A small project addressing a regression problem explains implementation of multiple linear regression techniques, hyperparameter tuning, collinearity, model overfitting and complexity using LASSO, Ridge and Elastic net
Key learnings:
- understanding effect of Collinearity on linear regression model
- analysing correlation among attributes
- practical understanding on output of linear regression model in
presence of correlated festures
4.implement, analyse Ridge regularization to avoid collinearity,
model overfitting and model complexity - implement, analyse Lasso regularization to avoid collinearity,
model overfitting and model complexity - discovering relevant features using Lasso model
- implement, analyse Elasticnet regularization to avoid collinearity , model overfitting and model complexity
- Analysing results of regularization
- comparing results of regularization with linear regression model