عنوان

Applied analytics through case studies using SAS and R :

شابک

1484235258

شابک

1484235266

شابک

1484240464

شابک

9781484235256

شابک

9781484235263

شابک

9781484240465

شابک اشتباه

148423524X

شابک اشتباه

9781484235249

عنوان اصلي

Applied analytics through case studies using SAS and R :

نام عام مواد

[Book]

ساير اطلاعات عنواني

implementing predictive models and machine learning techniques /

نام نخستين پديدآور

Deepti Gupta.

محل نشرو پخش و غیره

Boston, Massachusetts :

نام ناشر، پخش کننده و غيره

Apress,

تاریخ نشرو بخش و غیره

[2018]

تاریخ نشرو بخش و غیره

©2018

نام خاص و کميت اثر

1 online resource (xx, 404 pages) :

ساير جزييات

illustrations

متن يادداشت

Includes bibliographical references and index.

متن يادداشت

Intro; Table of Contents; About the Author; About the Contributor; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Data Analytics and Its Application in Various Industries; What Is Data Analytics?; Data Collection; Data Preparation; Data Analysis; Model Building; Results; Put into Use; Types of Analytics; Understanding Data and Its Types; What Is Big Data Analytics?; Big Data Analytics Challenges; Data Analytics and Big Data Tools; Role of Analytics in Various Industries; Who Are Analytical Competitors?; Key Models and Their Applications in Various Industries; Summary

متن يادداشت

Chapter 4: Telecommunication Case StudyTypes of Telecommunications Networks; Role of Analytics in the Telecommunications Industry; Predicting Customer Churn; Network Analysis and Optimization; Fraud Detection and Prevention; Price Optimization; Case Study: Predicting Customer Churn with Decision Tree Model; Advantages and Limitations of the Decision Tree; Handling Missing Values in the Decision Tree; Handling Model Overfitting in Decision Tree; Prepruning; Postpruning; How the Decision Tree Works; Measures of Choosing the Best Split Criteria in Decision Tree; Decision Tree Model Using R

متن يادداشت

Predictive Value Validation in Logistic Regression ModelLogistic Regression Model Using R; About Data; Performing Data Exploration; Model Building and Interpretation of Full Data; Model Building and Interpretation of Training and Testing Data; Predictive Value Validation; Logistic Regression Model Using SAS; Model Building and Interpretation of Full Data; Summary; References; Chapter 3: Retail Case Study; Supply Chain in the Retail Industry; Types of Retail Stores; Role of Analytics in the Retail Sector; Customer Engagement; Supply Chain Optimization; Price Optimization

متن يادداشت

Space Optimization and Assortment PlanningCase Study: Sales Forecasting for Gen Retailers with SARIMA Model; Overview of ARIMA Model; AutoRegressive Model; Moving Average Model; AutoRegressive Moving Average Model; The Integrated Model; Three Steps of ARIMA Modeling; Identification Stage; Estimation and Diagnostic Checking Stage; Forecasting Stage; Seasonal ARIMA Models or SARIMA; Evaluating Predictive Accuracy of Time Series Model; Seasonal ARIMA Model Using R; About Data; Performing Data Exploration for Time Series Data; Seasonal ARIMA Model Using SAS; Summary; References

بدون عنوان

0

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

متن يادداشت

Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. What You'll Learn Understand analytics and basic data concepts Use an analytical approach to solve Industrial business problems Build predictive model with machine learning techniques Create and apply analytical strategies Who This Book Is For Data scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9781484235256

عنوان

Applied analytics through case studies using SAS and R.

شماره استاندارد بين المللي کتاب و موسيقي

9781484235249

موضوع مستند نشده

SAS (Computer file)

موضوع مستند نشده

SAS (Computer file)

موضوع مستند نشده

Business enterprises-- Evaluation, Case studies.

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

R (Computer program language)

موضوع مستند نشده

BUSINESS & ECONOMICS-- Industries-- General.

موضوع مستند نشده

Business enterprises-- Evaluation.

موضوع مستند نشده

Business mathematics & systems.

موضوع مستند نشده

Computer programming-- software development.

موضوع مستند نشده

Databases.

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Maths for computer scientists.

موضوع مستند نشده

R (Computer program language)

موضوع مستند نشده

BUS-- 070000

موضوع مستند نشده

UN

موضوع مستند نشده

UN

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Gupta, Deepti

تاريخ عمليات

20200823032049.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y