Bitcoin Price Prediction Using RNN - LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
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       "      <th>Volume</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2869</th>\n",
       "      <td>2022-07-26</td>\n",
       "      <td>21361.121094</td>\n",
       "      <td>21361.121094</td>\n",
       "      <td>20776.816406</td>\n",
       "      <td>21239.753906</td>\n",
       "      <td>21239.753906</td>\n",
       "      <td>28624673855</td>\n",
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       "    <tr>\n",
       "      <th>2870</th>\n",
       "      <td>2022-07-27</td>\n",
       "      <td>21244.169922</td>\n",
       "      <td>22986.529297</td>\n",
       "      <td>21070.806641</td>\n",
       "      <td>22930.548828</td>\n",
       "      <td>22930.548828</td>\n",
       "      <td>31758955233</td>\n",
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       "    <tr>\n",
       "      <th>2871</th>\n",
       "      <td>2022-07-28</td>\n",
       "      <td>22933.640625</td>\n",
       "      <td>24110.470703</td>\n",
       "      <td>22722.265625</td>\n",
       "      <td>23843.886719</td>\n",
       "      <td>23843.886719</td>\n",
       "      <td>40212386158</td>\n",
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       "    <tr>\n",
       "      <th>2872</th>\n",
       "      <td>2022-07-29</td>\n",
       "      <td>23845.212891</td>\n",
       "      <td>24294.787109</td>\n",
       "      <td>23481.173828</td>\n",
       "      <td>23804.632813</td>\n",
       "      <td>23804.632813</td>\n",
       "      <td>35887249746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2873</th>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>23809.408203</td>\n",
       "      <td>23934.220703</td>\n",
       "      <td>23752.726563</td>\n",
       "      <td>23770.847656</td>\n",
       "      <td>23770.847656</td>\n",
       "      <td>35147595776</td>\n",
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      "text/plain": [
       "            Date          Open          High           Low         Close  \\\n",
       "2869  2022-07-26  21361.121094  21361.121094  20776.816406  21239.753906   \n",
       "2870  2022-07-27  21244.169922  22986.529297  21070.806641  22930.548828   \n",
       "2871  2022-07-28  22933.640625  24110.470703  22722.265625  23843.886719   \n",
       "2872  2022-07-29  23845.212891  24294.787109  23481.173828  23804.632813   \n",
       "2873  2022-07-30  23809.408203  23934.220703  23752.726563  23770.847656   \n",
       "\n",
       "         Adj Close       Volume  \n",
       "2869  21239.753906  28624673855  \n",
       "2870  22930.548828  31758955233  \n",
       "2871  23843.886719  40212386158  \n",
       "2872  23804.632813  35887249746  \n",
       "2873  23770.847656  35147595776  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('BTC-USD.csv', date_parser = True)\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>High</th>\n",
       "      <th>Low</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-17</td>\n",
       "      <td>465.864014</td>\n",
       "      <td>468.174011</td>\n",
       "      <td>452.421997</td>\n",
       "      <td>457.334015</td>\n",
       "      <td>457.334015</td>\n",
       "      <td>21056800</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>456.859985</td>\n",
       "      <td>456.859985</td>\n",
       "      <td>413.104004</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>34483200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>424.102997</td>\n",
       "      <td>427.834991</td>\n",
       "      <td>384.532013</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>37919700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-20</td>\n",
       "      <td>394.673004</td>\n",
       "      <td>423.295990</td>\n",
       "      <td>389.882996</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>36863600</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-21</td>\n",
       "      <td>408.084991</td>\n",
       "      <td>412.425995</td>\n",
       "      <td>393.181000</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>26580100</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2658</th>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>50802.609375</td>\n",
       "      <td>51956.328125</td>\n",
       "      <td>50499.468750</td>\n",
       "      <td>50640.417969</td>\n",
       "      <td>50640.417969</td>\n",
       "      <td>24324345758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2659</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>50679.859375</td>\n",
       "      <td>50679.859375</td>\n",
       "      <td>47414.210938</td>\n",
       "      <td>47588.855469</td>\n",
       "      <td>47588.855469</td>\n",
       "      <td>33430376883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2660</th>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>47623.871094</td>\n",
       "      <td>48119.742188</td>\n",
       "      <td>46201.496094</td>\n",
       "      <td>46444.710938</td>\n",
       "      <td>46444.710938</td>\n",
       "      <td>30049226299</td>\n",
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       "    <tr>\n",
       "      <th>2661</th>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>46490.605469</td>\n",
       "      <td>47879.964844</td>\n",
       "      <td>46060.312500</td>\n",
       "      <td>47178.125000</td>\n",
       "      <td>47178.125000</td>\n",
       "      <td>26686491018</td>\n",
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       "    <tr>\n",
       "      <th>2662</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>47169.371094</td>\n",
       "      <td>48472.527344</td>\n",
       "      <td>45819.953125</td>\n",
       "      <td>46306.445313</td>\n",
       "      <td>46306.445313</td>\n",
       "      <td>36974172400</td>\n",
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       "<p>2663 rows × 7 columns</p>\n",
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       "            Date          Open          High           Low         Close  \\\n",
       "0     2014-09-17    465.864014    468.174011    452.421997    457.334015   \n",
       "1     2014-09-18    456.859985    456.859985    413.104004    424.440002   \n",
       "2     2014-09-19    424.102997    427.834991    384.532013    394.795990   \n",
       "3     2014-09-20    394.673004    423.295990    389.882996    408.903992   \n",
       "4     2014-09-21    408.084991    412.425995    393.181000    398.821014   \n",
       "...          ...           ...           ...           ...           ...   \n",
       "2658  2021-12-27  50802.609375  51956.328125  50499.468750  50640.417969   \n",
       "2659  2021-12-28  50679.859375  50679.859375  47414.210938  47588.855469   \n",
       "2660  2021-12-29  47623.871094  48119.742188  46201.496094  46444.710938   \n",
       "2661  2021-12-30  46490.605469  47879.964844  46060.312500  47178.125000   \n",
       "2662  2021-12-31  47169.371094  48472.527344  45819.953125  46306.445313   \n",
       "\n",
       "         Adj Close       Volume  \n",
       "0       457.334015     21056800  \n",
       "1       424.440002     34483200  \n",
       "2       394.795990     37919700  \n",
       "3       408.903992     36863600  \n",
       "4       398.821014     26580100  \n",
       "...            ...          ...  \n",
       "2658  50640.417969  24324345758  \n",
       "2659  47588.855469  33430376883  \n",
       "2660  46444.710938  30049226299  \n",
       "2661  47178.125000  26686491018  \n",
       "2662  46306.445313  36974172400  \n",
       "\n",
       "[2663 rows x 7 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data_training = data[data['Date']< '2022-01-01'].copy()\n",
    "data_training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2664</th>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>47680.925781</td>\n",
       "      <td>47881.406250</td>\n",
       "      <td>46856.937500</td>\n",
       "      <td>47345.218750</td>\n",
       "      <td>47345.218750</td>\n",
       "      <td>27951569547</td>\n",
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       "      <th>2665</th>\n",
       "      <td>2022-01-03</td>\n",
       "      <td>47343.542969</td>\n",
       "      <td>47510.726563</td>\n",
       "      <td>45835.964844</td>\n",
       "      <td>46458.117188</td>\n",
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       "      <td>33071628362</td>\n",
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       "    <tr>\n",
       "      <th>2666</th>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>46458.851563</td>\n",
       "      <td>47406.546875</td>\n",
       "      <td>45752.464844</td>\n",
       "      <td>45897.574219</td>\n",
       "      <td>45897.574219</td>\n",
       "      <td>42494677905</td>\n",
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       "    <tr>\n",
       "      <th>2667</th>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>45899.359375</td>\n",
       "      <td>46929.046875</td>\n",
       "      <td>42798.222656</td>\n",
       "      <td>43569.003906</td>\n",
       "      <td>43569.003906</td>\n",
       "      <td>36851084859</td>\n",
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       "    <tr>\n",
       "      <th>2668</th>\n",
       "      <td>2022-01-06</td>\n",
       "      <td>43565.511719</td>\n",
       "      <td>43748.718750</td>\n",
       "      <td>42645.539063</td>\n",
       "      <td>43160.929688</td>\n",
       "      <td>43160.929688</td>\n",
       "      <td>30208048289</td>\n",
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       "      <th>2869</th>\n",
       "      <td>2022-07-26</td>\n",
       "      <td>21361.121094</td>\n",
       "      <td>21361.121094</td>\n",
       "      <td>20776.816406</td>\n",
       "      <td>21239.753906</td>\n",
       "      <td>21239.753906</td>\n",
       "      <td>28624673855</td>\n",
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       "    <tr>\n",
       "      <th>2870</th>\n",
       "      <td>2022-07-27</td>\n",
       "      <td>21244.169922</td>\n",
       "      <td>22986.529297</td>\n",
       "      <td>21070.806641</td>\n",
       "      <td>22930.548828</td>\n",
       "      <td>22930.548828</td>\n",
       "      <td>31758955233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2871</th>\n",
       "      <td>2022-07-28</td>\n",
       "      <td>22933.640625</td>\n",
       "      <td>24110.470703</td>\n",
       "      <td>22722.265625</td>\n",
       "      <td>23843.886719</td>\n",
       "      <td>23843.886719</td>\n",
       "      <td>40212386158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2872</th>\n",
       "      <td>2022-07-29</td>\n",
       "      <td>23845.212891</td>\n",
       "      <td>24294.787109</td>\n",
       "      <td>23481.173828</td>\n",
       "      <td>23804.632813</td>\n",
       "      <td>23804.632813</td>\n",
       "      <td>35887249746</td>\n",
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       "    <tr>\n",
       "      <th>2873</th>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>23809.408203</td>\n",
       "      <td>23934.220703</td>\n",
       "      <td>23752.726563</td>\n",
       "      <td>23770.847656</td>\n",
       "      <td>23770.847656</td>\n",
       "      <td>35147595776</td>\n",
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       "<p>210 rows × 7 columns</p>\n",
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      "text/plain": [
       "            Date          Open          High           Low         Close  \\\n",
       "2664  2022-01-02  47680.925781  47881.406250  46856.937500  47345.218750   \n",
       "2665  2022-01-03  47343.542969  47510.726563  45835.964844  46458.117188   \n",
       "2666  2022-01-04  46458.851563  47406.546875  45752.464844  45897.574219   \n",
       "2667  2022-01-05  45899.359375  46929.046875  42798.222656  43569.003906   \n",
       "2668  2022-01-06  43565.511719  43748.718750  42645.539063  43160.929688   \n",
       "...          ...           ...           ...           ...           ...   \n",
       "2869  2022-07-26  21361.121094  21361.121094  20776.816406  21239.753906   \n",
       "2870  2022-07-27  21244.169922  22986.529297  21070.806641  22930.548828   \n",
       "2871  2022-07-28  22933.640625  24110.470703  22722.265625  23843.886719   \n",
       "2872  2022-07-29  23845.212891  24294.787109  23481.173828  23804.632813   \n",
       "2873  2022-07-30  23809.408203  23934.220703  23752.726563  23770.847656   \n",
       "\n",
       "         Adj Close       Volume  \n",
       "2664  47345.218750  27951569547  \n",
       "2665  46458.117188  33071628362  \n",
       "2666  45897.574219  42494677905  \n",
       "2667  43569.003906  36851084859  \n",
       "2668  43160.929688  30208048289  \n",
       "...            ...          ...  \n",
       "2869  21239.753906  28624673855  \n",
       "2870  22930.548828  31758955233  \n",
       "2871  23843.886719  40212386158  \n",
       "2872  23804.632813  35887249746  \n",
       "2873  23770.847656  35147595776  \n",
       "\n",
       "[210 rows x 7 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data_test = data[data['Date']> '2022-01-01'].copy()\n",
    "data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>465.864014</td>\n",
       "      <td>468.174011</td>\n",
       "      <td>452.421997</td>\n",
       "      <td>457.334015</td>\n",
       "      <td>21056800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>456.859985</td>\n",
       "      <td>456.859985</td>\n",
       "      <td>413.104004</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>34483200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>424.102997</td>\n",
       "      <td>427.834991</td>\n",
       "      <td>384.532013</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>37919700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>394.673004</td>\n",
       "      <td>423.295990</td>\n",
       "      <td>389.882996</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>36863600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>408.084991</td>\n",
       "      <td>412.425995</td>\n",
       "      <td>393.181000</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>26580100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Open        High         Low       Close    Volume\n",
       "0  465.864014  468.174011  452.421997  457.334015  21056800\n",
       "1  456.859985  456.859985  413.104004  424.440002  34483200\n",
       "2  424.102997  427.834991  384.532013  394.795990  37919700\n",
       "3  394.673004  423.295990  389.882996  408.903992  36863600\n",
       "4  408.084991  412.425995  393.181000  398.821014  26580100"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data = data_training.drop(['Date', 'Adj Close'], axis = 1)\n",
    "training_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.28907290e-03, 3.73944128e-03, 4.24270741e-03, 4.14358659e-03,\n",
       "        4.31449241e-05],\n",
       "       [4.15542811e-03, 3.57446063e-03, 3.64887469e-03, 3.65546320e-03,\n",
       "        8.14009147e-05],\n",
       "       [3.66922344e-03, 3.15121937e-03, 3.21734240e-03, 3.21556748e-03,\n",
       "        9.11925723e-05],\n",
       "       ...,\n",
       "       [7.04244855e-01, 6.98592628e-01, 6.95206192e-01, 6.86563039e-01,\n",
       "        8.56027417e-02],\n",
       "       [6.87424046e-01, 6.95096205e-01, 6.93073850e-01, 6.97446374e-01,\n",
       "        7.60212627e-02],\n",
       "       [6.97498813e-01, 7.03736926e-01, 6.89443622e-01, 6.84511277e-01,\n",
       "        1.05334067e-01]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "training_data = scaler.fit_transform(training_data)\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "Y_train = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2663"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(60, training_data.shape[0]):\n",
    "    X_train.append(training_data[i-60:i])\n",
    "    Y_train.append(training_data[i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, Y_train = np.array(X_train), np.array(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2603, 60, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor.add(LSTM(units = 50, activation = 'relu', return_sequences = True, input_shape = (X_train.shape[1], 5)))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "regressor.add(LSTM(units = 60, activation = 'relu', return_sequences = True))\n",
    "regressor.add(Dropout(0.3))\n",
    "\n",
    "regressor.add(LSTM(units = 80, activation = 'relu', return_sequences = True))\n",
    "regressor.add(Dropout(0.4))\n",
    "\n",
    "regressor.add(LSTM(units = 120, activation = 'relu'))\n",
    "regressor.add(Dropout(0.5))\n",
    "\n",
    "regressor.add(Dense(units =1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 60, 50)            11200     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 60, 50)            0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 60, 60)            26640     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 60, 60)            0         \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 60, 80)            45120     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 60, 80)            0         \n",
      "                                                                 \n",
      " lstm_3 (LSTM)               (None, 120)               96480     \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 120)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 121       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 179,561\n",
      "Trainable params: 179,561\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "regressor.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "53/53 [==============================] - 7s 86ms/step - loss: 0.0217\n",
      "Epoch 2/20\n",
      "53/53 [==============================] - 5s 95ms/step - loss: 0.0047\n",
      "Epoch 3/20\n",
      "53/53 [==============================] - 5s 95ms/step - loss: 0.0043\n",
      "Epoch 4/20\n",
      "53/53 [==============================] - 5s 96ms/step - loss: 0.0034\n",
      "Epoch 5/20\n",
      "53/53 [==============================] - 5s 93ms/step - loss: 0.0035\n",
      "Epoch 6/20\n",
      "53/53 [==============================] - 5s 98ms/step - loss: 0.0033\n",
      "Epoch 7/20\n",
      "53/53 [==============================] - 5s 95ms/step - loss: 0.0035\n",
      "Epoch 8/20\n",
      "53/53 [==============================] - 5s 99ms/step - loss: 0.0041\n",
      "Epoch 9/20\n",
      "53/53 [==============================] - 5s 96ms/step - loss: 0.0031\n",
      "Epoch 10/20\n",
      "53/53 [==============================] - 5s 91ms/step - loss: 0.0028\n",
      "Epoch 11/20\n",
      "53/53 [==============================] - 5s 103ms/step - loss: 0.0028\n",
      "Epoch 12/20\n",
      "53/53 [==============================] - 5s 102ms/step - loss: 0.0027\n",
      "Epoch 13/20\n",
      "53/53 [==============================] - 5s 102ms/step - loss: 0.0028\n",
      "Epoch 14/20\n",
      "53/53 [==============================] - 5s 102ms/step - loss: 0.0028\n",
      "Epoch 15/20\n",
      "53/53 [==============================] - 6s 105ms/step - loss: 0.0031\n",
      "Epoch 16/20\n",
      "53/53 [==============================] - 6s 106ms/step - loss: 0.0029\n",
      "Epoch 17/20\n",
      "53/53 [==============================] - 5s 102ms/step - loss: 0.0025\n",
      "Epoch 18/20\n",
      "53/53 [==============================] - 5s 99ms/step - loss: 0.0025\n",
      "Epoch 19/20\n",
      "53/53 [==============================] - 5s 103ms/step - loss: 0.0026\n",
      "Epoch 20/20\n",
      "53/53 [==============================] - 6s 109ms/step - loss: 0.0025\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b2a7b1bd60>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train, Y_train, epochs = 20, batch_size = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RUSHI LUNAGARIYA\\AppData\\Local\\Temp\\ipykernel_19724\\1802083127.py:2: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df= past_60_days.append(data_test, ignore_index = True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60963.253906</td>\n",
       "      <td>64242.792969</td>\n",
       "      <td>60673.054688</td>\n",
       "      <td>63226.402344</td>\n",
       "      <td>37746665647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63254.335938</td>\n",
       "      <td>63516.937500</td>\n",
       "      <td>61184.238281</td>\n",
       "      <td>62970.046875</td>\n",
       "      <td>36124731509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62941.804688</td>\n",
       "      <td>63123.289063</td>\n",
       "      <td>60799.664063</td>\n",
       "      <td>61452.230469</td>\n",
       "      <td>32615846901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61460.078125</td>\n",
       "      <td>62541.468750</td>\n",
       "      <td>60844.609375</td>\n",
       "      <td>61125.675781</td>\n",
       "      <td>30605102446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61068.875000</td>\n",
       "      <td>61590.683594</td>\n",
       "      <td>60163.781250</td>\n",
       "      <td>61527.480469</td>\n",
       "      <td>29094934221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Open          High           Low         Close       Volume\n",
       "0  60963.253906  64242.792969  60673.054688  63226.402344  37746665647\n",
       "1  63254.335938  63516.937500  61184.238281  62970.046875  36124731509\n",
       "2  62941.804688  63123.289063  60799.664063  61452.230469  32615846901\n",
       "3  61460.078125  62541.468750  60844.609375  61125.675781  30605102446\n",
       "4  61068.875000  61590.683594  60163.781250  61527.480469  29094934221"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "past_60_days = data_training.tail(60)\n",
    "df= past_60_days.append(data_test, ignore_index = True)\n",
    "df = df.drop(['Date', 'Adj Close'], axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.90223834, 0.93369828, 0.91377496, 0.93559122, 0.10753514],\n",
       "       [0.93624436, 0.92311389, 0.92149553, 0.93178709, 0.10291375],\n",
       "       [0.93160553, 0.91737372, 0.91568718, 0.90926379, 0.09291584],\n",
       "       ...,\n",
       "       [0.33777327, 0.34849043, 0.34059156, 0.35118313, 0.11456075],\n",
       "       [0.35130353, 0.35117812, 0.35205361, 0.35060063, 0.10223709],\n",
       "       [0.35077209, 0.34592036, 0.35615496, 0.35009929, 0.10012958]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = scaler.transform(df)\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = []\n",
    "Y_test = []\n",
    "for i in range (60, inputs.shape[0]):\n",
    "    X_test.append(inputs[i-60:i])\n",
    "    Y_test.append(inputs[i, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((210, 60, 5), (210,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test, Y_test = np.array(X_test), np.array(Y_test)\n",
    "X_test.shape, Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 17ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([[0.69359803],\n",
       "        [0.692449  ],\n",
       "        [0.69055563],\n",
       "        [0.6878312 ],\n",
       "        [0.684098  ],\n",
       "        [0.67923504],\n",
       "        [0.6732032 ],\n",
       "        [0.66626126],\n",
       "        [0.6587129 ]], dtype=float32),\n",
       " array([0.70008401, 0.68695273, 0.67864831, 0.64400753, 0.63789318,\n",
       "        0.61426191, 0.6168336 , 0.61943856, 0.61809199]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = regressor.predict(X_test)\n",
    "Y_pred[1:10], Y_test[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.48427770e-05, 1.45819584e-05, 1.51033327e-05, 1.48392776e-05,\n",
       "       2.84931110e-12])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19298.903787912797"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale = 1/5.18164146e-05\n",
    "scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test = Y_test*scale\n",
    "Y_pred = Y_pred*scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13385.682 ],\n",
       "       [13363.507 ],\n",
       "       [13326.967 ],\n",
       "       [13274.389 ],\n",
       "       [13202.342 ],\n",
       "       [13108.492 ],\n",
       "       [12992.084 ],\n",
       "       [12858.112 ],\n",
       "       [12712.4375]], dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13510.85389903, 13257.43464296, 13097.16848337, 12428.63939768,\n",
       "       12310.63905999, 11854.58152308, 11904.21222554, 11954.48520094,\n",
       "       11928.49775737])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.plot(Y_test, color = 'red', label = 'Real Bitcoin Price')\n",
    "plt.plot(Y_pred, color = 'green', label = 'Predicted Bitcoin Price')\n",
    "plt.title('Bitcoin Price Prediction using RNN-LSTM')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}