Project Overrun Predictor

challenge

Challenge 4

brief

Project Overrun Predictor built a machine‑learning driven schedule‑forecasting prototype that predicts the likelihood of project overruns by analysing feature trends across completed and in‑progress energy projects, supported by an interactive Streamlit application.

Please be aware that this content was generated follwing an automated review so may not be perfectly accurate; refer to the original challenge brief and team files for authoritative information

key outcomes

Expected to enable earlier identification of schedule overrun risk, improve decision‑making for project managers, and reduce delays by highlighting high‑risk projects before variance becomes critical.

important files

• Data Modeling & Quality/Data Model & Scripts/app.py: Streamlit application allowing single‑project and batch prediction of schedule overrun risk using a trained ML model.
• Data Modeling & Quality/Data Model & Scripts/Completed_Energy_Projects_Lifecycle_Dataset.csv: Historical project dataset used to train and validate the schedule overrun prediction model.
• Data Modeling & Quality/Data Model & Scripts/Project Model.ipynb: Notebook documenting model training, evaluation, and feature engineering for schedule forecasting.

details

team: Project Overrun Predictor members: tbc topics: solution-centre, hack25, challenge4, python, scikit-learn, streamlit, pandas, joblib, schedule-forecasting, machine-learning, predictive-analytics, data-quality, project-controls, data-visualisation technologies: Python, Scikit-learn, Streamlit, Pandas, Joblib