The second set of project notes must include the model building aspect and be aligned to the analytical business problem the Capstone Project addresses. It should also include the rationale behind using/building one particular model (if applicable). The notes should be brief and succinct containing the depiction of the results for the models built aligned with the objective of the project. 

Below are the guidelines for the Project Notes-II:

1). Model building and interpretation

a. Build various models (You can choose to build models for either or all of descriptive, predictive or prescriptive purposes)

b. Test your predictive model against the test set using various appropriate performance metrics

c. Interpretation of the model(s) 

2). Model Tuning and business implication

a. Ensemble modelling

b. Any other model tuning measures

c. Interpretation of the most optimum model and its implication on the business

I need PYTHON CODES for the following:

 

1. Linear Regression (LR)
2. Lasso Linear Regression (LR-Lasso)
3. Ridge Linear Regression (LR-Ridge)
4. Decision Tree Regressor (DTR)
5. Adaptive Boosting Regressor (ADB)
6. Gradient Boosting Regressor (GBR)
7. Bagging Regressor (BGR)
8. Random Forest Regressor (RFR)
9. Extreme Gradient Boosting (XGB).

Dataset: https://docs.google.com/spreadsheets/d/15flTnySNohnTcpQpp4VZyIFP6Cbgu9riSvMVyylFY_E/edit#gid=1903205039

Note: This is a regression problem and hence I need to find out for both Train and Test set the following:

a) R2 Score (R square)

b) RMSE

c) MAE

d) MAPE