project (1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense\n",
        "\n",
        "# Get the Dataset\n",
        "df = pd.read_csv(\"Amazon.csv\", na_values=['null'], index_col='Date', parse_dates=True, infer_datetime_format=True)\n",
        "\n",
        "# Set Target Variable\n",
        "output_var = pd.DataFrame(df['Adj Close'])\n",
        "\n",
        "# Selecting the Features\n",
        "features = ['Open', 'High', 'Low', 'Volume']\n",
        "\n",
        "# Scaling\n",
        "scaler = MinMaxScaler()\n",
        "feature_transform = scaler.fit_transform(df[features])\n",
        "output_var = scaler.fit_transform(output_var)\n",
        "\n",
        "# Splitting into Training Set and Test Set\n",
        "timesplit = TimeSeriesSplit(n_splits=10)\n",
        "\n",
        "for train_index, test_index in timesplit.split(feature_transform):\n",
        "    X_train, X_test = feature_transform[train_index], feature_transform[test_index]\n",
        "    y_train, y_test = output_var[train_index], output_var[test_index]\n",
        "\n",
        "# Process the data for LSTM\n",
        "X_train = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])\n",
        "X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])\n",
        "\n",
        "# Building the LSTM Model\n",
        "lstm = Sequential()\n",
        "lstm.add(LSTM(32, input_shape=(1, X_train.shape[2]), activation='relu', return_sequences=False))\n",
        "lstm.add(Dense(1))\n",
        "lstm.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "# Model Training\n",
        "history = lstm.fit(X_train, y_train, epochs=50, batch_size=32, verbose=1, shuffle=True)\n",
        "\n",
        "# LSTM Prediction\n",
        "y_pred = lstm.predict(X_test)\n",
        "\n",
        "# Inverse transform the predictions and true values to their original scale\n",
        "y_pred = scaler.inverse_transform(y_pred)\n",
        "y_test = scaler.inverse_transform(y_test)\n",
        "\n",
        "# Calculate R-squared (Coefficient of Determination)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(\"R-squared (Coefficient of Determination):\", r2)\n",
        "\n",
        "# Calculate Mean Squared Error (MSE)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"Mean Squared Error (MSE):\", mse)\n",
        "\n",
        "# Calculate RMSE\n",
        "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
        "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
        "\n",
        "# Calculate Mean Absolute Error (MAE)\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "print(\"Mean Absolute Error (MAE):\", mae)\n",
        "\n",
        "# Calculate Mean Absolute Percentage Error (MAPE)\n",
        "mape = np.mean(np.abs((y_test - y_pred) / y_test)) * 100\n",
        "print(\"Mean Absolute Percentage Error (MAPE):\", mape)\n",
        "\n",
        "# Plotting Predicted vs True Adj Close Value – LSTM\n",
        "plt.plot(y_test, label='True Value')\n",
        "plt.plot(y_pred, label='LSTM Value')\n",
        "plt.title(\"Prediction by LSTM\")\n",
        "plt.xlabel('Time Scale')\n",
        "plt.ylabel('USD')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cvl1yjAWRKRA",
        "outputId": "2a9674b1-d5e1-4678-e5e1-e2f6fff5d089"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "175/175 [==============================] - 2s 3ms/step - loss: 0.0062\n",
            "Epoch 2/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 3.0448e-05\n",
            "Epoch 3/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 7.1903e-06\n",
            "Epoch 4/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 4.9771e-06\n",
            "Epoch 5/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 4.3191e-06\n",
            "Epoch 6/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 3.8726e-06\n",
            "Epoch 7/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 3.5388e-06\n",
            "Epoch 8/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 3.3020e-06\n",
            "Epoch 9/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 3.2628e-06\n",
            "Epoch 10/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 3.1280e-06\n",
            "Epoch 11/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 3.0599e-06\n",
            "Epoch 12/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 3.0819e-06\n",
            "Epoch 13/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 3.0990e-06\n",
            "Epoch 14/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.9256e-06\n",
            "Epoch 15/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.9430e-06\n",
            "Epoch 16/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.9442e-06\n",
            "Epoch 17/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.7822e-06\n",
            "Epoch 18/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.7140e-06\n",
            "Epoch 19/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.6780e-06\n",
            "Epoch 20/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.7965e-06\n",
            "Epoch 21/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.8901e-06\n",
            "Epoch 22/50\n",
            "175/175 [==============================] - 1s 4ms/step - loss: 2.6011e-06\n",
            "Epoch 23/50\n",
            "175/175 [==============================] - 1s 4ms/step - loss: 2.8275e-06\n",
            "Epoch 24/50\n",
            "175/175 [==============================] - 1s 4ms/step - loss: 2.9411e-06\n",
            "Epoch 25/50\n",
            "175/175 [==============================] - 1s 4ms/step - loss: 2.5731e-06\n",
            "Epoch 26/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.6598e-06\n",
            "Epoch 27/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.4836e-06\n",
            "Epoch 28/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.6353e-06\n",
            "Epoch 29/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.8566e-06\n",
            "Epoch 30/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.6865e-06\n",
            "Epoch 31/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.5518e-06\n",
            "Epoch 32/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.8349e-06\n",
            "Epoch 33/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.6544e-06\n",
            "Epoch 34/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.5522e-06\n",
            "Epoch 35/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.6060e-06\n",
            "Epoch 36/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.4796e-06\n",
            "Epoch 37/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.5331e-06\n",
            "Epoch 38/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.4664e-06\n",
            "Epoch 39/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.5782e-06\n",
            "Epoch 40/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.6210e-06\n",
            "Epoch 41/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.5789e-06\n",
            "Epoch 42/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.6307e-06\n",
            "Epoch 43/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.7088e-06\n",
            "Epoch 44/50\n",
            "175/175 [==============================] - 0s 2ms/step - loss: 2.4269e-06\n",
            "Epoch 45/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.6950e-06\n",
            "Epoch 46/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 2.5013e-06\n",
            "Epoch 47/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 2.5410e-06\n",
            "Epoch 48/50\n",
            "175/175 [==============================] - 1s 4ms/step - loss: 2.4119e-06\n",
            "Epoch 49/50\n",
            "175/175 [==============================] - 1s 4ms/step - loss: 2.2777e-06\n",
            "Epoch 50/50\n",
            "175/175 [==============================] - 1s 4ms/step - loss: 2.7263e-06\n",
            "18/18 [==============================] - 0s 2ms/step\n",
            "R-squared (Coefficient of Determination): 0.99610882919283\n",
            "Mean Squared Error (MSE): 1683.4554920936698\n",
            "Root Mean Squared Error (RMSE): 41.02993409809075\n",
            "Mean Absolute Error (MAE): 32.22313468330725\n",
            "Mean Absolute Percentage Error (MAPE): 1.1292299989708052\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}