Machine Learning Models Built End to End

I turn your raw data into working predictions. I build machine learning models for forecasting, classification, regression and recommendation, and I own the whole path from a messy export to a service your product can call. First I sit with your data and your goal, clean what needs cleaning, handle gaps and outliers, and engineer the features that actually carry signal instead of noise. Then I train and compare several model families, tune them honestly, and pick the one that earns its place on your real metrics rather than on a leaderboard.

Evaluation is where I refuse to cut corners. I measure on held-out and time-aware splits so the numbers you see are the numbers you will get in production, and I report precision, recall, error bands and calibration in plain language, not just a single score. I check for leakage, for drift, and for the quiet ways a model flatters itself during training. You get a clear picture of what the model is good at, where it is weak, and how confident it is on each prediction, so you can make decisions with your eyes open.

Once a model is proven, I package it as a clean, documented API you can call from anywhere, with versioning, monitoring hooks and a retraining routine so accuracy does not quietly decay over months. I write down how it works, hand over the training code, and stay reachable while your team gets comfortable running it. Whether you need demand forecasting, churn scoring, lead ranking, fraud flags or personalised recommendations, I deliver a model that is measured, deployable and yours to keep.

— Data cleaning, feature engineering and honest evaluation
— Forecasting, classification, regression and recommendation
— Deployment as a versioned, monitored API with retraining