Preface
About the Writer
Bakti Siregar, M.Sc., CDS is a Lecturer in the Data Science Program at ITSB. He obtained his Master’s degree in Applied Mathematics from the National Sun Yat-sen University, Taiwan. Alongside his academic role, Bakti also serves as a Freelance Data Scientist, collaborating with leading companies such as JNE, Samora Group, Pertamina, and PT. Green City Traffic.
His professional and research interests include Big Data Analytics, Machine Learning, Optimization, and Time Series Analysis, with a particular focus on finance and investment applications. His core expertise lies in statistical programming using R and Python, complemented by strong experience in database management systems such as MySQL and NoSQL. In addition, he is proficient in applying Big Data technologies, including Spark and Hadoop, for large-scale data processing and analysis.
Some of his projects can be viewed here: Rpubs, Github, Website, and Kaggle
Acknowledgments
Predictive Analytics is more than a technical discipline—it is a bridge between data and decision-making, transforming information into foresight that guides action. This book is crafted to help learners advance from foundational understanding toward building end-to-end predictive solutions that are robust, interpretable, and impactful.
The material explores a connected sequence of topics:
- Predictive Modeling Foundations: Regression, classification, ensembles, and interpretability.
- Forecasting and Sequential Models: Time series methods, Prophet, and LSTM for temporal data.
- Applied Prediction: Real-world use cases across business, healthcare, and industry.
- Prediction Engineering: Feature construction, optimization, and workflow integration.
- Intelligent Prediction: AI-driven approaches in NLP, vision, and AutoML.
- Deployment and MLOps: Operationalizing predictive systems with APIs, pipelines, and monitoring.
This work would not have been possible without the encouragement of colleagues, students, and mentors who provided constructive feedback, shared insights, and inspired new directions. My deepest gratitude goes to all who contributed, directly or indirectly, to the development of this resource. It is my hope that this book will serve as both a practical reference and a roadmap for learners and professionals applying predictive analytics in research, industry, and innovation.
Feedback & Suggestions
The evolution of this book depends on continuous learning and dialogue. Readers are warmly invited to share their perspectives on clarity, depth, case studies, and practical relevance. Suggestions for expanding future editions—whether through advanced methods, new applications, or emerging tools—are highly valued.
Your input will help refine this work into a comprehensive, living resource that grows with the rapidly changing field of predictive analytics. Thank you for your engagement and contributions to this journey.
For feedback and suggestions, please reach out via:
- dsciencelabs@outlook.com
- siregarbakti@gmail.com
- siregarbakti@itsb.ac.id