마케팅 애널리틱스: Python으로 고객 이탈 예측하기

Begin exploring the Telco Churn Dataset using pandas to compute summary statistics and Seaborn to create attractive visualizations.

Having explored your data, it's now time to preprocess it and get it ready for machine learning. Learn the why, what, and how of preprocessing, including feature selection and feature engineering.

With your data preprocessed and ready for machine learning, it's time to predict churn! Learn how to build supervised learning machine models in Python using scikit-learn.

Learn how to improve the performance of your models using hyperparameter tuning and gain a better understanding of the drivers of customer churn that you can take back to the business.