Task 2 - Stock Market Prediction And Forecasting Using Stacked LSTM.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "b6d96ab2",
   "metadata": {},
   "source": [
    "# LetsGrowMoreVIP - Data Science Internship\n",
    "Beginner Level Task-2 -Stock Market Prediction And Forecasting Using Stacked LSTM\n",
    "\n",
    "Completed by - Siddharth Maheshwar Wagh\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "594f93a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn as sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a95bd36c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Close</th>\n",
       "      <th>Total Trade Quantity</th>\n",
       "      <th>Turnover (Lacs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>234.05</td>\n",
       "      <td>235.95</td>\n",
       "      <td>230.20</td>\n",
       "      <td>233.50</td>\n",
       "      <td>233.75</td>\n",
       "      <td>3069914</td>\n",
       "      <td>7162.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>234.55</td>\n",
       "      <td>236.80</td>\n",
       "      <td>231.10</td>\n",
       "      <td>233.80</td>\n",
       "      <td>233.25</td>\n",
       "      <td>5082859</td>\n",
       "      <td>11859.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>240.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>232.50</td>\n",
       "      <td>235.00</td>\n",
       "      <td>234.25</td>\n",
       "      <td>2240909</td>\n",
       "      <td>5248.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>233.30</td>\n",
       "      <td>236.75</td>\n",
       "      <td>232.00</td>\n",
       "      <td>236.25</td>\n",
       "      <td>236.10</td>\n",
       "      <td>2349368</td>\n",
       "      <td>5503.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>233.55</td>\n",
       "      <td>239.20</td>\n",
       "      <td>230.75</td>\n",
       "      <td>234.00</td>\n",
       "      <td>233.30</td>\n",
       "      <td>3423509</td>\n",
       "      <td>7999.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Date    Open    High     Low    Last   Close  Total Trade Quantity  \\\n",
       "0  2018-09-28  234.05  235.95  230.20  233.50  233.75               3069914   \n",
       "1  2018-09-27  234.55  236.80  231.10  233.80  233.25               5082859   \n",
       "2  2018-09-26  240.00  240.00  232.50  235.00  234.25               2240909   \n",
       "3  2018-09-25  233.30  236.75  232.00  236.25  236.10               2349368   \n",
       "4  2018-09-24  233.55  239.20  230.75  234.00  233.30               3423509   \n",
       "\n",
       "   Turnover (Lacs)  \n",
       "0          7162.35  \n",
       "1         11859.95  \n",
       "2          5248.60  \n",
       "3          5503.90  \n",
       "4          7999.55  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('https://raw.githubusercontent.com/mwitiderrick/stockprice/master/NSE-TATAGLOBAL.csv')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7fc1afd9",
   "metadata": {},
   "outputs": [
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       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Close</th>\n",
       "      <th>Total Trade Quantity</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2010-07-27</td>\n",
       "      <td>117.6</td>\n",
       "      <td>119.50</td>\n",
       "      <td>112.00</td>\n",
       "      <td>118.80</td>\n",
       "      <td>118.65</td>\n",
       "      <td>586100</td>\n",
       "      <td>694.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>2010-07-26</td>\n",
       "      <td>120.1</td>\n",
       "      <td>121.00</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.60</td>\n",
       "      <td>658440</td>\n",
       "      <td>780.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>2010-07-23</td>\n",
       "      <td>121.8</td>\n",
       "      <td>121.95</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.35</td>\n",
       "      <td>120.65</td>\n",
       "      <td>281312</td>\n",
       "      <td>340.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>2010-07-22</td>\n",
       "      <td>120.3</td>\n",
       "      <td>122.00</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.75</td>\n",
       "      <td>120.90</td>\n",
       "      <td>293312</td>\n",
       "      <td>355.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>2010-07-21</td>\n",
       "      <td>122.1</td>\n",
       "      <td>123.00</td>\n",
       "      <td>121.05</td>\n",
       "      <td>121.10</td>\n",
       "      <td>121.55</td>\n",
       "      <td>658666</td>\n",
       "      <td>803.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            Date   Open    High     Low    Last   Close  Total Trade Quantity  \\\n",
       "2030  2010-07-27  117.6  119.50  112.00  118.80  118.65                586100   \n",
       "2031  2010-07-26  120.1  121.00  117.10  117.10  117.60                658440   \n",
       "2032  2010-07-23  121.8  121.95  120.25  120.35  120.65                281312   \n",
       "2033  2010-07-22  120.3  122.00  120.25  120.75  120.90                293312   \n",
       "2034  2010-07-21  122.1  123.00  121.05  121.10  121.55                658666   \n",
       "\n",
       "      Turnover (Lacs)  \n",
       "2030           694.98  \n",
       "2031           780.01  \n",
       "2032           340.31  \n",
       "2033           355.17  \n",
       "2034           803.56  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73911dc2",
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   "outputs": [
    {
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       "      <th>Low</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>2010-07-21</td>\n",
       "      <td>122.1</td>\n",
       "      <td>123.00</td>\n",
       "      <td>121.05</td>\n",
       "      <td>121.10</td>\n",
       "      <td>121.55</td>\n",
       "      <td>658666</td>\n",
       "      <td>803.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>2010-07-22</td>\n",
       "      <td>120.3</td>\n",
       "      <td>122.00</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.75</td>\n",
       "      <td>120.90</td>\n",
       "      <td>293312</td>\n",
       "      <td>355.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>2010-07-23</td>\n",
       "      <td>121.8</td>\n",
       "      <td>121.95</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.35</td>\n",
       "      <td>120.65</td>\n",
       "      <td>281312</td>\n",
       "      <td>340.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>2010-07-26</td>\n",
       "      <td>120.1</td>\n",
       "      <td>121.00</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.60</td>\n",
       "      <td>658440</td>\n",
       "      <td>780.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2010-07-27</td>\n",
       "      <td>117.6</td>\n",
       "      <td>119.50</td>\n",
       "      <td>112.00</td>\n",
       "      <td>118.80</td>\n",
       "      <td>118.65</td>\n",
       "      <td>586100</td>\n",
       "      <td>694.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           Date   Open    High     Low    Last   Close  Total Trade Quantity  \\\n",
       "2034 2010-07-21  122.1  123.00  121.05  121.10  121.55                658666   \n",
       "2033 2010-07-22  120.3  122.00  120.25  120.75  120.90                293312   \n",
       "2032 2010-07-23  121.8  121.95  120.25  120.35  120.65                281312   \n",
       "2031 2010-07-26  120.1  121.00  117.10  117.10  117.60                658440   \n",
       "2030 2010-07-27  117.6  119.50  112.00  118.80  118.65                586100   \n",
       "\n",
       "      Turnover (Lacs)  \n",
       "2034           803.56  \n",
       "2033           355.17  \n",
       "2032           340.31  \n",
       "2031           780.01  \n",
       "2030           694.98  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting the data with respect to date\n",
    "df1['Date']=pd.to_datetime(df1['Date'])\n",
    "df2=df1.sort_values(by='Date')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9d2ece76",
   "metadata": {},
   "outputs": [
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       "      <th>Date</th>\n",
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       "      <th>Last</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2034</td>\n",
       "      <td>2010-07-21</td>\n",
       "      <td>122.10</td>\n",
       "      <td>123.00</td>\n",
       "      <td>121.05</td>\n",
       "      <td>121.10</td>\n",
       "      <td>121.55</td>\n",
       "      <td>658666</td>\n",
       "      <td>803.56</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2033</td>\n",
       "      <td>2010-07-22</td>\n",
       "      <td>120.30</td>\n",
       "      <td>122.00</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.75</td>\n",
       "      <td>120.90</td>\n",
       "      <td>293312</td>\n",
       "      <td>355.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2032</td>\n",
       "      <td>2010-07-23</td>\n",
       "      <td>121.80</td>\n",
       "      <td>121.95</td>\n",
       "      <td>120.25</td>\n",
       "      <td>120.35</td>\n",
       "      <td>120.65</td>\n",
       "      <td>281312</td>\n",
       "      <td>340.31</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2031</td>\n",
       "      <td>2010-07-26</td>\n",
       "      <td>120.10</td>\n",
       "      <td>121.00</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.10</td>\n",
       "      <td>117.60</td>\n",
       "      <td>658440</td>\n",
       "      <td>780.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2030</td>\n",
       "      <td>2010-07-27</td>\n",
       "      <td>117.60</td>\n",
       "      <td>119.50</td>\n",
       "      <td>112.00</td>\n",
       "      <td>118.80</td>\n",
       "      <td>118.65</td>\n",
       "      <td>586100</td>\n",
       "      <td>694.98</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>4</td>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>233.55</td>\n",
       "      <td>239.20</td>\n",
       "      <td>230.75</td>\n",
       "      <td>234.00</td>\n",
       "      <td>233.30</td>\n",
       "      <td>3423509</td>\n",
       "      <td>7999.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>3</td>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>233.30</td>\n",
       "      <td>236.75</td>\n",
       "      <td>232.00</td>\n",
       "      <td>236.25</td>\n",
       "      <td>236.10</td>\n",
       "      <td>2349368</td>\n",
       "      <td>5503.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>240.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>232.50</td>\n",
       "      <td>235.00</td>\n",
       "      <td>234.25</td>\n",
       "      <td>2240909</td>\n",
       "      <td>5248.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>234.55</td>\n",
       "      <td>236.80</td>\n",
       "      <td>231.10</td>\n",
       "      <td>233.80</td>\n",
       "      <td>233.25</td>\n",
       "      <td>5082859</td>\n",
       "      <td>11859.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>0</td>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>234.05</td>\n",
       "      <td>235.95</td>\n",
       "      <td>230.20</td>\n",
       "      <td>233.50</td>\n",
       "      <td>233.75</td>\n",
       "      <td>3069914</td>\n",
       "      <td>7162.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2035 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index       Date    Open    High     Low    Last   Close  \\\n",
       "0      2034 2010-07-21  122.10  123.00  121.05  121.10  121.55   \n",
       "1      2033 2010-07-22  120.30  122.00  120.25  120.75  120.90   \n",
       "2      2032 2010-07-23  121.80  121.95  120.25  120.35  120.65   \n",
       "3      2031 2010-07-26  120.10  121.00  117.10  117.10  117.60   \n",
       "4      2030 2010-07-27  117.60  119.50  112.00  118.80  118.65   \n",
       "...     ...        ...     ...     ...     ...     ...     ...   \n",
       "2030      4 2018-09-24  233.55  239.20  230.75  234.00  233.30   \n",
       "2031      3 2018-09-25  233.30  236.75  232.00  236.25  236.10   \n",
       "2032      2 2018-09-26  240.00  240.00  232.50  235.00  234.25   \n",
       "2033      1 2018-09-27  234.55  236.80  231.10  233.80  233.25   \n",
       "2034      0 2018-09-28  234.05  235.95  230.20  233.50  233.75   \n",
       "\n",
       "      Total Trade Quantity  Turnover (Lacs)  \n",
       "0                   658666           803.56  \n",
       "1                   293312           355.17  \n",
       "2                   281312           340.31  \n",
       "3                   658440           780.01  \n",
       "4                   586100           694.98  \n",
       "...                    ...              ...  \n",
       "2030               3423509          7999.55  \n",
       "2031               2349368          5503.90  \n",
       "2032               2240909          5248.60  \n",
       "2033               5082859         11859.95  \n",
       "2034               3069914          7162.35  \n",
       "\n",
       "[2035 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ordering the index\n",
    "df2.reset_index(inplace=True)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d7132a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d7c01ecb50>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting graph at Open\n",
    "plt.plot(df2['Open'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08912c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=df2['Open']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d38eeef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.16626115],\n",
       "       [0.15896188],\n",
       "       [0.16504461],\n",
       "       ...,\n",
       "       [0.64436334],\n",
       "       [0.62226277],\n",
       "       [0.6202352 ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Scaling feature 'Open' \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "df3 = scaler.fit_transform(np.array(df3).reshape(-1,1))\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95f88225",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into training and testing \n",
    "\n",
    "training_size = int(len(df3)*0.70)\n",
    "test_size = len(df3) - training_size\n",
    "\n",
    "\n",
    "train_data = df3[0:training_size,:]\n",
    "test_data = df3[training_size:len(df3), :1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a8aceb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1628, 407)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "training_data, testing_data = train_test_split(df3, test_size=0.2, random_state=25)\n",
    "\n",
    "training_data.shape[0], testing_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07e07ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert an array of values into a dataset matrix\n",
    "def datasetCrtd(dataset, time_step=1):\n",
    "  dataX, dataY = [], []\n",
    "  for i in range(len(dataset)-time_step-1):\n",
    "    a = dataset[i:(i+time_step), 0]\n",
    "    dataX.append(a)\n",
    "    dataY.append(dataset[i + time_step, 0])\n",
    "\n",
    "  return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84ae9016",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "time_step = 100\n",
    "X_train, y_train = datasetCrtd(train_data, time_step)\n",
    "X_test, ytest = datasetCrtd(test_data, time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bed67e09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1323, 100)\n",
      "(1323,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train.shape), print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a195735",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape input required for LSTM\n",
    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ccb4bdf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Stacked LSTM\n",
    "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": 17,
   "id": "e6e69f51",
   "metadata": {},
   "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",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences=True, input_shape=(100, 1)))\n",
    "model.add(LSTM(50, return_sequences=True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "24806063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "19/19 [==============================] - 6s 154ms/step - loss: 0.0115 - val_loss: 0.0011\n",
      "Epoch 2/60\n",
      "19/19 [==============================] - 2s 114ms/step - loss: 0.0019 - val_loss: 0.0014\n",
      "Epoch 3/60\n",
      "19/19 [==============================] - 2s 115ms/step - loss: 9.4225e-04 - val_loss: 0.0012\n",
      "Epoch 4/60\n",
      "19/19 [==============================] - 2s 122ms/step - loss: 8.2767e-04 - val_loss: 0.0010\n",
      "Epoch 5/60\n",
      "19/19 [==============================] - 2s 117ms/step - loss: 8.3137e-04 - val_loss: 0.0011\n",
      "Epoch 6/60\n",
      "19/19 [==============================] - 2s 117ms/step - loss: 7.9819e-04 - val_loss: 0.0010\n",
      "Epoch 7/60\n",
      "19/19 [==============================] - 2s 111ms/step - loss: 7.8105e-04 - val_loss: 9.6851e-04\n",
      "Epoch 8/60\n",
      "19/19 [==============================] - 2s 101ms/step - loss: 7.5058e-04 - val_loss: 0.0010\n",
      "Epoch 9/60\n",
      "19/19 [==============================] - 3s 140ms/step - loss: 7.0601e-04 - val_loss: 8.9026e-04\n",
      "Epoch 10/60\n",
      "19/19 [==============================] - 3s 137ms/step - loss: 6.9967e-04 - val_loss: 9.1876e-04\n",
      "Epoch 11/60\n",
      "19/19 [==============================] - 2s 126ms/step - loss: 6.7608e-04 - val_loss: 8.2434e-04\n",
      "Epoch 12/60\n",
      "19/19 [==============================] - 2s 120ms/step - loss: 6.6577e-04 - val_loss: 7.9104e-04\n",
      "Epoch 13/60\n",
      "19/19 [==============================] - 2s 119ms/step - loss: 6.4056e-04 - val_loss: 7.6925e-04\n",
      "Epoch 14/60\n",
      "19/19 [==============================] - 2s 121ms/step - loss: 6.2971e-04 - val_loss: 7.3311e-04\n",
      "Epoch 15/60\n",
      "19/19 [==============================] - 2s 110ms/step - loss: 6.3628e-04 - val_loss: 7.2042e-04\n",
      "Epoch 16/60\n",
      "19/19 [==============================] - 2s 116ms/step - loss: 6.1351e-04 - val_loss: 7.1993e-04\n",
      "Epoch 17/60\n",
      "19/19 [==============================] - 2s 112ms/step - loss: 6.2562e-04 - val_loss: 6.7652e-04\n",
      "Epoch 18/60\n",
      "19/19 [==============================] - 2s 119ms/step - loss: 6.2246e-04 - val_loss: 7.1522e-04\n",
      "Epoch 19/60\n",
      "19/19 [==============================] - 2s 113ms/step - loss: 5.9196e-04 - val_loss: 7.4400e-04\n",
      "Epoch 20/60\n",
      "19/19 [==============================] - 2s 122ms/step - loss: 5.6570e-04 - val_loss: 6.4750e-04\n",
      "Epoch 21/60\n",
      "19/19 [==============================] - 3s 158ms/step - loss: 6.4296e-04 - val_loss: 6.2766e-04\n",
      "Epoch 22/60\n",
      "19/19 [==============================] - 3s 133ms/step - loss: 6.0254e-04 - val_loss: 6.2006e-04\n",
      "Epoch 23/60\n",
      "19/19 [==============================] - 2s 128ms/step - loss: 5.4040e-04 - val_loss: 5.9957e-04\n",
      "Epoch 24/60\n",
      "19/19 [==============================] - 2s 128ms/step - loss: 5.2269e-04 - val_loss: 5.6958e-04\n",
      "Epoch 25/60\n",
      "19/19 [==============================] - 2s 131ms/step - loss: 5.2287e-04 - val_loss: 5.4152e-04\n",
      "Epoch 26/60\n",
      "19/19 [==============================] - 3s 147ms/step - loss: 5.2186e-04 - val_loss: 5.6463e-04\n",
      "Epoch 27/60\n",
      "19/19 [==============================] - 2s 127ms/step - loss: 4.9805e-04 - val_loss: 5.3054e-04\n",
      "Epoch 28/60\n",
      "19/19 [==============================] - 2s 109ms/step - loss: 5.0152e-04 - val_loss: 5.2321e-04\n",
      "Epoch 29/60\n",
      "19/19 [==============================] - 5s 251ms/step - loss: 4.7694e-04 - val_loss: 5.0680e-04\n",
      "Epoch 30/60\n",
      "19/19 [==============================] - 5s 275ms/step - loss: 4.6093e-04 - val_loss: 4.8320e-04\n",
      "Epoch 31/60\n",
      "19/19 [==============================] - 5s 262ms/step - loss: 4.6540e-04 - val_loss: 4.7594e-04\n",
      "Epoch 32/60\n",
      "19/19 [==============================] - 7s 360ms/step - loss: 4.6837e-04 - val_loss: 5.4110e-04\n",
      "Epoch 33/60\n",
      "19/19 [==============================] - 6s 300ms/step - loss: 4.3813e-04 - val_loss: 4.5861e-04\n",
      "Epoch 34/60\n",
      "19/19 [==============================] - 6s 293ms/step - loss: 4.3413e-04 - val_loss: 4.4589e-04\n",
      "Epoch 35/60\n",
      "19/19 [==============================] - 7s 353ms/step - loss: 4.2276e-04 - val_loss: 4.8414e-04\n",
      "Epoch 36/60\n",
      "19/19 [==============================] - 7s 358ms/step - loss: 4.3756e-04 - val_loss: 4.2934e-04\n",
      "Epoch 37/60\n",
      "19/19 [==============================] - 5s 251ms/step - loss: 4.0697e-04 - val_loss: 4.8730e-04\n",
      "Epoch 38/60\n",
      "19/19 [==============================] - 5s 251ms/step - loss: 4.0834e-04 - val_loss: 5.3059e-04\n",
      "Epoch 39/60\n",
      "19/19 [==============================] - 7s 367ms/step - loss: 4.5748e-04 - val_loss: 3.9624e-04\n",
      "Epoch 40/60\n",
      "19/19 [==============================] - 6s 325ms/step - loss: 5.1245e-04 - val_loss: 3.9215e-04\n",
      "Epoch 41/60\n",
      "19/19 [==============================] - 6s 292ms/step - loss: 4.4241e-04 - val_loss: 4.3572e-04\n",
      "Epoch 42/60\n",
      "19/19 [==============================] - 6s 320ms/step - loss: 3.9439e-04 - val_loss: 3.6218e-04\n",
      "Epoch 43/60\n",
      "19/19 [==============================] - 6s 301ms/step - loss: 3.6342e-04 - val_loss: 3.4907e-04\n",
      "Epoch 44/60\n",
      "19/19 [==============================] - 5s 287ms/step - loss: 3.4422e-04 - val_loss: 3.3778e-04\n",
      "Epoch 45/60\n",
      "19/19 [==============================] - 6s 315ms/step - loss: 3.5200e-04 - val_loss: 3.2790e-04\n",
      "Epoch 46/60\n",
      "19/19 [==============================] - 6s 334ms/step - loss: 3.6221e-04 - val_loss: 3.2062e-04\n",
      "Epoch 47/60\n",
      "19/19 [==============================] - 7s 353ms/step - loss: 3.4833e-04 - val_loss: 4.2184e-04\n",
      "Epoch 48/60\n",
      "19/19 [==============================] - 6s 306ms/step - loss: 3.4866e-04 - val_loss: 2.9629e-04\n",
      "Epoch 49/60\n",
      "19/19 [==============================] - 6s 293ms/step - loss: 3.1730e-04 - val_loss: 2.8999e-04\n",
      "Epoch 50/60\n",
      "19/19 [==============================] - 5s 256ms/step - loss: 2.9844e-04 - val_loss: 3.4003e-04\n",
      "Epoch 51/60\n",
      "19/19 [==============================] - 6s 342ms/step - loss: 3.1472e-04 - val_loss: 2.6471e-04\n",
      "Epoch 52/60\n",
      "19/19 [==============================] - 7s 366ms/step - loss: 2.9219e-04 - val_loss: 2.7488e-04\n",
      "Epoch 53/60\n",
      "19/19 [==============================] - 7s 358ms/step - loss: 2.8680e-04 - val_loss: 2.8490e-04\n",
      "Epoch 54/60\n",
      "19/19 [==============================] - 6s 310ms/step - loss: 2.7680e-04 - val_loss: 2.3311e-04\n",
      "Epoch 55/60\n",
      "19/19 [==============================] - 7s 353ms/step - loss: 2.6288e-04 - val_loss: 2.3686e-04\n",
      "Epoch 56/60\n",
      "19/19 [==============================] - 7s 370ms/step - loss: 2.6738e-04 - val_loss: 2.1588e-04\n",
      "Epoch 57/60\n",
      "19/19 [==============================] - 3s 161ms/step - loss: 2.5572e-04 - val_loss: 2.3433e-04\n",
      "Epoch 58/60\n",
      "19/19 [==============================] - 3s 132ms/step - loss: 2.4320e-04 - val_loss: 2.0012e-04\n",
      "Epoch 59/60\n",
      "19/19 [==============================] - 3s 134ms/step - loss: 2.5573e-04 - val_loss: 2.3187e-04\n",
      "Epoch 60/60\n",
      "19/19 [==============================] - 2s 123ms/step - loss: 2.6031e-04 - val_loss: 3.3048e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2d7d084d3f0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, validation_split=0.1,\n",
    "          epochs=60, batch_size=64, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "38cd6645",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 2s 25ms/step\n"
     ]
    }
   ],
   "source": [
    "#Prediction of scaled test set\n",
    "test_predict = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f204d451",
   "metadata": {},
   "outputs": [],
   "source": [
    "#transform to original test set\n",
    "test_predict1 = scaler.inverse_transform(test_predict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8f348f5b",
   "metadata": {},
   "outputs": [
    {
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