storytelling on opendata

This project focuses on developing a customer churn prediction model using machine learning techniques in R. The primary objective is to analyze customer behavior data to identify factors leading to churn, enabling businesses to implement strategies to retain valuable customers. Key components of the project include data preprocessing, exploratory data analysis (EDA), model training using algorithms like logistic regression or decision trees, and performance evaluation through metrics such as ROC curves and confusion matrices. The final deliverable will be a Shiny application that visualizes the prediction results and allows users to explore various scenarios to understand churn dynamics better.

各临床资料在有无黏膜增厚分层中的差异分析

各临床资料在有无溃疡间的差异分析

各临床资料在有无肉芽肿的差异分析