intel_stock_price_prediction.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   },
   "source": [
    "Stock Price Prediction using LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e656cc7",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import array\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import LSTM\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b780c230",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:49:10.527282Z",
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     "exception": false,
     "start_time": "2024-02-04T14:49:10.518056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84fac536",
   "metadata": {
    "papermill": {
     "duration": 0.007337,
     "end_time": "2024-02-04T14:49:10.547882",
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     "start_time": "2024-02-04T14:49:10.540545",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Collect the Stock Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73a03c36",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>OpenInt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4534</th>\n",
       "      <td>1990-01-02</td>\n",
       "      <td>0.8579</td>\n",
       "      <td>0.8982</td>\n",
       "      <td>0.8499</td>\n",
       "      <td>0.8982</td>\n",
       "      <td>79600273</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4535</th>\n",
       "      <td>1990-01-03</td>\n",
       "      <td>0.8982</td>\n",
       "      <td>0.9061</td>\n",
       "      <td>0.8740</td>\n",
       "      <td>0.8740</td>\n",
       "      <td>86671242</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4536</th>\n",
       "      <td>1990-01-04</td>\n",
       "      <td>0.8820</td>\n",
       "      <td>0.8982</td>\n",
       "      <td>0.8579</td>\n",
       "      <td>0.8904</td>\n",
       "      <td>72928342</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4537</th>\n",
       "      <td>1990-01-05</td>\n",
       "      <td>0.8904</td>\n",
       "      <td>0.8982</td>\n",
       "      <td>0.8820</td>\n",
       "      <td>0.8820</td>\n",
       "      <td>46184758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4538</th>\n",
       "      <td>1990-01-08</td>\n",
       "      <td>0.8904</td>\n",
       "      <td>0.9061</td>\n",
       "      <td>0.8820</td>\n",
       "      <td>0.8982</td>\n",
       "      <td>54001929</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11551</th>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>46.6000</td>\n",
       "      <td>46.7400</td>\n",
       "      <td>46.0900</td>\n",
       "      <td>46.7000</td>\n",
       "      <td>34006271</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11552</th>\n",
       "      <td>2017-11-07</td>\n",
       "      <td>46.7000</td>\n",
       "      <td>47.0900</td>\n",
       "      <td>46.6400</td>\n",
       "      <td>46.7800</td>\n",
       "      <td>24422113</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11553</th>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>46.6200</td>\n",
       "      <td>46.7000</td>\n",
       "      <td>46.2800</td>\n",
       "      <td>46.7000</td>\n",
       "      <td>21556947</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11554</th>\n",
       "      <td>2017-11-09</td>\n",
       "      <td>46.0500</td>\n",
       "      <td>46.3900</td>\n",
       "      <td>45.6500</td>\n",
       "      <td>46.3000</td>\n",
       "      <td>25564257</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11555</th>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>46.0400</td>\n",
       "      <td>46.0900</td>\n",
       "      <td>45.3800</td>\n",
       "      <td>45.5800</td>\n",
       "      <td>24088569</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7022 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date     Open     High      Low    Close    Volume  OpenInt\n",
       "4534   1990-01-02   0.8579   0.8982   0.8499   0.8982  79600273        0\n",
       "4535   1990-01-03   0.8982   0.9061   0.8740   0.8740  86671242        0\n",
       "4536   1990-01-04   0.8820   0.8982   0.8579   0.8904  72928342        0\n",
       "4537   1990-01-05   0.8904   0.8982   0.8820   0.8820  46184758        0\n",
       "4538   1990-01-08   0.8904   0.9061   0.8820   0.8982  54001929        0\n",
       "...           ...      ...      ...      ...      ...       ...      ...\n",
       "11551  2017-11-06  46.6000  46.7400  46.0900  46.7000  34006271        0\n",
       "11552  2017-11-07  46.7000  47.0900  46.6400  46.7800  24422113        0\n",
       "11553  2017-11-08  46.6200  46.7000  46.2800  46.7000  21556947        0\n",
       "11554  2017-11-09  46.0500  46.3900  45.6500  46.3000  25564257        0\n",
       "11555  2017-11-10  46.0400  46.0900  45.3800  45.5800  24088569        0\n",
       "\n",
       "[7022 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/kaggle/input/price-volume-data-for-all-us-stocks-etfs/Stocks/intc.us.txt\", sep=',')\n",
    "df = df[df['Date']>'1990-01-01']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbdca265",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.8982\n",
       "1    0.8740\n",
       "2    0.8904\n",
       "3    0.8820\n",
       "4    0.8982\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.reset_index()['Close']\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87fd15b3",
   "metadata": {
    "execution": {
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     "start_time": "2024-02-04T14:49:10.691263",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bed92736",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:49:10.921148Z",
     "iopub.status.busy": "2024-02-04T14:49:10.920341Z",
     "iopub.status.idle": "2024-02-04T14:49:10.927692Z",
     "shell.execute_reply": "2024-02-04T14:49:10.926683Z"
    },
    "papermill": {
     "duration": 0.01932,
     "end_time": "2024-02-04T14:49:10.929809",
     "exception": false,
     "start_time": "2024-02-04T14:49:10.910489",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00284151]\n",
      " [0.00243317]\n",
      " [0.0027099 ]\n",
      " ...\n",
      " [0.77568245]\n",
      " [0.76893302]\n",
      " [0.75678403]]\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "df2 = scaler.fit_transform(np.array(df2).reshape(-1, 1))\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ac0ee0e",
   "metadata": {
    "papermill": {
     "duration": 0.009443,
     "end_time": "2024-02-04T14:49:10.949027",
     "exception": false,
     "start_time": "2024-02-04T14:49:10.939584",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preprocessing data with Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d622a063",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:49:10.970094Z",
     "iopub.status.busy": "2024-02-04T14:49:10.969793Z",
     "iopub.status.idle": "2024-02-04T14:49:10.974832Z",
     "shell.execute_reply": "2024-02-04T14:49:10.973932Z"
    },
    "papermill": {
     "duration": 0.018128,
     "end_time": "2024-02-04T14:49:10.976910",
     "exception": false,
     "start_time": "2024-02-04T14:49:10.958782",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset_size = int(len(df2) * 0.70)\n",
    "test_dataset_size = int(len(df2) - train_dataset_size)\n",
    "train_data, test_data = df2[0:train_dataset_size], df2[train_dataset_size: len(df2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3c9b117",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:49:10.998011Z",
     "iopub.status.busy": "2024-02-04T14:49:10.997328Z",
     "iopub.status.idle": "2024-02-04T14:49:11.003119Z",
     "shell.execute_reply": "2024-02-04T14:49:11.002243Z"
    },
    "papermill": {
     "duration": 0.018455,
     "end_time": "2024-02-04T14:49:11.005084",
     "exception": false,
     "start_time": "2024-02-04T14:49:10.986629",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Convert an arrary of values in dataset matrix\n",
    "def Create_dataset(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",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf0239ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:49:11.025028Z",
     "iopub.status.busy": "2024-02-04T14:49:11.024751Z",
     "iopub.status.idle": "2024-02-04T14:49:11.043895Z",
     "shell.execute_reply": "2024-02-04T14:49:11.043247Z"
    },
    "papermill": {
     "duration": 0.031169,
     "end_time": "2024-02-04T14:49:11.045696",
     "exception": false,
     "start_time": "2024-02-04T14:49:11.014527",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Reshape into X=t, t+1, t+2, t+3 and Y = t+4\n",
    "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": 10,
   "id": "cd852d8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:49:11.064070Z",
     "iopub.status.busy": "2024-02-04T14:49:11.063806Z",
     "iopub.status.idle": "2024-02-04T14:49:11.068162Z",
     "shell.execute_reply": "2024-02-04T14:49:11.067287Z"
    },
    "papermill": {
     "duration": 0.015764,
     "end_time": "2024-02-04T14:49:11.070302",
     "exception": false,
     "start_time": "2024-02-04T14:49:11.054538",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4814, 100)\n",
      "(4814,)\n",
      "(2006, 100)\n",
      "(2006,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape) \n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbd3c17",
   "metadata": {
    "papermill": {
     "duration": 0.008548,
     "end_time": "2024-02-04T14:49:11.087418",
     "exception": false,
     "start_time": "2024-02-04T14:49:11.078870",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Create an Stacked LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cc4aeb16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:49:11.105691Z",
     "iopub.status.busy": "2024-02-04T14:49:11.105388Z",
     "iopub.status.idle": "2024-02-04T14:49:11.110601Z",
     "shell.execute_reply": "2024-02-04T14:49:11.109759Z"
    },
    "papermill": {
     "duration": 0.016328,
     "end_time": "2024-02-04T14:49:11.112379",
     "exception": false,
     "start_time": "2024-02-04T14:49:11.096051",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4814, 100, 1)\n",
      "(2006, 100, 1)\n"
     ]
    }
   ],
   "source": [
    "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)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6728a93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:49:11.130920Z",
     "iopub.status.busy": "2024-02-04T14:49:11.130652Z",
     "iopub.status.idle": "2024-02-04T14:49:13.100250Z",
     "shell.execute_reply": "2024-02-04T14:49:13.099254Z"
    },
    "papermill": {
     "duration": 1.982261,
     "end_time": "2024-02-04T14:49:13.103317",
     "exception": false,
     "start_time": "2024-02-04T14:49:11.121056",
     "status": "completed"
    },
    "tags": []
   },
   "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: 50851 (198.64 KB)\n",
      "Trainable params: 50851 (198.64 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\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",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fafaf7d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:49:13.125030Z",
     "iopub.status.busy": "2024-02-04T14:49:13.124540Z",
     "iopub.status.idle": "2024-02-04T14:51:13.369428Z",
     "shell.execute_reply": "2024-02-04T14:51:13.368431Z"
    },
    "papermill": {
     "duration": 120.258179,
     "end_time": "2024-02-04T14:51:13.371755",
     "exception": false,
     "start_time": "2024-02-04T14:49:13.113576",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "76/76 [==============================] - 8s 34ms/step - loss: 0.0062 - val_loss: 3.4689e-04\n",
      "Epoch 2/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 7.2581e-04 - val_loss: 3.2944e-04\n",
      "Epoch 3/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 6.3258e-04 - val_loss: 4.7659e-04\n",
      "Epoch 4/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 5.8063e-04 - val_loss: 2.3935e-04\n",
      "Epoch 5/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 5.8060e-04 - val_loss: 4.4653e-04\n",
      "Epoch 6/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 4.5645e-04 - val_loss: 7.6653e-04\n",
      "Epoch 7/100\n",
      "76/76 [==============================] - 1s 17ms/step - loss: 4.0096e-04 - val_loss: 2.4216e-04\n",
      "Epoch 8/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 4.4606e-04 - val_loss: 2.1549e-04\n",
      "Epoch 9/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 3.5566e-04 - val_loss: 2.1895e-04\n",
      "Epoch 10/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 3.6871e-04 - val_loss: 1.9017e-04\n",
      "Epoch 11/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 3.8606e-04 - val_loss: 2.0968e-04\n",
      "Epoch 12/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 3.4658e-04 - val_loss: 1.5761e-04\n",
      "Epoch 13/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 3.4850e-04 - val_loss: 2.5688e-04\n",
      "Epoch 14/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 3.1959e-04 - val_loss: 3.4492e-04\n",
      "Epoch 15/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 2.9443e-04 - val_loss: 1.7988e-04\n",
      "Epoch 16/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 2.8136e-04 - val_loss: 1.1639e-04\n",
      "Epoch 17/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 2.5369e-04 - val_loss: 1.0569e-04\n",
      "Epoch 18/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 2.4576e-04 - val_loss: 2.0803e-04\n",
      "Epoch 19/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 2.5465e-04 - val_loss: 8.2150e-05\n",
      "Epoch 20/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 2.1341e-04 - val_loss: 1.2425e-04\n",
      "Epoch 21/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 2.0046e-04 - val_loss: 7.5912e-05\n",
      "Epoch 22/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.8256e-04 - val_loss: 9.9357e-05\n",
      "Epoch 23/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.8157e-04 - val_loss: 1.0251e-04\n",
      "Epoch 24/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 2.0255e-04 - val_loss: 6.8671e-05\n",
      "Epoch 25/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.6481e-04 - val_loss: 5.7096e-05\n",
      "Epoch 26/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.9664e-04 - val_loss: 6.2311e-05\n",
      "Epoch 27/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.6003e-04 - val_loss: 6.5814e-05\n",
      "Epoch 28/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.4967e-04 - val_loss: 8.0180e-05\n",
      "Epoch 29/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.5302e-04 - val_loss: 1.8114e-04\n",
      "Epoch 30/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.3849e-04 - val_loss: 8.7631e-05\n",
      "Epoch 31/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.3742e-04 - val_loss: 4.4838e-05\n",
      "Epoch 32/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.3158e-04 - val_loss: 8.1727e-05\n",
      "Epoch 33/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.6525e-04 - val_loss: 1.9678e-04\n",
      "Epoch 34/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.5336e-04 - val_loss: 9.6890e-05\n",
      "Epoch 35/100\n",
      "76/76 [==============================] - 1s 18ms/step - loss: 1.2425e-04 - val_loss: 4.2221e-05\n",
      "Epoch 36/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1791e-04 - val_loss: 1.5422e-04\n",
      "Epoch 37/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.4841e-04 - val_loss: 1.0348e-04\n",
      "Epoch 38/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.3819e-04 - val_loss: 4.2630e-05\n",
      "Epoch 39/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2438e-04 - val_loss: 4.4585e-05\n",
      "Epoch 40/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2603e-04 - val_loss: 4.0826e-05\n",
      "Epoch 41/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1755e-04 - val_loss: 2.1617e-04\n",
      "Epoch 42/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.6193e-04 - val_loss: 1.0623e-04\n",
      "Epoch 43/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1505e-04 - val_loss: 4.6429e-05\n",
      "Epoch 44/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.3012e-04 - val_loss: 8.6171e-05\n",
      "Epoch 45/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2495e-04 - val_loss: 4.8567e-05\n",
      "Epoch 46/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.2601e-04 - val_loss: 5.1947e-05\n",
      "Epoch 47/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2142e-04 - val_loss: 6.2010e-05\n",
      "Epoch 48/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2804e-04 - val_loss: 4.0523e-05\n",
      "Epoch 49/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1944e-04 - val_loss: 3.4639e-04\n",
      "Epoch 50/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2684e-04 - val_loss: 4.1098e-05\n",
      "Epoch 51/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2432e-04 - val_loss: 1.1931e-04\n",
      "Epoch 52/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.3511e-04 - val_loss: 4.3354e-05\n",
      "Epoch 53/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.3586e-04 - val_loss: 1.1057e-04\n",
      "Epoch 54/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.3086e-04 - val_loss: 1.4932e-04\n",
      "Epoch 55/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1900e-04 - val_loss: 4.1484e-05\n",
      "Epoch 56/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1594e-04 - val_loss: 1.1072e-04\n",
      "Epoch 57/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2045e-04 - val_loss: 6.4486e-05\n",
      "Epoch 58/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.3000e-04 - val_loss: 1.1822e-04\n",
      "Epoch 59/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2613e-04 - val_loss: 4.7004e-05\n",
      "Epoch 60/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1445e-04 - val_loss: 1.8934e-04\n",
      "Epoch 61/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1424e-04 - val_loss: 3.9845e-05\n",
      "Epoch 62/100\n",
      "76/76 [==============================] - 1s 16ms/step - loss: 1.1768e-04 - val_loss: 4.2130e-05\n",
      "Epoch 63/100\n",
      "76/76 [==============================] - 1s 17ms/step - loss: 1.2219e-04 - val_loss: 5.9101e-05\n",
      "Epoch 64/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.1957e-04 - val_loss: 9.1743e-05\n",
      "Epoch 65/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.4231e-04 - val_loss: 1.0586e-04\n",
      "Epoch 66/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.1183e-04 - val_loss: 1.0639e-04\n",
      "Epoch 67/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.2122e-04 - val_loss: 5.0439e-05\n",
      "Epoch 68/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1308e-04 - val_loss: 2.3790e-04\n",
      "Epoch 69/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.4700e-04 - val_loss: 5.5417e-05\n",
      "Epoch 70/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.0795e-04 - val_loss: 3.9561e-05\n",
      "Epoch 71/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2785e-04 - val_loss: 4.3365e-05\n",
      "Epoch 72/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1551e-04 - val_loss: 1.4388e-04\n",
      "Epoch 73/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2134e-04 - val_loss: 4.4870e-05\n",
      "Epoch 74/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1001e-04 - val_loss: 3.9900e-05\n",
      "Epoch 75/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1121e-04 - val_loss: 7.4619e-05\n",
      "Epoch 76/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1681e-04 - val_loss: 8.0009e-05\n",
      "Epoch 77/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.1433e-04 - val_loss: 7.3910e-05\n",
      "Epoch 78/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.4715e-04 - val_loss: 6.6225e-05\n",
      "Epoch 79/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.3845e-04 - val_loss: 6.9232e-05\n",
      "Epoch 80/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.1684e-04 - val_loss: 4.4040e-05\n",
      "Epoch 81/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.2090e-04 - val_loss: 3.8197e-05\n",
      "Epoch 82/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.1818e-04 - val_loss: 7.2218e-05\n",
      "Epoch 83/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2924e-04 - val_loss: 2.7638e-04\n",
      "Epoch 84/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.0910e-04 - val_loss: 4.1114e-05\n",
      "Epoch 85/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.1327e-04 - val_loss: 5.2463e-05\n",
      "Epoch 86/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.1317e-04 - val_loss: 4.9321e-05\n",
      "Epoch 87/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.0927e-04 - val_loss: 1.5595e-04\n",
      "Epoch 88/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2263e-04 - val_loss: 1.5210e-04\n",
      "Epoch 89/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2684e-04 - val_loss: 4.0532e-05\n",
      "Epoch 90/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.0450e-04 - val_loss: 4.4209e-05\n",
      "Epoch 91/100\n",
      "76/76 [==============================] - 1s 17ms/step - loss: 1.1329e-04 - val_loss: 3.9653e-05\n",
      "Epoch 92/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1179e-04 - val_loss: 1.6109e-04\n",
      "Epoch 93/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1454e-04 - val_loss: 3.8384e-05\n",
      "Epoch 94/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1145e-04 - val_loss: 4.0145e-05\n",
      "Epoch 95/100\n",
      "76/76 [==============================] - 1s 14ms/step - loss: 1.2886e-04 - val_loss: 1.2712e-04\n",
      "Epoch 96/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1512e-04 - val_loss: 4.1037e-05\n",
      "Epoch 97/100\n",
      "76/76 [==============================] - 1s 16ms/step - loss: 1.0404e-04 - val_loss: 4.1228e-05\n",
      "Epoch 98/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1150e-04 - val_loss: 8.6482e-05\n",
      "Epoch 99/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.2513e-04 - val_loss: 5.0319e-05\n",
      "Epoch 100/100\n",
      "76/76 [==============================] - 1s 15ms/step - loss: 1.1311e-04 - val_loss: 3.8110e-05\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=64, verbose=1)\n",
    "\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f95b12fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:13.679177Z",
     "iopub.status.busy": "2024-02-04T14:51:13.678762Z",
     "iopub.status.idle": "2024-02-04T14:51:13.684572Z",
     "shell.execute_reply": "2024-02-04T14:51:13.683716Z"
    },
    "papermill": {
     "duration": 0.15965,
     "end_time": "2024-02-04T14:51:13.686537",
     "exception": false,
     "start_time": "2024-02-04T14:51:13.526887",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.13.0'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f885d39c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:13.989030Z",
     "iopub.status.busy": "2024-02-04T14:51:13.988321Z",
     "iopub.status.idle": "2024-02-04T14:51:16.343340Z",
     "shell.execute_reply": "2024-02-04T14:51:16.342589Z"
    },
    "papermill": {
     "duration": 2.508708,
     "end_time": "2024-02-04T14:51:16.345309",
     "exception": false,
     "start_time": "2024-02-04T14:51:13.836601",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 [==============================] - 2s 5ms/step\n",
      "63/63 [==============================] - 0s 5ms/step\n"
     ]
    }
   ],
   "source": [
    "## Lets do prediction and check performance matrix.\n",
    "train_predict = model.predict(X_train)\n",
    "test_predict = model.predict(X_test)\n",
    "\n",
    "## Transform to original form \n",
    "train_predict = scaler.inverse_transform(train_predict)\n",
    "test_predict = scaler.inverse_transform(test_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e6ceb4b",
   "metadata": {
    "papermill": {
     "duration": 0.154654,
     "end_time": "2024-02-04T14:51:16.658903",
     "exception": false,
     "start_time": "2024-02-04T14:51:16.504249",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Predict the test data and plot the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89bc094a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:16.966328Z",
     "iopub.status.busy": "2024-02-04T14:51:16.965916Z",
     "iopub.status.idle": "2024-02-04T14:51:16.973035Z",
     "shell.execute_reply": "2024-02-04T14:51:16.972118Z"
    },
    "papermill": {
     "duration": 0.16298,
     "end_time": "2024-02-04T14:51:16.974964",
     "exception": false,
     "start_time": "2024-02-04T14:51:16.811984",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.239086827892965"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculate RMSE for Performance Metrics\n",
    "math.sqrt(mean_squared_error(y_train, train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0703523",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:17.294738Z",
     "iopub.status.busy": "2024-02-04T14:51:17.294381Z",
     "iopub.status.idle": "2024-02-04T14:51:17.301271Z",
     "shell.execute_reply": "2024-02-04T14:51:17.300484Z"
    },
    "papermill": {
     "duration": 0.163548,
     "end_time": "2024-02-04T14:51:17.303168",
     "exception": false,
     "start_time": "2024-02-04T14:51:17.139620",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.336480977901164"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Test data for RMSE\n",
    "math.sqrt(mean_squared_error(y_test, test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf09a2fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:17.610929Z",
     "iopub.status.busy": "2024-02-04T14:51:17.610594Z",
     "iopub.status.idle": "2024-02-04T14:51:17.910946Z",
     "shell.execute_reply": "2024-02-04T14:51:17.909989Z"
    },
    "papermill": {
     "duration": 0.45656,
     "end_time": "2024-02-04T14:51:17.913090",
     "exception": false,
     "start_time": "2024-02-04T14:51:17.456530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting \n",
    "## Shift Train prediction for plotting\n",
    "look_back = 100\n",
    "train_predict_plot = np.empty_like(df2)\n",
    "train_predict_plot[:,:] = np.nan \n",
    "train_predict_plot[look_back: len(train_predict)+look_back, : ] = train_predict\n",
    "\n",
    "## Shifting Test Prediction for Plotting \n",
    "test_predict_plot = np.empty_like(df2)\n",
    "test_predict_plot[:, :] = np.nan \n",
    "test_predict_plot[len(train_predict)+ (look_back*2)+1 : len(df2)-1, :] = test_predict\n",
    "\n",
    "## Plot Baseline and Prediction\n",
    "plt.plot(scaler.inverse_transform(df2), label=\"Main\")\n",
    "plt.plot(train_predict_plot, label=\"Train\")\n",
    "plt.plot(test_predict_plot, label=\"Test\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b249a59f",
   "metadata": {
    "papermill": {
     "duration": 0.154361,
     "end_time": "2024-02-04T14:51:18.222542",
     "exception": false,
     "start_time": "2024-02-04T14:51:18.068181",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Predict the future 30 days and plot the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "75f5cc10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:18.531313Z",
     "iopub.status.busy": "2024-02-04T14:51:18.530967Z",
     "iopub.status.idle": "2024-02-04T14:51:18.537634Z",
     "shell.execute_reply": "2024-02-04T14:51:18.536646Z"
    },
    "papermill": {
     "duration": 0.163953,
     "end_time": "2024-02-04T14:51:18.540140",
     "exception": false,
     "start_time": "2024-02-04T14:51:18.376187",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2107"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f807b492",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:18.850137Z",
     "iopub.status.busy": "2024-02-04T14:51:18.849827Z",
     "iopub.status.idle": "2024-02-04T14:51:18.856120Z",
     "shell.execute_reply": "2024-02-04T14:51:18.855253Z"
    },
    "papermill": {
     "duration": 0.163076,
     "end_time": "2024-02-04T14:51:18.858048",
     "exception": false,
     "start_time": "2024-02-04T14:51:18.694972",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_input = test_data[len(test_data)-100 : ].reshape(1, -1)\n",
    "x_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f732a0d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:19.166547Z",
     "iopub.status.busy": "2024-02-04T14:51:19.166185Z",
     "iopub.status.idle": "2024-02-04T14:51:19.172655Z",
     "shell.execute_reply": "2024-02-04T14:51:19.171879Z"
    },
    "papermill": {
     "duration": 0.163861,
     "end_time": "2024-02-04T14:51:19.174610",
     "exception": false,
     "start_time": "2024-02-04T14:51:19.010749",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.654665042302098,\n",
       " 0.6551712500970232,\n",
       " 0.6628824821730488,\n",
       " 0.6601995808599458,\n",
       " 0.6659028553494353,\n",
       " 0.6726185454287749,\n",
       " 0.674626503015311,\n",
       " 0.6717748657705663,\n",
       " 0.6813421930946507,\n",
       " 0.732502927568414,\n",
       " 0.7319967197734888,\n",
       " 0.7507939025583742,\n",
       " 0.7712615710665124,\n",
       " 0.7778085252142103,\n",
       " 0.7650520887820977,\n",
       " 0.7756824524755249,\n",
       " 0.7770323399286584,\n",
       " 0.7756824524755249,\n",
       " 0.7689330152098568,\n",
       " 0.7567840281316545]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_input = list(x_input)\n",
    "temp_input = temp_input[0].tolist()\n",
    "temp_input[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0aba0766",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:19.481450Z",
     "iopub.status.busy": "2024-02-04T14:51:19.481139Z",
     "iopub.status.idle": "2024-02-04T14:51:21.131814Z",
     "shell.execute_reply": "2024-02-04T14:51:21.130523Z"
    },
    "papermill": {
     "duration": 1.806764,
     "end_time": "2024-02-04T14:51:21.133897",
     "exception": false,
     "start_time": "2024-02-04T14:51:19.327133",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7559086]\n",
      "101\n",
      "1 day output [[0.75767905]]\n",
      "2 day output [[0.76017886]]\n",
      "3 day output [[0.75600576]]\n",
      "4 day output [[0.7553523]]\n",
      "5 day output [[0.7533582]]\n",
      "6 day output [[0.7493362]]\n",
      "7 day output [[0.74570864]]\n",
      "8 day output [[0.7413607]]\n",
      "9 day output [[0.7365326]]\n",
      "10 day output [[0.73188776]]\n",
      "11 day output [[0.7272604]]\n",
      "12 day output [[0.72282237]]\n",
      "13 day output [[0.71873546]]\n",
      "14 day output [[0.7149565]]\n",
      "15 day output [[0.71150625]]\n",
      "16 day output [[0.7083786]]\n",
      "17 day output [[0.70552415]]\n",
      "18 day output [[0.70291305]]\n",
      "19 day output [[0.70051485]]\n",
      "20 day output [[0.69829893]]\n",
      "21 day output [[0.6962469]]\n",
      "22 day output [[0.6943457]]\n",
      "23 day output [[0.69258624]]\n",
      "24 day output [[0.6909626]]\n",
      "25 day output [[0.6894686]]\n",
      "26 day output [[0.6880974]]\n",
      "27 day output [[0.68683964]]\n",
      "28 day output [[0.68568414]]\n",
      "29 day output [[0.68461627]]\n"
     ]
    }
   ],
   "source": [
    "lst_output = []\n",
    "n_steps = 100\n",
    "i = 0\n",
    "\n",
    "while(i<30):\n",
    "    if(len(temp_input)>100):\n",
    "#         print(temp_input)\n",
    "        x_input = np.array(temp_input[1:])\n",
    "#         print(f\"{i} day input {x_input}\")\n",
    "        x_input = x_input.reshape(1, -1)\n",
    "        x_input = x_input.reshape((1, n_steps, 1))\n",
    "        yhat = model.predict(x_input, verbose=0)\n",
    "        print(f\"{i} day output {yhat}\")\n",
    "        temp_input.extend(yhat[0].tolist())\n",
    "        temp_input = temp_input[1:]\n",
    "        lst_output.extend(yhat.tolist())\n",
    "        i = i+1\n",
    "    \n",
    "    else:\n",
    "        x_input = x_input.reshape((1, n_steps, 1))\n",
    "        yhat = model.predict(x_input, verbose=0)\n",
    "        print(yhat[0])\n",
    "        temp_input.extend(yhat[0].tolist())\n",
    "        print(len(temp_input))\n",
    "        lst_output.extend(yhat.tolist())\n",
    "        i = i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "442b55e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:21.481907Z",
     "iopub.status.busy": "2024-02-04T14:51:21.480818Z",
     "iopub.status.idle": "2024-02-04T14:51:21.488347Z",
     "shell.execute_reply": "2024-02-04T14:51:21.487383Z"
    },
    "papermill": {
     "duration": 0.194341,
     "end_time": "2024-02-04T14:51:21.490616",
     "exception": false,
     "start_time": "2024-02-04T14:51:21.296275",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7022"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_new = np.arange(1, 101)\n",
    "day_pred = np.arange(101, 131)\n",
    "len(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "14afa47e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-04T14:51:21.857827Z",
     "iopub.status.busy": "2024-02-04T14:51:21.857439Z",
     "iopub.status.idle": "2024-02-04T14:51:22.055399Z",
     "shell.execute_reply": "2024-02-04T14:51:22.054516Z"
    },
    "papermill": {
     "duration": 0.354852,
     "end_time": "2024-02-04T14:51:22.057360",
     "exception": false,
     "start_time": "2024-02-04T14:51:21.702508",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(day_new, scaler.inverse_transform(df2[len(df2)-100:]), label=\"Previous Price\")\n",
    "plt.plot(day_pred, scaler.inverse_transform(lst_output), label=\"Prediction Price\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "nvidiaTeslaT4",
   "dataSources": [
    {
     "datasetId": 4538,
     "sourceId": 7213,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 1130576,
     "sourceId": 1897283,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30588,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "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": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 152.713865,
   "end_time": "2024-02-04T14:51:25.704265",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-02-04T14:48:52.990400",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}