nyse_closing_price_prediction_520.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Modules\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/cxldata/datasets/project/ny_stock_prediction/prices-split-adjusted.csv', header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>date</th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>123.430000</td>\n",
       "      <td>125.839996</td>\n",
       "      <td>122.309998</td>\n",
       "      <td>126.250000</td>\n",
       "      <td>2163600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>125.239998</td>\n",
       "      <td>119.980003</td>\n",
       "      <td>119.940002</td>\n",
       "      <td>125.540001</td>\n",
       "      <td>2386400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>116.379997</td>\n",
       "      <td>114.949997</td>\n",
       "      <td>114.930000</td>\n",
       "      <td>119.739998</td>\n",
       "      <td>2489500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>115.480003</td>\n",
       "      <td>116.620003</td>\n",
       "      <td>113.500000</td>\n",
       "      <td>117.440002</td>\n",
       "      <td>2006300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-11</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>117.010002</td>\n",
       "      <td>114.970001</td>\n",
       "      <td>114.089996</td>\n",
       "      <td>117.330002</td>\n",
       "      <td>1408600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date symbol        open       close         low        high  \\\n",
       "0  2016-01-05   WLTW  123.430000  125.839996  122.309998  126.250000   \n",
       "1  2016-01-06   WLTW  125.239998  119.980003  119.940002  125.540001   \n",
       "2  2016-01-07   WLTW  116.379997  114.949997  114.930000  119.739998   \n",
       "3  2016-01-08   WLTW  115.480003  116.620003  113.500000  117.440002   \n",
       "4  2016-01-11   WLTW  117.010002  114.970001  114.089996  117.330002   \n",
       "\n",
       "      volume  \n",
       "0  2163600.0  \n",
       "1  2386400.0  \n",
       "2  2489500.0  \n",
       "3  2006300.0  \n",
       "4  1408600.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(851264, 7)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['WLTW', 'A', 'AAL', 'AAP', 'AAPL', 'ABC', 'ABT', 'ACN', 'ADBE',\n",
       "       'ADI', 'ADM', 'ADP', 'ADS', 'ADSK', 'AEE', 'AEP', 'AES', 'AET',\n",
       "       'AFL', 'AGN', 'AIG', 'AIZ', 'AJG', 'AKAM', 'ALB', 'ALK', 'ALL',\n",
       "       'ALXN', 'AMAT', 'AME', 'AMG', 'AMGN', 'AMP', 'AMT', 'AMZN', 'AN',\n",
       "       'ANTM', 'AON', 'APA', 'APC', 'APD', 'APH', 'ARNC', 'ATVI', 'AVB',\n",
       "       'AVGO', 'AVY', 'AWK', 'AXP', 'AYI', 'AZO', 'BA', 'BAC', 'BAX',\n",
       "       'BBBY', 'BBT', 'BBY', 'BCR', 'BDX', 'BEN', 'BHI', 'BIIB', 'BK',\n",
       "       'BLK', 'BLL', 'BMY', 'BSX', 'BWA', 'BXP', 'C', 'CA', 'CAG', 'CAH',\n",
       "       'CAT', 'CB', 'CBG', 'CBS', 'CCI', 'CCL', 'CELG', 'CERN', 'CF',\n",
       "       'CHD', 'CHK', 'CHRW', 'CI', 'CINF', 'CL', 'CLX', 'CMA', 'CMCSA',\n",
       "       'CME', 'CMG', 'CMI', 'CMS', 'CNC', 'CNP', 'COF', 'COG', 'COH',\n",
       "       'COL', 'COO', 'COP', 'COST', 'CPB', 'CRM', 'CSCO', 'CSX', 'CTAS',\n",
       "       'CTL', 'CTSH', 'CTXS', 'CVS', 'CVX', 'CXO', 'D', 'DAL', 'DD', 'DE',\n",
       "       'DFS', 'DG', 'DGX', 'DHI', 'DHR', 'DIS', 'DISCA', 'DISCK', 'DLR',\n",
       "       'DLTR', 'DNB', 'DOV', 'DOW', 'DPS', 'DRI', 'DTE', 'DUK', 'DVA',\n",
       "       'DVN', 'EA', 'EBAY', 'ECL', 'ED', 'EFX', 'EIX', 'EL', 'EMN', 'EMR',\n",
       "       'ENDP', 'EOG', 'EQIX', 'EQR', 'EQT', 'ES', 'ESRX', 'ESS', 'ETFC',\n",
       "       'ETN', 'ETR', 'EW', 'EXC', 'EXPD', 'EXPE', 'EXR', 'F', 'FAST',\n",
       "       'FCX', 'FDX', 'FE', 'FFIV', 'FIS', 'FISV', 'FITB', 'FL', 'FLIR',\n",
       "       'FLR', 'FLS', 'FMC', 'FOX', 'FOXA', 'FRT', 'FSLR', 'FTI', 'FTR',\n",
       "       'GD', 'GE', 'GGP', 'GILD', 'GIS', 'GLW', 'GOOG', 'GOOGL', 'GPC',\n",
       "       'GPN', 'GPS', 'GRMN', 'GS', 'GT', 'GWW', 'HAL', 'HAR', 'HAS',\n",
       "       'HBAN', 'HBI', 'HCN', 'HCP', 'HD', 'HES', 'HIG', 'HOG', 'HOLX',\n",
       "       'HON', 'HP', 'HPQ', 'HRB', 'HRL', 'HRS', 'HSIC', 'HST', 'HSY',\n",
       "       'HUM', 'IBM', 'ICE', 'IDXX', 'IFF', 'ILMN', 'INTC', 'INTU', 'IP',\n",
       "       'IPG', 'IR', 'IRM', 'ISRG', 'ITW', 'IVZ', 'JBHT', 'JCI', 'JEC',\n",
       "       'JNJ', 'JNPR', 'JPM', 'JWN', 'K', 'KEY', 'KIM', 'KLAC', 'KMB',\n",
       "       'KMX', 'KO', 'KR', 'KSS', 'KSU', 'L', 'LB', 'LEG', 'LEN', 'LH',\n",
       "       'LKQ', 'LLL', 'LLTC', 'LLY', 'LMT', 'LNC', 'LNT', 'LOW', 'LRCX',\n",
       "       'LUK', 'LUV', 'LVLT', 'M', 'MA', 'MAA', 'MAC', 'MAR', 'MAS', 'MAT',\n",
       "       'MCD', 'MCHP', 'MCK', 'MCO', 'MDLZ', 'MDT', 'MET', 'MHK', 'MJN',\n",
       "       'MKC', 'MLM', 'MMC', 'MMM', 'MNST', 'MO', 'MON', 'MOS', 'MRK',\n",
       "       'MRO', 'MSFT', 'MSI', 'MTB', 'MTD', 'MU', 'MUR', 'MYL', 'NBL',\n",
       "       'NDAQ', 'NEE', 'NEM', 'NFLX', 'NFX', 'NI', 'NKE', 'NOC', 'NOV',\n",
       "       'NRG', 'NSC', 'NTAP', 'NTRS', 'NUE', 'NVDA', 'NWL', 'O', 'OKE',\n",
       "       'OMC', 'ORCL', 'ORLY', 'OXY', 'PAYX', 'PBCT', 'PBI', 'PCAR', 'PCG',\n",
       "       'PCLN', 'PDCO', 'PEG', 'PEP', 'PFE', 'PFG', 'PG', 'PGR', 'PH',\n",
       "       'PHM', 'PKI', 'PLD', 'PM', 'PNC', 'PNR', 'PNW', 'PPG', 'PPL',\n",
       "       'PRGO', 'PRU', 'PSA', 'PVH', 'PWR', 'PX', 'PXD', 'QCOM', 'R',\n",
       "       'RAI', 'RCL', 'REGN', 'RF', 'RHI', 'RHT', 'RIG', 'RL', 'ROK',\n",
       "       'ROP', 'ROST', 'RRC', 'RSG', 'RTN', 'SBUX', 'SCG', 'SCHW', 'SE',\n",
       "       'SEE', 'SHW', 'SIG', 'SJM', 'SLB', 'SLG', 'SNA', 'SNI', 'SO',\n",
       "       'SPG', 'SPGI', 'SPLS', 'SRCL', 'SRE', 'STI', 'STT', 'STX', 'STZ',\n",
       "       'SWK', 'SWKS', 'SWN', 'SYK', 'SYMC', 'SYY', 'T', 'TAP', 'TDC',\n",
       "       'TEL', 'TGNA', 'TGT', 'TIF', 'TJX', 'TMK', 'TMO', 'TROW', 'TRV',\n",
       "       'TSCO', 'TSN', 'TSO', 'TSS', 'TWX', 'TXN', 'TXT', 'UAA', 'UAL',\n",
       "       'UDR', 'UHS', 'ULTA', 'UNH', 'UNM', 'UNP', 'UPS', 'URBN', 'URI',\n",
       "       'USB', 'UTX', 'V', 'VAR', 'VFC', 'VIAB', 'VLO', 'VMC', 'VNO',\n",
       "       'VRSK', 'VRSN', 'VRTX', 'VTR', 'VZ', 'WAT', 'WBA', 'WDC', 'WEC',\n",
       "       'WFC', 'WFM', 'WHR', 'WM', 'WMB', 'WMT', 'WU', 'WY', 'WYN', 'WYNN',\n",
       "       'XEC', 'XEL', 'XL', 'XLNX', 'XOM', 'XRAY', 'XRX', 'YHOO', 'YUM',\n",
       "       'ZBH', 'ZION', 'AIV', 'CHTR', 'LYB', 'GM', 'ABBV', 'DLPH', 'FB',\n",
       "       'FBHS', 'HCA', 'KMI', 'KORS', 'MPC', 'NLSN', 'PSX', 'TDG', 'TRIP',\n",
       "       'XYL', 'ZTS', 'COTY', 'MNK', 'NWS', 'NWSA', 'EVHC', 'ALLE', 'CFG',\n",
       "       'NAVI', 'QRVO', 'SYF', 'WRK', 'KHC', 'PYPL', 'HPE', 'CSRA', 'FTV'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.symbol.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_yahoo = df[df['symbol'] == 'YHOO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>date</th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>YHOO</td>\n",
       "      <td>16.940001</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>16.879999</td>\n",
       "      <td>17.200001</td>\n",
       "      <td>16587400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>YHOO</td>\n",
       "      <td>17.219999</td>\n",
       "      <td>17.230000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.230000</td>\n",
       "      <td>11718100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>YHOO</td>\n",
       "      <td>17.170000</td>\n",
       "      <td>17.170000</td>\n",
       "      <td>17.070000</td>\n",
       "      <td>17.299999</td>\n",
       "      <td>16422000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>YHOO</td>\n",
       "      <td>16.809999</td>\n",
       "      <td>16.700001</td>\n",
       "      <td>16.570000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>31816300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2585</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>YHOO</td>\n",
       "      <td>16.680000</td>\n",
       "      <td>16.700001</td>\n",
       "      <td>16.620001</td>\n",
       "      <td>16.760000</td>\n",
       "      <td>15470000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date symbol       open      close        low       high  \\\n",
       "713   2010-01-04   YHOO  16.940001  17.100000  16.879999  17.200001   \n",
       "1181  2010-01-05   YHOO  17.219999  17.230000  17.000000  17.230000   \n",
       "1649  2010-01-06   YHOO  17.170000  17.170000  17.070000  17.299999   \n",
       "2117  2010-01-07   YHOO  16.809999  16.700001  16.570000  16.900000   \n",
       "2585  2010-01-08   YHOO  16.680000  16.700001  16.620001  16.760000   \n",
       "\n",
       "          volume  \n",
       "713   16587400.0  \n",
       "1181  11718100.0  \n",
       "1649  16422000.0  \n",
       "2117  31816300.0  \n",
       "2585  15470000.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yahoo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1762, 7)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yahoo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of               date symbol       open      close        low       high  \\\n",
       "713     2010-01-04   YHOO  16.940001  17.100000  16.879999  17.200001   \n",
       "1181    2010-01-05   YHOO  17.219999  17.230000  17.000000  17.230000   \n",
       "1649    2010-01-06   YHOO  17.170000  17.170000  17.070000  17.299999   \n",
       "2117    2010-01-07   YHOO  16.809999  16.700001  16.570000  16.900000   \n",
       "2585    2010-01-08   YHOO  16.680000  16.700001  16.620001  16.760000   \n",
       "...            ...    ...        ...        ...        ...        ...   \n",
       "849257  2016-12-23   YHOO  38.459999  38.660000  38.369999  38.810001   \n",
       "849757  2016-12-27   YHOO  38.590000  38.919998  38.500000  39.070000   \n",
       "850257  2016-12-28   YHOO  39.119999  38.730000  38.709999  39.220001   \n",
       "850757  2016-12-29   YHOO  38.759998  38.639999  38.480000  38.930000   \n",
       "851257  2016-12-30   YHOO  38.720001  38.669998  38.430000  39.000000   \n",
       "\n",
       "            volume  \n",
       "713     16587400.0  \n",
       "1181    11718100.0  \n",
       "1649    16422000.0  \n",
       "2117    31816300.0  \n",
       "2585    15470000.0  \n",
       "...            ...  \n",
       "849257   3840100.0  \n",
       "849757   4240300.0  \n",
       "850257   4393500.0  \n",
       "850757   4170200.0  \n",
       "851257   6431600.0  \n",
       "\n",
       "[1762 rows x 7 columns]>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yahoo.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      0\n",
       "symbol    0\n",
       "open      0\n",
       "close     0\n",
       "low       0\n",
       "high      0\n",
       "volume    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yahoo.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/envs/tensorflow2/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_yahoo['date'] = pd.to_datetime(df_yahoo['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "print(df_yahoo.date.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_yahoo = df_yahoo.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>16.940001</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>16.879999</td>\n",
       "      <td>17.200001</td>\n",
       "      <td>16587400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>17.219999</td>\n",
       "      <td>17.230000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.230000</td>\n",
       "      <td>11718100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>17.170000</td>\n",
       "      <td>17.170000</td>\n",
       "      <td>17.070000</td>\n",
       "      <td>17.299999</td>\n",
       "      <td>16422000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>16.809999</td>\n",
       "      <td>16.700001</td>\n",
       "      <td>16.570000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>31816300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>16.680000</td>\n",
       "      <td>16.700001</td>\n",
       "      <td>16.620001</td>\n",
       "      <td>16.760000</td>\n",
       "      <td>15470000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol       open      close        low       high      volume\n",
       "date                                                                     \n",
       "2010-01-04   YHOO  16.940001  17.100000  16.879999  17.200001  16587400.0\n",
       "2010-01-05   YHOO  17.219999  17.230000  17.000000  17.230000  11718100.0\n",
       "2010-01-06   YHOO  17.170000  17.170000  17.070000  17.299999  16422000.0\n",
       "2010-01-07   YHOO  16.809999  16.700001  16.570000  16.900000  31816300.0\n",
       "2010-01-08   YHOO  16.680000  16.700001  16.620001  16.760000  15470000.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yahoo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1762, 6)\n"
     ]
    }
   ],
   "source": [
    "print(df_yahoo.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "yahoo_data = df_yahoo.asfreq('b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1825, 6)\n"
     ]
    }
   ],
   "source": [
    "print(yahoo_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>38.590000</td>\n",
       "      <td>38.919998</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>39.070000</td>\n",
       "      <td>4240300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>39.119999</td>\n",
       "      <td>38.730000</td>\n",
       "      <td>38.709999</td>\n",
       "      <td>39.220001</td>\n",
       "      <td>4393500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>38.759998</td>\n",
       "      <td>38.639999</td>\n",
       "      <td>38.480000</td>\n",
       "      <td>38.930000</td>\n",
       "      <td>4170200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>38.720001</td>\n",
       "      <td>38.669998</td>\n",
       "      <td>38.430000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>6431600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol       open      close        low       high     volume\n",
       "date                                                                    \n",
       "2016-12-26    NaN        NaN        NaN        NaN        NaN        NaN\n",
       "2016-12-27   YHOO  38.590000  38.919998  38.500000  39.070000  4240300.0\n",
       "2016-12-28   YHOO  39.119999  38.730000  38.709999  39.220001  4393500.0\n",
       "2016-12-29   YHOO  38.759998  38.639999  38.480000  38.930000  4170200.0\n",
       "2016-12-30   YHOO  38.720001  38.669998  38.430000  39.000000  6431600.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yahoo_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symbol    63\n",
       "open      63\n",
       "close     63\n",
       "low       63\n",
       "high      63\n",
       "volume    63\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yahoo_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "null_data = yahoo_data[yahoo_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  open  close  low  high  volume\n",
       "date                                             \n",
       "2010-01-18    NaN   NaN    NaN  NaN   NaN     NaN\n",
       "2010-02-15    NaN   NaN    NaN  NaN   NaN     NaN\n",
       "2010-04-02    NaN   NaN    NaN  NaN   NaN     NaN\n",
       "2010-05-31    NaN   NaN    NaN  NaN   NaN     NaN\n",
       "2010-07-05    NaN   NaN    NaN  NaN   NaN     NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "null_dates = null_data.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Timestamp('2012-10-29 00:00:00'), Timestamp('2012-10-30 00:00:00')]\n"
     ]
    }
   ],
   "source": [
    "import calendar\n",
    "import datetime\n",
    "\n",
    "holidays = []\n",
    "\n",
    "for date in null_dates:\n",
    "    week, day, month, year = date.weekday(), date.day, date.month, date.year\n",
    "    week_day = calendar.day_name[week]\n",
    "\n",
    "    if month==1:\n",
    "        if day==1:\n",
    "            # New year day\n",
    "            holidays.append(date)\n",
    "        elif day==2 and week_day=='Monday':\n",
    "            # Observed New Year Day\n",
    "            holidays.append(date)\n",
    "        elif day>=15 and day<=21 and week_day=='Monday':\n",
    "            # Martin Luther King, Jr. Day\n",
    "            holidays.append(date)\n",
    "\n",
    "    elif month==2:\n",
    "        # Washington's Birthday\n",
    "        if day>=15 and day<=21 and week_day=='Monday':\n",
    "            holidays.append(date)\n",
    "\n",
    "    elif month==5:\n",
    "        # Memorial day\n",
    "        if day>=25 and day<=31 and week_day=='Monday':\n",
    "            holidays.append(date)\n",
    "\n",
    "    elif month==7:\n",
    "        # Independence day\n",
    "        if day==4:\n",
    "            holidays.append(date)\n",
    "        # Observed Independence  Day\n",
    "        elif day==5 and week_day=='Monday':\n",
    "            holidays.append(date)\n",
    "        elif day==3 and week_day=='Friday':\n",
    "            holidays.append(date)\n",
    "\n",
    "    elif month == 9:\n",
    "        # Labour day\n",
    "        if day>=1 and day<=7 and week_day=='Monday':\n",
    "            holidays.append(date)\n",
    "\n",
    "    elif month==11:\n",
    "        # Thanksgiving Day\n",
    "        if week_day=='Thursday' and day>=22 and day<=28:\n",
    "            holidays.append(date)\n",
    "\n",
    "    elif month==12:\n",
    "        # Christmas Day\n",
    "        if day==25:\n",
    "            holidays.append(date)\n",
    "        # Observed Christmas Day\n",
    "        elif day==24 and week_day=='Friday':\n",
    "            holidays.append(date)\n",
    "        elif day==26 and week_day=='Monday':\n",
    "            holidays.append(date)\n",
    "\n",
    "good_fridays = [ datetime.date(2010,4,2), datetime.date(2011,4,22), datetime.date(2012,4,6), datetime.date(2013,3,29), datetime.date(2014,4,18), datetime.date(2015,4,3), datetime.date(2016,3,25) ]\n",
    "holidays = holidays + [pd.to_datetime(date) for date in good_fridays]\n",
    "\n",
    "non_holidays = [x for x in null_dates if x not in holidays]\n",
    "print(non_holidays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1825, 6)\n"
     ]
    }
   ],
   "source": [
    "print(yahoo_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "modified_df = yahoo_data.drop(holidays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1764, 6)\n"
     ]
    }
   ],
   "source": [
    "print(modified_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before filling missing values: \n",
      " symbol    2\n",
      "open      2\n",
      "close     2\n",
      "low       2\n",
      "high      2\n",
      "volume    2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Before filling missing values: \\n\", modified_df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "modified_df = modified_df.bfill(axis = 'rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After filling the missing values:\n",
      " symbol    0\n",
      "open      0\n",
      "close     0\n",
      "low       0\n",
      "high      0\n",
      "volume    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nAfter filling the missing values:\\n\", modified_df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotter(code):\n",
    "    global closing_stock\n",
    "    plt.subplot(211)\n",
    "    company_close = modified_df[modified_df['symbol']==code]\n",
    "    company_close = company_close.close.values.astype('float32')\n",
    "    company_close = company_close.reshape(-1, 1)\n",
    "    closing_stock = company_close\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel(code + \" close stock prices\")\n",
    "    plt.title('prices Vs Time')\n",
    "    plt.grid(True)\n",
    "    plt.plot(company_close , 'b')\n",
    "    plt.show()\n",
    "\n",
    "plotter(\"YHOO\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train samples: 1411 Validation Samples: 176 Test Samples: 177\n"
     ]
    }
   ],
   "source": [
    "n_train = int(len(closing_stock) * 0.80)\n",
    "n_remaining = len(closing_stock) - n_train\n",
    "\n",
    "n_val = int(n_remaining*0.50)\n",
    "n_test = n_remaining - n_val \n",
    "print(\"Train samples:\",n_train, \"Validation Samples:\",n_val,\"Test Samples:\", n_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1411, 1)\n"
     ]
    }
   ],
   "source": [
    "train_data = closing_stock[0:n_train]\n",
    "print(train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(176, 1)\n"
     ]
    }
   ],
   "source": [
    "val_data = closing_stock[n_train:n_train+n_val]\n",
    "print(val_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(177, 1)\n"
     ]
    }
   ],
   "source": [
    "test_data = closing_stock[n_train + n_val :]\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range = (0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = scaler.fit_transform(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "val = scaler.transform(val_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = scaler.transform(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(data , n_features):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(data)-n_features-1):\n",
    "        a = data[i:(i+n_features), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(data[i + n_features, 0])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_features = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, trainY = create_dataset(train, n_features)\n",
    "valX, valY = create_dataset(val, n_features)\n",
    "testX, testY = create_dataset(test, n_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1408, 2) (1408,) (173, 2) (173,) (174, 2) (174,)\n"
     ]
    }
   ],
   "source": [
    "print(trainX.shape , trainY.shape , valX.shape , valY.shape, testX.shape , testY.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX = trainX.reshape(trainX.shape[0] , 1 ,trainX.shape[1])\n",
    "valX = valX.reshape(valX.shape[0] , 1 ,valX.shape[1])\n",
    "testX = testX.reshape(testX.shape[0] , 1 ,testX.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1408, 1, 2) (1408,) (173, 1, 2) (173,) (174, 1, 2) (174,)\n"
     ]
    }
   ],
   "source": [
    "print(trainX.shape , trainY.shape , valX.shape , valY.shape, testX.shape , testY.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First GRU layer\n",
    "model.add(layers.GRU(units=100, return_sequences=True, input_shape=(1,n_features), activation='tanh'))\n",
    "model.add(layers.Dropout(0.2))\n",
    "\n",
    "# Second GRU layer\n",
    "model.add(layers.GRU(units=150, return_sequences=True, input_shape=(1,n_features), activation='tanh'))\n",
    "model.add(layers.Dropout(0.2))\n",
    "\n",
    "# Third GRU layer\n",
    "model.add(layers.GRU(units=100, activation='tanh'))\n",
    "model.add(layers.Dropout(0.2))\n",
    "\n",
    "# The output layer\n",
    "model.add(layers.Dense(units=1, kernel_initializer='he_uniform', activation='linear'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/envs/tensorflow2/lib/python3.6/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='mean_squared_error', optimizer=Adam(lr = 0.0005) , metrics = ['mean_squared_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru (GRU)                    (None, 1, 100)            31200     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 1, 100)            0         \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, 1, 150)            113400    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1, 150)            0         \n",
      "_________________________________________________________________\n",
      "gru_2 (GRU)                  (None, 100)               75600     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 220,301\n",
      "Trainable params: 220,301\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "11/11 [==============================] - 6s 162ms/step - loss: 0.1341 - mean_squared_error: 0.1341 - val_loss: 0.0953 - val_mean_squared_error: 0.0953\n",
      "Epoch 2/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0387 - mean_squared_error: 0.0387 - val_loss: 0.0014 - val_mean_squared_error: 0.0014\n",
      "Epoch 3/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0264 - mean_squared_error: 0.0264 - val_loss: 7.2537e-04 - val_mean_squared_error: 7.2537e-04\n",
      "Epoch 4/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0131 - mean_squared_error: 0.0131 - val_loss: 0.0044 - val_mean_squared_error: 0.0044\n",
      "Epoch 5/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 4.9766e-04 - val_mean_squared_error: 4.9766e-04\n",
      "Epoch 6/100\n",
      "11/11 [==============================] - 0s 18ms/step - loss: 0.0024 - mean_squared_error: 0.0024 - val_loss: 9.8298e-04 - val_mean_squared_error: 9.8298e-04\n",
      "Epoch 7/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0022 - mean_squared_error: 0.0022 - val_loss: 4.0266e-04 - val_mean_squared_error: 4.0266e-04\n",
      "Epoch 8/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0023 - mean_squared_error: 0.0023 - val_loss: 8.5219e-04 - val_mean_squared_error: 8.5219e-04\n",
      "Epoch 9/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0021 - mean_squared_error: 0.0021 - val_loss: 3.8591e-04 - val_mean_squared_error: 3.8591e-04\n",
      "Epoch 10/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0022 - mean_squared_error: 0.0022 - val_loss: 4.6885e-04 - val_mean_squared_error: 4.6885e-04\n",
      "Epoch 11/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0023 - mean_squared_error: 0.0023 - val_loss: 4.6394e-04 - val_mean_squared_error: 4.6394e-04\n",
      "Epoch 12/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 0.0019 - mean_squared_error: 0.0019 - val_loss: 4.3671e-04 - val_mean_squared_error: 4.3671e-04\n",
      "Epoch 13/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0021 - mean_squared_error: 0.0021 - val_loss: 5.3431e-04 - val_mean_squared_error: 5.3431e-04\n",
      "Epoch 14/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0020 - mean_squared_error: 0.0020 - val_loss: 4.6426e-04 - val_mean_squared_error: 4.6426e-04\n",
      "Epoch 15/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0018 - mean_squared_error: 0.0018 - val_loss: 4.0850e-04 - val_mean_squared_error: 4.0850e-04\n",
      "Epoch 16/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0020 - mean_squared_error: 0.0020 - val_loss: 4.9908e-04 - val_mean_squared_error: 4.9908e-04\n",
      "Epoch 17/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 0.0021 - mean_squared_error: 0.0021 - val_loss: 4.2390e-04 - val_mean_squared_error: 4.2390e-04\n",
      "Epoch 18/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0019 - mean_squared_error: 0.0019 - val_loss: 3.8848e-04 - val_mean_squared_error: 3.8848e-04\n",
      "Epoch 19/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0019 - mean_squared_error: 0.0019 - val_loss: 5.5109e-04 - val_mean_squared_error: 5.5109e-04\n",
      "Epoch 20/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0019 - mean_squared_error: 0.0019 - val_loss: 3.8139e-04 - val_mean_squared_error: 3.8139e-04\n",
      "Epoch 21/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0018 - mean_squared_error: 0.0018 - val_loss: 5.8009e-04 - val_mean_squared_error: 5.8009e-04\n",
      "Epoch 22/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 0.0017 - mean_squared_error: 0.0017 - val_loss: 4.2259e-04 - val_mean_squared_error: 4.2259e-04\n",
      "Epoch 23/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0018 - mean_squared_error: 0.0018 - val_loss: 3.7098e-04 - val_mean_squared_error: 3.7098e-04\n",
      "Epoch 24/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0018 - mean_squared_error: 0.0018 - val_loss: 4.6196e-04 - val_mean_squared_error: 4.6196e-04\n",
      "Epoch 25/100\n",
      "11/11 [==============================] - 0s 20ms/step - loss: 0.0018 - mean_squared_error: 0.0018 - val_loss: 3.7055e-04 - val_mean_squared_error: 3.7055e-04\n",
      "Epoch 26/100\n",
      "11/11 [==============================] - 0s 17ms/step - loss: 0.0016 - mean_squared_error: 0.0016 - val_loss: 3.7625e-04 - val_mean_squared_error: 3.7625e-04\n",
      "Epoch 27/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 0.0016 - mean_squared_error: 0.0016 - val_loss: 3.8844e-04 - val_mean_squared_error: 3.8844e-04\n",
      "Epoch 28/100\n",
      "11/11 [==============================] - 0s 17ms/step - loss: 0.0016 - mean_squared_error: 0.0016 - val_loss: 4.7066e-04 - val_mean_squared_error: 4.7066e-04\n",
      "Epoch 29/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 0.0017 - mean_squared_error: 0.0017 - val_loss: 3.9793e-04 - val_mean_squared_error: 3.9793e-04\n",
      "Epoch 30/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 0.0016 - mean_squared_error: 0.0016 - val_loss: 3.6802e-04 - val_mean_squared_error: 3.6802e-04\n",
      "Epoch 31/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 0.0016 - mean_squared_error: 0.0016 - val_loss: 3.7650e-04 - val_mean_squared_error: 3.7650e-04\n",
      "Epoch 32/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0014 - mean_squared_error: 0.0014 - val_loss: 3.6759e-04 - val_mean_squared_error: 3.6759e-04\n",
      "Epoch 33/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0014 - mean_squared_error: 0.0014 - val_loss: 3.6370e-04 - val_mean_squared_error: 3.6370e-04\n",
      "Epoch 34/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 0.0014 - mean_squared_error: 0.0014 - val_loss: 3.9104e-04 - val_mean_squared_error: 3.9104e-04\n",
      "Epoch 35/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0014 - mean_squared_error: 0.0014 - val_loss: 3.7623e-04 - val_mean_squared_error: 3.7623e-04\n",
      "Epoch 36/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0016 - mean_squared_error: 0.0016 - val_loss: 4.6603e-04 - val_mean_squared_error: 4.6603e-04\n",
      "Epoch 37/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0015 - mean_squared_error: 0.0015 - val_loss: 3.9354e-04 - val_mean_squared_error: 3.9354e-04\n",
      "Epoch 38/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0015 - mean_squared_error: 0.0015 - val_loss: 3.7294e-04 - val_mean_squared_error: 3.7294e-04\n",
      "Epoch 39/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0015 - mean_squared_error: 0.0015 - val_loss: 4.1085e-04 - val_mean_squared_error: 4.1085e-04\n",
      "Epoch 40/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0014 - mean_squared_error: 0.0014 - val_loss: 4.3209e-04 - val_mean_squared_error: 4.3209e-04\n",
      "Epoch 41/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0014 - mean_squared_error: 0.0014 - val_loss: 3.6417e-04 - val_mean_squared_error: 3.6417e-04\n",
      "Epoch 42/100\n",
      "11/11 [==============================] - 0s 18ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.5994e-04 - val_mean_squared_error: 3.5994e-04\n",
      "Epoch 43/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.6112e-04 - val_mean_squared_error: 3.6112e-04\n",
      "Epoch 44/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - val_loss: 3.5964e-04 - val_mean_squared_error: 3.5964e-04\n",
      "Epoch 45/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0014 - mean_squared_error: 0.0014 - val_loss: 4.1834e-04 - val_mean_squared_error: 4.1834e-04\n",
      "Epoch 46/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0014 - mean_squared_error: 0.0014 - val_loss: 4.7071e-04 - val_mean_squared_error: 4.7071e-04\n",
      "Epoch 47/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - val_loss: 5.6612e-04 - val_mean_squared_error: 5.6612e-04\n",
      "Epoch 48/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.6326e-04 - val_mean_squared_error: 3.6326e-04\n",
      "Epoch 49/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - val_loss: 4.0267e-04 - val_mean_squared_error: 4.0267e-04\n",
      "Epoch 50/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.5982e-04 - val_mean_squared_error: 3.5982e-04\n",
      "Epoch 51/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.5439e-04 - val_mean_squared_error: 3.5439e-04\n",
      "Epoch 52/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - val_loss: 3.6088e-04 - val_mean_squared_error: 3.6088e-04\n",
      "Epoch 53/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0014 - mean_squared_error: 0.0014 - val_loss: 5.8256e-04 - val_mean_squared_error: 5.8256e-04\n",
      "Epoch 54/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0016 - mean_squared_error: 0.0016 - val_loss: 3.6432e-04 - val_mean_squared_error: 3.6432e-04\n",
      "Epoch 55/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0015 - mean_squared_error: 0.0015 - val_loss: 5.3029e-04 - val_mean_squared_error: 5.3029e-04\n",
      "Epoch 56/100\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.8656e-04 - val_mean_squared_error: 3.8656e-04\n",
      "Epoch 57/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 4.0744e-04 - val_mean_squared_error: 4.0744e-04\n",
      "Epoch 58/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - val_loss: 4.5164e-04 - val_mean_squared_error: 4.5164e-04\n",
      "Epoch 59/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.6310e-04 - val_mean_squared_error: 3.6310e-04\n",
      "Epoch 60/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.5902e-04 - val_mean_squared_error: 3.5902e-04\n",
      "Epoch 61/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - val_loss: 3.5133e-04 - val_mean_squared_error: 3.5133e-04\n",
      "Epoch 62/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.5886e-04 - val_mean_squared_error: 3.5886e-04\n",
      "Epoch 63/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.7251e-04 - val_mean_squared_error: 3.7251e-04\n",
      "Epoch 64/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 4.8973e-04 - val_mean_squared_error: 4.8973e-04\n",
      "Epoch 65/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - val_loss: 3.6217e-04 - val_mean_squared_error: 3.6217e-04\n",
      "Epoch 66/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0010 - mean_squared_error: 0.0010 - val_loss: 3.5894e-04 - val_mean_squared_error: 3.5894e-04\n",
      "Epoch 67/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.6144e-04 - val_mean_squared_error: 3.6144e-04\n",
      "Epoch 68/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.8490e-04 - val_mean_squared_error: 3.8490e-04\n",
      "Epoch 69/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - val_loss: 3.6022e-04 - val_mean_squared_error: 3.6022e-04\n",
      "Epoch 70/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - val_loss: 3.9732e-04 - val_mean_squared_error: 3.9732e-04\n",
      "Epoch 71/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.8349e-04 - val_mean_squared_error: 3.8349e-04\n",
      "Epoch 72/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.4845e-04 - val_mean_squared_error: 3.4845e-04\n",
      "Epoch 73/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.6607e-04 - val_mean_squared_error: 3.6607e-04\n",
      "Epoch 74/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.6169e-04 - val_mean_squared_error: 3.6169e-04\n",
      "Epoch 75/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0010 - mean_squared_error: 0.0010 - val_loss: 3.4940e-04 - val_mean_squared_error: 3.4940e-04\n",
      "Epoch 76/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.5615e-04 - val_mean_squared_error: 3.5615e-04\n",
      "Epoch 77/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.5293e-04 - val_mean_squared_error: 3.5293e-04\n",
      "Epoch 78/100\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 4.5418e-04 - val_mean_squared_error: 4.5418e-04\n",
      "Epoch 79/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.5034e-04 - val_mean_squared_error: 3.5034e-04\n",
      "Epoch 80/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.7197e-04 - val_mean_squared_error: 3.7197e-04\n",
      "Epoch 81/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.6093e-04 - val_mean_squared_error: 3.6093e-04\n",
      "Epoch 82/100\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.8108e-04 - val_mean_squared_error: 3.8108e-04\n",
      "Epoch 83/100\n",
      "11/11 [==============================] - 0s 18ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.6038e-04 - val_mean_squared_error: 3.6038e-04\n",
      "Epoch 84/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 5.0640e-04 - val_mean_squared_error: 5.0640e-04\n",
      "Epoch 85/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 4.3119e-04 - val_mean_squared_error: 4.3119e-04\n",
      "Epoch 86/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.4369e-04 - val_mean_squared_error: 3.4369e-04\n",
      "Epoch 87/100\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.6957e-04 - val_mean_squared_error: 3.6957e-04\n",
      "Epoch 88/100\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.4824e-04 - val_mean_squared_error: 3.4824e-04\n",
      "Epoch 89/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.7838e-04 - val_mean_squared_error: 3.7838e-04\n",
      "Epoch 90/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 5.0198e-04 - val_mean_squared_error: 5.0198e-04\n",
      "Epoch 91/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.7261e-04 - val_mean_squared_error: 3.7261e-04\n",
      "Epoch 92/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.7743e-04 - val_mean_squared_error: 3.7743e-04\n",
      "Epoch 93/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - val_loss: 3.6303e-04 - val_mean_squared_error: 3.6303e-04\n",
      "Epoch 94/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.6875e-04 - val_mean_squared_error: 3.6875e-04\n",
      "Epoch 95/100\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.7240e-04 - val_mean_squared_error: 3.7240e-04\n",
      "Epoch 96/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0010 - mean_squared_error: 0.0010 - val_loss: 3.8231e-04 - val_mean_squared_error: 3.8231e-04\n",
      "Epoch 97/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.6808e-04 - val_mean_squared_error: 3.6808e-04\n",
      "Epoch 98/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 9.6624e-04 - mean_squared_error: 9.6624e-04 - val_loss: 3.8209e-04 - val_mean_squared_error: 3.8209e-04\n",
      "Epoch 99/100\n",
      "11/11 [==============================] - 0s 13ms/step - loss: 0.0010 - mean_squared_error: 0.0010 - val_loss: 3.8474e-04 - val_mean_squared_error: 3.8474e-04\n",
      "Epoch 100/100\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0011 - mean_squared_error: 0.0011 - val_loss: 3.6550e-04 - val_mean_squared_error: 3.6550e-04\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(trainX,trainY,epochs=100,batch_size=128, verbose=1, validation_data = (valX,valY))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate the performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score:\n",
      "MSE: 0.00019 , RMSE: 0.01\n",
      "Validation Score:\n",
      "MSE: 0.00034 , RMSE: 0.02\n",
      "Test Score:\n",
      "MSE: 0.00024 , RMSE: 0.02\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def model_score(model, X_train, y_train, X_val, y_val , X_test, y_test):\n",
    "    print('Train Score:')\n",
    "    train_score = model.evaluate(X_train, y_train, verbose=0)\n",
    "    print(\"MSE: {:.5f} , RMSE: {:.2f}\".format(train_score[0], math.sqrt(train_score[0])))\n",
    "\n",
    "    print('Validation Score:')\n",
    "    val_score = model.evaluate(X_val, y_val, verbose=0)\n",
    "    print(\"MSE: {:.5f} , RMSE: {:.2f}\".format (val_score[0], math.sqrt(val_score[0])))\n",
    "\n",
    "    print('Test Score:')\n",
    "    test_score = model.evaluate(X_test, y_test, verbose=0)\n",
    "    print(\"MSE: {:.5f} , RMSE: {:.2f}\".format (test_score[0], math.sqrt(test_score[0])))\n",
    "\n",
    "\n",
    "model_score(model, trainX, trainY ,valX, valY , testX, testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'mean_squared_error', 'val_loss', 'val_mean_squared_error'])\n"
     ]
    }
   ],
   "source": [
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])  # plotting train loss\n",
    "plt.plot(history.history['val_loss'])  # plotting validation loss\n",
    "\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1560.4054]\n",
      " [1551.1051]\n",
      " [1544.6035]\n",
      " [1538.6638]\n",
      " [1526.7004]\n",
      " [1522.3523]\n",
      " [1520.5519]\n",
      " [1505.3125]\n",
      " [1498.3419]\n",
      " [1524.1378]]\n"
     ]
    }
   ],
   "source": [
    "pred = scaler.inverse_transform(pred)\n",
    "print(pred[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[37.23]\n",
      " [37.11]\n",
      " [36.95]\n",
      " [36.59]\n",
      " [36.6 ]\n",
      " [36.53]\n",
      " [36.01]\n",
      " [36.  ]\n",
      " [36.94]\n",
      " [37.23]]\n"
     ]
    }
   ],
   "source": [
    "testY_actual = testY.reshape(testY.shape[0] , 1)\n",
    "testY_actual = scaler.inverse_transform(testY_actual)\n",
    "print(testY_actual[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tWgVAXl4e69ev3zF+ixYtmDZtGkCpaqZ169bRtGlTwE6Ku0qydm1JfT3Y+YBx4+D0022vunNnmD+/fNNUhf/8x44Ufv97OPtsOOOMnYfLz4c33rAN+8yZdvTwwguweDFLevYsGa5aNdvIX3CBbfALCuCss6yfiG3kZ8+2o4PBg22aDzwAr71mG/6OHWGffWyZpk6FVq2s2mr8eEsUAM2bw9//bsmnUyc72nj+eZgwoXzL7rJaOk9wDwV6hgtU9QJVLVDVAmAU8Eao97xIP1W9LlT+LHAN0DroSk1zdzJ8+HDOivxRA+eccw6LFy+md+/edOzYkYKCAh555BEALr/8cq677rodJ7jvuece+vfvT8eOHckJnVTp378/AwcO5KijjmJ7eOPl0mfcOGtN9Itf2IZx0CDo0ME27F9/DVdfbXvxRx0F/frZ8IlMngwnnGB77V98YSefR46EePfgErFqpf/7PzsqiGzAw3JyLBnccw8MGWIJJOLMMy0h3H23nUQ/+WQ7H7LffjB6tFVJnXuujf/jjzBmDPz5zxBUmUb1pz9BmzZWtVVYmHi5o9m40U7sh3aWXIbFetBFRXRAC2BGlHIBFgCtEwzXBJgd+nwh8Hwy84728KNZs2Yl9QCQdevWJTVctshEvMmuy2h2+4fHDB6sKqLavr1qu3aqdjygevjhqkOHqm7bZsPNm6d69tmq9etb/4cfjj6DL79U7dnThmnaVPXpp1XXr6+4eBMZP97m3aiR6sKFKc+3lI8/tnV0442xhykqUr3vPl3XurVqw4aqf/yjrYuzzlKtUcNiatNGdfbsiompguz2v984yMKHH3UBlqrqnFBZSxH5SkQmi0iXoKwpsDA0zMKgzLnMeeABOPZY+Ogj+PJLmDYNVqywayAuuwyqB6cCDzoIRo2yfuefb+cZHnvMziFMnmzVQQceCO3bWwukhx+GOXOsaWtubuUtzymnwEMP2VFD0wr6ex1/vJ1cf+aZmE18I0c7xbVqWZXdH/5g6+KDD+A3v7FqsdWrbV17676ME031wp5kJi7SAnhLVduWKX8WmKuqfw0+1wJyVXWliHQAxgCHA22AQap6SjBcF+AuVY16WbKI9AP6AeTn53cIX8cAdivvVq1aJYy7qKioVDVPtstEvHPnzmXt2rUpjVtYWEhuZW4Md1E43tpLltDpwguZc9NNLDrnnKSnIVu3csTdd9Nw6lSKa9Sg2rZtbGnYkDVHHcX6Nm1Y0qsX2ytonWTL+q22aRNHX301AF+89BLFders6JdTWMixl17K5v3248NBg8itV4+9p0whd948lvTqxbb69QGotWQJh993H/Vmz2bJL3/J4l69WHvEEaTUtr2CZMv6TVZ54u3evfs0VY3eVj/WIUdFdESpXsJaYC0FmsUZbxLQEa+GSopXQ6VXqXiHDLHqkW++Kf+Etm9XnTRJ9ZZbVJ98UnXjxgqLMSyr1u/kyba+TjpJdc0a1Q8/VP397+2ziOrUqYnj3bJFdeBA1Vq1bFrNm6s++qhqYWGlLEJZFbZ+f/xRdfr0iplWHLtzNdQpQQLYUb0kIvuISE7w/iDsRPYPqroYWCcinYJWUJcCYzMQs3NmwgRrHXT44eUfNycHTjzRqqJ+8xu7XmJP17WrXSvy4YfWaqprV6vGmzkT/vhHaxSQSM2adlJ9+XJ49VW7N9att1rLrBdftGbGmfLTT9Zy7fzzy3cyf+lSq6orKIBf/cqq6954A7ZuTV+suyidTWeHA/8BDhGRhSISXPVDH6DsTZG6Av8NmtKOBK5T1VVBvxuAl4C5wDzgnXTF7FxcqpYsTjop9esnqqK+fa3ZbseO1sJr/XpYsgR+97vyTScvzzbKkyfDxx/bOaF+/aJfOBj288/w73/HvmVD2IoV0KuXnUM5+2xrPbZqVfRhx42Ddu2s1daoUZYIk7nX1vbtdhX+6tV2Pcrnn8NNN1nLs5NOyt77dcU65NjdO6+GSq8qWQ01e7ZVgzz/fEbjSWS3Xb/lVVysesUV9p2MG7dz/02bVK++uqS1Wl6e6pIl8ad3xhmqNWuqnnKK6oEH2ngiqm3b2rwefVS/euQRa7FWrZpqx47W6u3tt62a7IYbEsc9aJBNd9gw+7xtm8X18suqdeqotmhhsVeQ3bkaqkrLycmhoKCAtm3bct555+24EC8V4VuQjxs3jkGDBsUcds2aNfztb38r9zzuvffeHdd9VHnvv2+vJ52U2TicEbHqm/bt7ehl7tySfmvW2J7+Sy9ZldXrr9tV9fGOZl58EcaOtSvX33vPriv54gurLmvWzO57deutFNx+ux0J9OgBkybZEc5pp9k1K6+9Zs8eiWXNGrsmp1cvuPRSK6te3S6y7NvXjmTmz7dWdlnGk0Ulq1OnDtOnT2fGjBnUrFmT58rcEkFVd9zSozx69+7NgAEDYvZPNVm4kNdft4vNDj4405G4iDp1rAooJ8eqjTZssOOIq6+Gr76y8wB//atdWBhpjvvZZztPZ80aa9p88sl2+xKwZNSxoyWYd96xqrOlS5n+yCN27mTsWKhbt2QaF19s1Vjjx8eO97HHbF733x+9f+Q2LZ9/ntr6SCNPFhnUpUsX5s6dy/z58znkkEO49NJLadu2LQsWLGD8+PF07tyZ9u3bc95551EYnDx79913OfTQQ2nfvj1vvFFyAfzQoUO56aabAFi6dClnnXUW7dq1o127dnz66acMGDCAefPmUVBQwB133AHAww8/zNFHH82RRx7JPffcs2NaDzzwAG3atOGEE07w251HLFhgdeUXX+znK7JNixYwfLhd5/LLX9p5jFGj7KR4+I4Jf/gD7LuvXSF/6ql2cjriiSdg3Tq7T1a1OJvFffdlTYcOdu6k7JX1PXpAw4Z2Y8Zo5syxZHHuuXZlfzRNmlhDgClTklr0ylRlbyQY7x7ldYqKUmvHncw9ygPbt2/nnXfeoWdwH585c+YwbNgwOnXqxIoVK7j//vt5//33qVu3Lg899BCPPvood955J9dccw0TJkygVatWXHDBBVGnffPNN3PiiScyevRoioqKKCwsZNCgQcyYMYPpwTKPHz+eOXPmMGXKFFSV3r178+GHH1K3bl1GjBjB9OnT2b59O+3bt6dDMi1W9nSRB1VddFFm43DRnXqq3RvrwQft3lq/+pXdtiSsQQOr3nnpJbsA8vrr4f/9P0sSjz9ut2opKEg9hpo1LYkMG2a3eqlZ0xLU8uXW6uuZZ6zKKdZRRcSxx2blkUXVTRYZsmnTJgqCH2SXLl246qqr+PnnnznwwAPp1KkTAJ999hmzZs3i+OOPB2Dr1q107tyZ2bNn07JlS1q3bg3AJZdcwgsvvLDTPCZMmMDLL78M2DmS+vXrs3r16lLDjB8/nvHjx3NUsIdTWFjInDlzWL9+PWedddaO26X37t07DWthN/Svf9mfOImLOl2GXH21dQsW2BFEtCOE/fe3DXhenp3LGDnSqpjWrLHyXXXNNXbe4bDDbP516thV+u+9ZzdZfPVVOOCA+NM49liLa/lya6adJapusohzBLBp/fq03KIcSs5ZlFU3VPepqvzyl7/c6bGr0cZLlaoycOBArr322lLljyd5ZFSV1P/mG7s54JNPZjoUl4zmzRMPc9NNdkL7/PPt8x132InyXdW+vZ2gfu01WLzYbnmy//7W/LZBg/hVXBHHHGOvU6bYEVKW8HMWWahTp0588sknzA1ad2zYsIHvv/+eQw89lPnz5zNv3jyAmM/wPvnkk3k2eFxmUVERa9eu3el25z169GDIkCE7zoUsWrSIZcuW0bVrV8aMGcOmTZtYv349b775ZjoXNfvNn8/h99xjF4L17ZvpaFxFqVHDkkWXLrbX/5e/VNy0mzSxO/gOGmSJAuxcRjKJAqzKqlq1rKuK8mSRhfbZZx+GDh3KhRdeyJFHHrmjCqp27dq88MIL/OpXv6J9+/bsu+++Ucd/4oknmDhxIkcccQQdOnRg1qxZNGrUiOOPP562bdtyxx13cOqpp3LRRRfRuXNnjjjiCM4991zWr19P+/btueCCC2jXrh2nnXYaRx99dCUvfYqWLoUbbrAH8+yq4mJr6XL99dC1K9W2brW67Wi3/3a7r+OPtyvLTzkl05GUVrcutG1r516i+fJLa7Jb2WJdgLG7d35RXnpl1UV5M2fahUygeuihqhs2pDad4mK7uKugwKZVv77qSSfpl08+WbHxplmVuSgvQyol3t/+1i4G/M9/SspWrlS99FL7bVarpjpmTOlxIrfGL8MvynNuyRI7mjjqKNi82ZolfvedlX37rT0Hevhwu0VH2QulVq+2J8StW2efv/nGbvHQu7eVDRtmbeY/+MDucupcZRowwKqzbrjBbg+yYYNdyDd8ONx1l13/0aePnUyfNs3e16hh1V0nnJCWkKruCW63e9u82ZpLzp4NV15pjyBt2tQSyEMP2cY+rGFDexrcZZfZE9zGBvejzMuD886zP2G9elaPfdll8Z9O51y65eVZI5zzz7erwzdtsqvJR42yK8WXL7fH2F4V3HKvdm07aV9cDEVFaQmpyiULVWU3fox3VrCj1Qy7+247GnjrrdItRh580J4XvWCBXcnbti388IM9+vS222y8oiJ7PfRQePNNe3509+7WPDY/P3PL5FzYuefajQbHjLGWVc8+a4kCrEntf/9r14pNn24XIyZqkruLqlSyqF27NitXrqRRo0aeMFKkqqxcuZLatWtnJoCtW60J66OP2iF62aaFIjsfhh95pF1w9Y9/2O0eBg2yKieASy6Bp56Cxo2Tb63iXGUQsaPkhx6yHZ+y26ycHGs5VUkXzVapZNGsWTMWLlzI8uXL4w63efPmzG0MU7B13TpqbtgAjRrZVaNpVrt2bZo1a1a+kZ56ChYuLLnvTlnvvQf33GO3RNi6FS64wPai3n7b9qpat7ZD7w8+sNs0nHaaXYWbLBG7cVvk5m1hMVqVOZc1smDntkolixo1atCyZcv4A73+Op/m5HDU2WdXTlC7aupUtp90EtU3brS95zFjKm/eo0fbg21eeCH+laZjxtjFSQBPPskh3brZCef69e1E9Esv2fMJWrSw8wcbNth0X3zRroA94AB7dkCDBnD00fDccxDcJsU5VzmqVLJIaM0auOIKjlG12x1ff72daNoVa9eW3ORszRq7Ed1pp5UeZsMGO+HaqJFd6h+rPX9xcemqks2b4fTT2VavHtUvucQ22jNnpvYUtwhVu1Pn11/Dli22Jx6ZXmGhnTj+7jvbux83zsrbtYv9AJq5c+2EcceONu7jj7PPK6/Au++WDNOqlbVkuv56qFXLygYNsvbkXbvad1BUZMueBXtYzlVJsdrU7u5dtOsskvL997q8c2dry1y3ruqVV6p++qm1wU/Wzz+r9u+veuqpqnvtZdPae2/Vxo3t/cUXlzx/+bHHVBs00B0PaKlWTbV3b9UJE0pP87337MEoX3xRUjZypCro9IceUl2xwubVp4/q5s3x45s3zx7acsYZqnPmlFp27dpVdzzwpXp11dq17ZnJ116r2rBhyfUH++1n5T16qO67b+x5nnaaDT9//o6iyW+/bQ+Leest1SlTyrduM8CvA0gvjze9Kuo6i4xv1NPVpZwsNFi5n35qiaJuXVtNnTur/vBD4pE3bVI9+mh72lbHjraRjWzgN29WveeekoQxYoS979FDddIk1fffV73jDtv4iqgOH14yzVatbNirriqZ1xlnqO63n056/337fOutNkxOjmq3bqqffGLTvesu1WeeUX33XXtt3NgSVG6uxTl8uOqqVXZh2957q77wgur27fb0rp49bZr16qmec46tl7B//1tLPfUrbMIE6/fwwzuv392Ix5teHm96ebJId7KIWLdO9dlnbe+4fn3VP/1J9fPPVdes2XnE4mJLMKA6enTsGdx/f8lG/bjjVLdsKd1/wwbVLl1sz/6RR1Svv96Gb9fONvCFhXYkUaOG6q23lsS7davqq6+qDhyomp+vpY5WIu8jVzl//73qokU2n5wc1fbtbXrhK0Yjy/TTT5Y8oikuVv3FL2z8suVHH63avPlOj4jck/9s2cDjTa89Od6MJAtgCLAMmBEquxdYBEwPul6hfgOBucB3QMx+fBwAABv+SURBVI9Qec+gbC4wINn5V1iyiPjhB9tbD290mzcvXS00apSV/+538WdQXKzat6+Nv2hR9GHWrFE99tiSeV14oerkySV78c88Y++/+ip6vOvXqz71lOorr1hymT/fxp8/v/SGf906O2oC1SeeSLRqonviCRs/fAuQ0aOtbMiQnQbfk/9s2cDjTa89Od5MJYuuQPsoyeL2KMMeBnwN1AJaAvOAnKCbBxwE1AyGOSyZ+Vd4sohYulT19detaqVFC3sI/IQJttFt2tT2/mPco2UnW7fG719UZBv3SZNsg19crHrwwXbUULOmzau4eNd/vOvWWRVVqucOFi2yarP77rPPxcWqHTpYrFHWxZ78Z8sGHm967cnxxksWabsKSVU/BFYlOfgZwAhV3aKqP2JHEccE3VxV/UFVtwIjgmEzZ9997crK22+Hjz6yWxCfdJK1GPr5Z7tSuHqSjcwS3VKiWjV7cMqJJ9qdKEXsOcJr19pl/m++WTGtg/Ly7JGQqU5r//3tQrjXX7fP48fb/WoGDEh+XTjnsppYMknTxEVaAG+patvg873A5cA6YCpwm6quFpGngc9U9ZVguMHAO8Fkeqrq1UF5X+BYVb0pxvz6Af0A8vPzO4wYMSKluAsLC8nNzU1q2JzCQvZ/6y2avPUWK7p04YcyDxOqcKrWhDb02NfyxJsuTd94g9ZPPcXU556j9ZNPUmv5cj7/5z/RKAkxG+ItD483vTze9CpPvN27d5+mqh2j9ox1yFERHdCC0tVQ+VjVUjXgAWBIUP40cElouMHAuUH3Uqi8L/B0MvNOWzVUFsqKeCNVUTVqWO3m4MExB82KeMvB400vjze9KqoaqlLrCFR1aeS9iLwIvBV8XASEn4XYLCgjTrnLJvvvD2efDT/+aHfL7NIl0xE55ypQpSYLEWmiqouDj2cBM4L344B/icijwP5Aa2AKIEBrEWmJJYk+wEWVGbMrh5EjMx2Bcy5N0pYsRGQ40A1oLCILgXuAbiJSACgwH7gWQFVnishrwCxgO3CjqhYF07kJ+DdWfTVEVWemK2bnnHPRpS1ZqOqFUYoHxxn+Aew8Rtnyt4G3KzA055xz5eQ38HfOOZeQJwvnnHMJebJwzjmXkCcL55xzCXmycM45l5AnC+eccwl5snDOOZeQJwvnnHMJebJwzjmXkCcL55xzCXmycM45l5AnC+eccwl5snDOOZeQJwvnnHMJebJwzjmXkCcL55xzCXmycM45l5AnC+eccwmlLVmIyBARWSYiM0JlD4vIbBH5r4iMFpEGQXkLEdkkItOD7rnQOB1E5BsRmSsiT4qIpCtm55xz0aXzyGIo0LNM2XtAW1U9EvgeGBjqN09VC4LuulD5s8A1QOugKztN55xzaZa2ZKGqHwKrypSNV9XtwcfPgGbxpiEiTYB6qvqZqirwMnBmOuJ1zjkXWybPWVwJvBP63FJEvhKRySLSJShrCiwMDbMwKHPOOVeJxHbY0zRxkRbAW6ratkz53UBH4GxVVRGpBeSq6koR6QCMAQ4H2gCDVPWUYLwuwF2qenqM+fUD+gHk5+d3GDFiREpxFxYWkpubm9K4meDxppfHm14eb3qVJ97u3btPU9WOUXuqato6oAUwo0zZ5cB/gL3ijDcJSyZNgNmh8guB55OZd4cOHTRVEydOTHncTPB408vjTS+PN73KEy8wVWNsUyu1GkpEegJ3Ar1VdWOofB8RyQneH4SdyP5BVRcD60SkU9AK6lJgbGXG7JxzDqqna8IiMhzoBjQWkYXAPVjrp1rAe0EL2M/UWj51Bf4oItuAYuA6VY2cHL8Ba1lVBzvHET7P4ZxzrhKkLVmo6oVRigfHGHYUMCpGv6lA22j9nHPOVQ6/gts551xCCZOFiPQXkXpiBovIlyJyamUE55xzLjskc2RxpaquA04F9gb6AoPSGpVzzrmskkyyiNyLqRfwD1WdGSpzzjlXBSSTLKaJyHgsWfxbRPKwFkvOOeeqiGRaQ10FFGDXPWwUkUbAFekNyznnXDZJ5shCgcOAm4PPdYHaaYvIOedc1kkmWfwN6IzdagNgPfBM2iJyzjmXdZKphjpWVduLyFcAqrpaRGqmOS7nnHNZJJkji23BfZsU7D5O+Alu55yrUpJJFk8Co4F9ReQB4GPgz2mNyjnnXFZJWA2lqv8UkWnAydj1FWeq6rdpj8w551zWSJgsRKQTMFNVnwk+1xORY1X187RH55xzLiskUw31LFAY+lwYlDnnnKsikrrdR/AEJQBUtZg03trcOedc9kkmWfwgIjeLSI2g6w/8kO7AnHPOZY9kksV1wHHAImAhcCzQL51BOeecyy7JtIZaBvSphFicc85lqZjJQkTuVNW/iMhTBBfkhanqzVFGc845tweKVw0VuZZiKjAtSpeQiAwRkWUiMiNU1lBE3hOROcHr3kG5iMiTIjJXRP4rIu1D41wWDD9HRC4r5zI655zbRTGPLFT1zeA2H0eo6u0pTn8o8DTwcqhsAPCBqg4SkQHB57uA04DWQXcs1jz3WBFpCNwDdMSOcKaJyDhVXZ1iTM4558op7gluVS0Cjk914qr6IbCqTPEZwLDg/TDgzFD5y2o+AxqISBOgB/Ceqq4KEsR7QM9UY3LOOVd+yVwvMV1ExgGvAxsihar6RorzzFfVxcH7JUB+8L4psCA03MKgLFb5TkSkH0FLrfz8fCZNmpRSgIWFhSmPmwkeb3p5vOnl8aZXRcWbTLKoDawETgqVKZBqsiiZiKqKyE4nz3dhei8ALwB07NhRu3XrltJ0Jk2aRKrjZoLHm14eb3p5vOlVUfEmkyzuUNUVuzynEktFpImqLg6qmZYF5YuA5qHhmgVli4BuZconVWA8zjnnEoh5zkJEfi0iy4H/ishCETmuguY5Doi0aLoMGBsqvzRoFdUJWBtUV/0bOFVE9g5aTp0alDnnnKsk8Y4sHgC6qOpsETkW+AtwYnkmLiLDsaOCxiKyEGvVNAh4TUSuAn4Czg8GfxvoBcwFNgJXAKjqKhH5E/BFMNwfVbXsSXPnnHNpFC9ZbFfV2QCq+rmI5JV34qp6YYxeJ0cZVoEbY0xnCDCkvPN3zjlXMeIli31F5NZYn1X10fSF5ZxzLpvESxYvAnlxPjvnnKsi4l3BfV9lBuKccy57JXOLcuecc1WcJwvnnHMJJUwWIlIrSlnD9ITjnHMuGyVzZPGGiNSIfAiuun4vfSE555zLNskkizHYRXQ5ItICu3p6YDqDcs45l12SeazqiyJSE0saLYBrVfXTdAfmnHMue8R7rGr4gjwBDgCmA51EpJNflOecc1VHvCOLshfgvRGj3Dnn3B7OL8pzzjmXUDJNZ98TkQahz3uLiN8i3DnnqpBkWkPto6prIh+C52Dvm76QnHPOZZtkkkWRiBwQ+SAiB2KPVXXOOVdFJPNY1buBj0VkMtYqqgvQL61ROeecyyrJXGfxroi0BzoFRbdU8DO5nXPOZblkjiwAjgO6hj6/lYZYnHPOZalkWkMNAvoDs4Kuv4j8OdUZisghIjI91K0TkVtE5F4RWRQq7xUaZ6CIzBWR70SkR6rzds45l5pkjix6AQWqWgwgIsOAr4DfpjJDVf0OKAimlQMsAkYDVwCPqeoj4eFF5DCgD3A4sD/wvoi0UdWiVObvnHOu/JJ9nkWD0Pv6FTj/k4F5qvpTnGHOAEao6hZV/RGYCxxTgTE455xLQFTjt4IVkQuBQcBErDVUV2Cgqo7Y5ZmLDAG+VNWnReRe4HJgHTAVuE1VV4vI08BnqvpKMM5g4B1VHRllev0IWmrl5+d3GDEitRALCwvJzc1NadxM8HjTy+NNL483vcoTb/fu3aepaseoPVU1YQc0AXoH3X7JjJPENGsCK4D84HM+kIMd7TwADAnKnwYuCY03GDg30fQ7dOigqZo4cWLK42aCx5teHm96ebzpVZ54gakaY5uazAnuD1R1saqOC7olIvJBUmkqvtOwo4qlQdJaqqpFaudGXqSkqmkR0Dw0XrOgzDnnXCWJmSxEpHbw+NTGwf2gGgZdC6BpBcz7QmB4aH5NQv3OAmYE78cBfUSkloi0BFoDUypg/s4555IUrzXUtcAtWAukadj5CrBzCk/vykxFpC7wy2AeEX8RkQLsViLzI/1UdaaIvIY1290O3KjeEso55ypVvFuUPwE8ISK/UdWnKnKmqroBaFSmrG+c4R/AzmM455zLgHjVUEeLyH6RRCEil4rIWBF5Mqiecs45V0XEO8H9PLAVQES6Ys1nXwbWAi+kPzTnnHPZIt45ixxVXRW8vwB4QVVHAaNEZHr6Q3POOZct4h1Z5IhIJJmcDEwI9Uv2BoTOOef2APE2+sOBySKyAtgEfAQgIq2wqijnnHNVRLzWUA8EF981AcYHV/eBHY38pjKCc845lx3iViep6mdRyr5PXzjOOeeyUbJ3nXXOOVeFebJwzjmXkCcL55xzCXmycM45l5AnC+eccwl5snDOOZeQJwvnnHMJebJwzjmXkCcL55xzCXmycM45l5AnC+eccwl5snDOOZdQxpKFiMwXkW9EZLqITA3KGorIeyIyJ3jdOyiX4HGuc0XkvyLSPlNxO+dcVZTpI4vuqlqgqh2DzwOAD1S1NfBB8BngNKB10PUDnq30SJ1zrgrLdLIo6wxgWPB+GHBmqPxlNZ8BDUSkSSYCdM65qkhKnmlUyTMW+RFYDSjwvKq+ICJrVLVB0F+A1araQETeAgap6sdBvw+Au1R1aplp9sOOPMjPz+8wYsSIlGIrLCwkNzc31UWrdB5venm86eXxpld54u3evfu0UE1PaaqakQ5oGrzuC3wNdAXWlBlmdfD6FnBCqPwDoGO86Xfo0EFTNXHixJTHzQSPN7083vTyeNOrPPECUzXGNjVj1VCquih4XQaMBo4Blkaql4LXZcHgi4DmodGbBWXOOecqQUaShYjUFZG8yHvgVGAGMA64LBjsMmBs8H4ccGnQKqoTsFZVF1dy2M45V2XFfQZ3GuUDo+20BNWBf6nquyLyBfCaiFwF/AScHwz/NtALmAtsBK6o/JCdc67qykiyUNUfgHZRylcCJ0cpV+DGSgjNOedcFNnWdNY551wW8mThnHMuIU8WzjnnEvJk4ZxzLiFPFs455xLyZOGccy4hTxbOOecS8mThnHMuIU8WzjnnEvJk4ZxzLiFPFs455xLyZOGccy4hTxbOOecS8mThnHMuIU8WzjnnEvJk4ZxzLiFPFs455xLyZOGccy6hSk8WItJcRCaKyCwRmSki/YPye0VkkYhMD7peoXEGishcEflORHpUdszOOVfVZeIZ3NuB21T1SxHJA6aJyHtBv8dU9ZHwwCJyGNAHOBzYH3hfRNqoalGlRu2cc1VYpR9ZqOpiVf0yeL8e+BZoGmeUM4ARqrpFVX8E5gLHpD9S55xzEaKqmZu5SAvgQ6AtcCtwObAOmIodfawWkaeBz1T1lWCcwcA7qjoyyvT6Af0A8vPzO4wYMSKluAoLC8nNzU1p3EzweNPL400vjze9yhNv9+7dp6lqx6g9VTUjHZALTAPODj7nAznY0c4DwJCg/GngktB4g4FzE02/Q4cOmqqJEyemPG4meLzp5fGml8ebXuWJF5iqMbapGWkNJSI1gFHAP1X1DQBVXaqqRapaDLxISVXTIqB5aPRmQZlzzrlKkonWUIIdHXyrqo+GypuEBjsLmBG8Hwf0EZFaItISaA1Mqax4nXPOZaY11PFAX+AbEZkelP0WuFBECgAF5gPXAqjqTBF5DZiFtaS6Ub0llHPOVapKTxaq+jEgUXq9HWecB7DzGM455zLAr+B2zjmXkCcL55xzCXmycM45l5AnC+eccwl5snDOOZeQJwvnnHMJebJwzjmXkCcL55xzCXmycM45l5AnC+eccwl5snDOOZeQJwvnnHMJebJwzjmXkCcL55xzCXmycM45l5AnC+eccwll4kl5WW3dOtiwIYf16+2zSPKvqtYVF5d0kc85OVCrlnUAGzfCpk1QrZp1OTnWRd5Hyrdtg/XroV49qFHDxt22DVavtmkUF8PatTUoKoKiIti+HWrXtnEjVKO/VqtWEve2bbBli40fiTvcRSurXx/y8krWQdjWrbBhg3VbtpRerpUra7J06c7TjfW+7OetW63bsqXkfa1a0KCBxVJUVDJ85LuJdGU/FxfbMm/bVvo1Mt2tW+E//2nK+PGwZAmsWVN63UWbdkTkffXq9t3VqGFl27eXnle8ruwwe+0F++5b8luIrJvId19UBCtXFuz4vdSqZb+HzZth+XKbngjk5tr3V7++fS+R72rTJvtO8/JsWmXXy7ZtVl69eumuRg17zckpeQ134bLt2+33u3WrLcPy5Yfx/PMl6zE8bKxpll33sTpVG75+fahTp2QdNmhQ8hstLLRuwwZbtsh/T6Tkfbj7/vsDmDzZxi8qgpo1bflr1iz9vnp1dvwvi4vtO9i40bqtW3ded7VqQcuW0KKFzSfefy/cL/Jbi8RXqxYcdVTUzdsu8WRRRpMmsHFjl7TOo1q1ki+5POPss0/Jj7u041OeR/Xq9gdKVc2a0Lix/QE3bizZ6MSf5nGpzzAjWlO9um2k9967ZCMU/hNDyWv4vWrpjW1xccnGIbyhiNbVrm0b9fCGeMMGS1rbt5cko2rVSm9MwV63bLGdn82bbQOyzz72Wlxsv6HFi2H2bNuY1a1rXe3aVv799zvHGN6oFRXZdKMlvHDiKttt326x7b23zUsVNm7MZeFCi7ts4gu/D5dF23BWhJyc0jsa0R0E2LqoVq0k6SUrsuMQXqaKlJ9vv5GK5smijAcfhNmz53Lwwa1i7pFHe1XdeU8k8jmyt7tli3XbtpXs6aiW7HmE90Ii72vVsj/xypX2J87Lg4YNratTx6Y/deocGjduTY0a9mOP/InL7v1C6dfIHnRkD7RWrZI/QGScaHvQkb2etWthxQrbY924sWSDU7aLbKAiy/Xtt9/RqtUhMedTdp5lP9esadOM7MnVrGnLHN7rjxzFlN2YlN3ARPZay264w9OePfsTfv3r40sdrWWzSZOm061bt0yHkbRJk6ZUWLxlv9/If2/NGjtqql7dXtesse82N9d+o7m5Jf+n8LTCtQSR7pNPJnPKKSfuGDbyH962reRoNJJEw7/FOnWsixwVRkT+Fxs3wrx58L//WXm0/16s/0T4CLPs9CvKbpMsRKQn8ASQA7ykqoPSMZ+bb4ZJkxbSrVurdEw+LQ48cBHdurXOdBhJmzRpMd26HZLpMJK2ePG23SZRVHXhHaOInBw7KkxlWuGjtYiaNbXU7yFSdVa9uiWD8orsXNavD+3bW5eNdou/gIjkAM8ApwGHAReKyGGZjco556qO3SJZAMcAc1X1B1XdCowAzshwTM45V2WIVtSZoTQSkXOBnqp6dfC5L3Csqt5UZrh+QD+A/Pz8DiNGjEhpfoWFheTm5u5a0JXI400vjze9PN70Kk+83bt3n6aqHaP2VNWs74BzsfMUkc99gafjjdOhQwdN1cSJE1MeNxM83vTyeNPL402v8sQLTNUY29TdpRpqEdA89LlZUOacc64S7C7J4gugtYi0FJGaQB9gXIZjcs65KmO3aDqrqttF5Cbg31jT2SGqOjPDYTnnXJWxWyQLAFV9G3g703E451xVtFu0hkqFiCwHfkpx9MbAigoMJ9083vTyeNPL402v8sR7oKruE63HHpssdoWITNVYzceykMebXh5venm86VVR8e4uJ7idc85lkCcL55xzCXmyiO6FTAdQTh5venm86eXxpleFxOvnLJxzziXkRxbOOecS8mThnHMuIU8WISLSU0S+E5G5IjIg0/GUJSLNRWSiiMwSkZki0j8ov1dEFonI9KDrlelYI0Rkvoh8E8Q1NShrKCLvicic4HXvTMcJICKHhNbhdBFZJyK3ZNP6FZEhIrJMRGaEyqKuTzFPBr/n/4pIpT9WJ0a8D4vI7CCm0SLSIChvISKbQuv5uSyJN+b3LyIDg/X7nYj0yJJ4Xw3FOl9Epgflu7Z+Y91hsKp12G1E5mEP2K0JfA0clum4ysTYBGgfvM8DvsceBnUvcHum44sR83ygcZmyvwADgvcDgIcyHWeM38MS4MBsWr9AV6A9MCPR+gR6Ae8AAnQCPs+SeE8FqgfvHwrF2yI8XBat36jff/Df+xqoBbQMth85mY63TP+/An+oiPXrRxYlsv4BS6q6WFW/DN6vB74FmmY2qpScAQwL3g8DzsxgLLGcDMxT1VTvApAWqvohsKpMcaz1eQbwsprPgAYi0qRyIjXR4lXV8aq6Pfj4GXYX6awQY/3GcgYwQlW3qOqPwFxsO1Jp4sUrIgKcDwyviHl5sijRFFgQ+ryQLN4Qi0gL4Cjg86DopuCwfki2VOsEFBgvItOCh1MB5Kvq4uD9EiA/M6HF1YfSf7JsXb8Qe33uDr/pK7Gjn4iWIvKViEwWkS6ZCiqKaN9/tq/fLsBSVZ0TKkt5/Xqy2A2JSC4wCrhFVdcBzwIHAwXAYuzQM1ucoKrtseen3ygiXcM91Y6Ps6r9dnAb/N7A60FRNq/fUrJxfcYiIncD24F/BkWLgQNU9SjgVuBfIlIvU/GF7DbffxkXUnqHZ5fWryeLErvFA5ZEpAaWKP6pqm8AqOpSVS1S1WLgRSr5UDgeVV0UvC4DRmOxLY1UhwSvyzIXYVSnAV+q6lLI7vUbiLU+s/Y3LSKXA6cDFwcJjqA6Z2Xwfhp2DqBNxoIMxPn+s3n9VgfOBl6NlO3q+vVkUSLrH7AU1EEOBr5V1UdD5eF66LOAGWXHzQQRqSsieZH32InNGdh6vSwY7DJgbGYijKnUHlm2rt+QWOtzHHBp0CqqE7A2VF2VMSLSE7gT6K2qG0Pl+4hITvD+IKA18ENmoiwR5/sfB/QRkVoi0hKLd0plxxfDKcBsVV0YKdjl9VuZZ+6zvcNaj3yPZdy7Mx1PlPhOwKoY/gtMD7pewD+Ab4LycUCTTMcaxHsQ1lrka2BmZJ0CjYAPgDnA+0DDTMcairkusBKoHyrLmvWLJbHFwDasjvyqWOsTawX1TPB7/gbomCXxzsXq+iO/4eeCYc8JfifTgS+BX2dJvDG/f+DuYP1+B5yWDfEG5UOB68oMu0vr12/34ZxzLiGvhnLOOZeQJwvnnHMJebJwzjmXkCcL55xzCXmycM45l1D1TAfg3O5ORCJNVwH2A4qA5cHnjap6XEYCc64CedNZ5yqQiNwLFKrqI5mOxbmK5NVQzqWRiBQGr92Cm7eNFZEfRGSQiFwsIlPEnvdxcDDcPiIySkS+CLrjM7sEzhlPFs5VnnbAdcAvgL5AG1U9BngJ+E0wzBPAY6p6NHbF7UuZCNS5svychXOV5wsN7s0kIvOA8UH5N0D34P0pwGF2GzAA6olIrqoWVmqkzpXhycK5yrMl9L449LmYkv9iNaCTqm6uzMCcS8SroZzLLuMpqZJCRAoyGItzO3iycC673Ax0DJ7KNgs7x+FcxnnTWeeccwn5kYVzzrmEPFk455xLyJOFc865hDxZOOecS8iThXPOuYQ8WTjnnEvIk4VzzrmE/j/7H/y1SYSuxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(testY_actual , 'b')\n",
    "plt.plot(pred , 'r')\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Prices')\n",
    "plt.title('Check the performance of the model with time')\n",
    "plt.legend(['Actual', 'Predicted'], loc='upper left')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow2",
   "language": "python",
   "name": "tensorflow2"
  },
  "language_info": {
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