MADS: Model Analysis & Decision Support
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Updated
May 21, 2024 - HTML
MADS: Model Analysis & Decision Support
📖Notes and remarks on Machine Learning related papers
A Text / Speech Summarizer
(Class) Computer Aided Analysis and Design (Optiomisation algorithms)
Evaluating k-nearest neighbors and singular value decomposition techniques for collaborative filtering recommender systems
Recommendation on data from the IBM Watson Studio platform
Recommendation System for IBM articles
Complete concepts behind implementing a Recommendation System using Association Rules, Collaborative Filtering, and Matrix Factorization.
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.
Analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles.
Recommender system from Yelp dataset
Articles recomendations for IBM Watson users
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