000			04046aam a22003971i 4500
001			0000372101
003			0001
008			221209s2018 enka ob 000 0 eng d
015			_aGBB999792 _2bnb
020			_a9781789138719 (paperback)
020			_a9781789134544 _q(electronic bk.)
020			_z9781789138719
040			_aUMI _beng _cUMI _dN$T _dOCLCF _dUk _erda _epn
042			_aukblsr
082	0	4	_a005.133 _223
084			_a005.133 _bFUE-H _223
100	1	0	_aFuentes, Alvaro, _eauthor.
245	1	0	_aHands-on predictive analytics with Python _hBook : _bmaster the complete predictive analytics process, from problem definition to model deployment / _cAlvaro Fuentes.
300			_av, 317 pages : _billustrations ; _c24 cm.
365			_a01 _b0.00
500			_aIncludes index
520			_aKey Features Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Get up to speed with advanced predictive modeling algorithms with the help of detailed explanations Learn to present a predictive model's results as an interactive application Book Description Predictive analytics is a field of applied analytics that employs a variety of quantitative methods to analyze your data and make predictions. This book guides you through the most important concepts related to predictive analytics. With the help of practical, step-by-step examples, you'll be able to build predictive analytics solutions while using cutting-edge Python tools and packages. You'll learn effectively by defining the problem and then moving on to identifying relevant data. As you advance, you'll get to grips with tasks such as data preparation, exploring and visualizing relationships, building models, and more. You will also work with models such as K-Nearest Neighbors (KNN), random forests, and neural networks using key libraries in Python's data science stack including NumPy, pandas, Matplotlib, and Seaborn. All along, you'll explore useful examples and Python code that will help you grasp the concepts and techniques effectively. In addition to this, you'll gain detailed insights into the core techniques and algorithms used in predictive analytics. By the end of this book, you will be equipped with the skills you need to build high-performance predictive analytics solutions using Python programming. What you will learn Get to grips with the core concepts and principles of predictive analytics Explore the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into a dataset Use Keras to build powerful neural network models that produce accurate predictions Build regression and classification models using scikit-learn Who this book is for This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and are interested in implementing predictive analytics solutions using Python's data stack. Anyone looking to get started in this exciting field will also find this book useful. Proficiency in Python programming and a basic understanding of statistics and college-level algebra are required.
521			_aAll.
650		7	_aCOMPUTERS / Programming Languages / General. _2bisacsh
650		0	_aPython (Computer program language)
650		0	_aApplication software _xDevelopment.
650		0	_aDecision making _xData processing.
650		0	_aData mining.
650		0	_aBig data.
650		7	_aApplication software _xDevelopment. _2fast _0(OCoLC)fst00811707
650		7	_aBig data. _2fast _0(OCoLC)fst01892965
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aDecision making _xData processing. _2fast _0(OCoLC)fst00889041
650		7	_aPython (Computer program language) _2fast _0(OCoLC)fst01084736
852			_p10001000062569 _910195.00 _h005.133 FUE-H 62569 _vAllied Book Company _bGround Floor _dBooks _t1 _q1-New _aJZL-CUI
999			_c65019 _d65019