Project 07: Hybrid Recommendation System Using Machine Learning

🚀 Welcome to the Multiverse of 100+ Data Science Project Series! 🌐 Project 07 immerses you in the realm of recommendation systems with a deep dive into building a Hybrid Recommendation System using Python.

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🎯 Project 07: Hybrid Recommendation System
Unlock the power of recommendation systems as we explore the hybrid approach, combining collaborative filtering and content-based filtering techniques to build a robust recommendation system. Learn how to leverage Python to integrate diverse recommendation strategies, providing users with personalized recommendations tailored to their preferences and behaviors.

🔧 Tools and Technologies:

Python
Jupyter Notebooks
Pandas
NumPy
Scikit-learn
Surprise Library
📈 What You'll Learn:

Understanding recommendation systems
Exploring collaborative filtering and content-based filtering
Implementing a hybrid recommendation system
Leveraging machine learning algorithms for recommendation
Evaluating recommendation system performance
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🔗 Resources:
Access the code, datasets, and additional materials on our GitHub repository. Follow along with the tutorial and unlock the secrets of building a Hybrid Recommendation System using Python.

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[DpHola]

Hey, could you please post a video on to develop thee frontend too with Streamlit for a hybrid recommendation System.

[francosbenitez]

Wao! Your ability to explain is amazing, man.

[dlcv]

For example you need to recommend the productID based on all productIDs against one user_ID then?, Because with every userID there are more than 1 productIDs are listed. So just giving the recommendation based on one productID is not correct.