Vaibhav Garg

Aspiring Data Scientist

[email protected]

Resume

Hello, I am

Vaibhav Garg.

1

Python
Project

2

Machine Learning
Projects

1

Deep Learning
Project

2

GenAI
Projects

About Me

👋 Welcome to my portfolio!

🧠 I’m a Computer Science graduate specializing in AI/ML, passionate about building intelligent systems that solve real-world problems through clean code, data, and automation.⚙️

My journey began with a strong foundation in Python, SQL, and libraries like Pandas, Scikit-learn and PyTorch .📊 I’ve created end-to-end projects, including dashboards and ML-powered web apps using Streamlit, showcasing my skills in turning data into interactive solutions.🌐

While I haven’t yet worked in a formal corporate setting, my projects demonstrate a solid grasp of data analysis, machine learning, and web app development.📑 Each one reflects my ability to write clean, modular code while solving real-world challenges creatively.

🚀 I’m continuously learning, exploring modern ML tools, and collaborating on exciting ideas.💡 Let’s connect or explore my portfolio to see how I turn ideas into useful, data-driven solutions.🌟

Key Skills

Python

MySQL

Exploratory Data Analysis (EDA)

Data Visualization

Statistical Data Analysis

Machine Learning

Deep Learning

Natural Language Processing

Generative AI

My Projects

Expense Tracking System

Domain/Function: Personal Finance Management

Health Insurance Premium Predictor

Domain/Function: Healthcare

Credit Risk Evaluator

Domain/Function: NBFC Credit Risk Modelling

Car Damage Detector

Domain/Function: Insurance Technology

Real Estate Research Tool

Domain/Function: Real Estate Analytics

E-commerce Chat Assistant

Domain/Function: E-commerce

My Experience

AI/ML Intern - IBM SkillsBuild, Remote

Jun 2024 - Jul 2024

● Contributed to the development of AquaGuardianBot, an AI-powered chatbot using IBM Watson Studio and NLP, reaching 500+ users with personalized water conservation guidance.

● Integrated real-time regional water usage and quality data, improving response relevance by 30% and enabling localized, actionable insights.

● Implemented multilingual and cross-platform support (5+ languages), boosting accessibility and increasing user engagement by over 60%.

Awards & Certificate

Python: Beginner to Advanced For Data Professionals

SQL for Data Science

Math and Statistics For AI, Data Science

Master Machine Learning for Data Science & AI: Beginner to Advanced

Deep Learning: Beginner to Advanced

Natural Language Processing

Let's Connect

Feel free to get in touch with me. I am always open to discussing new projects, creative ideas or opportunities to be part of your visions.

Mail Me

[email protected]

https://www.linkedin.com/in/vaibhavgarg-cse/

GitHub

https://github.com/vaibhavgarg2004

Python Project

Expense Tracking System

Domain/Function: Personal Finance Management

Built a personal finance dashboard using Streamlit and FastAPI to track, visualize, and analyze expenses, reducing manual effort by 40% compared to Excel workflows.

View Project
Engagement

View Project
on GitHUB

Other Tools

SQL

Streamlit

FastAPI

Add/Update Expenses Tab

Analytics By Category Tab

Project Details

Managing expenses manually can be time-consuming and error-prone. To solve this, I built a full-stack Expense Tracking System that lets users log, update, and analyze expenses through an interactive dashboard.

The system uses Streamlit for the frontend, FastAPI for the backend, and MySQL for persistent storage. It supports local deployment within minutes and real-time updates.

Key Features:
• Add or update expenses by date and category
• View monthly and category-wise summaries
• Visualize trends with dynamic charts
• Reduce manual effort by ~40%

This project demonstrates backend–frontend integration, SQL-based querying, and deployable Python development. It fits within the Personal Finance Management domain and serves as a base for more advanced budgeting tools.

Machine Learning Project

Health Insurance Premium Predictor

Domain/Function: Healthcare

Developed a Streamlit-based web app to predict health insurance premiums using machine learning. Included end-to-end preprocessing, feature engineering, and real-time prediction capabilities.

View Project
Engagement

View Project
on GitHUB

View Project
on Streamlit

Other Tools

Python

Scikit-learn

Streamlit

XGBoost

Prediction for Young User

Prediction for Adult User

Project Details

Estimating insurance premiums manually can be inaccurate and time-consuming. To solve this, I built a Health Insurance Premium Prediction system using machine learning and deployed it as a responsive Streamlit app.

The project features a complete ML pipeline with Pandas, Scikit-learn, and custom feature engineering. It supports real-time user input and delivers instant premium estimates based on demographic and health data.

Key Features:
• 10+ preprocessing steps including VIF-based reduction and outlier removal
• Reduced prediction error for young users from 73% to 2% via age-based segmentation
• Achieved R² of 0.988 on test data
• Real-time output in under 1 second

This project highlights applied ML in the insurance tech domain, combining data preprocessing, model optimization, and frontend deployment.

Machine Learning Project

Credit Risk Evaluator

Domain/Function: NBFC Credit Risk Modelling

Developed a credit risk prediction system using machine learning to assess loan default probability, offering real-time credit scoring and risk categorization through a responsive Streamlit web app.

View Project
Engagement

View Project
on GitHUB

View Project
on Streamlit

Other Tools

Pyhton

Scikit-learn

Streamlit

XGBoost

Low-risk applicant with strong profile

High-risk applicant with weak history

Project Details

Assessing loan risk manually is time-consuming and inconsistent. To address this, I developed a machine learning–based Credit Risk Evaluator that predicts the likelihood of loan default using borrower data and financial history.

The system is built with Streamlit for the frontend and integrates a full preprocessing and modeling pipeline in Scikit-learn. It uses engineered features like LTI Ratio and Average DPD to improve recall on risky applicants and supports real-time credit scoring.

Key Features:
• Predicts loan default risk using financial data
• Risk categorization from 300–900 based on model output
• Improved recall on default class with SMOTE Tomek + Optuna tuning
• Real-time web app for scoring new applicants

This project falls under the Credit Risk Modeling and Financial Risk Analytics domains and demonstrates the use of ML for practical, high-impact decision-making in finance.

Deep Learning Project

Car Damage Detector

Domain/Function: Insurance Technology

Built a Streamlit web app for automated car damage detection using deep learning. Included end-to-end image preprocessing, seamless user uploads, and real-time classification into six damage categories for instant assessments.

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Engagement

View Project
on GitHUB

View Project
on Streamlit

Other Tools

Python

PyTorch

Streamlit

ResNet50

Classification output of car image with Noticeable deformation at the front.

Classification output of car image with Undamaged rear section.

Project Details

Assessing car damage manually can be inconsistent and slow. To address this, I developed an automated Car Damage Detection system using deep learning, deployed through an interactive Streamlit app.

The project includes a complete computer vision pipeline with PyTorch and torchvision, applying extensive data augmentation and leveraging ResNet50 for image classification. Users can upload car images and receive instant damage category predictions.

Key Features:
• Applied 4+ image augmentation techniques to boost model robustness
• Improved classification accuracy from 57.7% to 79.5% with regularization and Optuna-based hyperparameter tuning
• Supports real-time inference directly in the web app
• Predicts one of six damage categories (e.g., Front Crushed, Rear Normal)

This project demonstrates practical deep learning for the automotive and insurance sectors, combining advanced modeling, optimization, and a user-friendly deployment.

GenAI Project

Real Estate Research Tool

Domain/Function: Real Estate Analytics

Designed a Streamlit-based AI tool for real estate research using RAG to extract insights from news articles. Integrated LLaMA3 via GROQ, MiniLM embeddings, and ChromaDB for semantic search over scraped content.

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Engagement

View Project
on GitHUB

View Project
on Streamlit

Other Tools

Python

ChromaDB

LangChain

Groq

Streamlit

Displays a user querying mortgage interest rates. The tool processes multiple URLs and returns an accurate, source-linked response using RAG.

Shows a user asking about luxury home sales trends. The app extracts key insights from real estate news and presents a concise answer with attribution

Project Details

Manually analyzing real estate news for market trends can be time-consuming and inconsistent. To streamline this, I built a Real Estate Research Tool using a conversational AI interface powered by LLaMA-3, deployed through an interactive Streamlit app.

The tool leverages Retrieval-Augmented Generation (RAG), semantic search, and live web scraping to provide grounded, explainable answers based on real estate news articles—making research faster and more reliable.

Key Features:
• Scrapes article content from user-provided URLs using BeautifulSoup
• Uses LangChain and MiniLM to chunk and embed text for semantic retrieval
• Stores and queries embeddings in ChromaDB for efficient RAG-based answering
• Displays generated answers along with source links in a clean Streamlit UI

This project demonstrates practical GenAI use in real estate analytics, integrating LLM reasoning, vector databases, and frontend deployment.

GenAI Project

E-commerce Chat Assistant

Domain/Function: E-commerce

Developed a Streamlit-based conversational AI app for e-commerce platforms to assist with FAQs, product discovery, and small talk. Integrated LLaMA3 via GROQ, semantic routing, and live SQL queries on a product database.

View Project
Engagement

View Project
on GitHUB

View Project
on Streamlit

Other Tools

Python

SQL

ChromaDB

Groq

Streamlit

Shows the chat assistant handling Small Talk, FAQs, and Product Search—delivering smart, contextual responses for a seamless shopping experience.

Project Details

Manually handling customer queries and product search on e-commerce platforms can be inefficient and limited. To solve this, I built an E-commerce Chat Assistant using a conversational AI interface powered by LLaMA-3 and deployed it via a fast, responsive Streamlit app.

The project combines Retrieval-Augmented Generation (RAG), SQL querying, and semantic intent routing to support dynamic assistance for FAQs, product discovery, and casual conversation—all without a backend server.

Key Features:
• Semantic intent routing to classify queries into FAQ, SQL, or small talk
• Dynamic product filtering via SQL on a local SQLite database
• RAG pipeline using ChromaDB and Sentence Transformers for policy-related questions
• Deployed using Streamlit with interactive product cards and dynamic outputs

This project demonstrates practical GenAI integration in e-commerce, showcasing full-stack capabilities including LLM-based logic, semantic search, and frontend deployment.