/diabetes_ml

Predicted diabetes progression after one year using seven different regression ML techniques on the LARS diabetes dataset

Primary LanguagePython

diabetes_ml

Predicted diabetes progression after 1 year using seven different regression ML techniques and the predictors (age, sex, BMI, etc) found in the LARS diabetes dataset. Selected best model of the seven (LASSO) using RMSE and R^2 on test data.

Required Packages/Modules

  • pandas
  • numpy
  • sklearn
  • matplotlib
  • math

Diabetes Predictors

  • age in years
  • sex
  • BMI
  • average blood pressure
  • s1 tc, T-Cells (a type of white blood cells)
  • s2 ldl, low-density lipoproteins
  • s3 hdl, high-density lipoproteins
  • s4 tch, thyroid stimulating hormone
  • s5 ltg, lamotrigine
  • s6 glu, blood sugar level

Regression Techniques Used

  • ElasticNet
  • LASSO
  • Ridge
  • Linear Regression
  • Polynomial Regression
  • Support Vector Regression (SVR)
  • Random Forest Regression