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

### 2️⃣ Create a Virtual Environment (Optional)
```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

### 3️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```

### 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

### 1️⃣ Build the Docker Image
```bash
docker build -t diamond-prediction .
```

### 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

| 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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