B.Sc IT graduate building intelligent systems using AI, ML & Data Science. Passionate about turning data into insight and code into products.
Email: aashuofficial2003@gmail.com
Phone: +91 9372559592
I'm a B.Sc. Information Technology graduate from Nirmala Memorial Foundation College, Mumbai, with a CGPA of 9.07. My passion lies at the intersection of AI, machine learning, and full-stack web development.
I've built AI-based systems including facial recognition applications, predictive analytics tools, and full-stack Django applications. I'm experienced with the complete ML pipeline — from data preprocessing and model training to deployment and visualization.
I'm fluent in Hindi (native) and English (professional working proficiency), and I'm actively looking for opportunities to apply my skills in backend, full-stack, or AI/ML engineering roles.
Full-stack Django application with task tracking and expense analytics. Includes a daily task system with add/update/delete and visual charts showing daily completion rates, a long-term task manager with deadline support, and an expense management system with daily spending visualization.
A REST API built with FastAPI for predicting workforce requirements based on historical employee data and business metrics.
A machine learning and Streamlit project for predicting bank customer churn, generating churn risk scores, and exploring customer retention scenarios through an interactive dashboard.
Factory-to-customer shipping route efficiency analysis for Nassau Candy Distributor using Python and Streamlit. This project cleans shipment data, maps products to factories, analyzes route performance by state and region, identifies geographic bottlenecks, compares ship modes, and presents insights through an interactive dashboard.
Face recognition system using OpenCV and ML models. Developed core facial recognition and classification logic trained on a folder-based dataset of family photos. Evaluated accuracy by testing on new photo sets, achieving measurable performance results.
Machine learning model analyzing employment data from 2018–2025 to identify trends and forecast workforce growth from 2026. Results are clearly visualized using Matplotlib graphs for easy interpretation by non-technical stakeholders.