/SM2324_GroupK

This repository contains the final project for the Statistical Methods course. MSc courses in Data Science and Scientific Computing, Data Science and Artificial Intelligence, Scientific and Data Intensive Computing. University of Trieste.

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SM2324_GroupK

This repository contains the final project for the Statistical Methods course.

MSc courses in Data Science and Scientific Computing, Data Science and Artificial Intelligence, Scientific and Data Intensive Computing.

University of Trieste.

TO DO

SHINYAPP

  1. Linear and GAM Models Step 4 --> Add some text explaining: - Remove less significant predictors
    - Introduce interactions between covariates - Log tranform covariates - Step AIC - refactoring covaraites - VIF - introduce best model and resiuduals analysis [ SANDRO ]

  2. Linear and GAM Models Step 5 --> Polynomial Regression - Step AIC - refactoring covaraites - VIF [ SANDRO ]

  3. Linear and GAM Models Step 6 --> Ridge and Lasso [ LUCA ]

  4. Linear and GAM Models Step 7 --> Splines [ PRANAY ]

  5. Linear and GAM Models Step 8 --> GAMs [ PRANAY ]

  6. Other models (find a better name!!!) --> -MARS, trees , random forest [ LUCA ]

  7. Conclusion [ ANNALISA ]

check number of occurences for NA district estimations saturated model!!!!!!!!!!!!!!!!!!!

check if it's needed to write something in each page

  • splines (with mostly correlated vars, with all vars, with most significant vars, with interaction, step AIC, categorical c_breastf)
  • GAM (most correlated vars, complete, removed less significant vars, interaction, categorical, linearized, ANOVA to compare different models)