Applied Advanced Analytics

1. Front Matter

   Pages i-ix

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2. Machine Learning for Streaming Data: Overview, Applications and Challenges

   • Shikha Verma

   Pages 1-9

3. Binary Prediction

   • Arnab Kumar Laha

   Pages 11-17

4. Reliability Shock Models: A Brief Excursion

   • Murari Mitra, Ruhul Ali Khan

   Pages 19-42

5. Explainable Artificial Intelligence Model: Analysis of Neural Network Parameters

   • Sandip Kumar Pal, Amol A. Bhave, Kingshuk Banerjee

   Pages 43-51

6. Style Scanner—Personalized Visual Search and Recommendations

   • Abhishek Kushwaha, Saurav Chakravorty, Paulami Das

   Pages 53-59

7. Artificial Intelligence-Based Cost Reduction for Customer Retention Management in the Indian Life Insurance Industry

   • Sanjay Thawakar, Vibhu Srivastava

   Pages 61-80

8. Optimization of Initial Credit Limit Using Comprehensive Customer Features

   • Shreya Piplani, Geetika Bansal

   Pages 81-86

9. Mitigating Agricultural Lending Risk: An Advanced Analytical Approach

   • Aditi Singh, Nishtha Jain

   Pages 87-102

10. Application of Association Rule Mining in a Clothing Retail Store

    • Akshay Jain, Shrey Jain, Nitin Merh

    Pages 103-114

11. Improving Blast Furnace Operations Through Advanced Analytics

    • Rishabh Agrawal, R. P. Suresh

    Pages 115-123

12. Food Index Forecasting

    • Kalyani Dacha, Ramya Cherukupalli, Abir Sinha

    Pages 125-134

13. Implementing Learning Analytic Tools in Predicting Students’ Performance in a Business School

    • R. Sujatha, B. Uma Maheswari

    Pages 135-146

14. An Optimal Response-Adaptive Design for Multi-treatment Clinical Trials with Circular Responses

    • Taranga Mukherjee, Rahul Bhattacharya, Atanu Biswas

    Pages 147-156

15. Stochastic Comparisons of Systems with Heterogeneous Log-Logistic Components

    • Shyamal Ghosh, Priyanka Majumder, Murari Mitra

    Pages 157-173

16. Stacking with Dynamic Weights on Base Models

    • Biswaroop Mookherjee, Abhishek Halder

    Pages 175-185

17. The Effect of Infrastructure and Taxation on Economic Growth: Insights from Middle-Income Countries

    • Rudra P. Pradhan

    Pages 187-197

18. Response Prediction and Ranking Models for Large-Scale Ecommerce Search

    • Seinjuti Chatterjee, Ravi Shankar Mishra, Sagar Raichandani, Prasad Joshi

    Pages 199-218

19. Connectedness of Markets with Heterogeneous Agents and the Information Cascades

    • Avijit Ghosh, Aditya Chourasiya, Lakshay Bansal, Abhijeet Chandra

    Pages 219-234

This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts, and consists of selected presentations at the 6^th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. The book is conceptually divided in seven parts. The first part gives expository briefs on some topics of current academic and practitioner interests, such as data streams,  binary prediction and reliability shock models. In the second part, the contributions look at artificial intelligence applications with chapters related to explainable AI,  personalized search and recommendation, and customer retention management. The third part deals with credit risk analytics, with chapters on optimization of credit limits and mitigation of agricultural lending risks. In its fourth part, the book explores analytics and data mining in the retail context. In the fifth part, the book presents some applications of analytics to operations management. This part has chapters related to improvement of furnace operations, forecasting food indices and analytics for improving student learning outcomes. The sixth part has contributions related to adaptive designs in clinical trials, stochastic comparisons of systems with heterogeneous components and stacking of models. The seventh and final part contains chapters related to finance and economics topics, such as role of infrastructure and taxation on economic growth of countries and connectedness of markets with heterogenous agents, The different themes ensure that the book would be of great value to practitioners, post-graduate students, research scholars and faculty teaching advanced business analytics courses.

• Data Streams
• Binary Prediction
• Shock Models on Reliability
• Artificial Intelligence Applications
• Credit Risk Analytics
• Retail Analytics
• Operations Management Analytics

• Production and Quantitative Methods, Indian Institute of Management Ahmedabad, Ahmedabad, India

  Arnab Kumar Laha

Arnab Kumar Laha is Professor of Production and Quantitative Methods (P&QM) at Indian Institute of Management Ahmedabad. He takes a keen interest in understanding how analytics, machine learning, and artificial intelligence can be leveraged to solve complex problems of business and society. He has published his research in national and international journals of repute, authored a popular book on analytics and an edited book volume published by Springer. He has been named as one of the "20 Most Prominent Analytics and Data Science Academicians in India" by Analytics India Magazine in 2018.

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