Passionate developer focused on AI/ML, MERN Stack, DSA, and building impactful real-world solutions.
🎓 Third Year Information Technology Student (CGPA : 9.72)
📌 JEE: 93.34%, MHT-CET: 99.12%
💡 AI | ML| XAI | MERN Stack | DSA | Learner | Hackathon Entusiast
🧠 300+ Problems on CodeChef ,with highest rating of 1413
🧠 250+ Problems on LeetCode
🥋 Karate(Shotokan) - Black Belt
🛡️ Saferi
🔹 AI-powered women safety and smart navigation platform.
🔹 Developed a route recommendation system that predicts safer travel paths by analyzing factors such as police station proximity, crime rates, and environmental data.
🔹 Integrated GraphHopper for real-time map visualization and route generation, enabling users to compare multiple route options.
🔹 Applied K-Means clustering to group locations based on safety-related parameters and support machine learning–based route evaluation.
🔹 Implemented an integrated SOS messaging feature that sends emergency alerts to predefined contacts for immediate assistance.
React.js , Node.js , MYSQL , Machine Learning , Graphhopper , Scikit-Learn
🔹 Developed a smart parcel collection system to securely receive deliveries when recipients are unavailable at home.
🔹 Integrated an ESP32 microcontroller with ultrasonic sensors to detect parcel placement inside the collection box.
🔹 Implemented a camera module to capture images of delivery personnel for identity verification and security logging.
🔹 Enabled real-time notifications and alerts using HiveMQ Cloud for instant delivery status updates.
🔹 Designed a Flutter-based mobile application to remotely control the opening and closing of the lock via message-based communication using MQTT.
Flutter , ESP32 , ESP32 Cam Module , HiveMQ Cloud
🔹 Developed an Explainable AI-based healthcare prediction system capable of predicting disease risk using patient health parameters and Machine Learning algorithms.
🔹 Implemented data preprocessing, feature encoding, scaling, and model training to improve prediction accuracy and overall system performance.
🔹 Integrated SHAP technique to provide transparent and interpretable predictions, allowing users to understand feature impact and model decision-making.
🔹 Designed an interactive user interface for real-time health data input, prediction visualization, and explainabilityanalysis to enhance usability and trust in AI-driven healthcare systems.
Python , Scikit-learn , SHAP , Flask
- 🥋 Karate Black Belt (Shotokan)
- 🥇 Finalist @ TechFiestta 2025
"Success is built from consistency, discipline, and the courage to keep learning every single day."