4/ds_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Properati: prediction of properties sales prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We develop a model that predicts the sale price of properties according to its attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the dataset and explore some of its statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.pipeline import Pipeline \n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>created_on</th>\n",
       "      <th>operation</th>\n",
       "      <th>property_type</th>\n",
       "      <th>place_with_parent_names</th>\n",
       "      <th>lat-lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>price_aprox_usd</th>\n",
       "      <th>surface_total_in_m2</th>\n",
       "      <th>surface_covered_in_m2</th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "      <th>floor</th>\n",
       "      <th>rooms</th>\n",
       "      <th>expenses</th>\n",
       "      <th>properati_url</th>\n",
       "      <th>barrio</th>\n",
       "      <th>comuna</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10728</td>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>sell</td>\n",
       "      <td>PH</td>\n",
       "      <td>|Argentina|Capital Federal|Parque Chacabuco|</td>\n",
       "      <td>-34.630152,-58.4317885</td>\n",
       "      <td>-34.630152</td>\n",
       "      <td>-58.431789</td>\n",
       "      <td>69500.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2044.117647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.properati.com.ar/1gohv_venta_ph_pa...</td>\n",
       "      <td>PARQUE CHACABUCO</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9181</td>\n",
       "      <td>2017-10-11</td>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>|Argentina|Capital Federal|Boedo|</td>\n",
       "      <td>-34.6242158,-58.4110815</td>\n",
       "      <td>-34.624216</td>\n",
       "      <td>-58.411082</td>\n",
       "      <td>153000.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1961.538462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>https://www.properati.com.ar/1g5qv_venta_depar...</td>\n",
       "      <td>SAN CRISTOBAL</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>2017-09-16</td>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>|Argentina|Capital Federal|Las Cañitas|</td>\n",
       "      <td>-34.5670326,-58.4330181</td>\n",
       "      <td>-34.567033</td>\n",
       "      <td>-58.433018</td>\n",
       "      <td>445000.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>3090.277778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.properati.com.ar/1ewc9_venta_depar...</td>\n",
       "      <td>PALERMO</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10905</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>|Argentina|Capital Federal|Caballito|</td>\n",
       "      <td>-34.628187,-58.4295863</td>\n",
       "      <td>-34.628187</td>\n",
       "      <td>-58.429586</td>\n",
       "      <td>209000.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3073.529412</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.properati.com.ar/1kb4c_venta_depar...</td>\n",
       "      <td>PARQUE CHACABUCO</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17590</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>sell</td>\n",
       "      <td>house</td>\n",
       "      <td>|Argentina|Capital Federal|Villa Lugano|</td>\n",
       "      <td>-34.6817313,-58.4751166</td>\n",
       "      <td>-34.681731</td>\n",
       "      <td>-58.475117</td>\n",
       "      <td>147000.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>735.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.properati.com.ar/1jctf_venta_casa_...</td>\n",
       "      <td>VILLA LUGANO</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       created_on operation property_type  \\\n",
       "10728  2017-10-17      sell            PH   \n",
       "9181   2017-10-11      sell     apartment   \n",
       "240    2017-09-16      sell     apartment   \n",
       "10905  2018-01-31      sell     apartment   \n",
       "17590  2018-01-08      sell         house   \n",
       "\n",
       "                            place_with_parent_names                  lat-lon  \\\n",
       "10728  |Argentina|Capital Federal|Parque Chacabuco|   -34.630152,-58.4317885   \n",
       "9181              |Argentina|Capital Federal|Boedo|  -34.6242158,-58.4110815   \n",
       "240         |Argentina|Capital Federal|Las Cañitas|  -34.5670326,-58.4330181   \n",
       "10905         |Argentina|Capital Federal|Caballito|   -34.628187,-58.4295863   \n",
       "17590      |Argentina|Capital Federal|Villa Lugano|  -34.6817313,-58.4751166   \n",
       "\n",
       "             lat        lon  price_aprox_usd  surface_total_in_m2  \\\n",
       "10728 -34.630152 -58.431789          69500.0                 34.0   \n",
       "9181  -34.624216 -58.411082         153000.0                 78.0   \n",
       "240   -34.567033 -58.433018         445000.0                144.0   \n",
       "10905 -34.628187 -58.429586         209000.0                 68.0   \n",
       "17590 -34.681731 -58.475117         147000.0                200.0   \n",
       "\n",
       "       surface_covered_in_m2  price_usd_per_m2  floor  rooms  expenses  \\\n",
       "10728                   32.0       2044.117647    NaN    NaN       NaN   \n",
       "9181                     NaN       1961.538462    NaN    4.0    2600.0   \n",
       "240                    144.0       3090.277778    NaN    5.0       NaN   \n",
       "10905                   64.0       3073.529412    4.0    NaN       NaN   \n",
       "17590                  200.0        735.000000    NaN    5.0       NaN   \n",
       "\n",
       "                                           properati_url            barrio  \\\n",
       "10728  https://www.properati.com.ar/1gohv_venta_ph_pa...  PARQUE CHACABUCO   \n",
       "9181   https://www.properati.com.ar/1g5qv_venta_depar...     SAN CRISTOBAL   \n",
       "240    https://www.properati.com.ar/1ewc9_venta_depar...           PALERMO   \n",
       "10905  https://www.properati.com.ar/1kb4c_venta_depar...  PARQUE CHACABUCO   \n",
       "17590  https://www.properati.com.ar/1jctf_venta_casa_...      VILLA LUGANO   \n",
       "\n",
       "       comuna  \n",
       "10728     7.0  \n",
       "9181      3.0  \n",
       "240      14.0  \n",
       "10905     7.0  \n",
       "17590     8.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_table('datos_properati.csv', sep=',')\n",
    "data = shuffle(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18979 entries, 10728 to 17720\n",
      "Data columns (total 17 columns):\n",
      "created_on                 18979 non-null object\n",
      "operation                  18979 non-null object\n",
      "property_type              18979 non-null object\n",
      "place_with_parent_names    18979 non-null object\n",
      "lat-lon                    18979 non-null object\n",
      "lat                        18979 non-null float64\n",
      "lon                        18979 non-null float64\n",
      "price_aprox_usd            17445 non-null float64\n",
      "surface_total_in_m2        16586 non-null float64\n",
      "surface_covered_in_m2      16876 non-null float64\n",
      "price_usd_per_m2           16037 non-null float64\n",
      "floor                      2756 non-null float64\n",
      "rooms                      13517 non-null float64\n",
      "expenses                   3815 non-null float64\n",
      "properati_url              18979 non-null object\n",
      "barrio                     18979 non-null object\n",
      "comuna                     18979 non-null float64\n",
      "dtypes: float64(10), object(7)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>price_aprox_usd</th>\n",
       "      <th>surface_total_in_m2</th>\n",
       "      <th>surface_covered_in_m2</th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "      <th>floor</th>\n",
       "      <th>rooms</th>\n",
       "      <th>expenses</th>\n",
       "      <th>comuna</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>18979.000000</td>\n",
       "      <td>18979.000000</td>\n",
       "      <td>1.744500e+04</td>\n",
       "      <td>16586.000000</td>\n",
       "      <td>16876.000000</td>\n",
       "      <td>16037.000000</td>\n",
       "      <td>2756.000000</td>\n",
       "      <td>13517.000000</td>\n",
       "      <td>3.815000e+03</td>\n",
       "      <td>18979.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-34.600838</td>\n",
       "      <td>-58.435161</td>\n",
       "      <td>2.963559e+05</td>\n",
       "      <td>142.784758</td>\n",
       "      <td>127.491171</td>\n",
       "      <td>2760.408525</td>\n",
       "      <td>11.040639</td>\n",
       "      <td>3.033661</td>\n",
       "      <td>4.884536e+03</td>\n",
       "      <td>7.981137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.026372</td>\n",
       "      <td>0.040787</td>\n",
       "      <td>4.108344e+05</td>\n",
       "      <td>1731.936727</td>\n",
       "      <td>1524.167396</td>\n",
       "      <td>1940.153216</td>\n",
       "      <td>53.448795</td>\n",
       "      <td>1.778162</td>\n",
       "      <td>3.024617e+04</td>\n",
       "      <td>5.007441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-34.695717</td>\n",
       "      <td>-58.530822</td>\n",
       "      <td>5.043300e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-34.619827</td>\n",
       "      <td>-58.464637</td>\n",
       "      <td>1.200000e+05</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1984.126984</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.600000e+03</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-34.601014</td>\n",
       "      <td>-58.434293</td>\n",
       "      <td>1.800000e+05</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2545.454545</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.800000e+03</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>-34.582748</td>\n",
       "      <td>-58.401513</td>\n",
       "      <td>3.200000e+05</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>3162.790698</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>-34.534315</td>\n",
       "      <td>-58.353227</td>\n",
       "      <td>1.350000e+07</td>\n",
       "      <td>184000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>142450.141026</td>\n",
       "      <td>904.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.843611e+06</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                lat           lon  price_aprox_usd  surface_total_in_m2  \\\n",
       "count  18979.000000  18979.000000     1.744500e+04         16586.000000   \n",
       "mean     -34.600838    -58.435161     2.963559e+05           142.784758   \n",
       "std        0.026372      0.040787     4.108344e+05          1731.936727   \n",
       "min      -34.695717    -58.530822     5.043300e+03             0.000000   \n",
       "25%      -34.619827    -58.464637     1.200000e+05            48.000000   \n",
       "50%      -34.601014    -58.434293     1.800000e+05            75.000000   \n",
       "75%      -34.582748    -58.401513     3.200000e+05           134.000000   \n",
       "max      -34.534315    -58.353227     1.350000e+07        184000.000000   \n",
       "\n",
       "       surface_covered_in_m2  price_usd_per_m2        floor         rooms  \\\n",
       "count           16876.000000      16037.000000  2756.000000  13517.000000   \n",
       "mean              127.491171       2760.408525    11.040639      3.033661   \n",
       "std              1524.167396       1940.153216    53.448795      1.778162   \n",
       "min                 1.000000          0.958333     1.000000      1.000000   \n",
       "25%                43.000000       1984.126984     2.000000      2.000000   \n",
       "50%                67.000000       2545.454545     4.000000      3.000000   \n",
       "75%               117.000000       3162.790698     8.000000      4.000000   \n",
       "max            150000.000000     142450.141026   904.000000     38.000000   \n",
       "\n",
       "           expenses        comuna  \n",
       "count  3.815000e+03  18979.000000  \n",
       "mean   4.884536e+03      7.981137  \n",
       "std    3.024617e+04      5.007441  \n",
       "min    1.000000e+00      1.000000  \n",
       "25%    1.600000e+03      3.000000  \n",
       "50%    2.800000e+03      7.000000  \n",
       "75%    5.000000e+03     13.000000  \n",
       "max    1.843611e+06     15.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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i6snnpUcDWEQkEg3gjN7e05jZtB8am9ZMqKelpWWab/HEeWoB9RRRTz4vPdobWsbAwCXefO/0vxFjontcW7hw4TSXTJynFlBPEfXk89KjR8AZy5Yti51QwcuLxcFXC6iniHryeenRABYRiUQDOMPbW5GXLFkSOyHlqQXUU0Q9+bz06I0YGWYWZmob8ETW+9jYGLNm+fgd6akF1FNEPflmukdvxJiAxsbG2AkVHnjggdgJKU8toJ4i6snnpUcDWEQkEg3gjLGxsdgJFWbP9vMqQU8toJ4i6snnpUfbgDO8bQMWkScHbQOegOXLl8dOqNDd3R07IeWpBdRTRD35vPRoAGfMnTs3dkKF8+fPx05IeWoB9RRRTz4vPRrAIiKRaABnnDp1KnZChfb2ie0zYiZ4agH1FFFPPi89GsAZ8+fPj51QwdMvBE8toJ4i6snnpUcDOMPLHpLKjh49Gjsh5akF1FNEPfm89GgAi4hEogGc4eWjqsvWrVsXOyHlqQXUU0Q9+bz0aABnePtQzurq6tgJKU8toJ4i6snnpUcDOGPp0qWxEyrs3bs3dkLKUwuop4h68nnp0QAWEYlEAzjj0qVLsRMqeHprtKcWUE8R9eTz0qOd8WTMmjUrvOmPvj3t1zPRnfGMjIy42WuTpxZQTxH15JvpniflznjMbNTMHjKzvWb2GTObn5zeP+5yrzGzPyta3urVq6cr9Zrs3LkzdkLKUwuop4h68nnpuaEHMDAQQrg1hLAZGALeGDtIRGSibvQBnPVN4KnXswC9DO3qPLWAeoqoJ5+XHj8bZa6Dmc0Gng/8S3JSjZk9lLlIPfDFq3zvXcBdAPPmzaNjTek94kfP1dI/NIdbGkpvznjsUjUHTy/mmWt6ARgdM7qOLmfTij5qq4cBePjRepYuuMzKxaUn83r6FjI0UsX65Y8BcPbiPI6cXch9993H9u3bqa6uZuvWrXR1ddHfX9pq0tHRwbFjxzh+/DgAJ06coKqqiv379wOwYsUK1q5dy65du0o3tKaGjo4OOjs700913rp1K0eOHOHkyZMAbNy4kdHRUQ4cOACUNrU0NjbS2dkJQG1tLe3t7ezatYvBwUEAtm3bxsGDB+ntLd3ezZs3c/z4cQ4dOgRAU1MTDQ0NdHWVdmC/aNEi2tra2LlzJyMjIwDcdttt7Nu3j7NnzwLQ2trKhQsXOHz4MADNzc3U19en+2atq6ujtbWVHTt2EELAzLj99tvZs2dP+iaZtrY2+vr6GBwcZPv27bS0tLBw4UL27NkDlD7tdtOmTelnfs2ePZtt27bR3d2d7oKwvb2dU6dOpW9HXbduHdXV1elLk5YvX8769evTP1Mncj9t2LCB3t5eF/fT4OBgun5i3089PT0AtLS0cO7cORf3U1VVVbp+Zup+upob+kk4MxsFHk6+/CbwmyGEITPrDyHUZi73GqA9hPCrectbsWJFeOnb/nnaessm+iRcV1eXm702eWoB9RRRT76Z7rnak3A3+iPggRDCrVO1sDlz5kzVoqZE+be4B55aQD1F1JPPS8+TaRuwiMgNRQM4o7yNx4uOjo7YCSlPLaCeIurJ56Xnhh7A2e28eaeHED5StP0XShvPPTl27FjshJSnFlBPEfXk89JzQw/gqeZtAJefufXAUwuop4h68nnp0QAWEYlEAzijr68vdkKFDRs2xE5IeWoB9RRRTz4vPRrAGd5eE11VVRU7IeWpBdRTRD35vPRoAGcsWbIkdkKF8ruqPPDUAuopop58Xno0gEVEItEAzrh48WLshAorVqyInZDy1ALqKaKefF56NIAzHn/88dgJFdauXRs7IeWpBdRTRD35vPRoAGesWrUqdkKF8h6aPPDUAuopop58Xno0gEVEItEAzijvH9WLmpqa2AkpTy2gniLqyeel54beH/BUM7Pw5nu7pv16Jro/YBF5cnhSfijnVPPyzGhZeQ/7HnhqAfUUUU8+Lz0awBmePjYbSD8SxQNPLaCeIurJ56VHA1hEJBJtA86oqqoKb3zfg9N+PRPdBjw4OOjm01s9tYB6iqgn30z3aBvwBCxevDh2QoUjR47ETkh5agH1FFFPPi89GsAZCxYsiJ1QwdNHJHlqAfUUUU8+Lz0awCIikWgAZ5w9ezZ2QoWNGzfGTkh5agH1FFFPPi89vl53FdmcOXP40N0/sp18yq1uvHlClxsdHZ3mkonz1ALqKaKefF569Ag4Y8WKFYQQpv1w7OgPJtRz4MCBab7FE+epBdRTRD35vPRoAIuIRKIBnDF37tzYCRVWr14dOyHlqQXUU0Q9+bz0aABnzJkzJ3ZChcbGxtgJKU8toJ4i6snnpUcDOMPbRxJ52WEI+GoB9RRRTz4vPRrAIiKRaABnVFVVxU6oUFtbGzsh5akF1FNEPfm89GhnPBnt7e2hq2v6d8guIj9etDOeCfC2DdjLBweCrxZQTxH15PPSowGcMTY2FjuhwuDgYOyElKcWUE8R9eTz0qMBLCISibYBZ2zZsiXs3r07dkZqZGTEzcckeWoB9RRRT76Z7tE24Ak4evQoZjajh8amNVftOXjw4Aze+nyeWkA9RdSTz0uPn19JDlRXVzMTH0uflbf3td7eXje7zfPUAuopop58Xnr0CFhEJBIN4IwzZ87ETqiwefPm2AkpTy2gniLqyeelRwM4w9s74by8VAZ8tYB6iqgnn5ceDeCMurq62AkVDh06FDsh5akF1FNEPfm89GgAi4hEogGcceHChdgJFZqammInpDy1gHqKqCeflx4N4IxLly7FTqjQ0NAQOyHlqQXUU0Q9+bz0aABneLlTyjztmc1TC6iniHryeenRABYRiUQDOGNoaCh2QoVFixbFTkh5agH1FFFPPi892hlPhpmFGG9F1n0g8uSmnfFMwKpVq2InVNi5c2fshJSnFlBPEfXk89KjAZwxa5av1TEyMhI7IeWpBdRTRD35vPT4mjgiIj9GtA04w9s24LGxMTePyj21gHqKqCffTPdoG/AELFmyJHZChX379sVOSHlqAfUUUU8+Lz0awBk1NTWxEyqcPXs2dkLKUwuop4h68nnp0QAWEYlEAzjj9OnTsRMqtLa2xk5IeWoB9RRRTz4vPRrAGXPnzo2dUMHT3tk8tYB6iqgnn5ceDeCMxYsXx06ocPjw4dgJKU8toJ4i6snnpSfaADazZjPbG+v6RURi0yPgjPPnz8dOqNDc3Bw7IeWpBdRTRD35vPTEHsBVZvZXZrbPzL5iZjVmdquZfcvMvmtm/2BmdQBmtt3M2pPjS82sJzm+ycweNLOHku9Zl5z+qszpf2FmhZ+4efny5Wm8qZNXX18fOyHlqQXUU0Q9+bz0zI58/euAV4QQ3mBmnwZeCvw28GshhB1m9h7g94G35izjjcCfhBA+bmZzKQ31W4CXA88KIQyb2YeAVwIfG//NZnYXcBeU7pSONacAOHqulv6hOdzScA6Axy5Vc/D0Yp65pheA0TGj6+hyNq3oo7Z6GICHH61n6YLLrFxc+mSNnr6FDI1UsX75YwCcvTiPI2cX0n5z6dUWw6Ol339dXV309/cD0NHRwbFjxzh+/Dj9/f1s2bKFqqoq9u/fD8CKFStYu3Ytu3btAkqvXe7o6KCzs5OBgQEAtm7dypEjRzh58iQAGzduZHR0lAMHDgCwevVqGhsb6ezsBKC2tpb29nZ27dqVflrstm3bOHjwIL29pds7MjLCLbfckn6YYVNTEw0NDemOrRctWkRbWxs7d+5M32d/2223sW/fvvQ1l62trVy4cCHd/tbc3Ex9fT3d3d1A6UNRW1tb2bFjByEEzIzbb7+dPXv2cO5c6X5oa2ujr6+PvXv3UltbS0tLCwsXLmTPnj1A6c00mzZt4oEHHgBg9uzZbNu2je7u7vQvnPb2dk6dOsXRo0cBWLduHdXV1ezdW9oitnz5ctavX5/usKW6upqtW7de9X4CGB4eprW1Nfr9tHnzZgYHB/nOd75DbW1t9Pupp6cHKO3q9Sd+4iei308bNmygqqqKBx98kNra2hm7n64m2luRzawZ+GoIofyI9W5gHnBnCOHm5LSnAJ8JIbSZ2Xbgt0IIXWa2FOgKITSb2S8Bb6c0XD8fQjhkZr8K/C7Qm1xdDfDJEMK78pqamprCC3/tC1N8S/PlvRV5+/bt3HHHHTPaczWeWkA9RdSTb6Z7rvZW5NiPgLO/HkaBm3IuO8ITm0zmlU8MIXzCzDqBnwPuN7PXAwZ8NIRwz2RivG2CqKuri52Q8tQC6iminnxeemJvAx7vceCcmf108vUvAzuS4z3AluT4y8rfYGYtwOEQwgeBLwL/Afg68DIzW55cpt7M1hRd+ZkzZ6biNkwZLy8WB18toJ4i6snnpcfbAAZ4NfB+M/sucCvwnuT0+4A3mdm/AUszl385sNfMHgKeBnwshLAfeAfwlWQ5XwVWFl3x6tWrp+5WTIEdO3YUX2iGeGoB9RRRTz4vPdE2QYQQeoDNma/vy5z9k1e4/PcpPbote0dy+vuA913h8p8CPjWZJjObzMWnnaddhXpqAfUUUU8+Lz0eHwFH4+VOKfP0C8FTC6iniHryeenRDtkzvO2QXUSeHLRD9glYunRp8YVmUPk1kx54agH1FFFPPi89GsAZ8+bNK77QDCq/qN0DTy2gniLqyeelRwNYRCQSDeCM8ts5vWhra4udkPLUAuopop58Xno0gDO8bYLo6+uLnZDy1ALqKaKefF56NIAzFi1aFDuhQnlHJh54agH1FFFPPi89GsAiIpFoAGc8/vjjsRMqtLS0xE5IeWoB9RRRTz4vPRrAGUNDQ7ETKixcuDB2QspTC6iniHryeenRAM5YtmxZ7IQKXl4sDr5aQD1F1JPPS48GsIhIJBrAGeWPIPFiyZIlsRNSnlpAPUXUk89Lj3bGk+FtZzxjY2PMmuXjd6SnFlBPEfXkm+ke7YxnAhobG2MnVCh/YKEHnlpAPUXUk89LjwawiEgkGsAZY2NjsRMqzJ4d+zNTn+CpBdRTRD35vPRoG3CGt23AIvLkoG3AE7B8+fLYCRW6u7tjJ6Q8tYB6iqgnn5ceH4/DnaiuruZDd//IL6lptbrx5qued/78+RksyeepBdRTRD35vPRoAGfU1NRoc4CIzBhtA85oa2sLXv40Aejv76e2tjZ2BuCrBdRTRD35ZrpH24AnYHh4OHZChVOnTsVOSHlqAfUUUU8+Lz0awBne9oZ29OjR2AkpTy2gniLqyeelRwNYRCQSDeAMb58Jt27dutgJKU8toJ4i6snnpUcDOMPMYidUqK6ujp2Q8tQC6iminnxeejSAM7ztjnLv3r2xE1KeWkA9RdSTz0uPBrCISCQawBlz5syJnVDB01ujPbWAeoqoJ5+XHr0RI2PLli1h9+7dsTNSIyMjbvba5KkF1FNEPflmukdvxJiA/v7+2AkVdu7cGTsh5akF1FNEPfm89GgAZwwMDGBmbg67d++O3uCxRT3qidXT2LRmSmeONkFkrFq1Krzkrf8UOyPV1nia7mPLYmcAvlpAPUXUk+9ae651/912lU0QGsAZFmGH7CJy45jqAaxNEBkNDQ2xEyo8feXZ2AkpTy2gniLqyeelRwM4w9vL0ObPHYmdkPLUAuopop58Xno0gEVEItEAzjh58mTshAoPHV8SOyHlqQXUU0Q9+bz0aABneNpjP8DKRX72TeGpBdRTRD35vPRoAGd4G8ANCy/FTkh5agH1FFFPPi89GsAiIpFoAGf09fXFTqhw+Myi2AkpTy2gniLqyeelRwM4w9ubUsaCnx3Ee2oB9RRRTz4vPRrAGUuW+HhmtOypyx6PnZDy1ALqKaKefF56NIBFRCLRAM64ePFi7IQKp/trYiekPLWAeoqoJ5+XHg3gjMcf9/FnSdnRxxbETkh5agH1FFFPPi89GsAZq1atip1Qoa3xTOyElKcWUE8R9eTz0qMBLCISiQZwxsiIjz0klQ0OV8VOSHlqAfUUUU8+Lz3aIXuGdsguInm0Q/ZptGLFitgJFW5d5WM7FfhqAfUUUU8+Lz0awBmePjYboHrOaOyElKcWUE8R9eTz0qMBLCISSbQBbGZ3mNlPebqeRx99dLpzJqX72NLYCSlPLaCeIurJ56UnygA2s9nAHf81VRUAAAx5SURBVMC0D+DJXM/ixYunt2SSmm7y8848Ty2gniLqyeelZ0ID2My+YGa7zWyfmd2VnNZvZv/DzLrN7Otmtiw5/Q1m9m0z22NmnzOz+cnpHzGz/2lm3wA+BbwR+A0ze8jMfjo5/8/N7BtmdtjMbjezvzaz75nZRzItP2tmu5Lr/YyZ1San95jZu5PTHzazp5lZ8/jrybudCxb4eHdM2bJaH3vtB18toJ4i6snnpWeizzq9LoTQZ2Y1wLfN7HPAAqA7hPCbZvZO4PeBXwU+H0L4KwAz+0PgTuBPk+WsB54TQhg1s3cB/SGE+5LL3gnUAc8GXgj8E/As4PXJdd4KHAPekSzjopndDbwNeE+y/DMhhDYzezPwWyGE15vZh7PXM17yC+UugPr6ejrWnALg6Lla+ofmcEvDOQAeu1TNwdOLeeaaXgBGx4yuo8vZtKKP2uphAB5+tJ6lCy6zcnFpb/s9fQsZGqli/fLHADh7cR5Hzi6k/ebTAAyPzqL72DKevvJs+imtDx1fwspFAzQsvMSCuSMsWzDAWLB0702n+2s4+tiC9J08g8NVPPToUm5ddSZ9YqH72FKabrqY/id75PRiZlmgZel5AE5dmM+J8zXcurr00dyXhmbz8IkltDWeZk7VGABdP1zG2iUXWLLgMgBVNkbDwks0118A4MTj8zlzcR5PX1Xah3L/4Bz2naynvamXqlmll+k8+IPlrF/2ODfNHwTge6fqqJ07TFNdPwDHHlvA4wPVbFpZWsb5y3P53qk6Om4+BQYE6PxhA7c0nGPRvCEA9p2oZ3HNIAvmjtCx5pSL+wlg9qwxlsy/HP1+Oth7E3Nnj6brJ/b91Jg80pwza4xF84ai30+HzyxiLFi6fiZ7P7Xcdx+9vb2Mjo5y4MABAFavXk1jYyOdnZ1A6ZN12tvb2bVrF4ODpXV6NRN6HXAyLF+SfNkMPBf4V6A6hDBiZi2UBu+tZnY78IfATUAtcH8I4Y3Jo9hvhBA+mllmdgB/BPhqCOHjyfLuDyGsS877GPB5YAT4CKVBDDAX2BVCuNPMeoBnhRCOm1kH8N4QwnPGX0+e+fPnh9e+65uF62OmLJl/mbOX5sXOAHy1gHqKqCfftfZM9euACx8Bm9kdwHOArSGES2a2HbhSebnqI8CLQwh7zOw1lLbBlhVteCn/uhjLHC9/PRsYpTSkX1Hw/aNM/NF9yszHTprLZpmfN8l4agH1FFFPPi89E9kGvBg4lwzfpwE/mfnelyXHfwnYmRxfCJwwsznAK3OWeyG57GR8C3iWmT0VwMzmm9n6gu+Z8PXU19dPMmd6lf8U9cBTC6iniHryeemZyAD+F2C2mX0X+ANKQxBKj2Y3mdluSttty9thfw/oBL4KfD9nuf8EvGQiT46VhRBOA68BPpn0fAt4WsG3Tfp6RERmwjXvC8LM+kMIvj7H/TrV1dWFX7rna7EzUs31F+jpm+wfCdPDUwuop4h68l1rj/YFMY36+/tjJ1Q4cd7HXvvBVwuop4h68nnpueYB/GR79AsOd8aTvPzIA08toJ4i6snnpUePgEVEItEAzhgeHo6dUOHSkJ+9s3lqAfUUUU8+Lz3aIXuGdsguInn0JNw0WrlyZeyECm2Np2MnpDy1gHqKqCeflx4N4IyqKh+fE1VWfr+/B55aQD1F1JPPS48GsIhIJNoGnDFr1qzwpj/6duyMVJWNMRp8/I701ALqKaKefNfao23A06iuri52QoW1Sy7ETkh5agH1FFFPPi89GsAZ8+fPj51QobyPVw88tYB6iqgnn5ceDWARkUg0gDPOnDkTO6HCwd6bYiekPLWAeoqoJ5+XHg3gDG8vQ5s7ezR2QspTC6iniHryeenRAM7w9iRc+fPXPPDUAuopop58Xno0gEVEItEAzrhwwcdvxbITj/t5VYanFlBPEfXk89KjAZxx6dKl2AkVzlz08ymynlpAPUXUk89LjwZwRkNDQ+yECk9f1Rc7IeWpBdRTRD35vPRoAIuIxBJC0CE5rFixIgBuDm95y1uiN3hsUY96YvWsbrw5XAug60ozRzvjyWhvbw9dXdohu4hMLe2MZwK8fSryzp07YyekPLWAeoqoJ5+XHg3gDG9/DYyMjMROSHlqAfUUUU8+Lz0awCIikWgbcIa3bcBjY2PMmuXjd6SnFlBPEfXkm+kebQOegIGBgdgJFfbt2xc7IeWpBdRTRD35vPRoAGd42S5Udvbs2dgJKU8toJ4i6snnpUcDWEQkEg3gDG8fSdTa2ho7IeWpBdRTRD35vPRoAGeMjvrYSXOZp72zeWoB9RRRTz4vPRrAGYODg7ETKhw+fDh2QspTC6iniHryeenRABYRiUSvA84wswvAgdgdGUsBL58U6qkF1FNEPflmumdNCGHZ+BNnz2DAjeDAlV4sHYuZdXnp8dQC6iminnxeerQJQkQkEg1gEZFINIAr/WXsgHE89XhqAfUUUU8+Fz16Ek5EJBI9AhYRiUQDGDCz55nZATN7xMx+Zxqvp8nMvmFm3zOzfWb268np9Wb2VTM7lPxbl5xuZvbBpOu7ZtaWWdark8sfMrNXX0dTlZl9x8y+lHy91sw6k+V+yszmJqdXJ18/kpzfnFnGPcnpB8zsudfakizrJjP7rJl9P1lPW2OtHzP7jeR+2mtmnzSzeTO9fszsr82s18z2Zk6bsvVhZlvM7OHkez5oZjbJlvcn99V3zewfzOymott9tZ+3q63byfRkzvstMwtmtnQm1s01i/1BmLEPQBXw70ALMBfYA2ycputaCbQlxxcCB4GNwB8Dv5Oc/jvAvcnxFwBfBgz4SaAzOb0eOJz8W5ccr7vGprcBnwC+lHz9aeAXk+MfBt6UHH8z8OHk+C8Cn0qOb0zWWTWwNlmXVdexjj4KvD45Phe4Kcb6AVYDR4CazHp5zUyvH+A2oA3YmzltytYH8CCwNfmeLwPPn2TLzwKzk+P3ZlqueLvJ+Xm72rqdTE9yehNwP/ADYOlMrJtr/v8+1Qu80Q7JCr4/8/U9wD0zdN3/CPxHSm/+WJmctpLS65EB/gJ4RebyB5LzXwH8Reb0istN4vobga8Dzwa+lPxHO5P5gUrXTfIfemtyfHZyORu/vrKXu4aeRZSGno07fcbXD6UBfDT5wZydrJ/nxlg/QDOVQ29K1kdy3vczp1dcbiIt4857CfDxK/0clW83V/l5y/u/N9ke4LNAK9DDEwN42tfNtRy0CeKJH7SyY8lp0yr5E/UZQCfQEEI4AZD8u7ygbaqaPwD8NjCWfL0EeCyEUN4xcna56XUm5z+eXH4q118LcBr4GyttFvk/ZraACOsnhHAcuA/4IXCC0u3dTdz1UzZV62N1cnyq2l5H6ZHitbTk/d+bMDN7IXA8hLBn3Fmx180VaQCXfvOON60vDTGzWuBzwFtDCOfzLnqF00LO6ZNp+HmgN4SwewLXN60tGbMp/Un55yGEZwAXKf2JfTXTuX7qgBdR+vN5FbAAeH7Ocmdi/RSZbMOUtZnZ24ER4OOxWsxsPvB24J1XOnumeyZCA7j0m60p83Uj8Oh0XZmZzaE0fD8eQvh8cvIpM1uZnL8S6C1om4rmZwEvNLMe4O8pbYb4AHCTmZXfop5dbnqdyfmLgb4paik7BhwLIXQmX3+W0kCOsX6eAxwJIZwOIQwDnwd+irjrp2yq1sex5Ph1tSVPXP088MqQ/L1+DS1nuPq6nainUPqFuSf5f90IdJvZimvomZJ1U2iqt2ncaAdKj7oOJ3dc+UmBTdN0XQZ8DPjAuNPfT+WTKn+cHP85Kp84eDA5vZ7SttK65HAEqL+Orjt44km4z1D5RMibk+O/QuWTTJ9Ojm+i8smWw1zfk3DfBDYkx9+VrJsZXz9AB7APmJ8s/6PAr8VYP/zoNuApWx/At5PLlp9oesEkW54H7AeWjbvcFW83OT9vV1u3k+kZd14PT2wDnvZ1c03/36d6gTfigdIzpAcpPTv79mm8nm2U/oz5LvBQcngBpe1fXwcOJf+W/wMY8L+TroeB9syyXgc8khxee51dd/DEAG6h9OzvI8kPRHVy+rzk60eS81sy3//2pPEA1/lMMXAr0JWsoy8kPxRR1g/wbuD7wF7gb5NhMqPrB/gkpW3Qw5Qeld05lesDaE9u378Df8a4J0An0PIIpW2o5f/PHy663Vzl5+1q63YyPePO7+GJATyt6+ZaD3onnIhIJNoGLCISiQawiEgkGsAiIpFoAIuIRKIBLCISiQawiEgkGsAiIpFoAIuIRPL/AVEGS5VWFGDyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('property_type')\n",
    "ax.barh(data.loc[:,'property_type'].dropna().value_counts().index, data.loc[:,'property_type'].dropna().value_counts().values, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([7.762e+03, 3.311e+03, 9.680e+02, 4.100e+02, 1.630e+02, 1.240e+02,\n",
       "        9.200e+01, 6.100e+01, 4.900e+01, 1.400e+01, 1.400e+01, 2.000e+01,\n",
       "        7.000e+00, 1.600e+01, 6.000e+00, 5.000e+00, 2.000e+00, 5.000e+00,\n",
       "        0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 1.000e+00, 1.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 2.000e+00]),\n",
       " array([5.04330000e+03, 2.27164654e+05, 4.49286008e+05, 6.71407362e+05,\n",
       "        8.93528716e+05, 1.11565007e+06, 1.33777142e+06, 1.55989278e+06,\n",
       "        1.78201413e+06, 2.00413549e+06, 2.22625684e+06, 2.44837819e+06,\n",
       "        2.67049955e+06, 2.89262090e+06, 3.11474226e+06, 3.33686361e+06,\n",
       "        3.55898496e+06, 3.78110632e+06, 4.00322767e+06, 4.22534903e+06,\n",
       "        4.44747038e+06, 4.66959173e+06, 4.89171309e+06, 5.11383444e+06,\n",
       "        5.33595580e+06, 5.55807715e+06, 5.78019850e+06, 6.00231986e+06,\n",
       "        6.22444121e+06, 6.44656257e+06, 6.66868392e+06, 6.89080527e+06,\n",
       "        7.11292663e+06, 7.33504798e+06, 7.55716934e+06, 7.77929069e+06,\n",
       "        8.00141204e+06, 8.22353340e+06, 8.44565475e+06, 8.66777611e+06,\n",
       "        8.88989746e+06, 9.11201881e+06, 9.33414017e+06, 9.55626152e+06,\n",
       "        9.77838288e+06, 1.00005042e+07, 1.02226256e+07, 1.04447469e+07,\n",
       "        1.06668683e+07, 1.08889896e+07, 1.11111110e+07]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('price_aprox_usd')\n",
    "ax.hist(data.query('rooms < 10.0').loc[:,'price_aprox_usd'].dropna().values, bins=50, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are some outliers for 'price_aprox_usd'. We will take this into account in the preprocess step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.6569e+04, 9.0000e+00, 5.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00]),\n",
       " array([     0.,   3680.,   7360.,  11040.,  14720.,  18400.,  22080.,\n",
       "         25760.,  29440.,  33120.,  36800.,  40480.,  44160.,  47840.,\n",
       "         51520.,  55200.,  58880.,  62560.,  66240.,  69920.,  73600.,\n",
       "         77280.,  80960.,  84640.,  88320.,  92000.,  95680.,  99360.,\n",
       "        103040., 106720., 110400., 114080., 117760., 121440., 125120.,\n",
       "        128800., 132480., 136160., 139840., 143520., 147200., 150880.,\n",
       "        154560., 158240., 161920., 165600., 169280., 172960., 176640.,\n",
       "        180320., 184000.]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('surface_total_in_m2')\n",
    "ax.hist(data.loc[:,'surface_total_in_m2'].dropna().values, bins=50, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.6854e+04, 1.3000e+01, 3.0000e+00, 1.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00]),\n",
       " array([1.0000000e+00, 3.0009800e+03, 6.0009600e+03, 9.0009400e+03,\n",
       "        1.2000920e+04, 1.5000900e+04, 1.8000880e+04, 2.1000860e+04,\n",
       "        2.4000840e+04, 2.7000820e+04, 3.0000800e+04, 3.3000780e+04,\n",
       "        3.6000760e+04, 3.9000740e+04, 4.2000720e+04, 4.5000700e+04,\n",
       "        4.8000680e+04, 5.1000660e+04, 5.4000640e+04, 5.7000620e+04,\n",
       "        6.0000600e+04, 6.3000580e+04, 6.6000560e+04, 6.9000540e+04,\n",
       "        7.2000520e+04, 7.5000500e+04, 7.8000480e+04, 8.1000460e+04,\n",
       "        8.4000440e+04, 8.7000420e+04, 9.0000400e+04, 9.3000380e+04,\n",
       "        9.6000360e+04, 9.9000340e+04, 1.0200032e+05, 1.0500030e+05,\n",
       "        1.0800028e+05, 1.1100026e+05, 1.1400024e+05, 1.1700022e+05,\n",
       "        1.2000020e+05, 1.2300018e+05, 1.2600016e+05, 1.2900014e+05,\n",
       "        1.3200012e+05, 1.3500010e+05, 1.3800008e+05, 1.4100006e+05,\n",
       "        1.4400004e+05, 1.4700002e+05, 1.5000000e+05]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('surface_covered_in_m2')\n",
    "ax.hist(data.loc[:,'surface_covered_in_m2'].dropna().values, bins=50, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 9 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('rooms')\n",
    "ax.barh((data.query('rooms < 10.0').loc[:,'rooms'].dropna().value_counts().index).astype(str), data.query('rooms < 10.0').loc[:,'rooms'].dropna().value_counts().values, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are some outliers for 'rooms'. We will take this into account in the preprocess step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 48 artists>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(b=True, linestyle='--')\n",
    "ax.set_title('barrio')\n",
    "ax.barh(data.loc[:,'barrio'].dropna().value_counts().index, data.loc[:,'barrio'].dropna().value_counts().values, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_on</th>\n",
       "      <th>operation</th>\n",
       "      <th>property_type</th>\n",
       "      <th>place_with_parent_names</th>\n",
       "      <th>lat-lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>price_aprox_usd</th>\n",
       "      <th>surface_total_in_m2</th>\n",
       "      <th>surface_covered_in_m2</th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "      <th>floor</th>\n",
       "      <th>rooms</th>\n",
       "      <th>expenses</th>\n",
       "      <th>properati_url</th>\n",
       "      <th>barrio</th>\n",
       "      <th>comuna</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>|Argentina|Capital Federal|Palermo|</td>\n",
       "      <td>-34.588912403,-58.4134942185</td>\n",
       "      <td>-34.588912</td>\n",
       "      <td>-58.413494</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2615.384615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>https://www.properati.com.ar/1dzv6_venta_depar...</td>\n",
       "      <td>PALERMO</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1560</td>\n",
       "      <td>2017-12-19</td>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>|Argentina|Capital Federal|Belgrano|</td>\n",
       "      <td>-34.5664783,-58.4341001</td>\n",
       "      <td>-34.566478</td>\n",
       "      <td>-58.434100</td>\n",
       "      <td>485000.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3730.769231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.properati.com.ar/1j3z7_venta_depar...</td>\n",
       "      <td>PALERMO</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      created_on operation property_type  \\\n",
       "18    2017-08-29      sell     apartment   \n",
       "1560  2017-12-19      sell     apartment   \n",
       "\n",
       "                   place_with_parent_names                       lat-lon  \\\n",
       "18     |Argentina|Capital Federal|Palermo|  -34.588912403,-58.4134942185   \n",
       "1560  |Argentina|Capital Federal|Belgrano|       -34.5664783,-58.4341001   \n",
       "\n",
       "            lat        lon  price_aprox_usd  surface_total_in_m2  \\\n",
       "18   -34.588912 -58.413494         340000.0                130.0   \n",
       "1560 -34.566478 -58.434100         485000.0                130.0   \n",
       "\n",
       "      surface_covered_in_m2  price_usd_per_m2  floor  rooms  expenses  \\\n",
       "18                    120.0       2615.384615    NaN    4.0    8000.0   \n",
       "1560                  120.0       3730.769231    NaN    4.0       NaN   \n",
       "\n",
       "                                          properati_url   barrio  comuna  \n",
       "18    https://www.properati.com.ar/1dzv6_venta_depar...  PALERMO    14.0  \n",
       "1560  https://www.properati.com.ar/1j3z7_venta_depar...  PALERMO    14.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[[18, 1560],:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With further exploration we obtain two examples that show that the dataset is problematic in the sense that two almost identical apartments have quite different sale price. We will try to take this into account in order to reduce the impact on the performanse of the models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function preprocess the dataset, creating and deleting features based on the past graphical analysis. This is done in preparation for the training and testing step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(data): \n",
    "    data = data.drop('created_on', axis=1, inplace=False)\n",
    "    data = data.drop('operation', axis=1, inplace=False)\n",
    "    data = data.drop('place_with_parent_names', axis=1, inplace=False)\n",
    "    data = data.drop('lat-lon', axis=1, inplace=False)\n",
    "    data = data.drop('lat', axis=1, inplace=False)\n",
    "    data = data.drop('lon', axis=1, inplace=False)\n",
    "    data = data.drop('price_usd_per_m2', axis=1, inplace=False)\n",
    "    data = data.drop('floor', axis=1, inplace=False)\n",
    "    data = data.drop('expenses', axis=1, inplace=False)\n",
    "    data = data.drop('properati_url', axis=1, inplace=False)\n",
    "    data = data.drop('comuna', axis=1, inplace=False)\n",
    "    data = data.dropna(subset=['price_aprox_usd','surface_total_in_m2','surface_covered_in_m2','rooms']) \n",
    "    mu = np.mean(data.loc[:,'price_aprox_usd'].dropna().values)\n",
    "    sigma = np.std(data.loc[:,'price_aprox_usd'].dropna().values)\n",
    "    data = data.query('price_aprox_usd < {}'.format(mu+0.5*sigma))\n",
    "    data = data.query('price_aprox_usd > {}'.format(mu-0.5*sigma))\n",
    "    data = data.query('rooms < 10')\n",
    "    data = pd.get_dummies(data)\n",
    "    data = shuffle(data)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       price_aprox_usd  surface_total_in_m2  surface_covered_in_m2  rooms  \\\n",
       "6563         131858.02                 64.0                   45.0    2.0   \n",
       "9586         323103.26                104.0                   67.0    3.0   \n",
       "2805         280000.00                103.0                   95.0    4.0   \n",
       "10218        161000.00                 45.0                   38.0    1.0   \n",
       "8300         209000.00                 67.0                   63.0    3.0   \n",
       "\n",
       "       property_type_PH  property_type_apartment  property_type_house  \\\n",
       "6563                  0                        1                    0   \n",
       "9586                  0                        1                    0   \n",
       "2805                  0                        1                    0   \n",
       "10218                 0                        1                    0   \n",
       "8300                  0                        1                    0   \n",
       "\n",
       "       property_type_store  barrio_AGRONOMIA  barrio_ALMAGRO  ...  \\\n",
       "6563                     0                 0               0  ...   \n",
       "9586                     0                 0               0  ...   \n",
       "2805                     0                 0               0  ...   \n",
       "10218                    0                 0               0  ...   \n",
       "8300                     0                 0               0  ...   \n",
       "\n",
       "       barrio_VILLA GRAL. MITRE  barrio_VILLA LUGANO  barrio_VILLA LURO  \\\n",
       "6563                          0                    0                  0   \n",
       "9586                          0                    0                  0   \n",
       "2805                          0                    0                  0   \n",
       "10218                         0                    0                  0   \n",
       "8300                          0                    0                  0   \n",
       "\n",
       "       barrio_VILLA ORTUZAR  barrio_VILLA PUEYRREDON  barrio_VILLA REAL  \\\n",
       "6563                      0                        0                  0   \n",
       "9586                      0                        0                  0   \n",
       "2805                      0                        0                  0   \n",
       "10218                     0                        0                  0   \n",
       "8300                      0                        0                  0   \n",
       "\n",
       "       barrio_VILLA RIACHUELO  barrio_VILLA SANTA RITA  barrio_VILLA SOLDATI  \\\n",
       "6563                        0                        0                     0   \n",
       "9586                        0                        0                     0   \n",
       "2805                        0                        0                     0   \n",
       "10218                       0                        0                     0   \n",
       "8300                        0                        0                     0   \n",
       "\n",
       "       barrio_VILLA URQUIZA  \n",
       "6563                      0  \n",
       "9586                      0  \n",
       "2805                      0  \n",
       "10218                     0  \n",
       "8300                      0  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = preprocess(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8445 entries, 6563 to 671\n",
      "Data columns (total 56 columns):\n",
      "price_aprox_usd             8445 non-null float64\n",
      "surface_total_in_m2         8445 non-null float64\n",
      "surface_covered_in_m2       8445 non-null float64\n",
      "rooms                       8445 non-null float64\n",
      "property_type_PH            8445 non-null uint8\n",
      "property_type_apartment     8445 non-null uint8\n",
      "property_type_house         8445 non-null uint8\n",
      "property_type_store         8445 non-null uint8\n",
      "barrio_AGRONOMIA            8445 non-null uint8\n",
      "barrio_ALMAGRO              8445 non-null uint8\n",
      "barrio_BALVANERA            8445 non-null uint8\n",
      "barrio_BARRACAS             8445 non-null uint8\n",
      "barrio_BELGRANO             8445 non-null uint8\n",
      "barrio_BOCA                 8445 non-null uint8\n",
      "barrio_BOEDO                8445 non-null uint8\n",
      "barrio_CABALLITO            8445 non-null uint8\n",
      "barrio_CHACARITA            8445 non-null uint8\n",
      "barrio_COGHLAN              8445 non-null uint8\n",
      "barrio_COLEGIALES           8445 non-null uint8\n",
      "barrio_CONSTITUCION         8445 non-null uint8\n",
      "barrio_FLORES               8445 non-null uint8\n",
      "barrio_FLORESTA             8445 non-null uint8\n",
      "barrio_LINIERS              8445 non-null uint8\n",
      "barrio_MATADEROS            8445 non-null uint8\n",
      "barrio_MONSERRAT            8445 non-null uint8\n",
      "barrio_MONTE CASTRO         8445 non-null uint8\n",
      "barrio_NUEVA POMPEYA        8445 non-null uint8\n",
      "barrio_NUEZ                 8445 non-null uint8\n",
      "barrio_PALERMO              8445 non-null uint8\n",
      "barrio_PARQUE AVELLANEDA    8445 non-null uint8\n",
      "barrio_PARQUE CHACABUCO     8445 non-null uint8\n",
      "barrio_PARQUE CHAS          8445 non-null uint8\n",
      "barrio_PARQUE PATRICIOS     8445 non-null uint8\n",
      "barrio_PATERNAL             8445 non-null uint8\n",
      "barrio_PUERTO MADERO        8445 non-null uint8\n",
      "barrio_RECOLETA             8445 non-null uint8\n",
      "barrio_RETIRO               8445 non-null uint8\n",
      "barrio_SAAVEDRA             8445 non-null uint8\n",
      "barrio_SAN CRISTOBAL        8445 non-null uint8\n",
      "barrio_SAN NICOLAS          8445 non-null uint8\n",
      "barrio_SAN TELMO            8445 non-null uint8\n",
      "barrio_VELEZ SARSFIELD      8445 non-null uint8\n",
      "barrio_VERSALLES            8445 non-null uint8\n",
      "barrio_VILLA CRESPO         8445 non-null uint8\n",
      "barrio_VILLA DEL PARQUE     8445 non-null uint8\n",
      "barrio_VILLA DEVOTO         8445 non-null uint8\n",
      "barrio_VILLA GRAL. MITRE    8445 non-null uint8\n",
      "barrio_VILLA LUGANO         8445 non-null uint8\n",
      "barrio_VILLA LURO           8445 non-null uint8\n",
      "barrio_VILLA ORTUZAR        8445 non-null uint8\n",
      "barrio_VILLA PUEYRREDON     8445 non-null uint8\n",
      "barrio_VILLA REAL           8445 non-null uint8\n",
      "barrio_VILLA RIACHUELO      8445 non-null uint8\n",
      "barrio_VILLA SANTA RITA     8445 non-null uint8\n",
      "barrio_VILLA SOLDATI        8445 non-null uint8\n",
      "barrio_VILLA URQUIZA        8445 non-null uint8\n",
      "dtypes: float64(4), uint8(52)\n",
      "memory usage: 758.7 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define the datasets for training and testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = len(data)*80//100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = (data.iloc[:size,1:].values).astype(float)\n",
    "Y_train = (data.iloc[:size,[0]].values).astype(float)\n",
    "\n",
    "X_test = (data.iloc[size:,1:].values).astype(float)\n",
    "Y_test = (data.iloc[size:,[0]].values).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.max(X_train, axis=0)+0.000001\n",
    "a = np.min(X_train, axis=0)-0.000001"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our first model and evaluate its performance. It is a KNN classifier with n=1 just to have a baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('lambda',\n",
       "                 FunctionTransformer(accept_sparse=False, check_inverse=True,\n",
       "                                     func=<function <lambda> at 0x7f9ebd9c12f0>,\n",
       "                                     inv_kw_args=None, inverse_func=None,\n",
       "                                     kw_args=None, validate=False)),\n",
       "                ('knn',\n",
       "                 KNeighborsRegressor(algorithm='auto', leaf_size=30,\n",
       "                                     metric='minkowski', metric_params=None,\n",
       "                                     n_jobs=None, n_neighbors=1, p=2,\n",
       "                                     weights='uniform'))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1 = Pipeline([('lambda', FunctionTransformer(lambda x:(x-a)/(b-a))),\n",
    "                ('knn', KNeighborsRegressor(n_neighbors=1))])\n",
    "\n",
    "model_1.fit(X_train, Y_train.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3939.727769390172"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train_pred = model_1.predict(X_train)\n",
    "\n",
    "np.mean(np.abs(Y_train.flatten()-Y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42748.84587329781"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test_pred = model_1.predict(X_test)\n",
    "\n",
    "np.mean(np.abs(Y_test.flatten()-Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([359., 195., 192., 125., 113., 123.,  68.,  55.,  50.,  33.,  35.,\n",
       "         60.,  29.,  30.,  27.,  20.,  11.,  18.,  15.,  18.,  17.,  10.,\n",
       "         12.,  14.,   6.,   7.,   6.,   4.,   4.,   3.,   7.,   5.,   4.,\n",
       "          3.,   2.,   2.,   2.,   0.,   0.,   0.,   2.,   0.,   0.,   1.,\n",
       "          0.,   1.,   0.,   0.,   0.,   1.]),\n",
       " array([     0.,   6700.,  13400.,  20100.,  26800.,  33500.,  40200.,\n",
       "         46900.,  53600.,  60300.,  67000.,  73700.,  80400.,  87100.,\n",
       "         93800., 100500., 107200., 113900., 120600., 127300., 134000.,\n",
       "        140700., 147400., 154100., 160800., 167500., 174200., 180900.,\n",
       "        187600., 194300., 201000., 207700., 214400., 221100., 227800.,\n",
       "        234500., 241200., 247900., 254600., 261300., 268000., 274700.,\n",
       "        281400., 288100., 294800., 301500., 308200., 314900., 321600.,\n",
       "        328300., 335000.]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax1.grid(b=True, linestyle='--')\n",
    "ax1.set_title('Training Error')\n",
    "ax1.hist(np.abs((Y_train.flatten()-Y_train_pred)), bins=50, color='#6A89CC', edgecolor='#000000')\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Testing Error')\n",
    "ax2.hist(np.abs((Y_test.flatten()-Y_test_pred)), bins=50, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is heavely overfitting. Nevertheless, it is a 'quick and dirty' baseline model that gives us a testing error to beat. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our second model and evaluate its performance. It is a simple Decision Tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('lambda',\n",
       "                 FunctionTransformer(accept_sparse=False, check_inverse=True,\n",
       "                                     func=<function <lambda> at 0x7f9ebda1b158>,\n",
       "                                     inv_kw_args=None, inverse_func=None,\n",
       "                                     kw_args=None, validate=False)),\n",
       "                ('adaboost',\n",
       "                 DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse',\n",
       "                                       max_depth=None, max_features=None,\n",
       "                                       max_leaf_nodes=None,\n",
       "                                       min_impurity_decrease=0.0,\n",
       "                                       min_impurity_split=None,\n",
       "                                       min_samples_leaf=1, min_samples_split=2,\n",
       "                                       min_weight_fraction_leaf=0.0,\n",
       "                                       presort='deprecated', random_state=None,\n",
       "                                       splitter='best'))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2 = Pipeline([('lambda', FunctionTransformer(lambda x:(x-a)/(b-a))),\n",
    "                     ('adaboost', DecisionTreeRegressor())]) \n",
    "\n",
    "model_2.fit(X_train, Y_train.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3574.6518410442927"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train_pred = model_2.predict(X_train)\n",
    "\n",
    "np.mean(np.abs(Y_train.flatten()-Y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42621.31172996137"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test_pred = model_2.predict(X_test)\n",
    "\n",
    "np.mean(np.abs(Y_test.flatten()-Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([358., 171., 151., 133., 137.,  76.,  78.,  58.,  52.,  56.,  43.,\n",
       "         42.,  55.,  28.,  32.,  25.,  21.,  15.,  14.,  11.,  22.,  12.,\n",
       "         12.,  10.,  15.,   7.,   6.,   8.,   9.,   5.,   5.,   1.,   5.,\n",
       "          1.,   1.,   3.,   3.,   2.,   0.,   1.,   3.,   0.,   0.,   0.,\n",
       "          1.,   0.,   0.,   0.,   0.,   1.]),\n",
       " array([     0.,   6220.,  12440.,  18660.,  24880.,  31100.,  37320.,\n",
       "         43540.,  49760.,  55980.,  62200.,  68420.,  74640.,  80860.,\n",
       "         87080.,  93300.,  99520., 105740., 111960., 118180., 124400.,\n",
       "        130620., 136840., 143060., 149280., 155500., 161720., 167940.,\n",
       "        174160., 180380., 186600., 192820., 199040., 205260., 211480.,\n",
       "        217700., 223920., 230140., 236360., 242580., 248800., 255020.,\n",
       "        261240., 267460., 273680., 279900., 286120., 292340., 298560.,\n",
       "        304780., 311000.]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax1.grid(b=True, linestyle='--')\n",
    "ax1.set_title('Training Error')\n",
    "ax1.hist(np.abs((Y_train.flatten()-Y_train_pred)), bins=50, color='#6A89CC', edgecolor='#000000')\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Testing Error')\n",
    "ax2.hist(np.abs((Y_test.flatten()-Y_test_pred)), bins=50, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is performing worse than the last one and it is also heavely overfitting, so we discard it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our third model and evaluate its performance. It is a AdaBoost Regressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('lambda',\n",
       "                 FunctionTransformer(accept_sparse=False, check_inverse=True,\n",
       "                                     func=<function <lambda> at 0x7f9ebd80d048>,\n",
       "                                     inv_kw_args=None, inverse_func=None,\n",
       "                                     kw_args=None, validate=False)),\n",
       "                ('adaboost',\n",
       "                 AdaBoostRegressor(base_estimator=None, learning_rate=1.0,\n",
       "                                   loss='linear', n_estimators=50,\n",
       "                                   random_state=None))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3 = Pipeline([('lambda', FunctionTransformer(lambda x:(x-a)/(b-a))),\n",
    "                     ('adaboost', AdaBoostRegressor())]) \n",
    "\n",
    "model_3.fit(X_train, Y_train.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54994.28945460679"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train_pred = model_3.predict(X_train)\n",
    "\n",
    "np.mean(np.abs(Y_train.flatten()-Y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56526.07668035368"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test_pred = model_3.predict(X_test)\n",
    "\n",
    "np.mean(np.abs(Y_test.flatten()-Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 71.,  72.,  73.,  66.,  71.,  77.,  94., 102.,  89.,  96.,  72.,\n",
       "         79.,  67.,  62.,  44.,  56.,  55.,  46.,  45.,  32.,  46.,  35.,\n",
       "         31.,  21.,  30.,  18.,  24.,  17.,  14.,  13.,  15.,   8.,   8.,\n",
       "          6.,   9.,   4.,   7.,   5.,   1.,   1.,   0.,   3.,   1.,   1.,\n",
       "          0.,   0.,   0.,   1.,   0.,   1.]),\n",
       " array([5.63000000e+00, 4.60501023e+03, 9.20439046e+03, 1.38037707e+04,\n",
       "        1.84031509e+04, 2.30025311e+04, 2.76019114e+04, 3.22012916e+04,\n",
       "        3.68006718e+04, 4.14000521e+04, 4.59994323e+04, 5.05988125e+04,\n",
       "        5.51981928e+04, 5.97975730e+04, 6.43969532e+04, 6.89963334e+04,\n",
       "        7.35957137e+04, 7.81950939e+04, 8.27944741e+04, 8.73938544e+04,\n",
       "        9.19932346e+04, 9.65926148e+04, 1.01191995e+05, 1.05791375e+05,\n",
       "        1.10390756e+05, 1.14990136e+05, 1.19589516e+05, 1.24188896e+05,\n",
       "        1.28788276e+05, 1.33387657e+05, 1.37987037e+05, 1.42586417e+05,\n",
       "        1.47185797e+05, 1.51785178e+05, 1.56384558e+05, 1.60983938e+05,\n",
       "        1.65583318e+05, 1.70182699e+05, 1.74782079e+05, 1.79381459e+05,\n",
       "        1.83980839e+05, 1.88580219e+05, 1.93179600e+05, 1.97778980e+05,\n",
       "        2.02378360e+05, 2.06977740e+05, 2.11577121e+05, 2.16176501e+05,\n",
       "        2.20775881e+05, 2.25375261e+05, 2.29974641e+05]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax1.grid(b=True, linestyle='--')\n",
    "ax1.set_title('Training Error')\n",
    "ax1.hist(np.abs((Y_train.flatten()-Y_train_pred)), bins=50, color='#6A89CC', edgecolor='#000000')\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Testing Error')\n",
    "ax2.hist(np.abs((Y_test.flatten()-Y_test_pred)), bins=50, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model performing worse than the baseline model but it is not overfitting so hard, so it could be taken as a new baseline model to beat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our fourth model and evaluate its performance. It is a Neural Network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NN_1():\n",
    "    model = tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Lambda(function=lambda x: (x-a)/(b-a),\n",
    "                                     input_shape=[X_train.shape[1]]))\n",
    "    model.add(tf.keras.layers.Dense(units=256, \n",
    "                                    activation='relu', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    model.add(tf.keras.layers.Dropout(rate=0.5))\n",
    "    model.add(tf.keras.layers.Dense(Y_train.shape[1], \n",
    "                                    activation='linear', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01, \n",
    "                                         beta_1=0.9, \n",
    "                                         beta_2=0.999, \n",
    "                                         epsilon=1e-07)\n",
    "    model.compile(loss='mse', metrics=['mae'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', \n",
    "                                                     patience=100,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1, \n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=200,\n",
    "                                                 min_delta=0.001)\n",
    "    model.fit(X_train, Y_train, epochs=1000, batch_size=64, callbacks=[callback1, callback2], verbose=True, validation_data=(X_test,Y_test))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda (Lambda)              (None, 55)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               14336     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 14,593\n",
      "Trainable params: 14,593\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 6756 samples, validate on 1689 samples\n",
      "Epoch 1/1000\n",
      "6756/6756 [==============================] - 0s 67us/sample - loss: 60106417539.4103 - mae: 224485.8281 - val_loss: 59521125534.2380 - val_mae: 223310.3281\n",
      "Epoch 2/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 58627506534.9153 - mae: 221218.0625 - val_loss: 57061729296.3694 - val_mae: 217812.7812\n",
      "Epoch 3/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 55221244061.6317 - mae: 213487.9531 - val_loss: 52795476519.7111 - val_mae: 207934.9531\n",
      "Epoch 4/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 50229069834.9130 - mae: 201707.7500 - val_loss: 47216773077.5607 - val_mae: 194269.4219\n",
      "Epoch 5/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 44210642725.7407 - mae: 186377.5469 - val_loss: 40884829167.6306 - val_mae: 177506.0781\n",
      "Epoch 6/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 37726601140.8218 - mae: 168389.8281 - val_loss: 34343101999.5927 - val_mae: 158365.3125\n",
      "Epoch 7/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 31239961445.3996 - mae: 148377.3750 - val_loss: 28097885564.1350 - val_mae: 137676.9375\n",
      "Epoch 8/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 25283185240.2131 - mae: 127250.2188 - val_loss: 22534497440.6631 - val_mae: 116284.0625\n",
      "Epoch 9/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 20238509870.8348 - mae: 106942.1016 - val_loss: 17970931815.0669 - val_mae: 97287.3203\n",
      "Epoch 10/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 16155714191.3843 - mae: 90680.5625 - val_loss: 14437871722.7046 - val_mae: 84183.2656\n",
      "Epoch 11/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 13127452634.4109 - mae: 80549.0000 - val_loss: 11934700927.1664 - val_mae: 76759.5625\n",
      "Epoch 12/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 11057359186.9082 - mae: 75295.2109 - val_loss: 10273277295.4032 - val_mae: 73045.9844\n",
      "Epoch 13/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 9754905960.1279 - mae: 72556.4688 - val_loss: 9260886384.0095 - val_mae: 71544.9609\n",
      "Epoch 14/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 8948850501.5702 - mae: 71920.3125 - val_loss: 8659188154.5814 - val_mae: 71061.6406\n",
      "Epoch 15/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 8506360585.8520 - mae: 71678.9297 - val_loss: 8310440250.0509 - val_mae: 71001.3750\n",
      "Epoch 16/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 8313268284.6276 - mae: 71993.3984 - val_loss: 8093124735.9242 - val_mae: 71024.9531\n",
      "Epoch 17/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 8138143124.9923 - mae: 72096.5547 - val_loss: 7943496224.4358 - val_mae: 70997.2578\n",
      "Epoch 18/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 7948292896.5873 - mae: 71865.2969 - val_loss: 7830635499.9929 - val_mae: 70803.8203\n",
      "Epoch 19/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 7927260284.2866 - mae: 71939.3125 - val_loss: 7728842822.3280 - val_mae: 70534.0000\n",
      "Epoch 20/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 7761040130.2735 - mae: 71377.5156 - val_loss: 7636643234.9366 - val_mae: 70239.4375\n",
      "Epoch 21/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 7777418066.6051 - mae: 71508.2266 - val_loss: 7549700710.1575 - val_mae: 69795.9844\n",
      "Epoch 22/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 7635126053.7407 - mae: 70977.0781 - val_loss: 7465392961.0231 - val_mae: 69430.0312\n",
      "Epoch 23/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 7598094175.9432 - mae: 70751.2266 - val_loss: 7384412863.8863 - val_mae: 69044.6406\n",
      "Epoch 24/1000\n",
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      "Epoch 25/1000\n",
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      "Epoch 26/1000\n",
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      "Epoch 27/1000\n",
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      "Epoch 28/1000\n",
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      "Epoch 29/1000\n",
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      "Epoch 30/1000\n",
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      "Epoch 31/1000\n",
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      "Epoch 32/1000\n",
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      "Epoch 33/1000\n",
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      "Epoch 34/1000\n",
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      "Epoch 35/1000\n",
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      "Epoch 36/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 6738842144.7389 - mae: 65803.9219 - val_loss: 6549667225.2362 - val_mae: 64467.8906\n",
      "Epoch 37/1000\n",
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      "Epoch 38/1000\n",
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      "Epoch 39/1000\n",
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      "Epoch 40/1000\n",
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      "Epoch 41/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 6469958369.2315 - mae: 64364.2070 - val_loss: 6305053834.8372 - val_mae: 62875.4609\n",
      "Epoch 42/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 6423229272.3647 - mae: 63905.7539 - val_loss: 6260738834.0367 - val_mae: 62590.4219\n",
      "Epoch 43/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 6390541757.6128 - mae: 63887.6133 - val_loss: 6216861268.5755 - val_mae: 62293.4414\n",
      "Epoch 44/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 22us/sample - loss: 6471413348.0355 - mae: 63852.2031 - val_loss: 6175090903.8342 - val_mae: 62027.0156\n",
      "Epoch 45/1000\n",
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      "Epoch 46/1000\n",
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      "Epoch 47/1000\n",
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      "Epoch 48/1000\n",
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      "Epoch 49/1000\n",
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      "Epoch 50/1000\n",
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      "Epoch 51/1000\n",
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      "Epoch 52/1000\n",
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      "Epoch 53/1000\n",
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      "Epoch 54/1000\n",
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      "Epoch 55/1000\n",
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      "Epoch 56/1000\n",
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      "Epoch 57/1000\n",
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      "Epoch 58/1000\n",
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      "Epoch 59/1000\n",
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      "Epoch 60/1000\n",
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      "Epoch 61/1000\n",
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      "Epoch 62/1000\n",
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      "Epoch 63/1000\n",
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      "Epoch 64/1000\n",
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      "Epoch 65/1000\n",
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      "Epoch 66/1000\n",
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      "Epoch 67/1000\n",
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      "Epoch 68/1000\n",
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      "Epoch 69/1000\n",
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      "Epoch 70/1000\n",
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      "Epoch 71/1000\n",
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      "Epoch 72/1000\n",
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      "Epoch 73/1000\n",
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      "Epoch 74/1000\n",
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      "Epoch 77/1000\n",
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      "Epoch 78/1000\n",
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      "Epoch 79/1000\n",
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      "Epoch 80/1000\n",
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      "Epoch 81/1000\n",
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      "Epoch 82/1000\n",
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      "Epoch 83/1000\n",
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      "Epoch 84/1000\n",
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      "Epoch 85/1000\n",
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      "Epoch 86/1000\n",
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      "Epoch 87/1000\n",
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      "Epoch 88/1000\n",
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      "Epoch 89/1000\n",
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      "Epoch 91/1000\n",
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      "Epoch 92/1000\n",
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      "Epoch 93/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 26us/sample - loss: 5238190938.7898 - mae: 55797.9727 - val_loss: 5096651316.7460 - val_mae: 54519.7852\n",
      "Epoch 94/1000\n",
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      "Epoch 95/1000\n",
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      "Epoch 96/1000\n",
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      "Epoch 97/1000\n",
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      "Epoch 98/1000\n",
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      "Epoch 99/1000\n",
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      "Epoch 100/1000\n",
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      "Epoch 101/1000\n",
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      "Epoch 102/1000\n",
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      "Epoch 103/1000\n",
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      "Epoch 104/1000\n",
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      "Epoch 105/1000\n",
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      "Epoch 106/1000\n",
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      "Epoch 107/1000\n",
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      "Epoch 108/1000\n",
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      "Epoch 109/1000\n",
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      "Epoch 110/1000\n",
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      "Epoch 111/1000\n",
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      "Epoch 112/1000\n",
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      "Epoch 113/1000\n",
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      "Epoch 114/1000\n",
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      "Epoch 115/1000\n",
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      "Epoch 116/1000\n",
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      "Epoch 117/1000\n",
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      "Epoch 118/1000\n",
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      "Epoch 119/1000\n",
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      "Epoch 120/1000\n",
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      "Epoch 121/1000\n",
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      "Epoch 122/1000\n",
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      "Epoch 123/1000\n",
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      "Epoch 124/1000\n",
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      "Epoch 125/1000\n",
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      "Epoch 126/1000\n",
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      "Epoch 127/1000\n",
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      "Epoch 128/1000\n",
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      "Epoch 133/1000\n",
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      "Epoch 134/1000\n",
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      "Epoch 135/1000\n",
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      "Epoch 136/1000\n",
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      "Epoch 138/1000\n",
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      "Epoch 139/1000\n",
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      "Epoch 140/1000\n",
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      "Epoch 141/1000\n",
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      "Epoch 142/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5172518761.4920 - mae: 55111.0898 - val_loss: 5002465702.8774 - val_mae: 53617.9219\n",
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      "Epoch 145/1000\n",
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      "Epoch 146/1000\n",
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      "Epoch 147/1000\n",
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      "Epoch 148/1000\n",
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      "Epoch 149/1000\n",
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      "Epoch 150/1000\n",
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      "Epoch 153/1000\n",
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      "Epoch 160/1000\n",
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      "Epoch 182/1000\n",
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      "Epoch 191/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5156510203.7561 - mae: 55062.2461 - val_loss: 4995351973.3618 - val_mae: 53603.5195\n",
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      "Epoch 193/1000\n",
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      "Epoch 194/1000\n",
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      "Epoch 195/1000\n",
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      "Epoch 196/1000\n",
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      "Epoch 197/1000\n",
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      "Epoch 199/1000\n",
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      "Epoch 200/1000\n",
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      "Epoch 207/1000\n",
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      "Epoch 211/1000\n",
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      "Epoch 212/1000\n",
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      "Epoch 213/1000\n",
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      "Epoch 214/1000\n",
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      "Epoch 222/1000\n",
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      "Epoch 224/1000\n",
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      "Epoch 225/1000\n",
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      "Epoch 228/1000\n",
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      "Epoch 231/1000\n",
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      "Epoch 232/1000\n",
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      "Epoch 233/1000\n",
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      "Epoch 234/1000\n",
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      "Epoch 235/1000\n",
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      "Epoch 236/1000\n",
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      "Epoch 237/1000\n",
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      "Epoch 238/1000\n",
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      "Epoch 239/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5118173845.4470 - mae: 54889.6719 - val_loss: 4997345527.6637 - val_mae: 53554.4492\n",
      "Epoch 240/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5202306519.6827 - mae: 55151.6719 - val_loss: 4997289107.3250 - val_mae: 53632.6875\n",
      "Epoch 241/1000\n",
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      "Epoch 242/1000\n",
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      "Epoch 243/1000\n",
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      "Epoch 244/1000\n",
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      "Epoch 245/1000\n",
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      "Epoch 246/1000\n",
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      "Epoch 247/1000\n",
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      "Epoch 248/1000\n",
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      "Epoch 249/1000\n",
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      "Epoch 250/1000\n",
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      "Epoch 251/1000\n",
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      "Epoch 252/1000\n",
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      "Epoch 253/1000\n",
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      "Epoch 254/1000\n",
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      "Epoch 255/1000\n",
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      "Epoch 256/1000\n",
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      "Epoch 257/1000\n",
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      "Epoch 258/1000\n",
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      "Epoch 259/1000\n",
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      "Epoch 260/1000\n",
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      "Epoch 261/1000\n",
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      "Epoch 262/1000\n",
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      "Epoch 263/1000\n",
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      "Epoch 264/1000\n",
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      "Epoch 265/1000\n",
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      "Epoch 266/1000\n",
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      "Epoch 267/1000\n",
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      "Epoch 268/1000\n",
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      "Epoch 269/1000\n",
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      "Epoch 270/1000\n",
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      "Epoch 271/1000\n",
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      "Epoch 272/1000\n",
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      "Epoch 273/1000\n",
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      "Epoch 274/1000\n",
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      "Epoch 275/1000\n",
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      "Epoch 276/1000\n",
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      "Epoch 277/1000\n",
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      "Epoch 278/1000\n",
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      "Epoch 279/1000\n",
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      "Epoch 280/1000\n",
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      "Epoch 281/1000\n",
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      "Epoch 282/1000\n",
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      "Epoch 283/1000\n",
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      "Epoch 284/1000\n",
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      "Epoch 285/1000\n",
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      "Epoch 286/1000\n",
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      "Epoch 287/1000\n",
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      "Epoch 288/1000\n",
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      "Epoch 289/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5122952812.8265 - mae: 54974.3984 - val_loss: 4994572926.4085 - val_mae: 53476.0820\n",
      "Epoch 290/1000\n",
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      "Epoch 291/1000\n",
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      "Epoch 292/1000\n",
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      "Epoch 293/1000\n",
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      "Epoch 294/1000\n",
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      "Epoch 295/1000\n",
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      "Epoch 296/1000\n",
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      "Epoch 297/1000\n",
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      "Epoch 298/1000\n",
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      "Epoch 299/1000\n",
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      "Epoch 300/1000\n",
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      "Epoch 301/1000\n",
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      "Epoch 302/1000\n",
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      "Epoch 303/1000\n",
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      "Epoch 304/1000\n",
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      "Epoch 305/1000\n",
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      "Epoch 306/1000\n",
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      "Epoch 307/1000\n",
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      "Epoch 308/1000\n",
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      "Epoch 309/1000\n",
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      "Epoch 310/1000\n",
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      "Epoch 311/1000\n",
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      "Epoch 312/1000\n",
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      "Epoch 313/1000\n",
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      "Epoch 314/1000\n",
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      "Epoch 315/1000\n",
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      "Epoch 316/1000\n",
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      "Epoch 317/1000\n",
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      "Epoch 318/1000\n",
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      "Epoch 319/1000\n",
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      "Epoch 320/1000\n",
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      "Epoch 321/1000\n",
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      "Epoch 322/1000\n",
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      "Epoch 323/1000\n",
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      "Epoch 324/1000\n",
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      "Epoch 325/1000\n",
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      "Epoch 326/1000\n",
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      "Epoch 327/1000\n",
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      "Epoch 328/1000\n",
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      "Epoch 329/1000\n",
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      "Epoch 330/1000\n",
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      "Epoch 331/1000\n",
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      "Epoch 332/1000\n",
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      "Epoch 334/1000\n",
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      "Epoch 336/1000\n",
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      "Epoch 337/1000\n",
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      "Epoch 338/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5105008134.0628 - mae: 54789.7773 - val_loss: 4996006209.3262 - val_mae: 53521.2773\n",
      "Epoch 339/1000\n",
      "6756/6756 [==============================] - 0s 20us/sample - loss: 5147980491.7087 - mae: 55012.9727 - val_loss: 4995800717.2623 - val_mae: 53534.2812\n",
      "Epoch 340/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5120120594.3398 - mae: 54862.0312 - val_loss: 4995777922.1978 - val_mae: 53546.3867\n",
      "Epoch 341/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5083313681.2789 - mae: 54507.2695 - val_loss: 4996326650.3919 - val_mae: 53517.6406\n",
      "Epoch 342/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 5135157778.4914 - mae: 54994.4062 - val_loss: 4998798918.9343 - val_mae: 53437.8398\n",
      "Epoch 343/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5120715072.7200 - mae: 54651.8633 - val_loss: 4994655151.3653 - val_mae: 53537.7188\n",
      "Epoch 344/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5118906467.5808 - mae: 54841.2227 - val_loss: 4994505318.1575 - val_mae: 53526.4141\n",
      "Epoch 345/1000\n",
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      "Epoch 346/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5086663422.0296 - mae: 54723.6094 - val_loss: 4994412912.9189 - val_mae: 53540.3867\n",
      "Epoch 347/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5111302927.0053 - mae: 54581.8555 - val_loss: 4994390892.0687 - val_mae: 53531.3828\n",
      "Epoch 348/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5105249925.8354 - mae: 54834.6562 - val_loss: 4994313835.6140 - val_mae: 53532.2070\n",
      "Epoch 349/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5161721413.4186 - mae: 54922.2891 - val_loss: 4994291424.0189 - val_mae: 53536.3711\n",
      "Epoch 350/1000\n",
      "6756/6756 [==============================] - 0s 20us/sample - loss: 5137597653.7123 - mae: 54921.5664 - val_loss: 4994213583.3464 - val_mae: 53541.4570\n",
      "Epoch 351/1000\n",
      "6756/6756 [==============================] - 0s 20us/sample - loss: 5148704796.6465 - mae: 55002.6094 - val_loss: 4994402524.9876 - val_mae: 53532.6602\n",
      "Epoch 352/1000\n",
      "6756/6756 [==============================] - 0s 20us/sample - loss: 5135978484.0261 - mae: 54886.3555 - val_loss: 4994361562.8656 - val_mae: 53535.5508\n",
      "Epoch 353/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5131218164.6323 - mae: 54767.2266 - val_loss: 4994202423.9290 - val_mae: 53538.6758\n",
      "Epoch 354/1000\n",
      "6756/6756 [==============================] - 0s 20us/sample - loss: 5132755729.2789 - mae: 54875.2891 - val_loss: 4994059143.9574 - val_mae: 53546.7773\n",
      "Epoch 355/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5074587102.6548 - mae: 54502.0469 - val_loss: 4994010707.3629 - val_mae: 53554.9102\n",
      "Epoch 356/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5186708436.0450 - mae: 55240.1367 - val_loss: 4993947257.5583 - val_mae: 53551.1719\n",
      "Epoch 357/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5074862460.4381 - mae: 54476.2930 - val_loss: 4993968147.7040 - val_mae: 53545.0625\n",
      "Epoch 358/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5144046914.8419 - mae: 54680.8555 - val_loss: 4994022762.8561 - val_mae: 53542.5352\n",
      "Epoch 359/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5096064662.9627 - mae: 54690.0234 - val_loss: 4994199020.5992 - val_mae: 53531.8086\n",
      "Epoch 360/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5159891766.1101 - mae: 55131.1055 - val_loss: 4994167561.8520 - val_mae: 53534.6445\n",
      "Epoch 361/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5142194928.6915 - mae: 54789.0508 - val_loss: 4994063938.9935 - val_mae: 53533.4609\n",
      "Epoch 362/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5129730650.4867 - mae: 54685.3008 - val_loss: 4993953350.0249 - val_mae: 53533.3516\n",
      "Epoch 363/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5164796877.9822 - mae: 54950.1680 - val_loss: 4994011171.4671 - val_mae: 53528.5273\n",
      "Epoch 364/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5079875213.2623 - mae: 54625.7617 - val_loss: 4993944559.9337 - val_mae: 53522.9805\n",
      "Epoch 365/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5177194839.4553 - mae: 55048.5625 - val_loss: 4993956043.7087 - val_mae: 53523.0586\n",
      "Epoch 366/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5128407548.3623 - mae: 54723.2812 - val_loss: 4993843682.2925 - val_mae: 53528.1289\n",
      "Epoch 367/1000\n",
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      "Epoch 368/1000\n",
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      "Epoch 369/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5138793960.0521 - mae: 54897.1797 - val_loss: 4993752763.0361 - val_mae: 53518.4219\n",
      "Epoch 370/1000\n",
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      "Epoch 371/1000\n",
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      "Epoch 372/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 5205281527.3606 - mae: 55128.1484 - val_loss: 4993849096.9426 - val_mae: 53516.0820\n",
      "Epoch 373/1000\n",
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      "Epoch 374/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 5143964284.2866 - mae: 54857.2031 - val_loss: 4993813607.3700 - val_mae: 53522.3828\n",
      "Epoch 375/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 5133479480.3837 - mae: 54864.4297 - val_loss: 4993844366.4748 - val_mae: 53521.7344\n",
      "Epoch 376/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 5098103253.8638 - mae: 54695.4609 - val_loss: 4993804162.8040 - val_mae: 53524.4375\n",
      "Epoch 377/1000\n",
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      "Epoch 378/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5102789910.2806 - mae: 54737.6523 - val_loss: 4993814099.0598 - val_mae: 53520.5820\n",
      "Epoch 379/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5119122222.0770 - mae: 54771.1562 - val_loss: 4993718114.3683 - val_mae: 53523.8984\n",
      "Epoch 380/1000\n",
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      "Epoch 381/1000\n",
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      "Epoch 382/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 5101434954.5719 - mae: 54657.3555 - val_loss: 4993636658.4725 - val_mae: 53529.7969\n",
      "Epoch 383/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5145391923.9882 - mae: 54970.1680 - val_loss: 4993583134.9201 - val_mae: 53531.4688\n",
      "Epoch 384/1000\n",
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      "Epoch 385/1000\n",
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      "Epoch 386/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 5166063230.4085 - mae: 55084.2539 - val_loss: 4993556129.5725 - val_mae: 53536.0625\n",
      "Epoch 387/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 25us/sample - loss: 5134330456.2131 - mae: 54853.0273 - val_loss: 4993547237.0207 - val_mae: 53540.6875\n",
      "Epoch 388/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5148653425.8283 - mae: 54897.4258 - val_loss: 4993683024.0284 - val_mae: 53539.2109\n",
      "Epoch 389/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 5150825543.6921 - mae: 54969.3789 - val_loss: 4993746406.8396 - val_mae: 53530.9492\n",
      "Epoch 390/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5120219926.5838 - mae: 54895.8281 - val_loss: 4993806418.4535 - val_mae: 53531.8164\n",
      "Epoch 391/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5105452265.4162 - mae: 54607.7969 - val_loss: 4993703808.6821 - val_mae: 53531.8320\n",
      "Epoch 392/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 5119877581.9822 - mae: 54574.3125 - val_loss: 4993658135.1901 - val_mae: 53539.7578\n",
      "Epoch 393/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5182386835.6282 - mae: 55081.0742 - val_loss: 4993818626.1220 - val_mae: 53531.8281\n",
      "Epoch 394/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 5145711974.3091 - mae: 54902.4922 - val_loss: 4993843673.1983 - val_mae: 53530.3359\n",
      "Epoch 395/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5109710338.7282 - mae: 54548.5742 - val_loss: 4993763339.5192 - val_mae: 53534.2617\n",
      "Epoch 396/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5080123500.0687 - mae: 54638.8047 - val_loss: 4993970055.0480 - val_mae: 53521.3008\n",
      "Epoch 397/1000\n",
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      "Epoch 398/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5064861823.3179 - mae: 54394.8281 - val_loss: 4993802837.4849 - val_mae: 53530.2305\n",
      "Epoch 399/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5097682511.2706 - mae: 54720.3164 - val_loss: 4993632001.3641 - val_mae: 53536.0859\n",
      "Epoch 400/1000\n",
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      "Epoch 401/1000\n",
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      "Epoch 402/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5144498592.5115 - mae: 54926.2812 - val_loss: 4993808866.8988 - val_mae: 53526.3320\n",
      "Epoch 403/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5133356372.7271 - mae: 54711.9141 - val_loss: 4993855606.8301 - val_mae: 53526.4570\n",
      "Epoch 404/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5101155331.6377 - mae: 54697.1758 - val_loss: 4993662296.9710 - val_mae: 53533.2070\n",
      "Epoch 405/1000\n",
      "6756/6756 [==============================] - 0s 20us/sample - loss: 5142374273.8946 - mae: 54875.3242 - val_loss: 4993796694.0912 - val_mae: 53527.1953\n",
      "Epoch 406/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5125431561.5488 - mae: 54680.6680 - val_loss: 4993864192.0000 - val_mae: 53524.8750\n",
      "Epoch 407/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5178704212.7271 - mae: 55155.1250 - val_loss: 4993704034.5198 - val_mae: 53531.7852\n",
      "Epoch 408/1000\n",
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      "Epoch 409/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5122271943.1616 - mae: 54894.8672 - val_loss: 4993526911.0148 - val_mae: 53535.9570\n",
      "Epoch 410/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5090554065.4683 - mae: 54528.2148 - val_loss: 4993852249.5773 - val_mae: 53532.5234\n",
      "Epoch 411/1000\n",
      "6756/6756 [==============================] - 0s 20us/sample - loss: 5163796827.6992 - mae: 54994.2578 - val_loss: 4993690126.8538 - val_mae: 53533.2578\n",
      "Epoch 412/1000\n",
      "6756/6756 [==============================] - 0s 20us/sample - loss: 5146295986.5483 - mae: 55143.6523 - val_loss: 4993661861.9680 - val_mae: 53534.3359\n",
      "Epoch 413/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5115319134.7306 - mae: 54731.0117 - val_loss: 4993661238.4133 - val_mae: 53536.1562\n",
      "Epoch 414/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5097906233.8993 - mae: 54590.1133 - val_loss: 4993520380.5139 - val_mae: 53538.8750\n",
      "Epoch 415/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5141526772.3292 - mae: 54954.7773 - val_loss: 4993608913.7715 - val_mae: 53534.2539\n",
      "Epoch 416/1000\n",
      "6756/6756 [==============================] - 0s 21us/sample - loss: 5164533184.3410 - mae: 55034.6562 - val_loss: 4993531516.2866 - val_mae: 53537.2734\n",
      "Epoch 417/1000\n",
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      "Epoch 418/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5086924724.8218 - mae: 54766.3086 - val_loss: 4993213923.5050 - val_mae: 53552.5312\n",
      "Epoch 419/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 5158840035.0503 - mae: 55007.1680 - val_loss: 4993081540.1303 - val_mae: 53553.3750\n",
      "Epoch 420/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5139743322.0320 - mae: 55010.1602 - val_loss: 4993207990.1859 - val_mae: 53546.7891\n",
      "Epoch 421/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5136370456.4026 - mae: 54819.3281 - val_loss: 4993322365.9538 - val_mae: 53537.8906\n",
      "Epoch 422/1000\n",
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      "Epoch 423/1000\n",
      "6756/6756 [==============================] - 0s 19us/sample - loss: 5104149346.3683 - mae: 54672.4023 - val_loss: 4993144898.6903 - val_mae: 53537.9805\n",
      "Epoch 424/1000\n",
      "6756/6756 [==============================] - 0s 19us/sample - loss: 5088791511.8342 - mae: 54647.3320 - val_loss: 4993233423.1569 - val_mae: 53531.8477\n",
      "Epoch 425/1000\n",
      "6756/6756 [==============================] - 0s 19us/sample - loss: 5135245582.3991 - mae: 54696.3867 - val_loss: 4993297222.1764 - val_mae: 53529.0312\n",
      "Epoch 426/1000\n",
      "6756/6756 [==============================] - 0s 20us/sample - loss: 5121018220.5234 - mae: 54818.6133 - val_loss: 4993314072.7057 - val_mae: 53529.2500\n",
      "Epoch 427/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5131014063.8200 - mae: 54788.0273 - val_loss: 4993210814.2191 - val_mae: 53532.2500\n",
      "Epoch 428/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5147583216.3884 - mae: 55096.5781 - val_loss: 4993221249.4399 - val_mae: 53529.4492\n",
      "Epoch 429/1000\n",
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      "Epoch 430/1000\n",
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      "Epoch 431/1000\n",
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      "Epoch 432/1000\n",
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      "Epoch 433/1000\n",
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      "Epoch 434/1000\n",
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      "Epoch 435/1000\n",
      "6756/6756 [==============================] - 0s 22us/sample - loss: 5168798738.1883 - mae: 55024.3828 - val_loss: 4993185181.4802 - val_mae: 53540.0820\n",
      "Epoch 436/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5189262359.6448 - mae: 55075.8711 - val_loss: 4993215494.0628 - val_mae: 53543.0781\n",
      "Epoch 437/1000\n",
      "6756/6756 [==============================] - 0s 19us/sample - loss: 5161690500.6229 - mae: 55102.2812 - val_loss: 4993245084.5708 - val_mae: 53534.9766\n",
      "Epoch 438/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5119853383.0858 - mae: 54642.3320 - val_loss: 4993221744.1610 - val_mae: 53537.2969\n",
      "Epoch 439/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5133171603.1735 - mae: 55020.1719 - val_loss: 4993256508.3245 - val_mae: 53531.4062\n",
      "Epoch 440/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5172000150.2049 - mae: 55001.8086 - val_loss: 4993191149.6602 - val_mae: 53537.2188\n",
      "Epoch 441/1000\n",
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      "Epoch 442/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5163744740.7176 - mae: 54972.0469 - val_loss: 4993465242.4488 - val_mae: 53525.6797\n",
      "Epoch 443/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 5118603689.9088 - mae: 54714.4531 - val_loss: 4993387136.8336 - val_mae: 53526.2539\n",
      "Epoch 444/1000\n",
      "6756/6756 [==============================] - 0s 23us/sample - loss: 5086651960.0805 - mae: 54756.8711 - val_loss: 4993494903.8911 - val_mae: 53521.8438\n"
     ]
    }
   ],
   "source": [
    "model_4 = NN_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52038.059170274566\n"
     ]
    }
   ],
   "source": [
    "Y_train_pred = model_4.predict(X_train)\n",
    "\n",
    "np.mean(np.abs(Y_train-Y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51789.953570274566\n"
     ]
    }
   ],
   "source": [
    "Y_test_pred = model_4.predict(X_test)\n",
    "\n",
    "np.mean(np.abs(Y_test-Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([144., 119., 116., 123., 138., 114., 117.,  94.,  88.,  85.,  72.,\n",
       "         66.,  51.,  46.,  41.,  38.,  43.,  27.,  18.,  10.,  15.,  16.,\n",
       "         11.,  10.,   8.,  10.,   5.,   9.,   4.,   9.,   8.,   6.,   6.,\n",
       "          6.,   4.,   5.,   0.,   0.,   1.,   0.,   0.,   0.,   0.,   1.,\n",
       "          3.,   0.,   0.,   1.,   0.,   1.]),\n",
       " array([3.46875000e+00, 6.15337062e+03, 1.23032725e+04, 1.84531744e+04,\n",
       "        2.46030762e+04, 3.07529781e+04, 3.69028800e+04, 4.30527819e+04,\n",
       "        4.92026837e+04, 5.53525856e+04, 6.15024875e+04, 6.76523894e+04,\n",
       "        7.38022912e+04, 7.99521931e+04, 8.61020950e+04, 9.22519969e+04,\n",
       "        9.84018987e+04, 1.04551801e+05, 1.10701702e+05, 1.16851604e+05,\n",
       "        1.23001506e+05, 1.29151408e+05, 1.35301310e+05, 1.41451212e+05,\n",
       "        1.47601114e+05, 1.53751016e+05, 1.59900917e+05, 1.66050819e+05,\n",
       "        1.72200721e+05, 1.78350623e+05, 1.84500525e+05, 1.90650427e+05,\n",
       "        1.96800329e+05, 2.02950231e+05, 2.09100132e+05, 2.15250034e+05,\n",
       "        2.21399936e+05, 2.27549838e+05, 2.33699740e+05, 2.39849642e+05,\n",
       "        2.45999544e+05, 2.52149446e+05, 2.58299347e+05, 2.64449249e+05,\n",
       "        2.70599151e+05, 2.76749053e+05, 2.82898955e+05, 2.89048857e+05,\n",
       "        2.95198759e+05, 3.01348661e+05, 3.07498562e+05]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax1.grid(b=True, linestyle='--')\n",
    "ax1.set_title('Training Error')\n",
    "ax1.hist(np.abs(Y_train-Y_train_pred), bins=50, color='#6A89CC', edgecolor='#000000')\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Testing Error')\n",
    "ax2.hist(np.abs(Y_test-Y_test_pred), bins=50, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is performing better than the last one, so we take it as the new baseline model to beat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define our fifth model and evaluate its performance. This new model will boost the last model. It will be an ensemble of that one and the one we trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_train_1 = Y_train - model_4.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NN_2():\n",
    "    model = tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Lambda(function=lambda x: (x-a)/(b-a),\n",
    "                                     input_shape=[X_train.shape[1]]))\n",
    "    model.add(tf.keras.layers.Dense(units=256, \n",
    "                                    activation='relu', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    model.add(tf.keras.layers.Dropout(rate=0.5))\n",
    "    model.add(tf.keras.layers.Dense(units=256, \n",
    "                                    activation='relu', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    model.add(tf.keras.layers.Dropout(rate=0.5))\n",
    "    model.add(tf.keras.layers.Dense(R_train_1.shape[1], \n",
    "                                    activation='linear', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01, \n",
    "                                         beta_1=0.9, \n",
    "                                         beta_2=0.999, \n",
    "                                         epsilon=1e-07)\n",
    "    model.compile(loss='mse', metrics=['mae'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', \n",
    "                                                     patience=100,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1, \n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=200,\n",
    "                                                 min_delta=0.001)\n",
    "    model.fit(X_train, R_train_1, epochs=1000, batch_size=64, callbacks=[callback1, callback2], verbose=True)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model_5():\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.nn = NN_2()\n",
    "    \n",
    "    def predict(self, X):\n",
    "        A1 = model_4.predict(X)\n",
    "        A2 = self.nn.predict(X)\n",
    "        return A1+A2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda_1 (Lambda)            (None, 55)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               14336     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 80,385\n",
      "Trainable params: 80,385\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 6756 samples\n",
      "Epoch 1/1000\n",
      "6756/6756 [==============================] - 0s 68us/sample - loss: 4910497081.4446 - mae: 53698.9414\n",
      "Epoch 2/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 4910454873.4257 - mae: 53704.5352\n",
      "Epoch 3/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4910554727.6732 - mae: 53718.2344\n",
      "Epoch 4/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4908449126.4606 - mae: 53712.1250\n",
      "Epoch 5/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 4900367817.4352 - mae: 53751.6016\n",
      "Epoch 6/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4871908154.9603 - mae: 53545.4336\n",
      "Epoch 7/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 4812848979.5145 - mae: 53231.0625\n",
      "Epoch 8/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 4759247387.1308 - mae: 52801.3203\n",
      "Epoch 9/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4709532518.3091 - mae: 52479.2617\n",
      "Epoch 10/1000\n",
      "6756/6756 [==============================] - 0s 30us/sample - loss: 4668442113.5157 - mae: 52159.6484\n",
      "Epoch 11/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 4652293258.6856 - mae: 52108.3867\n",
      "Epoch 12/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 4616761692.9118 - mae: 51784.6055\n",
      "Epoch 13/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4587491534.4369 - mae: 51553.6328\n",
      "Epoch 14/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4566089482.1551 - mae: 51367.9570\n",
      "Epoch 15/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4577953264.5400 - mae: 51510.6094\n",
      "Epoch 16/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4546351246.7780 - mae: 51240.4688\n",
      "Epoch 17/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 4546062612.4618 - mae: 51153.2188\n",
      "Epoch 18/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4539289089.2126 - mae: 51211.0977\n",
      "Epoch 19/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4531990461.0065 - mae: 50951.7734\n",
      "Epoch 20/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4506574435.1261 - mae: 50933.3398\n",
      "Epoch 21/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4510048107.4624 - mae: 51031.5156\n",
      "Epoch 22/1000\n",
      "6756/6756 [==============================] - 0s 30us/sample - loss: 4511451576.0047 - mae: 50789.6602\n",
      "Epoch 23/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4499486096.1421 - mae: 50860.4258\n",
      "Epoch 24/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4498754939.2256 - mae: 50847.9492\n",
      "Epoch 25/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4497832327.3511 - mae: 50677.7344\n",
      "Epoch 26/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4493945989.9870 - mae: 50587.4375\n",
      "Epoch 27/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4508879076.2629 - mae: 50858.4023\n",
      "Epoch 28/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4463240542.1243 - mae: 50535.8633\n",
      "Epoch 29/1000\n",
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      "Epoch 30/1000\n",
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      "Epoch 31/1000\n",
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      "Epoch 32/1000\n",
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      "Epoch 33/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4474312700.6655 - mae: 50491.7656\n",
      "Epoch 34/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4431941496.9520 - mae: 50246.5078\n",
      "Epoch 35/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4454908480.2652 - mae: 50416.0820\n",
      "Epoch 36/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4423409759.1853 - mae: 50243.5391\n",
      "Epoch 37/1000\n",
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      "Epoch 38/1000\n",
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      "Epoch 39/1000\n",
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      "Epoch 40/1000\n",
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      "Epoch 41/1000\n",
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      "Epoch 42/1000\n",
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      "Epoch 43/1000\n",
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      "Epoch 44/1000\n",
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      "Epoch 45/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 4421305574.0817 - mae: 49948.8555\n",
      "Epoch 46/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4402087302.8964 - mae: 50146.0898\n",
      "Epoch 47/1000\n",
      "6756/6756 [==============================] - 0s 28us/sample - loss: 4431265804.1255 - mae: 50261.8125\n",
      "Epoch 48/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 4424999811.1072 - mae: 50116.6094\n",
      "Epoch 49/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 4399090988.1066 - mae: 49929.3203\n",
      "Epoch 50/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 4401893234.7377 - mae: 49968.6250\n",
      "Epoch 51/1000\n",
      "6756/6756 [==============================] - 0s 29us/sample - loss: 4413346245.1912 - mae: 50080.3828\n",
      "Epoch 52/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4396735025.7146 - mae: 49977.3516\n",
      "Epoch 53/1000\n",
      "6756/6756 [==============================] - 0s 29us/sample - loss: 4382422273.9704 - mae: 49946.4062\n",
      "Epoch 54/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4380757071.4221 - mae: 49993.8203\n",
      "Epoch 55/1000\n",
      "6756/6756 [==============================] - 0s 30us/sample - loss: 4392027351.8342 - mae: 49990.6016\n",
      "Epoch 56/1000\n",
      "6756/6756 [==============================] - 0s 28us/sample - loss: 4380601889.6483 - mae: 49784.3477\n",
      "Epoch 57/1000\n",
      "6756/6756 [==============================] - 0s 29us/sample - loss: 4399517695.6969 - mae: 50079.4297\n",
      "Epoch 58/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4408712216.8573 - mae: 49980.5938\n",
      "Epoch 59/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4382271120.2937 - mae: 49945.9805\n",
      "Epoch 60/1000\n",
      "6756/6756 [==============================] - 0s 29us/sample - loss: 4376942259.4577 - mae: 49968.6953\n",
      "Epoch 61/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 27us/sample - loss: 4383102443.6898 - mae: 49916.3047\n",
      "Epoch 62/1000\n",
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      "Epoch 63/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4375310473.3215 - mae: 49894.4844\n",
      "Epoch 64/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 4370247539.9503 - mae: 49727.8281\n",
      "Epoch 65/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4368099575.0574 - mae: 49719.3906\n",
      "Epoch 66/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 4371799597.7738 - mae: 49841.7188\n",
      "Epoch 67/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4372988299.2919 - mae: 49805.6211\n",
      "Epoch 68/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 4389340456.1658 - mae: 49865.6289\n",
      "Epoch 69/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4390200314.2404 - mae: 49841.5820\n",
      "Epoch 70/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4373289580.0687 - mae: 49849.6055\n",
      "Epoch 71/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 4354478249.1510 - mae: 49784.8320\n",
      "Epoch 72/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4374285943.1332 - mae: 49817.2031\n",
      "Epoch 73/1000\n",
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      "Epoch 74/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 4347257757.7833 - mae: 49583.0430\n",
      "Epoch 75/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 4360370593.7241 - mae: 49783.0664\n",
      "Epoch 76/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4382457648.0474 - mae: 49856.8047\n",
      "Epoch 77/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4340232106.2120 - mae: 49612.6641\n",
      "Epoch 78/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4365076015.8958 - mae: 49726.6094\n",
      "Epoch 79/1000\n",
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      "Epoch 80/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 4356171015.5784 - mae: 49597.6016\n",
      "Epoch 81/1000\n",
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      "Epoch 82/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 4339509813.9586 - mae: 49567.4375\n",
      "Epoch 83/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 4359072114.7377 - mae: 49672.8398\n",
      "Epoch 84/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 4324919113.2078 - mae: 49554.9258\n",
      "Epoch 85/1000\n",
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      "Epoch 86/1000\n",
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      "Epoch 87/1000\n",
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      "Epoch 88/1000\n",
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      "Epoch 89/1000\n",
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      "Epoch 90/1000\n",
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      "Epoch 91/1000\n",
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      "Epoch 92/1000\n",
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      "Epoch 93/1000\n",
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      "Epoch 94/1000\n",
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      "Epoch 95/1000\n",
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      "Epoch 96/1000\n",
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      "Epoch 97/1000\n",
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      "Epoch 98/1000\n",
      "6756/6756 [==============================] - 0s 30us/sample - loss: 4336570580.3481 - mae: 49501.5625\n",
      "Epoch 99/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 4338288218.0320 - mae: 49557.7695\n",
      "Epoch 100/1000\n",
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      "Epoch 101/1000\n",
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      "Epoch 102/1000\n",
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      "Epoch 103/1000\n",
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      "Epoch 104/1000\n",
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      "Epoch 105/1000\n",
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      "Epoch 106/1000\n",
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      "Epoch 107/1000\n",
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      "Epoch 108/1000\n",
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      "Epoch 109/1000\n",
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      "Epoch 110/1000\n",
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      "Epoch 111/1000\n",
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      "Epoch 112/1000\n",
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      "Epoch 113/1000\n",
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      "Epoch 114/1000\n",
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      "Epoch 115/1000\n",
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      "Epoch 116/1000\n",
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      "Epoch 117/1000\n",
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      "Epoch 118/1000\n",
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      "Epoch 119/1000\n",
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      "Epoch 120/1000\n",
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      "Epoch 121/1000\n",
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      "Epoch 122/1000\n",
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      "Epoch 123/1000\n",
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      "Epoch 124/1000\n",
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      "Epoch 125/1000\n",
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      "Epoch 127/1000\n",
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      "Epoch 128/1000\n",
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      "Epoch 129/1000\n",
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      "Epoch 130/1000\n",
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      "Epoch 131/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 28us/sample - loss: 4321067968.7957 - mae: 49369.2930\n",
      "Epoch 132/1000\n",
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      "Epoch 133/1000\n",
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      "Epoch 134/1000\n",
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      "Epoch 135/1000\n",
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      "Epoch 136/1000\n",
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      "Epoch 137/1000\n",
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      "Epoch 138/1000\n",
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      "Epoch 139/1000\n",
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      "Epoch 140/1000\n",
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      "Epoch 141/1000\n",
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      "Epoch 142/1000\n",
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      "Epoch 143/1000\n",
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      "Epoch 144/1000\n",
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      "Epoch 145/1000\n",
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      "Epoch 146/1000\n",
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      "Epoch 147/1000\n",
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      "Epoch 148/1000\n",
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      "Epoch 149/1000\n",
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      "Epoch 150/1000\n",
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      "Epoch 151/1000\n",
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     ]
    },
    {
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     ]
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     ]
    },
    {
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     ]
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
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     ]
    },
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     ]
    },
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      "6756/6756 [==============================] - 0s 25us/sample - loss: 2998852700.3055 - mae: 40475.9414\n",
      "Epoch 902/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 3054080614.8396 - mae: 40744.7578\n",
      "Epoch 903/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3047271271.2185 - mae: 40755.7852\n",
      "Epoch 904/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 3099269533.7833 - mae: 40985.9023\n",
      "Epoch 905/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 3060034978.3304 - mae: 40553.3828\n",
      "Epoch 906/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3083609268.6702 - mae: 40882.1992\n",
      "Epoch 907/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3063580850.2451 - mae: 40819.1797\n",
      "Epoch 908/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 3005893113.6341 - mae: 40507.8359\n",
      "Epoch 909/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2998011540.2345 - mae: 40214.7070\n",
      "Epoch 910/1000\n",
      "6756/6756 [==============================] - 0s 29us/sample - loss: 3078437215.1853 - mae: 41123.7500\n",
      "Epoch 911/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3064910337.9704 - mae: 41069.6523\n",
      "Epoch 912/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2984633889.9515 - mae: 40263.6953\n",
      "Epoch 913/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3011066141.2528 - mae: 40527.2852\n",
      "Epoch 914/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 3056468010.7425 - mae: 40990.9922\n",
      "Epoch 915/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2981723786.8372 - mae: 40222.0078\n",
      "Epoch 916/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3020935400.5068 - mae: 40653.7969\n",
      "Epoch 917/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 3066120143.0432 - mae: 40718.6875\n",
      "Epoch 918/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3085549007.6495 - mae: 40816.6328\n",
      "Epoch 919/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3092212919.7016 - mae: 41180.9453\n",
      "Epoch 920/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3051188984.2700 - mae: 40794.1406\n",
      "Epoch 921/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 3018173495.1711 - mae: 40770.3398\n",
      "Epoch 922/1000\n",
      "6756/6756 [==============================] - 0s 28us/sample - loss: 3051142745.1226 - mae: 40327.0039\n",
      "Epoch 923/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3097619351.7205 - mae: 40970.2031\n",
      "Epoch 924/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2977009179.1308 - mae: 40357.2852\n",
      "Epoch 925/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 3008876308.7649 - mae: 40423.4609\n",
      "Epoch 926/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 3032745383.4837 - mae: 40746.3320\n",
      "Epoch 927/1000\n",
      "6756/6756 [==============================] - 0s 29us/sample - loss: 3023472245.3144 - mae: 40525.6680\n",
      "Epoch 928/1000\n",
      "6756/6756 [==============================] - 0s 30us/sample - loss: 3026218288.9568 - mae: 40594.8164\n",
      "Epoch 929/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 3014174329.1036 - mae: 40608.3359\n",
      "Epoch 930/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3031823620.0924 - mae: 40564.7070\n",
      "Epoch 931/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 3014489937.9988 - mae: 40558.3594\n",
      "Epoch 932/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 3080480549.1344 - mae: 40945.8281\n",
      "Epoch 933/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 3078523217.8472 - mae: 41039.7969\n",
      "Epoch 934/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 3036460362.8751 - mae: 40621.0859\n",
      "Epoch 935/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 3027689449.5678 - mae: 40519.5820\n",
      "Epoch 936/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3070860593.2599 - mae: 40760.0000\n",
      "Epoch 937/1000\n",
      "6756/6756 [==============================] - 0s 24us/sample - loss: 3013969070.6075 - mae: 40705.1445\n",
      "Epoch 938/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 3037956143.5927 - mae: 40542.0156\n",
      "Epoch 939/1000\n",
      "6756/6756 [==============================] - 0s 28us/sample - loss: 3103253922.6335 - mae: 41133.6133\n",
      "Epoch 940/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 3029670749.2149 - mae: 40738.9141\n",
      "Epoch 941/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3073208010.7235 - mae: 40926.0117\n",
      "Epoch 942/1000\n",
      "6756/6756 [==============================] - 0s 28us/sample - loss: 3083966005.5796 - mae: 41084.0078\n",
      "Epoch 943/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 3079441485.6033 - mae: 40839.0938\n",
      "Epoch 944/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 3016815923.6850 - mae: 40430.6680\n",
      "Epoch 945/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 3060519490.0841 - mae: 40803.2227\n",
      "Epoch 946/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 3043276486.5554 - mae: 40766.1953\n",
      "Epoch 947/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2929860407.6258 - mae: 39599.6133\n",
      "Epoch 948/1000\n",
      "6756/6756 [==============================] - 0s 29us/sample - loss: 2830906511.0811 - mae: 39087.5234\n",
      "Epoch 949/1000\n",
      "6756/6756 [==============================] - 0s 28us/sample - loss: 2792909890.0841 - mae: 38914.3984\n",
      "Epoch 950/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2755729447.3321 - mae: 38639.7383\n",
      "Epoch 951/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2848431416.0805 - mae: 39205.3711\n",
      "Epoch 952/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2818348266.6288 - mae: 39032.3164\n",
      "Epoch 953/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2780396345.2173 - mae: 38743.4336\n",
      "Epoch 954/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2775308671.4695 - mae: 38511.9336\n",
      "Epoch 955/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2741450255.1569 - mae: 38449.9219\n",
      "Epoch 956/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2764618969.6530 - mae: 38734.1914\n",
      "Epoch 957/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2740694982.7069 - mae: 38661.5234\n",
      "Epoch 958/1000\n",
      "6756/6756 [==============================] - 0s 28us/sample - loss: 2767288564.1018 - mae: 38730.0078\n",
      "Epoch 959/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2772098750.8253 - mae: 38748.5000\n",
      "Epoch 960/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2712662736.7105 - mae: 38448.4141\n",
      "Epoch 961/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2741726949.7786 - mae: 38550.4844\n",
      "Epoch 962/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2740765344.2084 - mae: 38397.8750\n",
      "Epoch 963/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2688222412.6181 - mae: 38166.9023\n",
      "Epoch 964/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2696506697.2078 - mae: 38153.7344\n",
      "Epoch 965/1000\n",
      "6756/6756 [==============================] - 0s 29us/sample - loss: 2708782924.0876 - mae: 38349.9180\n",
      "Epoch 966/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2739374877.1012 - mae: 38360.3945\n",
      "Epoch 967/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2734914886.3280 - mae: 38515.8750\n",
      "Epoch 968/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2684800664.7815 - mae: 38196.6680\n",
      "Epoch 969/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2758795258.5435 - mae: 38352.6758\n",
      "Epoch 970/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2778857778.1693 - mae: 38842.1289\n",
      "Epoch 971/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2781776967.2374 - mae: 38857.2188\n",
      "Epoch 972/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2699788701.4423 - mae: 38094.9961\n",
      "Epoch 973/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2713567456.0189 - mae: 38408.6289\n",
      "Epoch 974/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2740742093.0728 - mae: 38571.9375\n",
      "Epoch 975/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2696141615.0622 - mae: 38200.0469\n",
      "Epoch 976/1000\n",
      "6756/6756 [==============================] - 0s 28us/sample - loss: 2740363520.7578 - mae: 38373.6055\n",
      "Epoch 977/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2716832344.8194 - mae: 38287.4609\n",
      "Epoch 978/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2746950122.7803 - mae: 38487.5898\n",
      "Epoch 979/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2716594435.4861 - mae: 38198.5000\n",
      "Epoch 980/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2757828869.1533 - mae: 38395.5820\n",
      "Epoch 981/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2682450562.8798 - mae: 38072.5078\n",
      "Epoch 982/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2722319705.2741 - mae: 38363.9883\n",
      "Epoch 983/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2764698505.6246 - mae: 38713.4336\n",
      "Epoch 984/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2723130260.9923 - mae: 38550.6797\n",
      "Epoch 985/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2685721303.5311 - mae: 38060.1562\n",
      "Epoch 986/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2727297035.1403 - mae: 38209.4570\n",
      "Epoch 987/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2700680705.5157 - mae: 38123.4219\n",
      "Epoch 988/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2695837727.8295 - mae: 38161.4531\n",
      "Epoch 989/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2746212003.5429 - mae: 38446.3086\n",
      "Epoch 990/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2708442289.0326 - mae: 38126.2070\n",
      "Epoch 991/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2719072907.4435 - mae: 38361.9922\n",
      "Epoch 992/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2681290391.2658 - mae: 38120.7148\n",
      "Epoch 993/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2716787985.2789 - mae: 38247.8594\n",
      "Epoch 994/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2753520787.4766 - mae: 38552.8516\n",
      "Epoch 995/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2694113826.2546 - mae: 38049.4336\n",
      "Epoch 996/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2692303282.6998 - mae: 38043.7305\n",
      "Epoch 997/1000\n",
      "6756/6756 [==============================] - 0s 27us/sample - loss: 2732765520.6347 - mae: 38485.1719\n",
      "Epoch 998/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2745642218.3256 - mae: 38545.8203\n",
      "Epoch 999/1000\n",
      "6756/6756 [==============================] - 0s 25us/sample - loss: 2758903703.1143 - mae: 38694.9883\n",
      "Epoch 1000/1000\n",
      "6756/6756 [==============================] - 0s 26us/sample - loss: 2713068434.8703 - mae: 38364.5352\n"
     ]
    }
   ],
   "source": [
    "model_5 = Model_5()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37038.456670274566\n"
     ]
    }
   ],
   "source": [
    "Y_train_pred = model_5.predict(X_train)\n",
    "\n",
    "np.mean(np.abs(Y_train-Y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38711.744970273133\n"
     ]
    }
   ],
   "source": [
    "Y_test_pred = model_5.predict(X_test)\n",
    "\n",
    "np.mean(np.abs(Y_test-Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([205., 211., 179., 161., 181., 124.,  99.,  97.,  81.,  52.,  44.,\n",
       "         40.,  30.,  29.,  14.,  18.,  27.,   6.,  10.,  13.,  12.,   7.,\n",
       "          5.,   9.,   5.,   6.,   3.,   4.,   4.,   1.,   2.,   1.,   0.,\n",
       "          3.,   0.,   1.,   2.,   0.,   0.,   1.,   0.,   0.,   0.,   0.,\n",
       "          1.,   0.,   0.,   0.,   0.,   1.]),\n",
       " array([3.86562500e+01, 6.46257969e+03, 1.28865031e+04, 1.93104266e+04,\n",
       "        2.57343500e+04, 3.21582734e+04, 3.85821969e+04, 4.50061203e+04,\n",
       "        5.14300437e+04, 5.78539672e+04, 6.42778906e+04, 7.07018141e+04,\n",
       "        7.71257375e+04, 8.35496609e+04, 8.99735844e+04, 9.63975078e+04,\n",
       "        1.02821431e+05, 1.09245355e+05, 1.15669278e+05, 1.22093202e+05,\n",
       "        1.28517125e+05, 1.34941048e+05, 1.41364972e+05, 1.47788895e+05,\n",
       "        1.54212819e+05, 1.60636742e+05, 1.67060666e+05, 1.73484589e+05,\n",
       "        1.79908512e+05, 1.86332436e+05, 1.92756359e+05, 1.99180283e+05,\n",
       "        2.05604206e+05, 2.12028130e+05, 2.18452053e+05, 2.24875977e+05,\n",
       "        2.31299900e+05, 2.37723823e+05, 2.44147747e+05, 2.50571670e+05,\n",
       "        2.56995594e+05, 2.63419517e+05, 2.69843441e+05, 2.76267364e+05,\n",
       "        2.82691287e+05, 2.89115211e+05, 2.95539134e+05, 3.01963058e+05,\n",
       "        3.08386981e+05, 3.14810905e+05, 3.21234828e+05]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax1.grid(b=True, linestyle='--')\n",
    "ax1.set_title('Training Error')\n",
    "ax1.hist(np.abs(Y_train-Y_train_pred), bins=50, color='#6A89CC', edgecolor='#000000')\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Testing Error')\n",
    "ax2.hist(np.abs(Y_test-Y_test_pred), bins=50, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is performing better than the last one. It is overfitting a bit but nevertheless we take it as the new baseline model to beat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define our sixth and last model and evaluate its performance. This new model will boost the last model. It will be an ensemble of that one and the one we trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_train_2 = Y_train - model_5.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NN_3():\n",
    "    model = tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Lambda(function=lambda x: (x-a)/(b-a),\n",
    "                                     input_shape=[X_train.shape[1]]))\n",
    "    model.add(tf.keras.layers.Dense(units=256, \n",
    "                                    activation='relu', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    model.add(tf.keras.layers.Dropout(rate=0.5))\n",
    "    model.add(tf.keras.layers.Dense(units=256, \n",
    "                                    activation='relu', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    model.add(tf.keras.layers.Dropout(rate=0.5))\n",
    "    model.add(tf.keras.layers.Dense(units=256, \n",
    "                                    activation='relu', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    model.add(tf.keras.layers.Dropout(rate=0.5))\n",
    "    model.add(tf.keras.layers.Dense(R_train_2.shape[1], \n",
    "                                    activation='linear', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01, \n",
    "                                         beta_1=0.9, \n",
    "                                         beta_2=0.999, \n",
    "                                         epsilon=1e-07)\n",
    "    model.compile(loss='mse', metrics=['mae'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', \n",
    "                                                     patience=100,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1, \n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=200,\n",
    "                                                 min_delta=0.001)\n",
    "    model.fit(X_train, R_train_2, epochs=1000, batch_size=64, callbacks=[callback1, callback2], verbose=True)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model_6():\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.nn = NN_3()\n",
    "    \n",
    "    def predict(self, X):\n",
    "        A1 = model_5.predict(X)\n",
    "        A2 = self.nn.predict(X)\n",
    "        return A1+A2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda_2 (Lambda)            (None, 55)                0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 256)               14336     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 146,177\n",
      "Trainable params: 146,177\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 6756 samples\n",
      "Epoch 1/1000\n",
      "6756/6756 [==============================] - 1s 119us/sample - loss: 2381986185.0184 - mae: 35451.6055\n",
      "Epoch 2/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2375095578.8277 - mae: 35597.6562\n",
      "Epoch 3/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2374277487.7063 - mae: 35569.9062\n",
      "Epoch 4/1000\n",
      "6756/6756 [==============================] - 0s 30us/sample - loss: 2372450794.9319 - mae: 35569.7148\n",
      "Epoch 5/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2370602072.5163 - mae: 35658.9727\n",
      "Epoch 6/1000\n",
      "6756/6756 [==============================] - 0s 34us/sample - loss: 2371234936.1942 - mae: 35517.2305\n",
      "Epoch 7/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2369178330.0320 - mae: 35670.6016\n",
      "Epoch 8/1000\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
    {
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     ]
    },
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     ]
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    {
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     ]
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     ]
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     ]
    },
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      "Epoch 613/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2352636349.6128 - mae: 35308.5000\n",
      "Epoch 614/1000\n",
      "6756/6756 [==============================] - 0s 35us/sample - loss: 2353222839.0953 - mae: 35310.7461\n",
      "Epoch 615/1000\n",
      "6756/6756 [==============================] - 0s 35us/sample - loss: 2351485521.9988 - mae: 35300.9219\n",
      "Epoch 616/1000\n",
      "6756/6756 [==============================] - 0s 35us/sample - loss: 2348911874.5767 - mae: 35297.4922\n",
      "Epoch 617/1000\n",
      "6756/6756 [==============================] - 0s 38us/sample - loss: 2353280379.8319 - mae: 35335.4883\n",
      "Epoch 618/1000\n",
      "6756/6756 [==============================] - 0s 36us/sample - loss: 2350550643.7987 - mae: 35312.2266\n",
      "Epoch 619/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 34us/sample - loss: 2350031608.2700 - mae: 35318.0781\n",
      "Epoch 620/1000\n",
      "6756/6756 [==============================] - 0s 35us/sample - loss: 2354656719.4979 - mae: 35312.1406\n",
      "Epoch 621/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2352738334.0107 - mae: 35322.3008\n",
      "Epoch 622/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2352452778.9698 - mae: 35320.1758\n",
      "Epoch 623/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2353392371.6471 - mae: 35313.2539\n",
      "Epoch 624/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2353783444.0071 - mae: 35342.5430\n",
      "Epoch 625/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2351663053.0728 - mae: 35284.9414\n",
      "Epoch 626/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2348858796.7128 - mae: 35292.4961\n",
      "Epoch 627/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2352379560.2416 - mae: 35308.1953\n",
      "Epoch 628/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2352699364.4144 - mae: 35339.5703\n",
      "Epoch 629/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2349938260.2724 - mae: 35285.0508\n",
      "Epoch 630/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2351650128.9378 - mae: 35319.9102\n",
      "Epoch 631/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2346635022.2475 - mae: 35273.3477\n",
      "Epoch 632/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2344978914.2925 - mae: 35285.7773\n",
      "Epoch 633/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2354543058.3020 - mae: 35338.6094\n",
      "Epoch 634/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2354510200.8005 - mae: 35297.2422\n",
      "Epoch 635/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2351108418.3872 - mae: 35285.4531\n",
      "Epoch 636/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2354169830.8396 - mae: 35334.6211\n",
      "Epoch 637/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2353915561.4541 - mae: 35317.0625\n",
      "Epoch 638/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2352653178.9224 - mae: 35343.9883\n",
      "Epoch 639/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2348343456.5115 - mae: 35269.1055\n",
      "Epoch 640/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2349182001.4873 - mae: 35267.9609\n",
      "Epoch 641/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2352932425.2078 - mae: 35314.8477\n",
      "Epoch 642/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2352706988.3339 - mae: 35299.6406\n",
      "Epoch 643/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2350691778.2356 - mae: 35301.5742\n",
      "Epoch 644/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2351643537.9609 - mae: 35296.1250\n",
      "Epoch 645/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2352266268.3434 - mae: 35308.7891\n",
      "Epoch 646/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2350100783.5927 - mae: 35326.0195\n",
      "Epoch 647/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2350341833.4352 - mae: 35283.0000\n",
      "Epoch 648/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2352406721.5536 - mae: 35341.2422\n",
      "Epoch 649/1000\n",
      "6756/6756 [==============================] - 0s 30us/sample - loss: 2352642409.3404 - mae: 35348.3516\n",
      "Epoch 650/1000\n",
      "6756/6756 [==============================] - 0s 37us/sample - loss: 2355248385.9704 - mae: 35364.7695\n",
      "Epoch 651/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2353375222.6027 - mae: 35301.5508\n",
      "Epoch 652/1000\n",
      "6756/6756 [==============================] - 0s 35us/sample - loss: 2351473265.9799 - mae: 35305.0391\n",
      "Epoch 653/1000\n",
      "6756/6756 [==============================] - 0s 36us/sample - loss: 2350244643.8461 - mae: 35318.2266\n",
      "Epoch 654/1000\n",
      "6756/6756 [==============================] - 0s 38us/sample - loss: 2353605764.6229 - mae: 35298.8008\n",
      "Epoch 655/1000\n",
      "6756/6756 [==============================] - 0s 38us/sample - loss: 2352357550.6075 - mae: 35325.4883\n",
      "Epoch 656/1000\n",
      "6756/6756 [==============================] - 0s 36us/sample - loss: 2350875577.2173 - mae: 35287.8984\n",
      "Epoch 657/1000\n",
      "6756/6756 [==============================] - 0s 36us/sample - loss: 2352945706.2877 - mae: 35313.6484\n",
      "Epoch 658/1000\n",
      "6756/6756 [==============================] - 0s 40us/sample - loss: 2350553758.6169 - mae: 35305.1602\n",
      "Epoch 659/1000\n",
      "6756/6756 [==============================] - 0s 35us/sample - loss: 2350395531.2919 - mae: 35279.5508\n",
      "Epoch 660/1000\n",
      "6756/6756 [==============================] - 0s 41us/sample - loss: 2350967859.6092 - mae: 35282.9570\n",
      "Epoch 661/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2349481174.1670 - mae: 35311.5820\n",
      "Epoch 662/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2353891277.4517 - mae: 35299.0156\n",
      "Epoch 663/1000\n",
      "6756/6756 [==============================] - 0s 34us/sample - loss: 2350256609.9136 - mae: 35295.8438\n",
      "Epoch 664/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2351248432.1989 - mae: 35272.8125\n",
      "Epoch 665/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2354575326.5033 - mae: 35315.5938\n",
      "Epoch 666/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2352718481.9609 - mae: 35277.9648\n",
      "Epoch 667/1000\n",
      "6756/6756 [==============================] - 0s 34us/sample - loss: 2354314318.9674 - mae: 35338.8789\n",
      "Epoch 668/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2351381277.1770 - mae: 35318.1914\n",
      "Epoch 669/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2349209814.3943 - mae: 35263.3672\n",
      "Epoch 670/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2351155414.8490 - mae: 35312.1445\n",
      "Epoch 671/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2355248988.9118 - mae: 35345.3945\n",
      "Epoch 672/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2352814696.2795 - mae: 35347.3125\n",
      "Epoch 673/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2352512267.9739 - mae: 35311.7031\n",
      "Epoch 674/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2349687305.8520 - mae: 35286.8867\n",
      "Epoch 675/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2352866285.8875 - mae: 35315.1914\n",
      "Epoch 676/1000\n",
      "6756/6756 [==============================] - 0s 54us/sample - loss: 2352745622.3564 - mae: 35310.6250\n",
      "Epoch 677/1000\n",
      "6756/6756 [==============================] - 0s 36us/sample - loss: 2355304038.7638 - mae: 35319.2461\n",
      "Epoch 678/1000\n",
      "6756/6756 [==============================] - 0s 33us/sample - loss: 2357865785.1415 - mae: 35353.6523\n",
      "Epoch 679/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2353501744.8052 - mae: 35323.5938\n",
      "Epoch 680/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2353944289.9893 - mae: 35323.9688\n",
      "Epoch 681/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2351712301.7738 - mae: 35325.4688\n",
      "Epoch 682/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2354421942.3375 - mae: 35332.2695\n",
      "Epoch 683/1000\n",
      "6756/6756 [==============================] - 0s 35us/sample - loss: 2353401939.2114 - mae: 35312.8047\n",
      "Epoch 684/1000\n",
      "6756/6756 [==============================] - 0s 34us/sample - loss: 2350640291.9218 - mae: 35331.0195\n",
      "Epoch 685/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2353586589.8591 - mae: 35298.9883\n",
      "Epoch 686/1000\n",
      "6756/6756 [==============================] - 0s 32us/sample - loss: 2352702929.0136 - mae: 35298.5430\n",
      "Epoch 687/1000\n",
      "6756/6756 [==============================] - 0s 31us/sample - loss: 2353586074.4488 - mae: 35324.7500\n",
      "Epoch 688/1000\n",
      "6756/6756 [==============================] - 0s 43us/sample - loss: 2353615517.1770 - mae: 35285.4258\n",
      "Epoch 689/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6756/6756 [==============================] - 0s 34us/sample - loss: 2353981013.9396 - mae: 35320.9805\n"
     ]
    }
   ],
   "source": [
    "model_6 = Model_6()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33230.53258926199\n"
     ]
    }
   ],
   "source": [
    "Y_train_pred = model_6.predict(X_train)\n",
    "\n",
    "np.mean(np.abs(Y_train-Y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32942.25261992857\n"
     ]
    }
   ],
   "source": [
    "Y_test_pred = model_6.predict(X_test)\n",
    "\n",
    "np.mean(np.abs(Y_test-Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([210., 200., 168., 155., 177., 135., 106.,  88.,  85.,  62.,  45.,\n",
       "         46.,  26.,  33.,  12.,  23.,  21.,  10.,   9.,  14.,   9.,   4.,\n",
       "          9.,   7.,   6.,   5.,   4.,   4.,   5.,   0.,   2.,   0.,   0.,\n",
       "          1.,   1.,   1.,   2.,   1.,   0.,   1.,   0.,   0.,   0.,   0.,\n",
       "          0.,   1.,   0.,   0.,   0.,   1.]),\n",
       " array([2.60937500e+00, 6.39749781e+03, 1.27923862e+04, 1.91872747e+04,\n",
       "        2.55821631e+04, 3.19770516e+04, 3.83719400e+04, 4.47668284e+04,\n",
       "        5.11617169e+04, 5.75566053e+04, 6.39514937e+04, 7.03463822e+04,\n",
       "        7.67412706e+04, 8.31361591e+04, 8.95310475e+04, 9.59259359e+04,\n",
       "        1.02320824e+05, 1.08715713e+05, 1.15110601e+05, 1.21505490e+05,\n",
       "        1.27900378e+05, 1.34295267e+05, 1.40690155e+05, 1.47085043e+05,\n",
       "        1.53479932e+05, 1.59874820e+05, 1.66269709e+05, 1.72664597e+05,\n",
       "        1.79059486e+05, 1.85454374e+05, 1.91849262e+05, 1.98244151e+05,\n",
       "        2.04639039e+05, 2.11033928e+05, 2.17428816e+05, 2.23823705e+05,\n",
       "        2.30218593e+05, 2.36613482e+05, 2.43008370e+05, 2.49403258e+05,\n",
       "        2.55798147e+05, 2.62193035e+05, 2.68587924e+05, 2.74982812e+05,\n",
       "        2.81377701e+05, 2.87772589e+05, 2.94167477e+05, 3.00562366e+05,\n",
       "        3.06957254e+05, 3.13352143e+05, 3.19747031e+05]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax1.grid(b=True, linestyle='--')\n",
    "ax1.set_title('Training Error')\n",
    "ax1.hist(np.abs(Y_train-Y_train_pred), bins=50, color='#6A89CC', edgecolor='#000000')\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "ax2.grid(b=True, linestyle='--')\n",
    "ax2.set_title('Testing Error')\n",
    "ax2.hist(np.abs(Y_test-Y_test_pred), bins=50, color='#6A89CC', edgecolor='#000000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is performing much better than the last one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This last model could be considered a candidate solution for the original problem presented. Additional exploration of hyperparameters would be necessary to make a final conclusion."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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