Predicting Customer Churn in E-commerce Using Supervised Learning
In an era where customer retention is as crucial as acquisition, understanding and predicting customer churn becomes a centerpiece of business strategy, especially in e-commerce. This blog presents an in-depth guide on how to use Python and machine learning techniques for effective churn prediction.
The Significance of Churn Prediction
Churn prediction involves identifying customers likely to leave a service or stop buying products. In e-commerce, where competition is fierce, retaining an existing customer is often more cost-effective than acquiring a new one. Accurate churn predictions can help businesses tailor their retention strategies more effectively.
In the first part of our series on churn analysis in e-commerce, we focused on analyzing customer churn. In this continuation, we will explore the practical application of supervised learning techniques in Python for predicting churn.
Dataset and Preprocessing
Data Exploration
Our journey begins with importing the libraries and loading the E-Commerce dataset: