IMPROVE_LSTM.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Revant-1/LSTM-Stock-Prediction-Model/blob/main/IMPROVE_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lm7spboOslSk"
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      "outputs": [],
      "source": [
        "# Import necessary libraries\n",
        "import yfinance as yf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fetch data for a stock (e.g., Apple Inc. - AAPL) from Yahoo Finance\n",
        "ticker_symbol = 'RELIANCE.NS'\n",
        "stock_data = yf.download(ticker_symbol, start='2010-01-01', end='2024-01-28')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ArppmG0asxjY",
        "outputId": "da0b48d8-e30e-4bb0-adf0-842a5d992d15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess the data\n",
        "data = stock_data['Close'].values.reshape(-1, 1)  # Convert to 2D array for scaling\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "scaled_data = scaler.fit_transform(data)"
      ],
      "metadata": {
        "id": "yY1hzMjUs9aW"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create the training and testing datasets\n",
        "train_data = scaled_data[:int(len(scaled_data) * 0.8)]\n",
        "test_data = scaled_data[int(len(scaled_data) * 0.8):]\n",
        "\n",
        "def create_dataset(dataset, time_step=1):\n",
        "    X, y = [], []\n",
        "    for i in range(len(dataset) - time_step - 1):\n",
        "        X.append(dataset[i:(i + time_step), 0])\n",
        "        y.append(dataset[i + time_step, 0])\n",
        "    return np.array(X), np.array(y)\n",
        "\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)\n",
        "\n"
      ],
      "metadata": {
        "id": "aWLPoyLqtC3R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape input to be [samples, time steps, features] for LSTM\n",
        "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
        "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
      ],
      "metadata": {
        "id": "CJslncdStELd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dropout, Dense\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import layers\n",
        "from keras.regularizers import l2\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.callbacks import EarlyStopping\n",
        "\n",
        "# Define the model with increased complexity and regularization\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=128, return_sequences=True, input_shape=(X_train.shape[1], 1), kernel_regularizer=l2(0.001)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(LSTM(units=64, return_sequences=True, kernel_regularizer=l2(0.001)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(LSTM(units=32, kernel_regularizer=l2(0.001)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(units=1))\n",
        "\n",
        "\n",
        "# Compile the model with RMSprop optimizer and adjusted learning rate\n",
        "model.compile(loss='mse', optimizer=RMSprop(lr=0.0005), metrics=['mean_absolute_error'])\n",
        "\n",
        "\n",
        "# Add early stopping to prevent overfitting\n",
        "#early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88f4f8c1-1109-4658-92e0-59a7a6bbe2da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.RMSprop.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnsOYIrWtO69",
        "outputId": "6f252fe8-bf47-42bc-80f6-6b5b7358fc75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_9 (LSTM)               (None, 100, 128)          66560     \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 100, 128)          0         \n",
            "                                                                 \n",
            " lstm_10 (LSTM)              (None, 100, 64)           49408     \n",
            "                                                                 \n",
            " dropout_10 (Dropout)        (None, 100, 64)           0         \n",
            "                                                                 \n",
            " lstm_11 (LSTM)              (None, 32)                12416     \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 32)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 128417 (501.63 KB)\n",
            "Trainable params: 128417 (501.63 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Train the model with increased epochs and batch size\n",
        "history = model.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "g8jY18emtSeO",
        "outputId": "c19fb4d8-bb14-48ba-d7de-27a632a92d41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "42/42 [==============================] - 25s 481ms/step - loss: 0.2157 - mean_absolute_error: 0.0485 - val_loss: 0.1998 - val_mean_absolute_error: 0.1794\n",
            "Epoch 2/100\n",
            "42/42 [==============================] - 17s 404ms/step - loss: 0.1380 - mean_absolute_error: 0.0334 - val_loss: 0.1099 - val_mean_absolute_error: 0.0481\n",
            "Epoch 3/100\n",
            "42/42 [==============================] - 17s 405ms/step - loss: 0.0887 - mean_absolute_error: 0.0323 - val_loss: 0.0689 - val_mean_absolute_error: 0.0343\n",
            "Epoch 4/100\n",
            "42/42 [==============================] - 18s 436ms/step - loss: 0.0570 - mean_absolute_error: 0.0326 - val_loss: 0.1137 - val_mean_absolute_error: 0.2645\n",
            "Epoch 5/100\n",
            "42/42 [==============================] - 17s 408ms/step - loss: 0.0364 - mean_absolute_error: 0.0300 - val_loss: 0.0627 - val_mean_absolute_error: 0.1859\n",
            "Epoch 6/100\n",
            "42/42 [==============================] - 17s 404ms/step - loss: 0.0238 - mean_absolute_error: 0.0289 - val_loss: 0.0224 - val_mean_absolute_error: 0.0621\n",
            "Epoch 7/100\n",
            "42/42 [==============================] - 17s 405ms/step - loss: 0.0162 - mean_absolute_error: 0.0314 - val_loss: 0.0128 - val_mean_absolute_error: 0.0350\n",
            "Epoch 8/100\n",
            "42/42 [==============================] - 19s 452ms/step - loss: 0.0111 - mean_absolute_error: 0.0299 - val_loss: 0.0173 - val_mean_absolute_error: 0.0913\n",
            "Epoch 9/100\n",
            "42/42 [==============================] - 17s 405ms/step - loss: 0.0083 - mean_absolute_error: 0.0313 - val_loss: 0.0108 - val_mean_absolute_error: 0.0661\n",
            "Epoch 10/100\n",
            "42/42 [==============================] - 17s 408ms/step - loss: 0.0066 - mean_absolute_error: 0.0319 - val_loss: 0.0283 - val_mean_absolute_error: 0.1518\n",
            "Epoch 11/100\n",
            "42/42 [==============================] - 17s 417ms/step - loss: 0.0051 - mean_absolute_error: 0.0302 - val_loss: 0.0046 - val_mean_absolute_error: 0.0357\n",
            "Epoch 12/100\n",
            "42/42 [==============================] - 18s 437ms/step - loss: 0.0047 - mean_absolute_error: 0.0312 - val_loss: 0.0134 - val_mean_absolute_error: 0.0973\n",
            "Epoch 13/100\n",
            "42/42 [==============================] - 18s 441ms/step - loss: 0.0042 - mean_absolute_error: 0.0321 - val_loss: 0.0100 - val_mean_absolute_error: 0.0800\n",
            "Epoch 14/100\n",
            "42/42 [==============================] - 17s 411ms/step - loss: 0.0037 - mean_absolute_error: 0.0306 - val_loss: 0.0308 - val_mean_absolute_error: 0.1660\n",
            "Epoch 15/100\n",
            "42/42 [==============================] - 19s 447ms/step - loss: 0.0038 - mean_absolute_error: 0.0323 - val_loss: 0.0133 - val_mean_absolute_error: 0.1006\n",
            "Epoch 16/100\n",
            "42/42 [==============================] - 18s 417ms/step - loss: 0.0035 - mean_absolute_error: 0.0306 - val_loss: 0.0032 - val_mean_absolute_error: 0.0359\n",
            "Epoch 17/100\n",
            "42/42 [==============================] - 18s 426ms/step - loss: 0.0036 - mean_absolute_error: 0.0318 - val_loss: 0.0134 - val_mean_absolute_error: 0.1021\n",
            "Epoch 18/100\n",
            "42/42 [==============================] - 17s 415ms/step - loss: 0.0032 - mean_absolute_error: 0.0298 - val_loss: 0.0063 - val_mean_absolute_error: 0.0592\n",
            "Epoch 19/100\n",
            "42/42 [==============================] - 19s 447ms/step - loss: 0.0034 - mean_absolute_error: 0.0323 - val_loss: 0.0133 - val_mean_absolute_error: 0.1021\n",
            "Epoch 20/100\n",
            "42/42 [==============================] - 17s 410ms/step - loss: 0.0032 - mean_absolute_error: 0.0318 - val_loss: 0.0194 - val_mean_absolute_error: 0.1286\n",
            "Epoch 21/100\n",
            "42/42 [==============================] - 17s 414ms/step - loss: 0.0031 - mean_absolute_error: 0.0294 - val_loss: 0.0354 - val_mean_absolute_error: 0.1808\n",
            "Epoch 22/100\n",
            "42/42 [==============================] - 18s 431ms/step - loss: 0.0031 - mean_absolute_error: 0.0320 - val_loss: 0.0055 - val_mean_absolute_error: 0.0547\n",
            "Epoch 23/100\n",
            "42/42 [==============================] - 18s 425ms/step - loss: 0.0032 - mean_absolute_error: 0.0310 - val_loss: 0.0245 - val_mean_absolute_error: 0.1474\n",
            "Epoch 24/100\n",
            "42/42 [==============================] - 17s 414ms/step - loss: 0.0031 - mean_absolute_error: 0.0313 - val_loss: 0.0086 - val_mean_absolute_error: 0.0766\n",
            "Epoch 25/100\n",
            "42/42 [==============================] - 17s 413ms/step - loss: 0.0031 - mean_absolute_error: 0.0305 - val_loss: 0.0053 - val_mean_absolute_error: 0.0536\n",
            "Epoch 26/100\n",
            "42/42 [==============================] - 21s 511ms/step - loss: 0.0031 - mean_absolute_error: 0.0316 - val_loss: 0.0040 - val_mean_absolute_error: 0.0433\n",
            "Epoch 27/100\n",
            "42/42 [==============================] - 17s 416ms/step - loss: 0.0030 - mean_absolute_error: 0.0301 - val_loss: 0.0150 - val_mean_absolute_error: 0.1108\n",
            "Epoch 28/100\n",
            "42/42 [==============================] - 17s 418ms/step - loss: 0.0029 - mean_absolute_error: 0.0300 - val_loss: 0.0030 - val_mean_absolute_error: 0.0367\n",
            "Epoch 29/100\n",
            "42/42 [==============================] - 19s 466ms/step - loss: 0.0029 - mean_absolute_error: 0.0315 - val_loss: 0.0029 - val_mean_absolute_error: 0.0361\n",
            "Epoch 30/100\n",
            "42/42 [==============================] - 17s 406ms/step - loss: 0.0028 - mean_absolute_error: 0.0294 - val_loss: 0.0146 - val_mean_absolute_error: 0.1090\n",
            "Epoch 31/100\n",
            "42/42 [==============================] - 17s 409ms/step - loss: 0.0029 - mean_absolute_error: 0.0286 - val_loss: 0.0028 - val_mean_absolute_error: 0.0358\n",
            "Epoch 32/100\n",
            "42/42 [==============================] - 17s 415ms/step - loss: 0.0030 - mean_absolute_error: 0.0302 - val_loss: 0.0028 - val_mean_absolute_error: 0.0353\n",
            "Epoch 33/100\n",
            "42/42 [==============================] - 19s 449ms/step - loss: 0.0028 - mean_absolute_error: 0.0288 - val_loss: 0.0144 - val_mean_absolute_error: 0.1082\n",
            "Epoch 34/100\n",
            "42/42 [==============================] - 17s 411ms/step - loss: 0.0030 - mean_absolute_error: 0.0319 - val_loss: 0.0124 - val_mean_absolute_error: 0.0987\n",
            "Epoch 35/100\n",
            "42/42 [==============================] - 17s 409ms/step - loss: 0.0027 - mean_absolute_error: 0.0300 - val_loss: 0.0057 - val_mean_absolute_error: 0.0568\n",
            "Epoch 36/100\n",
            "42/42 [==============================] - 19s 450ms/step - loss: 0.0027 - mean_absolute_error: 0.0282 - val_loss: 0.0083 - val_mean_absolute_error: 0.0755\n",
            "Epoch 37/100\n",
            "42/42 [==============================] - 19s 462ms/step - loss: 0.0029 - mean_absolute_error: 0.0307 - val_loss: 0.0626 - val_mean_absolute_error: 0.2450\n",
            "Epoch 38/100\n",
            "42/42 [==============================] - 17s 406ms/step - loss: 0.0028 - mean_absolute_error: 0.0298 - val_loss: 0.0041 - val_mean_absolute_error: 0.0448\n",
            "Epoch 39/100\n",
            "42/42 [==============================] - 17s 398ms/step - loss: 0.0027 - mean_absolute_error: 0.0283 - val_loss: 0.0116 - val_mean_absolute_error: 0.0949\n",
            "Epoch 40/100\n",
            "42/42 [==============================] - 18s 436ms/step - loss: 0.0028 - mean_absolute_error: 0.0304 - val_loss: 0.0066 - val_mean_absolute_error: 0.0639\n",
            "Epoch 41/100\n",
            "42/42 [==============================] - 17s 411ms/step - loss: 0.0028 - mean_absolute_error: 0.0299 - val_loss: 0.0061 - val_mean_absolute_error: 0.0605\n",
            "Epoch 42/100\n",
            "42/42 [==============================] - 17s 402ms/step - loss: 0.0026 - mean_absolute_error: 0.0288 - val_loss: 0.0139 - val_mean_absolute_error: 0.1065\n",
            "Epoch 43/100\n",
            "42/42 [==============================] - 17s 403ms/step - loss: 0.0027 - mean_absolute_error: 0.0291 - val_loss: 0.0077 - val_mean_absolute_error: 0.0720\n",
            "Epoch 44/100\n",
            "42/42 [==============================] - 18s 442ms/step - loss: 0.0025 - mean_absolute_error: 0.0273 - val_loss: 0.0093 - val_mean_absolute_error: 0.0820\n",
            "Epoch 45/100\n",
            "42/42 [==============================] - 17s 411ms/step - loss: 0.0028 - mean_absolute_error: 0.0306 - val_loss: 0.0185 - val_mean_absolute_error: 0.1263\n",
            "Epoch 46/100\n",
            "42/42 [==============================] - 17s 409ms/step - loss: 0.0028 - mean_absolute_error: 0.0294 - val_loss: 0.0111 - val_mean_absolute_error: 0.0925\n",
            "Epoch 47/100\n",
            "42/42 [==============================] - 17s 412ms/step - loss: 0.0026 - mean_absolute_error: 0.0288 - val_loss: 0.0145 - val_mean_absolute_error: 0.1094\n",
            "Epoch 48/100\n",
            "42/42 [==============================] - 19s 461ms/step - loss: 0.0028 - mean_absolute_error: 0.0316 - val_loss: 0.0065 - val_mean_absolute_error: 0.0635\n",
            "Epoch 49/100\n",
            "42/42 [==============================] - 17s 417ms/step - loss: 0.0026 - mean_absolute_error: 0.0285 - val_loss: 0.0109 - val_mean_absolute_error: 0.0910\n",
            "Epoch 50/100\n",
            "42/42 [==============================] - 18s 419ms/step - loss: 0.0026 - mean_absolute_error: 0.0290 - val_loss: 0.0054 - val_mean_absolute_error: 0.0557\n",
            "Epoch 51/100\n",
            "42/42 [==============================] - 19s 451ms/step - loss: 0.0024 - mean_absolute_error: 0.0288 - val_loss: 0.0066 - val_mean_absolute_error: 0.0649\n",
            "Epoch 52/100\n",
            "42/42 [==============================] - 18s 414ms/step - loss: 0.0025 - mean_absolute_error: 0.0287 - val_loss: 0.0034 - val_mean_absolute_error: 0.0410\n",
            "Epoch 53/100\n",
            "42/42 [==============================] - 17s 401ms/step - loss: 0.0027 - mean_absolute_error: 0.0292 - val_loss: 0.0028 - val_mean_absolute_error: 0.0361\n",
            "Epoch 54/100\n",
            "42/42 [==============================] - 17s 408ms/step - loss: 0.0027 - mean_absolute_error: 0.0300 - val_loss: 0.0053 - val_mean_absolute_error: 0.0569\n",
            "Epoch 55/100\n",
            "42/42 [==============================] - 19s 457ms/step - loss: 0.0025 - mean_absolute_error: 0.0280 - val_loss: 0.0043 - val_mean_absolute_error: 0.0476\n",
            "Epoch 56/100\n",
            "42/42 [==============================] - 17s 411ms/step - loss: 0.0026 - mean_absolute_error: 0.0304 - val_loss: 0.0075 - val_mean_absolute_error: 0.0708\n",
            "Epoch 57/100\n",
            "42/42 [==============================] - 17s 400ms/step - loss: 0.0024 - mean_absolute_error: 0.0274 - val_loss: 0.0201 - val_mean_absolute_error: 0.1328\n",
            "Epoch 58/100\n",
            "42/42 [==============================] - 17s 403ms/step - loss: 0.0026 - mean_absolute_error: 0.0287 - val_loss: 0.0082 - val_mean_absolute_error: 0.0757\n",
            "Epoch 59/100\n",
            "42/42 [==============================] - 19s 459ms/step - loss: 0.0026 - mean_absolute_error: 0.0301 - val_loss: 0.0027 - val_mean_absolute_error: 0.0359\n",
            "Epoch 60/100\n",
            "42/42 [==============================] - 17s 395ms/step - loss: 0.0027 - mean_absolute_error: 0.0299 - val_loss: 0.0531 - val_mean_absolute_error: 0.2253\n",
            "Epoch 61/100\n",
            "42/42 [==============================] - 17s 411ms/step - loss: 0.0025 - mean_absolute_error: 0.0282 - val_loss: 0.0058 - val_mean_absolute_error: 0.0604\n",
            "Epoch 62/100\n",
            "42/42 [==============================] - 17s 413ms/step - loss: 0.0025 - mean_absolute_error: 0.0293 - val_loss: 0.0031 - val_mean_absolute_error: 0.0405\n",
            "Epoch 63/100\n",
            "42/42 [==============================] - 19s 457ms/step - loss: 0.0025 - mean_absolute_error: 0.0290 - val_loss: 0.0034 - val_mean_absolute_error: 0.0413\n",
            "Epoch 64/100\n",
            "42/42 [==============================] - 17s 416ms/step - loss: 0.0025 - mean_absolute_error: 0.0277 - val_loss: 0.0092 - val_mean_absolute_error: 0.0819\n",
            "Epoch 65/100\n",
            "42/42 [==============================] - 17s 418ms/step - loss: 0.0025 - mean_absolute_error: 0.0289 - val_loss: 0.0449 - val_mean_absolute_error: 0.2062\n",
            "Epoch 66/100\n",
            "42/42 [==============================] - 19s 464ms/step - loss: 0.0024 - mean_absolute_error: 0.0288 - val_loss: 0.0028 - val_mean_absolute_error: 0.0383\n",
            "Epoch 67/100\n",
            "42/42 [==============================] - 17s 416ms/step - loss: 0.0025 - mean_absolute_error: 0.0291 - val_loss: 0.0044 - val_mean_absolute_error: 0.0486\n",
            "Epoch 68/100\n",
            "42/42 [==============================] - 17s 403ms/step - loss: 0.0025 - mean_absolute_error: 0.0283 - val_loss: 0.0028 - val_mean_absolute_error: 0.0376\n",
            "Epoch 69/100\n",
            "42/42 [==============================] - 17s 398ms/step - loss: 0.0025 - mean_absolute_error: 0.0287 - val_loss: 0.0041 - val_mean_absolute_error: 0.0485\n",
            "Epoch 70/100\n",
            "42/42 [==============================] - 19s 466ms/step - loss: 0.0024 - mean_absolute_error: 0.0290 - val_loss: 0.0074 - val_mean_absolute_error: 0.0709\n",
            "Epoch 71/100\n",
            "42/42 [==============================] - 17s 396ms/step - loss: 0.0024 - mean_absolute_error: 0.0281 - val_loss: 0.0057 - val_mean_absolute_error: 0.0588\n",
            "Epoch 72/100\n",
            "42/42 [==============================] - 17s 404ms/step - loss: 0.0025 - mean_absolute_error: 0.0290 - val_loss: 0.0029 - val_mean_absolute_error: 0.0387\n",
            "Epoch 73/100\n",
            "42/42 [==============================] - 17s 404ms/step - loss: 0.0025 - mean_absolute_error: 0.0288 - val_loss: 0.0029 - val_mean_absolute_error: 0.0378\n",
            "Epoch 74/100\n",
            "42/42 [==============================] - 19s 458ms/step - loss: 0.0023 - mean_absolute_error: 0.0281 - val_loss: 0.0288 - val_mean_absolute_error: 0.1626\n",
            "Epoch 75/100\n",
            "42/42 [==============================] - 17s 399ms/step - loss: 0.0026 - mean_absolute_error: 0.0284 - val_loss: 0.0177 - val_mean_absolute_error: 0.1236\n",
            "Epoch 76/100\n",
            "42/42 [==============================] - 17s 416ms/step - loss: 0.0024 - mean_absolute_error: 0.0295 - val_loss: 0.0321 - val_mean_absolute_error: 0.1725\n",
            "Epoch 77/100\n",
            "42/42 [==============================] - 19s 460ms/step - loss: 0.0023 - mean_absolute_error: 0.0281 - val_loss: 0.0135 - val_mean_absolute_error: 0.1052\n",
            "Epoch 78/100\n",
            "42/42 [==============================] - 17s 413ms/step - loss: 0.0024 - mean_absolute_error: 0.0292 - val_loss: 0.0118 - val_mean_absolute_error: 0.0968\n",
            "Epoch 79/100\n",
            "42/42 [==============================] - 17s 418ms/step - loss: 0.0023 - mean_absolute_error: 0.0273 - val_loss: 0.0074 - val_mean_absolute_error: 0.0710\n",
            "Epoch 80/100\n",
            "42/42 [==============================] - 17s 410ms/step - loss: 0.0023 - mean_absolute_error: 0.0275 - val_loss: 0.0164 - val_mean_absolute_error: 0.1183\n",
            "Epoch 81/100\n",
            "42/42 [==============================] - 19s 459ms/step - loss: 0.0024 - mean_absolute_error: 0.0279 - val_loss: 0.0027 - val_mean_absolute_error: 0.0375\n",
            "Epoch 82/100\n",
            "42/42 [==============================] - 17s 396ms/step - loss: 0.0025 - mean_absolute_error: 0.0285 - val_loss: 0.0052 - val_mean_absolute_error: 0.0552\n",
            "Epoch 83/100\n",
            "42/42 [==============================] - 17s 408ms/step - loss: 0.0023 - mean_absolute_error: 0.0283 - val_loss: 0.0029 - val_mean_absolute_error: 0.0380\n",
            "Epoch 84/100\n",
            "42/42 [==============================] - 22s 524ms/step - loss: 0.0025 - mean_absolute_error: 0.0288 - val_loss: 0.0035 - val_mean_absolute_error: 0.0424\n",
            "Epoch 85/100\n",
            "42/42 [==============================] - 17s 396ms/step - loss: 0.0023 - mean_absolute_error: 0.0284 - val_loss: 0.0082 - val_mean_absolute_error: 0.0763\n",
            "Epoch 86/100\n",
            "42/42 [==============================] - 17s 398ms/step - loss: 0.0024 - mean_absolute_error: 0.0287 - val_loss: 0.0033 - val_mean_absolute_error: 0.0407\n",
            "Epoch 87/100\n",
            "42/42 [==============================] - 17s 397ms/step - loss: 0.0026 - mean_absolute_error: 0.0294 - val_loss: 0.0025 - val_mean_absolute_error: 0.0351\n",
            "Epoch 88/100\n",
            "42/42 [==============================] - 19s 451ms/step - loss: 0.0024 - mean_absolute_error: 0.0288 - val_loss: 0.0628 - val_mean_absolute_error: 0.2462\n",
            "Epoch 89/100\n",
            "42/42 [==============================] - 17s 403ms/step - loss: 0.0024 - mean_absolute_error: 0.0281 - val_loss: 0.0025 - val_mean_absolute_error: 0.0350\n",
            "Epoch 90/100\n",
            "42/42 [==============================] - 17s 413ms/step - loss: 0.0024 - mean_absolute_error: 0.0281 - val_loss: 0.0057 - val_mean_absolute_error: 0.0596\n",
            "Epoch 91/100\n",
            "42/42 [==============================] - 17s 415ms/step - loss: 0.0024 - mean_absolute_error: 0.0289 - val_loss: 0.0192 - val_mean_absolute_error: 0.1299\n",
            "Epoch 92/100\n",
            "42/42 [==============================] - 19s 449ms/step - loss: 0.0022 - mean_absolute_error: 0.0267 - val_loss: 0.0038 - val_mean_absolute_error: 0.0453\n",
            "Epoch 93/100\n",
            "42/42 [==============================] - 17s 411ms/step - loss: 0.0023 - mean_absolute_error: 0.0277 - val_loss: 0.0046 - val_mean_absolute_error: 0.0527\n",
            "Epoch 94/100\n",
            "42/42 [==============================] - 17s 402ms/step - loss: 0.0024 - mean_absolute_error: 0.0281 - val_loss: 0.0066 - val_mean_absolute_error: 0.0658\n",
            "Epoch 95/100\n",
            "42/42 [==============================] - 17s 408ms/step - loss: 0.0023 - mean_absolute_error: 0.0287 - val_loss: 0.0045 - val_mean_absolute_error: 0.0519\n",
            "Epoch 96/100\n",
            "42/42 [==============================] - 19s 438ms/step - loss: 0.0023 - mean_absolute_error: 0.0276 - val_loss: 0.0026 - val_mean_absolute_error: 0.0354\n",
            "Epoch 97/100\n",
            "42/42 [==============================] - 17s 416ms/step - loss: 0.0023 - mean_absolute_error: 0.0286 - val_loss: 0.0032 - val_mean_absolute_error: 0.0423\n",
            "Epoch 98/100\n",
            "42/42 [==============================] - 17s 407ms/step - loss: 0.0024 - mean_absolute_error: 0.0285 - val_loss: 0.0109 - val_mean_absolute_error: 0.0927\n",
            "Epoch 99/100\n",
            "42/42 [==============================] - 19s 458ms/step - loss: 0.0024 - mean_absolute_error: 0.0290 - val_loss: 0.0039 - val_mean_absolute_error: 0.0462\n",
            "Epoch 100/100\n",
            "42/42 [==============================] - 17s 410ms/step - loss: 0.0023 - mean_absolute_error: 0.0275 - val_loss: 0.0058 - val_mean_absolute_error: 0.0617\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Save the model\n",
        "model.save('stock_prediction_model_9.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kg_nBjz8wbe",
        "outputId": "38d553f4-5b3e-4bed-c57c-137d8f1ab253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Evaluate the model with additional metrics\n",
        "loss, mae = model.evaluate(X_test, y_test)\n",
        "print(f'Test Loss: {loss}, Test MAE: {mae}')\n",
        "\n",
        "# Visualize training history\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "ZQEnZd4En1__",
        "outputId": "759aadc5-24e1-4581-ec7a-4bb8eb73481d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 1s 73ms/step - loss: 0.0058 - mean_absolute_error: 0.0617\n",
            "Test Loss: 0.005846407264471054, Test MAE: 0.06169433519244194\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "ysfFe_xQEZ01",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac739b72-e40f-434d-8fdb-36524a464b5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "train_predict = model.predict(X_train)\n",
        "test_predict = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hb10wZwzEPh",
        "outputId": "1e159246-1c1c-4615-ffc1-2d6075bbbb27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "84/84 [==============================] - 12s 117ms/step\n",
            "19/19 [==============================] - 1s 70ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure both y_train and train_predict have compatible shapes\n",
        "print(f\"y_train shape: {y_train.shape}\")\n",
        "print(f\"train_predict shape: {train_predict.shape}\")\n",
        "print(f\"y_test shape: {y_test.shape}\")\n",
        "print(f\"test_predict shape: {test_predict.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnCNRlcMKtw_",
        "outputId": "cd87c3d9-a5ec-4075-a9c1-d2a779e3369e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_train shape: (2677,)\n",
            "train_predict shape: (2677, 1)\n",
            "y_test shape: (594,)\n",
            "test_predict shape: (594, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Inverse transform the predictions to get actual stock prices\n",
        "train_predict = scaler.inverse_transform(train_predict)\n",
        "y_train = scaler.inverse_transform([y_train])\n",
        "test_predict = scaler.inverse_transform(test_predict)\n",
        "y_test = scaler.inverse_transform([y_test])"
      ],
      "metadata": {
        "id": "aKUXwb_LzKhY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model evaluation\n",
        "from sklearn.metrics import mean_squared_error\n",
        "train_rmse = np.sqrt(mean_squared_error(y_train[0], train_predict[:, 0]))\n",
        "test_rmse = np.sqrt(mean_squared_error(y_test[0], test_predict[:, 0]))\n",
        "print(f'Train RMSE: {train_rmse}')\n",
        "print(f'Test RMSE: {test_rmse}')"
      ],
      "metadata": {
        "id": "PmUf50hRzM9g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfedc2cf-cac8-46f4-b4e7-65338dd47beb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train RMSE: 99.52320261717898\n",
            "Test RMSE: 180.70703944840392\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the results\n",
        "plt.style.use('fivethirtyeight')\n",
        "# Plot the data\n",
        "plt.figure(figsize=(20, 10))\n",
        "\n",
        "# Plot training predictions and actuals\n",
        "plt.plot(stock_data.index[time_step+1:time_step+len(train_predict)+1], train_predict, label='Train Predictions')\n",
        "plt.plot(stock_data.index[time_step+1:time_step+len(train_predict)+1], y_train[0], label='Train Actuals')\n",
        "\n",
        "# Plot testing predictions and actuals\n",
        "test_index = stock_data.index[time_step+len(train_predict)+1:time_step+len(train_predict)+1+len(test_predict)]\n",
        "plt.plot(test_index, test_predict, label='Test Predictions')\n",
        "plt.plot(test_index[:len(test_predict)], y_test[0][:len(test_predict)], label='Test Actuals')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "Sklp93jkzP9B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "outputId": "faf51d7a-afbe-413d-ed92-0bad75a8d4df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extend predictions for the next 10 days\n",
        "future_time_steps = 10\n",
        "future_predictions = []"
      ],
      "metadata": {
        "id": "omymYGaG0fke"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the last sequence from the testing data to start predicting the future\n",
        "last_sequence = X_test[-1]\n",
        "\n",
        "for i in range(future_time_steps):\n",
        "    # Predict the next price\n",
        "    next_prediction = model.predict(np.array([last_sequence]))[0, 0]\n",
        "\n",
        "    # Append the prediction to the list of future predictions\n",
        "    future_predictions.append(next_prediction)\n",
        "\n",
        "    # Update the last sequence with the new prediction\n",
        "    last_sequence = np.roll(last_sequence, -1)\n",
        "    last_sequence[-1] = next_prediction"
      ],
      "metadata": {
        "id": "tOdshhuA0mEU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6585e4b2-6f17-4583-d500-5586bdb78b67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 49ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 55ms/step\n",
            "1/1 [==============================] - 0s 49ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Inverse transform the future predictions to get the actual stock prices\n",
        "future_predictions = scaler.inverse_transform(np.array(future_predictions).reshape(-1, 1))"
      ],
      "metadata": {
        "id": "EMy31wVI0skD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Get the last date in the original dataset\n",
        "last_date = stock_data.index[-1]\n",
        "\n",
        "# Generate dates for the next 10 days\n",
        "next_dates = pd.date_range(start=last_date, periods=future_time_steps + 1)[1:]  # Skip the last date"
      ],
      "metadata": {
        "id": "ZerKI4IE08O7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " #Print the predicted prices for the next 10 days along with dates\n",
        "print(\"Predicted prices for the next 10 days:\")\n",
        "for date, prediction in zip(next_dates, future_predictions):\n",
        "    print(f\"{date.strftime('%Y-%m-%d')}: {prediction[0]:.2f}\")"
      ],
      "metadata": {
        "id": "Xb7kvUf-0tvy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e259d8b-8b4a-42cc-f484-186e45d18541"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted prices for the next 10 days:\n",
            "2024-01-26: 2768.04\n",
            "2024-01-27: 2776.13\n",
            "2024-01-28: 2784.13\n",
            "2024-01-29: 2792.00\n",
            "2024-01-30: 2799.72\n",
            "2024-01-31: 2807.27\n",
            "2024-02-01: 2814.66\n",
            "2024-02-02: 2821.87\n",
            "2024-02-03: 2828.92\n",
            "2024-02-04: 2835.79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extend predictions for the next 100 days\n",
        "future_time_steps = 100\n",
        "future_predictions = []\n",
        "\n",
        "# Use the last sequence from the testing data to start predicting the future\n",
        "last_sequence = X_test[-1]\n",
        "\n",
        "for i in range(future_time_steps):\n",
        "    # Predict the next price\n",
        "    next_prediction = model.predict(np.array([last_sequence]))[0, 0]\n",
        "\n",
        "    # Append the prediction to the list of future predictions\n",
        "    future_predictions.append(next_prediction)\n",
        "\n",
        "    # Update the last sequence with the new prediction\n",
        "    last_sequence = np.roll(last_sequence, -1)\n",
        "    last_sequence[-1] = next_prediction\n",
        "\n",
        "# Inverse transform the future predictions to get the actual stock prices\n",
        "future_predictions = scaler.inverse_transform(np.array(future_predictions).reshape(-1, 1))\n",
        "\n",
        "# Generate dates for the next 100 days\n",
        "next_dates = pd.date_range(start=last_date, periods=future_time_steps + 1)[1:]  # Skip the last date\n",
        "\n",
        "# Plot the original and predicted prices for the next 100 days\n",
        "plt.figure(figsize=(16, 8))\n",
        "plt.plot(stock_data.index[-len(y_test[0]):], y_test[0], label='Original Prices')\n",
        "plt.plot(next_dates, future_predictions, label='Predicted Prices')\n",
        "plt.title('Original vs. Predicted Prices for the Next 100 Days')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XvSfHrZsdLCO",
        "outputId": "f33557d8-c62a-4a5a-bc75-1abacdc5797f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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  ]
}