Project Overview:

The "Food Delivery Time Prediction" project, aims to accurately predict the delivery performance time of food orders. This project leverages advanced machine learning techniques to analyze various factors that influence delivery times, providing valuable insights for optimizing delivery operations.

GitHub Repository: https://github.com/jmdtanalyst/ML_Food_Delivery

Access the Running Project:  https://mldelivery.jmcloudpro.com/ 

Key Features:

• Objective: To predict the delivery performance time of food orders.

• Machine Learning Models Used:

  • Random Forest

  • Decision Tree

  • Gradient Boosting

  • Linear Regression

  • XGBoost

  • CatBoosting Regressor

  • AdaBoost Regressor

Features for Prediction:

• • Delivery person ratings

  • Multiple deliveries

  • Distance

  • Weather conditions

  • Road traffic density

  • Type of vehicle

  • Festival

  • City

Project Highlights:

• Comprehensive Analysis: The project considers a wide range of factors, from weather conditions to traffic density, ensuring a robust prediction model.

• Diverse Model Application: By employing multiple machine learning models, the project identifies the most effective algorithms for accurate predictions.

• Practical Utility: The insights gained from this project can help food delivery services optimize their operations, improve customer satisfaction, and reduce delivery times.