- This was part of a class project where different variables are used to predict the concrete compressive strength
- Exploratory Data analysis was performed to clean the data
- Data was normalized using Z-score approach
- Algorithms used: Lasso and Ridge Regression
- Models were developed using the two ML algorithms and for each model, different values were assumed for the penalty function
- The effect of different values of penalty parameter on feature selection was studied.
- 5-fold validation was used to chose the penalty parameter for the predictive model based on Lasso Regression.
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View Code? Open in Web Editor NEWA project focused on using Lasso and Ridge Regression models to predict an output