CHURN ANALYSIS AND PREDICTION IN TELECOMBANKING CUSTOMERS

Customer churn is one of the most significant challenges faced by telecom and banking industries, as it directly
impacts revenue generation, customer base stability, and long-term profitability. In today's highly competitive
market, retaining existing customers is more cost-effective than acquiring new ones, making churn prediction an
essential business strategy. This project focuses on analysing customer behavior and developing an intelligent
system to predict churn using advanced machine learning techniques. The study utilizes a variety of features,
including customer demographics (age, gender, location), service usage patterns (call duration, internet usage,
subscription plans), transaction history, and customer satisfaction indicators. To build an effective prediction
model, data pre-processing techniques such as data cleaning, handling missing values, feature scaling, and
encoding categorical variables are applied. Exploratory Data Analysis (EDA) is performed to identify patterns,
trends, and correlations between features and churn behavior. Multiple classification algorithms, including
Logistic Regression, Decision Trees, Random Forest, and Extreme Gradient Boosting (XGBoost), are
implemented and compared based on performance metrics such as accuracy, precision, recall, and F1-score.
Among these, ensemble methods like Random Forest and XGBoost often provide higher accuracy due to their
ability to handle complex patterns and reduce overfitting. The model's output helps in identifying customers who
are at a high risk of leaving the company. Based on these predictions, businesses can take proactive measures such
as personalized marketing strategies, targeted offers, improved customer support, and service enhancements. This
not only reduces churn rate but also improves customer satisfaction, loyalty, and overall business performance.