A curated collection of domain-specific data projects grounded in real-world scenarios.
These case studies simulate the role of a data analyst or data scientist in industries such as real estate, automotive, finance, and more.
Each project begins with a practical business challenge, walks through data exploration and modeling, and ends with actionable insights or solutions. Whether it's pricing a used car, detecting fraud, or forecasting housing trends, this repository showcases how data science can drive value across domains.
- Customer Interaction Analysis
Analyze real-world support chat data to extract user intent patterns and improve AI system training.
- Residential Price Prediction
Predict house prices using square footage, number of rooms, and other features.
- Ford Car Resale Pricing Model
Build and tune regression models to optimize resale quotations for Ford vehicles.
- Credit Card Fraud Detection
Use classification techniques to identify potentially fraudulent transactions.
Each folder includes:
- A self-contained Jupyter notebook
- Cleaned datasets stored locally in a
data/directory - Visualizations and insight summaries
- A project-specific README with business context
This repository complements my mini-projects collection, which focuses on technical experimentation and smaller-scale, skill-driven projects such as automation scripts, data cleaning workflows, and quick machine learning prototypes.