In the customer management, customer churn refers to a decision made by the customer about ending the business relationship. This analysis shows how logistic regression model, support vector machines and the random forest model can be used to identify the customer churn in the telecom dataset. The “churn” data set was developed to predict telecom customer churn based on information about their account. The data files state that the data are “artificial based on claims similar to real world”.

The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed.