In the telecom industry, large-scale of data is generated on daily basis by an enormous amount of customer base. Here, getting a new customer base is costlier than holding the current customers where churn is the process of customers switching from one firm to another in a given stipulated time. Telecom management and analysts are finding the explanations behind customers leaving subscriptions and behavior activities of the holding churn customers’ data. This system uses classification techniques to find out the leave subscriptions and collects the reasons behind the leave subscription of customers in the telecom industry. The major goal of this system is to analyze the diversified machine learning algorithms which are required to develop customer churn prediction models and identify churn reasons in order to give them with retention strategies and plans.
After you clearly define the Machine Learning problem it's time to finalize the approach to achieve it. There are many algorithms, tools and libraries to choose from. In this case we are going to build Artificial Neural Network (ANN) to predict the customer attrition, also known as customer churn, customer turnover, or customer defection. We will use TensorFlow deep learning framework to build our model. Customer churn (rate) is the measure of the number of individuals or items moving out of a collective group over a specific period. It is one of two primary factors that determine the steady-state level of customers a business will support.
- Data set contains 7,043 customers and 21 data points for each customer.
- Each row represents a customer, and each column contains the customer’s attributes.
- Column Churn contains the class labels value, 'Yes' means the customer will churn else 'No'
- Customer attributes/features are as below