Machine Learning for Business Analytics Real-Time Data Analysis for Decision-Making
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Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions.
The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing at a CAGR of 44.06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of machine learning for business analytics are explained with real case studies.
Essentially, this book acts as a guide to all business analysts. The authors blend the basics of data analytics and machine learning and extend its application to business analytics. This book acts as a superb introduction and covers the applications and implications of machine learning. The authors provide first-hand experience of the applications of machine learning for business analytics in the section on real-time analysis. Case studies put the theory into practice so that you may receive hands-on experience with machine learning and data analytics. This book is a valuable source for practitioners, industrialists, technologists, and researchers.
About the Editors
List of Contributors
1. Introduction to Machine Learning for Data Analytics
Dr.L.K.Indumathi1, Mr.Abdul Rais Abdul Waheed, Ms. Juvairia Begum
2. Role of Machine Learning in Promoting Sustainability
Dr. Muneza Kagzi
3. Addressing the Utilization of Popular Regression Models in business applications
Meganathan Kumar Satheesh and Korupalli V Rajesh Kumar
4. CHATBOTS: The Uses and Impact In The Hospitality Sector
Princy Sera Rajan, Darsana S Babu, and Sameena M.H
5. Traversing Through the Use of Robotics in Medical Industry: Outlining Emerging Trends and Perspectives for Future Growth
Gaurav Nagpal, Kshitiz Sinha, Himanshu Seth, Namita Ruparel
6. Integration of AI in Insurance and Health Care: What Does It Mean?
Dr. A.Kannan, Dr. B. Justus Rabi, and Dr. M. Anand
7. Artificial Intelligence in Agriculture – A Review
Harshitha Sirineni, Thakur Santosh, and Dr.S.Deepajothi
8. Machine Learning and Artificial Intelligence-based Tools in Digital Marketing: An Integrated Approach
Dr.Preetha Mary George, Dr Sanjeev Ganguly, Venkat Reddy Yasa
9. Application Of Artificial Intelligence In Market Knowledge And B2b Marketing Co-Creation
H. Raghupathi, Debdutta Choudhury, and Dr. Cynthia Jabbour Sfeir
10 A Systematic Literature Review of Artificial Intelligence's Impact on Customer Experience
Dr. M.A. Sikandar, Praveen kumar Munari, and Meghraj Arli
11. The Impact of Artificial Intelligence on Customer Experience and the Purchasing Process
Dr. Laxmi Shaw1, Megha Mankal, and Chinnapani Kiran Kumar
12. Application of Artificial Intelligence in Banking – A Review
Syed Hasan Jaffar, Viplap Dhandhukia, Bijay Kumar G
13. Digital Ethics: Towards a socially preferable development of AI systems
C. Guzmán-Velásquez, J. G. Lalinde-Pulido
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