Solving real-world problems using machine learning, Deep Learning, and computer vision
Welcome! I'm a developer of intelligent systems. I design models, scale architectures, and build real-time apps. Let's explore my work and create something incredible together.
2024–2026 | CGPA: 8.74
Specializing in AI/ML and Data Science
2020–2024 | CGPA: 9.39
Computer Science & Engineering
A highly motivated AI/ML Engineer with a passion for transforming real-world problems into scalable solutions using AI, ML, and deep learning. Strong in building models, developing intelligent systems, and working across domains.
An intelligent travel planning application powered by Groq 80B model via API. Takes destination preferences and generates detailed, personalized itineraries with LangChain integration.
A powerful chatbot built by locally fine-tuning the Qwen base model using QLoRA and the Alpaca dataset. Optimized on personal hardware, it delivers intelligent, context-aware conversations with a sleek UI, offering strong Q&A capability.
Advanced stock prediction system using hybrid LSTM-CNN architecture. Trained on comprehensive dataset of 2126 Indian stocks with sophisticated UI and multiple analytical features.
An intelligent computer vision system for accurate license plate detection and recognition using image processing and EasyOCR. Efficiently reads plate numbers from real-world scenes in real time, ideal for surveillance, traffic, and security.
Research project on improving spectral clustering scalability through intelligent sampling methods. Published in EAI International Conference on Body Area Networks.
A smart task allocation system that uses machine learning to dynamically balance computational loads between IoT devices and cloud servers. It evaluates task complexity, device status, and energy constraints to make real-time decisions, improving efficiency and reducing latency.
A high-performance NLP system for real-time fake news detection, using advanced machine learning and text analysis. Delivered via a robust Flask web app, it ensures good accuracy and user-friendly misinformation detection.
Advanced medical image classification system using ensemble learning. Combines DenseNet, VGG16, and InceptionResNet architectures with sophisticated data augmentation techniques.
Conventional spectral clustering methods provide essential information about the structure within the dataset; nonetheless, they are not scalable over large datasets. In this research, an ensemble of density-based and cluster-based sampling techniques is used to improve the scalability of spectral clustering in a novel way. By carefully choosing a representative sample of data points, the suggested strategy lowers computing complexity while speeding up the clustering process without sacrificing accuracy. Our tests show that the suggested approach performs better in terms of clustering performance (as determined by the Silhouette Score, Adjusted Rand Index, and Normalized Mutual Information) and computing efficiency than conventional spectral clustering and other cutting-edge approaches. In large-scale datasets, the approach reduces execution time by 134.4\% while improving clustering accuracy by 61.5\%. This method is potentially used in large-scale data analysis applications where scalability and efficiency are crucial, including body area networks (BANs).
Began my journey in Computer Science & Engineering
ML Intern @ Inrainz (Remote) – Fraud Payment Detection
Developed first major Deep Learning project
Completed B.Tech and cleared GATE
Major project in final semester
IT Support Intern @ ChemProcessSystem (Ahmedabad)
Currently pursuing specialization in AI/ML
Presented at EAI BodyNets 2024, IIT BHU – Springer publication
Did several AI projects and research work
I’m open to full-time, internship, and collaborative roles in AI/ML, data science, and tech innovation. Let’s connect to explore how I can bring value to your team.