Alisha-21-cloud

Alisha-21-cloud / DiamondPriceX

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DiamondPriceX is a full-stack web application that predicts the market price of a diamond based on its physical and qualitative attributes. The platform combines a trained machine learning model with a clean, modern web interface to deliver real-time price estimates.

diamondpricex.onrender.com
13
3
100% credibility
Found May 13, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

DiamondPriceX is a web application that lets users input diamond attributes like carat, cut, color, clarity, and dimensions to receive an AI-predicted market price.

How It Works

1
🌐 Discover DiamondPriceX

You find the website and visit the home page to check out this handy tool for estimating diamond prices.

2
📖 Learn how it works

Browse the about and model pages to see how it predicts prices based on diamond qualities like size and clarity.

3
Sign in or join
📝
Create account

Share your name, email, and pick a secure password to get started.

🔑
Log in

Enter your username and password to access your account right away.

4
💎 Enter diamond details

Fill out a simple form with the diamond's weight, cut, color, clarity, and measurements.

5
✨ See the prediction

Click submit and get an instant estimate of the diamond's market value in dollars.

✅ Get your valuation

Celebrate having a clear price prediction and detailed diamond profile to guide your decisions.

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Star Growth

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AI-Generated Review

What is DiamondPriceX?

DiamondPriceX is a full-stack web application built in Python with Flask and Scikit-Learn that predicts the market price of a diamond based on its physical and qualitative attributes like carat, cut, color, clarity, and dimensions. Users log in, fill a form with nine key inputs, and get instant USD price estimates from a trained Random Forest model, solving the opacity of manual diamond valuation. A Jupyter Notebook handles model training on a 54K-record dataset for easy retraining.

Why is it gaining traction?

It combines machine learning with a clean, responsive Bootstrap interface to deliver real-time estimates in a SaaS-style app, complete with user authentication and profile summaries. Developers appreciate the quick local setup via pip and venv, plus pages showcasing model comparisons and training results. The modern UI and modular design make it a solid demo for blending ML predictions with web forms.

Who should use this?

Jewelers and diamond buyers needing quick attribute-based price checks during trades. ML beginners prototyping regression apps or learning to deploy Scikit-Learn models via web UIs. Backend devs evaluating Flask for lightweight full-stack projects with SQLite auth.

Verdict

Grab it for a polished ML-web starter if you're into diamond market tools or teaching demos—docs are thorough, setup is straightforward. With 13 stars and 1.0% credibility, it's early-stage and untested in production; fork and harden before real use.

(187 words)

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