Customer churn prediction using logistic regression is a vital tool for businesses to anticipate and mitigate customer attrition. Logistic regression, a popular machine learning algorithm for binary classification tasks, is well-suited for this purpose. In customer churn prediction, historical data containing customer attributes, transaction history, interaction patterns, and demographic information are used to train the logistic regression model. These features serve as input variables to predict the likelihood of a customer churning or leaving the service. Logistic regression calculates the probability of a customer churning based on the input features. By fitting a logistic function to the data, the model estimates the probability of churn as a function of the input variables. During training, the model parameters are adjusted to minimize the difference between predicted and actual churn outcomes. Once trained, the logistic regression model can predict the probability of churn for new customers. By setting a threshold probability, businesses can identify customers at high risk of churning and take proactive measures to retain them, such as targeted marketing campaigns, personalized offers, or improved customer service. The performance of the logistic regression model is evaluated using metrics like accuracy, precision, recall, and F1-score. Continuous monitoring and periodic retraining of the model are essential to adapt to changing customer behavior and maintain predictive accuracy. Overall, customer churn prediction using logistic regression empowers businesses to anticipate and prevent customer attrition, thereby enhancing customer retention, revenue stability, and long-term profitability.