• Geetanjali Rokade
• Dhruv Shirsath
• Abhishek Nikam
• Aditya Patil

Customer churn prediction is a crucial task for businesses, especially those in subscription-based industries such as telecommunications, finance, and SaaS. This project aims to develop a machine learning model to predict customer churn using the Telco Customer Churn dataset from Kaggle. The project involves comprehensive data preprocessing, including handling missing values, encoding categorical variables, and normalizing numerical features. Exploratory data analysis (EDA) is performed to identify patterns and relationships between variables and customer churn. Various machine learning models, including Logistic Regression are trained and evaluated based on performance metrics such as accuracy, precision, recall, and F1-score. The best-performing model is further analyzed to identify significant factors contributing to customer churn. Feature importance analysis and visualization techniques are used to present the results, providing valuable insights for business decision-makers. These insights help in understanding the key drivers of churn and formulating strategies to improve customer retention. The project demonstrates the effectiveness of machine learning in solving real-world business problems and highlights the importance of data-driven approaches in enhancing customer satisfaction and business profitability.

Customer Churn, Machine Learning, Exploratory Data Analysis, Predictive Modeling, Telco Customer Churn Dataset, Data Visualization

"Customer Churn Behaviour Prediction and visualisation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.a459-a468, December-2024, Available :http://www.jetir.org/papers/JETIR2412039.pdf

"Customer Churn Behaviour Prediction and visualisation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppa459-a468, December-2024, Available at : http://www.jetir.org/papers/JETIR2412039.pdf