Housing_Model3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use some functions from tensorflow_docs\n",
    "# !pip install -q git+https://github.com/tensorflow/docs\n",
    "import tensorflow_docs as tfdocs\n",
    "import tensorflow_docs.plots\n",
    "import tensorflow_docs.modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>Integer_Year</th>\n",
       "      <th>Median List Price per SqFt Calculated</th>\n",
       "      <th>Month</th>\n",
       "      <th>No. of Bedrooms</th>\n",
       "      <th>Zip_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>116.973244</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>121.058559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>114.574016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18</td>\n",
       "      <td>128.642487</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>124.783862</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4745</th>\n",
       "      <td>20</td>\n",
       "      <td>75.297652</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>46214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4746</th>\n",
       "      <td>21</td>\n",
       "      <td>71.947874</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>46214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4747</th>\n",
       "      <td>22</td>\n",
       "      <td>79.283366</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>46214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4748</th>\n",
       "      <td>23</td>\n",
       "      <td>91.824038</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>46214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4749</th>\n",
       "      <td>24</td>\n",
       "      <td>99.132039</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>46214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2441 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Integer_Year  Median List Price per SqFt Calculated  Month  \\\n",
       "13              15                             116.973244      1   \n",
       "14              16                             121.058559      1   \n",
       "15              17                             114.574016      1   \n",
       "16              18                             128.642487      1   \n",
       "17              19                             124.783862      1   \n",
       "...            ...                                    ...    ...   \n",
       "4745            20                              75.297652     12   \n",
       "4746            21                              71.947874     12   \n",
       "4747            22                              79.283366     12   \n",
       "4748            23                              91.824038     12   \n",
       "4749            24                              99.132039     12   \n",
       "\n",
       "      No. of Bedrooms  Zip_Code  \n",
       "13                  1     46220  \n",
       "14                  1     46220  \n",
       "15                  1     46220  \n",
       "16                  1     46220  \n",
       "17                  1     46220  \n",
       "...               ...       ...  \n",
       "4745                5     46214  \n",
       "4746                5     46214  \n",
       "4747                5     46214  \n",
       "4748                5     46214  \n",
       "4749                5     46214  \n",
       "\n",
       "[2441 rows x 5 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Indianapolis_Housing = pd.read_csv(\"Indianapolis_Housing_Cleaned.csv\")\n",
    "Indianapolis_Housing.head()\n",
    "Indianapolis_Housing = Indianapolis_Housing[Indianapolis_Housing.Year >= 2010]\n",
    "Indianapolis_Housing = Indianapolis_Housing.drop([\"Unnamed: 0\", \"Unnamed: 0.1\", \"Year\", \"Date\",\\\n",
    "                                                  \"Median Market Value (Single Family)\", \"Median Bedroom Price\",\\\n",
    "                                                  \"Median List Price Calculated\"], axis=1)\n",
    "Indianapolis_Housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = Indianapolis_Housing_February[[\"Integer_Year\", \"Zip_Code_Integer\", \"No. of Bedrooms\", \n",
    "                                   #\"Median Market Value (Single Family)\",\n",
    "                                  #\"Median List Price per SqFt Calculated\",\n",
    "                                  #\"Median List Price Calculated\"]].values\n",
    "\n",
    "#y = Indianapolis_Housing_February[\"Median Bedroom Price\"].values\n",
    "#y = y.reshape(-1, 1)\n",
    "#print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "# from sklearn.preprocessing import LabelEncoder, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train, X_test, y_train, y_test = train_test_split(\n",
    "    #X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_scaler = MinMaxScaler().fit(X_train)\n",
    "#y_scaler = MinMaxScaler().fit(y_train)\n",
    "#X_train_scaled = X_scaler.transform(X_train)\n",
    "#X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Zip_Code'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2646\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\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[0m\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Zip_Code'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-124-15ecdfe64a0e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mIndianapolis_Housing\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Zip_Code'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIndianapolis_Housing\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Zip_Code'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;36m46220\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'46220'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m46219\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'46219'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m46227\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'46227'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m46214\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'46214'\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      2\u001b[0m \u001b[0mIndianapolis_Housing\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[0mIndianapolis_Housing\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Zip_Code\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix_sep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mIndianapolis_Housing\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"No. of Bedrooms\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIndianapolis_Housing\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'No. of Bedrooms'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'1 Bedroom'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'2 Bedroom'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'3 Bedroom'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'4 Bedroom'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'5 Bedroom'\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      5\u001b[0m \u001b[0mIndianapolis_Housing\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[0mIndianapolis_Housing\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"No. of Bedrooms\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix_sep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\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   2798\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\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[0;32m   2799\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\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-> 2800\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\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[0m\u001b[0;32m   2801\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\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   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\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[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2646\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\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[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2648\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\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[0m\u001b[0;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\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[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Zip_Code'"
     ]
    }
   ],
   "source": [
    "Indianapolis_Housing['Zip_Code'] = Indianapolis_Housing['Zip_Code'].map({46220: '46220', 46219: '46219', 46227: '46227', 46214: '46214'})\n",
    "Indianapolis_Housing = pd.get_dummies(Indianapolis_Housing,columns=[\"Zip_Code\"], prefix='', prefix_sep='')\n",
    "\n",
    "Indianapolis_Housing[\"No. of Bedrooms\"] = Indianapolis_Housing['No. of Bedrooms'].map({1: '1 Bedroom', 2: '2 Bedroom', 3: '3 Bedroom', 4: '4 Bedroom', 5: '5 Bedroom'})\n",
    "Indianapolis_Housing = pd.get_dummies(Indianapolis_Housing,columns=[\"No. of Bedrooms\"], prefix='', prefix_sep='')\n",
    "\n",
    "Indianapolis_Housing[\"Month\"] = Indianapolis_Housing['Month'].map({1: 'January', 2: 'February', 3: 'March',\n",
    "                                                                   4: 'April', 5: 'May', 6: 'June',\n",
    "                                                                   7: 'July', 8: 'August', 9: 'September',\n",
    "                                                                   10: 'October', 11: 'November', 12: 'December'})\n",
    "Indianapolis_Housing = pd.get_dummies(Indianapolis_Housing,columns=[\"Month\"], prefix='', prefix_sep='')\n",
    "\n",
    "Indianapolis_Housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Integer_Year', 'Median List Price per SqFt Calculated', '46214',\n",
       "       '46219', '46220', '46227', '1 Bedroom', '2 Bedroom', '3 Bedroom',\n",
       "       '4 Bedroom', '5 Bedroom', 'April', 'August', 'December', 'February',\n",
       "       'January', 'July', 'June', 'March', 'May', 'November', 'October',\n",
       "       'September'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Indianapolis_Housing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Indianapolis_Housing.to_csv(\"Indianapolis_Housing_Predictions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_dataset = Indianapolis_Housing.sample(frac=0.8,random_state=0)\n",
    "test_dataset = Indianapolis_Housing.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Integer_Year</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>19.615463</td>\n",
       "      <td>2.942604</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46214</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.242704</td>\n",
       "      <td>0.428827</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46219</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.248336</td>\n",
       "      <td>0.432158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46220</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.437682</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46227</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.250896</td>\n",
       "      <td>0.433640</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 Bedroom</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.190988</td>\n",
       "      <td>0.393180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Bedroom</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.205837</td>\n",
       "      <td>0.404416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 Bedroom</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.204301</td>\n",
       "      <td>0.403293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 Bedroom</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.198157</td>\n",
       "      <td>0.398713</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Bedroom</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.200717</td>\n",
       "      <td>0.400639</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.080389</td>\n",
       "      <td>0.271964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.269578</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.087558</td>\n",
       "      <td>0.282723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.090118</td>\n",
       "      <td>0.286424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.092166</td>\n",
       "      <td>0.289334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.079877</td>\n",
       "      <td>0.271172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.080901</td>\n",
       "      <td>0.272753</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.091654</td>\n",
       "      <td>0.288611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.081413</td>\n",
       "      <td>0.273539</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.077317</td>\n",
       "      <td>0.267162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.076805</td>\n",
       "      <td>0.266350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>0.082949</td>\n",
       "      <td>0.275876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count       mean       std   min   25%   50%   75%   max\n",
       "Integer_Year  1953.0  19.615463  2.942604  15.0  17.0  20.0  22.0  25.0\n",
       "46214         1953.0   0.242704  0.428827   0.0   0.0   0.0   0.0   1.0\n",
       "46219         1953.0   0.248336  0.432158   0.0   0.0   0.0   0.0   1.0\n",
       "46220         1953.0   0.258065  0.437682   0.0   0.0   0.0   1.0   1.0\n",
       "46227         1953.0   0.250896  0.433640   0.0   0.0   0.0   1.0   1.0\n",
       "1 Bedroom     1953.0   0.190988  0.393180   0.0   0.0   0.0   0.0   1.0\n",
       "2 Bedroom     1953.0   0.205837  0.404416   0.0   0.0   0.0   0.0   1.0\n",
       "3 Bedroom     1953.0   0.204301  0.403293   0.0   0.0   0.0   0.0   1.0\n",
       "4 Bedroom     1953.0   0.198157  0.398713   0.0   0.0   0.0   0.0   1.0\n",
       "5 Bedroom     1953.0   0.200717  0.400639   0.0   0.0   0.0   0.0   1.0\n",
       "April         1953.0   0.080389  0.271964   0.0   0.0   0.0   0.0   1.0\n",
       "August        1953.0   0.078853  0.269578   0.0   0.0   0.0   0.0   1.0\n",
       "December      1953.0   0.087558  0.282723   0.0   0.0   0.0   0.0   1.0\n",
       "February      1953.0   0.090118  0.286424   0.0   0.0   0.0   0.0   1.0\n",
       "January       1953.0   0.092166  0.289334   0.0   0.0   0.0   0.0   1.0\n",
       "July          1953.0   0.079877  0.271172   0.0   0.0   0.0   0.0   1.0\n",
       "June          1953.0   0.080901  0.272753   0.0   0.0   0.0   0.0   1.0\n",
       "March         1953.0   0.091654  0.288611   0.0   0.0   0.0   0.0   1.0\n",
       "May           1953.0   0.081413  0.273539   0.0   0.0   0.0   0.0   1.0\n",
       "November      1953.0   0.077317  0.267162   0.0   0.0   0.0   0.0   1.0\n",
       "October       1953.0   0.076805  0.266350   0.0   0.0   0.0   0.0   1.0\n",
       "September     1953.0   0.082949  0.275876   0.0   0.0   0.0   0.0   1.0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"Median List Price per SqFt Calculated\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop(\"Median List Price per SqFt Calculated\")\n",
    "test_labels = test_dataset.pop(\"Median List Price per SqFt Calculated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "  return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Integer_Year', '46214', '46219', '46220', '46227', '1 Bedroom',\n",
       "       '2 Bedroom', '3 Bedroom', '4 Bedroom', '5 Bedroom', 'April', 'August',\n",
       "       'December', 'February', 'January', 'July', 'June', 'March', 'May',\n",
       "       'November', 'October', 'September'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "  model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1)\n",
    "  ])\n",
    "\n",
    "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "\n",
    "  model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 64)                1472      \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 5,697\n",
      "Trainable params: 5,697\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.26787338],\n",
       "       [ 0.16218096],\n",
       "       [ 0.08661722],\n",
       "       [ 0.08953816],\n",
       "       [ 0.89993703],\n",
       "       [ 0.7822533 ],\n",
       "       [-0.1462408 ],\n",
       "       [ 0.6881495 ],\n",
       "       [ 0.21004666],\n",
       "       [ 0.4529076 ]], dtype=float32)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_batch = normed_train_data[:10]\n",
    "example_result = model.predict(example_batch)\n",
    "example_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 0, loss:9157.4267,  mae:90.1638,  mse:9157.4268,  val_loss:7803.6142,  val_mae:82.8491,  val_mse:7803.6143,  \n",
      "....................................................................................................\n",
      "Epoch: 100, loss:6.7647,  mae:1.9644,  mse:6.7647,  val_loss:11.0677,  val_mae:2.5920,  val_mse:11.0677,  \n",
      "....................................................................................................\n",
      "Epoch: 200, loss:5.3295,  mae:1.6838,  mse:5.3295,  val_loss:9.9664,  val_mae:2.4227,  val_mse:9.9664,  \n",
      "....................................................................................................\n",
      "Epoch: 300, loss:3.8602,  mae:1.4504,  mse:3.8602,  val_loss:10.4519,  val_mae:2.4504,  val_mse:10.4519,  \n",
      "....................................................................................................\n",
      "Epoch: 400, loss:3.4260,  mae:1.3612,  mse:3.4260,  val_loss:9.3618,  val_mae:2.3539,  val_mse:9.3618,  \n",
      "....................................................................................................\n",
      "Epoch: 500, loss:2.9034,  mae:1.2753,  mse:2.9034,  val_loss:10.4065,  val_mae:2.4525,  val_mse:10.4065,  \n",
      "....................................................................................................\n",
      "Epoch: 600, loss:2.6169,  mae:1.1950,  mse:2.6169,  val_loss:9.6294,  val_mae:2.3347,  val_mse:9.6294,  \n",
      "....................................................................................................\n",
      "Epoch: 700, loss:2.4782,  mae:1.1440,  mse:2.4782,  val_loss:9.4352,  val_mae:2.3521,  val_mse:9.4352,  \n",
      "....................................................................................................\n",
      "Epoch: 800, loss:2.4042,  mae:1.1121,  mse:2.4042,  val_loss:8.9824,  val_mae:2.2820,  val_mse:8.9824,  \n",
      "....................................................................................................\n",
      "Epoch: 900, loss:2.0982,  mae:1.0559,  mse:2.0982,  val_loss:10.0991,  val_mae:2.4253,  val_mse:10.0991,  \n",
      "...................................................................................................."
     ]
    }
   ],
   "source": [
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
    "  callbacks=[tfdocs.modeling.EpochDots()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.049446</td>\n",
       "      <td>1.041286</td>\n",
       "      <td>2.049446</td>\n",
       "      <td>9.374491</td>\n",
       "      <td>2.339757</td>\n",
       "      <td>9.374491</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1.899946</td>\n",
       "      <td>1.005392</td>\n",
       "      <td>1.899947</td>\n",
       "      <td>9.358198</td>\n",
       "      <td>2.329623</td>\n",
       "      <td>9.358197</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.054541</td>\n",
       "      <td>1.046586</td>\n",
       "      <td>2.054541</td>\n",
       "      <td>8.841883</td>\n",
       "      <td>2.252895</td>\n",
       "      <td>8.841882</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.052260</td>\n",
       "      <td>1.047737</td>\n",
       "      <td>2.052260</td>\n",
       "      <td>9.142134</td>\n",
       "      <td>2.289797</td>\n",
       "      <td>9.142134</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.061020</td>\n",
       "      <td>1.033958</td>\n",
       "      <td>2.061020</td>\n",
       "      <td>9.454328</td>\n",
       "      <td>2.308683</td>\n",
       "      <td>9.454329</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss       mae       mse  val_loss   val_mae   val_mse  epoch\n",
       "995  2.049446  1.041286  2.049446  9.374491  2.339757  9.374491    995\n",
       "996  1.899946  1.005392  1.899947  9.358198  2.329623  9.358197    996\n",
       "997  2.054541  1.046586  2.054541  8.841883  2.252895  8.841882    997\n",
       "998  2.052260  1.047737  2.052260  9.142134  2.289797  9.142134    998\n",
       "999  2.061020  1.033958  2.061020  9.454328  2.308683  9.454329    999"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()\n",
    "#print(hist[\"mse\"].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotter = tfdocs.plots.HistoryPlotter(smoothing_std=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MAE [Median Bedroom Price]')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plotter.plot({'Basic': history}, metric = \"mae\")\n",
    "plt.ylim([0, 100000])\n",
    "plt.ylabel('MAE [Median Bedroom Price]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MSE [Median Bedroom Price]')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sC3So1xBwB3BHxl5D7wOWRMQz6ails4CjWixzOnBZRLwKEBEvdyx8MzPbUlkvFl9BMgLpx9L5E0h6DR3TzjoDgedL5huA/Vss824ASf8HVAPfiIi5LTckaTowHaB///7U19dnDLtnW716tdsi5bZo5rZo5rbIJs9eQ2qlLFrM1wBDgAlAHXCPpH0j4rWNVoqYCcwEGDp0aEyYMCFj2D1bfX09bouE26KZ26KZ2yKbPHsNNZA82rJJHfBiK8vcHBFrI+JZ4CmSxGBmZhWS9Yjg08A1JdcFXgVOKrPOQ8AQSYOBF4DjgE+0WOYmYBpwlaRdSU4VPZMxJjMz6wRlE4GkKmBoRHSo11BErJN0BnAryfn/KyLicUkXAg9HxOy0bpKkxcB64EsRsWILvo+ZmXVQ2UQQEY3pD/q1WRJAi3XnAHNalJ1fMh3AWenLzMy6QNZrBPMknS1pD0m7NL1yjczMzCoi6zWCf03fP1tSFsDenRuOmZlVWtY7iwfnHYiZmXWNdhOBpPZuGCMibujccMzMrNLKHREcmb4PAA4gGWIC4GCgHnAiMDPr5tpNBBFxCoCkW4DhEfFSOr8byYByZmbWzWXtNTSoKQmk/k46TpCZmXVvWXsN1Uu6Ffg1SW+h44A7c4vKzMwqJmuvoTMkHQ0clBbNjIgb8wvLzMwqJesRAcAC4PWIuE3SdpL6RMTreQVmZmaVkfXBNKcD1wM/S4sGkgwYZ2Zm3VzWi8WfBQ4EVgFExNMkXUrNzKyby5oI3kofNwmApBo2fciMmZl1Q1kTwV2SzgO2lfRB4Drgf/MLy8zMKiVrIjgXWAY8BnyKZGjpr+YVlJmZVU7W7qONwM/Tl5mZ9SDtHhFIGiLpKkmXSKqT9HtJqyU9IumfKxWkmZnlp9ypoSuBP5I8dP4B4ApgV+Bs4Mf5hmZmZpVQLhHsEBEzI+Ji4M2IuC4i1kTEPKBXBeIzM7OclUsEjSXTLZ9X3IiZmXV75S4WD5P0KCDgXek06bwfU2lm1gOUSwT7VCQKMzPrMuUeTPNcpQIxM7OukfWGMjMz66GcCMzMCs6JwMys4DINMSHpQOAbwF7pOgIiItxzyMysm8v6hLJfAF8E5gPr8wvHzMwqLWsiWBkRv881EjMz6xJZE8Gdkr4L3AC81VQYEQtyicrMzComayLYP30fW1IWwMTODcfMzCot6/MIDs47EDMz6xpZjwiQdATwHqB3U1lEXJhHUGZmVjmZ7iOQ9FPg48CZJF1HP0rSldTMzLq5rDeUHRARJwKvRsQFwHhgj/zCMjOzSsmaCN5M3/8haXdgLTC43EqSJkt6StISSee2s9xHJIWksW0tY2Zm+ciaCG6RtBPwXWABsBSY1d4KkqqBy4DDgeHANEnDW1muD/A5kkdhmplZhWVKBBHxzYh4LSJ+S3JtYFhEfK3Mau8DlkTEMxHxNkniOKqV5b4J/CewpgNxm5lZJ2m315CkiRFxh6RjWqkjIm5oZ/WBwPMl8w0034/QtI3RwB4RcYuks9uJYzowHaB///7U19e3F3ZhrF692m2Rcls0c1s0c1tkU6776AeAO4AjW6kLkjuN26I21kkqpSrg+8DJZWIgImYCMwGGDh0aEyZMKLdKIdTX1+O2SLgtmrktmrktsin3hLKvp++nbMa2G9i4Z1Ed8GLJfB9gX6BeEsA7gdmSpkbEw5vxeWZmthnKnRo6q736iLikneqHgCGSBgMvAMcBnyhZdyWwa8ln1QNnOwmYmVVWuVNDfdL3ocA/A7PT+SOBu9tbMSLWSToDuBWoBq6IiMclXQg8HBGz21vfzMwqo9ypoQsAJP0BGBMRr6fz3wCuK7fxiJgDzGlRdn4by07IFLGZmXWqrPcR7Am8XTL/NjCo06MxM7OKyzro3C+BByXdSNLz52jgmtyiMjOzisk6DPV/SPo98P606JSI+FN+YZmZWaVkPTUEsB2wKiJ+CDSkvYHMzKybyzoM9deBc4AZaVEt8Ku8gjIzs8rJekRwNDAVeAMgIl6kuWupmZl1Y1kTwdsREaRDREjaPr+QzMyskrImgmsl/QzYSdLpwG3Az/MLy8zMKiVrr6GLJX0QWEVyl/H5ETEv18jMzKwiMj+8Pv3h94+/mVkPU27QudcpGTq6tAqIiNgxl6jMzKxiyh0R3E4yPPQNwKyI+Gv+IZmZWSW1e7E4Ij4MHAYsA34u6S5J/yZpl4pEZ2ZmuSvbaygiVkbElSQPof8pcCEZnipmZmbdQ9mLxZIOAKaRjDN0L3B0RNyTd2BmZlYZ5S4WLwVeA2aRPDx+XVo+BiAiFuQcn5mZ5azcEcFSkl5DhwGT2PiB9AFMzCcsMzOrlHJPKJtQoTjMzKyLtHuxuOkU0JYuY2ZmW69yp4aulDSBjU8JtfQLYHSnRWRmZhVVLhH0BebTfiJY1nnhmJlZpZW7RjCoQnGYmVkX6cijKs3MrAdyIjAzKzgnAjOzgivXffSTJdMHtqg7I6+gzMyscsodEZxVMv2jFnX/2smxmJlZFyiXCNTGdGvzZmbWDZVLBNHGdGvzZmbWDZW7oWyYpEdJ/vp/VzpNOr93rpGZmVlFlEsE+1QkCjMz6zLl7ix+rnReUj/gIOCvETE/z8DMzKwyynUfvUXSvun0bsAikt5Cv5T0hQrEZ2ZmOSt3sXhwRCxKp08B5kXEkcD+uPuomVmPUC4RrC2ZPgSYAxARrwON5TYuabKkpyQtkXRuK/VnSVos6VFJt0vaqyPBm5nZliuXCJ6XdKako4ExwFwASdsCte2tKKkauAw4HBgOTJM0vMVifwLGRsQI4HrgPzv+FczMbEuUSwSnAu8BTgY+HhGvpeXjgCvLrPs+YElEPBMRbwOzgKNKF4iIOyPiH+ns/UBdB2I3M7NOUK7X0MvAp1spvxO4s8y2BwLPl8w3kFxbaMupwO9bq5A0HZgO0L9/f+rr68t8dDGsXr3abZFyWzRzWzRzW2TTbiKQNLu9+oiY2t7qra3Sxud8EhgLfKCNz5kJzAQYOnRoTJgwob2wCqO+vh63RcJt0cxt0cxtkU25G8rGk/xV/2vgATo2vlADsEfJfB3wYsuFJB0KfAX4QES81YHtm5lZJyiXCN4JfBCYBnwC+B3w64h4PMO2HwKGSBoMvAAcl25jA0mjgZ8Bk9PTUGZmVmHtXiyOiPURMTciTiK5QLwEqJd0ZrkNR8Q64AzgVuAJ4NqIeFzShZKaTil9F9gBuE7SwnKnoszMrPOVOyJAUi/gCJKjgkHApcANWTYeEXNI7z0oKTu/ZPrQDsRqZmY5KHex+GpgX5LePBeU3GVsZmY9RLkjghOAN4B3A5+TNlwrFhARsWOOsZmZWQWUu4/AD7c3M+vh/ENvZlZwTgRmZgXnRGBmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZwTgRmZgXnRGBmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZwTgRmZgXnRGBmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZwTgRmZgXnRGBmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZwTgRmZgXnRGBmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZwTgRmZgWXayKQNFnSU5KWSDq3lfpekn6T1j8gaVCe8ZiZ2aZySwSSqoHLgMOB4cA0ScNbLHYq8GpE/BPwfeA7ecVjZmatU0Tks2FpPPCNiDgsnZ8BEBEXlSxza7rMfZJqgL8B/aOdoHrtNiQGnvyDjcpqqkRtdRURwZp1jZusU66+tkrUVFfRGMFbrdVXV1FTpTbrt6muorpKrG8M3l7f+fW9aqqokljXGKwtqY8AqaR+fSNrGzdtut41VUhi7fpG1m1G/ba11QCt1gvonda/vb6R9e3Vr2tkfYt/Wkn0rkn+HnlrXSONLeqrJHplqF+/fj1rQ7TcdaoltknXX7N2PS2/XXWV2Ka67fqmfQfgzbXraWlr3Pea9ou26sutX6qtfW+T+q1036utgurq6lz3PYA16xq3+n3v2W9PmR8RYzdZEKhprbCTDASeL5lvAPZva5mIWCdpJdAPWF66kKTpwPR09q1nvz1lUS4Rdz+70qKtCsxt0cxt0cxt0WyvtiryTARqpaxl0suyDBExE5gJIOnhtrJa0bgtmrktmrktmrktssnzYnEDsEfJfB3wYlvLpKeG+gKv5BiTmZm1kGcieAgYImmwpG2A44DZLZaZDZyUTn8EuKO96wNmZtb5cjs1lJ7zPwO4FagGroiIxyVdCDwcEbOBXwC/lLSE5EjguAybnplXzN2Q26KZ26KZ26KZ2yKD3HoNmZlZ9+A7i83MCs6JwMys4LpVIig3ZEVPImkPSXdKekLS45I+n5bvImmepKfT953Tckm6NG2bRyWN6dpv0PkkVUv6k6Rb0vnB6dAkT6dDlWyTlvfooUsk7STpeklPpvvH+KLuF5K+mP7/WCTp15J6F3W/2BLdJhFkHLKiJ1kH/HtE7AOMAz6bft9zgdsjYghwezoPSbsMSV/TgZ9UPuTcfR54omT+O8D307Z4lWTIEuj5Q5f8EJgbEcOAkSRtUrj9QtJA4HPA2IjYl6RTynEUd7/YfBHRLV7AeODWkvkZwIyujquC3/9m4IPAU8BuadluwFPp9M+AaSXLb1iuJ7xI7kO5HZgI3EJyM+JyoKbl/kHSU218Ol2TLqeu/g6d1A47As+2/D5F3C9oHplgl/Tf+RbgsCLuF1v66jZHBLQ+ZMXALoqlotJD2NHAA8A7IuIlgPR9QLpYT2+fHwBfBpoGVekHvBYR69L50u+70dAlQNPQJT3B3sAy4Mr0NNnlkrangPtFRLwAXAz8FXiJ5N95PsXcL7ZId0oEmYaj6Gkk7QD8FvhCRKxqb9FWynpE+0iaArwcEfNLi1tZNDLUdXc1wBjgJxExGniD5tNAremxbZFeBzkKGAzsDmxPciqspSLsF1ukOyWCLENW9CiSakmSwH9HxA1p8d8l7ZbW7wa8nJb35PY5EJgqaSkwi+T00A+AndKhSWDj79uThy5pABoi4oF0/nqSxFDE/eJQ4NmIWBYRa4EbgAMo5n6xRbpTIsgyZEWPIUkkd14/ERGXlFSVDstxEsm1g6byE9NeIuOAlU2nCrq7iJgREXURMYjk3/2OiDgeuJNkaBLYtC165NAlEfE34HlJQ9OiQ4DFFHC/IDklNE7Sdun/l6a2KNx+scW6+iJFR17Ah4A/A38BvtLV8eT8Xf+F5LD1UWBh+voQyTnN24Gn0/dd0uVF0qvqL8BjJD0puvx75NAuE4Bb0um9gQeBJcB1QK+0vHc6vySt37ur4+7kNhgFPJzuGzcBOxd1vwAuAJ4EFgG/BHoVdb/YkpeHmDAzK7judGrIzMxy4ERgZlZwTgRmZgXnRGBmVnBOBGZmBedEYJaStF7SwpJXp41wK2mQpEWdtT2zzpTboyrNuqE3I2JUVwdhVmk+IjArQ9JSSd+R9GD6+qe0fC9Jt6fj/N8uac+0/B2SbpT0SPo6IN1UtaSfp+Pn/0HStunyn5O0ON3OrC76mlZgTgRmzbZtcWro4yV1qyLifcCPScY5Ip2+JiJGAP8NXJqWXwrcFREjScYBejwtHwJcFhHvAV4Djk3LzwVGp9v5dF5fzqwtvrPYLCVpdUTs0Er5UmBiRDyTDgT4t4joJ2k5ydj+a9PylyJiV0nLgLqIeKtkG4OAeZE8LAVJ5wC1EfEtSXOB1STDRdwUEatz/qpmG/ERgVk20cZ0W8u05q2S6fU0X6M7gmQ8oPcC80tGzjSrCCcCs2w+XvJ+Xzr9R5LRUAGOB+5Np28HPgMbnrO8Y1sblVQF7BERd5I8eGcnYJOjErM8+S8Ps2bbSlpYMj83Ipq6kPaS9ADJH0/T0rLPAVdI+hLJU8NOScs/D8yUdCrJX/6fIXmCVmuqgV9J6ksyUuj3I+K1TvtGZhn4GoFZGek1grERsbyrYzHLg08NmZkVnI8IzMwKzkcEZmYF50RgZlZwTgRmZgXnRGBmVnBOBGZmBff/AQYi5G57/xzwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter.plot({'Basic': history}, metric = \"mse\")\n",
    "plt.ylim([0, 1000000000])\n",
    "plt.ylabel('MSE [Median Bedroom Price]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "488/488 - 0s - loss: 8.2350 - mae: 2.2570 - mse: 8.2350\n",
      "Testing set Mean Abs Error:  2.26 \n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} \".format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1953/1953 - 0s - loss: 3.6287 - mae: 1.3077 - mse: 3.6287\n",
      "Training set Mean Abs Error:  1.31 \n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_train_data, train_labels, verbose=2)\n",
    "\n",
    "print(\"Training set Mean Abs Error: {:5.2f} \".format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "a = plt.axes(aspect='equal')\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [Median Bedroom Price]')\n",
    "plt.ylabel('Predictions [Median Bedroom Price]')\n",
    "lims = [0, 500000]\n",
    "plt.xlim(lims)\n",
    "plt.ylim(lims)\n",
    "_ = plt.plot(lims, lims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([118.739235, 124.29423 , 152.03894 , 119.458725, 125.348755,\n",
       "       126.56666 ,  78.22609 ,  61.423275,  68.094795,  86.90941 ,\n",
       "        63.350582,  61.55908 ,  61.438915,  64.906075,  63.012577,\n",
       "        62.775585,  74.11642 ,  81.15546 ,  82.11763 , 104.95576 ,\n",
       "        80.51366 ,  76.06513 , 101.15238 , 111.1295  ,  81.1999  ,\n",
       "       108.58861 , 113.72783 ,  67.26402 ,  72.68548 ,  75.09412 ,\n",
       "        98.20839 ,  72.95581 ,  74.4615  ,  72.94248 ,  67.804504,\n",
       "        74.04031 , 100.92509 ,  71.760345,  80.710236, 124.24176 ,\n",
       "       176.63844 , 160.32796 , 161.38095 , 176.20755 ,  61.75697 ,\n",
       "        62.227722,  63.321934,  80.801605,  66.84675 ,  66.884445,\n",
       "        65.01951 ,  67.46932 ,  65.962524,  65.70289 ,  63.831554,\n",
       "        80.169266, 100.365875,  79.2117  ,  74.712395,  80.43471 ,\n",
       "       113.808815, 100.948006,  73.61679 , 100.78866 ,  81.39894 ,\n",
       "        82.34198 ,  91.25299 ,  96.12155 ,  74.204834,  75.07942 ,\n",
       "        78.71719 ,  73.22828 ,  86.478134,  71.48496 ,  75.38431 ,\n",
       "        79.24403 ,  86.335915, 123.569565, 130.39232 , 131.68843 ,\n",
       "       139.53432 , 137.94562 , 125.057785, 128.0323  ,  94.02306 ,\n",
       "        66.938736,  62.438   ,  61.311745,  71.35309 ,  96.614975,\n",
       "        84.304085,  76.50282 ,  84.85127 ,  91.53366 ,  78.39751 ,\n",
       "        75.05212 ,  83.47316 ,  74.39619 ,  76.72376 ,  81.464554,\n",
       "       110.96222 ,  69.61548 ,  70.24462 ,  81.10062 ,  72.40408 ,\n",
       "        70.980316,  72.986435,  92.60245 ,  97.030716,  72.13806 ,\n",
       "        70.5906  ,  74.1085  , 127.98507 , 131.4073  , 128.91803 ,\n",
       "       131.16644 , 137.3342  , 184.90771 ,  64.659424,  67.77321 ,\n",
       "        83.320816,  63.29906 ,  73.99733 ,  62.38477 ,  65.21183 ,\n",
       "        68.195465,  96.050285,  65.020546,  63.819977,  69.21968 ,\n",
       "        73.91984 , 100.64619 , 103.10774 ,  85.177734,  76.41304 ,\n",
       "        79.89271 ,  93.45409 ,  76.68086 , 103.62084 ,  67.92236 ,\n",
       "        72.84677 ,  79.0661  ,  89.87287 ,  72.08899 ,  68.947395,\n",
       "        90.79265 ,  74.4206  ,  76.240906,  72.00174 ,  84.81064 ,\n",
       "        92.202934, 100.040825,  72.06902 ,  84.67464 , 132.28505 ,\n",
       "       140.69925 , 171.75018 , 132.84056 , 135.02515 , 185.11472 ,\n",
       "       150.75954 , 161.7369  , 186.99792 , 133.9971  , 172.5425  ,\n",
       "        74.66412 ,  61.49123 , 100.70421 ,  61.775776,  67.373215,\n",
       "        66.38469 ,  66.784454,  65.83312 ,  66.78235 , 100.06537 ,\n",
       "        85.23084 , 100.409775,  80.426384,  76.297585,  87.36377 ,\n",
       "        94.237274, 107.579605,  79.06893 ,  83.63556 ,  68.263596,\n",
       "        95.739525,  70.19685 ,  67.89112 ,  78.60166 ,  76.82684 ,\n",
       "        81.68123 ,  68.48116 ,  89.07667 ,  78.03966 , 124.834076,\n",
       "       131.62903 , 177.5853  , 150.55402 , 128.76746 , 124.08966 ,\n",
       "       139.74963 ,  93.352325,  62.48445 ,  72.08324 ,  79.18068 ,\n",
       "        88.270065,  69.05373 ,  69.20557 ,  84.39488 ,  69.642334,\n",
       "        71.11342 ,  93.76218 ,  94.98114 , 105.00696 ,  79.163216,\n",
       "        82.45909 ,  95.55226 , 105.015594,  74.678696,  93.16882 ,\n",
       "        76.17166 ,  83.74008 ,  75.84538 ,  93.36366 ,  74.42391 ,\n",
       "        68.51428 ,  75.22673 ,  78.60829 ,  77.80615 ,  70.90871 ,\n",
       "        75.1341  ,  76.42425 ,  83.00632 ,  92.7104  ,  93.8562  ,\n",
       "       124.508995, 129.49811 , 141.14694 , 154.9961  , 169.48032 ,\n",
       "       139.84335 , 152.7692  , 173.73465 , 133.93674 , 141.04369 ,\n",
       "        68.79628 ,  68.34169 ,  66.41431 ,  65.43441 ,  68.02002 ,\n",
       "        65.64715 ,  64.19859 ,  70.05322 ,  77.681   ,  94.12189 ,\n",
       "        78.18309 ,  77.98236 ,  81.27023 ,  88.4482  , 104.9406  ,\n",
       "        98.955925,  83.348404,  97.47517 ,  78.80029 ,  85.57437 ,\n",
       "       107.16208 ,  68.17818 ,  78.69575 ,  83.335976,  69.74188 ,\n",
       "        70.13974 ,  78.23527 ,  69.89192 ,  80.52562 ,  74.06046 ,\n",
       "        71.83917 ,  80.36722 ,  92.441734, 119.50551 , 127.82834 ,\n",
       "       133.07724 , 175.93481 , 130.92984 , 142.26091 , 154.38925 ,\n",
       "       168.92612 , 186.97095 , 148.21054 , 162.15918 , 119.47645 ,\n",
       "       141.51944 ,  66.13554 ,  63.898335,  99.6449  ,  86.74506 ,\n",
       "        95.2604  ,  65.89415 ,  81.40196 ,  90.09268 ,  99.0352  ,\n",
       "       101.709076, 104.480515,  82.56227 ,  84.745575, 101.850395,\n",
       "       105.02143 ,  79.629135,  79.872665,  80.52087 ,  75.85574 ,\n",
       "        82.64763 ,  88.16761 , 108.750595,  88.00575 ,  71.86115 ,\n",
       "        74.71653 ,  68.78029 ,  76.34685 ,  82.69989 ,  76.04235 ,\n",
       "        68.18316 ,  90.36217 ,  94.667984, 129.44244 , 177.638   ,\n",
       "       127.00177 , 177.35413 , 138.6639  , 180.84583 , 122.39757 ,\n",
       "       124.45087 , 176.80539 ,  66.06278 ,  80.277664,  83.318146,\n",
       "        89.917984,  62.83556 ,  65.38167 ,  82.364456,  69.12145 ,\n",
       "        85.46889 ,  63.274937,  83.05937 ,  91.27518 ,  87.45861 ,\n",
       "        80.28292 , 107.25798 , 107.67938 ,  95.00955 ,  83.58948 ,\n",
       "        88.96294 ,  67.89332 ,  76.40325 ,  95.84595 ,  75.10107 ,\n",
       "        68.01002 ,  66.74826 ,  84.43036 ,  72.04461 ,  85.911804,\n",
       "       127.718315, 150.18211 , 178.3357  , 128.1035  , 129.5404  ,\n",
       "       163.99893 , 121.625656, 150.79108 , 174.0505  , 178.62158 ,\n",
       "       125.788895, 176.01535 ,  65.714066,  72.54778 ,  93.656555,\n",
       "        64.23872 ,  64.78253 ,  85.04332 ,  96.02089 ,  62.04685 ,\n",
       "        66.40955 ,  97.82683 ,  65.02535 ,  99.04893 ,  67.49989 ,\n",
       "        99.77583 ,  78.82928 ,  82.017914,  79.6948  ,  82.74904 ,\n",
       "        85.72938 , 112.88231 ,  77.69504 ,  79.19521 ,  81.913864,\n",
       "       101.528755,  90.06795 ,  68.851295,  75.64376 ,  66.83218 ,\n",
       "        83.012115,  92.903336,  71.07741 ,  74.192314,  70.50742 ,\n",
       "        70.5724  ,  74.495834,  71.651855,  75.33714 , 100.44122 ,\n",
       "       131.20006 , 138.7124  , 171.5581  , 174.03198 , 122.415375,\n",
       "       120.25319 , 140.9062  , 183.01639 , 119.70755 , 128.02647 ,\n",
       "       172.6854  ,  65.999954,  63.984158,  72.15059 ,  70.77318 ,\n",
       "        97.62361 ,  67.8539  ,  65.78248 ,  64.22978 ,  90.742294,\n",
       "        73.24197 ,  82.881584, 112.13672 ,  76.1331  ,  79.10245 ,\n",
       "        82.0956  , 103.653305,  75.48416 ,  78.18783 , 102.32229 ,\n",
       "       111.989624,  75.18755 ,  84.15713 ,  87.99263 , 113.20769 ,\n",
       "        82.988815,  88.325424,  70.31366 ,  74.962524,  72.96623 ,\n",
       "        91.57535 ,  75.99472 ,  68.7737  ,  65.60768 ,  72.95915 ,\n",
       "        69.99157 ,  68.70502 ,  71.06781 ,  70.37487 , 116.69354 ,\n",
       "       127.32256 , 118.33765 , 145.59375 , 184.78146 , 122.04802 ,\n",
       "       120.27582 , 123.54112 , 159.82593 ,  66.58069 ,  63.154438,\n",
       "        66.10332 ,  98.66627 ,  65.79681 ,  66.311775,  80.444565,\n",
       "        88.49345 ,  78.523705,  82.94436 ,  74.00767 ,  73.930504,\n",
       "        79.982155,  77.12844 ,  74.74931 ,  69.025955,  74.21233 ,\n",
       "        68.76942 ,  68.85254 ,  74.56201 ], dtype=float32)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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": [
    "error = test_predictions - test_labels\n",
    "plt.hist(error, bins = 25)\n",
    "plt.xlabel(\"Prediction Error [Median Bedroom Price]\")\n",
    "_ = plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save(\"Indianapolis_Housing2.h5\")\n",
    "# from tensorflow.keras.models import load_model\n",
    "# model = load_model(\"Indianapolis_Housing3.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionsData = pd.read_csv(\"predictionsDataInit.csv\")\n",
    "predictionsData = predictionsData.drop(\"Unnamed: 0\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "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>Integer_Year</th>\n",
       "      <th>46214</th>\n",
       "      <th>46219</th>\n",
       "      <th>46220</th>\n",
       "      <th>46227</th>\n",
       "      <th>1 Bedroom</th>\n",
       "      <th>2 Bedroom</th>\n",
       "      <th>3 Bedroom</th>\n",
       "      <th>4 Bedroom</th>\n",
       "      <th>5 Bedroom</th>\n",
       "      <th>...</th>\n",
       "      <th>December</th>\n",
       "      <th>February</th>\n",
       "      <th>January</th>\n",
       "      <th>July</th>\n",
       "      <th>June</th>\n",
       "      <th>March</th>\n",
       "      <th>May</th>\n",
       "      <th>November</th>\n",
       "      <th>October</th>\n",
       "      <th>September</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</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>1</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Integer_Year  46214  46219  46220  46227  1 Bedroom  2 Bedroom  3 Bedroom  \\\n",
       "0            25      0      0      1      0          1          0          0   \n",
       "1            25      0      0      1      0          0          1          0   \n",
       "2            25      0      0      1      0          0          0          1   \n",
       "3            25      0      0      1      0          0          0          0   \n",
       "4            25      0      0      1      0          0          0          0   \n",
       "\n",
       "   4 Bedroom  5 Bedroom  ...  December  February  January  July  June  March  \\\n",
       "0          0          0  ...         0         0        1     0     0      0   \n",
       "1          0          0  ...         0         0        1     0     0      0   \n",
       "2          0          0  ...         0         0        1     0     0      0   \n",
       "3          1          0  ...         0         0        1     0     0      0   \n",
       "4          0          1  ...         0         0        1     0     0      0   \n",
       "\n",
       "   May  November  October  September  \n",
       "0    0         0        0          0  \n",
       "1    0         0        0          0  \n",
       "2    0         0        0          0  \n",
       "3    0         0        0          0  \n",
       "4    0         0        0          0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictionsData = predictionsData[['Integer_Year', '46214', '46219', '46220',\n",
    "       '46227', '1 Bedroom', '2 Bedroom', '3 Bedroom', '4 Bedroom',\n",
    "       '5 Bedroom', 'April', 'August', 'December', 'February', 'January',\n",
    "       'July', 'June', 'March', 'May', 'November', 'October', 'September']]\n",
    "\n",
    "predictionsData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Integer_Year', '46214', '46219', '46220', '46227', '1 Bedroom',\n",
      "       '2 Bedroom', '3 Bedroom', '4 Bedroom', '5 Bedroom', 'April', 'August',\n",
      "       'December', 'February', 'January', 'July', 'June', 'March', 'May',\n",
      "       'November', 'October', 'September'],\n",
      "      dtype='object')\n",
      "Index(['Integer_Year', '46214', '46219', '46220', '46227', '1 Bedroom',\n",
      "       '2 Bedroom', '3 Bedroom', '4 Bedroom', '5 Bedroom', 'April', 'August',\n",
      "       'December', 'February', 'January', 'July', 'June', 'March', 'May',\n",
      "       'November', 'October', 'September'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(predictionsData.columns)\n",
    "print(train_dataset.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Integer_Year    int64\n",
       "46214           int64\n",
       "46219           int64\n",
       "46220           int64\n",
       "46227           int64\n",
       "1 Bedroom       int64\n",
       "2 Bedroom       int64\n",
       "3 Bedroom       int64\n",
       "4 Bedroom       int64\n",
       "5 Bedroom       int64\n",
       "April           int64\n",
       "August          int64\n",
       "December        int64\n",
       "February        int64\n",
       "January         int64\n",
       "July            int64\n",
       "June            int64\n",
       "March           int64\n",
       "May             int64\n",
       "November        int64\n",
       "October         int64\n",
       "September       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictionsData.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "predictionsData[['46214', '46219', '46220',\n",
    "       '46227', '1 Bedroom', '2 Bedroom', '3 Bedroom', '4 Bedroom',\n",
    "       '5 Bedroom', 'April', 'August', 'December', 'February', 'January',\n",
    "       'July', 'June', 'March', 'May', 'November', 'October', 'September']] = predictionsData[['46214', '46219', '46220',\n",
    "       '46227', '1 Bedroom', '2 Bedroom', '3 Bedroom', '4 Bedroom',\n",
    "       '5 Bedroom', 'April', 'August', 'December', 'February', 'January',\n",
    "       'July', 'June', 'March', 'May', 'November', 'October', 'September']].astype(np.uint8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Integer_Year    int64\n",
       "46214           uint8\n",
       "46219           uint8\n",
       "46220           uint8\n",
       "46227           uint8\n",
       "1 Bedroom       uint8\n",
       "2 Bedroom       uint8\n",
       "3 Bedroom       uint8\n",
       "4 Bedroom       uint8\n",
       "5 Bedroom       uint8\n",
       "April           uint8\n",
       "August          uint8\n",
       "December        uint8\n",
       "February        uint8\n",
       "January         uint8\n",
       "July            uint8\n",
       "June            uint8\n",
       "March           uint8\n",
       "May             uint8\n",
       "November        uint8\n",
       "October         uint8\n",
       "September       uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictionsData.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Integer_Year    int64\n",
       "46214           uint8\n",
       "46219           uint8\n",
       "46220           uint8\n",
       "46227           uint8\n",
       "1 Bedroom       uint8\n",
       "2 Bedroom       uint8\n",
       "3 Bedroom       uint8\n",
       "4 Bedroom       uint8\n",
       "5 Bedroom       uint8\n",
       "April           uint8\n",
       "August          uint8\n",
       "December        uint8\n",
       "February        uint8\n",
       "January         uint8\n",
       "July            uint8\n",
       "June            uint8\n",
       "March           uint8\n",
       "May             uint8\n",
       "November        uint8\n",
       "October         uint8\n",
       "September       uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "normed_prediction_data = norm(predictionsData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>Integer_Year</th>\n",
       "      <th>46214</th>\n",
       "      <th>46219</th>\n",
       "      <th>46220</th>\n",
       "      <th>46227</th>\n",
       "      <th>1 Bedroom</th>\n",
       "      <th>2 Bedroom</th>\n",
       "      <th>3 Bedroom</th>\n",
       "      <th>4 Bedroom</th>\n",
       "      <th>5 Bedroom</th>\n",
       "      <th>...</th>\n",
       "      <th>December</th>\n",
       "      <th>February</th>\n",
       "      <th>January</th>\n",
       "      <th>July</th>\n",
       "      <th>June</th>\n",
       "      <th>March</th>\n",
       "      <th>May</th>\n",
       "      <th>November</th>\n",
       "      <th>October</th>\n",
       "      <th>September</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.829854</td>\n",
       "      <td>-0.565971</td>\n",
       "      <td>-0.574641</td>\n",
       "      <td>1.695148</td>\n",
       "      <td>-0.578582</td>\n",
       "      <td>2.057609</td>\n",
       "      <td>-0.508974</td>\n",
       "      <td>-0.506582</td>\n",
       "      <td>-0.496991</td>\n",
       "      <td>-0.500992</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.309694</td>\n",
       "      <td>-0.314631</td>\n",
       "      <td>3.137667</td>\n",
       "      <td>-0.294562</td>\n",
       "      <td>-0.29661</td>\n",
       "      <td>-0.317569</td>\n",
       "      <td>-0.29763</td>\n",
       "      <td>-0.289401</td>\n",
       "      <td>-0.288361</td>\n",
       "      <td>-0.300676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.829854</td>\n",
       "      <td>-0.565971</td>\n",
       "      <td>-0.574641</td>\n",
       "      <td>1.695148</td>\n",
       "      <td>-0.578582</td>\n",
       "      <td>-0.485752</td>\n",
       "      <td>1.963729</td>\n",
       "      <td>-0.506582</td>\n",
       "      <td>-0.496991</td>\n",
       "      <td>-0.500992</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.309694</td>\n",
       "      <td>-0.314631</td>\n",
       "      <td>3.137667</td>\n",
       "      <td>-0.294562</td>\n",
       "      <td>-0.29661</td>\n",
       "      <td>-0.317569</td>\n",
       "      <td>-0.29763</td>\n",
       "      <td>-0.289401</td>\n",
       "      <td>-0.288361</td>\n",
       "      <td>-0.300676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.829854</td>\n",
       "      <td>-0.565971</td>\n",
       "      <td>-0.574641</td>\n",
       "      <td>1.695148</td>\n",
       "      <td>-0.578582</td>\n",
       "      <td>-0.485752</td>\n",
       "      <td>-0.508974</td>\n",
       "      <td>1.973003</td>\n",
       "      <td>-0.496991</td>\n",
       "      <td>-0.500992</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.309694</td>\n",
       "      <td>-0.314631</td>\n",
       "      <td>3.137667</td>\n",
       "      <td>-0.294562</td>\n",
       "      <td>-0.29661</td>\n",
       "      <td>-0.317569</td>\n",
       "      <td>-0.29763</td>\n",
       "      <td>-0.289401</td>\n",
       "      <td>-0.288361</td>\n",
       "      <td>-0.300676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.829854</td>\n",
       "      <td>-0.565971</td>\n",
       "      <td>-0.574641</td>\n",
       "      <td>1.695148</td>\n",
       "      <td>-0.578582</td>\n",
       "      <td>-0.485752</td>\n",
       "      <td>-0.508974</td>\n",
       "      <td>-0.506582</td>\n",
       "      <td>2.011079</td>\n",
       "      <td>-0.500992</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.309694</td>\n",
       "      <td>-0.314631</td>\n",
       "      <td>3.137667</td>\n",
       "      <td>-0.294562</td>\n",
       "      <td>-0.29661</td>\n",
       "      <td>-0.317569</td>\n",
       "      <td>-0.29763</td>\n",
       "      <td>-0.289401</td>\n",
       "      <td>-0.288361</td>\n",
       "      <td>-0.300676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.829854</td>\n",
       "      <td>-0.565971</td>\n",
       "      <td>-0.574641</td>\n",
       "      <td>1.695148</td>\n",
       "      <td>-0.578582</td>\n",
       "      <td>-0.485752</td>\n",
       "      <td>-0.508974</td>\n",
       "      <td>-0.506582</td>\n",
       "      <td>-0.496991</td>\n",
       "      <td>1.995020</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.309694</td>\n",
       "      <td>-0.314631</td>\n",
       "      <td>3.137667</td>\n",
       "      <td>-0.294562</td>\n",
       "      <td>-0.29661</td>\n",
       "      <td>-0.317569</td>\n",
       "      <td>-0.29763</td>\n",
       "      <td>-0.289401</td>\n",
       "      <td>-0.288361</td>\n",
       "      <td>-0.300676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Integer_Year     46214     46219     46220     46227  1 Bedroom  2 Bedroom  \\\n",
       "0      1.829854 -0.565971 -0.574641  1.695148 -0.578582   2.057609  -0.508974   \n",
       "1      1.829854 -0.565971 -0.574641  1.695148 -0.578582  -0.485752   1.963729   \n",
       "2      1.829854 -0.565971 -0.574641  1.695148 -0.578582  -0.485752  -0.508974   \n",
       "3      1.829854 -0.565971 -0.574641  1.695148 -0.578582  -0.485752  -0.508974   \n",
       "4      1.829854 -0.565971 -0.574641  1.695148 -0.578582  -0.485752  -0.508974   \n",
       "\n",
       "   3 Bedroom  4 Bedroom  5 Bedroom  ...  December  February   January  \\\n",
       "0  -0.506582  -0.496991  -0.500992  ... -0.309694 -0.314631  3.137667   \n",
       "1  -0.506582  -0.496991  -0.500992  ... -0.309694 -0.314631  3.137667   \n",
       "2   1.973003  -0.496991  -0.500992  ... -0.309694 -0.314631  3.137667   \n",
       "3  -0.506582   2.011079  -0.500992  ... -0.309694 -0.314631  3.137667   \n",
       "4  -0.506582  -0.496991   1.995020  ... -0.309694 -0.314631  3.137667   \n",
       "\n",
       "       July     June     March      May  November   October  September  \n",
       "0 -0.294562 -0.29661 -0.317569 -0.29763 -0.289401 -0.288361  -0.300676  \n",
       "1 -0.294562 -0.29661 -0.317569 -0.29763 -0.289401 -0.288361  -0.300676  \n",
       "2 -0.294562 -0.29661 -0.317569 -0.29763 -0.289401 -0.288361  -0.300676  \n",
       "3 -0.294562 -0.29661 -0.317569 -0.29763 -0.289401 -0.288361  -0.300676  \n",
       "4 -0.294562 -0.29661 -0.317569 -0.29763 -0.289401 -0.288361  -0.300676  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normed_prediction_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = model.predict(normed_prediction_data).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       112.53897 , 113.675705, 117.03175 , 116.051956, 117.283035,\n",
       "       112.44595 , 114.13486 , 115.673325, 112.14942 , 113.974556,\n",
       "       120.07011 , 120.514084, 119.4424  , 115.480316, 119.27833 ,\n",
       "       116.26678 , 117.52858 , 120.750725, 116.77399 , 115.247086,\n",
       "       124.398254, 119.6649  , 125.50166 , 124.03093 , 120.83244 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save(\"Indianapolis_Housing.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indianapolis_Housing = pd.read_csv(\"Indianapolis_Housing_Cleaned.csv\")\n",
    "Indianapolis_Housing = Indianapolis_Housing.drop([\"Unnamed: 0\", \"Unnamed: 0.1\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indianapolis_Housing = Indianapolis_Housing[Indianapolis_Housing.Integer_Year != 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Integer_Year</th>\n",
       "      <th>Median Bedroom Price</th>\n",
       "      <th>Median List Price Calculated</th>\n",
       "      <th>Median List Price per SqFt Calculated</th>\n",
       "      <th>Median Market Value (Single Family)</th>\n",
       "      <th>Month</th>\n",
       "      <th>No. of Bedrooms</th>\n",
       "      <th>Year</th>\n",
       "      <th>Zip_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1997-01-31</td>\n",
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       "      <td>2015-12-31</td>\n",
       "      <td>20</td>\n",
       "      <td>141884.0</td>\n",
       "      <td>117900.000000</td>\n",
       "      <td>75.297652</td>\n",
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       "      <td>2016-12-31</td>\n",
       "      <td>21</td>\n",
       "      <td>153075.0</td>\n",
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       "      <td>2017-12-31</td>\n",
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       "      <td>2018-12-31</td>\n",
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       "      <td>5</td>\n",
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       "            Date  Integer_Year  Median Bedroom Price  \\\n",
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       "2     1999-01-31             4              122400.0   \n",
       "3     2000-01-31             5               97772.0   \n",
       "4     2001-01-31             6               96466.0   \n",
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       "4748  2018-12-31            23              179149.0   \n",
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       "\n",
       "      Median List Price Calculated  Median List Price per SqFt Calculated  \\\n",
       "0                    125900.776510                              70.844378   \n",
       "1                    141319.035095                              66.742279   \n",
       "2                    141325.632469                              71.362276   \n",
       "3                    128316.922949                              45.601351   \n",
       "4                    132249.910303                              54.573311   \n",
       "...                            ...                                    ...   \n",
       "4745                 117900.000000                              75.297652   \n",
       "4746                 128900.000000                              71.947874   \n",
       "4747                 135000.000000                              79.283366   \n",
       "4748                 149900.000000                              91.824038   \n",
       "4749                 159450.000000                              99.132039   \n",
       "\n",
       "      Median Market Value (Single Family)  Month  No. of Bedrooms  Year  \\\n",
       "0                                126400.0      1                1  1997   \n",
       "1                                151900.0      1                1  1998   \n",
       "2                                150600.0      1                1  1999   \n",
       "3                                154967.0      1                1  2000   \n",
       "4                                155566.0      1                1  2001   \n",
       "...                                   ...    ...              ...   ...   \n",
       "4745                             119662.0     12                5  2015   \n",
       "4746                             126031.0     12                5  2016   \n",
       "4747                             136849.0     12                5  2017   \n",
       "4748                             152612.0     12                5  2018   \n",
       "4749                             161163.0     12                5  2019   \n",
       "\n",
       "      Zip_Code  \n",
       "0        46220  \n",
       "1        46220  \n",
       "2        46220  \n",
       "3        46220  \n",
       "4        46220  \n",
       "...        ...  \n",
       "4745     46214  \n",
       "4746     46214  \n",
       "4747     46214  \n",
       "4748     46214  \n",
       "4749     46214  \n",
       "\n",
       "[4700 rows x 10 columns]"
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     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "Indianapolis_Housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>192.488129</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>235</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>124.398254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>119.664902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>25</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>1</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>125.501663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>25</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>1</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>124.030930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>25</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>1</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>120.832443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Integer_Year  46214  46219  46220  46227  1 Bedroom  2 Bedroom  \\\n",
       "0              25      0      0      1      0          1          0   \n",
       "1              25      0      0      1      0          0          1   \n",
       "2              25      0      0      1      0          0          0   \n",
       "3              25      0      0      1      0          0          0   \n",
       "4              25      0      0      1      0          0          0   \n",
       "..            ...    ...    ...    ...    ...        ...        ...   \n",
       "235            25      0      0      0      1          1          0   \n",
       "236            25      0      0      0      1          0          1   \n",
       "237            25      0      0      0      1          0          0   \n",
       "238            25      0      0      0      1          0          0   \n",
       "239            25      0      0      0      1          0          0   \n",
       "\n",
       "     3 Bedroom  4 Bedroom  5 Bedroom  ...  February  January  July  June  \\\n",
       "0            0          0          0  ...         0        1     0     0   \n",
       "1            0          0          0  ...         0        1     0     0   \n",
       "2            1          0          0  ...         0        1     0     0   \n",
       "3            0          1          0  ...         0        1     0     0   \n",
       "4            0          0          1  ...         0        1     0     0   \n",
       "..         ...        ...        ...  ...       ...      ...   ...   ...   \n",
       "235          0          0          0  ...         0        0     0     0   \n",
       "236          0          0          0  ...         0        0     0     0   \n",
       "237          1          0          0  ...         0        0     0     0   \n",
       "238          0          1          0  ...         0        0     0     0   \n",
       "239          0          0          1  ...         0        0     0     0   \n",
       "\n",
       "     March  May  November  October  September  \\\n",
       "0        0    0         0        0          0   \n",
       "1        0    0         0        0          0   \n",
       "2        0    0         0        0          0   \n",
       "3        0    0         0        0          0   \n",
       "4        0    0         0        0          0   \n",
       "..     ...  ...       ...      ...        ...   \n",
       "235      0    0         0        0          0   \n",
       "236      0    0         0        0          0   \n",
       "237      0    0         0        0          0   \n",
       "238      0    0         0        0          0   \n",
       "239      0    0         0        0          0   \n",
       "\n",
       "     Median List Price per SqFt Calculated  \n",
       "0                               192.221832  \n",
       "1                               192.805878  \n",
       "2                               193.027939  \n",
       "3                               193.173187  \n",
       "4                               192.488129  \n",
       "..                                     ...  \n",
       "235                             124.398254  \n",
       "236                             119.664902  \n",
       "237                             125.501663  \n",
       "238                             124.030930  \n",
       "239                             120.832443  \n",
       "\n",
       "[240 rows x 23 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PredictionData = pd.read_csv(\"Predicted_Data_Final.csv\")\n",
    "# PredictionData = PredictionData.drop([\"Unnamed: 0\", \"Unnamed: 0.1\"], axis=1)\n",
    "predictionsData[\"Median List Price per SqFt Calculated\"] = test_predictions\n",
    "predictionsData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionsData.to_csv(\"Predicted_Data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run \"UnDummied.ipynb\" here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionsData = pd.read_csv(\"Predicted_Data_Final.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_Machine = pd.concat([Indianapolis_Housing, predictionsData])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>Date</th>\n",
       "      <th>Integer_Year</th>\n",
       "      <th>Median Bedroom Price</th>\n",
       "      <th>Median List Price Calculated</th>\n",
       "      <th>Median List Price per SqFt Calculated</th>\n",
       "      <th>Median Market Value (Single Family)</th>\n",
       "      <th>Month</th>\n",
       "      <th>No. of Bedrooms</th>\n",
       "      <th>Year</th>\n",
       "      <th>Zip_Code</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997-01-31</td>\n",
       "      <td>2</td>\n",
       "      <td>112900.0</td>\n",
       "      <td>125900.776510</td>\n",
       "      <td>70.844378</td>\n",
       "      <td>126400.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>46220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998-01-31</td>\n",
       "      <td>3</td>\n",
       "      <td>118000.0</td>\n",
       "      <td>141319.035095</td>\n",
       "      <td>66.742279</td>\n",
       "      <td>151900.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>46220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-31</td>\n",
       "      <td>4</td>\n",
       "      <td>122400.0</td>\n",
       "      <td>141325.632469</td>\n",
       "      <td>71.362276</td>\n",
       "      <td>150600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>46220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-31</td>\n",
       "      <td>5</td>\n",
       "      <td>97772.0</td>\n",
       "      <td>128316.922949</td>\n",
       "      <td>45.601351</td>\n",
       "      <td>154967.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>46220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-01-31</td>\n",
       "      <td>6</td>\n",
       "      <td>96466.0</td>\n",
       "      <td>132249.910303</td>\n",
       "      <td>54.573311</td>\n",
       "      <td>155566.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>46220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124.030930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46227</td>\n",
       "      <td>238.0</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108.584900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46214</td>\n",
       "      <td>179.0</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108.538956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46219</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186.925660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46220</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.832440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46227</td>\n",
       "      <td>239.0</td>\n",
       "      <td>239.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4940 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Integer_Year  Median Bedroom Price  \\\n",
       "0    1997-01-31             2              112900.0   \n",
       "1    1998-01-31             3              118000.0   \n",
       "2    1999-01-31             4              122400.0   \n",
       "3    2000-01-31             5               97772.0   \n",
       "4    2001-01-31             6               96466.0   \n",
       "..          ...           ...                   ...   \n",
       "235         NaN            25                   NaN   \n",
       "236         NaN            25                   NaN   \n",
       "237         NaN            25                   NaN   \n",
       "238         NaN            25                   NaN   \n",
       "239         NaN            25                   NaN   \n",
       "\n",
       "     Median List Price Calculated  Median List Price per SqFt Calculated  \\\n",
       "0                   125900.776510                              70.844378   \n",
       "1                   141319.035095                              66.742279   \n",
       "2                   141325.632469                              71.362276   \n",
       "3                   128316.922949                              45.601351   \n",
       "4                   132249.910303                              54.573311   \n",
       "..                            ...                                    ...   \n",
       "235                           NaN                             124.030930   \n",
       "236                           NaN                             108.584900   \n",
       "237                           NaN                             108.538956   \n",
       "238                           NaN                             186.925660   \n",
       "239                           NaN                             120.832440   \n",
       "\n",
       "     Median Market Value (Single Family)  Month  No. of Bedrooms    Year  \\\n",
       "0                               126400.0      1                1  1997.0   \n",
       "1                               151900.0      1                1  1998.0   \n",
       "2                               150600.0      1                1  1999.0   \n",
       "3                               154967.0      1                1  2000.0   \n",
       "4                               155566.0      1                1  2001.0   \n",
       "..                                   ...    ...              ...     ...   \n",
       "235                                  NaN     12                4     NaN   \n",
       "236                                  NaN     12                5     NaN   \n",
       "237                                  NaN     12                5     NaN   \n",
       "238                                  NaN     12                5     NaN   \n",
       "239                                  NaN     12                5     NaN   \n",
       "\n",
       "     Zip_Code  Unnamed: 0  Unnamed: 0.1  \n",
       "0       46220         NaN           NaN  \n",
       "1       46220         NaN           NaN  \n",
       "2       46220         NaN           NaN  \n",
       "3       46220         NaN           NaN  \n",
       "4       46220         NaN           NaN  \n",
       "..        ...         ...           ...  \n",
       "235     46227       238.0         238.0  \n",
       "236     46214       179.0         179.0  \n",
       "237     46219       119.0         119.0  \n",
       "238     46220        59.0          59.0  \n",
       "239     46227       239.0         239.0  \n",
       "\n",
       "[4940 rows x 12 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indianapolis_Housing['Zip_Code'] = Indianapolis_Housing['Zip_Code'].map({46220: '46220', 46219: '46219', 46227: '46227', 46214: '46214'})\n",
    "# Indianapolis_Housing[\"No. of Bedrooms\"] = Indianapolis_Housing['No. of Bedrooms'].map({1: '1 Bedroom', 2: '2 Bedroom', 3: '3 Bedroom', 4: '4 Bedroom', 5: '5 Bedroom'})\n",
    "\n",
    "# Indianapolis_Housing[\"Month\"] = Indianapolis_Housing['Month'].map({1: 'January', 2: 'February', 3: 'March',\n",
    "#                                                                    4: 'April', 5: 'May', 6: 'June',\n",
    "#                                                                    7: 'July', 8: 'August', 9: 'September',\n",
    "#                                                                    10: 'October', 11: 'November', 12: 'December'})\n",
    "Data_Machine_MLPC = Data_Machine\n",
    "Data_Machine_MLPC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MonthFrame = PredictionData[[\"Integer_Year\", \"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\", \"Median Bedroom Price\"]]\n",
    "# MonthFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_Machine_MLPC.to_csv(\"Data_Machine2.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correct Year in Data_Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      Unnamed: 0 Date  Integer_Year  Median Bedroom Price  \\\n",
       "4935         235  NaN            25                   NaN   \n",
       "4936         236  NaN            25                   NaN   \n",
       "4937         237  NaN            25                   NaN   \n",
       "4938         238  NaN            25                   NaN   \n",
       "4939         239  NaN            25                   NaN   \n",
       "\n",
       "      Median List Price Calculated  Median List Price per SqFt Calculated  \\\n",
       "4935                           NaN                             124.030930   \n",
       "4936                           NaN                             108.584900   \n",
       "4937                           NaN                             108.538956   \n",
       "4938                           NaN                             186.925660   \n",
       "4939                           NaN                             120.832440   \n",
       "\n",
       "      Median Market Value (Single Family)  Month  No. of Bedrooms  Year  \\\n",
       "4935                                  NaN     12                4  2020   \n",
       "4936                                  NaN     12                5  2020   \n",
       "4937                                  NaN     12                5  2020   \n",
       "4938                                  NaN     12                5  2020   \n",
       "4939                                  NaN     12                5  2020   \n",
       "\n",
       "      Zip_Code  Unnamed: 0.1  Unnamed: 0.1.1  \n",
       "4935     46227         238.0           238.0  \n",
       "4936     46214         179.0           179.0  \n",
       "4937     46219         119.0           119.0  \n",
       "4938     46220          59.0            59.0  \n",
       "4939     46227         239.0           239.0  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_Machine = pd.read_csv(\"Data_Machine2.csv\")\n",
    "Data_Machine.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
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       "      <td>1</td>\n",
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       "      <td>46220</td>\n",
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       "      <td>239.0</td>\n",
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       "<p>4940 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "           Date  Integer_Year  Median List Price per SqFt Calculated  Month  \\\n",
       "0    1997-01-28             2                              70.844378      1   \n",
       "1    1998-01-28             3                              66.742279      1   \n",
       "2    1999-01-28             4                              71.362276      1   \n",
       "3    2000-01-28             5                              45.601351      1   \n",
       "4    2001-01-28             6                              54.573311      1   \n",
       "...         ...           ...                                    ...    ...   \n",
       "4935 2020-12-28            25                             124.030930     12   \n",
       "4936 2020-12-28            25                             108.584900     12   \n",
       "4937 2020-12-28            25                             108.538956     12   \n",
       "4938 2020-12-28            25                             186.925660     12   \n",
       "4939 2020-12-28            25                             120.832440     12   \n",
       "\n",
       "      No. of Bedrooms  Year  Zip_Code  Unnamed: 0.1.1  \n",
       "0                   1  1997     46220             NaN  \n",
       "1                   1  1998     46220             NaN  \n",
       "2                   1  1999     46220             NaN  \n",
       "3                   1  2000     46220             NaN  \n",
       "4                   1  2001     46220             NaN  \n",
       "...               ...   ...       ...             ...  \n",
       "4935                4  2020     46227           238.0  \n",
       "4936                5  2020     46214           179.0  \n",
       "4937                5  2020     46219           119.0  \n",
       "4938                5  2020     46220            59.0  \n",
       "4939                5  2020     46227           239.0  \n",
       "\n",
       "[4940 rows x 8 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_Machine[\"Date\"] = pd.to_datetime(Data_Machine.Year*10000+Data_Machine.Month*100+28,format='%Y%m%d')\n",
    "Data_Machine = Data_Machine.drop([\"Unnamed: 0\", \"Unnamed: 0.1\", \\\n",
    "                                                  \"Median Market Value (Single Family)\", \"Median Bedroom Price\",\\\n",
    "                                                  \"Median List Price Calculated\"], axis=1)\n",
    "Data_Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
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       "      <td>46220</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-01-28</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>108.538956</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>46219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4938</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>186.925660</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
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       "      <td>46220</td>\n",
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       "    <tr>\n",
       "      <th>4939</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>120.832440</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>46227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4940 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Integer_Year  Median List Price per SqFt Calculated  Month  \\\n",
       "0    1997-01-28             2                              70.844378      1   \n",
       "1    1998-01-28             3                              66.742279      1   \n",
       "2    1999-01-28             4                              71.362276      1   \n",
       "3    2000-01-28             5                              45.601351      1   \n",
       "4    2001-01-28             6                              54.573311      1   \n",
       "...         ...           ...                                    ...    ...   \n",
       "4935 2020-12-28            25                             124.030930     12   \n",
       "4936 2020-12-28            25                             108.584900     12   \n",
       "4937 2020-12-28            25                             108.538956     12   \n",
       "4938 2020-12-28            25                             186.925660     12   \n",
       "4939 2020-12-28            25                             120.832440     12   \n",
       "\n",
       "      No. of Bedrooms  Year  Zip_Code  \n",
       "0                   1  1997     46220  \n",
       "1                   1  1998     46220  \n",
       "2                   1  1999     46220  \n",
       "3                   1  2000     46220  \n",
       "4                   1  2001     46220  \n",
       "...               ...   ...       ...  \n",
       "4935                4  2020     46227  \n",
       "4936                5  2020     46214  \n",
       "4937                5  2020     46219  \n",
       "4938                5  2020     46220  \n",
       "4939                5  2020     46227  \n",
       "\n",
       "[4940 rows x 7 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_Machine = Data_Machine.drop([\"Unnamed: 0.1.1\"], axis=1)\n",
    "Data_Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataMachine[\"Median List Price per SqFt Calculated\"] = Data_Machine[\"Median List Price per SqFt Calculated\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Integer_Year</th>\n",
       "      <th>Median Bedroom Price</th>\n",
       "      <th>Month</th>\n",
       "      <th>No. of Bedrooms</th>\n",
       "      <th>Year</th>\n",
       "      <th>Zip_Code</th>\n",
       "      <th>Median List Price Calculated</th>\n",
       "      <th>Median Market Value (Single Family)</th>\n",
       "      <th>Median List Price per SqFt Calculated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1997-01-28</td>\n",
       "      <td>2</td>\n",
       "      <td>112900.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1997</td>\n",
       "      <td>46220</td>\n",
       "      <td>125900.7765</td>\n",
       "      <td>126400.00</td>\n",
       "      <td>70.844378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1998-01-28</td>\n",
       "      <td>3</td>\n",
       "      <td>118000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1998</td>\n",
       "      <td>46220</td>\n",
       "      <td>141319.0351</td>\n",
       "      <td>151900.00</td>\n",
       "      <td>66.742279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1999-01-28</td>\n",
       "      <td>4</td>\n",
       "      <td>122400.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1999</td>\n",
       "      <td>46220</td>\n",
       "      <td>141325.6325</td>\n",
       "      <td>150600.00</td>\n",
       "      <td>71.362276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2000-01-28</td>\n",
       "      <td>5</td>\n",
       "      <td>97772.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "      <td>46220</td>\n",
       "      <td>128316.9229</td>\n",
       "      <td>154967.00</td>\n",
       "      <td>45.601351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-28</td>\n",
       "      <td>6</td>\n",
       "      <td>96466.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2001</td>\n",
       "      <td>46220</td>\n",
       "      <td>132249.9103</td>\n",
       "      <td>155566.00</td>\n",
       "      <td>54.573311</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4935</th>\n",
       "      <td>4935</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>199679.34</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>46227</td>\n",
       "      <td>177148.6900</td>\n",
       "      <td>175601.23</td>\n",
       "      <td>124.030930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4936</th>\n",
       "      <td>4936</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>206903.75</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>46214</td>\n",
       "      <td>177020.4800</td>\n",
       "      <td>180276.45</td>\n",
       "      <td>108.584900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>4937</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>204817.40</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>46219</td>\n",
       "      <td>146339.4000</td>\n",
       "      <td>141159.89</td>\n",
       "      <td>108.538956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4938</th>\n",
       "      <td>4938</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>482764.25</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>46220</td>\n",
       "      <td>270187.1200</td>\n",
       "      <td>264129.25</td>\n",
       "      <td>186.925660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4939</th>\n",
       "      <td>4939</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>223116.60</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>46227</td>\n",
       "      <td>181176.6400</td>\n",
       "      <td>175851.08</td>\n",
       "      <td>120.832440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4940 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0        Date  Integer_Year  Median Bedroom Price  Month  \\\n",
       "0              0  1997-01-28             2             112900.00      1   \n",
       "1              1  1998-01-28             3             118000.00      1   \n",
       "2              2  1999-01-28             4             122400.00      1   \n",
       "3              3  2000-01-28             5              97772.00      1   \n",
       "4              4  2001-01-28             6              96466.00      1   \n",
       "...          ...         ...           ...                   ...    ...   \n",
       "4935        4935  2020-12-28            25             199679.34     12   \n",
       "4936        4936  2020-12-28            25             206903.75     12   \n",
       "4937        4937  2020-12-28            25             204817.40     12   \n",
       "4938        4938  2020-12-28            25             482764.25     12   \n",
       "4939        4939  2020-12-28            25             223116.60     12   \n",
       "\n",
       "      No. of Bedrooms  Year  Zip_Code  Median List Price Calculated  \\\n",
       "0                   1  1997     46220                   125900.7765   \n",
       "1                   1  1998     46220                   141319.0351   \n",
       "2                   1  1999     46220                   141325.6325   \n",
       "3                   1  2000     46220                   128316.9229   \n",
       "4                   1  2001     46220                   132249.9103   \n",
       "...               ...   ...       ...                           ...   \n",
       "4935                4  2020     46227                   177148.6900   \n",
       "4936                5  2020     46214                   177020.4800   \n",
       "4937                5  2020     46219                   146339.4000   \n",
       "4938                5  2020     46220                   270187.1200   \n",
       "4939                5  2020     46227                   181176.6400   \n",
       "\n",
       "      Median Market Value (Single Family)  \\\n",
       "0                               126400.00   \n",
       "1                               151900.00   \n",
       "2                               150600.00   \n",
       "3                               154967.00   \n",
       "4                               155566.00   \n",
       "...                                   ...   \n",
       "4935                            175601.23   \n",
       "4936                            180276.45   \n",
       "4937                            141159.89   \n",
       "4938                            264129.25   \n",
       "4939                            175851.08   \n",
       "\n",
       "      Median List Price per SqFt Calculated  \n",
       "0                                 70.844378  \n",
       "1                                 66.742279  \n",
       "2                                 71.362276  \n",
       "3                                 45.601351  \n",
       "4                                 54.573311  \n",
       "...                                     ...  \n",
       "4935                             124.030930  \n",
       "4936                             108.584900  \n",
       "4937                             108.538956  \n",
       "4938                             186.925660  \n",
       "4939                             120.832440  \n",
       "\n",
       "[4940 rows x 11 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataMachine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataMachine = DataMachine.drop([\"Unnamed: 0\", \"Unnamed: 0.1\"],  axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataMachine.to_csv(\"Data_MachineV2.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save(\"Indianapolis_Housing3.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Integer_Year</th>\n",
       "      <th>Median Bedroom Price</th>\n",
       "      <th>Month</th>\n",
       "      <th>No. of Bedrooms</th>\n",
       "      <th>Year</th>\n",
       "      <th>Zip_Code</th>\n",
       "      <th>Median List Price Calculated</th>\n",
       "      <th>Median Market Value (Single Family)</th>\n",
       "      <th>Median List Price per SqFt Calculated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997-01-28</td>\n",
       "      <td>2</td>\n",
       "      <td>112900.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1997</td>\n",
       "      <td>46220</td>\n",
       "      <td>125900.7765</td>\n",
       "      <td>126400.00</td>\n",
       "      <td>70.844378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998-01-28</td>\n",
       "      <td>3</td>\n",
       "      <td>118000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1998</td>\n",
       "      <td>46220</td>\n",
       "      <td>141319.0351</td>\n",
       "      <td>151900.00</td>\n",
       "      <td>66.742279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-28</td>\n",
       "      <td>4</td>\n",
       "      <td>122400.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1999</td>\n",
       "      <td>46220</td>\n",
       "      <td>141325.6325</td>\n",
       "      <td>150600.00</td>\n",
       "      <td>71.362276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-28</td>\n",
       "      <td>5</td>\n",
       "      <td>97772.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "      <td>46220</td>\n",
       "      <td>128316.9229</td>\n",
       "      <td>154967.00</td>\n",
       "      <td>45.601351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-01-28</td>\n",
       "      <td>6</td>\n",
       "      <td>96466.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2001</td>\n",
       "      <td>46220</td>\n",
       "      <td>132249.9103</td>\n",
       "      <td>155566.00</td>\n",
       "      <td>54.573311</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4935</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>199679.34</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>46227</td>\n",
       "      <td>177148.6900</td>\n",
       "      <td>175601.23</td>\n",
       "      <td>124.030930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4936</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>206903.75</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>46214</td>\n",
       "      <td>177020.4800</td>\n",
       "      <td>180276.45</td>\n",
       "      <td>108.584900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>204817.40</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>46219</td>\n",
       "      <td>146339.4000</td>\n",
       "      <td>141159.89</td>\n",
       "      <td>108.538956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4938</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>482764.25</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>46220</td>\n",
       "      <td>270187.1200</td>\n",
       "      <td>264129.25</td>\n",
       "      <td>186.925660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4939</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>25</td>\n",
       "      <td>223116.60</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "      <td>46227</td>\n",
       "      <td>181176.6400</td>\n",
       "      <td>175851.08</td>\n",
       "      <td>120.832440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Integer_Year  Median Bedroom Price  Month  No. of Bedrooms  \\\n",
       "0     1997-01-28             2             112900.00      1                1   \n",
       "1     1998-01-28             3             118000.00      1                1   \n",
       "2     1999-01-28             4             122400.00      1                1   \n",
       "3     2000-01-28             5              97772.00      1                1   \n",
       "4     2001-01-28             6              96466.00      1                1   \n",
       "...          ...           ...                   ...    ...              ...   \n",
       "4935  2020-12-28            25             199679.34     12                4   \n",
       "4936  2020-12-28            25             206903.75     12                5   \n",
       "4937  2020-12-28            25             204817.40     12                5   \n",
       "4938  2020-12-28            25             482764.25     12                5   \n",
       "4939  2020-12-28            25             223116.60     12                5   \n",
       "\n",
       "      Year  Zip_Code  Median List Price Calculated  \\\n",
       "0     1997     46220                   125900.7765   \n",
       "1     1998     46220                   141319.0351   \n",
       "2     1999     46220                   141325.6325   \n",
       "3     2000     46220                   128316.9229   \n",
       "4     2001     46220                   132249.9103   \n",
       "...    ...       ...                           ...   \n",
       "4935  2020     46227                   177148.6900   \n",
       "4936  2020     46214                   177020.4800   \n",
       "4937  2020     46219                   146339.4000   \n",
       "4938  2020     46220                   270187.1200   \n",
       "4939  2020     46227                   181176.6400   \n",
       "\n",
       "      Median Market Value (Single Family)  \\\n",
       "0                               126400.00   \n",
       "1                               151900.00   \n",
       "2                               150600.00   \n",
       "3                               154967.00   \n",
       "4                               155566.00   \n",
       "...                                   ...   \n",
       "4935                            175601.23   \n",
       "4936                            180276.45   \n",
       "4937                            141159.89   \n",
       "4938                            264129.25   \n",
       "4939                            175851.08   \n",
       "\n",
       "      Median List Price per SqFt Calculated  \n",
       "0                                 70.844378  \n",
       "1                                 66.742279  \n",
       "2                                 71.362276  \n",
       "3                                 45.601351  \n",
       "4                                 54.573311  \n",
       "...                                     ...  \n",
       "4935                             124.030930  \n",
       "4936                             108.584900  \n",
       "4937                             108.538956  \n",
       "4938                             186.925660  \n",
       "4939                             120.832440  \n",
       "\n",
       "[4940 rows x 10 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataMachine = pd.read_csv(\"Data_MachineV2.csv\")\n",
    "DataMachine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
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 },
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