Predicting Supply Chain Delays with Machine Learning

during my internship with CSRBOX IBM SkillsBuild, I had the opportunity to work on an exciting supply chain management project inspired by the JNADatathon 2022 challenge. The goal was to tackle the critical issue of late deliveries and build a predictive model to forecast potential delays using historical order data.

💻 What I did:

Performed descriptive analytics to identify key factors causing delays in supply chains. Engineered features like order size, distance between cities, and product handling risk for better model accuracy. Built a logistic regression model to predict delays and evaluated its performance using the ROC-AUC curve. Applied more advanced techniques, like Random Forest, to improve the model's predictive power. Delivered actionable insights to optimize supply chain performance and resilience. This project helped me further develop my skills in data analysis, feature engineering, and machine learning, and I’m excited to share it with you all now!