Customer Classification Horeca or Retail – XGBoost + K-Fold-CV

In this project, we implement XGBoost with Python and Scikit-Learn to classify the customers from two different channels as Horeca (Hotel/Retail/Café) customers or Retail channel (nominal) customers.

• The y labels contain values as 1 and 2. We have converted them into 0 and 1 for further analysis.
• Trained the XGBoost classifier and found the accuracy score to be 91.67%.
• Performed k-fold cross-validation with XGBoost.

GitHub Repo: https://github.com/dev-nanthan/customer-classification-XGBoost-KCV

Finding the Most Important Feature

The most important feature of the Dataset is found using XGBoost. plot_importance() function in XGBoost is used to achieve the most importace feature of the Design Matrix