This research aims to predict customer churn in a telecommunications company using Logistic Regression (LR) and Gradient Boosting Classifier (GBC) algorithms. Customer churn poses a significant challenge as acquiring new customers is costlier than retaining existing ones. The dataset from Kaggle comprises 7043 records and 21 attributes. The process includes data pre-processing, cleaning, transformation, and normalization using a Min-Max Scaler. The data is split into features (X) and target (y), then divided into training and testing sets with an 80:20 ratio. Both models were trained and evaluated using a confusion matrix. Results show that the GBC model outperforms the LR model, with an accuracy of 83% compared to LR's 81%. This study demonstrates the effectiveness of GBC in predicting customer churn.