Bias-Aware Personalized News Recommendation Web Application with Credibility Scoring

A personalised news recommendation web application that is aware of bias and scores the credibility of stories.

A personalised news recommendation web application that is aware of bias and scores the credibility of stories. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are personalised recommendations ranked by relevance, data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.