Using decision tree for New Yorks taxi tip prediction.
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Updated
Sep 2, 2023 - Jupyter Notebook
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taxi-tip-prediction
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Developed a machine learning model to predict whether taxi passengers 🚖 will give a generous tip (≥ 20%) 💸 based on trip and payment data. Utilized Python 🐍, Pandas 🐼, NumPy 🔢, Scikit-learn 🤖, and XGBoost 🚀 for data preprocessing, feature engineering, modeling, and evaluation.
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Mar 19, 2024 - Jupyter Notebook
A decision tree regressor from SnapML by IBM was used to predict taxi tips on a dataset from the NYC TL Commission. The model was trained on over 3 million data samples in 0.636 seconds using multithreaded CPU/GPU acceleration. Achieved mean squared error = 1.62 on test data.
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Updated
Jul 12, 2024 - Jupyter Notebook
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