DiamondPricingAnalytics
AI-driven pricing and market analytics for the diamond industry.
A dual-pipeline system for automated competitor price tracking and fair value prediction using Machine Learning.
Project Information
Client
Jewelry Retail Tech
Year
2024
Role
Data Engineer
Scope
- Data Mining
- Machine Learning
- Web Scraping
Overview
Built an automated system for scraping competitor pricing and predicting fair market value for diamonds based on the 4Cs.
The Challenge
Manual pricing surveys were time-consuming and prone to human error. The client lacked real-time data to price their inventory competitively.
The Solution
Created a pipeline with Selenium for web scraping and a Machine Learning pricing engine (Linear/Lasso Regression). The system automates data collection and provides "Buy/Sell" recommendations.
Technology Stack
Core
- Python
- Pandas
- NumPy
ML
- Scikit-Learn
- Linear Regression
Scraping
- Selenium
Key Impact
Saved ~120 hours/month of manual research. Scraped and processed 53k+ records. Provided accurate fair value predictions to guide inventory decisions.
Pricing Trends
Analytics Dashboard
"We used to price based on gut feeling. Now we know exactly what a 1.5 Carat VVS1 should cost. It completely changed how we compete."
