Create README2.md

README2.md

@@ -0,0 +1,125 @@
+Here's a `README.md` file for your **AI-based Diamond Price Prediction and Classification** project, incorporating details from the proposal while maintaining a clear and structured format.  
+
+```markdown
+# 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 |
+|----------|--------|-------------