1- Types of Predictive Models

Regression Models:

• Predict a continuous outcome variable based on input features.
• Utilize linear regression, multiple regression, or polynomial regression.

b. Classification Models:

• Assign observations to categories or classes.
• Explore models such as logistic regression, decision trees, or support vector machines.

2- Model Selection and Evaluation Criteria

a. Model Selection:

• Choose appropriate models based on the nature of the problem.
• Consider factors like interpretability, complexity, and model assumptions.

b. Evaluation Criteria:

• Use metrics like accuracy, precision, recall, F1 score, and ROC-AUC for classification models.
• For regression, assess metrics such as Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).