This book presents a selection of the latest and representative developments in predictive analytics using big data technologies. It focuses on some critical aspects of big data and machine learning and provides studies for readers.
The chapters address a comprehensive range of advanced data technologies used for statistical modelling towards predictive analytics.
Topics included in this book include:
- Categorized machine learning algorithms
- Player monopoly in cricket teams.
- Chain type estimators
- Log type estimators
- Bivariate survival data using shared inverse Gaussian frailty models
- Web log analysis
- COVID-19 epidemiology
This reference book will be of significant benefit to the predictive analytics community as a useful guide of the latest research in this emerging field.
Table of Contents
Chapter 1 Data Analytics on Various Domains with Categorized Machine Learning Algorithms
- R. Suguna and R. Uma Rani
- Introduction
- Data Analytics
- Machine Learning
- Supervised Machine Learning
- Classification
- Regression
- Unsupervised Machine Learning
- Reinforcement Learning
- Background of Data Analytics
- Various Domains
- Medical Domain-Autism Data
- Agriculture Domain-Rainfall Data
- Social Domain-Child Abuse Data
- Illustration of Regression with Various Domains
- Logistic Regression
- Linear Regression
- Multiple Linear Regression
- Results and Discussion
- Logistic Regression for Autism Data
- Roc Curve
- Linear Regression for Child Abuse Data
- Multiple Linear Regression with Rainfall Data
- Anova (Analysis of Variance Table)
- Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgements
- References
Chapter 2 Quantifying Players’ Monopoly in a Cricket Team: An Application of Bootstrap Sampling
- Bireshwar Bhattacharjee and Dibyojyoti Bhattacharjee
- Introduction
- Motivation of the Study
- Review of Literature
- Objectives of the Study
- Methodology
- Data Source
- Data Collection Process
- Results and Discussion
- Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgements
- References
Chapter 3 on Mean Estimation Using a Generalized Class of Chain Type Estimator Under Successive Sampling
- Shashi Bhushan, Nishi Rastogi and Shailja Pandey
- Introduction
- Sampling Methodology
- Sample Structure and Notations
- Formulation of the Proposed Generalized Class
- Important Special Cases of Cases of Class of Estimator For
- Unmatched Proportion
- Important Special Cases of Class of Estimator for Matched
- Proportion
- Mean Square Error of the Proposed Generalized Class
- Analytical Study
- Optimal Replacement Policy
- Efficiency Comparison
- Numerical Study
- Conclusion and Interpretation
- Consent for Publication
- Conflict of Interest
- Acknowledgements
- References
Chapter 4 Log Type Estimators of Population Mean Under Ranked Set Sampling
- Shashi Bhushan and Anoop Kumar
- Introduction
- Literature Review
- Proposed Estimators
- Theoretical Comparison
- Simulation Study
- Results of the Simulation Study
- Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgements
- Appendix I
- References
Chapter 5 Analysis of Bivariate Survival Data Using Shared Inverse Gaussian Frailty Models: a Bayesian Approach
- Arvind Pandey, Shashi Bhushan, Lalpawimawha and Shikhar Tyagi
- Introduction
- General Shared Frailty Model
- Inverse Gaussian Frailty
- Baseline Distributions
- Proposed Models
- Bayesian Estimation of Parameters and Model Comparisons
- Simulation Study
- Analysis of Kidney Infection Data
- Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgements
- References
Chapter 6 an Efficient Approach for Weblog Analysis Using Machine Learning Techniques
- Brijesh Bakariya
- Introduction
- Machine Learning Techniques
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Python
- Pandas
- Related Work
- Proposed Work
- Experimental Results
- Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgements
- References
Chapter 7 an Epidemic Analysis of COVID-19 Using Exploratory Data Analysis Approach
- Chemmalar Selvi G. and Lakshmi Priya G. G.
- Introduction
- Is Eda a Critical Task?
- How Does the Data Scientist Use the Eda?
- Univariate Eda Methods
- Descriptive Statistics
- Box Plot
- Histogram
- Multivariate Eda Methods
- Cross-Tabulation
- Correlation Matrix
- Maps
- Graphs
- Does Programming Knowledge Required in the Eda Process?
- Protocol Guiding When and Where Eda is Efficient
- Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgements
- References
- Subject Index
Author
- Arvind Pandey
- Dharmendra Singh Rajput
- Krishna Kumar Mohbey
