RNN02.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNH2QPBzn5w0nB5wwYYIO4P",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kanishkautag/RNN-Google-Stock-Price/blob/main/RNN02.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "G0bpL1xqv8Q-",
        "outputId": "b0746876-2a29-42cd-dff2-89a9b8134d20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 16s 165ms/step - loss: 0.1448 - val_loss: 0.0862\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 4s 112ms/step - loss: 0.0392 - val_loss: 0.0667\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 6s 188ms/step - loss: 0.0306 - val_loss: 0.0329\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 5s 160ms/step - loss: 0.0325 - val_loss: 0.0803\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 5s 148ms/step - loss: 0.0262 - val_loss: 0.0433\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 6s 182ms/step - loss: 0.0275 - val_loss: 0.0455\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 4s 118ms/step - loss: 0.0283 - val_loss: 0.0139\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 5s 157ms/step - loss: 0.0253 - val_loss: 0.0406\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0250 - val_loss: 0.0324\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0235 - val_loss: 0.0219\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 5s 159ms/step - loss: 0.0268 - val_loss: 0.0260\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0241 - val_loss: 0.0486\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0210 - val_loss: 0.0150\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 6s 166ms/step - loss: 0.0242 - val_loss: 0.0097\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 6s 160ms/step - loss: 0.0225 - val_loss: 0.0110\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 4s 117ms/step - loss: 0.0217 - val_loss: 0.0159\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 5s 138ms/step - loss: 0.0209 - val_loss: 0.0122\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 5s 145ms/step - loss: 0.0247 - val_loss: 0.0454\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0213 - val_loss: 0.0366\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 4s 116ms/step - loss: 0.0201 - val_loss: 0.0110\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 6s 173ms/step - loss: 0.0184 - val_loss: 0.0080\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0197 - val_loss: 0.0285\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0207 - val_loss: 0.0350\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 5s 158ms/step - loss: 0.0189 - val_loss: 0.0149\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 4s 116ms/step - loss: 0.0183 - val_loss: 0.0106\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0191 - val_loss: 0.0085\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 6s 167ms/step - loss: 0.0185 - val_loss: 0.0124\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0174 - val_loss: 0.0457\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0186 - val_loss: 0.0073\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 6s 165ms/step - loss: 0.0192 - val_loss: 0.0099\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0184 - val_loss: 0.0064\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0163 - val_loss: 0.0055\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 5s 155ms/step - loss: 0.0159 - val_loss: 0.0054\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0151 - val_loss: 0.0054\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0156 - val_loss: 0.0070\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 5s 139ms/step - loss: 0.0144 - val_loss: 0.0157\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 5s 137ms/step - loss: 0.0161 - val_loss: 0.0082\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0163 - val_loss: 0.0106\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 5s 134ms/step - loss: 0.0145 - val_loss: 0.0181\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 5s 138ms/step - loss: 0.0176 - val_loss: 0.0410\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 4s 116ms/step - loss: 0.0161 - val_loss: 0.0090\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 4s 126ms/step - loss: 0.0137 - val_loss: 0.0090\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 5s 146ms/step - loss: 0.0150 - val_loss: 0.0107\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 6s 164ms/step - loss: 0.0146 - val_loss: 0.0062\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 5s 151ms/step - loss: 0.0144 - val_loss: 0.0152\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 4s 119ms/step - loss: 0.0156 - val_loss: 0.0061\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0144 - val_loss: 0.0043\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 5s 143ms/step - loss: 0.0157 - val_loss: 0.0120\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 5s 134ms/step - loss: 0.0156 - val_loss: 0.0085\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 4s 117ms/step - loss: 0.0134 - val_loss: 0.0111\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 4s 132ms/step - loss: 0.0137 - val_loss: 0.0180\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 5s 142ms/step - loss: 0.0146 - val_loss: 0.0060\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 4s 116ms/step - loss: 0.0122 - val_loss: 0.0052\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 6s 171ms/step - loss: 0.0127 - val_loss: 0.0117\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 5s 149ms/step - loss: 0.0126 - val_loss: 0.0070\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 4s 112ms/step - loss: 0.0110 - val_loss: 0.0105\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0140 - val_loss: 0.0108\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 6s 163ms/step - loss: 0.0131 - val_loss: 0.0037\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0131 - val_loss: 0.0059\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0131 - val_loss: 0.0107\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 5s 162ms/step - loss: 0.0128 - val_loss: 0.0045\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0116 - val_loss: 0.0051\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 6s 164ms/step - loss: 0.0130 - val_loss: 0.0236\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 5s 155ms/step - loss: 0.0139 - val_loss: 0.0111\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0118 - val_loss: 0.0049\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0118 - val_loss: 0.0194\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 5s 160ms/step - loss: 0.0129 - val_loss: 0.0083\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0118 - val_loss: 0.0304\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0120 - val_loss: 0.0094\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 5s 160ms/step - loss: 0.0117 - val_loss: 0.0051\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0131 - val_loss: 0.0105\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0127 - val_loss: 0.0039\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 5s 150ms/step - loss: 0.0114 - val_loss: 0.0077\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 4s 121ms/step - loss: 0.0110 - val_loss: 0.0046\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0108 - val_loss: 0.0030\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 5s 135ms/step - loss: 0.0126 - val_loss: 0.0029\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 5s 135ms/step - loss: 0.0128 - val_loss: 0.0091\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0123 - val_loss: 0.0184\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 4s 128ms/step - loss: 0.0114 - val_loss: 0.0029\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 5s 144ms/step - loss: 0.0118 - val_loss: 0.0086\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0103 - val_loss: 0.0175\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0104 - val_loss: 0.0067\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 5s 160ms/step - loss: 0.0102 - val_loss: 0.0088\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0108 - val_loss: 0.0062\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0112 - val_loss: 0.0093\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 6s 163ms/step - loss: 0.0106 - val_loss: 0.0249\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 4s 113ms/step - loss: 0.0135 - val_loss: 0.0031\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0116 - val_loss: 0.0128\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 5s 161ms/step - loss: 0.0114 - val_loss: 0.0106\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0114 - val_loss: 0.0057\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0111 - val_loss: 0.0118\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 5s 151ms/step - loss: 0.0103 - val_loss: 0.0181\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 6s 167ms/step - loss: 0.0119 - val_loss: 0.0053\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 4s 114ms/step - loss: 0.0108 - val_loss: 0.0031\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 5s 159ms/step - loss: 0.0103 - val_loss: 0.0149\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0099 - val_loss: 0.0124\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 4s 118ms/step - loss: 0.0100 - val_loss: 0.0029\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 5s 151ms/step - loss: 0.0104 - val_loss: 0.0146\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 4s 119ms/step - loss: 0.0103 - val_loss: 0.0046\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 4s 115ms/step - loss: 0.0103 - val_loss: 0.0150\n",
            "1/1 [==============================] - 2s 2s/step\n",
            "MAE: 28.089739013671867, RMSE: 29.14485770349158\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dropout, Dense\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "\n",
        "# Load the data\n",
        "dataset_train = pd.read_csv(\"/content/Google_Stock_Price_Train.csv\", thousands=',',\n",
        "                            dtype={'Date': str, 'Open': float, 'High': float, 'Low': float, 'Close': float, 'Volume': int})\n",
        "dataset_test = pd.read_csv(\"/content/Google_Stock_Price_Test.csv\", thousands=',',\n",
        "                           dtype={'Date': str, 'Open': float, 'High': float, 'Low': float, 'Close': float, 'Volume': int})\n",
        "\n",
        "# Prepare the training data\n",
        "training_set = dataset_train.iloc[:, 1:2].values  # Using only 'Open' price\n",
        "\n",
        "# Scale the features\n",
        "sc = StandardScaler()\n",
        "training_set_scaled = sc.fit_transform(training_set)\n",
        "\n",
        "# Prepare sequences\n",
        "sequence_length = 60\n",
        "X_train = []\n",
        "y_train = []\n",
        "for i in range(sequence_length, len(training_set_scaled)):\n",
        "    X_train.append(training_set_scaled[i-sequence_length:i, 0])\n",
        "    y_train.append(training_set_scaled[i, 0])\n",
        "\n",
        "X_train, y_train = np.array(X_train), np.array(y_train)\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
        "\n",
        "# Build the LSTM model\n",
        "regressor = Sequential()\n",
        "regressor.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
        "regressor.add(Dropout(0.2))\n",
        "regressor.add(LSTM(units=50, return_sequences=True))\n",
        "regressor.add(Dropout(0.2))\n",
        "regressor.add(LSTM(units=50, return_sequences=True))\n",
        "regressor.add(Dropout(0.2))\n",
        "regressor.add(LSTM(units=50))\n",
        "regressor.add(Dropout(0.2))\n",
        "regressor.add(Dense(units=1))\n",
        "\n",
        "# Compile and train the model\n",
        "regressor.compile(optimizer='adam', loss='mean_squared_error')\n",
        "history = regressor.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.1, verbose=1)\n",
        "\n",
        "# Prepare the test data\n",
        "dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis=0)\n",
        "inputs = dataset_total[len(dataset_total) - len(dataset_test) - sequence_length:].values\n",
        "inputs = inputs.reshape(-1, 1)\n",
        "inputs = sc.transform(inputs)\n",
        "\n",
        "# Create sequences for prediction\n",
        "X_test = []\n",
        "for i in range(sequence_length, len(inputs)):\n",
        "    X_test.append(inputs[i-sequence_length:i, 0])\n",
        "X_test = np.array(X_test)\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "\n",
        "# Make predictions\n",
        "predicted_stock_price = regressor.predict(X_test)\n",
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price)\n",
        "\n",
        "# Get the real stock prices\n",
        "real_stock_price = dataset_test.iloc[:, 1:2].values\n",
        "\n",
        "# Calculate MAE and RMSE\n",
        "mae = mean_absolute_error(real_stock_price, predicted_stock_price)\n",
        "rmse = np.sqrt(mean_squared_error(real_stock_price, predicted_stock_price))\n",
        "print(f\"MAE: {mae}, RMSE: {rmse}\")\n",
        "\n",
        "# Visualize the results\n",
        "plt.plot(real_stock_price, color='red', label='Real Google Stock Price')\n",
        "plt.plot(predicted_stock_price, color='blue', label='Predicted Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Stock Price')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Visualize the training loss\n",
        "plt.figure()\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2rPWqZVwwK0M"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}