Predictive modeling leverages patterns in historical data to discern which outcomes are most likely to occur in the future. Developing a predictive model first begins with a process called feature engineering which involves creating new variables from the existing dataset, often to summarize more granular data points into more useful information. Next, the data is split into training, testing, and validation partitions to aid in model development and final performance evaluation. Various iterations or versions of initial models are tested to see which methods offer the greatest chance of generating accurate predictions. The model performing the strongest is then improved upon to maximize accuracy, before finally being tested on unseen data to determine how well it predicts the actual outcomes observed. Once the model is finalized, the methodology, key insights into the variables which most impact predictions, and example predictions are presented in a final report. Depending on your unique needs, the final model may then be integrated into your existing workflow to help inform decision making in an ongoing basis.