This project focuses on analyzing and predicting customer churn in a telecommunications company. Customer churn, also known as customer attrition, refers to the phenomenon where customers cease their relationship with a company. By understanding the factors that contribute to churn and building predictive models, businesses can take proactive measures to retain customers.
The dataset used for this project can be found here.
The EDA phase involves exploring the dataset to gain insights into the underlying patterns and relationships. Key steps in the EDA process include:
- Data Loading and Understanding
- Univariate Analysis
- Bivariate Analysis
- Multivariate Analysis
- Data Cleaning and Preprocessing
- Data Visualization
After gaining insights from EDA, predictive models are trained to forecast customer churn. Commonly used machine learning algorithms for churn prediction include:
- Random Forest
Customer churn analysis and prediction are essential for businesses to maintain customer satisfaction and maximize revenue. By leveraging data analytics and machine learning techniques, companies can identify at-risk customers and implement targeted retention strategies.
This README provides an overview of the Customer Churn Analysis & Prediction project, including data sources, EDA, and model training. For detailed implementation and code, please refer to the project files.