A repository for a machine learning project about developing a hybrid movie recommender system.

Objective of the project is to build a hybrid-filtering personalized news articles recommendation system which can suggest articles from popular news service providers based on reading history of twitter users who share similar interests (Collaborative filtering) and content similarity of the article and user’s tweets (Content-based filtering).

A react native(UI), FastAPI (Server) and MySQL(DB) non-fungible token market place with a machine learning content-based filtering recommendation engine.

Movie Website built on python Django framework; Uses Content Based Predictive Model approach to predict similar movies based on the contents/genres similarities

Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows

Netflix Recommender with both Content Based and Collaborative Based Filtering

Code repo of solution of 11th place in Recsys Challenge 2022

Transforming skincare recommendations: our hybrid system combines KNN, CNN, and EfficientNet B0 for personalized advice. Published in IEEE, with 80% validation accuracy and 87.10% training accuracy.

recommending recipes with content-based filtering approach

Movie recommendation system to find common movie interests among a group of people.

Machine Learning Terapan - Submission 2

Project was done as a part of Machine Learning (CSE343) at IIIT Delhi.

I developed an advanced game classification model using cutting-edge deep learning techniques for ten different games with high accuracy. It also provides personalized game recommendations using FastAPI and a database of 20,000 similar games.

Comparison of performance evaluation of the baseline and hybrid recommendation systems using various metrics, to prove that hybrid systems perform better

Creating recommendation system with #project-of-the-week in DataTalks.Club

The sample code repository leverages Azure Text Analytics to extract key phrases from the product description as additional product features. And perform text relationship analysis with TF-IDF vectorization and Cosine Similarity for product recommendation.

Indonesia Tourism Destination Recommendation System with Content-based Filtering & Collaborative Filtering.

Restaurant Recommendation Application

Music recommender system with collaborative and content-based filtering