{"product_id":"practical-data-modeling-and-machine-learning-with-python-from-data-preparation-to-model-evaluation-and-optimization-9781067559250","title":"Practical Data Modeling and Machine Learning with Python: From Data Preparation to Model Evaluation and Optimization","description":"\u003cp\u003e • Author(s): Shouke Wei\u003cbr\u003e • Publisher: Deepsim Press\u003cbr\u003e • Publisher Imprint: Deepsim Press\u003cbr\u003e • BISAC: Languages - Python\u003c\/p\u003e\u003cp\u003eData is abundant, but understanding is not. Between raw data and meaningful decisions lies a crucial process: the ability to build, evaluate, and refine models that capture structure in the world. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eThis book, \u003ci\u003ePractical Data Modeling and Machine Learning with Python\u003c\/i\u003e, focuses on that process.\u003cbr\u003eIt is the second volume in the *Practical Data Science with Python* series. The first book introduced data exploration and visualization-how to observe patterns, clean data, and ask the right questions. This volume moves one step further: from understanding data to **modeling it**, and from intuition to \u003cb\u003equantitative reasoning.\u003c\/b\u003e.\u003cb\u003ePurpose of This Book \u003c\/b\u003eThe central goal of this book is not simply to present algorithms, but to develop a coherent approach to **data modeling**.\u003cbr\u003eIn practice, modeling is not a single step. It is a system: \u003cul\u003e\n\u003cli\u003edefining a problem clearly\u003c\/li\u003e\n\u003cli\u003epreparing data carefully\u003c\/li\u003e\n\u003cli\u003eselecting appropriate models\u003c\/li\u003e\n\u003cli\u003eevaluating performance rigorously\u003c\/li\u003e\n\u003cli\u003erefining and improving results\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eThis book follows that system. It integrates statistical modeling and modern machine learning into a unified workflow, emphasizing both \u003cb\u003eprinciples\u003c\/b\u003e and\u003cb\u003e practical implementation.\u003c\/b\u003e.\u003cb\u003eWhat This Book Covers\u003c\/b\u003eThis book is organized into six parts, each corresponding to a key stage in the data modeling and machine learning workflow. \u003cp\u003e\u003c\/p\u003e\u003cb\u003ePart I - Foundations of Data Modeling\u003c\/b\u003e introduces the fundamental concepts of data modeling and analytical thinking. It covers the practical setup of a Python environment and the essential steps of data preparation and feature engineering, establishing a solid foundation for all subsequent work.\u003cbr\u003e\u003cb\u003ePart II - Statistical Modeling Foundations\u003c\/b\u003e provides the necessary statistical background for modeling. Topics such as probability distributions, estimation, and hypothesis testing are presented with a focus on interpretation and practical relevance.\u003cbr\u003e\u003cb\u003ePart III - Statistical Modeling Techniques\u003c\/b\u003e develops core modeling approaches, including linear regression, regularization, and generalized linear models. These methods form the bridge between classical statistics and modern machine learning.\u003cbr\u003e\u003cb\u003ePart IV - Foundations of Machine Learning\u003c\/b\u003e introduces the principles that govern machine learning systems, including training and validation strategies, the bias-variance tradeoff, and the role of cross-validation and preprocessing pipelines in building reliable models.\u003cbr\u003e\u003cb\u003ePart V - Core Machine Learning Models\u003c\/b\u003e presents practical machine learning methods, including classification models, regression techniques, and ensemble approaches. Emphasis is placed on understanding model behavior and comparing different methods in realistic settings.\u003cbr\u003e\u003cb\u003ePart VI - Model Evaluation and Optimization\u003c\/b\u003e focuses on assessing and improving models. It covers performance metrics, validation strategies, hyperparameter tuning, and model interpretation techniques, providing a complete framework for building robust and trustworthy models. \u003cp\u003e\u003c\/p\u003eTogether, these parts form a coherent progression from data preparation to model evaluation and optimization, reflecting the full lifecycle of data-driven modeling. \u003cp\u003e\u003c\/p\u003eRather than focusing only on algorithms, this book emphasizes how to think about modeling problems, avoid common pitfalls, and develop reliable solutions in practice.","brand":"Deepsim Press","offers":[{"title":"Paperback","offer_id":47883412668567,"sku":"9781067559250","price":5041.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781067559250.webp?v=1781101711","url":"https:\/\/atlanticbooks.com\/products\/practical-data-modeling-and-machine-learning-with-python-from-data-preparation-to-model-evaluation-and-optimization-9781067559250","provider":"Atlantic Books","version":"1.0","type":"link"}