A python code that helps to identify customers likely to churn and develop focused intervention plans to retain as many customers as possible. It is implemented with logistic regression, random forest, and logistic regression + LASSO machine learning algorithms. The datset used is Telco-Customer-Churn Data from kaggle.
#challenge Customer churn is a critical and challenging problem affecting business and industry, in particularly, the rapidly growing, highly competitive telecommunication sector because most customers have multiple options from which to choose within a geographic location. There are 400 millions subscribers in the US telecommunication industry. The availability of wireless devices and networks is everywhere. Everyone has access to cell phones and internet. From social network to shopping or travelling, people can retrieve their needs and enjoy their leisure time online. Everything is accessible through mobile devices and Internet. Therefore, this is the prosperous market to thrive in. Nevertheless, competitions also arises in this market. With similar services, different companies compete vigorously for a segment in the telecom industry. The ultimate goal is to expand its coverage area and retrieve more customers loyalty. The core to succeed in this market lies in the customer itself.
#Link to colab code Link to google colab