This research paper presents a comprehensive analysis of Google search trends to understand public interests, behavioral patterns, and their correlation with real-world events. Using Python and libraries such as Pytrends, Pandas, and Matplotlib, the study performs data extraction, cleaning, and visualization to identify trends over time. The analysis focuses on key technology-related keywords including Machine Learning, Data Science, and Cloud Computing. The results reveal that search patterns are strongly influenced by real-world events, seasonal variations, and societal behavior. Machine Learning demonstrates the highest growth trend, Data Science shows moderate but consistent growth, while Cloud Computing remains relatively stable. The study highlights the significance of Google Trends as a cost-effective and real-time data source for various domains such as healthcare, education, marketing, and economics. It also discusses key limitations including lack of demographic data, keyword ambiguity, and noise in the dataset. The findings suggest that with the integration of Artificial Intelligence and Machine Learning, Google Trends can evolve into a predictive system for forecasting future trends. This research contributes to understanding how digital search behavior can be leveraged for data-driven decision making and real-world insights.
Vijay Kumar Verma (Sat,) studied this question.