Rajneesh Pandey

Conducted a comparative study of four machine learning models for fake news detection: Random Forest Classifier, Logistic Regression, Decision Tree, and Gradient Boosting Classifier. Employed various performance metrics such as precision, accuracy, recall, and F1-score for model evaluation.

Developed a responsive website for detecting fake news using machine learning algorithms, allowing users to input headlines and check authenticity. Utilized natural language processing techniques to achieve 95% accuracy in fake news detection.

Developed a fully functional and responsive website for storing and displaying student results. Collaborated with a team, primarily managing and deploying the database for the project.

Developed a responsive website for detecting credit defaulters using machine learning algorithms. The system predicts a person's name based on their previous repayment history.

Grade: 8.08 CGPA

Pursued a Bachelor of Engineering in Computer Science, gaining foundational knowledge and practical skills in the field. Achieved a CGPA of 8.08, demonstrating strong academic performance.

Grade: 80%

Completed Intermediate education in Science, achieving a strong academic record with 80% marks. Focused on core scientific principles and analytical skills.

Grade: 89.5%

Achieved Matriculation with an excellent academic performance, securing 89.5% marks. Developed a strong foundation in various subjects during this period.