Food Delivery Time Prediction Model #109
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SaiNivedh26
approved these changes
Oct 6, 2024
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This project builds a machine learning model to predict food delivery times, utilizing XGBoost with an accuracy score of 82%. The model was evaluated through cross-validation using four algorithms: Linear Regression, Decision Tree, Random Forest, and XGBoost, with XGBoost providing the best results.
The model takes various user inputs for prediction, including location information, which is processed using the OpenCage API to obtain accurate geographic coordinates. The deployment is done via Streamlit, featuring a user-friendly interface that allows users to input key details and receive real-time delivery time predictions.
This implementation offers a practical and accessible solution, making it suitable for real-world applications.