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Handbook of Regression Modeling in People Analytics: With Examples in R and Python

on executing the analyses. * Clear guidance on how to interpret results. * Primary instruction in R but added sections for Python coders;

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Python: Advanced Predictive Analytics

NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression;

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Learning Predictive Analytics With Python

of Predictive Analytics with Python * Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic;

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Python for R Users

languages and features concise tutorials with command-by-command translations-complete with sample code-of R to Python and Python to R;

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Building Better Models with JMP Pro

-validation, model comparison and selection, and data reduction techniques. Full of rich examples, Building Better Models with JMP Pro is an applied;

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Data Science for Marketing Analytics

of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not;

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Applied Regression and Modeling

models in regressions. All these sections provide examples with complete computer analysis and instructions commonly used in modeling and;

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Hands-On Predictive Analytics with Python

-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step;

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Python

and the libraries supported by Python with the help of real-world examples and datasets. It also helps you gain practical insights into;

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Training Systems Using Python Statistical Modeling

-world examples on training end-to-end machine learning systems in Python Book DescriptionPython's ease of use and multi-purpose nature has led;

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Statistical Regression Modeling with R

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear;

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Statistical Regression Modeling with R

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear;

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Data Science Using Python and R

the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R;

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Advanced R Statistical Programming and Data Models

multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical;

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Mastering Predictive Analytics with Python

using Python. You're expected to have basic development experience with Python. What You Will Learn * Gain an insight into components and;

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Statistical Application Development with R and Python -

Software Implementation Illustrated with R and Python About This Book * Learn the nature of data through software which takes the;

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Handbook for Applied Modeling

. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling;

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Handbook of Regression Analysis With Applications in R

Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical;

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Data Mining and Business Analytics with R

computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony;

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Handbook for Applied Modeling

. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling;

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Spreadsheet Modeling and Decision Analysis

Master key spreadsheet and business analytics skills with SPREADSHEET MODELING AND DECISION ANALYSIS: A PRACTICAL INTRODUCTION TO BUSINESS;

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Predictive Analytics

body of the text of each chapter with basic statistical methodology illustrated by real data examples. Derivations, proofs and extensions are;

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Advanced Data Analytics Using Python

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;

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Advanced Data Analytics Using Python

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;

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Predictive Data Mining Models

This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R') and WEKA. Knowledge;

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Healthcare Analytics Made Simple

Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python;

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Handbook of Regression Methods

provides a broad overview of regression models, diagnostic procedures, and inference procedures, with emphasis on how these methods are applied;

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