An AI-powered system for dynamic car price prediction, trend analysis, and automated updates. This project includes data processing, machine learning-based price prediction, an API for real-time queries, SQL database integration, and automated email reports.

• Data Cleaning & Processing: Prepares used car price data for accurate predictions.
• ML-based Price Prediction: Uses regression models (XGBoost, Random Forest, etc.).
• REST API: Provides real-time price predictions via FastAPI/Flask.
• Automated Updates: Periodic price recalculations using scheduled scripts.
• SQL Database Integration: Stores price history for trend analysis.
• Email Reporting System: Sends weekly reports with market insights.

• Languages: Python
• Machine Learning: Scikit-learn, XGBoost, Pandas
• Backend API: Flask / FastAPI
• Database: PostgreSQL / MySQL
• Deployment: AWS / Render / Vercel

📦 predictive-pricing-automation
├── 📁 data               # Dataset & preprocessing scripts
├── 📁 models             # Trained machine learning models
├── 📁 api                # API implementation (Flask/FastAPI)
├── 📁 reports            # Generated reports & visualizations
├── src/
│   ├── preprocess.py     # Data cleaning & feature engineering
│   ├── train.py          # Model training script
│   ├── predict.py        # Price prediction logic
│   ├── api.py            # API server script
│   ├── email_report.py   # Automated email sender
│   └── config.py         # Configuration settings
└── README.md             # Project documentation

This project is licensed under the MIT License.

Developed by Muhammad Saad | AutoPrice AI