Mansurali Anifa

One of the most crucial macroeconomic indicators for assessing inflation is the Consumer Price Index (CPI), it influences a number of financial markets, productivity, and economic variables and a nation's ability to forecast changes in the CPI is critical to its economic growth. Increase in CPI may lead to market decline. India's CPI increased by 0.3% and stood at 185.80 in February 2024. Thus, it is essential to estimate the CPI variation precisely. This chapter aims to compare predictive regression algorithms such as Random Forest, Decision Tree, and Support vector models in predicting the CPI of India and uses classical regression to examine the relation between the economic indicators like crude oil rate, gold rate and Federal fund effective rate (FFER) with CPI. Predictive models have been validated using robust metrics including MAE, RMSE, MSE and R2 from which Random Forest regression is measured as the best algorithm in predicting CPI. Additionally, from Multiple Linear regression (MLR) results changes in the FFER and gold rate have a greater impact on the India’s CPI.

The rapid advancement of artificial intelligence (AI) is transforming the global tourism industry, offering new opportunities for service personalization and enhanced customer experiences. AI-driven personalization is rapidly reshaping the tourism industry by tailoring services to meet individual customer preferences. In India, this technological advancement is particularly impactful, offering significant potential to improve tourist satisfaction and business performance. This research article investigates the Impact of AI-driven personalization on the tourism industry in India, focusing on how AI technologies can enhance tourist experiences and improve the performance of tourism businesses. The study explores the implementation of AI tools such as recommendation engines, chatbots, and predictive analytics in customizing tourism services to meet individual preferences and needs. It aims to provide a comprehensive understanding of the benefits and challenges of AI-driven personalization in the context of India's diverse and rapidly growing tourism sector.

In the challenging world of complex projects, the influx of “data analytics” has offered project managers a game-changing asset, assisting them in various aspects from reporting to taking decisions, reducing cost, optimized resources utilization, effective risk management, and has ushered an era of unprecedented possibilities and opportunities. This chapter begins by elucidating the compelling need for DA adoption among project managers. The next part investigates into the application of DA across diverse project domains. The next section highlights the benefits of leveraging DA in projects and the barriers to DA adoption in projects. Furthermore, the chapter introduces a data analytics project management (DAPM) maturity model tailored to assess organizations' readiness and maturity levels in embracing DA within project management frameworks. In its final section, the chapter presents a practical roadmap for firms to navigate the complexities of DA adoption in projects.