Project: Riyadh Traffic Congestion Prediction

This is a machine learning project that aims to predict the TrafficIndexLive in Riyadh, based on a dataset from Kaggle.

The model analyzes historical traffic patterns based on factors such as:

• Time (hour, day, month).
• Historical data (traffic index from a week ago, historical travel time).
• The model was built using a RandomForestRegressor and showed good results (Mean R² ~0.98).

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📊 Dataset

This project uses the "Traffic Index in Saudi Arabia and Middle East" dataset available on Kaggle.

• Link to the Dataset

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📓 Code & Notebook

The complete analysis, data processing steps, and model building can be found in my public Kaggle Notebook.

• My Kaggle Profile
• Direct Link to the Notebook

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🛠️ Technologies Used

• Python 3
• Pandas: For data manipulation and analysis.
• Numpy: For numerical operations.
• Scikit-learn: For model building (RandomForestRegressor) and evaluation (cross_val_score, mean_squared_error).
• Jupyter / Kaggle Notebooks: As the development environment.

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📞 Contact

• Name: [Meshal AL-Qushaym]
• Email: [meshalqushim@outlook.com]
• Kaggle Profile: kaggle.com/meshalfalah