Credit card fraud detection is crucial for financial institutions to protect customers and prevent monetary losses. Logistic regression, a machine learning algorithm, is widely employed for this task due to its effectiveness in binary classification problems like fraud detection. In credit card fraud detection using logistic regression, the algorithm learns from historical transaction data, including features like transaction amount, location, time, and user behavior patterns. These features are used to train the model to distinguish between legitimate and fraudulent transactions. Logistic regression works by estimating the probability of a transaction being fraudulent based on the input features. It calculates a decision boundary that separates legitimate and fraudulent transactions in the feature space. During training, the algorithm adjusts the model parameters to minimize the difference between predicted and actual outcomes. Once trained, the logistic regression model can predict the likelihood of fraud for new transactions. Transactions with probabilities above a predefined threshold are flagged as potentially fraudulent, prompting further verification steps such as additional authentication or investigation. 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 evolving fraud patterns and maintain effectiveness in detecting new fraud schemes. Overall, credit card fraud detection using logistic regression offers a reliable and scalable solution to mitigate financial risks associated with fraudulent transactions while ensuring a seamless experience for legitimate cardholders.