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 # AI-Based Diamond Price Prediction and Classification
-This project aims to predict diamond grading prices, GIA-certified prices, and classification-based changes in diamond attributes using machine learning models. The system processes diamond attributes from engineer plans and provides AI-driven insights into pricing and parameter variations.
 ---
 ## 🚀 Project Overview
-The goal of this project is to develop an AI-driven pipeline for automating diamond grading and certification price predictions. The system leverages machine learning models to analyze historical diamond data, predict pricing estimates, and provide classification-based recommendations.
-### 🔹 Features:
-- Predict **GIA-certified prices** and **grading prices** for diamonds.
-- Classify and recommend **potential changes in diamond parameters**.
-- Analyze **historical data trends** for better forecasting.
-- Provide **real-time AI predictions via a web-based interface**.
 ---
-## 📌 Problem Statement
-Diamond pricing and grading involve complex, time-consuming manual evaluations. This project automates the process by utilizing **machine learning models** to predict pricing, detect parameter changes, and generate valuable insights for decision-making.
 ---
-## ⚙️ Tech Stack
-| Component        | Tools & Technologies |
-|-----------------|---------------------|
-| **Data Collection** | Python (Requests, SQL) |
-| **Data Preprocessing** | Pandas, NumPy, Scikit-learn Pipelines |
-| **Model Development** | Scikit-learn, XGBoost, LightGBM, TensorFlow/PyTorch |
-| **Model Evaluation** | Scikit-learn metrics (RMSE, MAE, R²), Evidently, Prometheus |
-| **Deployment** | Flask, FastAPI, Docker |
 ---
-## 🛠 Setup & Installation
-### 1️⃣ Create a Virtual Environment
 ```bash
 python -m venv venv
 source venv/bin/activate  # On Windows: venv\Scripts\activate
 ```
-### 2️⃣ Install Dependencies
 ```bash
 pip install -r requirements.txt
 ```
-### 3️⃣ Run the Application
 ```bash
 python app.py
 ```
-OR (if using Docker)
-```bash
-docker-compose up --build
-```
 ---
-## 📊 Application Workflow
-### **🔹 Prediction Module**
-**Input:** Diamond parameters from engineer plans:
-`Tag, EngCts, EngShp, EngQua, EngCol, EngCut, EngPol, EngSym, EngFlo, EngNts, EngMikly, EngLab, EngAmt`
-**Process:**
-1. **Historical Learning:** AI model learns from past diamond data.
-2. **Training:** Identifies patterns linking diamond attributes to final pricing.
-3. **Deployment:** Predicts `GrdAmt, ByGrdAmt, GiaAmt` for new inputs.
-**Output:**
-- AI-generated price estimates with **>95% accuracy**.
 ---
-### **🔹 Classification Module**
-**Input:** Engineer Plan data with additional attributes:
-`Tag, EngCts, EngShp, EngQua, EngCol, EngCut, EngPol, EngSym, EngFlo, EngNts, EngMikly, EngLab, EngAmt, Carat, Black_Code, White_Code`
-**Process:**
-1. **AI Model Training:** Learns from past cases where Carat, Black_Code, or White_Code led to different outcomes.
-2. **Alert Generation:** Detects discrepancies in new inputs and suggests corrections.
-**Output:**
-- **Alerts and recommendations** for potential adjustments in diamond parameters.
 ---
-## 📂 App Structure
 ```
 .
 ├── app.py                    # Flask application
 ├── templates/
-│   ├── index.html            # Home page
-│   ├── output.html           # Prediction result display
-├── uploads/                  # Uploaded diamond datasets
-├── Model/                    # Trained AI models (joblib)
-├── Label_encoders/           # Encoders for categorical variables
-├── requirements.txt          # Dependencies
-├── Dockerfile                # Containerization setup
-├── README.md                 # Documentation
 ```
 ---
-## 🌟 Key Features
-✔ **GIA Price Prediction** – Estimates diamond grading costs.
-✔ **Parameter Classification** – Identifies changes in carat, shape, and other factors.
-✔ **Real-time AI Predictions** – Instant price estimates based on historical data.
-✔ **User-friendly Web Interface** – Upload diamond data and get instant insights.
-✔ **Downloadable Reports** – Export predictions and analysis as CSV files.
 ---
-## 🛠 API Endpoints
-| Endpoint | Method | Description |
-|----------|--------|-------------

 # 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.
 ---
 ## 🚀 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.
 ---
+## ⚙️ 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 |
 ---
+## 📌 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.
 ---
+## 🛠️ Setup Instructions
+### 1️⃣ Clone the Repository
+```bash
+git clone https://github.com/your-repo/diamond-price-prediction.git
+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.
 ---
+## 📦 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.
 ---
+## 📜 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 |
 ---
+## 📂 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
 ```
 ---
+## 📊 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.
 ---
+## 🔍 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.
+---
+## 📜 License
+This project is licensed under the **MIT License**.
+---
+## 💡 Credits
+Developed by **Webashlar**, a leading IT company specializing in AI, data science, and software solutions.
+Happy predicting! 💎✨
+```
+---