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In this unit, we take a significant step forward in predictive analytics by exploring the application of neural networks for both regression and classification tasks. Neural networks offer powerful capabilities for capturing complex patterns in data, making them an essential tool for digital transformation across industries. By leveraging the scalability and efficiency of cloud platforms, we will learn to build, train, and evaluate neural network models capable of addressing real-world challenges. We begin by revisiting familiar datasets, such as expanded versions of the readmission dataset for classification and the market size dataset for regression. Through these examples, we鈥檒l explore how neural networks handle continuous and discrete predictions, enabling us to address diverse business problems. To optimize model performance, we鈥檒l incorporate techniques like cross-validation to fine-tune hyperparameters, such as regularization, and understand how these choices impact the trade-off between underfitting and overfitting. The unit also introduces advanced diagnostics to evaluate model performance, using metrics such as mean absolute error and mean squared error for regression, and confusion matrices for classification tasks with skewed datasets. These metrics help us refine our models and ensure their robustness. Additionally, we will tackle common data preparation challenges, such as handling missing or erroneous data, merging datasets, and transforming categorical variables into usable formats. Finally, we delve into practical examples, such as predicting flight delays, to illustrate the end-to-end workflow of cleaning, processing, and modeling data with neural networks. By iterating through models of varying complexity鈥攍inear, quadratic, and cubic鈥攚e鈥檒l identify how to balance complexity and generalizability to avoid overfitting. By the end of this unit, you will have a comprehensive understanding of how to implement and evaluate neural networks in cloud environments, preparing you to harness their full potential for data-driven decision-making.