Open Issues Need Help
View All on GitHubAI Summary: Create a `config.py` file to store all hardcoded constants currently present in the StockSentry project's Python scripts. This includes API keys, column names, file paths, and other similar values. The goal is to improve maintainability and make it easier to modify project settings without altering the core code.
AI Summary: The task requires writing unit tests in Python to validate the accuracy of the model evaluation metrics used in the StockSentry project. This involves creating test cases that check the calculated metrics (e.g., R-squared, Mean Squared Error) against expected values for known datasets or scenarios.
AI Summary: Refactor the StockSentry project to improve API key management. This involves replacing the Google Colab-specific method of accessing the News API key with a more portable and secure approach using a .env file and the python-dotenv library. The changes include adding python-dotenv to dependencies, creating a .env.example file, updating the notebook to load the key from the .env file, updating the README, and adding .env to .gitignore.
AI Summary: Enhance the StockSentry project by implementing a more robust machine learning pipeline. This involves incorporating multiple regression algorithms (XGBoost, Support Vector Regression, Ridge/Lasso, and potentially ensemble methods), performing hyperparameter tuning using GridSearchCV, employing time series cross-validation, and evaluating model performance using a wider range of metrics (MAE, RMSE, directional accuracy). The goal is to improve prediction accuracy and provide a more realistic assessment of model performance, addressing overfitting issues in the current RandomForestRegressor implementation.