Daily Archives: July 1, 2025

Authors: Shreya Dabral

Abstract: The growing market for beauty serums, especially on online shopping websites such as Nykaa, offers a strong research case to study consumer repurchase behavior. This research examines the drivers of consumers' loyalty towards certain serum brands, with specific emphasis on the efficacy of Korean beauty serums and the importance of ingredient transparency. The study utilizes a quantitative approach, conducting a survey of 100 participants made up mainly of young women, a group that accounts for a large part of the serum market. Key takeaways indicate that 67% of consumers focus on product efficacy when making purchasing decisions, while 51% consider ingredients, showing a turn towards ingredient-driven purchasing habits. The findings also indicate that 37% of respondents buy serums every 2-3 months, showing moderate devotion to serum consumption and indicating the development of brand loyalty. Most importantly, 55% of consumers prefer buying online, indicating the vital role of ecommerce in the beauty sector. Even though brands such as Dot & Key and L'Oréal enjoy popularity, according to the research, 60% of the respondents have never used Korean serums and are also indifferent to their effectiveness, with 36.7% viewing them as a fleeting trend. This lack of trust is a challenge to K-beauty brands that need to present strong evidence of their product's effectiveness in order to change consumer attitudes. In addition, before-and-after outcome importance, highly rated by 57% of participants, shows the necessity to boost consumer faith by making strategies more transparent and open. Solving this research issue is essential because not only does it enrich the knowledge about consumer behaviour within the beauty segment, but also offers practical solutions for brands willing to boost market presence and cultivate consumer loyalty. Through an analysis of repurchase intentions, the present study tries to inform marketing strategies that would appeal to changing consumer preferences within the serum sector.

DOI: http://doi.org/10.5281/zenodo.15780127

Published by: vikaspatanker

Authors: Ayush Vishwakarma, Yashi Verma

 

 

Abstract: Accurate estimation of travel time is no longer a luxury but a necessity in modern navigation systems, directly impacting user trust and urban transportation efficiency. As cities grow more complex and dynamic, conventional prediction models struggle to adapt to real-time changes. This paper explores the transformative role of big data and artificial intelligence (AI) in refining Estimated Time of Arrival (ETA) predictions, with a focus on Google Maps. Leveraging massive datasets—including GPS trajectories, historical travel data, real-time traffic flows, and userreported incidents—Google Maps employs advanced machine learning algorithms to make adaptive and reliable ETA forecasts [3][4][8][9]. This study investigates how these AI models interpret multilayered traffic data to generate predictions, even under volatile traffic conditions. It further examines how deep learning architectures and neural networks detect patterns, anomalies, and geographic variations in travel behaviours [1][2][19]. A time-based graphical analysis illustrates the improvements in ETA prediction accuracy from 2017 to 2025, emphasizing the system’s continual evolution. Additionally, the paper breaks down the core data sources that fuel this predictive engine, offering insights into the structure and effectiveness of Google Maps’ data pipeline [5][6][7]. As part of this research, we also propose a novel real-time user feedback mechanism designed to enhance live traffic prediction by incorporating human intelligence in the loop. The system enables commuters to quickly report congestion, blockages, or discrepancies, providing hyper-local input that can improve ETA accuracy, especially in under-reported areas.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Mr. Muskan Aherwar, Tushar Ahirwar, Dr. Jasbir Kaur, Ms. Ifrah Kampoo

Abstract: This paper aims to build a model to predict used car’s reasonable prices based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, trans- mission, fuel type, and engine size. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user’s input. The model building process involves machine learning and data science. The dataset used was scraped from listings of used cars. Various regression methods, including linear regression, deci- sion tree regression, and random forest regression, were applied in the research to achieve the highest accuracy. Before the actual start of model-building, this project visualized the data to under- stand the dataset better. The dataset was divided and modified to fit the regression, thus ensure the performance of the regression. To evaluate the performance of each regression, R-square was calculated. Among all regressions in this project, random forest achieved the highest R-square of 0.90416. Compared to previous research, the resulting model includes more aspects of used cars while also having a higher prediction accuracy

Published by: vikaspatanker