test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "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\n",
    "import xlrd\n",
    "import pandas as pd\n",
    "import seaborn\n",
    "import csv\n",
    "from csv import reader\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.lines import Line2D\n",
    "from scipy.stats import pearsonr\n",
    "import pickle\n",
    "RF_SEED = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184189"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Property)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180360"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_col = ['Property_Sales_Price','Total_Assessed_Value', 'Parcel_Acreage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 11,
     "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['Parcel_Vacancy', 'Class_Code_Translated', 'County_Name'], dtype='object')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-47-d979911c21d4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#One Hot encoding for non Numerical\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mProperty_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mProperty_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Parcel_Vacancy'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Class_Code_Translated'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'County_Name'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m#new_df.head()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\reshape.py\u001b[0m in \u001b[0;36mget_dummies\u001b[1;34m(data, prefix, prefix_sep, dummy_na, columns, sparse, drop_first, dtype)\u001b[0m\n\u001b[0;32m    866\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Input must be a list-like for parameter `columns`\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    867\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 868\u001b[1;33m             \u001b[0mdata_to_encode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m         \u001b[1;31m# validate prefixes and separator to avoid silently dropping cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2804\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2805\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2806\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2807\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2808\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1550\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1551\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1552\u001b[1;33m         self._validate_read_indexer(\n\u001b[0m\u001b[0;32m   1553\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1554\u001b[0m         )\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1638\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1639\u001b[0m                 \u001b[0maxis_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1640\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1641\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1642\u001b[0m             \u001b[1;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['Parcel_Vacancy', 'Class_Code_Translated', 'County_Name'], dtype='object')] are in the [columns]\""
     ]
    }
   ],
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Git Rid of Index\n",
    "Property_df.drop(['index'],axis=1,inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Property_df = Property_df.loc[Property_df['Total_Assessed_Value']>0,:]\n",
    "df = Property_df.loc[Property_df['Parcel_Acreage']>0,:]\n",
    "#len(new_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Seperate X and Y\n",
    "y_data = df['Property_Sales_Price']\n",
    "\n",
    "x_data = df.loc[:, Property_df.columns != 'Property_Sales_Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0          179900.0\n",
      "1           48000.0\n",
      "2          195000.0\n",
      "3         1785000.0\n",
      "4          257575.0\n",
      "            ...    \n",
      "179710      90000.0\n",
      "179711      78000.0\n",
      "179712      11250.0\n",
      "179713     200000.0\n",
      "179714     184000.0\n",
      "Name: Property_Sales_Price, Length: 171928, dtype: float64         Total_Assessed_Value  Parcel_Acreage  Parcel_Vacancy_N  \\\n",
      "0                   182300.0           0.169                 1   \n",
      "1                    93500.0           0.134                 1   \n",
      "2                   188000.0           0.172                 1   \n",
      "3                  1802900.0           2.155                 1   \n",
      "4                   210900.0           0.201                 1   \n",
      "...                      ...             ...               ...   \n",
      "179710               64500.0        4000.000                 1   \n",
      "179711               46700.0        2000.000                 1   \n",
      "179712               67200.0        2700.000                 1   \n",
      "179713                9200.0        1564.000                 1   \n",
      "179714              105700.0        9700.000                 1   \n",
      "\n",
      "        Parcel_Vacancy_Y  Class_Code_Translated_Agricultural  \\\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_Commercial  Class_Code_Translated_Exempt  \\\n",
      "0                                      0                             0   \n",
      "1                                      0                             0   \n",
      "2                                      0                             0   \n",
      "3                                      1                             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_Industrial  \\\n",
      "0                                      0   \n",
      "1                                      0   \n",
      "2                                      0   \n",
      "3                                      0   \n",
      "4                                      0   \n",
      "...                                  ...   \n",
      "179710                                 0   \n",
      "179711                                 0   \n",
      "179712                                 0   \n",
      "179713                                 0   \n",
      "179714                                 0   \n",
      "\n",
      "        Class_Code_Translated_Locally Assessed  Class_Code_Translated_Mineral  \\\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_Vermillion  County_Name_Vigo  County_Name_Wabash  \\\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_Warren  County_Name_Warrick  County_Name_Washington  \\\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                   1                    0                       0   \n",
      "179711                   1                    0                       0   \n",
      "179712                   1                    0                       0   \n",
      "179713                   1                    0                       0   \n",
      "179714                   1                    0                       0   \n",
      "\n",
      "        County_Name_Wayne  County_Name_Wells  County_Name_White  \\\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_Whitley  \n",
      "0                         0  \n",
      "1                         0  \n",
      "2                         0  \n",
      "3                         0  \n",
      "4                         0  \n",
      "...                     ...  \n",
      "179710                    0  \n",
      "179711                    0  \n",
      "179712                    0  \n",
      "179713                    0  \n",
      "179714                    0  \n",
      "\n",
      "[171928 rows x 101 columns] Index(['Total_Assessed_Value', 'Parcel_Acreage', 'Parcel_Vacancy_N',\n",
      "       'Parcel_Vacancy_Y', 'Class_Code_Translated_Agricultural',\n",
      "       'Class_Code_Translated_Commercial', 'Class_Code_Translated_Exempt',\n",
      "       'Class_Code_Translated_Industrial',\n",
      "       'Class_Code_Translated_Locally Assessed',\n",
      "       'Class_Code_Translated_Mineral',\n",
      "       ...\n",
      "       'County_Name_Vermillion', 'County_Name_Vigo', 'County_Name_Wabash',\n",
      "       'County_Name_Warren', 'County_Name_Warrick', 'County_Name_Washington',\n",
      "       'County_Name_Wayne', 'County_Name_Wells', 'County_Name_White',\n",
      "       'County_Name_Whitley'],\n",
      "      dtype='object', length=101)\n"
     ]
    }
   ],
   "source": [
    "    data = df.loc[:, df.columns !='Property_Sales_Price'] \n",
    "    print( df['Property_Sales_Price'], data, data.columns )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data_train_model(labels, data):\n",
    "    # 20% examples in test data\n",
    "    train, test, train_labels, test_labels = train_test_split(data,\n",
    "                                                              labels,\n",
    "                                                              test_size=0.2,\n",
    "                                                              random_state=RF_SEED)\n",
    "\n",
    "# training data fit\n",
    "    regressor = RandomForestRegressor(n_estimators = 1000, random_state = RF_SEED)\n",
    "    regressor.fit(train, train_labels)\n",
    " \n",
    "    return test, test_labels, regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test, x_test_labels, regressor = split_data_train_model(y_data, x_data)\n",
    " \n",
    "predictions = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.79068\n"
     ]
    }
   ],
   "source": [
    "# find the correlation between real answer and prediction\n",
    "correlation = round(pearsonr(predictions, x_test_labels)[0], 5)\n",
    " \n",
    "\n",
    "title_name = \"Random Forest Regression - Property Price vs - correlation ({})\".format(correlation)\n",
    "x_axis_label = \"Real House Price\"\n",
    "y_axis_label = \"Predicted House Price\"\n",
    " \n",
    "# plot data\n",
    "#simple_scatter_plot(x_test_labels, predictions, title_name, x_axis_label, y_axis_label)\n",
    "\n",
    "print(correlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. Total_Assessed_Value (0.6491825351741952)\n",
      "2. Parcel_Acreage (0.14663858330869195)\n",
      "3. Parcel_Vacancy_N (0.015141772618275354)\n",
      "4. Parcel_Vacancy_Y (0.016700170350763963)\n",
      "5. Class_Code_Translated_Agricultural (0.002524415812214859)\n",
      "6. Class_Code_Translated_Commercial (0.013078085544542909)\n",
      "7. Class_Code_Translated_Exempt (0.011064429134688948)\n",
      "8. Class_Code_Translated_Industrial (0.011674943717703291)\n",
      "9. Class_Code_Translated_Locally Assessed (8.779080903282581e-05)\n",
      "10. Class_Code_Translated_Mineral (1.118724273312944e-07)\n",
      "11. Class_Code_Translated_Residential (0.012871622064979682)\n",
      "12. County_Name_Allen (0.005793336634473317)\n",
      "13. County_Name_Bartholomew (0.00040543787803269237)\n",
      "14. County_Name_Benton (1.7986549685053467e-05)\n",
      "15. County_Name_Blackford (2.6788113444614147e-05)\n",
      "16. County_Name_Boone (0.010798741750864534)\n",
      "17. County_Name_Brown (0.0002479567278459378)\n",
      "18. County_Name_Carroll (3.533130187558316e-05)\n",
      "19. County_Name_Cass (0.00021572778265076844)\n",
      "20. County_Name_Clark (0.0043731022549691355)\n",
      "21. County_Name_Clay (0.0008510827144795228)\n",
      "22. County_Name_Clinton (0.00047944024666498887)\n",
      "23. County_Name_Crawford (6.819454809773408e-06)\n",
      "24. County_Name_Daviess (0.00033497892575039457)\n",
      "25. County_Name_DeKalb (9.461885783246874e-05)\n",
      "26. County_Name_Dearborn (0.0004950309638489088)\n",
      "27. County_Name_Decatur (0.00013436883641141688)\n",
      "28. County_Name_Delaware (0.0007581329304641847)\n",
      "29. County_Name_Dubois (0.0003055393802029221)\n",
      "30. County_Name_Elkhart (0.005384484634392501)\n",
      "31. County_Name_Fayette (0.00012869223199151453)\n",
      "32. County_Name_Floyd (0.0016670862048498423)\n",
      "33. County_Name_Fountain (0.00014762393175042434)\n",
      "34. County_Name_Franklin (0.00010750363145849506)\n",
      "35. County_Name_Fulton (5.581070187480423e-05)\n",
      "36. County_Name_Gibson (0.0006686338918333485)\n",
      "37. County_Name_Grant (0.00012502725280793964)\n",
      "38. County_Name_Greene (8.900186793395638e-05)\n",
      "39. County_Name_Hamilton (0.004453095054683365)\n",
      "40. County_Name_Hancock (0.004031410165220671)\n",
      "41. County_Name_Harrison (3.853149274677182e-05)\n",
      "42. County_Name_Hendricks (0.005955568580191076)\n",
      "43. County_Name_Henry (6.348452489191989e-05)\n",
      "44. County_Name_Howard (0.0004341852343357734)\n",
      "45. County_Name_Huntington (0.00026574440847299154)\n",
      "46. County_Name_Jackson (0.00010460391357151156)\n",
      "47. County_Name_Jasper (0.00015477460450819748)\n",
      "48. County_Name_Jay (0.00010365413628746724)\n",
      "49. County_Name_Jefferson (0.000385901390790381)\n",
      "50. County_Name_Jennings (3.3440912798657635e-05)\n",
      "51. County_Name_Johnson (0.002928500804618565)\n",
      "52. County_Name_Knox (0.00014624771475315042)\n",
      "53. County_Name_Kosciusko (0.0005985314895699953)\n",
      "54. County_Name_LaGrange (0.0004651714657403681)\n",
      "55. County_Name_LaPorte (0.0031703949177048192)\n",
      "56. County_Name_Lake (0.00727234866866329)\n",
      "57. County_Name_Lawrence (0.00033350059286202865)\n",
      "58. County_Name_Madison (0.0033449295652988615)\n",
      "59. County_Name_Marion (0.01407384482957418)\n",
      "60. County_Name_Marshall (0.005473451822645171)\n",
      "61. County_Name_Martin (1.2625961901929714e-05)\n",
      "62. County_Name_Miami (0.00015046971035889941)\n",
      "63. County_Name_Monroe (0.0026694137088554247)\n",
      "64. County_Name_Morgan (0.0005311307875879802)\n",
      "65. County_Name_Newton (0.0008287287544320786)\n",
      "66. County_Name_Noble (9.789163126698525e-05)\n",
      "67. County_Name_Ohio (2.125210618739649e-05)\n",
      "68. County_Name_Orange (4.912373306830128e-05)\n",
      "69. County_Name_Owen (6.623193988791566e-05)\n",
      "70. County_Name_Parke (0.00012195680018219462)\n",
      "71. County_Name_Perry (3.1909614920749224e-05)\n",
      "72. County_Name_Pike (2.0567079953438352e-05)\n",
      "73. County_Name_Porter (0.002159946872734998)\n",
      "74. County_Name_Posey (0.0002411472621007241)\n",
      "75. County_Name_Pulaski (6.937515861699658e-05)\n",
      "76. County_Name_Putnam (0.00011411837490239984)\n",
      "77. County_Name_Randolph (5.1387170489184376e-05)\n",
      "78. County_Name_Ripley (0.00014002159305266323)\n",
      "79. County_Name_Rush (0.0001367406133000358)\n",
      "80. County_Name_Scott (3.476007537226165e-05)\n",
      "81. County_Name_Shelby (0.0009804096939753529)\n",
      "82. County_Name_Spencer (2.7122614204672036e-05)\n",
      "83. County_Name_St. Joseph (0.012422996127543865)\n",
      "84. County_Name_Starke (0.00017851791381144963)\n",
      "85. County_Name_Steuben (0.00197109902372508)\n",
      "86. County_Name_Sullivan (0.00015448478896579502)\n",
      "87. County_Name_Switzerland (1.5021502947966248e-05)\n",
      "88. County_Name_Tippecanoe (0.0017022574875088382)\n",
      "89. County_Name_Tipton (5.361588195360655e-05)\n",
      "90. County_Name_Union (7.653889844395649e-06)\n",
      "91. County_Name_Vanderburgh (0.0010941128840995808)\n",
      "92. County_Name_Vermillion (0.0009064524851516917)\n",
      "93. County_Name_Vigo (0.002375323263079016)\n",
      "94. County_Name_Wabash (0.00015607498998004393)\n",
      "95. County_Name_Warren (1.0153951203559799e-05)\n",
      "96. County_Name_Warrick (0.00020640214476474024)\n",
      "97. County_Name_Washington (0.00024287533498432832)\n",
      "98. County_Name_Wayne (9.958655268226176e-05)\n",
      "99. County_Name_Wells (0.0024509248625674477)\n",
      "100. County_Name_White (0.00017080805335198304)\n",
      "101. County_Name_Whitley (0.0006812452538369785)\n"
     ]
    }
   ],
   "source": [
    "features_importance = regressor.feature_importances_\n",
    " \n",
    "print(\"Feature ranking:\")\n",
    "for i, data_class in enumerate(feature_names):\n",
    "    print(\"{}. {} ({})\".format(i + 1, data_class, features_importance[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)\n",
    "# Fit the model on training set\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, Y_train)\n",
    "# save the model to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'model'\n",
    "outfile = open(model, 'wb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(feature_importance, outfile)\n",
    "outfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "\n",
    "infile = open(filename,'rb')\n",
    "new_dict = pickle.load(infile)\n",
    "infile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(new_dict)\n",
    "print(new_dict==dogs_dict)\n",
    "print(type(new_dict))"
   ]
  }
 ],
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