Data leakage is a critical issue that can significantly impact the accuracy and reliability of machine learning models. It occurs when information from the test set leaks into the training process, leading to artificially inflated performance metrics and misleading results. Preventing data leakage is essential for building robust and trustworthy AI/ML models. In this article, we will explore some valuable tips and tricks in AI/ML with Python to avoid data leakage and ensure the integrity of our models. Train-Test Split: Incorporating insights from effective Python training , the initial step in preventing data leakage is executing a meticulous train-test split. When splitting your dataset into training and testing subsets, it's crucial to maintain the data's integrity. Python's scikit-learn library provides convenient methods to perform this task. Remember to never use any information from the test set during model training, as it can lead to overfitting and inaccurate eval