Indian Exchange Rate Pred By using ml

[PRIYANSHU2026]

We will implement the LSTM Recurrent Neural Network to predict the foreign exchange rate. The LSTM model will be trained to learn the series of previous observations and predict the next observation in the sequence. We will apply this model in predicting the foreign exchange rate of India.

1. Enhance Data Preprocessing:

   • Issue: Improve the data preprocessing steps to handle missing values, outliers, and feature scaling.
   • Description: Currently, the data preprocessing may not adequately address missing values and outliers. Implement robust methods to handle these issues, and ensure proper feature scaling for improved model performance.

2. Optimize LSTM Hyperparameters:

   • Issue: Fine-tune LSTM hyperparameters for better accuracy.
   • Description: Experiment with different hyperparameters for the LSTM model, such as the number of layers, neurons per layer, dropout rates, and learning rates, to achieve better prediction accuracy.

3. Implement Early Stopping:

   • Issue: Add early stopping to prevent overfitting.
   • Description: Incorporate early stopping in the training process to monitor the validation loss and stop training when the model starts overfitting.

4. Add Model Evaluation Metrics:

   • Issue: Include additional model evaluation metrics.
   • Description: Besides accuracy, add other evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared to provide a comprehensive assessment of the model's performance.

5. Visualize Training Process:

   • Issue: Add visualizations for the training process.
   • Description: Implement visualizations to monitor the training and validation loss over epochs, helping in understanding the model's learning curve and identifying potential overfitting.

6. Cross-Validation:

   • Issue: Implement cross-validation to ensure model robustness.
   • Description: Use K-fold cross-validation to evaluate the model's performance across different subsets of the data, ensuring its robustness and reliability.

7. Enhance Documentation:

   • Issue: Improve project documentation.
   • Description: Provide detailed documentation for each step of the project, including data preprocessing, model architecture, hyperparameter tuning, and evaluation metrics. This will help users understand and replicate the analysis.

8. Deploy Model:

   • Issue: Deploy the trained model for real-time predictions.
   • Description: Develop a simple web interface using Flask or Streamlit to deploy the trained model, allowing users to input new data and get real-time predictions of the foreign exchange rates.

9. Integrate External Data Sources:

   • Issue: Incorporate additional external data sources to improve model accuracy.
   • Description: Integrate additional relevant data sources, such as economic indicators or global financial news, to enhance the predictive power of the model.

10. Model Versioning and Experiment Tracking:

    • Issue: Implement model versioning and experiment tracking.
    • Description: Use tools like MLflow or DVC to track different model versions and experiments, allowing for better reproducibility and comparison of model performance across different runs.

These issues aim to enhance the project by improving the preprocessing steps, optimizing the model, providing better evaluation, and making the project more user-friendly and accessible.

[invigorzz313]

This issue has been assigned. Do follow the project guidelines for PR.