Machine Learning for Data Mining: Advanced Predictive Modeling Techniques for Data Analysis – Non Fiction – Paperback

Title:
Machine Learning for Data Mining: Improve your data mining capabilities with advanced predictive modeling

Condition: BRAND NEW
Format: Paperback
ISBN: 9781838828974

Overview:
Machine Learning for Data Mining offers a practical, hands-on pathway to elevate your data mining projects with advanced predictive modeling. Authored by Jesus Salcedo and published by Packt Publishing in 2019 as a first edition, this paperback guide is written for data scientists, analysts, and students who want to blend the strengths of machine learning with traditional data mining techniques. The book demystifies when and how to apply various ML methods within data mining workflows, from initial data preparation to final model deployment. Youll learn a clear, step-by-step approach to building, evaluating, and refining predictive models, with an emphasis on interpretability and actionable insights. Topics include selecting the right algorithm for different data landscapes, designing robust experiments, handling real-world data challenges, and translating model outcomes into business value. The tone remains practical and outcome-focused, guiding you through common pitfalls such as overfitting, data leakage, and imbalanced datasets. If youre aiming to upgrade your toolkit and deliver data-driven decisions with confidence, this is a essential companion for your data science journey.

What Makes This Book Stand Out:
What sets this guide apart is its deliberate integration of machine learning techniques into data mining practice, giving you a concrete blueprint for smarter analyses. Salcedo emphasizes a pragmatic, results-first mindset: choose the right ML approach based on the data, validate it rigorously, and interpret outcomes in business terms. Unlike theory-only texts, this book foregrounds actionable stepsfeature engineering strategies, model selection criteria, and stepwise evaluation plansthat you can apply immediately to real datasets. The content is designed to help you improve model development and performance in a range of contexts, from fraud detection and customer analytics to operational monitoring and beyond. By focusing on when and how to apply each technique, the book helps you avoid common missteps and accelerate the journey from data to decision.

Who This Book Is Perfect For:
This book is ideal for data scientists, data analysts, and ML practitioners who want to deepen their data mining capabilities with predictive modeling. It also serves students and researchers seeking practical, implementable guidance on combining ML with data mining workflows. If youre responsible for turning large, messy datasets into reliable insights, or youre preparing for projects in market research, security analytics, or IoT data streams, this accessible paperback will become a go-to reference. Beginners will appreciate the clear, step-by-step approach, while more experienced readers will value the concrete techniques and decision-making frameworks that speed up project delivery.

Key Highlights:

• Clear guidance on when to apply machine learning within data mining projects
• Step-by-step workflow from data preparation to model evaluation
• Practical strategies for feature engineering and algorithm selection
• Coverage of diverse techniques including classification, regression, clustering, and anomaly detection
• Techniques to avoid overfitting, data leakage, and imbalanced data issues
• Focus on interpreting results and translating them into business actions
• Hungry for real-world applicability, with insights drawn from typical data science scenarios

About the Author:
Jesus Salcedo is the author of Machine Learning for Data Mining, published by Packt Publishing in 2019. This work reflects a practical, reader-friendly approach to bridging machine learning with data mining, designed to help professionals implement robust predictive modeling in real-world data environments. Salcedos writing emphasizes clarity, hands-on techniques, and accessible explanations, making advanced concepts approachable for both newcomers and seasoned practitioners. Readers can expect a focused, results-driven perspective that translates theory into implementable steps and measurable improvements in data mining projects.

Why Youll Love This Book:
If youre aiming to upgrade your data analysis capabilities, this book delivers a practical, immediately usable framework for applying ML to data mining. It helps you build stronger models, interpret outcomes with confidence, and present findings in business-friendly language. The paperback edition offers a portable, approachable format for on-the-job reference, classroom use, or study groups. With its clear structure and concrete guidance, its a reliable companion for driving tangible improvements in data-driven decision making, whether youre optimizing campaigns, detecting anomalies, or forecasting future trends.

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