Passionate about technology and education, I developed expertise in Python and data analysis at Dhyanahitha, training AI models and extracting insights from complex datasets. As a GRS Tutor at the University of Cincinnati, I mentored students in web development and programming, helping them build a strong foundation in key concepts.
I developed a Galaxy classification system using deep convolutional neural networks (CNNs) to automate the labeling process. The model classifies galaxies into 10 categories based on features like spiral structure, bulge size, and smoothness. I compared pre-trained models (LeNet5, ResNet50) and built a custom CNN from scratch, achieving 95.30% accuracy on the Galaxy10 dataset. The project highlights the limitations of crowd-sourced data while suggesting improvements for better reliability.
Technologies: CNN (LeNet5, ResNet50), KNN, Python, Machine Learning
During my Data Science internship at Dhyanahitha, I gained hands-on experience in statistics, data wrangling, and data visualization while working with large datasets. I trained an AI model to predict heart disease using the Heart Dataset and applied various machine learning algorithms for classification and prediction.
Technologies: Python, NumPy, Pandas, Seaborn, Matplotlib, Logistic Regression, Random Forest, KNN