Learn How To Build RECOMMENDER SYSTEM with Python (TOO EASY!!!)

🌟 Dive into the world of recommender systems with Python! 🚀 Unlock the secrets behind creating personalized recommendations and enhancing user experiences. 📈 Explore two fascinating projects where we demystify the algorithms driving recommendation engines. 💡 Learn how to implement recommendation systems effortlessly and add a personalized touch to your projects!
Get the Code file of the video here:-
github.com/SMDS-Studio/Recomm...
Useful Resources:
🔗 Supercharge your recommender system skills with these resources:
📘 Discover the Dataset Here: grouplens.org/datasets/moviel...
📚 Access Python libraries for building recommenders: pypi.org/project/scikit-surpr...
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Recommender Systems, Python, Data Science, Personalized Recommendations, Collaborative Filtering, Algorithm Demystified, TechExplorers, LearnWithData, DataDrivenInsights, PythonProjects, MachineLearning, CodingTutorial, TechEducation 🌟📈🚀

[@matross3097]

I have a Question:
Why did you create a grand df with all the genre information when you are only going to use userId, movieId, rating from the dataframe ?
data = Dataset.load_from_df(train_df[['userId' , 'movieId', 'rating']], reader)

[@user-nd9fn2ci5j]

will this work for binary rating? For instance: Like = 1, Dislike = 0, or "have this product" = 1, "doesn't have this product" = 0?

[@forheuristiclifeksh7836]

0:36

[@pumkinegro2167]

Does the genres of the movies affect the predictions?

[@SMDS_Studio]

It very well might, you could consider this as an indicator out of 100 indicators before making a suggestion to the user.
Also I really do like Gnar

[@fidha254]

I stand corrected but I think the genres become relevant if you decide you wanted content-based filtering as opposed to collaborative (users) filtering. Collaborative you only need movieID, UserID and Rating as far as I know.