MCA Graduate | Machine Learning & Data Analytics Enthusiast
I am an MCA graduate with a strong problem-solving and research-driven mindset, enthusiastic about Data Science, Machine Learning, and Data Analytics. Adaptable, attentive, and driven by a curious mindset, I am always eager to learn and grow. I enjoy applying technology to uncover insights and create practical solutions for real-world challenges.
Implemented Machine Learning and Deep Learning models for EEG data classification. Designed and tested hybrid deep learning architectures, including attention mechanisms, applying EDA, preprocessing, and fine-tuning to improve reliability. Achieved up to 99.81% accuracy with the best-performing model, named "DeepSeizNet".
View ProjectConducted EDA and normalization on heart disease dataset. Experimented with ML and hybrid Deep Learning models, achieving accuracy between 78%–98% across models for reliable risk classification.
View ProjectBuilt an interactive Power BI dashboard to analyze employee attrition trends. Applied heatmaps, drill-through pages, and slicers to provide HR teams with actionable insights.
View ProjectResearched and analyzed AI adoption trends across industries using secondary data. Compiled findings in structured reports with supporting charts and references.
View ReportCollaborated on a demo festival website showcasing event details and highlights. Integrated ticket booking and payment features to enhance user convenience.
Visit WebsiteDeveloped a FastAPI demo project that parses PDF blood reports and generates structured health insights with simulated AI responses.
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