Predicting Cricket Match Outcomes with Machine Learning

One of my first end-to-end machine-learning projects: a pipeline that predicts cricket match outcomes and player performance from historical match data.

What I built

The work ran from raw data all the way to results. I engineered features from historical match records — form, venue, match context — then trained and evaluated models with scikit-learn. On top of that I built a set of visualisations exploring runs, wickets and win probability, so the model's behaviour was something you could look at rather than just a score on a page.

What I took from it

This was where I learned that the model is the small part. Most of the effort went into getting the data into a shape worth modelling — deciding what a "feature" even is when the raw records are messy and inconsistent — and into checking that the evaluation was honest rather than flattering.