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Multivariate Analysis and Machine Learning Techniques
Feature Analysis in Data Science using Python
Engels
207 b
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This book covers multivariate analysis and other computational techniques for solving data analytics problems using Python. The topics covered in the book consist: of (a) a working introduction to programming with Python for data analytics, (b) a comprehensive overview of important statistical techniques-probability; hypothesis testing; correlation and regression; factor analysis; classification techniques-logit, linear discriminant analysis, decision tree, support vector machines; clustering techniques; and survival analysis, and (c) introduction to other computational techniques such as market basket analysis, graph theory, and machine learning techniques. This book has a collection of 150 tutorials and worked-out exercises for solving problems using statistical and computational techniques using the programming language Python. This book comes in handy as it provides worked-out examples that conceptualize real-world problems using data curated from popular databases. The book provides a jump start for a self-learning analytics student, a beginner in statistics, or someone new to programming in Python. The book is used as a supplementary academic textbook for analytics students for courses on statistics, multivariate analysis, data mining, and business analytics. The book is also used as a reference handbook by a business analytics professional.
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