• Amol Chole
• Trupti Mate
• Rushikesh Chavan
• Shradha Mandan

Customer churn is a major problem and one of the most important concerns for large companies, especially in the telecom field, companies are seeking to develop means to predict potential customer to churn. The main contribution of our work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. The model developed in this work uses machine learning and Deep Learning techniques on big data platform and builds a model that predicts the Customer Churn. The model was prepared and tested by working on a large dataset created by transforming big raw data provided by GitHub. The dataset contained all customers information, and was used to train, test, and evaluate the system. The model experimented four algorithms: Random Forest, Support Vector Machine, K-Nearest Neighbour and Convolutional Neural Network. However, the best results were obtained by applying Random Forest algorithm. The Big Data used from GitHub contains 56000 user’s data from different Telecom Sectors.

RF (Random Forest), SVM (Support Vector Machine), KNN (K-nearest Neighbour’s), CNN (Convolutional Neural Networks).

"Customers Churn Prediction using Machine Learning and Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.632-635, May-2023, Available :http://www.jetir.org/papers/JETIRFX06111.pdf

"Customers Churn Prediction using Machine Learning and Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. pp632-635, May-2023, Available at : http://www.jetir.org/papers/JETIRFX06111.pdf