Food Delivery Time Prediction System Using Machine Learning

Food Delivery Time Prediction System

Project Overview

So basically, this project is about using AI to guess how long food delivery will take. It looks at stuff like customer orders, delivery guy info, locations, and even the weather to figure out a pretty accurate delivery time.The whole idea is to make food delivery apps work better, give customers a better idea of when their food’s coming, and help drivers find the best routes.

Project Details Table

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Key Features Available

• Predicts food delivery time using real-time data inputs.
• Regression model built using advanced ML algorithms (Linear Regression, Decision Tree, Random Forest, XGBoost).
• Integrated OpenCage API for converting address into geolocation (latitude and longitude).
• Real-time prediction interface built using Streamlit.
• Well-documented and structured code for both frontend and backend logic.
• Packaged with necessary .pkl files for the trained model and scaler.
• Includes a sample dataset (train.csv) for testing and retraining.
• Portable deployment-ready application with full requirements list.

Methodology

1. Data Collection

• Food delivery dataset collected from the project data source.
• The dataset includes attributes like order details, delivery person info, city, weather, and actual delivery time.

2. Data Preprocessing

• Cleaning: Managed missing values and outliers.
• Feature Engineering: Created useful features such as distance, delivery personnel rating, etc.

3. Model Development

• Trained and compared several regression models:
  • Linear Regression
  • Decision Tree
  • Random Forest
  • XGBoost

4. Model Evaluation

• Evaluated using industry-standard metrics:
  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)
  • R-squared Score (R²)

5. Deployment

• Frontend developed in Streamlit for live model interaction.
• OpenCage Geocoding API integration to fetch geolocation data for user input addresses.
• The main application logic is located in main.py.

Technology Stack

• Programming Language: Python
• Development Environment: Jupyter Notebook
• Application Framework: Streamlit

Python Libraries Used

• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn
• XGBoost

Project Files Included

• Food-Delivery-Time-Prediction-Using-Machine-Learning.ipynb – Model development
• Location_finder_api.ipynb – Geolocation API usage
• main.py – Streamlit deployment script
• model.pkl – Trained model file
• scaler.pkl – Data scaler file
• requirements.txt – All necessary package dependencies
• train.csv – Dataset used for training and testing

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