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In this unit, we bridge the gap between foundational predictive analytics and practical implementation in modern digital transformation contexts. We begin by exploring Predictive Analytics in Python, where we leverage Python鈥檚 powerful libraries to build, train, and evaluate regression and classification models. Through hands-on exercises, you鈥檒l learn how to process data, apply linear and logistic regression, and visualize results effectively. Next, we extend our focus to Linear Regression on the Cloud, demonstrating how cloud platforms enable scalable, efficient training of regression models on large datasets. You鈥檒l gain practical experience in using cloud-based tools and services to handle real-world data challenges, such as forecasting trends and optimizing resource allocation. We also delve into Logistic Regression on the Cloud, emphasizing its applications in predicting discrete outcomes. By hosting and training logistic regression models in a cloud environment, we unlock the ability to process high-volume, real-time data, essential for tasks like customer behavior prediction, fraud detection, and healthcare analytics. Throughout the unit, we鈥檒l highlight the role of predictive analytics in digital transformation, showing how cloud computing and Python empower organizations to make data-driven decisions.