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Demonstrates improved delivery timing prediction in food delivery systems, highlighting efficiency gains.
This paper presents a Smart Food Delivery Analytics and Prediction System developed using Python, Streamlit, Pandas, Matplotlib and Random Forest Classifier. The system provides interactive analytics and predicts whether food deliveries will be On Time or Late based on delivery factors such as traffic, weather, rider experience and delivery distance. The proposed system supports data-driven decision making and improves operational efficiency.
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A et al. (2026) studied this question.
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