Rameshwor Chaudhary

About

Machine Learning Engineering student with experience building, training, evaluating, and deploying machine learning models end-to-end. Built full-stack AI and e-commerce platforms and has a strong foundation in Python, SQL, data structures, and version control.

Experience

• Data Science Virtual Experience Program
  BCG X (Forage)
  2026 – 2026
• Quantitative Research Virtual Experience Program
  JPMorgan Chase & Co. (Forage)
  2026 – 2026

Education

• B.E. Computer Science Engineering — Specialization in Artificial Intelligence & Machine Learning
  Chandigarh University
  Artificial Intelligence & Machine Learning · 2024 – 2028
• Higher Secondary Education (+2 Science)
  Nobel Academy
  Physics, Chemistry, Mathematics · 2022 – 2024
• Secondary Education
  Himchuli Academy
  Nursery to class 10th · 2008 – 2022

Skills

• SQL
• C
• Data Visualization
• Generative AI Tools
• Python
• Machine Learning
• Prompt Engineering
• Java
• Classification
• Data Cleaning
• Feature Engineering
• Matplotlib
• Model Evaluation
• NumPy
• Pandas
• Streamlit
• scikit-learn
• Seaborn
• Supervised Learning
• Unsupervised Learning
• Statistical Analysis
• Natural Language Processing
• Predictive Modeling
• Data Structures and Algorithms
• Regression
• Model Training
• APIs and integrations
• Generative AI (ChatGPT, Copilot, Gemini)

Projects

• NexShop (MeroPasal) — Nepal-Focused E-Commerce Platform ↗
  Next.js 15, TypeScript, Firebase, Tailwind CSS, ShadCN UI

  Developed a production-ready e-commerce platform with customer flows, payment gateways, an admin panel, analytics, and Nepal-specific address selection.

• Employee Salary Prediction ↗
  Python, Scikit-learn, Pandas, Matplotlib

  Built a regression model to predict employee salaries and performed end-to-end preprocessing including imputation, encoding, and scaling.

• Comparison of Machine Learning Algorithms ↗
  Python, Scikit-learn, Pandas, Seaborn

  Ran a benchmarking study comparing six classification algorithms across multiple datasets and evaluated performance using accuracy, precision, recall, F1-score, and training time.

• PadhaiHub — AI-Powered Student OS ↗
  Python, Next.js, Groq AI, Vercel

  Architected and deployed a full-stack AI platform with six core features including syllabus analysis, PYQ prediction, AI notes generation, assignment conversion, chatbot, and study planning. Integrated Groq AI and scaled to 50+ active users.

Courses & certifications

• Mathematics for Machine Learning: Linear Algebra · Coursera
• Python for Data Science, AI & Development · Coursera / IBM
• Prompt Engineering for ChatGPT · Coursera

📚 Publications

• Prediction of Heart Disease using Machine Learning: A Comprehensive Review of Approaches, Obstacles, and Future Perspective · 2026