Rutvik Satav

Developed a comprehensive stock analytics platform leveraging XGBoost for stock price prediction, analyzing equity market trends, and providing investment insights on defaulted or matured loans over time with 79% of accuracy. Implemented a Deep Learning model (BERT) for sentiment analysis on Twitter data, achieving an 84% accuracy in sentiment classification. Enhanced customer sentiment tracking for marketing strategies, thereby contributing to improved decision-making. Designed and deployed a computer vision-based parking space detection system using YOLOv4, resulting in an 82% improvement in parking efficiency and reduced congestion in urban areas.

Gained proficiency in data analytics, extracting actionable insights from complex datasets using Python. Developed strong skills in Python for data manipulation, analysis, and visualization, along with expertise in SQL for efficient data retrieval, management, and optimization. Collaborated on real-world projects to deliver data-driven solutions, contributing to Machine Learning and Deep Learning for predictive modeling and analysis, and actively participated in data preprocessing and report creation.