Project Demos
Try live demonstrations of my key projects. Click to watch videos or interact with live demos.
AI-Driven Project Management Assistant
AI agents for automated project planning and management
AI Customer Support Ticket Resolver
Automated customer support using AI agents and MCP
AI Spreadsheet and PDF Assistant
Interactive file analysis with chat interface
Generative AI Travel Chatbot
AI-powered travel assistant with web scraping
About Me
Data Scientist / AI Engineer with 3.9 years of experience developing full-stack data science and AI projects primarily in the e-commerce domain at Versatile Commerce, leveraging machine learning, deep learning, Generative AI, and MLOps to solve complex business challenges and drive data-driven solutions.
Technical Skills
Programming & Data Analysis
Python
SQL
PySpark
Pandas
NumPy
Matplotlib
Seaborn
Statistics
Hypothesis Testing
Probability
Statistical Analysis
A/B Testing
Machine Learning
Scikit Learn
Linear Regression
Logistic Regression
Ridge Regression
Decision Trees
Random Forest
SVM
XGBoost
k-NN
Naive Bayes
K-Means Clustering
Deep Learning
Neural Networks
CNN
RNN
LSTM
Transformers
TensorFlow
PyTorch
Natural Language Processing
word2vec
BERT
GPT-based models
Fine-tuning
Text Classification
Named Entity Recognition
Generative AI
LLM
LangChain
LangGraph
AI Agents
Multi Models
RAG Databases
Vector Embeddings
Cloud & DevOps
Amazon SageMaker
Amazon Bedrock
Azure DevOps
Azure AI
CI/CD
MLOps
Git
DVC
Dagshub
MLFLOW
Docker
Airflow
Work Experience
Versatile Commerce
Milton Keynes, England, United Kingdom
Data Scientist
Full-timeMay 2022 – Present()
Data Science Intern
InternshipNov 2021 – Apr 2022(6 mos)
iNeuron.ai
Remote
Data Science Intern
InternshipNov 2020 – Mar 2021(5 mos)
My Certifications
My Projects
Project Management Assistant Using AI Agents
- Developed a Streamlit application that leverages AI agents to automate end-to-end project planning and management.
- Task Generation: Breaks down project goals into detailed, actionable tasks.
- Task Dependency Identification: Maps relationships and sequencing between tasks.
- Scheduling: Builds realistic timelines respecting dependencies and resources.
- Task Allocation: Assigns tasks to team members based on skills for balanced workload.
- Risk Assessment: Identifies and scores risks at the task level to mitigate issues early.
- Insight Generation: Provides project health summaries, bottleneck detection, and actionable advice.
AI Agents
Project Management
Streamlit
Task Automation
Risk Assessment
Team Allocation
AI Customer Support Ticket Resolver Using MCP
- This Project uses large language models to automate customer support. It classifies tickets, analyzes content, generate and send responses automatically to the given customer email address.
- Built with Streamlit and MCP (Model Context Protocol) Inspector Tool.
- Accepts customer support messages or queries and uses AI to understand the issue and generate a helpful reply.
- Detects urgency and classifies the type of request, automatically sends responses via email.
- Automatically logs tickets into a Google Sheet and provides a simple Streamlit web interface with MCP Inspector Tool.
LLM
MCP
Streamlit
AI Agents
Google Sheets API
Email Automation
AI Spreadsheet and PDF Assistant
- Developed a system to summarize uploaded CSV files based on user prompts, enabling dynamic data insights.
- Added support for PDF file uploads and extraction for summarization.
- Built an interactive chat interface allowing users to ask questions about the uploaded files with context-aware responses.
- Designed and implemented an auto-generated structured summary view for easy understanding of file contents.
File Analysis
PDF Processing
CSV Analysis
Chat Interface
AI Summarization
Data Insights
Generative AI-Powered Travel Chatbot
- Developed a Generative AI-powered travel chatbot for my travel website, designed to retrieve and present information dynamically using web scraping and conversational AI.
- The chatbot delivers an interactive user experience by engaging in real-time conversations.
- It features AI agents that handle cases where information is unavailable on the website.
- Upon user consent, these agents fetch the required data from external sources, ensuring a seamless and user-controlled interaction.
LangChain
Chroma DB
FastAPI
Groq
Cache Augmented Generation (CAG) Chatbot
- Developed a professional chatbot that reduces response time and improves performance using smart caching mechanisms.
- Integrated custom vector embeddings, Python subprocess-based LLM querying, and Mistral-7B-Instruct-v0.3.
- Designed an intuitive front-end using Streamlit for seamless interaction.
Streamlit
Mistral-7B
Vector Embeddings
LLM
Fashion Product Recommendation System
- Built a content-based recommender system with outfit compatibility and image-based recommendations.
- Implemented TF-IDF for text-based product search, fine-tuned ResNet50 for image recommendations, and optimized FAISS for fast similarity search.
- Deployed on Streamlit with Google Gemini API for real-time outfit analysis.
TF-IDF
ResNet50
Google Gemini API
FAISS
Automated Predictive Maintenance Pipeline for APS Using Machine Learning
- Developed an automated pipeline to predict failures in heavy-duty vehicle APS by distinguishing component failures from other vehicle issues.
- Utilized Apache Kafka for data ingestion, transferring sensor data to MongoDB, and trained with various machine learning models (Random Forest, XGBoost) for accurate failure predictions.
- Implemented a scalable End-to-End pipeline with CI/CD using GitHub Actions, deploying on EC2 and containerizing with Docker.
Apache Kafka
MongoDB
Docker
EC2
XGBoost
Book Recommender System Using ML
- This project demonstrates a Book Recommender System utilizing Collaborative Filtering to recommend books to users based on their preferences.
- The system helps users find books they are likely to enjoy, based on the choices of others with similar tastes.
- It trained on a collaborative filtering model on the dataset using user ratings.
Collaborative Filtering
Recommendations
Python
IPL Score Prediction Using ML
- Developed a machine learning model to predict IPL match scores using Linear Regression and Ridge Regression.
- Performed data preprocessing and model evaluation to ensure optimal performance.
- Utilized Flask to deploy the model in a web application for real-time predictions.
Linear Regression
Ridge Regression
Flask
Python