My Projects
Here's a collection of the projects I've worked on, showcasing my skills in data science, machine learning, and
full-stack development.
Each project reflects my passion for solving real-world problems and building impactful solutions through
technology.
Academic Portal for University
A full-stack university management platform designed to manage students, schools, faculty activities, and academic operations. Built with a scalable architecture to support digital transformation and enterprise-level academic workflows.
Tech Stack
AP Web Stack – Business Website
A scalable, SEO-friendly business website built with Django, featuring a custom backend and content management system. Designed for real-world business deployment with a clean architecture, performance optimization, and long-term scalability.
Tech Stack
Kualakubs World School Website
Designed and developed a custom school website for a Gurugram-based institution from scratch. Built with a modern, high-performance React frontend, featuring a fully responsive UI, clean UX, and production-ready deployment for real-world usage.
Tech Stack
Fake New Classification
This project is designed to classify news articles as Fake or Real using various machine learning models. The dataset is processed using TF-IDF vectorization, and multiple models including Naïve Bayes (NB), Random Forest (RF), and Logistic Regression (LR) are trained. The best model is selected based on performance metrics.
Tech Stack
Flight Price Prediction
AeroPredict is an innovative machine learning project designed to make flying more predictable, specifically targeting flights within the Indian route. Utilizing a basic ML model, AeroPredict accurately forecasts flight fares, providing travelers with valuable insights into pricing trends.
Tech Stack
Diamond Price Prediction
Built a regression model to estimate diamond prices using Python and Scikit-learn. This project involves exploratory data analysis and feature engineering to accurately predict market values.
Tech Stack
Seed Classification Model
Built a machine learning model to classify wheat seed types using Scikit-learn, achieving 97% accuracy. This classification model evaluates geometric properties of seeds.
Tech Stack
Obesity Classification
This project implements an Artificial Neural Network (ANN) model to classify obesity levels based on user inputs. The model is trained on obesity classification data and predicts the obesity level, providing valuable insights for users. The project is built with Flask for web deployment, and it also includes various utilities, data preprocessing, and model training pipelines.