The text features a running case study to help readers apply theoretical knowledge to real-world scenarios. It includes practical examples demonstrating business analytics applications such as customer churn prediction, fraud detection, and sales forecasting. Each chapter contains learning objectives, practical exercises, and summaries to support the learning process.
The book covers both supervised and unsupervised machine learning techniques, data visualization, and data manipulation using key libraries like Pandas, NumPy, and Matplotlib. It emphasizes actionable insights and decision-making rather than just technical implementation, making it ideal for business professionals looking to enhance their analytical capabilities.
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