Using the same dataset leveraged for Portfolio Builder Exercise #1, write up a second report that answers the following:
- Depending on the type of response variable, apply a linear or logistic regression model.
- First, apply the model to your data without pre-applying feature engineering processes.
- Create and a apply a blueprint of feature engineering processes that you think will help your model improve.
- Now reapply the model to your data that has been feature engineered.
- Did your model performance improve?
- Apply a principal component regression model.
- Perform a grid search over several components.
- Identify and explain the performance of the optimal model.
- Apply a partial least squares regression model.
- Perform a grid search over several components.
- Identify and explain the performance of the optimal model.
- Apply a regularized regression model.
- Perform a grid search across penalty magnitudes (size of \(\lambda\) and type of penalty - ridge, lasso, elastic net).
- What are the optimal parameter values?
- What is the RMSE for this optimal model?
- How does it compare to your previous models?
- Pick the best performing model from above.
- Identify the most influential features for this model.
- Plot the top 10 most influential features.
- Do these features have positive or negative impacts on your response variable?
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