Performance Analysis of Algorithms in Imbalanced Data Environments Imbalanced data is a common challenge in machine learning applications, where class distributions are highly unequal. In such datasets, one class significantly outnumbers the other, often representing normal cases, while the minority class may correspond to rare but critical events such as fraud detection, rare disease diagnosis, or cybersecurity threats. When traditional algorithms are applied to imbalanced datasets, they may achieve high accuracy; however, this metric can be misleading. A model that predicts only the majority class can still obtain high accuracy while completely failing to detect minority instances. Therefore, more informative evaluation metrics such as Recall, Precision, F1-score, ROC curves, and Precision-Recall curves are essential for reliable performance assessment. Different classification algorithms respond differently to data imbalance. Many models tend to bias toward the majority class, reducing their ability to detect rare events. To address this issue, several techniques have been developed, including resampling strategies such as oversampling and undersampling, as well as synthetic data generation methods like SMOTE. Another approach involves adjusting class weights within the learning algorithm. These techniques are widely implemented in practical applications, especially in fraud detection systems and AI-based medical diagnosis tools. Modern machine learning libraries such as Scikit-learn provide built-in tools to handle imbalanced datasets efficiently. Ultimately, analyzing algorithm performance in imbalanced environments requires not only selecting appropriate models but also understanding data distribution and choosing suitable evaluation metrics to ensure fairness and effectiveness.