Movie Recommender System (Content-Based)

[kanak227]

Description
The Movie Recommender System helps users discover movies they might like based on their past interactions, ratings, or preferences. The system will use machine learning techniques to analyze movie data and generate personalized recommendations. This project is ideal for learning data preprocessing, recommendation algorithms, and deployment techniques.

Scope
The recommender system will use:

Content-Based Filtering: Recommends movies similar to those the user has liked, based on movie attributes like genre, director, and actors.
Collaborative Filtering: Suggests movies based on the preferences of similar users.
Hybrid Model: A combination of both techniques for better accuracy.
Deployment: The final system can be deployed using Streamlit.
Expected deliverables include:
✅ A clean dataset (e.g., from IMDb, TMDB,)
✅ Preprocessing using pandas & scikit-learn
✅ Model training and evaluation using ML algorithms
✅ A functional web application to display recommendations

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[abhisheks008]

Can you please clarify in which open source event you are participating?
@kanak227

[kanak227]

I am into IWOC 25 @abhisheks008

[kanak227]

@abhisheks008 Assign it to me under IWOC

[abhisheks008]

Make sure you implement 3-4 deep learning models along with the machine learning models. As this repository mainly focuses on deep learning methods, you need to focus on deep learning models only.

Assigning this issue to you @kanak227 under IWOC2025.