Feature request - Hybrid Recommendation System for Model Selection

[sanchitc05]

Fixes #116

Related Issues or bug

• Info about Issue or bug

Fixes: #[issue number that will be closed through this PR]

Proposed Changes

Description:

I have implemented a Hybrid Recommendation System that helps users select the most appropriate machine learning models based on the type of dataset and the task (e.g., classification, regression). This system combines content-based filtering (recommending models based on dataset features) and collaborative filtering (ranking models based on user feedback).

Code Overview:

recommend_based_on_data_type(data, task_type):

This function analyzes the dataset and task type to suggest suitable models. For example, if the data is images and the task is classification, models like CNN and ResNet are recommended.

get_user_feedback(model):

This function retrieves user feedback for a specific model. If feedback exists, it returns the average rating for the model (on a scale of 1-5); otherwise, it informs that no feedback is available.

hybrid_recommendation_system(data, task_type):

This function combines the two approaches:

1. It first recommends models based on the dataset type and task (content-based filtering).
2. Then, it ranks these recommendations based on user feedback (collaborative filtering), so models with better feedback are prioritized.

Additional info:

The system can be enhanced by adding more models and feedback, or integrating it into a larger platform for ML model selection.
I am happy to collaborate on improving this further!

Please review the changes and consider merging this into the project. Let me know if there are any changes or suggestions!

[github-actions]

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[github-actions]

🎉🎉 Thank you for your contribution! Your PR #324 has been merged! 🎉🎉