k-aboelfetoh/SmartFare-Egypt

SmartFare Egypt is a machine learning-powered web app that estimates and compares ride fares from Uber, Careem, and InDrive in Egypt based on distance, duration, traffic, and vehicle type — helping users find the most cost-effective ride. # 🚕 SmartFare Egypt – Intelligent Ride Fare Estimator SmartFare Egypt is a **machine learning-powered** application that predicts ride-hailing fares in Egypt and compares prices between **Uber**, **Careem**, and **InDrive**. The project uses **synthetic ride data** and machine learning models to estimate fares based on trip **distance**, **duration**, **peak hours**, and **vehicle type**. It helps users make cost-effective ride choices by showing real-time fare comparisons. ⚠️ **Note:** Since the dataset is synthetic, the results will differ from actual fares provided by the companies in real-time. --- ## 🌟 Features - **Ride fare prediction** using ML models - **Provider comparison** (Uber, Careem, InDrive) - Considers **distance, duration, and peak hours** - Detailed **EDA report** with insights on data trends - Visual comparison of model performance - Prepared for deployment with **Streamlit** interface --- ## 🛠 Tools & Technologies Used - **Python** – Main programming language - **Pandas** – Data manipulation - **NumPy** – Numerical operations - **Matplotlib** & **Seaborn** – Data visualization - **Scikit-learn** – Machine learning models - **XGBoost** – Gradient boosting model - **ydata_profiling** – Automated EDA report - **Joblib** – Model saving/loading - **Streamlit** – Web application framework - **ChatGPT** – Assisted in project documentation --- ## 📂 Project Structure ``` smartfare-egypt/ ├── app.py # Streamlit main app ├── model.pkl # Trained ML model (Random Forest) ├── egypt_eda_report.html # EDA analysis report ├── assets/ # Project images │ ├── mae_comparison.png │ ├── r2_score_comparison.png │ └── demo_streamlit.webm ├── requirements.txt # Python dependencies ├── notebooks/ # Jupyter notebooks for model development │ └── model_development.ipynb └── README.md ``` --- ## 🚀 How to Run 1. **Clone the repository:** ```bash git clone github.com …