What It Does
Machine learning pipelines designed to turn business data into structured, repeatable outputs such as predictions, classifications, and customer segments. Each pipeline includes data preparation, model training, and evaluation so results can be reproduced and improved over time.
The Business Problem
Many organisations collect large volumes of data but lack the workflows needed to convert that data into reliable decisions. One off analysis in notebooks does not scale. Production pipelines ensure models can be reused, monitored, and continuously improved.
What Was Built
Classification models used for predictive analysis such as health risk prediction and categorisation tasks
Dimensionality reduction using PCA to manage high dimensional datasets
Hyperparameter tuning using GridSearchCV and cross validation
Unsupervised clustering using K Means and K Medoids for segmentation
Modular preprocessing pipelines reusable across different datasets
Business Impact
Improves decision accuracy through structured modelling
Replaces manual analysis with repeatable workflows
Enables segmentation, forecasting, and risk scoring
Applicable across healthcare, operations, and commercial analytics
Allows pipelines to adapt quickly to new datasets