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@article{174604,
        author = {P Ratna Rohith and D Hima Bindu and P Hemanth Nani and K Niharika and T Vinay},
        title = {CUSTOMER CHURN PREDICTION USING CUSTOM RECURRENT NEURAL NETWORK MODEL},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {50-55},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174604},
        abstract = {},
        keywords = {Customer churn, Revenue losses, Attrition forecasting, Client retention, BiLSTM-CNN, Machine learning, Deep learning, Consumer behavior, Class imbalance, SMOTEENN strategy, Recurrent Neural Network (RNN), Customer data, Demographics, Service consumption, Contract details, Billing details, Sequential nature of RNNs, Resampling efficiency, Accuracy (96.61%), Detection rates, Precision, Recall, F1-scores, Flask-based web application, Real-time churn predictions, Data-driven decision-making, Scalable solution, Customer attrition, Service improvement.},
        month = {March},
        }