I have a dataset with around 40,000 rows and 36 variables, half of which are continuous and half of which are categorical. I have created dummy variables for the categorical variables and standardized the scale of the dataset. Now I am struggling with how to select features and what type of model to build. I was thinking of doing Lasso regularization to select features or a chi-square test, but it seems like neither of these can be implemented for both categorical and continuous variables if they are in one dataset. Would I need to analyze the variables separately? The dependent variable I am trying to predict is also continuous, so I am planning to try linear regression.