The development of predictive models involves several steps:
1. Data Collection: Gathering relevant and high-quality data. 2. Feature Selection: Identifying which variables are most predictive of the outcome. 3. Model Training: Using statistical or machine learning algorithms to create the model. 4. Validation and Testing: Ensuring the model's accuracy and reliability by testing it on separate datasets.