MADS: Model Analysis & Decision Support

📖Notes and remarks on Machine Learning related papers

A Text / Speech Summarizer

A Cloud Based Personalised Recommendation System for movies and books.

Numerical Analysis Projects

Predicting Nobel Physics Prize winners. Final project for Harvard CS109a 2017 edition https://github.com/covuworie/a-2017.

Complete concepts behind implementing a Recommendation System using Association Rules, Collaborative Filtering, and Matrix Factorization.

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

Recommendation on data from the IBM Watson Studio platform

This repository analyze user interactions with articles on the IBM Watson Studio platform and develop recommendation systems to suggest new articles that align with their interests.

EDA, Pre-processing, 6 Recommendation Systems Techniques: * Popularity-Based, * Cosine Similarity Collaborative Filtering, * Matrix Factorization Collaborative Filtering, * Clustering, * Content-Based Filtering, * Hybrid Recommendation System.

(Class) Computer Aided Analysis and Design (Optiomisation algorithms)

Evaluating k-nearest neighbors and singular value decomposition techniques for collaborative filtering recommender systems

Movie recommendations

Project 3 in Data Scientist Nanodegree with Udacity. Build a recommender engine for IBM Watson.

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.

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.