Predictive-Modeling-for-Hospital-Readmission

The objective of this research is to develop and evaluate predictive models for hospital readmission using the Medical Cost Personal Datasets. The dataset includes crucial patient information such as age, sex, BMI, number of dependents, smoking status, place of residence, and specific medical costs. Our aim is to predict the likelihood of hospital readmission using various machine learning techniques, including supportvector regression , random forests, decision trees, linear regression, and gradient boosting. This process involves thorough data preprocessing, such as handling missing values, encoding categorical variables, and standardizing numerical features to ensure robust model performance. Feature selection is conducted to identify the most influential pred ictors of readmission, thereby enhancing the accuracy and interpretability of the models.