Pre-SMOTE Analysis: It's commendable that you modeled before applying SMOTE. This helps visualize the bias towards the majority class in the imbalanced data, which is valuable information.
Notebook Cleanliness: While your notebook has some strengths, consider cleaning it up. Remove unused code lines and ensure all remaining code executes and produces outputs. This will improve readability and maintainability.
Linear Separability Exploration: You've effectively explored the data's linear separability, gaining valuable insights into its structure.
Areas for Further Enhancement:
Multicollinearity: Although you used a correlation matrix, commenting on potential multicollinearity within the data would be beneficial. If present, suggest strategies to address it (e.g., feature selection, dimensionality reduction).
Model Choice Expansion: Your exploration of linear separability could inform the selection of more appropriate models. While logistic regression and linear SVM are good starting points, consider models like Random Forest or Support Vector Machines with kernels that can handle potentially non-linear relationships in the data.
Cross Validation Integration: Implementing cross-validation during model fitting is a recommended practice. This helps ensure your model generalizes well to unseen data.
Credit Score Enhancements: Generating credit scores as an output is a valuable step. Consider appending these scores to the original data frame. This allows for further analysis and visualization opportunities.
Actionable Insights for the Bank: Incorporate insights from your model findings into clear and actionable feedback for the bank. For example, how can the bank use these scores to assess customer creditworthiness or segment customers for targeted strategies?
Error Analysis and Tuning: While you performed hyperparameter tuning, consider prioritizing error analysis first. Identify the most relevant metric based on the bank's business case (e.g., accuracy, precision, recall, F1-score) and use this metric to guide hyperparameter tuning.
Overall:
This is a promising project! By incorporating the suggested improvements, you can take your project to an even higher level and provide the bank with valuable insights to support their decision-making.
Strengths:
Areas for Further Enhancement:
Overall:
This is a promising project! By incorporating the suggested improvements, you can take your project to an even higher level and provide the bank with valuable insights to support their decision-making.