NSE Stock Price Prediction using LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('max_columns', None)\n",
    "import datetime\n",
    "import math, time\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, BatchNormalization, Dropout, Activation, LSTM, GRU\n",
    "from tensorflow.keras.optimizers import Adam,RMSprop,SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "stocks_df = pd.read_csv(\"/kaggle/input/nyse/prices-split-adjusted.csv\",index_col=0)\n",
    "firm_df = pd.read_csv(\"/kaggle/input/nyse/securities.csv\",header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\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>2016-01-06</th>\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>2016-01-07</th>\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>2016-01-08</th>\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>2016-01-11</th>\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",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>WLTW</td>\n",
       "      <td>115.510002</td>\n",
       "      <td>115.550003</td>\n",
       "      <td>114.500000</td>\n",
       "      <td>116.059998</td>\n",
       "      <td>1098000.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>WLTW</td>\n",
       "      <td>116.459999</td>\n",
       "      <td>112.849998</td>\n",
       "      <td>112.589996</td>\n",
       "      <td>117.070000</td>\n",
       "      <td>949600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>WLTW</td>\n",
       "      <td>113.510002</td>\n",
       "      <td>114.379997</td>\n",
       "      <td>110.050003</td>\n",
       "      <td>115.029999</td>\n",
       "      <td>785300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>WLTW</td>\n",
       "      <td>113.330002</td>\n",
       "      <td>112.529999</td>\n",
       "      <td>111.919998</td>\n",
       "      <td>114.879997</td>\n",
       "      <td>1093700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>WLTW</td>\n",
       "      <td>113.660004</td>\n",
       "      <td>110.379997</td>\n",
       "      <td>109.870003</td>\n",
       "      <td>115.870003</td>\n",
       "      <td>1523500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           symbol        open       close         low        high     volume\n",
       "date                                                                        \n",
       "2016-01-05   WLTW  123.430000  125.839996  122.309998  126.250000  2163600.0\n",
       "2016-01-06   WLTW  125.239998  119.980003  119.940002  125.540001  2386400.0\n",
       "2016-01-07   WLTW  116.379997  114.949997  114.930000  119.739998  2489500.0\n",
       "2016-01-08   WLTW  115.480003  116.620003  113.500000  117.440002  2006300.0\n",
       "2016-01-11   WLTW  117.010002  114.970001  114.089996  117.330002  1408600.0\n",
       "2016-01-12   WLTW  115.510002  115.550003  114.500000  116.059998  1098000.0\n",
       "2016-01-13   WLTW  116.459999  112.849998  112.589996  117.070000   949600.0\n",
       "2016-01-14   WLTW  113.510002  114.379997  110.050003  115.029999   785300.0\n",
       "2016-01-15   WLTW  113.330002  112.529999  111.919998  114.879997  1093700.0\n",
       "2016-01-19   WLTW  113.660004  110.379997  109.870003  115.870003  1523500.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Ticker symbol</th>\n",
       "      <th>Security</th>\n",
       "      <th>SEC filings</th>\n",
       "      <th>GICS Sector</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>3M Company</td>\n",
       "      <td>reports</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Conglomerates</td>\n",
       "      <td>St. Paul, Minnesota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66740</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>Health Care</td>\n",
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       "      <td>1467373</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATVI</td>\n",
       "      <td>Activision Blizzard</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Home Entertainment Software</td>\n",
       "      <td>Santa Monica, California</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>718877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AYI</td>\n",
       "      <td>Acuity Brands Inc</td>\n",
       "      <td>reports</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Electrical Components &amp; Equipment</td>\n",
       "      <td>Atlanta, Georgia</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>1144215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>Adobe Systems Inc</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>San Jose, California</td>\n",
       "      <td>1997-05-05</td>\n",
       "      <td>796343</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AAP</td>\n",
       "      <td>Advance Auto Parts</td>\n",
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       "      <td>Consumer Discretionary</td>\n",
       "      <td>Automotive Retail</td>\n",
       "      <td>Roanoke, Virginia</td>\n",
       "      <td>2015-07-09</td>\n",
       "      <td>1158449</td>\n",
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       "      <th>8</th>\n",
       "      <td>AES</td>\n",
       "      <td>AES Corp</td>\n",
       "      <td>reports</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>Independent Power Producers &amp; Energy Traders</td>\n",
       "      <td>Arlington, Virginia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>874761</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>Aetna Inc</td>\n",
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       "      <td>Health Care</td>\n",
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       "      <td>Hartford, Connecticut</td>\n",
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       "      <td>1122304</td>\n",
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      "text/plain": [
       "  Ticker symbol             Security SEC filings             GICS Sector  \\\n",
       "0           MMM           3M Company     reports             Industrials   \n",
       "1           ABT  Abbott Laboratories     reports             Health Care   \n",
       "2          ABBV               AbbVie     reports             Health Care   \n",
       "3           ACN        Accenture plc     reports  Information Technology   \n",
       "4          ATVI  Activision Blizzard     reports  Information Technology   \n",
       "5           AYI    Acuity Brands Inc     reports             Industrials   \n",
       "6          ADBE    Adobe Systems Inc     reports  Information Technology   \n",
       "7           AAP   Advance Auto Parts     reports  Consumer Discretionary   \n",
       "8           AES             AES Corp     reports               Utilities   \n",
       "9           AET            Aetna Inc     reports             Health Care   \n",
       "\n",
       "                              GICS Sub Industry   Address of Headquarters  \\\n",
       "0                      Industrial Conglomerates       St. Paul, Minnesota   \n",
       "1                         Health Care Equipment   North Chicago, Illinois   \n",
       "2                               Pharmaceuticals   North Chicago, Illinois   \n",
       "3                IT Consulting & Other Services           Dublin, Ireland   \n",
       "4                   Home Entertainment Software  Santa Monica, California   \n",
       "5             Electrical Components & Equipment          Atlanta, Georgia   \n",
       "6                          Application Software      San Jose, California   \n",
       "7                             Automotive Retail         Roanoke, Virginia   \n",
       "8  Independent Power Producers & Energy Traders       Arlington, Virginia   \n",
       "9                           Managed Health Care     Hartford, Connecticut   \n",
       "\n",
       "  Date first added      CIK  \n",
       "0              NaN    66740  \n",
       "1       1964-03-31     1800  \n",
       "2       2012-12-31  1551152  \n",
       "3       2011-07-06  1467373  \n",
       "4       2015-08-31   718877  \n",
       "5       2016-05-03  1144215  \n",
       "6       1997-05-05   796343  \n",
       "7       2015-07-09  1158449  \n",
       "8              NaN   874761  \n",
       "9       1976-06-30  1122304  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firm_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symbol    0\n",
       "open      0\n",
       "close     0\n",
       "low       0\n",
       "high      0\n",
       "volume    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticker symbol                0\n",
       "Security                     0\n",
       "SEC filings                  0\n",
       "GICS Sector                  0\n",
       "GICS Sub Industry            0\n",
       "Address of Headquarters      0\n",
       "Date first added           198\n",
       "CIK                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firm_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols = list(set(stocks_df.symbol))\n",
    "len(symbols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AON', 'ULTA', 'PRGO', 'GPC', 'BHI', 'CCL', 'FISV', 'JBHT', 'NRG', 'FBHS']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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       "      <th>304</th>\n",
       "      <td>MCHP</td>\n",
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       "      <td>reports</td>\n",
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       "      <td>Semiconductors</td>\n",
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       "      <td>MU</td>\n",
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       "      <td>MSFT</td>\n",
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       "      <td>Information Technology</td>\n",
       "      <td>Systems Software</td>\n",
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       "      <td>1994-06-01</td>\n",
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      "text/plain": [
       "    Ticker symbol              Security SEC filings             GICS Sector  \\\n",
       "304          MCHP  Microchip Technology     reports  Information Technology   \n",
       "305            MU     Micron Technology     reports  Information Technology   \n",
       "306          MSFT       Microsoft Corp.     reports  Information Technology   \n",
       "\n",
       "    GICS Sub Industry Address of Headquarters Date first added     CIK  \n",
       "304    Semiconductors       Chandler, Arizona       2007-09-07  827054  \n",
       "305    Semiconductors            Boise, Idaho       1994-09-27  723125  \n",
       "306  Systems Software     Redmond, Washington       1994-06-01  789019  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firm_df.loc[firm_df.Security.str.startswith('Micro'),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks_msft = stocks_df[stocks_df.symbol == 'MSFT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
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       "      <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>MSFT</td>\n",
       "      <td>30.620001</td>\n",
       "      <td>30.950001</td>\n",
       "      <td>30.590000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>38409100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>30.850000</td>\n",
       "      <td>30.959999</td>\n",
       "      <td>30.639999</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>49749600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>30.879999</td>\n",
       "      <td>30.770000</td>\n",
       "      <td>30.520000</td>\n",
       "      <td>31.080000</td>\n",
       "      <td>58182400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>30.629999</td>\n",
       "      <td>30.450001</td>\n",
       "      <td>30.190001</td>\n",
       "      <td>30.700001</td>\n",
       "      <td>50559700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>30.280001</td>\n",
       "      <td>30.660000</td>\n",
       "      <td>30.240000</td>\n",
       "      <td>30.879999</td>\n",
       "      <td>51197400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>30.709999</td>\n",
       "      <td>30.270000</td>\n",
       "      <td>30.120001</td>\n",
       "      <td>30.760000</td>\n",
       "      <td>68754700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-12</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>30.150000</td>\n",
       "      <td>30.070000</td>\n",
       "      <td>29.910000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>65912100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-13</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>30.260000</td>\n",
       "      <td>30.350000</td>\n",
       "      <td>30.010000</td>\n",
       "      <td>30.520000</td>\n",
       "      <td>51863500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-14</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>30.309999</td>\n",
       "      <td>30.959999</td>\n",
       "      <td>30.260000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>63228100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-15</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>31.080000</td>\n",
       "      <td>30.860001</td>\n",
       "      <td>30.709999</td>\n",
       "      <td>31.240000</td>\n",
       "      <td>79913200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol       open      close        low       high      volume\n",
       "date                                                                     \n",
       "2010-01-04   MSFT  30.620001  30.950001  30.590000  31.100000  38409100.0\n",
       "2010-01-05   MSFT  30.850000  30.959999  30.639999  31.100000  49749600.0\n",
       "2010-01-06   MSFT  30.879999  30.770000  30.520000  31.080000  58182400.0\n",
       "2010-01-07   MSFT  30.629999  30.450001  30.190001  30.700001  50559700.0\n",
       "2010-01-08   MSFT  30.280001  30.660000  30.240000  30.879999  51197400.0\n",
       "2010-01-11   MSFT  30.709999  30.270000  30.120001  30.760000  68754700.0\n",
       "2010-01-12   MSFT  30.150000  30.070000  29.910000  30.400000  65912100.0\n",
       "2010-01-13   MSFT  30.260000  30.350000  30.010000  30.520000  51863500.0\n",
       "2010-01-14   MSFT  30.309999  30.959999  30.260000  31.100000  63228100.0\n",
       "2010-01-15   MSFT  31.080000  30.860001  30.709999  31.240000  79913200.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_msft.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1762, 6)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_msft.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'prices')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(stocks_msft.open.values.astype('float32'),color='red',label=open)\n",
    "plt.title('Opening Stock Prices for Microsoft')\n",
    "plt.grid(True)\n",
    "plt.xlabel('time(days)')\n",
    "plt.ylabel('prices')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(stocks_msft.close.values.astype('float32'),color='green',label=open)\n",
    "plt.title('Closing Stock Prices for Microsoft')\n",
    "plt.grid(True)\n",
    "plt.xlabel('time(days)')\n",
    "plt.ylabel('prices')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Volume')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,10))\n",
    "plt.plot(stocks_msft.volume.values,color='black')\n",
    "plt.xlabel('time(days)')\n",
    "plt.ylabel('Volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix random seed for reproducibility\n",
    "np.random.seed(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back, 0])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**LSTMs are sensitive to the scale of the input data, specifically when the sigmoid (default) or tanh activation functions are used. It can be a good practice to rescale the data to the range of 0-to-1, also called normalizing.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_kg_hide-input": false,
    "_kg_hide-output": false
   },
   "outputs": [],
   "source": [
    "msft = stocks_msft.copy()\n",
    "#msft = msft.drop('symbol',axis=1)\n",
    "closing = msft.close.values\n",
    "closing = closing.reshape(len(closing),1)\n",
    "\n",
    "#normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "closing = scaler.fit_transform(closing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train and test sets\n",
    "train_size = int(len(closing) * 0.75)\n",
    "test_size = len(closing) - train_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1321 441\n"
     ]
    }
   ],
   "source": [
    "print(train_size , test_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = closing[0:train_size,:], closing[train_size:len(closing),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.19551837]\n",
      " [0.19576457]\n",
      " [0.19108594]\n",
      " ...\n",
      " [0.43462202]\n",
      " [0.43609952]\n",
      " [0.42551099]]\n",
      "\n",
      " ----------------------------\n",
      "[[0.45653779]\n",
      " [0.4560453 ]\n",
      " [0.45333658]\n",
      " [0.4548141 ]\n",
      " [0.460724  ]\n",
      " [0.4617089 ]\n",
      " [0.45900031]\n",
      " [0.47402114]\n",
      " [0.47155874]\n",
      " [0.4582615 ]\n",
      " [0.4900271 ]\n",
      " [0.48337847]\n",
      " [0.49199711]\n",
      " [0.50061562]\n",
      " [0.61216448]\n",
      " [0.6161044 ]\n",
      " [0.64393008]\n",
      " [0.64146766]\n",
      " [0.63112533]\n",
      " [0.63161784]\n",
      " [0.62127561]\n",
      " [0.60551585]\n",
      " [0.57301156]\n",
      " [0.58335389]\n",
      " [0.60920957]\n",
      " [0.59985224]\n",
      " [0.59935973]\n",
      " [0.60625466]\n",
      " [0.63309534]\n",
      " [0.62275301]\n",
      " [0.61561188]\n",
      " [0.60502346]\n",
      " [0.60502346]\n",
      " [0.60108344]\n",
      " [0.58827881]\n",
      " [0.58064518]\n",
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      " [0.58729381]\n",
      " [0.59640484]\n",
      " [0.5887712 ]\n",
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      " [0.57498157]\n",
      " [0.56956414]\n",
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      " [0.58113769]\n",
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      " [0.56537802]\n",
      " [0.55331201]\n",
      " [0.56193062]\n",
      " [0.56537802]\n",
      " [0.58384638]\n",
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      " [0.5547895 ]\n",
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      " [0.55011077]\n",
      " [0.54986458]\n",
      " [0.57325786]\n",
      " [0.5877863 ]\n",
      " [0.58335389]\n",
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      " [0.60403845]\n",
      " [0.60502346]\n",
      " [0.58138389]\n",
      " [0.5843389 ]\n",
      " [0.59886734]\n",
      " [0.57621277]\n",
      " [0.5843389 ]\n",
      " [0.5840926 ]\n",
      " [0.59074121]\n",
      " [0.59862104]\n",
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      " [0.58113769]\n",
      " [0.55774441]\n",
      " [0.49396702]\n",
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      " [0.4299434 ]\n",
      " [0.48510218]\n",
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      " [0.51514407]\n",
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      " [0.50110814]\n",
      " [0.50455554]\n",
      " [0.48263978]\n",
      " [0.51415906]\n",
      " [0.49396702]\n",
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      " [0.5230239 ]\n",
      " [0.50406305]\n",
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      " [0.49938443]\n",
      " [0.50307805]\n",
      " [0.52327009]\n",
      " [0.53188874]\n",
      " [0.55552821]\n",
      " [0.58163018]\n",
      " [0.58458509]\n",
      " [0.58581629]\n",
      " [0.60182225]\n",
      " [0.59344993]\n",
      " [0.59074121]\n",
      " [0.58803249]\n",
      " [0.58286138]\n",
      " [0.59098741]\n",
      " [0.60329965]\n",
      " [0.60600836]\n",
      " [0.60970206]\n",
      " [0.59566613]\n",
      " [0.6161044 ]\n",
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      " [0.76926867]\n",
      " [0.75547894]\n",
      " [0.76262006]\n",
      " [0.74735291]\n",
      " [0.72962324]\n",
      " [0.744398  ]\n",
      " [0.76680627]\n",
      " [0.77296239]\n",
      " [0.77246988]\n",
      " [0.78576702]\n",
      " [0.76705247]\n",
      " [0.75104651]\n",
      " [0.75449403]\n",
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      " [0.76779118]\n",
      " [0.76926867]\n",
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      " [0.79315444]\n",
      " [0.79290815]\n",
      " [0.76803747]\n",
      " [0.8101453 ]\n",
      " [0.80768288]\n",
      " [0.80719039]\n",
      " [0.78724454]\n",
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      " [0.8079291 ]\n",
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      " [0.7138636 ]\n",
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      " [0.69810396]\n",
      " [0.716326  ]\n",
      " [0.69662644]\n",
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      " [0.72814584]\n",
      " [0.73725688]\n",
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      " [0.7150948 ]\n",
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      " [0.8069441 ]\n",
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      " [0.8396947 ]\n",
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      " [0.85003693]\n",
      " [0.8431421 ]\n",
      " [0.90248709]\n",
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      " [0.9352377 ]\n",
      " [0.92637286]\n",
      " [0.91332182]\n",
      " [0.90765821]\n",
      " [0.90888941]\n",
      " [0.9059345 ]\n",
      " [0.89682346]\n",
      " [0.89140605]\n",
      " [0.87909382]\n",
      " [0.92120165]\n",
      " [0.92243295]\n",
      " [0.91504553]\n",
      " [0.87884762]\n",
      " [0.88672743]\n",
      " [0.86456537]\n",
      " [0.88303373]\n",
      " [0.9022409 ]\n",
      " [0.92661906]\n",
      " [0.91947793]\n",
      " [0.93203649]\n",
      " [0.93843881]\n",
      " [0.92070926]\n",
      " [0.92391037]\n",
      " [0.92588037]\n",
      " [0.9377001 ]\n",
      " [0.91726173]\n",
      " [0.89115986]\n",
      " [0.89239106]\n",
      " [0.91627683]\n",
      " [0.90962822]\n",
      " [0.94459493]\n",
      " [0.93573009]\n",
      " [0.95936966]\n",
      " [0.96429448]\n",
      " [0.98424036]\n",
      " [0.97685302]\n",
      " [0.97439062]\n",
      " [0.96749569]\n",
      " [1.        ]\n",
      " [0.99803009]\n",
      " [0.99803009]\n",
      " [0.99827629]\n",
      " [0.99064277]\n",
      " [0.99162768]\n",
      " [0.98448665]\n",
      " [0.98227045]\n",
      " [0.96355577]]\n"
     ]
    }
   ],
   "source": [
    "print(train)\n",
    "print(\"\\n ----------------------------\")\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape into X=t and Y=t+1\n",
    "look_back = 1   # Taking 1 past values to predict a single value\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our data is in the form of sample and features , we need to convert it to the form of [sample,timestamp,features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "#Timestamp here is taken as 1 ie looking at the stock of time t we will predict at t+1\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1319, 1, 1), (1319,), (439, 1, 1), (439,))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape,trainY.shape,testX.shape,testY.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we prepare the LSTM network for predicting the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a call back functionn\n",
    "class myCallback(tf.keras.callbacks.Callback):\n",
    "    def on_epoch_end(self,epoch,logs={}):\n",
    "        if(logs.get('accuracy')>0.95):\n",
    "            print(\"\\n Reaching 95% so stopping the training now\")\n",
    "            self.model.stop_training = True\n",
    "            \n",
    "callbacks = myCallback()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = 'weights'\n",
    "modelcheckpoint = tf.keras.callbacks.ModelCheckpoint(filepath,monitor = 'val_accuracy', verbose=0, save_freq='epoch',save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_platue = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_accuracy',factor=0.1,patience=5,verbose=0,mode='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    \n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(LSTM(256, input_shape=(1, look_back), return_sequences=True))\n",
    "    model.add(Dropout(0.4))\n",
    "        \n",
    "    model.add(LSTM(256))\n",
    "    model.add(Dropout(0.4))\n",
    "        \n",
    "    model.add(Dense(64,kernel_initializer=\"uniform\",activation='relu'))        \n",
    "    model.add(Dense(1,kernel_initializer=\"uniform\",activation='linear'))\n",
    "    \n",
    "        \n",
    "    start = time.time()\n",
    "    model.compile(loss='mse',optimizer=Adam(lr = 0.0005), metrics=['mean_squared_error'])\n",
    "    print(\"Compilation Time : \", time.time() - start)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compilation Time :  0.01087641716003418\n",
      "Epoch 1/100\n",
      "11/11 [==============================] - 1s 71ms/step - loss: 0.0763 - mean_squared_error: 0.0763 - val_loss: 0.5066 - val_mean_squared_error: 0.5066 - lr: 5.0000e-04\n",
      "Epoch 2/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 0.0679 - mean_squared_error: 0.0679 - val_loss: 0.4557 - val_mean_squared_error: 0.4557 - lr: 5.0000e-04\n",
      "Epoch 3/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 0.0514 - mean_squared_error: 0.0514 - val_loss: 0.3367 - val_mean_squared_error: 0.3367 - lr: 5.0000e-04\n",
      "Epoch 4/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.0266 - mean_squared_error: 0.0266 - val_loss: 0.1386 - val_mean_squared_error: 0.1386 - lr: 5.0000e-04\n",
      "Epoch 5/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.0188 - mean_squared_error: 0.0188 - val_loss: 0.0957 - val_mean_squared_error: 0.0957 - lr: 5.0000e-04\n",
      "Epoch 6/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 0.0151 - mean_squared_error: 0.0151 - val_loss: 0.1220 - val_mean_squared_error: 0.1220 - lr: 5.0000e-04\n",
      "Epoch 7/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.0126 - mean_squared_error: 0.0126 - val_loss: 0.0793 - val_mean_squared_error: 0.0793 - lr: 5.0000e-04\n",
      "Epoch 8/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 0.0094 - mean_squared_error: 0.0094 - val_loss: 0.0384 - val_mean_squared_error: 0.0384 - lr: 5.0000e-04\n",
      "Epoch 9/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.0057 - mean_squared_error: 0.0057 - val_loss: 0.0249 - val_mean_squared_error: 0.0249 - lr: 5.0000e-04\n",
      "Epoch 10/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 0.0026 - mean_squared_error: 0.0026 - val_loss: 0.0025 - val_mean_squared_error: 0.0025 - lr: 5.0000e-04\n",
      "Epoch 11/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 8.9205e-04 - mean_squared_error: 8.9205e-04 - val_loss: 0.0011 - val_mean_squared_error: 0.0011 - lr: 5.0000e-04\n",
      "Epoch 12/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 5.4045e-04 - mean_squared_error: 5.4045e-04 - val_loss: 0.0030 - val_mean_squared_error: 0.0030 - lr: 5.0000e-04\n",
      "Epoch 13/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 6.8074e-04 - mean_squared_error: 6.8074e-04 - val_loss: 0.0013 - val_mean_squared_error: 0.0013 - lr: 5.0000e-04\n",
      "Epoch 14/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 5.2103e-04 - mean_squared_error: 5.2103e-04 - val_loss: 8.7239e-04 - val_mean_squared_error: 8.7239e-04 - lr: 5.0000e-04\n",
      "Epoch 15/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 5.1715e-04 - mean_squared_error: 5.1715e-04 - val_loss: 5.6075e-04 - val_mean_squared_error: 5.6075e-04 - lr: 5.0000e-04\n",
      "Epoch 16/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 5.4326e-04 - mean_squared_error: 5.4326e-04 - val_loss: 7.7321e-04 - val_mean_squared_error: 7.7321e-04 - lr: 5.0000e-04\n",
      "Epoch 17/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 5.7858e-04 - mean_squared_error: 5.7858e-04 - val_loss: 6.5704e-04 - val_mean_squared_error: 6.5704e-04 - lr: 5.0000e-04\n",
      "Epoch 18/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 5.4736e-04 - mean_squared_error: 5.4736e-04 - val_loss: 9.0521e-04 - val_mean_squared_error: 9.0521e-04 - lr: 5.0000e-04\n",
      "Epoch 19/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.9753e-04 - mean_squared_error: 4.9753e-04 - val_loss: 7.3240e-04 - val_mean_squared_error: 7.3240e-04 - lr: 5.0000e-04\n",
      "Epoch 20/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.4511e-04 - mean_squared_error: 4.4511e-04 - val_loss: 7.2959e-04 - val_mean_squared_error: 7.2959e-04 - lr: 5.0000e-04\n",
      "Epoch 21/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.9809e-04 - mean_squared_error: 4.9809e-04 - val_loss: 8.4804e-04 - val_mean_squared_error: 8.4804e-04 - lr: 5.0000e-04\n",
      "Epoch 22/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 5.2091e-04 - mean_squared_error: 5.2091e-04 - val_loss: 9.7513e-04 - val_mean_squared_error: 9.7513e-04 - lr: 5.0000e-04\n",
      "Epoch 23/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 5.1473e-04 - mean_squared_error: 5.1473e-04 - val_loss: 7.7353e-04 - val_mean_squared_error: 7.7353e-04 - lr: 5.0000e-04\n",
      "Epoch 24/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.7100e-04 - mean_squared_error: 4.7100e-04 - val_loss: 8.3349e-04 - val_mean_squared_error: 8.3349e-04 - lr: 5.0000e-04\n",
      "Epoch 25/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.8322e-04 - mean_squared_error: 4.8322e-04 - val_loss: 5.3744e-04 - val_mean_squared_error: 5.3744e-04 - lr: 5.0000e-04\n",
      "Epoch 26/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.7756e-04 - mean_squared_error: 4.7756e-04 - val_loss: 7.2607e-04 - val_mean_squared_error: 7.2607e-04 - lr: 5.0000e-04\n",
      "Epoch 27/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.8558e-04 - mean_squared_error: 4.8558e-04 - val_loss: 5.2019e-04 - val_mean_squared_error: 5.2019e-04 - lr: 5.0000e-04\n",
      "Epoch 28/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 5.0438e-04 - mean_squared_error: 5.0438e-04 - val_loss: 6.1872e-04 - val_mean_squared_error: 6.1872e-04 - lr: 5.0000e-04\n",
      "Epoch 29/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 5.4750e-04 - mean_squared_error: 5.4750e-04 - val_loss: 0.0011 - val_mean_squared_error: 0.0011 - lr: 5.0000e-04\n",
      "Epoch 30/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.5438e-04 - mean_squared_error: 4.5438e-04 - val_loss: 5.8425e-04 - val_mean_squared_error: 5.8425e-04 - lr: 5.0000e-04\n",
      "Epoch 31/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.7083e-04 - mean_squared_error: 4.7083e-04 - val_loss: 6.6508e-04 - val_mean_squared_error: 6.6508e-04 - lr: 5.0000e-04\n",
      "Epoch 32/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.5199e-04 - mean_squared_error: 4.5199e-04 - val_loss: 0.0010 - val_mean_squared_error: 0.0010 - lr: 5.0000e-04\n",
      "Epoch 33/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.6719e-04 - mean_squared_error: 4.6719e-04 - val_loss: 6.4943e-04 - val_mean_squared_error: 6.4943e-04 - lr: 5.0000e-04\n",
      "Epoch 34/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 5.1768e-04 - mean_squared_error: 5.1768e-04 - val_loss: 5.2319e-04 - val_mean_squared_error: 5.2319e-04 - lr: 5.0000e-04\n",
      "Epoch 35/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.7699e-04 - mean_squared_error: 4.7699e-04 - val_loss: 9.7718e-04 - val_mean_squared_error: 9.7718e-04 - lr: 5.0000e-04\n",
      "Epoch 36/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.4776e-04 - mean_squared_error: 4.4776e-04 - val_loss: 7.9146e-04 - val_mean_squared_error: 7.9146e-04 - lr: 5.0000e-04\n",
      "Epoch 37/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.7363e-04 - mean_squared_error: 4.7363e-04 - val_loss: 4.7986e-04 - val_mean_squared_error: 4.7986e-04 - lr: 5.0000e-04\n",
      "Epoch 38/100\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 4.5743e-04 - mean_squared_error: 4.5743e-04 - val_loss: 5.1317e-04 - val_mean_squared_error: 5.1317e-04 - lr: 5.0000e-04\n",
      "Epoch 39/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.4884e-04 - mean_squared_error: 4.4884e-04 - val_loss: 8.7305e-04 - val_mean_squared_error: 8.7305e-04 - lr: 5.0000e-04\n",
      "Epoch 40/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.3525e-04 - mean_squared_error: 4.3525e-04 - val_loss: 5.9275e-04 - val_mean_squared_error: 5.9275e-04 - lr: 5.0000e-04\n",
      "Epoch 41/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.2851e-04 - mean_squared_error: 4.2851e-04 - val_loss: 6.2143e-04 - val_mean_squared_error: 6.2143e-04 - lr: 5.0000e-04\n",
      "Epoch 42/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.1079e-04 - mean_squared_error: 4.1079e-04 - val_loss: 5.8912e-04 - val_mean_squared_error: 5.8912e-04 - lr: 5.0000e-04\n",
      "Epoch 43/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.6122e-04 - mean_squared_error: 4.6122e-04 - val_loss: 7.4892e-04 - val_mean_squared_error: 7.4892e-04 - lr: 5.0000e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 44/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.1125e-04 - mean_squared_error: 4.1125e-04 - val_loss: 6.9177e-04 - val_mean_squared_error: 6.9177e-04 - lr: 5.0000e-04\n",
      "Epoch 45/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.2368e-04 - mean_squared_error: 4.2368e-04 - val_loss: 5.1318e-04 - val_mean_squared_error: 5.1318e-04 - lr: 5.0000e-04\n",
      "Epoch 46/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.4516e-04 - mean_squared_error: 4.4516e-04 - val_loss: 4.8159e-04 - val_mean_squared_error: 4.8159e-04 - lr: 5.0000e-04\n",
      "Epoch 47/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.5629e-04 - mean_squared_error: 4.5629e-04 - val_loss: 4.4857e-04 - val_mean_squared_error: 4.4857e-04 - lr: 5.0000e-04\n",
      "Epoch 48/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.4768e-04 - mean_squared_error: 4.4768e-04 - val_loss: 5.1314e-04 - val_mean_squared_error: 5.1314e-04 - lr: 5.0000e-04\n",
      "Epoch 49/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.4958e-04 - mean_squared_error: 4.4958e-04 - val_loss: 6.6573e-04 - val_mean_squared_error: 6.6573e-04 - lr: 5.0000e-04\n",
      "Epoch 50/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.1578e-04 - mean_squared_error: 4.1578e-04 - val_loss: 4.5975e-04 - val_mean_squared_error: 4.5975e-04 - lr: 5.0000e-04\n",
      "Epoch 51/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.9386e-04 - mean_squared_error: 4.9386e-04 - val_loss: 5.5828e-04 - val_mean_squared_error: 5.5828e-04 - lr: 5.0000e-04\n",
      "Epoch 52/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.0097e-04 - mean_squared_error: 4.0097e-04 - val_loss: 6.8487e-04 - val_mean_squared_error: 6.8487e-04 - lr: 5.0000e-04\n",
      "Epoch 53/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.6068e-04 - mean_squared_error: 4.6068e-04 - val_loss: 4.4266e-04 - val_mean_squared_error: 4.4266e-04 - lr: 5.0000e-04\n",
      "Epoch 54/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.3883e-04 - mean_squared_error: 4.3883e-04 - val_loss: 4.4841e-04 - val_mean_squared_error: 4.4841e-04 - lr: 5.0000e-04\n",
      "Epoch 55/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.4919e-04 - mean_squared_error: 4.4919e-04 - val_loss: 5.3485e-04 - val_mean_squared_error: 5.3485e-04 - lr: 5.0000e-04\n",
      "Epoch 56/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.2343e-04 - mean_squared_error: 4.2343e-04 - val_loss: 5.0328e-04 - val_mean_squared_error: 5.0328e-04 - lr: 5.0000e-04\n",
      "Epoch 57/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.2425e-04 - mean_squared_error: 4.2425e-04 - val_loss: 6.7954e-04 - val_mean_squared_error: 6.7954e-04 - lr: 5.0000e-04\n",
      "Epoch 58/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.8511e-04 - mean_squared_error: 3.8511e-04 - val_loss: 4.6491e-04 - val_mean_squared_error: 4.6491e-04 - lr: 5.0000e-04\n",
      "Epoch 59/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.1964e-04 - mean_squared_error: 4.1964e-04 - val_loss: 4.3682e-04 - val_mean_squared_error: 4.3682e-04 - lr: 5.0000e-04\n",
      "Epoch 60/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.0122e-04 - mean_squared_error: 4.0122e-04 - val_loss: 6.8785e-04 - val_mean_squared_error: 6.8785e-04 - lr: 5.0000e-04\n",
      "Epoch 61/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.1190e-04 - mean_squared_error: 4.1190e-04 - val_loss: 4.3304e-04 - val_mean_squared_error: 4.3304e-04 - lr: 5.0000e-04\n",
      "Epoch 62/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.1278e-04 - mean_squared_error: 4.1278e-04 - val_loss: 4.2032e-04 - val_mean_squared_error: 4.2032e-04 - lr: 5.0000e-04\n",
      "Epoch 63/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.2202e-04 - mean_squared_error: 4.2202e-04 - val_loss: 4.2817e-04 - val_mean_squared_error: 4.2817e-04 - lr: 5.0000e-04\n",
      "Epoch 64/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.3043e-04 - mean_squared_error: 4.3043e-04 - val_loss: 4.0975e-04 - val_mean_squared_error: 4.0975e-04 - lr: 5.0000e-04\n",
      "Epoch 65/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.5261e-04 - mean_squared_error: 4.5261e-04 - val_loss: 4.7472e-04 - val_mean_squared_error: 4.7472e-04 - lr: 5.0000e-04\n",
      "Epoch 66/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.0670e-04 - mean_squared_error: 4.0670e-04 - val_loss: 4.4076e-04 - val_mean_squared_error: 4.4076e-04 - lr: 5.0000e-04\n",
      "Epoch 67/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.1409e-04 - mean_squared_error: 4.1409e-04 - val_loss: 4.4400e-04 - val_mean_squared_error: 4.4400e-04 - lr: 5.0000e-04\n",
      "Epoch 68/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.1119e-04 - mean_squared_error: 4.1119e-04 - val_loss: 3.9923e-04 - val_mean_squared_error: 3.9923e-04 - lr: 5.0000e-04\n",
      "Epoch 69/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 4.3136e-04 - mean_squared_error: 4.3136e-04 - val_loss: 4.0476e-04 - val_mean_squared_error: 4.0476e-04 - lr: 5.0000e-04\n",
      "Epoch 70/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.8774e-04 - mean_squared_error: 3.8774e-04 - val_loss: 4.0172e-04 - val_mean_squared_error: 4.0172e-04 - lr: 5.0000e-04\n",
      "Epoch 71/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.0384e-04 - mean_squared_error: 4.0384e-04 - val_loss: 4.0924e-04 - val_mean_squared_error: 4.0924e-04 - lr: 5.0000e-04\n",
      "Epoch 72/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 3.6598e-04 - mean_squared_error: 3.6598e-04 - val_loss: 4.4729e-04 - val_mean_squared_error: 4.4729e-04 - lr: 5.0000e-04\n",
      "Epoch 73/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 3.9670e-04 - mean_squared_error: 3.9670e-04 - val_loss: 4.3748e-04 - val_mean_squared_error: 4.3748e-04 - lr: 5.0000e-04\n",
      "Epoch 74/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.4281e-04 - mean_squared_error: 4.4281e-04 - val_loss: 4.4167e-04 - val_mean_squared_error: 4.4167e-04 - lr: 5.0000e-04\n",
      "Epoch 75/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.9979e-04 - mean_squared_error: 3.9979e-04 - val_loss: 5.6606e-04 - val_mean_squared_error: 5.6606e-04 - lr: 5.0000e-04\n",
      "Epoch 76/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.7939e-04 - mean_squared_error: 3.7939e-04 - val_loss: 5.1460e-04 - val_mean_squared_error: 5.1460e-04 - lr: 5.0000e-04\n",
      "Epoch 77/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.6394e-04 - mean_squared_error: 3.6394e-04 - val_loss: 3.9469e-04 - val_mean_squared_error: 3.9469e-04 - lr: 5.0000e-04\n",
      "Epoch 78/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 3.9124e-04 - mean_squared_error: 3.9124e-04 - val_loss: 3.8866e-04 - val_mean_squared_error: 3.8866e-04 - lr: 5.0000e-04\n",
      "Epoch 79/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.9206e-04 - mean_squared_error: 3.9206e-04 - val_loss: 4.4263e-04 - val_mean_squared_error: 4.4263e-04 - lr: 5.0000e-04\n",
      "Epoch 80/100\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 3.6326e-04 - mean_squared_error: 3.6326e-04 - val_loss: 3.8470e-04 - val_mean_squared_error: 3.8470e-04 - lr: 5.0000e-04\n",
      "Epoch 81/100\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 3.9349e-04 - mean_squared_error: 3.9349e-04 - val_loss: 3.9170e-04 - val_mean_squared_error: 3.9170e-04 - lr: 5.0000e-04\n",
      "Epoch 82/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 3.8091e-04 - mean_squared_error: 3.8091e-04 - val_loss: 3.8441e-04 - val_mean_squared_error: 3.8441e-04 - lr: 5.0000e-04\n",
      "Epoch 83/100\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 3.6355e-04 - mean_squared_error: 3.6355e-04 - val_loss: 3.8997e-04 - val_mean_squared_error: 3.8997e-04 - lr: 5.0000e-04\n",
      "Epoch 84/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 3.3664e-04 - mean_squared_error: 3.3664e-04 - val_loss: 4.4033e-04 - val_mean_squared_error: 4.4033e-04 - lr: 5.0000e-04\n",
      "Epoch 85/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.4531e-04 - mean_squared_error: 3.4531e-04 - val_loss: 5.0437e-04 - val_mean_squared_error: 5.0437e-04 - lr: 5.0000e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 86/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.6278e-04 - mean_squared_error: 3.6278e-04 - val_loss: 4.4651e-04 - val_mean_squared_error: 4.4651e-04 - lr: 5.0000e-04\n",
      "Epoch 87/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.4715e-04 - mean_squared_error: 3.4715e-04 - val_loss: 3.8620e-04 - val_mean_squared_error: 3.8620e-04 - lr: 5.0000e-04\n",
      "Epoch 88/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 3.5818e-04 - mean_squared_error: 3.5818e-04 - val_loss: 4.4846e-04 - val_mean_squared_error: 4.4846e-04 - lr: 5.0000e-04\n",
      "Epoch 89/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 4.0241e-04 - mean_squared_error: 4.0241e-04 - val_loss: 3.8349e-04 - val_mean_squared_error: 3.8349e-04 - lr: 5.0000e-04\n",
      "Epoch 90/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.2715e-04 - mean_squared_error: 3.2715e-04 - val_loss: 3.8033e-04 - val_mean_squared_error: 3.8033e-04 - lr: 5.0000e-04\n",
      "Epoch 91/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.8058e-04 - mean_squared_error: 3.8058e-04 - val_loss: 4.3470e-04 - val_mean_squared_error: 4.3470e-04 - lr: 5.0000e-04\n",
      "Epoch 92/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 3.4509e-04 - mean_squared_error: 3.4509e-04 - val_loss: 5.7221e-04 - val_mean_squared_error: 5.7221e-04 - lr: 5.0000e-04\n",
      "Epoch 93/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.6334e-04 - mean_squared_error: 3.6334e-04 - val_loss: 3.8306e-04 - val_mean_squared_error: 3.8306e-04 - lr: 5.0000e-04\n",
      "Epoch 94/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 3.3070e-04 - mean_squared_error: 3.3070e-04 - val_loss: 3.9317e-04 - val_mean_squared_error: 3.9317e-04 - lr: 5.0000e-04\n",
      "Epoch 95/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.2232e-04 - mean_squared_error: 3.2232e-04 - val_loss: 6.8030e-04 - val_mean_squared_error: 6.8030e-04 - lr: 5.0000e-04\n",
      "Epoch 96/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.4059e-04 - mean_squared_error: 3.4059e-04 - val_loss: 3.9988e-04 - val_mean_squared_error: 3.9988e-04 - lr: 5.0000e-04\n",
      "Epoch 97/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.5053e-04 - mean_squared_error: 3.5053e-04 - val_loss: 3.8028e-04 - val_mean_squared_error: 3.8028e-04 - lr: 5.0000e-04\n",
      "Epoch 98/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 3.2024e-04 - mean_squared_error: 3.2024e-04 - val_loss: 4.1560e-04 - val_mean_squared_error: 4.1560e-04 - lr: 5.0000e-04\n",
      "Epoch 99/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 3.2716e-04 - mean_squared_error: 3.2716e-04 - val_loss: 4.3022e-04 - val_mean_squared_error: 4.3022e-04 - lr: 5.0000e-04\n",
      "Epoch 100/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 3.5760e-04 - mean_squared_error: 3.5760e-04 - val_loss: 5.6620e-04 - val_mean_squared_error: 5.6620e-04 - lr: 5.0000e-04\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = model.fit(trainX,trainY,epochs=100,batch_size=128,\n",
    "                   callbacks = [lr_platue,modelcheckpoint], validation_data = (testX,testY))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history['mean_squared_error'])\n",
    "plt.plot(history.history['val_mean_squared_error'])\n",
    "plt.title('model mean squared error')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.00014 MSE (0.01 RMSE)\n",
      "Test Score: 0.00057 MSE (0.02 RMSE)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.00013951794244349003, 0.0005661967443302274)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def model_score(model, X_train, y_train, X_test, y_test):\n",
    "    trainScore = model.evaluate(X_train, y_train, verbose=0)\n",
    "    print('Train Score: %.5f MSE (%.2f RMSE)' % (trainScore[0], math.sqrt(trainScore[0])))\n",
    "    testScore = model.evaluate(X_test, y_test, verbose=0)\n",
    "    print('Test Score: %.5f MSE (%.2f RMSE)' % (testScore[0], math.sqrt(testScore[0])))\n",
    "    return trainScore[0], testScore[0]\n",
    "\n",
    "model_score(model, trainX, trainY , testX, testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(testX)\n",
    "pred = scaler.inverse_transform(pred) #Performing Denormalization of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "testY = testY.reshape(testY.shape[0] , 1)\n",
    "testY = scaler.inverse_transform(testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(pred,color='red', label='Prediction')\n",
    "plt.plot(testY,color='blue', label='Actual')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Prices')\n",
    "plt.title('Model Accuracy with time')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}