Bike Delivery Data Analysis: A Study on Delivery Efficiency and Income Prediction Using Python and R

This article introduces the courier-delivery-analysis project by magdus-data-science on GitHub. Combining Python and R languages, this project uses machine learning techniques to analyze bike delivery data, focusing on delivery time prediction, income optimization, and the impact of weather factors on delivery efficiency. Its goal is to provide data support for delivery riders to optimize their work strategies and for platforms to improve scheduling algorithms.

With the development of the gig economy, bike delivery has become an important part of urban logistics, offering advantages such as flexibility, environmental friendliness, and low cost. However, delivery riders face challenges like unstable income, high work intensity, and significant influence from external factors. This project targets this scenario and uses data science methods to reveal key factors affecting delivery efficiency and income.

Python Advantages: Machine learning ecosystem (scikit-learn, XGBoost, etc.), efficient data processing (pandas, NumPy), support for engineering deployment; R Advantages: Mature statistical analysis, powerful ggplot2 visualization, rich time series tools (forecast, etc.), R Markdown support for reproducible research; The dual-language strategy allows choosing the optimal tool for different analysis stages, avoiding the limitations of a single language.

Operational Efficiency

• Decomposition of delivery time (identifying bottlenecks in links like order response and in-store waiting)
• Analysis of route efficiency, time distribution, regional differences, and rider experience effects

Income Analysis

• Income composition (proportion of base fee, subsidies, etc.), hourly wage distribution, influencing factors, optimal work strategies, income inequality

Weather Impact

• Integration of weather data, relationship between weather and order volume/delivery efficiency/income, prediction applications

Delivery Time Prediction

• Problem definition (prediction of total time/subdivided links)
• Feature engineering (order, spatio-temporal, rider, real-time, platform features)
• Model selection (linear regression, tree models, deep learning)
• Evaluation metrics (MAE, RMSE, etc.) and business applications (ETA, route planning, capacity scheduling)

Exploratory Data Analysis (EDA)

Data quality check, distribution analysis, correlation exploration, visual insights

Statistical Inference (R Language Application)

Hypothesis testing, confidence interval estimation, multiple regression analysis

Machine Learning Modeling (Python Application)

Time series-aware data splitting, feature engineering, hyperparameter tuning, cross-validation, model evaluation and interpretation (SHAP values, etc.)

Recommendations for Riders

Optimal working hours, regional selection strategies, weather decision guidelines, efficiency improvement tips

Recommendations for Platforms

Pricing and subsidy optimization, scheduling algorithm improvement, capacity management, rider experience enhancement

This project demonstrates the ability of data science to solve practical business problems. By leveraging the technical advantages of dual-language collaboration, it converts data insights into strategic recommendations, helping to promote the sustainable development of the delivery ecosystem. For learners, it is an excellent practical case covering the complete data science process and soft skills such as dual-language collaboration and business implementation.