nNe3gt8PSUneRLIzZuguTBERkXuhcCZhc85xvPo4B68eJOACJMUnsSB/ATmBGezZaNTVeftNmOCNLUtLi269IiIi/ZHCmYSlob2BHeU7qG7xZo2dlDOJuXmLOXYolbfPevtkZnprYY4ZE8VCRURE+jmFM7mroAty7PoxSipLCLgAaYlpLC9YQdu1Al7/T+jo8OYsmzPH+0jQd5SIiMgD0Z9SuaO6tjp2lO+gprUGgGl505iR+Qh7dyVx7Zq3z+jRXmuZ5iwTERHpHQpn8j5BF+TItSMcqjpE0AXJSMpgxbgVNFaMuT3gPzXVm0h20qRoVysiIjKwKJzJe7T6WtlaupWq5ioAZgybwYyshezbk0RFhbfPpEmwdCkkJ0exUBERkQFK4Uxuq2qqYkvpFtr8baQlprF6/Gqaq/J5fRt0dkJKiteFOWFCtCsVEREZuBTOBICj145y4OoBHI78IfksHrmaknfSKC/3to8b500mq+kxREREIkvhbJDrDHSyo3wH5Q3lAMwZOYdRroif/jiO9nZvMtklS2DKlOjWKSIiMlgonA1ita21bC7dTGNHI0nxSawqXEXjlXG8tc9bqDw/H1au9BYsFxERkb6hcDZInas9x+5Luwm4ALmpuawat46jBzI5f97bPncuFBVp6SUREZG+pnA2yASCAfZe2cvpmtOAN3fZwzlL2LYpgRs3vElkV67UoH8REZFoUTgbRJo7m9lSuoXqlmriLZ6lBUsZGpjG6z+GtjYYMgQeewxycqJdqYiIyOClcDZIVDZVsqV0C+3+djKSMlg3YR21V4bxxh4IBr3xZWvXetNliIiISPQonA0CXafJGJM5hpXjVnPoQAqnTnnbH3oIHnnEWyNTREREokvhbADzBXzsKN9BWUMZAHNHzmVGdhGb3zSuX4f4eG/uMk2TISIiEjsUzgao6pZqtpdt52bHTZLik1hZuJKU9kJeew1aW73pMdatg2HDol2piIiIdKVwNsAEggGKK4s5dv0YDsfQlKGsn7ieiotZbHnn3fFla9Z4i5eLiIhIbFE4G0CqW6rZUb6DhvYGDOPhEQ8zZ3gR7+yN59w5b5/Zs2HhQo0vExERiVUKZwNA99ay7JRsVhauJM0N56c/gZoab/6yRx+FiROjXa2IiIjcjcJZP9dTa1lRfhHlZfG8uQc6OiAzE9av1/xlIiIi/UFEO7fMbIOZnTWzC2b2Yg/bV5jZITPzm9nT3bZ9zMzOhz4+Fsk6+6POQCd7r+zlv878Fw3tDWSnZPPUtKd4OG8R27fFs3WrF8wKCuDDH1YwExER6S8i1nJmZvHAPwDrgArgoJm97pw71WW3y8DzwBe6HZsD/AlQBDigJHRsfaTq7U8u1F1gX8U+Wn2tt1vL5ufP5+qVBDbu8mb7T0yExYth2rRoVysiIiL3IpLdmguBC865UgAzewV4Crgdzpxz5aFtwW7HPgZsds7VhbZvBjYA/x7BemNefVs9b195m8qmSgBGpI9gWcEyhiTksmcXtwf9jxrlrY85ZEj0ahUREZH7E8lwNhq40uV5BbDoAY4d3Ut19Tu+gI9DVYc4Xn2coAuSkpDCotGLmJI7hcpKY+NOaG72JpVduNCb8d8s2lWLiIjI/ejXNwSY2QvACwAFBQVRrqb3Oee4WH+R/RX7afG1ADA9bzoLRy8k0JnMzp3vtpYNH+61lmVnR61cERER6QWRDGdXgbFdno8JvRbusSu7Hbuj+07OuZeBlwGKiorc/RQZi26FskNVh2hobwAgLy2P5QXLyU0dxokTUFICPp83X9m8eTBnjuYuExERGQgiGc4OApPNbDxe2HoW+EiYx24E/tzMhoaerwf+oPdLjC09hbIhSUOYO2ouU3OncvWq8epPocHbxLhx3qD/zMyolSwiIiK9LGLhzDnnN7NP4wWteODfnHMnzezLQLFz7nUzWwC8BgwFnjSzLznnZjrn6szsT/ECHsCXb90cMBDdLZRNyZ1Cc1McmzdDebm3f1YWLFkCY8fe8ZQiIiLST5lzA6M3sKioyBUXF0e7jHviC/g4X3ee49ePc7PjJvDeUNbZEcexY3D8OAQC3vQY8+bBrFnqwhQREenPzKzEOVfU07Z+fUNAf9Xc2cyJ6hOcqTlDZ6ATeH8oKz4IJ06A3+8dM3kyLFoEaWlRLFxEREQiTuGsD11rvsbx68cpbyjH4bVYjkgfwawRsyjMLsTXGUdJsRfKfD7vmLFjYf58725MERERGfgUziLMOUdZQxlHrh2hprUGgDiLY+LQicwaPoth6cPo6EChTERERACFs4i5Ncj/cNVh6tu9VadSElKYMWwGM4bNIC0xjY4OKA6Fsk6vd5MxY7xQNmJEFIsXERGRqFE462VBF+RinXfn5a1B/hlJGcwZOYcpuVNIiEugs1OhTERERHqmcNZLgi7I+drzHL52mMaORuC9g/zjLI7OTig57t19eSuUjR7thbKRI6NYvIiIiMQMhbMHdCuUHao6RFNnEwCZyZnMGzWPSTmTiLM4fD44chyOHXs3lOXnQ1GRQpmIiIi8l8LZfbrVfVlSVXK7pSw7JZt5o+YxYegE4sybiKysDPbuhRZvaUzy872WslGjolW5iIiIxDKFs3t0a6B/SWXJ7TFlWclZzM+fz8ShEzEzABob4e234coV77i8PHjkES+ciYiIiNyJwlmYbk2JUVxZfHuJpe7dl+DN5H/0KBw+7D1OSoKFC2H6dAjlNhEREZE7UjgLU1lDGVtKtwDeQP95o+YxOXfy7VAGUFEBe/Z4rWbgzer/yCOQmhqNikVERKQ/UjgLU2F2IaOHjGbC0AlMzZv6nlDm98Pu3XD+vPd86FBYtkzjykREROTeKZyFKc7ieGLKE+97vakJNm2C2lpISPAG+2thchEREblfCmcPoKICtm6Fjg7IyoL1671WMxEREZH7pXB2n44ehQMHwDkoKIDVq73B/yIiIiIPQuHsHvn9sHMnXLzoPZ83z+vK1J2YIiIi0hsUzu5BY6M3vqyuDhITYdUqKCyMdlUiIiIykCichenaNdi48d3xZY89BtnZ0a5KREREBhqFszClp3v/anyZiIiIRJLCWZiGDIEPf9j7V+PLREREJFIUzu5BZma0KxAREZGBTlOlioiIiMQQhTMRERGRGKJwJiIiIhJDFM5EREREYojCmYiIiEgMUTgTERERiSEKZyIiIiIxROFMREREJIYonImIiIjEEIUzERERkRiicCYiIiISQxTORERERGKIOeeiXUOvMLMbwKX7PDwPqOnFcqR36frENl2f2KVrE9t0fWJXX1ybcc65YT1tGDDh7EGYWbFzrijadUjPdH1im65P7NK1iW26PrEr2tdG3ZoiIiIiMUThTERERCSGKJx5Xo52AXJXuj6xTdcndunaxDZdn9gV1WujMWciIiIiMUQtZyIiIiIxZMCHMzPbYGZnzeyCmb3Yw/ZkM/tBaPt+Myvssu0PQq+fNbPH+rTwQeJ+r4+Z5ZrZdjNrNrNv9Hnhg8ADXJt1ZlZiZsdD/67u8+IHgQe4PgvN7Ejo46iZfbjPix/gHuTvTmh7Qeh32xf6rOhB5AF+dgrNrK3Lz88/RqxI59yA/QDigYvABCAJOArM6LbPp4B/DD1+FvhB6PGM0P7JwPjQeeKj/Z4G0scDXp90YBnwCeAb0X4vA+3jAa/NXCA/9Pgh4Gq0389A+3jA65MGJIQejwKqbz3XR3SvTZftrwL/AXwh2u9noH084M9OIXCiL+oc6C1nC4ELzrlS51wn8ArwVLd9ngK+HXr8KrDGzCz0+ivOuQ7nXBlwIXQ+6T33fX2ccy3OuT1Ae9+VO6g8yLU57JyrDL1+Ekg1s+Q+qXrweJDr0+qc84deTwE08Lh3PcjfHczsF4AyvJ8d6X0PdH36ykAPZ6OBK12eV4Re63Gf0C+sm0BumMfKg3mQ6yOR1VvX5peAQ865jgjVOVg90PUxs0VmdhI4DnyiS1iTB3ff18bMMoDfB77UB3UOVg/6u228mR02s51mtjxSRSZE6sQiMriZ2UzgL4H10a5F3ss5tx+YaWbTgW+b2ZvOObVCR99LwN8455r7uKFGwlMFFDjnas1sPvBjM5vpnGvs7U800FvOrgJjuzwfE3qtx33MLAHIAmrDPFYezINcH4msB7o2ZjYGeA34qHPuYsSrHXx65WfHOXcaaMYbGyi940GuzSLgq2ZWDnwe+EMz+3SE6x1s7vv6hIY51QI450rwxq5NiUSRAz2cHQQmm9l4M0vCG9j3erd9Xgc+Fnr8NLDNeSP/XgeeDd21MR6YDBzoo7oHiwe5PhJZ931tzCwb+CnwonPu7b4qeJB5kOszPvQHBzMbB0wDyvum7EHhvq+Nc265c67QOVcIfB34c+ec7kbvXQ/yszPMzOIBzGwCXi4ojUiV0b5zItIfwM8B5/AS7hdDr30Z+FDocQreXTEX8MLXhC7HfjF03Fng8Wi/l4H48YDXpxyow/uffwXd7rjRR3SuDfBHQAtwpMvH8Gi/n4H28QDX59fwBpsfAQ4BvxDt9zLQPh7k91qXc7yE7taMqeuDN4a268/Ok5GqUSsEiIiIiMSQgd6tKSIiItKvKJyJiIiIxBCFMxEREZEYonAmIiIiEkMUzkRERERiiMKZiPRrZlZuZnkPuk8v1lNoZif64nOJyMCkcCYiIiISQxTORKRfMLMfm1mJmZ00sxd62F5oZmfM7HtmdtrMXjWztC67fMbMDpnZcTObFjpmoZm9E1rIeK+ZTe3hvK+Y2RNdnn/LzJ4Ofb7doXMeMrMlPRz7vJl9o8vzn5jZytDj9aHPfcjM/iO06DVm9r/M7JSZHTOzrz3Al0xE+imFMxHpL37dOTcfKAI+a2a5PewzFfg/zrnpQCPwqS7bapxz84D/C3wh9NoZYLlzbi7wx8Cf93DOHwC/AhBa7mUN3vJU1cC60DmfAf4u3DcS6mL9I2Bt6Phi4HdC7+nDwEzn3GzgK+GeU0QGjoRoFyAiEqbPmtmHQ4/H4q1rV9ttnyvu3fU8/x/wWeBW69N/hv4tAX4x9DgL+LaZTQYckNjD530T+FszSwY2ALucc21mlgV8w8zmAAHubQHkR4AZwNtmBpAEvAPcBNqBfzWznwA/uYdzisgAoXAmIjEv1BW4FljsnGs1sx1469911309uq7PO0L/Bnj3d9+fAtudcx82s0Jgx/tO6Fx76PM9htdC9kpo028D14GH8Xoh2nuox897eyhu1WzAZufcc90PMLOFeK1zTwOfBlb3cF4RGcDUrSki/UEWUB8KZtPwWp56UmBmi0OPPwLsCeO8V0OPn7/Lfj8APg4sB97qcmyVcy6It5h4fA/HlQNzzCzOzMYCC0Ov7wOWmtkkADNLN7MpoXFnWc65n+GFv4c/oH4RGYAUzkSkP3gLSDCz08D/wgs3PTkL/PfQfkPxxpfdzVeBvzCzw9y9J2ET8CiwxTnXGXrt/wAfM7OjwDSgpYfj3gbKgFN4Y9IOATjnbuCFwX83s2N4XZrTgCHAT0Kv7QF+5wPqF5EByJzr3gsgItL/hLolf+KceyjatYiIPAi1nImIiIjEELWciYiIiMQQtZyJiIiIxBCFMxEREZEYonAmIiIiEkMUzkRERERiiMKZiIiISAxROBMRERGJIf8/oHHvSuveLPYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso = Lasso()\n",
    "alpha = np.linspace(0.001,0.05,70) \n",
    "alpha = np.array([float(e) for e in alpha])\n",
    "lasso_a, train_lasso, test_lasso = calc_params(X_train,y_train,lasso,alpha,'alpha',5)\n",
    "lasso_set_a = lasso_a[np.where(test_lasso == test_lasso.min())]\n",
    "print('\\nLasso - best alpha value:', lasso_set_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the above graph shows us the mean absolute error across different alpha values for lasso regression. The difference between the RMSE value on the training data and cross validation is minimum.\n",
    "\n",
    "The function returns the best aplha value of 0.001. \n",
    "###### Calling calc parameter function\n",
    "Using the calc parameter function to pass the ridge model and calculate the test error and train error on different alpha values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha  =  0.01\n",
      "alpha  =  0.5183050847457626\n",
      "alpha  =  1.0266101694915253\n",
      "alpha  =  1.534915254237288\n",
      "alpha  =  2.0432203389830503\n",
      "alpha  =  2.5515254237288127\n",
      "alpha  =  3.0598305084745756\n",
      "alpha  =  3.5681355932203385\n",
      "alpha  =  4.076440677966101\n",
      "alpha  =  4.584745762711863\n",
      "alpha  =  5.093050847457626\n",
      "alpha  =  5.601355932203389\n",
      "alpha  =  6.109661016949151\n",
      "alpha  =  6.617966101694914\n",
      "alpha  =  7.126271186440677\n",
      "alpha  =  7.6345762711864396\n",
      "alpha  =  8.142881355932202\n",
      "alpha  =  8.651186440677964\n",
      "alpha  =  9.159491525423727\n",
      "alpha  =  9.66779661016949\n",
      "alpha  =  10.176101694915252\n",
      "alpha  =  10.684406779661016\n",
      "alpha  =  11.192711864406778\n",
      "alpha  =  11.70101694915254\n",
      "alpha  =  12.209322033898303\n",
      "alpha  =  12.717627118644065\n",
      "alpha  =  13.225932203389828\n",
      "alpha  =  13.73423728813559\n",
      "alpha  =  14.242542372881354\n",
      "alpha  =  14.750847457627117\n",
      "alpha  =  15.25915254237288\n",
      "alpha  =  15.767457627118642\n",
      "alpha  =  16.275762711864406\n",
      "alpha  =  16.78406779661017\n",
      "alpha  =  17.29237288135593\n",
      "alpha  =  17.800677966101695\n",
      "alpha  =  18.308983050847456\n",
      "alpha  =  18.81728813559322\n",
      "alpha  =  19.32559322033898\n",
      "alpha  =  19.833898305084745\n",
      "alpha  =  20.342203389830505\n",
      "alpha  =  20.85050847457627\n",
      "alpha  =  21.358813559322034\n",
      "alpha  =  21.867118644067794\n",
      "alpha  =  22.37542372881356\n",
      "alpha  =  22.88372881355932\n",
      "alpha  =  23.392033898305083\n",
      "alpha  =  23.900338983050844\n",
      "alpha  =  24.408644067796608\n",
      "alpha  =  24.916949152542372\n",
      "alpha  =  25.425254237288133\n",
      "alpha  =  25.933559322033897\n",
      "alpha  =  26.441864406779658\n",
      "alpha  =  26.950169491525422\n",
      "alpha  =  27.458474576271183\n",
      "alpha  =  27.966779661016947\n",
      "alpha  =  28.47508474576271\n",
      "alpha  =  28.98338983050847\n",
      "alpha  =  29.491694915254236\n",
      "alpha  =  30.0\n",
      "\n",
      "Ridge - best alpha value: [5.093051]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ridge = Ridge()\n",
    "alpha = np.linspace(0.01,30,60) \n",
    "alpha = np.array([float(e) for e in alpha])\n",
    "ridge_a,train_ridge, test_ridge = calc_params(X_train,y_train,ridge,alpha,'alpha',5)\n",
    "ridge_set_a = ridge_a[np.where(test_ridge == test_ridge.min())]\n",
    "print('\\nRidge - best alpha value:', ridge_set_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the above graph shows us the mean absolute error across different alpha values for ridge regression. The difference between the RMSE value on the training data and cross validation is large at the beginning and  then converges as the aplha value increases.\n",
    "\n",
    "The function returns the best aplha value of 5.09. \n",
    "\n",
    "##### Calling Evaluate model function\n",
    "Using the Evaluate model function to pass the ridge model and calculate the RMSE on training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: Ridge(alpha=array([5.093051]), copy_X=True, fit_intercept=True, max_iter=None,\n",
      "      normalize=False, random_state=None, solver='auto', tol=0.001)\n",
      "RMSE on train set: 0.1325\n",
      "RMSE on test set: 0.1322\n"
     ]
    }
   ],
   "source": [
    "ridge = Ridge(alpha=ridge_set_a )\n",
    "evaluate_model(ridge, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the Evaluate model function to pass the lasso model and calculate the RMSE on training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: Lasso(alpha=array([0.001]), copy_X=True, fit_intercept=True, max_iter=1000,\n",
      "      normalize=False, positive=False, precompute=False, random_state=None,\n",
      "      selection='cyclic', tol=0.0001, warm_start=False)\n",
      "RMSE on train set: 0.1364\n",
      "RMSE on test set: 0.1334\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso(alpha=lasso_set_a )\n",
    "evaluate_model(lasso, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the Evaluate model function to pass the lasso model and calculate the RMSE on training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
      "RMSE on train set: 0.1293\n",
      "RMSE on test set: 0.1304\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(linreg, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from the above cells, Ridge method with aplha value 5.093051 is the best performing model. The RMSE using Ridge regression returned a value of 0.1325 from training set and a value of 0.1322 on the test set.\n",
    "The linear regression and lasso regression performs poorly as compared to the ridge regression.\n",
    "#### Section e)\n",
    "##### Stochastic Gradient Descent\n",
    "We can perform the scaling function on the training set. We are also setting the parameters for different penalty and alpha values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "x_s = scaler.transform(X_train)\n",
    "\n",
    "parameters = {\n",
    "    'penalty': ['l1','l2'],\n",
    "    'alpha': np.linspace(0.0001, 10, 1000, dtype=int)\n",
    "}\n",
    "\n",
    "sgdreg = SGDRegressor()\n",
    "\n",
    "gs = GridSearchCV(sgdreg, parameters, verbose=1, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2000 candidates, totalling 10000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3min 59s\n",
      "Best estimator: SGDRegressor(alpha=0, average=False, early_stopping=False, epsilon=0.1,\n",
      "             eta0=0.01, fit_intercept=True, l1_ratio=0.15,\n",
      "             learning_rate='invscaling', loss='squared_loss', max_iter=1000,\n",
      "             n_iter_no_change=5, penalty='l2', power_t=0.25, random_state=None,\n",
      "             shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
      "             warm_start=False)\n",
      "Best parameters: {'alpha': 0, 'penalty': 'l2'}\n",
      "Best score: 0.6444987171362777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 10000 out of 10000 | elapsed:  4.0min finished\n"
     ]
    }
   ],
   "source": [
    "%time _ = gs.fit(x_s, y_train)\n",
    "print(\"Best estimator:\",gs.best_estimator_)\n",
    "print(\"Best parameters:\", gs.best_params_)\n",
    "print(\"Best score:\", gs.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell returns the best estimator, best parameters and best scores using the Stochastic Gradient Descent.\n",
    "\n",
    "We are using the best parameters returned from the above process and set on the testing set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: Stochastic Gradient Descent\n",
      "Following is the mean absolute error on testing data.\n",
      "Mean Absolute Error:  0.09519535343352002\n",
      "Method: SGDRegressor(alpha=0, average=False, early_stopping=False, epsilon=0.1,\n",
      "             eta0=0.01, fit_intercept=True, l1_ratio=0.15,\n",
      "             learning_rate='invscaling', loss='squared_loss', max_iter=1000,\n",
      "             n_iter_no_change=5, penalty='l2', power_t=0.25, random_state=None,\n",
      "             shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
      "             warm_start=False)\n",
      "RMSE on train set: 0.1352\n",
      "RMSE on test set: 0.1348\n"
     ]
    }
   ],
   "source": [
    "xtest_s = scaler.transform(X_test)\n",
    "sgdreg_test = SGDRegressor(alpha = 0, penalty = 'l2')\n",
    "\n",
    "sgdreg_test.fit(xtest_s, y_test)\n",
    "mae = measure_performance(xtest_s, y_test, sgdreg_test)\n",
    "print('Method: Stochastic Gradient Descent')\n",
    "print('Following is the mean absolute error on testing data.')\n",
    "print(\"Mean Absolute Error: \", mae)\n",
    "evaluate_model(sgdreg_test, x_s, y_train, xtest_s, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGDRegressor with  the \"elasticnet\" penalty parameter\n",
      "Best estimator: SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1,\n",
      "             eta0=0.01, fit_intercept=True, l1_ratio=0.8775632653061224,\n",
      "             learning_rate='invscaling', loss='squared_loss', max_iter=1000,\n",
      "             n_iter_no_change=5, penalty='l2', power_t=0.25, random_state=None,\n",
      "             shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
      "             warm_start=False)\n",
      "Best parameters: {'l1_ratio': 0.8775632653061224}\n",
      "Best score: 0.6473788055180415\n"
     ]
    }
   ],
   "source": [
    "print('SGDRegressor with  the \"elasticnet\" penalty parameter')\n",
    "alpha = np.linspace(.0001,1,50)\n",
    "t_rmse = np.array([])\n",
    "cv_rmse = np.array([])\n",
    "    \n",
    "for a in alpha:\n",
    "    sgdreg_elastic = SGDRegressor(l1_ratio=a)\n",
    "    \n",
    "    # computing the RMSE on training data\n",
    "    sgdreg_elastic.fit(x_s, y_train)\n",
    "    p = sgdreg_elastic.predict(x_s)\n",
    "    err = p-y_train\n",
    "    total_error = np.dot(err,err)\n",
    "    rmse_train = np.sqrt(total_error/len(p))\n",
    "\n",
    "    rmse_10cv = cross_validate(sgdreg_elastic, x_s, y_train, 10)\n",
    "    \n",
    "    t_rmse = np.append(t_rmse, [rmse_train])\n",
    "    cv_rmse = np.append(cv_rmse, [rmse_10cv])\n",
    "    #print('{:.3f}\\t {:.4f}\\t\\t {:.4f}'.format(a,rmse_train,rmse_10cv))\n",
    "\n",
    "\n",
    "parameters = {'l1_ratio':alpha}\n",
    "gs1 = GridSearchCV(sgdreg_elastic, parameters,  cv=5)\n",
    "gs1.fit(x_s, y_train)\n",
    "print(\"Best estimator:\",gs1.best_estimator_)\n",
    "print(\"Best parameters:\", gs1.best_params_)\n",
    "print(\"Best score:\", gs1.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  0.0907731784716062\n",
      "Fold  1 RMSE: 0.1478\n",
      "Fold  2 RMSE: 0.1197\n",
      "Fold  3 RMSE: 0.1460\n",
      "Fold  4 RMSE: 0.1505\n",
      "Fold  5 RMSE: 0.1370\n",
      "RMSE on 5-fold CV: 0.1402\n",
      "Method: SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1,\n",
      "             eta0=0.01, fit_intercept=True, l1_ratio=0.8775,\n",
      "             learning_rate='invscaling', loss='squared_loss', max_iter=1000,\n",
      "             n_iter_no_change=5, penalty='l2', power_t=0.25, random_state=None,\n",
      "             shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
      "             warm_start=False)\n",
      "RMSE on train set: 0.1352\n",
      "RMSE on test set: 0.1368\n"
     ]
    }
   ],
   "source": [
    "sgdreg_elastic_test = SGDRegressor(l1_ratio = 0.8775)\n",
    "sgdreg_elastic_test.fit(xtest_s,y_test)\n",
    "mae = measure_performance(xtest_s, y_test, sgdreg_elastic_test)\n",
    "print(\"Mean Absolute Error: \", mae)\n",
    "rmse_5cv = cross_validate(sgdreg_elastic_test, x_s, y_train, 5, verbose=True)\n",
    "print('RMSE on 5-fold CV: %.4f' %rmse_5cv)\n",
    "evaluate_model(sgdreg_elastic_test, x_s, y_train, xtest_s, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the RMSE value for training and testing set using Stochastic Gradient Descent. This model is performing better compared to Linear, Lasso and Ridge regression method. The RMSE between the training and testing set is minimum.\n",
    "\n",
    "We used a combination of parameters of penalty and alpha to find the best scores and the estimators. The data was scaled and then applied to SGDRegressor function. The RMSE returned from SGDRegressor with l1_ratio is better compared to RMSE returned from SGDRegressor with l2 parameter. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}