greyatomtestuser3 / Xgboost_project

Extreme Gradient Boosting(XGBoost) Project

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Welcome to the Extreme Gradient Boosting Project!

This is Extreme Gradient Boosting Project

In this project, you will demonstrate what you have learned in this course by conducting an experiment dealing with Loan Prediction.

We have seen in the lectures How Extreme Gradient Boosting works.

What we have learned so far:

• Boosting
• Adaboost
• Gradient Boosting
• XGBoost

What we are going to do?

• You have clean data-set. We will use an approach similar to previous grid search but will divide the parmeter in two parts.
• Choose default values for Xgboost Classifier.
• Tune tree-specific parameters ( max_depth, min_child_weight, gamma, subsample, colsample_bytree) for decided learning rate and number of trees. Note that we can choose different parameters to define a tree.

What your will learn by doing this assignment ?

• You will learn to build Xgboost model.

Dataset

To perform Extreme gradient boosting task we will use Loan Prediction dataset which we have used while doing logistic regression project.

Details information is mentioned in each task.