house_pricing_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('expand_frame_repr', False)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import os\n",
    "import datetime as dt\n",
    "import sklearn\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw dataset shape: (293, 34)\n",
      "Raw dataset shape with duplicates removed: (293, 34)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>ARCH</th>\n",
       "      <th>BEDROOMS</th>\n",
       "      <th>BATHROOMS</th>\n",
       "      <th>HALF_BATHROOMS</th>\n",
       "      <th>BASE_AREA</th>\n",
       "      <th>TOTAL_AREA</th>\n",
       "      <th>BASEMENT_AREA</th>\n",
       "      <th>BASEMENT_FINISHED_AREA</th>\n",
       "      <th>PARKING_AREA</th>\n",
       "      <th>YRBLT</th>\n",
       "      <th>PHYS</th>\n",
       "      <th>QUALITY_CODE</th>\n",
       "      <th>LOT_SIZE</th>\n",
       "      <th>LAND_VALUE</th>\n",
       "      <th>LAST_SALE_PRICE</th>\n",
       "      <th>LAST_SALE_DATE</th>\n",
       "      <th>MARKET_VALUE</th>\n",
       "      <th>USPF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2STY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3622</td>\n",
       "      <td>3622</td>\n",
       "      <td>1645</td>\n",
       "      <td>54</td>\n",
       "      <td>651</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>0.22</td>\n",
       "      <td>58000</td>\n",
       "      <td>550000</td>\n",
       "      <td>Aug 25 2020 12:00:00:000AM</td>\n",
       "      <td>561275.00</td>\n",
       "      <td>154.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2STY</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3506</td>\n",
       "      <td>3506</td>\n",
       "      <td>1768</td>\n",
       "      <td>0</td>\n",
       "      <td>630</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>0.21</td>\n",
       "      <td>58000</td>\n",
       "      <td>435000</td>\n",
       "      <td>Apr  4 2019 12:00:00:000AM</td>\n",
       "      <td>469738.25</td>\n",
       "      <td>133.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2STY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2587</td>\n",
       "      <td>2587</td>\n",
       "      <td>1180</td>\n",
       "      <td>75</td>\n",
       "      <td>420</td>\n",
       "      <td>2007</td>\n",
       "      <td>8</td>\n",
       "      <td>40</td>\n",
       "      <td>0.21</td>\n",
       "      <td>58000</td>\n",
       "      <td>364900</td>\n",
       "      <td>Jun 22 2020 12:00:00:000AM</td>\n",
       "      <td>375372.63</td>\n",
       "      <td>145.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2STY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2544</td>\n",
       "      <td>2544</td>\n",
       "      <td>1232</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>2010</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "      <td>0.14</td>\n",
       "      <td>58000</td>\n",
       "      <td>315000</td>\n",
       "      <td>Aug  9 2019 12:00:00:000AM</td>\n",
       "      <td>336955.50</td>\n",
       "      <td>132.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2STY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2766</td>\n",
       "      <td>2766</td>\n",
       "      <td>1291</td>\n",
       "      <td>71</td>\n",
       "      <td>400</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>0.18</td>\n",
       "      <td>58000</td>\n",
       "      <td>355000</td>\n",
       "      <td>Jul 18 2019 12:00:00:000AM</td>\n",
       "      <td>375830.00</td>\n",
       "      <td>135.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ARCH  BEDROOMS  BATHROOMS  HALF_BATHROOMS  BASE_AREA  TOTAL_AREA  BASEMENT_AREA  BASEMENT_FINISHED_AREA  PARKING_AREA  YRBLT  PHYS  QUALITY_CODE  LOT_SIZE  LAND_VALUE  LAST_SALE_PRICE              LAST_SALE_DATE  MARKET_VALUE    USPF\n",
       "0  2STY       4.0        4.0             1.0       3622        3622           1645                      54           651   2012     8            45      0.22       58000           550000  Aug 25 2020 12:00:00:000AM     561275.00  154.96\n",
       "1  2STY       6.0        4.0             1.0       3506        3506           1768                       0           630   2013     7            40      0.21       58000           435000  Apr  4 2019 12:00:00:000AM     469738.25  133.98\n",
       "2  2STY       4.0        3.0             1.0       2587        2587           1180                      75           420   2007     8            40      0.21       58000           364900  Jun 22 2020 12:00:00:000AM     375372.63  145.10\n",
       "3  2STY       4.0        2.0             1.0       2544        2544           1232                       0           400   2010    11            35      0.14       58000           315000  Aug  9 2019 12:00:00:000AM     336955.50  132.45\n",
       "4  2STY       4.0        3.0             1.0       2766        2766           1291                      71           400   2008     8            35      0.18       58000           355000  Jul 18 2019 12:00:00:000AM     375830.00  135.87"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.getcwd()[9:14] == 'ssoma':\n",
    "    file_path = \"C:\\\\Users\\\\ssoma\\\\OneDrive - Bayer\\\\Personal Data-R90PQ30R\\\\Analytics\\\\house-pricing\\\\input-data\\\\\"\n",
    "else:\n",
    "    file_path = \"C:\\\\Users\\\\sivac\\\\Documents\\\\Analytics\\\\house-pricing\\\\input-data\\\\\"\n",
    "\n",
    "\n",
    "file_list = os.listdir(file_path)\n",
    "for i in file_list:\n",
    "    i = file_path + i\n",
    "    file = pd.read_csv(i)\n",
    "    \n",
    "    if 'df' in globals():\n",
    "        df = pd.concat([df, file])\n",
    "    else:\n",
    "        df = file\n",
    "\n",
    "print(f'Raw dataset shape: {df.shape}')\n",
    "\n",
    "\n",
    "\n",
    "df_new = df[['ARCH','BEDROOMS','BATHROOMS','HALF_BATHROOMS','BASE_AREA','TOTAL_AREA','BASEMENT_AREA','BASEMENT_FINISHED_AREA',\n",
    "             'PARKING_AREA', 'YRBLT','PHYS','QUALITY_CODE', 'LOT_SIZE', 'LAND_VALUE', 'LAST_SALE_PRICE', 'LAST_SALE_DATE', \n",
    "             'MARKET_VALUE', 'USPF']].copy()\n",
    "df = df.drop_duplicates().reset_index(drop=True)\n",
    "print(f'Raw dataset shape with duplicates removed: {df.shape}')\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>BEDROOMS</th>\n",
       "      <th>BATHROOMS</th>\n",
       "      <th>HALF_BATHROOMS</th>\n",
       "      <th>BASE_AREA</th>\n",
       "      <th>TOTAL_AREA</th>\n",
       "      <th>BASEMENT_AREA</th>\n",
       "      <th>BASEMENT_FINISHED_AREA</th>\n",
       "      <th>PARKING_AREA</th>\n",
       "      <th>QUALITY_CODE</th>\n",
       "      <th>LOT_SIZE</th>\n",
       "      <th>LAND_VALUE</th>\n",
       "      <th>LAST_SALE_PRICE</th>\n",
       "      <th>MARKET_VALUE</th>\n",
       "      <th>SALE_DATE_YR</th>\n",
       "      <th>AGE_OF_HOUSE_AT_SALE</th>\n",
       "      <th>SALE_DATE_MNTH</th>\n",
       "      <th>SALE_DATE_DT</th>\n",
       "      <th>ARCH_1STY</th>\n",
       "      <th>ARCH_2STY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3622</td>\n",
       "      <td>3622</td>\n",
       "      <td>1645</td>\n",
       "      <td>54</td>\n",
       "      <td>651</td>\n",
       "      <td>45</td>\n",
       "      <td>0.22</td>\n",
       "      <td>58000</td>\n",
       "      <td>550000</td>\n",
       "      <td>561275.00</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3506</td>\n",
       "      <td>3506</td>\n",
       "      <td>1768</td>\n",
       "      <td>0</td>\n",
       "      <td>630</td>\n",
       "      <td>40</td>\n",
       "      <td>0.21</td>\n",
       "      <td>58000</td>\n",
       "      <td>435000</td>\n",
       "      <td>469738.25</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2587</td>\n",
       "      <td>2587</td>\n",
       "      <td>1180</td>\n",
       "      <td>75</td>\n",
       "      <td>420</td>\n",
       "      <td>40</td>\n",
       "      <td>0.21</td>\n",
       "      <td>58000</td>\n",
       "      <td>364900</td>\n",
       "      <td>375372.63</td>\n",
       "      <td>2020</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2544</td>\n",
       "      <td>2544</td>\n",
       "      <td>1232</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>35</td>\n",
       "      <td>0.14</td>\n",
       "      <td>58000</td>\n",
       "      <td>315000</td>\n",
       "      <td>336955.50</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2766</td>\n",
       "      <td>2766</td>\n",
       "      <td>1291</td>\n",
       "      <td>71</td>\n",
       "      <td>400</td>\n",
       "      <td>35</td>\n",
       "      <td>0.18</td>\n",
       "      <td>58000</td>\n",
       "      <td>355000</td>\n",
       "      <td>375830.00</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BEDROOMS  BATHROOMS  HALF_BATHROOMS  BASE_AREA  TOTAL_AREA  BASEMENT_AREA  BASEMENT_FINISHED_AREA  PARKING_AREA  QUALITY_CODE  LOT_SIZE  LAND_VALUE  LAST_SALE_PRICE  MARKET_VALUE  SALE_DATE_YR  AGE_OF_HOUSE_AT_SALE  SALE_DATE_MNTH  SALE_DATE_DT  ARCH_1STY  ARCH_2STY\n",
       "0       4.0        4.0             1.0       3622        3622           1645                      54           651            45      0.22       58000           550000     561275.00          2020                     8               8            25          0          1\n",
       "1       6.0        4.0             1.0       3506        3506           1768                       0           630            40      0.21       58000           435000     469738.25          2019                     6               4             4          0          1\n",
       "2       4.0        3.0             1.0       2587        2587           1180                      75           420            40      0.21       58000           364900     375372.63          2020                    13               6            22          0          1\n",
       "3       4.0        2.0             1.0       2544        2544           1232                       0           400            35      0.14       58000           315000     336955.50          2019                     9               8             9          0          1\n",
       "4       4.0        3.0             1.0       2766        2766           1291                      71           400            35      0.18       58000           355000     375830.00          2019                    11               7            18          0          1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['LAST_SALE_DATE'] = pd.to_datetime(df_new['LAST_SALE_DATE'], format=\"%b %d %Y %I:%M:%S:%f%p\")\n",
    "\n",
    "# Generating useful information from date values\n",
    "\n",
    "df_new['SALE_DATE_YR'] = df_new['LAST_SALE_DATE'].dt.year\n",
    "df_new['AGE_OF_HOUSE_AT_SALE'] = df_new['SALE_DATE_YR'] - df_new['YRBLT']\n",
    "df_new['SALE_DATE_MNTH'] = df_new['LAST_SALE_DATE'].dt.month\n",
    "df_new['SALE_DATE_DT'] = df_new['LAST_SALE_DATE'].dt.day\n",
    "df_new.drop(['LAST_SALE_DATE', 'YRBLT', 'USPF', 'PHYS'], axis=1, inplace=True)\n",
    "df_new = pd.get_dummies(df_new)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Training dataset shape: (219, 18) | Testing dataset shape: (74, 18)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_y = df_new['LAST_SALE_PRICE']\n",
    "df_x = df_new.drop('LAST_SALE_PRICE', axis=1)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size = 0.25, random_state = 1)\n",
    "print(f' Training dataset shape: {x_train.shape} | Testing dataset shape: {x_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Actual      Predicted\n",
      "5   268000  269287.879192\n",
      "30  484000  482666.885334\n",
      "29  339000  333620.046310\n",
      "17  330000  327469.350382\n",
      "28  375000  376308.999026 \n",
      "\n",
      "Mean Absolute Error: 2339.1468243361323\n",
      "Mean Squared Error: 8491902.980470242\n",
      "Root Mean Squared Error: 2914.0869891734947\n",
      "R-squared: 0.9982507653133185\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "# print(model.intercept_)\n",
    "# print(model.coef_)\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "print(f'{df.head()} \\n')\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "print('R-squared:', metrics.r2_score(y_test, y_pred, multioutput='uniform_average'))\n",
    "\n",
    "# https://www.analyticsvidhya.com/blog/2021/05/all-you-need-to-know-about-your-first-machine-learning-model-linear-regression/\n",
    "# https://www.analyticsvidhya.com/blog/2021/07/an-introduction-to-linear-regression/\n",
    "# https://www.analyticsvidhya.com/blog/2021/06/getting-started-with-machine-learning%e2%80%8a-%e2%80%8aimplementing-linear-regression-from-scratch/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting unknown dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>BEDROOMS</th>\n",
       "      <th>BATHROOMS</th>\n",
       "      <th>HALF_BATHROOMS</th>\n",
       "      <th>BASE_AREA</th>\n",
       "      <th>TOTAL_AREA</th>\n",
       "      <th>BASEMENT_AREA</th>\n",
       "      <th>BASEMENT_FINISHED_AREA</th>\n",
       "      <th>PARKING_AREA</th>\n",
       "      <th>QUALITY_CODE</th>\n",
       "      <th>LOT_SIZE</th>\n",
       "      <th>LAND_VALUE</th>\n",
       "      <th>MARKET_VALUE</th>\n",
       "      <th>SALE_DATE_YR</th>\n",
       "      <th>AGE_OF_HOUSE_AT_SALE</th>\n",
       "      <th>SALE_DATE_MNTH</th>\n",
       "      <th>SALE_DATE_DT</th>\n",
       "      <th>ARCH_1STY</th>\n",
       "      <th>ARCH_2STY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1664</td>\n",
       "      <td>1664</td>\n",
       "      <td>1664</td>\n",
       "      <td>1200</td>\n",
       "      <td>400</td>\n",
       "      <td>40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0</td>\n",
       "      <td>319155</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1312</td>\n",
       "      <td>2544</td>\n",
       "      <td>1232</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>35</td>\n",
       "      <td>0.14</td>\n",
       "      <td>58000</td>\n",
       "      <td>318206</td>\n",
       "      <td>2021</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BEDROOMS  BATHROOMS  HALF_BATHROOMS  BASE_AREA  TOTAL_AREA  BASEMENT_AREA  BASEMENT_FINISHED_AREA  PARKING_AREA  QUALITY_CODE  LOT_SIZE  LAND_VALUE  MARKET_VALUE  SALE_DATE_YR  AGE_OF_HOUSE_AT_SALE  SALE_DATE_MNTH  SALE_DATE_DT  ARCH_1STY  ARCH_2STY\n",
       "0         3          3               0       1664        1664           1664                    1200           400            40      0.20           0        319155          2021                    12               9             7          1          0\n",
       "1         4          2               1       1312        2544           1232                       0           400            35      0.14       58000        318206          2021                    11               9             7          0          1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_data = file_path.replace(\"input-data\\\\\",\"\")\n",
    "unseen_data = pd.read_csv(unseen_data + \"unseen_data.csv\")\n",
    "address = unseen_data['ADDRESS']\n",
    "unseen_data.drop('ADDRESS', axis=1, inplace=True)\n",
    "\n",
    "# Data Preparation\n",
    "unseen_data['SALE_DATE_YR'] = dt.date.today().year\n",
    "unseen_data['AGE_OF_HOUSE_AT_SALE'] = (dt.date.today().year)  - (unseen_data['YRBLT'])\n",
    "unseen_data['SALE_DATE_MNTH'] = dt.date.today().month\n",
    "unseen_data['SALE_DATE_DT'] = dt.date.today().day\n",
    "unseen_data.drop(['YRBLT'], axis=1, inplace=True)\n",
    "unseen_data = pd.get_dummies(unseen_data)\n",
    "unseen_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Address</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1900 BRIDGECREST XING</td>\n",
       "      <td>352949.488952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1945 BRIDGECREST XING</td>\n",
       "      <td>331541.154427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Address      Predicted\n",
       "0  1900 BRIDGECREST XING  352949.488952\n",
       "1  1945 BRIDGECREST XING  331541.154427"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_price = model.predict(unseen_data)\n",
    "df = pd.DataFrame({'Address': address, 'Predicted': predicted_price})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numericCols = df_new.select_dtypes(include=['float64','int64'])\n",
    "\n",
    "# Calculate Correlation\n",
    "corr = numericCols.corr()\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  }
 ],
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