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.

A platform to read and share 📚 with other users, the platform tracks users by collecting there data and exposing the collected data as an API

A recommendationn system for movies using Python and machine learning algorithms (k nearest neighbours, logistic regression). numpy. scikit-learn

Using XGboost to predict accommodation listing prices

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

Auto Complete / Suggestion feature using Trie data structure

Articles recommendation engine for IBM Watson Studio platform

An audio book recommendation engine for Audible users.

Recommendation engine for IBM Watson.

Content recommendation API

Analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles.

P&G Hack - Recommendation platform

Building out a recommendation engine with IBM Studio for the Udacity Data Science Nano-degree Program

🦕 example of recon-engine

Recommendations with IBM Data (knowledge-based plus collaborative filtering both model-based and neighborhood-based)

Build a recommendation system using IBM Watson Studio platform.

As part of the udacity.com Data Scientist nanodegree, this is a blog to document my experience with the program. This project was created using a GitHub template created by user chabaldwin. The blog discusses the 4 different projects starting with a dataset I selected from Kaggle that shows Real Estate Data for NYC Property Sales across the 5 bo…