Building intelligent software solutions with Java, Spring Boot, and AI/ML 🚀 — turning ideas into impactful applications.
Education 🎓
| Degree / Course | Institution | Year |
|---|---|---|
| Bachelor of Technology (CSE) | Kalinga Institute of Industrial Technology, Bhubaneswar | 2022 - 2026 |
| Senior Secondary School | City Montessori School (ISC), Lucknow | 2021 |
| High School | City Montessori School (ICSE), Lucknow | 2019 |
Skills 🛠️
My technical skills include programming, web development, and AI/ML.
Java
Machine Learning
Backend Development
Frontend
Projects
Projects here are written in a blog-style, sharing my personal experience, while GitHub documentation focuses on technical details and implementation.| Name | Tags | Decription |
|---|---|---|
| cnn deep-learning keras tensorflow | Developed a deep learning model using a Convolutional Neural Network (CNN) to accurately recognize handwritten digits. | |
| dialogflow FastApi ngrok nlp python | Foodoo Bot is an AI-powered food assistant designed to help users discover, order, and manage meals seamlessly. It can recommend dishes based on preferences, track orders in real-time, suggest recipes, and provide nutritional insights, making the entire food experience smarter and more personalized. | |
| Docker Hibernate/JPA Java Maven Postman RestAPI SpringBoot | A robust backend system designed to streamline school operations by managing students, teachers, courses, and academic records with efficiency and scalability. | |
| Docker Hibernate/JPA Java Maven RestAPI SpringBoot | Designed a scalable backend for an e-commerce platform using Spring Boot and MySQL, featuring RESTful APIs for products, customers, carts, and orders with secure data handling. | |
| classification machine-learning Numpy Pandas RandomForest Scikit-Learn | Designed and trained a machine learning model to classify credit card transactions as fraudulent or legitimate. | |
| Docker Hibernate/JPA Java Maven RestAPI SpringBoot | Backend healthcare management system built with Spring Boot, MySQL, and Docker — featuring secure role-based access and scalable RESTful APIs. | |
| numpy pandas python scikit-learn seaborn streamlit(optional) | Developed a machine learning model using Logistic Regression to support early autism screening with questionnaire-based data. Achieved ~86.9% accuracy with high recall, minimizing false negatives, and prepared the model for user-friendly deployment via Streamlit. |