D209 Task 2: Data Mining I

This project uses the Random Forest algorithm to predict hospital readmissions based on survey responses.

Research Question: Can hospital readmissions be predicted using patient survey responses?

Data Preparation: The dataset was cleaned, variables renamed for clarity, and target variables were converted for analysis. Data was split into training and testing sets.

Key Findings: The model achieved an accuracy of 60.6% and an AUC of 0.4916, suggesting limited predictive ability.

Implications: Survey responses alone are insufficient for accurate readmission prediction; additional data is needed.

Tools and Techniques: R was used for Random Forest modeling and evaluation, with metrics including confusion matrix and AUC​.