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Mohamedsayed01/car_price

Type de record:

softwaremodel
Créateur:
Moh
Hôte:
Carvo — ML-powered web app that predicts used car prices in Egypt using XGBoost, with user accounts and prediction history. # 🚗 CARVO — Egyptian Car Price Predictor Know your car's true value. Instant, accurate price prediction for the Egyptian car market — powered by machine learning and trained on real local data. --- ## 📖 About **Carvo** is a full-stack web application built with **Flask** that predicts the fair market price of used cars in Egypt. It uses a trained **XGBoost** regression model to turn a few simple inputs — brand, model, year, mileage, city, and more — into an instant, data-driven price estimate. Beyond the prediction engine, Carvo ships with a complete user system: secure registration and login, a personal prediction history, and a profile dashboard — so every estimate you make is saved and easy to revisit. --- ## 🖼️ Screenshots Home page Prediction result page --- ## ✨ Features - 🔐 **Full user authentication** — register, log in, and update account details, with passwords securely hashed via `werkzeug`. - 🤖 **Machine learning model (XGBoost)** for accurate car price predictions. - 🏷️ **Dynamic brand/model selection** — car models update automatically based on the selected brand via an internal API. - 🧠 **Smart feature engineering**, including: - Car age (`car_age`) - Average kilometers driven per year (`km_per_year`) - Automatic flags for luxury, Chinese, and electric brands - 📊 **Per-user prediction history**, stored in a SQLite database. - 👤 **Profile dashboard** to view/edit account details and browse past predictions. - 🌐 **JSON API endpoints** (`/api/predict`, `/api/models/ `) for AJAX-driven, asynchronous requests. --- ## 🛠️ Tech Stack | Category | Technology | |---|---| | Backend | Python, Flask | | Machine Learning | XGBoost, scikit-learn, pandas, NumPy | | Database | SQLite3 | | Security | Werkzeug (password hashing) | | Frontend | HTML, CSS, JavaScript (Jinja2 templates) | --- ## 📂 Project Structure ``` car_price/ ├── model/ # Trained XGBoost model + encoders │ └── xgb_egypt_model.pkl ├── static/ …