DataCo Supply Chain Analytics

This project analysed 180,519 order records from the DataCo Supply Chain dataset to identify major drivers of late deliveries, examine operational patterns across shipping modes, customer segments, and regions, and develop machine learning models for late-delivery prediction. Three classifiers — Logistic Regression, Decision Tree, and Random Forest — were trained and compared using precision, recall, and F1-score. The Decision Tree achieved the best F1-score of 0.763.

• Examined order volume, shipping modes, customer segments, product categories, and regional distribution.
• Profiled missing values, data types, and feature distributions prior to modelling.
• Identified patterns in order processing time, scheduled shipping days, and real shipping days.

• 57.28% of all 180,519 orders were classified as late deliveries.
• Investigated late-delivery drivers across shipping modes, customer segments, and regions.
• Examined the gap between scheduled and actual shipping days as a key indicator of delays.

• Selected features relevant to late-delivery prediction based on EDA findings.
• Encoded categorical variables and handled missing values prior to model training.
• Split the dataset into training and test sets for model evaluation.

• Trained Logistic Regression, Decision Tree, and Random Forest classifiers on the prepared dataset.
• Evaluated models using precision, recall, and F1-score on the held-out test set.
• The Decision Tree achieved the best F1-score of 0.763 among the three models compared.

• More than half of all orders (57.28%) were classified as late deliveries, indicating a systemic delay pattern in the dataset.
• The Decision Tree classifier achieved the best predictive performance with an F1-score of 0.763.
• Comparison across Logistic Regression, Decision Tree, and Random Forest provided a structured view of model trade-offs for this classification task.