README2.md

AI-Based Diamond Price Prediction and Classification

This project utilizes machine learning and AI techniques to predict diamond grading prices (GIA-certified prices, grading prices, and bygrading prices) based on various diamond attributes. Additionally, it provides classification-based recommendations for changes in diamond parameters. The system is built using Flask, scikit-learn, and XGBoost, and it is deployed as a web application with a user-friendly interface.

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🚀 Project Overview

Problem Statement

Manually evaluating diamond prices and certification costs is a time-consuming and error-prone task. This project automates the process by leveraging AI models to analyze historical data and provide accurate predictions and recommendations based on diamond attributes.

Key Features

✅ Diamond Price Prediction: Predicts GIA, grading, and bygrading prices using AI.
✅ Parameter Change Analysis: Identifies and suggests modifications in diamond attributes.
✅ Automated Data Processing: Cleans and preprocesses input data for better model accuracy.
✅ Web-Based Interface: Flask-based UI for easy file uploads and result visualization.
✅ Downloadable Reports: Users can download CSV reports for predictions and analysis.

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⚙️ Tech Stack

Component	Tools/Technologies Used
Backend	Flask, scikit-learn, XGBoost, NumPy, Pandas
Frontend	HTML, CSS, Jinja Templates
Database	CSV/Excel file-based input
Deployment	Docker, Gunicorn
Machine Learning	Linear Regression, Decision Trees, Random Forest, K-Nearest Neighbors, XGBoost

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📌 Project Workflow

🔹 Input:

• Users upload a CSV/Excel file containing diamond attributes (Tag, Carat, Shape, Quality, Color, Cut, Polish, Symmetry, Fluorescence, etc.).

🔹 Processing:

• Prediction Models estimate GIA prices, grading prices, and bygrading prices.
• Classification Models analyze changes in diamond parameters (e.g., carat, color, cut).

🔹 Output:

• Users receive predicted values and recommendations based on AI models.
• Results are displayed in a structured table.
• Users can download reports as CSV files.

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🛠️ Setup Instructions

1️⃣ Clone the Repository

git clone https://huggingface.co/spaces/WebashalarForML/DiamRapo
cd diamond-price-prediction

2️⃣ Create a Virtual Environment (Optional)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the Application

python app.py

Visit http://127.0.0.1:5000 in your browser.

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📦 Running with Docker

1️⃣ Build the Docker Image

docker build -t diamond-prediction .

2️⃣ Run the Container

docker run -p 7860:7860 diamond-prediction

Now, visit http://localhost:7860 to use the app.

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📜 API Endpoints

Endpoint	Method	Description
/	GET	Home page
/predict	POST	Uploads a CSV/Excel file and predicts diamond prices
/download_pred	GET	Downloads prediction results as CSV
/download_class	GET	Downloads classification analysis as CSV

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📂 Project Structure

.
├── app.py                    # Flask application
├── templates/
│   ├── index.html             # Home page template
│   ├── output.html            # Output display template
├── static/                    # CSS and static files
├── Model/                     # Trained ML models (.joblib)
├── Label_encoders/            # Pretrained label encoders
├── uploads/                   # Uploaded files storage
├── data/                      # Processed data files
├── requirements.txt           # Dependencies list
├── Dockerfile                 # Docker setup
└── README.md                  # Documentation

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📊 Example Use Cases

1️⃣ Predicting Diamond Prices

• Upload a diamond dataset (CSV/Excel).
• The AI model predicts GIA price, grading price, and bygrading price.
• Download the results as a structured report.

2️⃣ Identifying Diamond Parameter Changes

• AI analyzes changes in carat, cut, color, and other attributes.
• Alerts users to potential modifications in the diamond properties.

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🔍 Future Enhancements

• ✅ Improve model accuracy with deep learning.
• ✅ Add support for real-time API integration with diamond pricing databases.
• ✅ Extend the system to predict market trends using time-series forecasting.

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💡 Credits

Developed by Webashlar, a leading IT company specializing in AI, data science, and software solutions.

Happy predicting! 💎✨

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