Extreme Gradient Boosting(XGBoost) Project

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:

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.

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.

Name	Name	Last commit message	Last commit date
Latest commit History 17 Commits 17 Commits
.idea	.idea
.ipynb_checkpoints	.ipynb_checkpoints
data	data
q01_myXGBoost	q01_myXGBoost
q02_param2	q02_param2
q03_xgboost	q03_xgboost
README.md	README.md
__init__.py	__init__.py
__init__.pyc	__init__.pyc
config.json	config.json
q01_myXGBoost.ipynb	q01_myXGBoost.ipynb
q02_param2.ipynb	q02_param2.ipynb
q03_xgboost.ipynb	q03_xgboost.ipynb