predicting_customer_churn

Predicting Customer Churn for Subscription-Based Services

Table of Contents

• Introduction
• Dataset
• Project Structure
• Data Preprocessing
• Feature Engineering
• Model Training and Evaluation

Introduction

In this project, we aim to predict customer churn for subscription-based services. Customer churn, or the rate at which customers discontinue their subscription, is a crucial metric for businesses. By leveraging machine learning techniques, we can analyze historical customer data and build a predictive model to identify customers who are likely to churn. This enables businesses to take proactive measures to retain those customers and optimize their services.

Dataset

The dataset used in this project contains information about customers and their subscription-related attributes. It includes features such as gender, age, partner status, contract type, monthly charges, total charges, and churn status (target variable). The dataset is a CSV file named customer_data.csv located in the data directory.