Numerical Analysis Projects
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Updated
Nov 15, 2022 - HTML
Numerical Analysis Projects
Articles recommendation engine for IBM Watson Studio platform
This projects shows some techniques for recommendation engines using data from the IBM Watson Studio Platform.
Performed EDA, created user-article matrix, calculated similarity using dot product, implemented Rank-Based, User-User CF, Content-Based, and Matrix Factorization, evaluated model with precision, recall, and F1-score.
Predicting Nobel Physics Prize winners. Final project for Harvard CS109a 2017 edition https://github.com/covuworie/a-2017.
analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations on new articles they will like.
In the IBM Watson Studio, there is a large collaborative community ecosystem of articles, datasets, notebooks, and other A.I. and ML. assets. Users of the system interact with all of this. This is a recommendation system project to enhance the user experience and connect them with assets. This personalizes the experience for each user.
This is a movie recommendation system that recommends movie based on the ratings given by the user, uses user-user collaborative filter, item-item collaborative filter and matrix factorisation
A Cloud Based Personalised Recommendation System for movies and books.
Recommender system from Yelp dataset
Articles recomendations for IBM Watson users
Analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles.
EDA, Pre-processing, 6 Recommendation Systems Techniques: * Popularity-Based, * Cosine Similarity Collaborative Filtering, * Matrix Factorization Collaborative Filtering, * Clustering, * Content-Based Filtering, * Hybrid Recommendation System.
Recommender system for IBM Watson public articles
Recommendations with IBM Data (knowledge-based plus collaborative filtering both model-based and neighborhood-based)
This project is to analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles they might like. Recommending articles that are most pertinent to specific users is beneficial to both service providers and users.
Project 3 in Data Scientist Nanodegree with Udacity. Build a recommender engine for IBM Watson.
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