Implement Explainable AI (XAI)

[kaishwarya24]

Is this a unique feature?

• I have checked "open" AND "closed" issues and this is not a duplicate

Is your feature request related to a problem/unavailable functionality? Please describe.

To improve the transparency and trustworthiness of our predictions, I propose integrating Explainable AI (XAI) techniques into the project.

Proposed Solution

Integration of XAI Techniques:

Implement SHAP (SHapley Additive exPlanations) , LIME (Local Interpretable Model-agnostic Explanations) to provide insights into feature importance and model predictions across different algorithms used in the project.

Visualization:

Create visualizations that clearly illustrate the influence of different features on the predicted stock prices. This can include:
    Feature importance plots.

Screenshots

No response

Do you want to work on this issue?

Yes

If "yes" to above, please explain how you would technically implement this (issue will not be assigned if this is skipped)

To enhance the stock price prediction project with Explainable AI (XAI), I propose the following steps:

1. Library Selection

I will start by researching and selecting appropriate XAI libraries such as SHAP , LIME. These libraries are widely used for interpreting model predictions and are well-suited for regression tasks like ours.
2. Integration with Existing Models

Next, I will review the existing models, to determine how best to integrate XAI methods. I will ensure that the chosen XAI techniques can work seamlessly with these models.
3. Training the Models

Before applying XAI techniques, I will confirm that the models are adequately trained on the historical stock data.
4. Implementing SHAP

I will utilize SHAP to calculate Shapley values for the predictions made by our models. This will involve setting up a SHAP explainer specific to the model type . The Shapley values will help identify which features are most influential in the predictions.

I will generate summary plots and dependence plots to visualize these influences, making it easier for users to understand how each feature impacts stock price predictions.
5. Implementing LIME

In addition to SHAP, I will use LIME to provide local explanations for individual predictions. This involves creating a LIME explainer that can highlight how variations in input features affect specific outputs.

By selecting various test instances, I will generate explanations that detail the contributions of different features for those specific predictions.
6. Documentation Updates

Once the XAI techniques are integrated, I will update the project documentation to include comprehensive explanations of the XAI methods used. This will cover how users can interpret the outputs of SHAP and LIME, along with examples to illustrate the insights gained.
8. Testing and Validation

Finally, I will conduct tests to validate the effectiveness of the XAI implementations. This includes reviewing the clarity and relevance of the explanations provided. Gathering feedback from potential users will also be a part of this process to ensure that the explanations meet their needs and expectations.

[kaishwarya24]

please assign me this as a part of gssoc'24 extended

[rohitinu6]

@kaishwarya24 , Please ensure to star this repo,
All the best

[github-actions]

✅ This issue has been successfully closed. Thank you for your contribution and helping us improve the project! If you have any more ideas or run into other issues, feel free to open a new one. Happy coding! 🚀