Abstract
This research examines the customer churn issues in the telecommunications industry, which significantly impact companies by leading to revenue loss and high costs associated with acquiring new customers. Therefore, effective prediction and preventing churn are still significant challenges in the industry. This research utilises deep learning techniques to implement a model that can predict customer churn. The dataset used is the "Customer Churn Prediction" dataset from Kaggle, which contains 100,000 customer records with 100 features, including demographic information, service usage patterns, billing details, and customer behaviour metrics collected over a 12-month period from a major telecommunications provider. Moreover, the Genetic Algorithm is used as a feature selection technique to find the best features from the dataset and help to improve the model's performance. The most crucial factors that impact churn, such as customer usage patterns, network quality, and engagement with customer care, were identified. Moreover, the implemented Deep Learning model, Temporal Convolutional Network, used train and test data to calculate several metrics. Hyperparameters for TCN are used to improve model performance. The results are an accuracy of 70.4%, a sensitivity of 71%, and an AUC of 0.8. These results outperform the single models, such as Extreme Gradient Boosting (XGBoost), Random Forests (RF), Light Gradient Boosting Machine (LGBM), Logistic Regression (LR), K-nearest neighbours (KNN), Support Vector Machines (SVM), Naïve Bayes, and AdaBoost. The suggested model helps telecom companies keep their customers and reduce churn rates by providing a practical solution.