Machine Learning Approaches to Classification of Online Users by Exploiting Information Seeking Behaviours

In today’s digital age, understanding how users interact with online platforms has become more important than ever, especially for reshape experiences and protect security. This project introduces an innovative approach to analyzing and classifying user behavior by using machine learning, with a focus on predicting information-seeking patterns based on social media and locating data. Inspired by real world needs, we developed a system that uses a fine-tuned Random Forest Classifier to categorize user activities into "uncertain Behavior, Good Behavior, or Neutral Behavior using features like gender, age, location latitude and longitude, and social metrics such as followers, friends, favorites, and statuses. The model does a great job reaching an impressive accuracy of 90.21%. What makes this project special is its interactive edge we built a user friendly interface using Jupyter allowing anyone to input their own data think of it like filling out a digital profile and get instant predictions about their behavior type. It is for marketer wanting to personalize ads, security teams detecting possible risk, or researcher studying online habits, this tool delivers action able insights with a simple click. The system also save predictions to a CSV file for future reference and offers a peek into advanced possibilitie with plans for real time deployment using Flask and Drawing from established research on user direction and machine learning, this project balances technical culture with practical usability aiming to enhance our understanding of digital behavior while keeping privacy and ethics in mind. It a step toward smarter more natural online environments crafted with care and Interest.