How I Used Machine Learning to Predict 41% of Project Delays Before They Happened

A machine learning model can be used to predict project delays by analyzing data from project management tools like Jira. The process involves creating a dataset with features such as ticket priority, story points, and dependencies to train a predictive model. Through exploratory data analysis, patterns are identified, confirming that higher priority and more dependencies increase the likelihood of a delay. By engineering new features like 'complexity per person', the model's accuracy is improved to help project managers anticipate risks. This data-driven approach allows for proactive intervention, potentially saving time and reducing costs associated with project overruns.