Feature request - Hybrid Recommendation System for Model Selection

[sanchitc05]

I would like to propose the addition of a hybrid recommendation system that suggests the most suitable machine learning models based on the type of dataset and the task at hand. The system could analyze the dataset (e.g., tabular, image, text) and recommend models such as:

CNNs (Convolutional Neural Networks) for image-related tasks,
RNNs (Recurrent Neural Networks) or LSTMs (Long Short-Term Memory Networks) for sequential data, and
Random Forests or Gradient Boosting for tabular data.
By integrating this recommendation system, contributors—whether they are beginners or experts—can streamline the process of selecting the right model for their task. The system can be enhanced over time by incorporating user feedback to further refine its recommendations based on past performance.

Why this feature is important:

Choosing the right model is a critical step in machine learning, and this system would help reduce trial and error, saving time for contributors. It will also provide a learning opportunity for new contributors to understand which models work best for different datasets.

Given my background in building recommendation systems (I've worked on a YouTube video recommendation system for an AI-integrated e-learning platform), I am confident that I can contribute effectively to building this feature. I would love to work on this issue and collaborate with others to bring it to life.

Please consider assigning this issue to me. Looking forward to your feedback!

[github-actions]

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. Your contributions are highly appreciated! 😊

[github-actions]

Hello @sanchitc05! Your issue #116 has been closed. Thank you for your contribution!