Challenge 6
Delivery Risk Classifier explored predictive classification to identify tasks likely to complete late, focusing on understanding the limits of snapshot‑based delivery data and how modelling could still provide early warning signals. The team combined exploratory modelling with clear recommendations on how delivery datasets need to evolve to support more reliable prediction.
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Improves organisational learning by clarifying what is and is not predictable from snapshot data today, and by outlining how better‑structured datasets could enable earlier and more reliable delivery risk prediction.
Classification Model Hack 27-checkpoint.ipynb: Notebook developing and evaluating a classification model to predict late task completion.Script.docx: Written explanation of modelling choices, data limitations, findings and recommendations.Python DATA - activity_data_synthetic_generic (1).csv: Synthetic activity‑level delivery data used for model training and evaluation.
team: Delivery Risk Classifier members: Jas, Josh topics: solution-centre, hack27, challenge6, python, pandas, scikit-learn, data-analytics, delivery-confidence, early-warning, predictive-modelling, capacity-management, decision-support technologies: Python, pandas, scikit-learn, data-analytics