Data Collection: Gathering accurate and timely data is the foundation of any predictive model. Model Selection: Choosing the appropriate statistical or machine learning model to best fit the data. Parameter Estimation: Fine-tuning model parameters to improve prediction accuracy. Validation: Comparing model predictions with real-world outcomes to assess performance. Scenario Analysis: Exploring different intervention strategies and their potential impacts.
