• Sindhu R Kashyap
• Shivumanjesh P

Fast-paced tech has had a big impact on how companies operate. With so many options to choose from, churning has become a major issue for businesses. Customer churn is a major challenge for businesses of all sizes. When a customer churns, they stop using the company's products or services. This can lead to lost revenue and profits. It is therefore important for businesses to develop strategies to reduce customer churn.One way to reduce customer churn is to use machine learning models to predict which customers are likely to churn. This information can then be used to target these customers with interventions to prevent them from churning.Ensemble techniques are a powerful way to improve the performance of machine learning models. Ensemble techniques combine the predictions of multiple base learners to produce a more accurate prediction.

Ensemble Techniques, Telco Dataset, Cat Boost, LightGBM, Logistic regression

"Customer Churn Prediction Using Ensemble Techniques on Telco Dataset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.d376-d383, November-2023, Available :http://www.jetir.org/papers/JETIR2311350.pdf

"Customer Churn Prediction Using Ensemble Techniques on Telco Dataset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppd376-d383, November-2023, Available at : http://www.jetir.org/papers/JETIR2311350.pdf