The EU is a major trade partner with India, and economic linkages continue to deepen. India and the EU have increased their political interactions and are crucial partners in promoting sustainable development. Recent trends in India-EU relations show that New Delhi and Brussels are keen to expand their partnership in multiple areas, including sustainable development and natural resources management. This volume takes a unique approach to exploring the efficient management of resources in the era of resource depletion due to climate changes and business expansion, in conjunction with considering the multiple dimensions of India-European Union cooperation. It analyzes regional and global trends in the process of globalization and sustainable development, particularly in the context of natural resource management and resource efficiency. It offers a variety of perspectives through useful and current information in this field, providing a concise and holistic understanding of the issues and challenges faced when exploiting natural resources for sustainable and efficient resource utilization.

This proceedings volume documents the contributions presented at the CONIAPS XXVII international Conference on Recent Advances in Pure and Applied Algebra. The entries focus on modern trends and techniques in various branches of pure and applied Algebra and highlight their applications in coding theory, cryptography, graph theory, and fuzzy theory.

The world is approaching a point where big data will start to play a beneficial role in many industries and organizations. Today, analyzing data for new insights has become an everyday norm, increasing the need for data analysts to use efficient and appropriate tools to provide quick and valuable results to clients. Existing research in the field currently lacks a full coverage of all essential algorithms, leaving a knowledge void for practical implementation and code in Python with all needed libraries and links to datasets used. Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science serves as a one-stop book to help emerging data scientists gain hands-on skills needed through real-world data and completely up-to-date Python code. It covers all the technical details, from installing the needed software to importing libraries and using the latest data sets; deciding on the right model; training, testing, and evaluating the model; and including NumPy, Pandas, and matplotlib. With coverage on various machine learning algorithms like regression, linear and logical regression, classification, support vector machine (SVM), clustering, k-nearest neighbor, market basket analysis, Apriori, k-means clustering, and visualization using Seaborne, it is designed for academic researchers, undergraduate students, postgraduate students, executive education program leaders, and practitioners.