3. Methods of Analysis Data Preparation: Explain the steps taken for data cleaning, transformation, and feature selection. Discuss encoding of categorical variables and scaling of numerical features. Model Selection: Justify the choice of models (e.g., Logistic Regression, Decision Trees, Random Forests). Describe the rationale behind selecting specific evaluation metrics (e.g., accuracy, precision, recall, F1 score). Model Training and Validation: Explain the train-test split process and any cross-validation techniques used. Discuss how hyperparameter tuning was conducted, if applicable