ML-E1: Which machine learning algo & python library to use for which real world use-case?
Here we’ll not only cover which ML algo to try, but which specific off-the-shelf python library to use for real world problems.
Like product demand prediction, churn prediction, automated customer service, recommendation, weather prediction, time-series, credit card fraud, spam, security, people tracking, self-driving, grocery checkout and more.
ML series menu: E1 E2 E3 E4 E5 E6 E7 E8 E9
Customer Churn Prediction
- Machine Learning Algorithm: Gradient Boosting Machines (GBM)
- ML Library/Module: XGBoost, specifically the
XGBClassifierclass
Customer churn prediction, which aims to identify customers who are likely to stop using a product or service, is a common use case of machine learning in business. Gradient Boosting Machines (GBM) are often used in churn prediction for their high performance and flexibility.
In Python, XGBoost is a library that provides an efficient and flexible implementation of the GBM algorithm. The XGBClassifier class is used for classification tasks, such as predicting whether a customer will churn (leave) or not.
