A naukri.com initiative
Nycdatascience
1M
141
Image Credit: Nycdatascience
Predicting the Unpredictable: Revolutionizing E-commerce Delivery with Machine Learning
- Accurately predicting delivery timelines in the e-commerce industry is a complex challenge due to various factors.
- A machine learning system utilizing the Olist e-commerce dataset was developed to forecast delivery times with high accuracy.
- Challenges include extreme variability, geographic complexity in Brazil, and non-linear relationships affecting delivery predictions.
- A multi-layered approach involving meticulous data preparation, feature engineering, and advanced modeling strategies was implemented.
- A region-specific clustering model identified three distinct delivery regions: Business Centers, Mid-Tier Regions, and Remote Areas.
- Specialized XGBoost models tailored to each cluster improved prediction accuracy significantly.
- The system also quantified prediction uncertainty, transforming point estimates into probability distributions.
- Results showed dramatic improvement in prediction accuracy, particularly in Business Centers compared to Remote Areas.
- An interactive chatbot interface was developed to make complex predictions accessible to business users.
- The system offers benefits like smarter promise dates, region-specific strategies, and operational optimization for businesses.
- Future research directions include temporal modeling, external data integration, online learning, and causal modeling.
Read Full Article
8 Likes
For uninterrupted reading, download the app