RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from sklearn import tree\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\coset\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\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>Parcel_ID</th>\n",
       "      <th>Parcel_Acreage</th>\n",
       "      <th>Property_Street_Address</th>\n",
       "      <th>Property_City</th>\n",
       "      <th>Property_State</th>\n",
       "      <th>Property_Zip</th>\n",
       "      <th>Parcel_Vacancy</th>\n",
       "      <th>Conveyance_Date</th>\n",
       "      <th>Property_Sales_Price</th>\n",
       "      <th>Seller_City</th>\n",
       "      <th>...</th>\n",
       "      <th>Buyer_ZIP</th>\n",
       "      <th>Primary_Residence</th>\n",
       "      <th>Assessed_Value_Land</th>\n",
       "      <th>Assessed_Value_Improvement</th>\n",
       "      <th>Total_Assessed_Value</th>\n",
       "      <th>Property_Class_Code</th>\n",
       "      <th>Class_Code_General</th>\n",
       "      <th>Class_Code_Translated</th>\n",
       "      <th>County_ID</th>\n",
       "      <th>County_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4574368</td>\n",
       "      <td>0.169</td>\n",
       "      <td>3909 MARIAH DRIVE                             ...</td>\n",
       "      <td>NEW ALBANY</td>\n",
       "      <td>IN</td>\n",
       "      <td>41750</td>\n",
       "      <td>N</td>\n",
       "      <td>6/2/2020</td>\n",
       "      <td>179900.0</td>\n",
       "      <td>NEW ALBANY</td>\n",
       "      <td>...</td>\n",
       "      <td>47150</td>\n",
       "      <td>Y</td>\n",
       "      <td>43800.0</td>\n",
       "      <td>138500.0</td>\n",
       "      <td>182300.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>22</td>\n",
       "      <td>Floyd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4515880</td>\n",
       "      <td>0.134</td>\n",
       "      <td>4110 E 10TH STREET</td>\n",
       "      <td>INDIAAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>42601</td>\n",
       "      <td>N</td>\n",
       "      <td>10/19/2020</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Petaluma</td>\n",
       "      <td>...</td>\n",
       "      <td>46074</td>\n",
       "      <td>N</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>88500.0</td>\n",
       "      <td>93500.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>49</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4454504</td>\n",
       "      <td>0.172</td>\n",
       "      <td>5404 OAK HARBOR CT</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>42637</td>\n",
       "      <td>N</td>\n",
       "      <td>6/18/2020</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>...</td>\n",
       "      <td>46237</td>\n",
       "      <td>Y</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>158000.0</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>49</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4421719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6516 EMERALD HILL COURT #310</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>42637</td>\n",
       "      <td>N</td>\n",
       "      <td>5/1/2020</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>...</td>\n",
       "      <td>42637</td>\n",
       "      <td>Y</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>77900.0</td>\n",
       "      <td>88300.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Residential</td>\n",
       "      <td>49</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4531964</td>\n",
       "      <td>2.155</td>\n",
       "      <td>6335 Intech Commons Drive</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>IN</td>\n",
       "      <td>42678</td>\n",
       "      <td>N</td>\n",
       "      <td>12/16/2020</td>\n",
       "      <td>1785000.0</td>\n",
       "      <td>MIAMI BEACH</td>\n",
       "      <td>...</td>\n",
       "      <td>75093</td>\n",
       "      <td>N</td>\n",
       "      <td>845000.0</td>\n",
       "      <td>957900.0</td>\n",
       "      <td>1802900.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>400</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>49</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parcel_ID  Parcel_Acreage  \\\n",
       "0    4574368           0.169   \n",
       "1    4515880           0.134   \n",
       "2    4454504           0.172   \n",
       "3    4421719             NaN   \n",
       "4    4531964           2.155   \n",
       "\n",
       "                             Property_Street_Address  \\\n",
       "0  3909 MARIAH DRIVE                             ...   \n",
       "1                                 4110 E 10TH STREET   \n",
       "2                                 5404 OAK HARBOR CT   \n",
       "3                       6516 EMERALD HILL COURT #310   \n",
       "4                          6335 Intech Commons Drive   \n",
       "\n",
       "                    Property_City Property_State Property_Zip Parcel_Vacancy  \\\n",
       "0  NEW ALBANY                                 IN        41750              N   \n",
       "1                     INDIAAPOLIS             IN        42601              N   \n",
       "2                    INDIANAPOLIS             IN        42637              N   \n",
       "3                    INDIANAPOLIS             IN        42637              N   \n",
       "4                    Indianapolis             IN        42678              N   \n",
       "\n",
       "  Conveyance_Date  Property_Sales_Price                     Seller_City  ...  \\\n",
       "0        6/2/2020              179900.0  NEW ALBANY                      ...   \n",
       "1      10/19/2020               48000.0                        Petaluma  ...   \n",
       "2       6/18/2020              195000.0                    Indianapolis  ...   \n",
       "3        5/1/2020              120000.0                        Franklin  ...   \n",
       "4      12/16/2020             1785000.0                    MIAMI BEACH   ...   \n",
       "\n",
       "  Buyer_ZIP Primary_Residence Assessed_Value_Land Assessed_Value_Improvement  \\\n",
       "0     47150                 Y             43800.0                   138500.0   \n",
       "1     46074                 N              5000.0                    88500.0   \n",
       "2     46237                 Y             30000.0                   158000.0   \n",
       "3     42637                 Y             10400.0                    77900.0   \n",
       "4     75093                 N            845000.0                   957900.0   \n",
       "\n",
       "  Total_Assessed_Value Property_Class_Code Class_Code_General  \\\n",
       "0             182300.0               510.0                500   \n",
       "1              93500.0               510.0                500   \n",
       "2             188000.0               520.0                500   \n",
       "3              88300.0               550.0                500   \n",
       "4            1802900.0               425.0                400   \n",
       "\n",
       "   Class_Code_Translated  County_ID  County_Name  \n",
       "0            Residential         22        Floyd  \n",
       "1            Residential         49       Marion  \n",
       "2            Residential         49       Marion  \n",
       "3            Residential         49       Marion  \n",
       "4             Commercial         49       Marion  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the csv file into a pandas DataFrame\n",
    "\n",
    "Property = pd.read_csv('Resources/Property.csv')\n",
    "Property.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184189"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Property)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180360"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop NA's from DF\n",
    "pd.DataFrame(Property).dropna()\n",
    "Property.head()\n",
    "Property_df = Property[~Property.isin([np.nan, np.inf, -np.inf]).any(1)]\n",
    "len(Property_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select 5 Columns of DF\n",
    "#Property_df=Property_df[['Property_Sales_Price','Total_Assessed_Value','Parcel_Acreage','Parcel_Vacancy','Class_Code_Translated']]\n",
    "Property_df=Property_df[['Property_Sales_Price','Class_Code_Translated','Total_Assessed_Value','Parcel_Acreage','Parcel_Vacancy', 'County_Name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_col=['Property_Sales_Price','Total_Assessed_Value', 'Parcel_Acreage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(646, 6)\n",
      "(179715, 6)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# outliers\n",
    "print(Property_df[(np.abs(stats.zscore(Property_df[num_col])) > 3)].shape)\n",
    "# good data\n",
    "print(Property_df[(np.abs(stats.zscore(Property_df[num_col])) < 3).all(axis=1)].shape)"
   ]
  },
  {
   "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>index</th>\n",
       "      <th>Property_Sales_Price</th>\n",
       "      <th>Class_Code_Translated</th>\n",
       "      <th>Total_Assessed_Value</th>\n",
       "      <th>Parcel_Acreage</th>\n",
       "      <th>Parcel_Vacancy</th>\n",
       "      <th>County_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>179900.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>182300.0</td>\n",
       "      <td>0.169</td>\n",
       "      <td>N</td>\n",
       "      <td>Floyd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>93500.0</td>\n",
       "      <td>0.134</td>\n",
       "      <td>N</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>0.172</td>\n",
       "      <td>N</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1785000.0</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>1802900.0</td>\n",
       "      <td>2.155</td>\n",
       "      <td>N</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>257575.0</td>\n",
       "      <td>Residential</td>\n",
       "      <td>210900.0</td>\n",
       "      <td>0.201</td>\n",
       "      <td>N</td>\n",
       "      <td>Marion</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Property_Sales_Price Class_Code_Translated  Total_Assessed_Value  \\\n",
       "0      0              179900.0           Residential              182300.0   \n",
       "1      1               48000.0           Residential               93500.0   \n",
       "2      2              195000.0           Residential              188000.0   \n",
       "3      4             1785000.0            Commercial             1802900.0   \n",
       "4      5              257575.0           Residential              210900.0   \n",
       "\n",
       "   Parcel_Acreage Parcel_Vacancy County_Name  \n",
       "0           0.169              N       Floyd  \n",
       "1           0.134              N      Marion  \n",
       "2           0.172              N      Marion  \n",
       "3           2.155              N      Marion  \n",
       "4           0.201              N      Marion  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Git Rid of Outliers\n",
    "Property_df = Property_df[(np.abs(stats.zscore(Property_df[num_col])) < 3).all(axis=1)].reset_index()\n",
    "Property_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#One Hot encoding for non Numerical \n",
    "Property_df=pd.get_dummies(data=Property_df, columns=['Parcel_Vacancy','Class_Code_Translated','County_Name'])\n",
    "#new_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Git Rid of Index\n",
    "Property_df.drop(['index'],axis=1,inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seperate X and Y\n",
    "y = Property_df['Property_Sales_Price']\n",
    "\n",
    "x=Property_df.loc[:, Property_df.columns != 'Property_Sales_Price']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Property_df=Property_df.loc[Property_df['Total_Assessed_Value']>0,:]\n",
    "Property_df=Property_df.loc[Property_df['Parcel_Acreage']>0,:]\n",
    "#len(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Property_Sales_Price</th>\n",
       "      <th>Total_Assessed_Value</th>\n",
       "      <th>Parcel_Acreage</th>\n",
       "      <th>Parcel_Vacancy_N</th>\n",
       "      <th>Parcel_Vacancy_Y</th>\n",
       "      <th>Class_Code_Translated_Agricultural</th>\n",
       "      <th>Class_Code_Translated_Commercial</th>\n",
       "      <th>Class_Code_Translated_Exempt</th>\n",
       "      <th>Class_Code_Translated_Industrial</th>\n",
       "      <th>Class_Code_Translated_Locally Assessed</th>\n",
       "      <th>...</th>\n",
       "      <th>County_Name_Vermillion</th>\n",
       "      <th>County_Name_Vigo</th>\n",
       "      <th>County_Name_Wabash</th>\n",
       "      <th>County_Name_Warren</th>\n",
       "      <th>County_Name_Warrick</th>\n",
       "      <th>County_Name_Washington</th>\n",
       "      <th>County_Name_Wayne</th>\n",
       "      <th>County_Name_Wells</th>\n",
       "      <th>County_Name_White</th>\n",
       "      <th>County_Name_Whitley</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>179900.0</td>\n",
       "      <td>182300.0</td>\n",
       "      <td>0.169</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48000.0</td>\n",
       "      <td>93500.0</td>\n",
       "      <td>0.134</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>195000.0</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>0.172</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1785000.0</td>\n",
       "      <td>1802900.0</td>\n",
       "      <td>2.155</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>257575.0</td>\n",
       "      <td>210900.0</td>\n",
       "      <td>0.201</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179710</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>64500.0</td>\n",
       "      <td>4000.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179711</th>\n",
       "      <td>78000.0</td>\n",
       "      <td>46700.0</td>\n",
       "      <td>2000.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179712</th>\n",
       "      <td>11250.0</td>\n",
       "      <td>67200.0</td>\n",
       "      <td>2700.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179713</th>\n",
       "      <td>200000.0</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>1564.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179714</th>\n",
       "      <td>184000.0</td>\n",
       "      <td>105700.0</td>\n",
       "      <td>9700.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171928 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Property_Sales_Price  Total_Assessed_Value  Parcel_Acreage  \\\n",
       "0                   179900.0              182300.0           0.169   \n",
       "1                    48000.0               93500.0           0.134   \n",
       "2                   195000.0              188000.0           0.172   \n",
       "3                  1785000.0             1802900.0           2.155   \n",
       "4                   257575.0              210900.0           0.201   \n",
       "...                      ...                   ...             ...   \n",
       "179710               90000.0               64500.0        4000.000   \n",
       "179711               78000.0               46700.0        2000.000   \n",
       "179712               11250.0               67200.0        2700.000   \n",
       "179713              200000.0                9200.0        1564.000   \n",
       "179714              184000.0              105700.0        9700.000   \n",
       "\n",
       "        Parcel_Vacancy_N  Parcel_Vacancy_Y  \\\n",
       "0                      1                 0   \n",
       "1                      1                 0   \n",
       "2                      1                 0   \n",
       "3                      1                 0   \n",
       "4                      1                 0   \n",
       "...                  ...               ...   \n",
       "179710                 1                 0   \n",
       "179711                 1                 0   \n",
       "179712                 1                 0   \n",
       "179713                 1                 0   \n",
       "179714                 1                 0   \n",
       "\n",
       "        Class_Code_Translated_Agricultural  Class_Code_Translated_Commercial  \\\n",
       "0                                        0                                 0   \n",
       "1                                        0                                 0   \n",
       "2                                        0                                 0   \n",
       "3                                        0                                 1   \n",
       "4                                        0                                 0   \n",
       "...                                    ...                               ...   \n",
       "179710                                   0                                 0   \n",
       "179711                                   0                                 0   \n",
       "179712                                   0                                 0   \n",
       "179713                                   0                                 0   \n",
       "179714                                   0                                 0   \n",
       "\n",
       "        Class_Code_Translated_Exempt  Class_Code_Translated_Industrial  \\\n",
       "0                                  0                                 0   \n",
       "1                                  0                                 0   \n",
       "2                                  0                                 0   \n",
       "3                                  0                                 0   \n",
       "4                                  0                                 0   \n",
       "...                              ...                               ...   \n",
       "179710                             0                                 0   \n",
       "179711                             0                                 0   \n",
       "179712                             0                                 0   \n",
       "179713                             0                                 0   \n",
       "179714                             0                                 0   \n",
       "\n",
       "        Class_Code_Translated_Locally Assessed  ...  County_Name_Vermillion  \\\n",
       "0                                            0  ...                       0   \n",
       "1                                            0  ...                       0   \n",
       "2                                            0  ...                       0   \n",
       "3                                            0  ...                       0   \n",
       "4                                            0  ...                       0   \n",
       "...                                        ...  ...                     ...   \n",
       "179710                                       0  ...                       0   \n",
       "179711                                       0  ...                       0   \n",
       "179712                                       0  ...                       0   \n",
       "179713                                       0  ...                       0   \n",
       "179714                                       0  ...                       0   \n",
       "\n",
       "        County_Name_Vigo  County_Name_Wabash  County_Name_Warren  \\\n",
       "0                      0                   0                   0   \n",
       "1                      0                   0                   0   \n",
       "2                      0                   0                   0   \n",
       "3                      0                   0                   0   \n",
       "4                      0                   0                   0   \n",
       "...                  ...                 ...                 ...   \n",
       "179710                 0                   0                   1   \n",
       "179711                 0                   0                   1   \n",
       "179712                 0                   0                   1   \n",
       "179713                 0                   0                   1   \n",
       "179714                 0                   0                   1   \n",
       "\n",
       "        County_Name_Warrick  County_Name_Washington  County_Name_Wayne  \\\n",
       "0                         0                       0                  0   \n",
       "1                         0                       0                  0   \n",
       "2                         0                       0                  0   \n",
       "3                         0                       0                  0   \n",
       "4                         0                       0                  0   \n",
       "...                     ...                     ...                ...   \n",
       "179710                    0                       0                  0   \n",
       "179711                    0                       0                  0   \n",
       "179712                    0                       0                  0   \n",
       "179713                    0                       0                  0   \n",
       "179714                    0                       0                  0   \n",
       "\n",
       "        County_Name_Wells  County_Name_White  County_Name_Whitley  \n",
       "0                       0                  0                    0  \n",
       "1                       0                  0                    0  \n",
       "2                       0                  0                    0  \n",
       "3                       0                  0                    0  \n",
       "4                       0                  0                    0  \n",
       "...                   ...                ...                  ...  \n",
       "179710                  0                  0                    0  \n",
       "179711                  0                  0                    0  \n",
       "179712                  0                  0                    0  \n",
       "179713                  0                  0                    0  \n",
       "179714                  0                  0                    0  \n",
       "\n",
       "[171928 rows x 102 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Property_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.46558113808729595\n"
     ]
    }
   ],
   "source": [
    "#Fit Model\n",
    "#from sklearn.linear_model import LinearRegression\n",
    "#model = LinearRegression()\n",
    "\n",
    "# Fitting our model with all of our features in X\n",
    "#model.fit(x, y)\n",
    "\n",
    "#score = model.score(x, y)\n",
    "#print(f\"R2 Score: {score}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlrd\n",
    " \n",
    "import numpy as np\n",
    "import seaborn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as matplotlib\n",
    " \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    " \n",
    "from matplotlib.lines import Line2D\n",
    "from scipy.stats import pearsonr\n",
    " \n",
    "# set seed to make results reproducible\n",
    "RF_SEED = 30\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomforestregressor = RandomForestRegressor(n_estimators=100)\n",
    "randomforestregressor.fit(X_train,y_train)\n",
    "randomforestregressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = ['Class_Code_Translated','Total_Assessed_Value','Parcel_Acreage','Parcel_Vacancy', 'County_Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 32.88811439, -20.89529887,  12.78945477, -31.27423237,\n",
       "        29.74010213,   0.49437206, -10.01243576,  -8.44943809,\n",
       "        18.29457124, -19.65898892,  -6.76542804, -40.13255868,\n",
       "       -40.67233681, -41.31777649,  76.78915444, -25.3188002 ,\n",
       "        20.49270501,  45.53444262, -12.8842887 , -60.18657088])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions=randomforestregressor.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. 0.2488345810825234 (0.2488345810825234)\n",
      "2. 0.7188481722569962 (0.7188481722569962)\n",
      "3. 0.016606310006802592 (0.016606310006802592)\n",
      "4. 0.015710936653677947 (0.015710936653677947)\n"
     ]
    }
   ],
   "source": [
    "features_importance = randomforestregressor.feature_importances_\n",
    "print(\"Feature ranking:\")\n",
    "for i, data_class in enumerate(features_importance):\n",
    "    print(\"{}. {} ({})\".format(i + 1, data_class, features_importance[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 Residuals\n",
    "predictions = model.predict(x)\n",
    "# Plot Residuals\n",
    "plt.scatter(predictions, predictions - y)\n",
    "plt.hlines(y=0, xmin=predictions.min(), xmax=predictions.max())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}