| # AI-Based Diamond Price Prediction and Classification |
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| 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 |
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| ### 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. |
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| ### 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 |
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| | 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 |
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| ### 🔹 Input: |
| - Users upload a CSV/Excel file containing **diamond attributes** (Tag, Carat, Shape, Quality, Color, Cut, Polish, Symmetry, Fluorescence, etc.). |
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| ### 🔹 Processing: |
| - **Prediction Models** estimate GIA prices, grading prices, and bygrading prices. |
| - **Classification Models** analyze changes in diamond parameters (e.g., carat, color, cut). |
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| ### 🔹 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 |
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| ### 1️⃣ Clone the Repository |
| ```bash |
| git clone https://huggingface.co/spaces/WebashalarForML/DiamRapo |
| cd diamond-price-prediction |
| ``` |
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| ### 2️⃣ Create a Virtual Environment (Optional) |
| ```bash |
| python -m venv venv |
| source venv/bin/activate # On Windows: venv\Scripts\activate |
| ``` |
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| ### 3️⃣ Install Dependencies |
| ```bash |
| pip install -r requirements.txt |
| ``` |
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| ### 4️⃣ Run the Application |
| ```bash |
| python app.py |
| ``` |
| Visit `http://127.0.0.1:5000` in your browser. |
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| ## 📦 Running with Docker |
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| ### 1️⃣ Build the Docker Image |
| ```bash |
| docker build -t diamond-prediction . |
| ``` |
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| ### 2️⃣ Run the Container |
| ```bash |
| docker run -p 7860:7860 diamond-prediction |
| ``` |
| Now, visit `http://localhost:7860` to use the app. |
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| ## 📜 API Endpoints |
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| | 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 |
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| ``` |
| . |
| ├── 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 |
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| ### 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. |
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| ### 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. |
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| Happy predicting! 💎✨ |
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