Multiple-Linear-Regression-with-Regularization

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:

1. understanding effect of Collinearity on linear regression model
2. analysing correlation among attributes
3. 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
4. implement, analyse Lasso regularization to avoid collinearity,
   model overfitting and model complexity
5. discovering relevant features using Lasso model
6. implement, analyse Elasticnet regularization to avoid collinearity , model overfitting and model complexity
7. Analysing results of regularization
8. comparing results of regularization with linear regression model