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Lakshya172/README.md

Lakshya Agarwal

Computer Science Student β€’ Software Developer β€’ Full-Stack Developer β€’ AI/ML Enthusiast

B.Tech Computer Science & Engineering @ Lovely Professional University

Building practical software, scalable backend systems, and intelligent applications.


πŸ‘¨β€πŸ’» About Me

I am a B.Tech Computer Science & Engineering student at Lovely Professional University, focused on building practical and scalable software solutions.

My interests span full-stack development, backend engineering, databases, problem solving, and applied AI/ML. I enjoy turning ideas into functional applications while continuously improving my understanding of software architecture and engineering practices.


🎯 Current Focus

Area Focus
πŸ’» Full-Stack Development Building complete web applications
βš™οΈ Backend Development REST APIs, authentication & server-side systems
🧠 Problem Solving Data Structures & Algorithms
πŸ—„οΈ Databases MongoDB, SQL & database design
πŸ€– Applied AI/ML Practical machine learning applications
πŸ”¬ Research Data-driven and reproducible systems

πŸ› οΈ Tech Stack

Languages

Python JavaScript TypeScript C++ HTML CSS

Backend & APIs

Node.js Express.js REST APIs

Frontend

React Next.js Astro Vanilla JavaScript

Databases

MongoDB MongoDB Atlas SQL Mongoose

AI / ML & Data

PyTorch scikit-learn OpenCV pandas NumPy

Tools & Platforms

Git GitHub Docker GitHub Actions Render GitHub Pages


πŸš€ Featured Projects

πŸ”¬ AdityaNet

Verifiable Research Platform Β· Aditya-L1 Solar X-ray Archive

A data engineering and research platform built around reproducible processing and analysis of ISRO Aditya-L1 solar X-ray data.

Stack: Python pandas scikit-learn Parquet TypeScript Astro React GitHub Actions Docker

Highlights

  • Built a fail-loud data extraction and transformation pipeline.
  • Generated a digest-addressed SHA-256 dataset for data integrity.
  • Processed thousands of Parquet artifacts across a large historical dataset.
  • Used chronological validation to prevent temporal data leakage.
  • Benchmarked learned models against threshold-based baselines.
  • Added CI validation gates for data integrity and metric drift.
  • Optimized the frontend for minimal client-side JavaScript.

πŸ”— GitHub Repository


πŸ’Ž Rinky Commerce Engine

Production Full-Stack E-Commerce Platform Β· Rinky Jewellers

A production full-stack e-commerce platform built for Rinky Jewellers, Kanpur, providing a digital storefront and centralized product management system.

Stack: Node.js Express.js MongoDB Mongoose HTML CSS JavaScript Brevo Render

Highlights

  • Full-stack commerce application
  • RESTful backend with Node.js & Express
  • MongoDB Atlas database
  • User authentication & role-based authorization
  • Product catalogue and management
  • Customer wishlist functionality
  • Admin management system
  • Transactional email workflows
  • Security middleware and rate limiting
  • Responsive Vanilla JavaScript frontend
  • Production deployment with Render

🌐 Live Application

πŸ”— GitHub Repository


🌐 Personal Portfolio

Personal Developer Portfolio Β· GitHub Pages

A responsive personal portfolio website showcasing my projects, skills, experience, and technical background.

Stack: HTML CSS JavaScript GitHub Pages

Highlights

  • Multi-page portfolio website
  • Custom JavaScript animations
  • Responsive design
  • Lightweight framework-free architecture
  • No build step or external dependencies
  • Deployed using GitHub Pages

🌐 Live Portfolio

πŸ”— GitHub Repository


πŸ‘€ Facial Recognition Attendance System

Real-Time Face Recognition Β· Automated Attendance

A computer vision application that detects and recognizes faces through a webcam and records attendance automatically.

Stack: Python OpenCV KNN Streamlit Pickle CSV

Highlights

  • Real-time face detection and recognition
  • KNN-based face classification
  • Webcam integration
  • Automated attendance recording
  • CSV-based attendance storage
  • Streamlit-based interactive interface

πŸ”— GitHub Repository


🌌 ASTRA

Automated Stellar Transient Recognition & Analysis

An end-to-end machine learning and research platform for analyzing stellar variability using TESS light curves.

Stack: Python PyTorch CNN Transformers Next.js ONNX

Highlights

  • Stellar variability classification
  • Hybrid CNN + Transformer architecture
  • TESS light-curve data processing
  • Model training and evaluation
  • Dataset verification
  • Confidence and uncertainty analysis
  • Web-based inference platform

πŸ”— Project Repository


πŸ“š Currently Learning

  • Full-Stack Web Development
  • Backend Engineering & REST APIs
  • Data Structures & Algorithms
  • Database Systems
  • Software Engineering
  • Machine Learning & AI
  • Natural Language Processing
  • System Design
  • Building practical production applications

πŸŽ“ Education

B.Tech β€” Computer Science & Engineering

Lovely Professional University

2024 – 2028


πŸ“œ Certifications

  • Data Science & Analytics β€” HP LIFE
  • Effective Communication Skills β€” Skillera

πŸ“Š GitHub Stats

Lakshya's GitHub Stats

Lakshya's Top Languages


πŸ”₯ GitHub Streak

Lakshya's GitHub Streak


πŸ“ˆ Contribution Graph

Lakshya's GitHub Contribution Graph


🀝 Connect With Me


πŸš€ Building scalable systems, one line of code at a time.

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