/CS273A_project_diabetes

Course Project for CS273A: Machine Learning at UCI

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Predicting early hospital readmission rates for patients with Diabetes

  • Used UCI Machine Learning Repository’s Diabetes 130-Hospital Dataset to find the best fitting model for predicting early hospital admission rates in Diabetic patients
  • Performed feature engineering steps such as removing unimportant features, replacing and grouping feature values, one hot encoding categorical features and rescaling numerical features
  • Employed SMOTE to tackle class imbalance in the target feature
  • Performed Grid Search on the following models: Logistic Regression, Random Forest, and Neural Network to select the best model using Cross Validation