Zoom Clone
Full-stack Zoom web-client clone: start, join and schedule meetings, with a pre-join camera and microphone preview and host controls in the meeting room.
- TypeScript
- Next.js
- Tailwind CSS
- FastAPI
- Pydantic
- SQLAlchemy
- SQLite
Open to internships & entry-level roles
Hi, I'm
Om Prakash Chaubey
Full-Stack & AI Engineer
Full-Stack Developer and B.Tech Computer Science student at Thapar Institute building web apps, LangGraph AI agents, and ML pipelines.
- Projects
- 7
- Skills & Tools
- 35
- Attack Detection
- 0.96 F1
Skills & Tools
Languages, machine learning frameworks, data analysis tools, and full-stack web technologies.
Languages
- Python
- C++
- C
- TypeScript
- JavaScript
- R
- SQL
AI & ML
- PyTorch
- TensorFlow
- Keras
- Scikit-learn
- XGBoost
- Random Forest
- Isolation Forest
- SMOTE
- LangChain
- LangGraph
Data Science & Analysis
- Pandas
- NumPy
- Statistics
- Predictive Modeling
- Matplotlib
- Seaborn
- Streamlit
- TOPSIS
- Linear Programming (PuLP)
Backend & Cloud
- FastAPI
- Flask
- Node.js
- Docker
- CI/CD
- Git
Frontend
- React
- Next.js
- Tailwind CSS
Projects
Selected full-stack engineering, conversational AI, MLOps, and applied machine learning projects.
Restaurant Order Agent (LangGraph)
LangGraph agent that takes a food order, checks stock, then simulates cooking and serving with retries at each stage.
- Python
- LangGraph
- LangChain
- Groq
- Pydantic
YouTube Sentiment MLOps Pipeline
Chrome extension and FastAPI service that classify the sentiment of YouTube comments as positive, neutral or negative with a TF-IDF and Logistic Regression pipeline.
- Python
- Scikit-learn
- NLTK
- Pandas
- FastAPI
- Docker
- GitHub Actions
- Pytest
- JavaScript
Network Traffic Anomaly Detection
0.96 F1 · attack classHybrid intrusion-detection pipeline combining K-Means clustering, Isolation Forest anomaly scores and a Random Forest classifier, evaluated on UNSW-NB15 and cross-tested on CIC-IDS2017.
- Python
- Scikit-learn
- K-Means
- Isolation Forest
- Random Forest
- SMOTE
More Projects
Additional machine learning, deep learning, and mathematical optimisation projects from academic and independent work.
E-Commerce Data Analysis & Prediction Dashboard
2025XGBoost model for predictive analytics reaching ~90% overall accuracy and improving recall from 54% to 82%; interactive Streamlit dashboard visualising business KPIs, including an Average Order Value of INR 158 and delivery-delay and seasonal-sales insights.
- Python
- XGBoost
- Streamlit
Emotion Recognition with Deep Learning
2025Convolutional model reaching 85.75% accuracy on held-out test data, using data augmentation (noise addition, pitch shifting); diagnosed real-world acoustic domain mismatch with live microphone demonstrations.
- Python
- Deep Learning (CNN)
TOPSIS Ranking Framework for NLP Models
Feb 2025Python implementation of the TOPSIS algorithm ranking BERT, RoBERTa and DistilBERT on Cosine Similarity, Accuracy, Inference Time and Model Size; automated NumPy/Pandas pipeline exporting ranked CSVs and bar charts.
- Python
- NumPy
- Pandas
About
I am a pre-final year Computer Science and Engineering student at Thapar Institute, specializing in AI/ML and full-stack web development. I focus on architecting end-to-end solutions that bridge the gap between complex machine learning models and scalable, user-centric applications. My recent technical work spans building autonomous AI agents using LangGraph, deploying containerized MLOps pipelines, and engineering decentralized IoT platforms like De-Insure. I am driven by a passion for leveraging data-driven insights and modern backend frameworks to build robust, impactful software.
Education
Aug 2023 – May 2027 (Expected)
B.Tech in Computer Science and Engineering
Thapar Institute of Engineering & Technology · Patiala, Punjab, India
CGPA: 7.18/10.0
Spoken Languages
English
Professional
Hindi
Fluent
Nepali
Native
Certifications
Google AI Specialization
Coursera
NVIDIA: Fundamentals of Deep Learning
NVIDIA
Deep Learning with PyTorch
Transfer Learning for NLP with TensorFlow Hub
Generative AI & Machine Learning with PySpark