This story is a walk-through of a notebook I uploaded on Kaggle. Originally, it only used machine learning models and since then I have added a couple of basic neural network models. The churn prediction topic has been extensively covered by many blogs on Medium and notebooks on Kaggle, however, there are very few using neural networks. The application of neural networks to structured data in itself is seldom covered in the literature. I learned neural networks through the deeplearning.ai specialization on Coursera and the documentation of Tensorflow with Keras.

Introduction

Customer attrition or customer churn occurs when customers or subscribers stop doing business with a company or service. Customer churn is a critical metric because it is much more cost effective to retain existing customers than it is to acquire new customers as it saves cost of sales and marketing. Customer retention is more cost-effective as you’ve already earned the trust and loyalty of existing customers.

There are various ways to calculate this metric as churn rate may represent the total number of customers lost, the percentage of customers lost compared to the company’s total customer count, the value of recurring business lost, or the percent of recurring value lost. However, in this dataset, it is defined as a binary variable for each customer and calculating the rate is not the objective. Thus the objective here is to identify and quantify the factors which influence churn rate.

This is a fairly easy and beginner level project with fewer variables. It is not a useful application for neural networks as number of training examples are comparatively less but it is easy to understand neural networks using this.

#python #data-science #neural-networks #machine-learning #deep-learning