stacked LSTM for Apple stock market prediction and forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Info\n",
    "* Apple Stock Data had been collected from Date **2016-07-28** to **2021-07-26**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps to collect the data::\n",
    "**step-1:** Create an Account at <a href=\"https://www.tiingo.com/\"> Tiingo</a> and Login.\n",
    "\n",
    "**step-2:** Go <a href=\"https://api.tiingo.com/documentation/general/overview\"> here</a> and copy your ``Api Token``\n",
    "\n",
    "**step-3:** Initialize your Api key to the ``key`` variable down below and follow the below steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "\n",
    "* [Collect Stock Data](#collect)\n",
    "* [Load Data](#load)\n",
    "* [Train Test Split](#split)\n",
    "* [Data Sequencing](#seq)\n",
    "* [MinMax Scaling](#scale)\n",
    "* [Reshape into 3D data](#reshape)\n",
    "* [Model Creation](#model)\n",
    "* [Load Saved Model](#ld_saved)\n",
    "* [Analyse RMSE](#rmse)\n",
    "* [Plotting Results](#results)\n",
    "* [Forecast on future 30 days](#forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"collect\"></a>\n",
    "# Collect Stock Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader as pdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "key = ### YOUR TIINGO API KEY "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>volume</th>\n",
       "      <th>adjClose</th>\n",
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       "      <th>adjLow</th>\n",
       "      <th>adjOpen</th>\n",
       "      <th>adjVolume</th>\n",
       "      <th>divCash</th>\n",
       "      <th>splitFactor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AAPL</th>\n",
       "      <th>2016-07-28 00:00:00+00:00</th>\n",
       "      <td>104.34</td>\n",
       "      <td>104.45</td>\n",
       "      <td>102.82</td>\n",
       "      <td>102.83</td>\n",
       "      <td>39869839</td>\n",
       "      <td>24.364793</td>\n",
       "      <td>24.390480</td>\n",
       "      <td>24.009853</td>\n",
       "      <td>24.012188</td>\n",
       "      <td>159479356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-29 00:00:00+00:00</th>\n",
       "      <td>104.21</td>\n",
       "      <td>104.55</td>\n",
       "      <td>103.68</td>\n",
       "      <td>104.19</td>\n",
       "      <td>27733688</td>\n",
       "      <td>24.334436</td>\n",
       "      <td>24.413831</td>\n",
       "      <td>24.210674</td>\n",
       "      <td>24.329766</td>\n",
       "      <td>110934752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01 00:00:00+00:00</th>\n",
       "      <td>106.05</td>\n",
       "      <td>106.15</td>\n",
       "      <td>104.41</td>\n",
       "      <td>104.41</td>\n",
       "      <td>38167871</td>\n",
       "      <td>24.764101</td>\n",
       "      <td>24.787452</td>\n",
       "      <td>24.381139</td>\n",
       "      <td>24.381139</td>\n",
       "      <td>152671484</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-02 00:00:00+00:00</th>\n",
       "      <td>104.48</td>\n",
       "      <td>106.07</td>\n",
       "      <td>104.00</td>\n",
       "      <td>106.05</td>\n",
       "      <td>33816556</td>\n",
       "      <td>24.397485</td>\n",
       "      <td>24.768771</td>\n",
       "      <td>24.285399</td>\n",
       "      <td>24.764101</td>\n",
       "      <td>135266224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-03 00:00:00+00:00</th>\n",
       "      <td>105.79</td>\n",
       "      <td>105.84</td>\n",
       "      <td>104.77</td>\n",
       "      <td>104.81</td>\n",
       "      <td>30202641</td>\n",
       "      <td>24.703388</td>\n",
       "      <td>24.715063</td>\n",
       "      <td>24.465204</td>\n",
       "      <td>24.474544</td>\n",
       "      <td>120810564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "                                   close    high     low    open    volume  \\\n",
       "symbol date                                                                  \n",
       "AAPL   2016-07-28 00:00:00+00:00  104.34  104.45  102.82  102.83  39869839   \n",
       "       2016-07-29 00:00:00+00:00  104.21  104.55  103.68  104.19  27733688   \n",
       "       2016-08-01 00:00:00+00:00  106.05  106.15  104.41  104.41  38167871   \n",
       "       2016-08-02 00:00:00+00:00  104.48  106.07  104.00  106.05  33816556   \n",
       "       2016-08-03 00:00:00+00:00  105.79  105.84  104.77  104.81  30202641   \n",
       "\n",
       "                                   adjClose    adjHigh     adjLow    adjOpen  \\\n",
       "symbol date                                                                    \n",
       "AAPL   2016-07-28 00:00:00+00:00  24.364793  24.390480  24.009853  24.012188   \n",
       "       2016-07-29 00:00:00+00:00  24.334436  24.413831  24.210674  24.329766   \n",
       "       2016-08-01 00:00:00+00:00  24.764101  24.787452  24.381139  24.381139   \n",
       "       2016-08-02 00:00:00+00:00  24.397485  24.768771  24.285399  24.764101   \n",
       "       2016-08-03 00:00:00+00:00  24.703388  24.715063  24.465204  24.474544   \n",
       "\n",
       "                                  adjVolume  divCash  splitFactor  \n",
       "symbol date                                                        \n",
       "AAPL   2016-07-28 00:00:00+00:00  159479356      0.0          1.0  \n",
       "       2016-07-29 00:00:00+00:00  110934752      0.0          1.0  \n",
       "       2016-08-01 00:00:00+00:00  152671484      0.0          1.0  \n",
       "       2016-08-02 00:00:00+00:00  135266224      0.0          1.0  \n",
       "       2016-08-03 00:00:00+00:00  120810564      0.0          1.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pdr.get_data_tiingo('AAPL', api_key= key)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('datasets/Apple Stock Price.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='load'></a>\n",
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <td>AAPL</td>\n",
       "      <td>2016-07-28 00:00:00+00:00</td>\n",
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       "      <th>1</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2016-07-29 00:00:00+00:00</td>\n",
       "      <td>104.21</td>\n",
       "      <td>104.55</td>\n",
       "      <td>103.68</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2016-08-01 00:00:00+00:00</td>\n",
       "      <td>106.05</td>\n",
       "      <td>106.15</td>\n",
       "      <td>104.41</td>\n",
       "      <td>104.41</td>\n",
       "      <td>38167871</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2016-08-02 00:00:00+00:00</td>\n",
       "      <td>104.48</td>\n",
       "      <td>106.07</td>\n",
       "      <td>104.00</td>\n",
       "      <td>106.05</td>\n",
       "      <td>33816556</td>\n",
       "      <td>24.397485</td>\n",
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       "      <td>24.285399</td>\n",
       "      <td>24.764101</td>\n",
       "      <td>135266224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2016-08-03 00:00:00+00:00</td>\n",
       "      <td>105.79</td>\n",
       "      <td>105.84</td>\n",
       "      <td>104.77</td>\n",
       "      <td>104.81</td>\n",
       "      <td>30202641</td>\n",
       "      <td>24.703388</td>\n",
       "      <td>24.715063</td>\n",
       "      <td>24.465204</td>\n",
       "      <td>24.474544</td>\n",
       "      <td>120810564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "  symbol                       date   close    high     low    open    volume  \\\n",
       "0   AAPL  2016-07-28 00:00:00+00:00  104.34  104.45  102.82  102.83  39869839   \n",
       "1   AAPL  2016-07-29 00:00:00+00:00  104.21  104.55  103.68  104.19  27733688   \n",
       "2   AAPL  2016-08-01 00:00:00+00:00  106.05  106.15  104.41  104.41  38167871   \n",
       "3   AAPL  2016-08-02 00:00:00+00:00  104.48  106.07  104.00  106.05  33816556   \n",
       "4   AAPL  2016-08-03 00:00:00+00:00  105.79  105.84  104.77  104.81  30202641   \n",
       "\n",
       "    adjClose    adjHigh     adjLow    adjOpen  adjVolume  divCash  splitFactor  \n",
       "0  24.364793  24.390480  24.009853  24.012188  159479356      0.0          1.0  \n",
       "1  24.334436  24.413831  24.210674  24.329766  110934752      0.0          1.0  \n",
       "2  24.764101  24.787452  24.381139  24.381139  152671484      0.0          1.0  \n",
       "3  24.397485  24.768771  24.285399  24.764101  135266224      0.0          1.0  \n",
       "4  24.703388  24.715063  24.465204  24.474544  120810564      0.0          1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/Apple Stock Price.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>AAPL</td>\n",
       "      <td>2021-07-20 00:00:00+00:00</td>\n",
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       "      <td>2021-07-21 00:00:00+00:00</td>\n",
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       "      <td>AAPL</td>\n",
       "      <td>2021-07-22 00:00:00+00:00</td>\n",
       "      <td>146.80</td>\n",
       "      <td>148.1950</td>\n",
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       "      <th>1255</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2021-07-23 00:00:00+00:00</td>\n",
       "      <td>148.56</td>\n",
       "      <td>148.7177</td>\n",
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       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2021-07-26 00:00:00+00:00</td>\n",
       "      <td>148.99</td>\n",
       "      <td>149.8300</td>\n",
       "      <td>147.70</td>\n",
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      "text/plain": [
       "     symbol                       date   close      high     low     open  \\\n",
       "1252   AAPL  2021-07-20 00:00:00+00:00  146.15  147.0997  142.96  143.460   \n",
       "1253   AAPL  2021-07-21 00:00:00+00:00  145.40  146.1300  144.63  145.530   \n",
       "1254   AAPL  2021-07-22 00:00:00+00:00  146.80  148.1950  145.81  145.935   \n",
       "1255   AAPL  2021-07-23 00:00:00+00:00  148.56  148.7177  146.92  147.550   \n",
       "1256   AAPL  2021-07-26 00:00:00+00:00  148.99  149.8300  147.70  148.270   \n",
       "\n",
       "        volume  adjClose   adjHigh  adjLow  adjOpen  adjVolume  divCash  \\\n",
       "1252  96350036    146.15  147.0997  142.96  143.460   96350036      0.0   \n",
       "1253  74993460    145.40  146.1300  144.63  145.530   74993460      0.0   \n",
       "1254  77338156    146.80  148.1950  145.81  145.935   77338156      0.0   \n",
       "1255  71447416    148.56  148.7177  146.92  147.550   71447416      0.0   \n",
       "1256  72434089    148.99  149.8300  147.70  148.270   72434089      0.0   \n",
       "\n",
       "      splitFactor  \n",
       "1252          1.0  \n",
       "1253          1.0  \n",
       "1254          1.0  \n",
       "1255          1.0  \n",
       "1256          1.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1257, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1257,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_df = df['close'] # this is our dependent variable for our stock price \n",
    "\n",
    "close_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1257.000000\n",
       "mean      186.231333\n",
       "std        72.414174\n",
       "min       103.130000\n",
       "25%       133.500000\n",
       "50%       170.420000\n",
       "75%       209.680000\n",
       "max       506.090000\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "close_df.hist()\n",
    "plt.xlabel('stocks')\n",
    "plt.ylabel('count')\n",
    "plt.title('Distribution of stocks')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* we can see that huge number of (close) stock price is between **100-200**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph visualisation of stock data\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.plot(close_df)\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Stock Price(Close)')\n",
    "plt.title('Apple Stock Price(Close)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='split'></a>\n",
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting dataset into trian and test\n",
    "\n",
    "training_size = int(len(close_df) * 0.65) # 65% train size\n",
    "test_size = len(close_df) - training_size # 35% test size\n",
    "train_data, test_data = close_df[0:training_size], close_df[training_size:] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(817,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='seq'></a>\n",
    "# Data Sequencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting an array of values into a dataset matrix\n",
    "\n",
    "def create_dataset(dataset, time_step=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - time_step):\n",
    "        a = dataset.iloc[i:(i+time_step)]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset.iloc[i + time_step])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 100\n",
    "x_train ,y_train = create_dataset(train_data, time_step)\n",
    "x_test, y_test = create_dataset(test_data, time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(717, 100)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(717,)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='scale'></a>\n",
    "# MinMax Scaling\n",
    "* **LSTM's** are very ``sensitive`` to ``scale`` of data so we have to apply minmaxscalar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler_x = MinMaxScaler(feature_range= (0,1)) \n",
    "scaler_y = MinMaxScaler(feature_range= (0,1)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit on train data to scale down in between (0,1)\n",
    "\n",
    "scaled_x_train = scaler_x.fit_transform(x_train)\n",
    "scaled_y_train = scaler_y.fit_transform(y_train.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transformt the test data\n",
    "\n",
    "scaled_x_test = scaler_x.transform(x_test)\n",
    "scaled_y_test = scaler_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the x and y scaler\n",
    "\n",
    "import pickle\n",
    "\n",
    "pickle.dump(scaler_x, open('./saved models/MinMaxScaler_x', 'wb'))\n",
    "pickle.dump(scaler_y, open('./saved models/MinMaxScaler_y', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='reshape'></a>\n",
    "# Reshape into 3D data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(717, 100, 1) (340, 100, 1)\n"
     ]
    }
   ],
   "source": [
    "# reshape input into 3D in order to fit into LSTM\n",
    "\n",
    "scaled_x_train = scaled_x_train.reshape(scaled_x_train.shape + (1,))\n",
    "scaled_x_test = scaled_x_test.reshape(scaled_x_test.shape + (1,))\n",
    "print(scaled_x_train.shape, scaled_x_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model'></a>\n",
    "# Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n"
     ]
    }
   ],
   "source": [
    "# set the random seed to initialize the same weights at each time we train the model\n",
    "\n",
    "import tensorflow as tf\n",
    "print(tf.__version__)\n",
    "tf.random.set_seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences = True, input_shape=(100,1))) # Lstm Layer with Input layer\n",
    "model.add(LSTM(50,return_sequences = True)) # Lstm Layer-2\n",
    "model.add(LSTM(50)) # Lstm Layer-3\n",
    "model.add(Dense(1)) # output layer\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* while adding more than 1 **LSTM's** we should add an argument ``return_sequences = True`` until we reached last LSTM Layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 100, 50)           10400     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 100, 50)           20200     \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### verbose: \n",
    "        0, 1, or 2. Verbosity mode.\n",
    "        0 = silent, 1 = progress bar, 2 = one line per epoch.\n",
    "        Note that the progress bar is not particularly useful when\n",
    "        logged to a file, so verbose=2 is recommended when not running\n",
    "        interactively (eg, in a production environment)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implementing early stopping and model check point \n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "\n",
    "# stop training process if the val_loss of the train data is not decreased after 5 epochs\n",
    "es = EarlyStopping(monitor= \"val_loss\" , min_delta= 0, patience= 5, verbose=1)\n",
    "\n",
    "# do not save the weights until and unless the validation loss is not decreased.\n",
    "mc = ModelCheckpoint(filepath= \"saved models/Stacked LSTM for Apple Stocks.h5\",\n",
    "                     monitor=\"val_loss\", verbose=1, save_best_only= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "239.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape[0]/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "238/239 [============================>.] - ETA: 0s - loss: 0.0096\n",
      "Epoch 00001: val_loss improved from inf to 0.13344, saving model to saved models/Stacked LSTM for Apple Stocks.h5\n",
      "239/239 [==============================] - 11s 44ms/step - loss: 0.0096 - val_loss: 0.1334\n",
      "Epoch 2/100\n",
      "238/239 [============================>.] - ETA: 0s - loss: 0.0039\n",
      "Epoch 00002: val_loss improved from 0.13344 to 0.10543, saving model to saved models/Stacked LSTM for Apple Stocks.h5\n",
      "239/239 [==============================] - 10s 40ms/step - loss: 0.0039 - val_loss: 0.1054\n",
      "Epoch 3/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 0.0028\n",
      "Epoch 00003: val_loss improved from 0.10543 to 0.08787, saving model to saved models/Stacked LSTM for Apple Stocks.h5\n",
      "239/239 [==============================] - 11s 44ms/step - loss: 0.0028 - val_loss: 0.0879\n",
      "Epoch 4/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 0.0020\n",
      "Epoch 00004: val_loss improved from 0.08787 to 0.07478, saving model to saved models/Stacked LSTM for Apple Stocks.h5\n",
      "239/239 [==============================] - 12s 50ms/step - loss: 0.0020 - val_loss: 0.0748\n",
      "Epoch 5/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 0.0019\n",
      "Epoch 00005: val_loss improved from 0.07478 to 0.05721, saving model to saved models/Stacked LSTM for Apple Stocks.h5\n",
      "239/239 [==============================] - 12s 50ms/step - loss: 0.0019 - val_loss: 0.0572\n",
      "Epoch 6/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 0.0018\n",
      "Epoch 00006: val_loss improved from 0.05721 to 0.04286, saving model to saved models/Stacked LSTM for Apple Stocks.h5\n",
      "239/239 [==============================] - 12s 51ms/step - loss: 0.0018 - val_loss: 0.0429\n",
      "Epoch 7/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 0.0013\n",
      "Epoch 00007: val_loss improved from 0.04286 to 0.03453, saving model to saved models/Stacked LSTM for Apple Stocks.h5\n",
      "239/239 [==============================] - 12s 49ms/step - loss: 0.0013 - val_loss: 0.0345\n",
      "Epoch 8/100\n",
      "238/239 [============================>.] - ETA: 0s - loss: 0.0012\n",
      "Epoch 00008: val_loss improved from 0.03453 to 0.03320, saving model to saved models/Stacked LSTM for Apple Stocks.h5\n",
      "239/239 [==============================] - 12s 49ms/step - loss: 0.0012 - val_loss: 0.0332\n",
      "Epoch 9/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 0.0010\n",
      "Epoch 00009: val_loss improved from 0.03320 to 0.03182, saving model to saved models/Stacked LSTM for Apple Stocks.h5\n",
      "239/239 [==============================] - 12s 48ms/step - loss: 0.0010 - val_loss: 0.0318\n",
      "Epoch 10/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 8.8807e-04\n",
      "Epoch 00010: val_loss improved from 0.03182 to 0.02800, saving model to saved models/Stacked LSTM for Apple Stocks.h5\n",
      "239/239 [==============================] - 12s 49ms/step - loss: 8.8807e-04 - val_loss: 0.0280\n",
      "Epoch 11/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 8.5296e-04\n",
      "Epoch 00011: val_loss did not improve from 0.02800\n",
      "239/239 [==============================] - 12s 48ms/step - loss: 8.5296e-04 - val_loss: 0.0317\n",
      "Epoch 12/100\n",
      "238/239 [============================>.] - ETA: 0s - loss: 8.7764e-04\n",
      "Epoch 00012: val_loss did not improve from 0.02800\n",
      "239/239 [==============================] - 12s 48ms/step - loss: 8.7501e-04 - val_loss: 0.0370\n",
      "Epoch 13/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 8.2001e-04\n",
      "Epoch 00013: val_loss did not improve from 0.02800\n",
      "239/239 [==============================] - 12s 49ms/step - loss: 8.2001e-04 - val_loss: 0.0379\n",
      "Epoch 14/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 7.4042e-04\n",
      "Epoch 00014: val_loss did not improve from 0.02800\n",
      "239/239 [==============================] - 12s 49ms/step - loss: 7.4042e-04 - val_loss: 0.0419\n",
      "Epoch 15/100\n",
      "239/239 [==============================] - ETA: 0s - loss: 8.7085e-04\n",
      "Epoch 00015: val_loss did not improve from 0.02800\n",
      "239/239 [==============================] - 12s 48ms/step - loss: 8.7085e-04 - val_loss: 0.0366\n",
      "Epoch 00015: early stopping\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(scaled_x_train, scaled_y_train, validation_data=(scaled_x_test, scaled_y_test),\n",
    "                 epochs=100, batch_size=3, verbose=1, callbacks=[es,mc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'val_loss'])\n"
     ]
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(hist.history.keys())  #loss and accuracy belongs to input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for loss\n",
    "plt.plot(hist.history['loss'])\n",
    "plt.plot(hist.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='ld_saved'></a>\n",
    "# Load Saved Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "Model = load_model('saved models/Stacked LSTM for Apple Stocks.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0012\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0011547202011570334"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## evaluation on train data\n",
    "\n",
    "Model.evaluate(scaled_x_train, scaled_y_train, batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "340/340 [==============================] - 3s 8ms/step - loss: 0.0280\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.028002560138702393"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## evaluation on test data\n",
    "\n",
    "Model.evaluate(scaled_x_test, scaled_y_test, batch_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='rmse'></a>\n",
    "# Analyse RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred = Model.predict(scaled_x_train)\n",
    "test_pred = Model.predict(scaled_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RMSE on scaled Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16733962895536464"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## rmse on test data\n",
    "math.sqrt(mean_squared_error(scaled_y_test, test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.033981186301246614"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## rmse on train data\n",
    "math.sqrt(mean_squared_error(scaled_y_train, train_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RMSE on Inverse-scaled Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform Prediction data back into original form\n",
    "\n",
    "inv_train_pred = scaler_y.inverse_transform(train_pred)\n",
    "inv_test_pred = scaler_y.inverse_transform(test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(717, 1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_train_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.341435548465249"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate RMSE Performance metrics(Root Mean squared Error) for Train data\n",
    "\n",
    "math.sqrt(mean_squared_error(y_train, inv_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.379310924839917"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE for test data\n",
    "\n",
    "math.sqrt(mean_squared_error(y_test, inv_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='results'></a>\n",
    "# Plotting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plotting \n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# shift train predictions for plotting(orange)\n",
    "look_back=100\n",
    "trainPredictPlot = np.empty_like(close_df)\n",
    "trainPredictPlot[:] = np.nan\n",
    "trainPredictPlot[look_back:len(inv_train_pred)+look_back] = inv_train_pred.reshape(inv_train_pred.shape[0])\n",
    "\n",
    "# shift test predictions for plotting(green)\n",
    "testPredictPlot = np.empty_like(close_df)\n",
    "testPredictPlot[:] = np.nan\n",
    "testPredictPlot[len(inv_train_pred)+(look_back*2):len(close_df)] = inv_test_pred.reshape(inv_test_pred.shape[0])\n",
    "\n",
    "# plot baseline and predictions (blue)\n",
    "plt.plot(close_df, label='Original')\n",
    "plt.plot(trainPredictPlot, label='Train_pred')\n",
    "plt.plot(testPredictPlot, label='Test_pred')\n",
    "\n",
    "plt.title('Results of Stock Price Prediction')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Stock Price(close)')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='forecast'></a>\n",
    "# Forecast on future 30 days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Collect previous 100 days data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "340"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_input = y_test[240:].reshape(1,-1)\n",
    "x_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale the input data\n",
    "sc_x_input = scaler_x.transform(x_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_input = sc_x_input[0].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**predict for future 30 days**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "n_steps = len(temp_input)\n",
    "for i in range(30):\n",
    "    input_ = temp_input[i:]\n",
    "    input_ = np.array(input_).reshape(1, n_steps, 1)\n",
    "    output = Model.predict(input_)[0][0]\n",
    "    temp_input.append(output)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(temp_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# collect numbers for 100 and 30 days\n",
    "day_new = np.arange(1,101)\n",
    "day_pred = np.arange(101,131)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1257"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(close_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Partial Plot in which Blue plot is forecast on 30 days\n",
    "\n",
    "plt.plot(day_new, scaler_y.inverse_transform(np.array(temp_input[:100]).reshape(-1,1)), label='100 days')\n",
    "plt.plot(day_pred, scaler_y.inverse_transform(np.array(temp_input[100:]).reshape(-1,1)), label='30 days')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.title('Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the partial Forecast\n",
    "\n",
    "plt.plot(scaler_y.inverse_transform(np.array(temp_input).reshape(-1,1)))\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.title('Forecast')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the complete Graph along with 30 days Forecast\n",
    "\n",
    "stock_previous = close_df.to_numpy()\n",
    "stock_30 = scaler_y.inverse_transform(np.array(temp_input[100:]).reshape(1, -1))[0]\n",
    "Total_stock = np.concatenate([stock_previous, stock_30])\n",
    "plt.plot(Total_stock)\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.title('Complete Stock Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}