NIKITH VARMA VUYYURI

Project Title: A Survey on Fake Review Detection using Machine Learning Techniques

• Conducted research and developed a machine learning model to detect fake reviews on online platforms, enhancing consumer trust and product authenticity.
• Collected and preprocessed review data from e-commerce sites, implementing techniques such as data cleaning, stemming, and feature extraction.
• Employed various machine learning algorithms including Logistic Regression, Support Vector Machines, Naive Bayes, and XG Boost, achieving a predictive accuracy of up to 91.8%.
• Developed a web application using Flask to enable users to assess the authenticity of reviews in real-time, improving user experience and decision-making.

• Covers AI, ML, neural networks, fuzzy systems, and evolutionary computation.
• Organizes tech talks, hands-on sessions, and project mentorship.
• Influenced positive change within the organization through strategic thinking, innovation, problem-solving abilities, and consistent leadership style.
• Collected, arranged, and input information into database system.
• Attended conventions to represent organisation, gather information and help build new partnerships
• Prepared statements detailing operational detail and key performance indicators

• Involves robotics, automation, embedded systems, and control systems.
• Conducts hackathons, Arduino/Raspberry Pi workshops, and robotics challenges.
• Design and lead workshops on Machine Learning, Deep Learning, and Neural Networks using tools like Python, TensorFlow, PyTorch.
• Develop and mentor projects related to Natural Language Processing, Computer Vision, Reinforcement Learning, etc.
• Conduct research paper review sessions and guide members on IEEE-standard paper writing.
• Collaborate with teams to build solutions for real-world AI problems (e.g., prediction systems, automation tools).
• Trained on data preprocessing, model evaluation, and deployment basics.