This project aims to predict customer churn for a business using machine learning techniques. By understanding customer behavior and predicting which customers are likely to churn, businesses can take proactive actions to retain valuable clients and improve customer satisfaction.
- Project Overview
- Dataset
- Installation
- Usage
- Models Implemented
- Results
- Directory Structure
- Contributing
- License
Customer churn refers to the percentage of customers who stop using a company’s product or service during a certain timeframe. This project uses machine learning to predict which customers are at risk of leaving (churning) based on historical data. Understanding churn helps businesses take action to retain customers, improve services, and maximize revenue.
The dataset used in this project is Telco Customer Churn, a dataset provided by IBM, which includes information about customers, such as:
- Customer demographics (e.g., age, gender, etc.)
- Subscription details (e.g., contract type, payment method)
- Account details (e.g., tenure, usage)
- Whether or not the customer churned (target variable)
The dataset is stored in the Telco_Customer_Churn.csv file and is loaded and preprocessed in the code to prepare it for model training.
Follow these steps to set up the project environment and run the code:
Clone the repository to your local machine using the following command:
git clone https://github.com/AbhinavH296/Customer_Churn_Prediction.git
cd Customer_Churn_Prediction