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E-commerce Delivery Delay Prediction

Classification 100,756 orders

Overview

This project focuses on predicting delivery delays in e-commerce orders using machine learning. By analyzing historical order data, we can identify patterns and factors that contribute to delayed deliveries, enabling proactive interventions and improved customer satisfaction.

92.7%
XGBoost Accuracy
91.4%
Random Forest
96,478
Delivered Orders
37.6%
Shipping Cost Impact

Problem Statement

E-commerce platforms face significant challenges with delivery delays, which directly impact customer satisfaction and retention. The goal of this project was to build a predictive model that can identify orders at risk of delay before they occur, allowing for proactive measures.

Key Questions:

  • What factors contribute most to delivery delays?
  • Can we predict delays with high accuracy?
  • How can we use these insights to improve operations?

Data & Methodology

The dataset contains 100,756 Brazilian e-commerce orders with features including order details, payment information, customer location, and product characteristics. After cleaning, 96,478 delivered orders were used for analysis.

Feature Engineering:

  • Order value and payment installments
  • Shipping cost and delivery distance
  • Seller history and ratings
  • Time-based features (season, day of week)
  • Geographic indicators

Models Tested:

  • XGBoost (Best Performance: 92.7%)
  • Random Forest (91.4%)
  • Logistic Regression (Baseline)

Key Insights

Top Predictors of Delivery Delay:

  • Shipping cost (37.6%) - Higher shipping costs correlate with faster delivery
  • Order value (34.0%) - More expensive orders tend to arrive sooner
  • Payment installments (28.3%) - More installments often indicate higher-value purchases

Geographic Patterns:

  • Major cities (SP, RJ, MG) show faster delivery times
  • Remote areas experience more delays

Results & Impact

The final XGBoost model achieved 92.7% accuracy in predicting delivery delays, with the following business implications:

  • Proactive Alerts: System can flag at-risk orders 48 hours in advance
  • Resource Allocation: Optimize logistics for high-risk regions
  • Customer Communication: Set realistic expectations for delivery times

Conclusion

This project demonstrates how machine learning can optimize e-commerce logistics by predicting delivery delays, enabling better resource allocation and improved customer satisfaction.