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Publications on Amazon.com and Lulu.com

Featured Books:


operations-research-using-open-source-tools     data-analytics-using-open-source-tools    


ALL TITLES
Category
ISBN

Biography
978-1-257-86014-2

Biography
978-1-257-76188-3

Biography
978-1-257-97624-9

Christian
978-1-329-16078-1

Christian
978-1-365-83555-1

Christian
978-1-257-90451-8
The Devil did not make me do it (Coming Soon)
Christian
978-1-365-98214-9

Church security
978-1-300-97329-4
To Watch Over Them Day and Night
Church security
978-1-365-88075-9

Data Science
978-1-365-27041-3

Data Science
978-1-329-28062-5

Data Science
978-1-312-19311-6

Data Science
978-1-365-81915-5

Data Science
978-1-312-84101-7

General
978-1-312-71732-9

History
978-1-312-38213-8
Dear Mr. President (Under rewrite)
Leadership
978-1-105-55514-5

Leadership/Biography
978-1-329-56566-1

Math Modeling
978-1-257-00583-3

Math Modeling
978-1-4583-9255-8

Math Modeling
978-1-257-83225-5

Operations Research
978-1-329-00404-7
Discrete Event Simulation using ExtendSim 8 (Buy PB) (Buy HC) Simulation Modeling 978-1-300-79058-7



Missile Flight Simulation - Surface-to-Air Missiles (Buy HC)
Simulation Modeling
978-1-329-64495-3
Simulation Conceptual Modeling
Simulation Modeling
978-1-105-18162-7
Verification and Validation for Modeling and Simulation
Simulation Modeling
978-1-312-74061-7
Predictive Modeling and Analytics
Statistical Modeling
978-1-312-37544-4
Systems Engineering Processes and Practice (Buy HC) Systems Engineering 978-1-257-09273-4

Comments

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  2. DLJuly 7, 2024 at 9:21 PM

    Predictive modeling and analytics are essential components of data science, focusing on creating models that predict future outcomes based on historical data. There are many comprehensive books available that cover these topics in depth, providing both theoretical understanding and practical applications.

    "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" by Eric Siegel

    Overview: This book provides a broad introduction to predictive analytics and its applications. It’s written in an accessible style, making it suitable for readers without a deep technical background.
    Key Topics: Real-world applications, case studies, basic concepts of predictive modeling.

    Deep Learning Projects for Final Year

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