In real-world situations, data scientists often start an analysis with a simple and easy to implement model such as linear or logistic regression. There are various advantages of this approach such as getting a sense of the data with a minimum cost and giving food for thoughts on how to solve a business problem. In this blog post, I decided to start from the opposite side by applying a multilayer perceptron model (neural network) to predict customer churn. I think it is quite fun and exciting to try different algorithms or at least to know how you can solve a problem in a more sophisticated way. Customer churn is when a customer decides to stop using services, content, or products from a company.

Customer churn is a key predictor of the long term success or failure of a business. But when it comes to all this data, what's the best model to use? This post shows that gradient boosting is the most accurate way of predicting customer attrition. I'll show you how you can create your own data analysis using gradient boosting to identify and save those at risk customers! Customer retention should be a top priority of any business as acquiring new customers is often far more expensive that keeping existing ones.