<![CDATA[Customer churn represents a major challenge for telecommunication companies because of its significant influence on revenue stability and customer retention efforts. Intense competition among service providers has increased the need for reliable predictive models capable of identifying customers with a high probability of terminating their subscriptions. This study focuses on the analysis and prediction of customer churn by applying machine learning techniques to the Telco Customer Churn dataset. The research workflow includes data preprocessing stages such as duplicate removal, treatment of missing values, and transformation of both categorical and numerical features. Exploratory data analysis supported by visualization techniques is employed to examine customer behavior and feature relationships. Subsequently, the dataset is partitioned into training and testing subsets using an 80:20 stratified split. A preprocessing pipeline is applied, incorporating feature scaling for numerical variables and one-hot encoding for categorical variables. Predictive models are developed using Logistic Regression and Random Forest algorithms, and their performance is assessed through accuracy measurements and classification reports. The results indicate that the Random Forest model delivers better predictive performance than Logistic Regression, demonstrating its effectiveness in modeling complex data patterns. Overall, the study confirms that machine learning-based approaches can serve as effective tools for churn prediction and offer meaningful insights to support strategic decision-making in customer retention within the telecommunication sector.]]>
