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In this unit, we bring together the key concepts and techniques of predictive analytics to build robust, generalizable models that address real-world challenges. We start by diagnosing two critical issues鈥攐verfitting and underfitting鈥攚hich can significantly impact a model鈥檚 performance. Using diagnostic tools, we will explore how to systematically identify and mitigate these problems to enhance model accuracy and reliability. Next, we introduce cross-validation, a powerful method to ensure models perform well on unseen data. By dividing data into training, validation, and test sets, we鈥檒l learn how to make informed decisions about features, model complexity, and regularization parameters. This approach ensures that the predictive analytics models we develop are optimized for generalizability, a key requirement for leveraging digital transformation technologies effectively. We also tackle the challenges posed by skewed datasets, especially in classification problems with binary labels. Through practical examples, we鈥檒l understand why standard metrics like misclassification error may fall short in such scenarios. To address this, we鈥檒l introduce more nuanced evaluation metrics鈥攑recision, recall, and F-score鈥攁nd demonstrate how to balance these measures by adjusting decision thresholds. By the end of this unit, you鈥檒l have a comprehensive understanding of how to diagnose and refine predictive analytics models.