Our objective here is to perform hyperparameter tuning of the native XGBoost API in order to improve its regression performance while addressing bias-variance trade-off -- especially to alleviate Boosting Machine's tendency of overfitting. In order to conduct hyperparameter tuning, this analysis uses the grid search method. In other words, we select the search grid of hyperparameters and calculate the model performance over all the hyperparameter datapoints on the search-grid. Then, we identify the global local minimum of the performance -- or the hyperparameter datapoint which yields the best performance (the minimum value of the Objective Function) -- as the best hyperparameter values for the tuned model. Hyperparameter tuning can be computationally very expensive depending on how you set the search grid.