Challenge 6
Early Slip Predictor focused on identifying early indicators of delivery slippage by analysing capacity pressure and task behaviour across work centres. Using simple machine‑learning techniques and clear capacity metrics, the team demonstrated how likely future slip can be predicted early and translated into understandable risk signals.
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Improves foresight by predicting which tasks are likely to slip before deadlines move, helping teams rebalance capacity earlier and reduce late recovery actions.
KNN_script/KNN_script.ipynb: Notebook training and evaluating a KNN model to predict task slippage risk.Challenge_6_capacity_data_synthetic_generic.csv: Synthetic capacity dataset used to identify work‑centre pressure and risk status.KNN_script/Active_activity_KNN.csv: Model outputs showing predicted slippage for active tasks.
team: Early Slip Predictor members: tbc topics: solution-centre, hack27, challenge6, python, scikit-learn, data-analytics, k-nearest-neighbours, delivery-confidence, early-warning, capacity-management, planning-behaviour, decision-support technologies: Python, scikit-learn, data-analytics, k-nearest-neighbours