LSTM  Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e088b11d-b851-434c-bac7-30515dc5c4a0",
   "metadata": {},
   "source": [
    "## **1. Importing Libraries**\n",
    "\n",
    "numpy: For numerical operations.\n",
    "\n",
    "pandas: For data manipulation and analysis.\n",
    "\n",
    "matplotlib.pyplot: For data visualization.\n",
    "\n",
    "keras.models.load_model: For loading pre-trained Keras models.\n",
    "\n",
    "datetime: For handling date and time.\n",
    "\n",
    "yfinance: For downloading stock data from Yahoo Finance.\n",
    "\n",
    "tensorflow: For building and training deep learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f79aac2b-2ddd-4b6b-850c-3d50ac8c3718",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import load_model\n",
    "import datetime as dt\n",
    "import yfinance as yf\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020c1be9-a5eb-447a-a6ff-0413754213c1",
   "metadata": {},
   "source": [
    "## **2. Downloading Stock Data**\n",
    "\n",
    "\n",
    "STOCK: Ticker symbol of the stock (State Bank of India in this case).\n",
    "\n",
    "start_date: Starting date for the stock data.\n",
    "\n",
    "end_date: Ending date for the stock data.\n",
    "\n",
    "yf.download: Downloads historical stock data from Yahoo Finance.\n",
    "\n",
    "df.head(): Displays the first five rows of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9236f2d8-3599-436b-a6de-bca041bd2a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>22.267092</td>\n",
       "      <td>22.988886</td>\n",
       "      <td>22.101974</td>\n",
       "      <td>22.988886</td>\n",
       "      <td>15.844799</td>\n",
       "      <td>25152894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>22.988886</td>\n",
       "      <td>24.720245</td>\n",
       "      <td>22.535995</td>\n",
       "      <td>24.446625</td>\n",
       "      <td>16.849533</td>\n",
       "      <td>47648560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>23.493668</td>\n",
       "      <td>24.974997</td>\n",
       "      <td>23.116261</td>\n",
       "      <td>23.441774</td>\n",
       "      <td>16.156950</td>\n",
       "      <td>36396207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>23.776726</td>\n",
       "      <td>25.286358</td>\n",
       "      <td>23.776726</td>\n",
       "      <td>24.625893</td>\n",
       "      <td>16.973083</td>\n",
       "      <td>70573968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>24.673071</td>\n",
       "      <td>26.409147</td>\n",
       "      <td>24.059780</td>\n",
       "      <td>25.786425</td>\n",
       "      <td>17.772964</td>\n",
       "      <td>83453217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2000-01-03  22.267092  22.988886  22.101974  22.988886  15.844799  25152894\n",
       "2000-01-04  22.988886  24.720245  22.535995  24.446625  16.849533  47648560\n",
       "2000-01-05  23.493668  24.974997  23.116261  23.441774  16.156950  36396207\n",
       "2000-01-06  23.776726  25.286358  23.776726  24.625893  16.973083  70573968\n",
       "2000-01-07  24.673071  26.409147  24.059780  25.786425  17.772964  83453217"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STOCK = 'SBIN.NS'\n",
    "start_date = dt.date(2000,1,1)\n",
    "end_date = dt.datetime.now().date()\n",
    "df = yf.download(STOCK,start=start_date, end=end_date, progress=False)\n",
    "\n",
    "# read DataSet\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f598c53c-bf68-4220-b89f-09a20759520e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 6111 entries, 2000-01-03 to 2024-06-28\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       6111 non-null   float64\n",
      " 1   High       6111 non-null   float64\n",
      " 2   Low        6111 non-null   float64\n",
      " 3   Close      6111 non-null   float64\n",
      " 4   Adj Close  6111 non-null   float64\n",
      " 5   Volume     6111 non-null   int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 334.2 KB\n"
     ]
    }
   ],
   "source": [
    "#  Dataset Analysis\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481cb5a0-0246-40e0-abc1-e5633c010730",
   "metadata": {},
   "source": [
    "## **3. Calculating Moving Averages**\n",
    "\n",
    "rolling(100).mean(): Calculates the 100-day moving average.\n",
    "\n",
    "rolling(200).mean(): Calculates the 200-day moving average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "83139000-c8f8-488a-95d6-ddeedb6dfefe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb89abdc490>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph of Close Price history\n",
    "fig=plt.figure(figsize=(12,6))\n",
    "plt.plot(df.Close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3a644383-d9f3-41df-8b13-898c723cda11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2000-01-03         NaN\n",
       "2000-01-04         NaN\n",
       "2000-01-05         NaN\n",
       "2000-01-06         NaN\n",
       "2000-01-07         NaN\n",
       "                ...   \n",
       "2024-06-24    771.1490\n",
       "2024-06-25    773.3885\n",
       "2024-06-26    775.7145\n",
       "2024-06-27    777.9250\n",
       "2024-06-28    780.1470\n",
       "Name: Close, Length: 6111, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma100 = df.Close.rolling(100).mean()\n",
    "ma100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9e901cf0-1629-4bf1-b023-6cde3a517627",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2000-01-03           NaN\n",
       "2000-01-04           NaN\n",
       "2000-01-05           NaN\n",
       "2000-01-06           NaN\n",
       "2000-01-07           NaN\n",
       "                 ...    \n",
       "2024-06-24    683.687001\n",
       "2024-06-25    685.034001\n",
       "2024-06-26    686.386501\n",
       "2024-06-27    687.769501\n",
       "2024-06-28    689.207501\n",
       "Name: Close, Length: 6111, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma200=df.Close.rolling(200).mean()\n",
    "ma200"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c528c7ca-97da-4d96-879f-014e1c653760",
   "metadata": {},
   "source": [
    "## **4. Plotting Stock Data and Moving Averages**\n",
    "\n",
    "plt.plot(df.Close): Plots the closing prices of the stock.\n",
    "\n",
    "plt.plot(ma100, 'r'): Plots the 100-day moving average in red.\n",
    "\n",
    "plt.plot(ma200, 'g'): Plots the 200-day moving average in green."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "419be231-0013-4a2d-b876-8ba2e8772cfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb899eda310>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(12,6))\n",
    "plt.plot(df.Close)\n",
    "plt.plot(ma100, 'r')\n",
    "plt.plot(ma200, 'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "90844ba5-f73b-4d8e-8020-3f592bca5e03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6111, 6)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d15856d-c544-4b49-b89f-bacde3410d80",
   "metadata": {},
   "source": [
    "## **5. Splitting Data into Training and Testing**\n",
    "\n",
    "data_training: DataFrame containing 70% of the data for training.\n",
    "\n",
    "data_testing: DataFrame containing the remaining 30% of the data for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "41491209-fc36-4c2d-8e7b-a4cf75cb53c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4277, 1)\n",
      "(1834, 1)\n"
     ]
    }
   ],
   "source": [
    "data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])\n",
    "data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70): int(len(df))])\n",
    "\n",
    "print(data_training.shape)\n",
    "print(data_testing.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71b950c-36de-4d6f-ae9c-630f36644b95",
   "metadata": {},
   "source": [
    "## **6. Scaling Data**\n",
    "\n",
    "MinMaxScaler: Scales data to a specified range (0 to 1 in this case).\n",
    "\n",
    "fit_transform: Fits the scaler to the data and transforms it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "df2a8cef-73a9-4cac-b348-263bfad05d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "996e2d34-e73f-477f-97f3-d19a2a971fb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02922066],\n",
       "       [0.03363806],\n",
       "       [0.03059305],\n",
       "       ...,\n",
       "       [0.74243855],\n",
       "       [0.74259003],\n",
       "       [0.72031725]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocessing\n",
    "data_training_array = scaler.fit_transform(data_training)\n",
    "data_training_array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14eedadd-8321-426b-a0cf-73507e3c8b9e",
   "metadata": {},
   "source": [
    "## **7. Creating Training Data**\n",
    "\n",
    "x_train: List of sequences of 100 data points for each input sample.\n",
    "\n",
    "y_train: List of corresponding labels for each input sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d6f3eeed-7f6a-4c4e-a003-8dbc55e782ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "for i in range(100,data_training_array.shape[0]):\n",
    "    x_train.append(data_training_array[i-100:i])\n",
    "    y_train.append(data_training_array[i,0])\n",
    "\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "11a6b779-2295-4226-a4c1-883e761bb20b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4177, 100, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccec4a63-2706-400a-8c7e-3d82d51064a7",
   "metadata": {},
   "source": [
    "## **8. Building the LSTM Model**\n",
    "\n",
    "Sequential(): Initializes the sequential model.\n",
    "\n",
    "LSTM(units=50, ...): Adds LSTM layers with specified units.\n",
    "\n",
    "Dropout(0.2): Adds dropout layers to prevent overfitting.\n",
    "\n",
    "Dense(units=1): Adds a dense layer with 1 unit.\n",
    "\n",
    "model.summary(): Prints the model architecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ea011328-3589-4d4d-bbc9-b6a861aea06e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model fitting of Long Short Term Memory architecture\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a0571bee-d167-4ac0-ade8-05b82b8c3057",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(units = 50, activation= 'relu', return_sequences=True, input_shape = (x_train.shape[1],1)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units = 60, activation = 'relu', return_sequences=True))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(LSTM(units = 80, activation = 'relu', return_sequences=True))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(LSTM(units = 120, activation = 'relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(units=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4c954df3-6775-4480-ab97-b2efeb711e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">26,640</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">45,120</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">96,480</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">121</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m60\u001b[0m)        │        \u001b[38;5;34m26,640\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m60\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m80\u001b[0m)        │        \u001b[38;5;34m45,120\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m80\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m)            │        \u001b[38;5;34m96,480\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m121\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">178,761</span> (698.29 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m178,761\u001b[0m (698.29 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">178,761</span> (698.29 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m178,761\u001b[0m (698.29 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c8cfcb-774b-4dc7-a68b-22af6a41ea15",
   "metadata": {},
   "source": [
    "## **9. Compiling and Training the Model**\n",
    "\n",
    "compile: Compiles the model with the Adam optimizer and mean squared error loss function.\n",
    "\n",
    "fit: Trains the model on the training data for 50 epochs.\n",
    "\n",
    "save: Saves the trained model to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ce0273fb-350c-460e-ba61-a9d4cd989328",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 182ms/step - loss: 0.0576\n",
      "Epoch 2/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 175ms/step - loss: 0.0081\n",
      "Epoch 3/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 172ms/step - loss: 0.0067\n",
      "Epoch 4/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 171ms/step - loss: 0.0057\n",
      "Epoch 5/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 172ms/step - loss: 0.0051\n",
      "Epoch 6/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 174ms/step - loss: 0.0055\n",
      "Epoch 7/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0041\n",
      "Epoch 8/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0048\n",
      "Epoch 9/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0041\n",
      "Epoch 10/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0037\n",
      "Epoch 11/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0038\n",
      "Epoch 12/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0035\n",
      "Epoch 13/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0033\n",
      "Epoch 14/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0034\n",
      "Epoch 15/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - loss: 0.0031\n",
      "Epoch 16/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0031\n",
      "Epoch 17/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0031\n",
      "Epoch 18/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0032\n",
      "Epoch 19/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0027\n",
      "Epoch 20/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0026\n",
      "Epoch 21/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0026\n",
      "Epoch 22/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0027\n",
      "Epoch 23/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0025\n",
      "Epoch 24/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 153ms/step - loss: 0.0023\n",
      "Epoch 25/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0028\n",
      "Epoch 26/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0025\n",
      "Epoch 27/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0024\n",
      "Epoch 28/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0027\n",
      "Epoch 29/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0024\n",
      "Epoch 30/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0025\n",
      "Epoch 31/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0024\n",
      "Epoch 32/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0025\n",
      "Epoch 33/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0025\n",
      "Epoch 34/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 154ms/step - loss: 0.0026\n",
      "Epoch 35/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0022\n",
      "Epoch 36/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0024\n",
      "Epoch 37/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - loss: 0.0023\n",
      "Epoch 38/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0024\n",
      "Epoch 39/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 150ms/step - loss: 0.0024\n",
      "Epoch 40/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - loss: 0.0024\n",
      "Epoch 41/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0023\n",
      "Epoch 42/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - loss: 0.0024\n",
      "Epoch 43/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0025\n",
      "Epoch 44/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - loss: 0.0025\n",
      "Epoch 45/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - loss: 0.0025\n",
      "Epoch 46/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0024\n",
      "Epoch 47/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 152ms/step - loss: 0.0025\n",
      "Epoch 48/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0025\n",
      "Epoch 49/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0023\n",
      "Epoch 50/50\n",
      "\u001b[1m131/131\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 151ms/step - loss: 0.0023\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x7fb892863f50>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply Training\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "model.fit(x_train, y_train, epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6e6a831f-55a4-4439-a6d6-e3b6893e7839",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "model.save('keras_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6522f9c-f9bb-4c38-a63b-9891ab5c73c7",
   "metadata": {},
   "source": [
    "## **10. Preparing Testing Data**\n",
    "\n",
    "past_100_days: Last 100 days of the training data.\n",
    "\n",
    "final_df: Concatenation of the last 100 days of training data and the testing data.\n",
    "\n",
    "input_data: Scales the concatenated data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "107b197d-fd7a-4d97-82e6-7d991e2953b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>254.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-24</th>\n",
       "      <td>254.899994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>259.200012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-27</th>\n",
       "      <td>266.450012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-30</th>\n",
       "      <td>263.950012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close\n",
       "Date                  \n",
       "2017-01-23  254.149994\n",
       "2017-01-24  254.899994\n",
       "2017-01-25  259.200012\n",
       "2017-01-27  266.450012\n",
       "2017-01-30  263.950012"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_testing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f7f11fa1-c944-4936-affe-1eaa8224e54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-26</th>\n",
       "      <td>246.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-29</th>\n",
       "      <td>249.050003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-30</th>\n",
       "      <td>252.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-31</th>\n",
       "      <td>252.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-01</th>\n",
       "      <td>252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-16</th>\n",
       "      <td>255.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>258.350006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-19</th>\n",
       "      <td>258.399994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-20</th>\n",
       "      <td>251.050003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close\n",
       "Date                  \n",
       "2016-08-26  246.600006\n",
       "2016-08-29  249.050003\n",
       "2016-08-30  252.250000\n",
       "2016-08-31  252.500000\n",
       "2016-09-01  252.000000\n",
       "...                ...\n",
       "2017-01-16  255.750000\n",
       "2017-01-17  256.000000\n",
       "2017-01-18  258.350006\n",
       "2017-01-19  258.399994\n",
       "2017-01-20  251.050003\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_training.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f4c5ed1c-8487-482b-b737-388b252b86f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "past_100_days = data_training.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d01b9d69-3515-4944-9b84-6477300291a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.concat([past_100_days, data_testing], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f88eeba3-74ef-4cfe-9301-e89f7e4f4cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>246.600006</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>249.050003</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>252.250000</td>\n",
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       "      <th>3</th>\n",
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       "        Close\n",
       "0  246.600006\n",
       "1  249.050003\n",
       "2  252.250000\n",
       "3  252.500000\n",
       "4  252.000000"
      ]
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     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4f96bf3d-0928-45b9-a876-2f37f02ab174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12685479],\n",
       "       [0.13010068],\n",
       "       [0.13434021],\n",
       "       ...,\n",
       "       [0.92011123],\n",
       "       [0.9183227 ],\n",
       "       [0.92488075]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data = scaler.fit_transform(final_df)\n",
    "input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "747c7c57-898b-40f9-a2d7-c6c5c31fa3ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1934, 1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ff9fc5-299f-41e2-8f86-2680ce353c7f",
   "metadata": {},
   "source": [
    "## **11. Creating Testing Data**\n",
    "\n",
    "x_test: List of sequences of 100 data points for each input sample.\n",
    "\n",
    "y_test: List of corresponding labels for each input sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fa93ba48-1a04-4144-86e4-86aa3aa039bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1834, 100, 1)\n",
      "(1834,)\n"
     ]
    }
   ],
   "source": [
    "x_test = []\n",
    "y_test = []\n",
    "for i in range(100,input_data.shape[0]):\n",
    "    x_test.append(input_data[i-100:i])\n",
    "    y_test.append(input_data[i,0])\n",
    "\n",
    "x_test, y_test = np.array(x_test), np.array(y_test)\n",
    "\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf4ad43-c1d7-49be-9713-94edbf513516",
   "metadata": {},
   "source": [
    "## **12. Making Predictions**\n",
    "\n",
    "predict: Predicts the stock prices using the trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a19f82b7-7b5f-42f2-9ec6-c928e20fa115",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 53ms/step\n"
     ]
    }
   ],
   "source": [
    "y_predicted = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "74a765ca-a9c0-4b0c-8714-34728b15524e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1834, 1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "afae3f28-1be3-49be-a497-154c368adc21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22682048],\n",
       "       [0.22771865],\n",
       "       [0.22828051],\n",
       "       ...,\n",
       "       [0.86302966],\n",
       "       [0.8648549 ],\n",
       "       [0.8673492 ]], dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a36b6a2e-2044-4965-8156-bd65a0e9852b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.13685743, 0.13785107, 0.14354796, ..., 0.92011123, 0.9183227 ,\n",
       "       0.92488075])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbf61f8d-4c58-4811-af79-3dfa1ee62fa5",
   "metadata": {},
   "source": [
    "## **13. Inversing Scaling and Plotting Predictions**\n",
    "\n",
    "scale_factor: Factor used to inverse the scaling.\n",
    "\n",
    "plt.plot: Plots the original and predicted prices.\n",
    "\n",
    "plt.legend: Adds a legend to the plot.\n",
    "\n",
    "plt.title: Adds a title to the plot.\n",
    "\n",
    "plt.xlabel: Adds a label to the x-axis.\n",
    "\n",
    "plt.ylabel: Adds a label to the y-axis.\n",
    "\n",
    "plt.show(): Displays the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ebc70cd4-b1b1-40cb-b4f0-c8f78a1b3133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00132485])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4e01ae9f-944f-4174-a5c2-535aa8c694b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_factor = 1/0.00107175\n",
    "y_predicted = y_predicted * scale_factor\n",
    "y_test = y_test * scale_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2d11e364-db44-47d0-ba67-ffa2b0f2de86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicted Graph\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(y_test, 'r')\n",
    "plt.plot(y_predicted, 'g')\n",
    "plt.legend(['Original Price', 'Predicted Price'])\n",
    "plt.title(\"Predection Vs Original\")\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}