Rohan Karna

Developed an emotion detection model using OpenCV, achieving 83% accuracy. Utilized 5-fold cross-validation to enhance model performance and deployed it for security and classroom applications.

Developed an emotion detection model using OpenCV, achieving 83% accuracy. Employed 5-fold cross-validation for training, enhancing model performance for security and classroom applications.

Conducted a project on text classification for spam detection, achieving 98% accuracy with Naive Bayes and 89% with a Neural Network using BERT embeddings. Performed EDA on a dataset of 5,572 messages.

Collected and preprocessed a Kaggle dataset for spam detection, trained models using Naive Bayes and Neural Networks, achieving 98% and 89% accuracy respectively. Conducted exploratory data analysis to extract key features for model training.

Applied K-Means clustering for customer segmentation based on purchasing behavior, leading to a 20% increase in targeted marketing efficiency. Conducted data preprocessing and utilized Python for insights.

Applied K-Means clustering for customer segmentation based on purchasing behavior, leading to a 20% increase in targeted marketing efficiency. Utilized Python and data visualization tools for insights.

Analyzed data to enhance supply chain management, collaborated with the Supply Chain Planning Team to develop a dynamic planning dashboard, and prepared detailed reports for the U.S. team. Ensured accurate supply chain tracking through timely report submissions.

Analyzed data using Microsoft Excel to enhance supply chain management and collaborated with the Supply Chain Planning Team to develop a dynamic planning dashboard. Prepared detailed reports on supply metrics and submitted JVU reports for accurate tracking.

Grade: 8.33/10

Activities and societies: Senior Executive Member of ECCF Club, organized various invited talks and workshops; Executive Member of IEEE Sensors Club, increased student participation through multiple events.

Completed a Bachelor of Technology with a focus on data science and engineering principles, achieving a CGPA of 8.33/10. Developed strong analytical skills and proficiency in programming languages and data visualization techniques.

Grade: 93.4%

Completed 12th standard with a focus on science, achieving a percentage of 93.4%. Developed foundational knowledge in various scientific disciplines.

Grade: 87.5%

Completed 10th standard under the NEB curriculum, achieving a percentage of 87.5%. Gained essential skills and knowledge in various subjects.