Food Delivery Multi-Factor Dataset

Key Features of the Dataset

1. Delivery Personnel Attributes

• ID: A unique identifier for each delivery instance to maintain record distinction.
• Delivery_person_ID: A unique identifier for each delivery person, enabling performance tracking.
• Delivery_person_Age: Age of the delivery person, which may influence delivery efficiency.
• Delivery_person_Ratings: Customer ratings reflecting service quality and performance.

2. Geographical and Distance Metrics

• Restaurant_latitude and longitude: GPS coordinates of the restaurant’s location.
• Delivery_location_latitude and longitude: GPS coordinates of the delivery destination.
• Distance (km): The calculated distance between the restaurant and the delivery location in kilometers.

3. Environmental and Real-Time Factors

• Type_of_order: Categories such as meal, snacks, drinks, and buffet, providing insights into preparation times.
• Type_of_vehicle: Delivery vehicle types (e.g., scooter, motorcycle, cycle, EV scooter) to analyze their impact on speed and time.
• Temperature: Ambient temperature during delivery, affecting efficiency.
• Humidity: Atmospheric moisture levels that could influence travel conditions.
• Precipitation: Rainfall or snowfall data for identifying weather disruptions.
• Weather_description: A textual description of weather conditions like sunny, cloudy, or stormy.
• Traffic_Level: Traffic congestion levels categorized as low, medium, or high.

4. Target Variable

• TARGET (Delivery Time in Minutes): The core variable to predict delivery time for each instance, crucial for training machine learning models.

Advantages of the Dataset

1. Comprehensive Insights:
   • By integrating delivery personnel, environmental, and order-specific features, the dataset provides a 360-degree view of food delivery scenarios.
2. Real-World Relevance:
   • Reflects realistic delivery challenges, such as weather disruptions and traffic congestion, for improved model accuracy.
3. Diverse Applications:
   • Ideal for machine learning tasks such as time-series prediction, feature analysis, and optimization of delivery systems.
4. Benchmarking for the Industry:
   • Enables businesses to refine logistics and enhance user satisfaction by reducing delivery times.
5. Scalable Use:
   • Suitable for academic research, AI model development, and improving the operational efficiency of food delivery platforms.

Applications of the Food Delivery Time Dataset

• Delivery Time Prediction Models: Develop predictive models to estimate delivery times based on various influencing factors.
• Operational Optimization: Enhance logistics by identifying key variables that cause delays.
• Environmental Impact Analysis: Understand how weather and traffic affect delivery efficiency.
• Customer Experience Improvement: Use insights from ratings and engagement metrics to elevate service quality.

Why Choose This Dataset?

The Food Delivery Time: A Multi-Factor Dataset goes beyond basic variables, offering detailed attributes and real-world conditions for unparalleled insights. Whether you’re building predictive models, optimizing delivery systems, or conducting research, this dataset is a game-changer for food delivery logistics.