Customer Churn Prediction Model and UI Dashboard Visualization

The increasing competition in sectors including telecommunications, banking, ecommerce, and subscription- based services has made customer churn prediction an important academic and commercial issue. Early detection of churn-prone consumers is a strategic advantage for businesses since retaining current customers is far less expensive than recruiting new ones. This study looks at the use of machine learning methods to forecast customer loss and pinpoint the main causes. Analysis is done using a structured churn dataset that includes billing, behavioural, demographic, and service-related characteristics. The research methodology follows a methodical pipeline that comprises of feature engineering, model creation, exploratory data analysis (EDA) to help understand the data patterns and anomalies, and data pretreatment. EDA highlights the churn patterns, that includes the contract type and the monthly charges. Data preprocessing and feature engineering used in the models help enhance the model quality, and the models are assessed using system of measurement appropriate for imbalanced data. Gradient Boosting was found to be the most effective for identifying high-risk churners. Tenure, contract type, and monthly fees are the most significant indicators, according to feature importance analysis. Overall, the study offers a methodology which is useful for integrating the churn predictions into customer relationships management systems and strike a balance between predictive accuracy and interpretability.