Unveiling Instagram Trends: An Exploratory Data Analysis Approach

This research paper explores Instagram user behavior, focusing on privacy settings, posting patterns, engagement activities (such as following, liking, and commenting), and content enhancement practices like the use of filters. Social media platforms have emerged as primary tools for personal expression and communication, and understanding how users engage with such platforms can offer insights into behavioral trends and digital preferences. The dataset used for this study was sourced from Kaggle and analyzed using Python in Jupyter Notebook. Various visualizations, including bar charts and histograms, were used to uncover behavioral patterns. The analysis found that public accounts are slightly more common, users tend to engage more with accounts they follow, and most users utilize Instagram filters to enhance their content. Additionally, posting activity shows a long-tail distribution, where most users have a lower post count. The findings contribute to broader digital sociology discussions and can inform platform design, marketing strategies, and digital literacy initiatives. Acknowledgement I would like to express my sincere gratitude to Mrs. Elizabeth Leah George and Dr. Jyotshna Ma’am for their invaluable guidance, constant support, and encouragement throughout the course of this research. Their expert insights and mentorship greatly contributed to the successful completion of this work