My Projects

LendMate - Loan Management System

LendMate - Loan Management System

A comprehensive Loan Management System using Flutter and Firebase for real-time data handling.

FlutterFirebaseGCPJavaScriptNode.js
Celebraisions - Rewards Management Web App

Celebraisions - Rewards Management Web App

Rewards management webApp for local business, made using MERN stack.

MongoDBReactNode.jsAWS
Noted - Flutter Note-taking App

Noted - Flutter Note-taking App

A sleek note-taking app built using Flutter and Hive database. Noted allows users to create, edit, and delete notes effortlessly. The app offers a minimalistic design with a fast, offline-first experience thanks to Hives lightweight and powerful local storage solution.

FlutterHive (local database)Dart
Digital Wallet App UI

Digital Wallet App UI

This digital wallet app UI, developed using Flutter, offers users a sleek and modern interface for managing their finances. The app features a card management system with smooth page transitions, action buttons for sending money, paying bills, and tracking expenses, along with easy access to transaction history and statistics. With a focus on simplicity and functionality, the app provides a user-friendly experience tailored for efficient financial management.

FlutterDartSmooth Page Indicator
Fat Tree Data Center Topology

Fat Tree Data Center Topology

This Java project models a fat-tree network topology, commonly used in data centers for scalable and efficient connectivity. It calculates the number of core, aggregation, edge switches, and physical machines based on the input value K and determines the distance between any two nodes within the network.

Java
Traveling Salesman Problem with Reinforcement Learning

Traveling Salesman Problem with Reinforcement Learning

This Python project addresses the Traveling Salesman Problem (TSP) using a reinforcement learning approach. The goal is to find an optimal path that maximizes rewards collected from visiting cities while minimizing travel distances. The project involves: - Reading city data (name, latitude, longitude, and prize) from a file. - Computing the distance matrix between cities based on their geographic coordinates. - Using Q-learning to train an agent to navigate through cities and collect rewards. - Evaluating the best travel path based on learned Q-values to maximize the total reward.

PythonNumPyMath Library