categorize the dataset based on distinct characteristics or patterns. These segments can provide valuable insights for formulating targeted marketing strategies. Since each segment exhibits different purchase capacities, understanding these variations allows for tailored approaches to maximize effectiveness and meet the unique needs of each customer group.

categorize the dataset based on distinct characteristics or patterns. These segments can provide valuable insights for formulating targeted marketing strategies. Since each segment exhibits different purchase capacities, understanding these variations allows for tailored approaches to maximize effectiveness and meet the unique needs of each customer group.

This project is finished by Daniela Quintero Narváez and Zihua Lai. We cooperate to use PCA, K- Means do clustering for spatial data and use casual forest model to do analysis.

This R Markdown document describes the steps for creating a credit risk prediction model using logistic regression. We start by loading the necessary libraries and the dataset, followed by data preprocessing and model training and evaluation. Lastly, we focus on visualizing our results.