Categorical Outcomes

Requirements Clarification & Assessment

1. Understanding the Problem:

   • We are dealing with a classification problem since the dependent variable is categorical.
   • The dataset includes both continuous and categorical independent variables, requiring careful preprocessing and selection of suitable models.

2. Data Exploration:

   • Assess the distribution of the categorical dependent variable to understand class balance.
   • Identify the types of categorical independent variables (ordinal vs nominal).
   • Determine the range and distribution of continuous variables.

3. Data Preprocessing Needs:

   • Categorical variables may need encoding (e.g., one-hot, label, or ordinal encoding) before being used in certain algorithms.
   • Continuous variables might require normalization or standardization, depending on the selected model.

4. Model Selection Criteria:

   • Consider the interpretability of the model, computational efficiency, and scalability.
   • Evaluate the model's ability to handle imbalanced classes if applicable.

5. Evaluation Metrics:

   • Define metrics for model evaluation such as accuracy, precision, recall, F1-score, and AUC-ROC, depending on the class distribution.