Discover how data science enhances supply chain optimization and industrial network analysis, leveraging techniques like predictive analytics, machine learning, and graph theory to optimize operations.
Linear Programming is the foundation of optimization in operations research. We explore its traditional methods, challenges in scaling large instances, and introduce PDLP, a scalable solver using first-order methods, designed for modern computational infrastructures.
Explore key metrics for evaluating classification and regression models.
Understand how decision tree algorithms split data and how pruning improves generalization.
Learn how to design robust data preprocessing pipelines that prepare raw data for modeling.
This article explores the use of K-means clustering in crime analysis, including practical implementation, case studies, and future directions.
A step-by-step guide to implementing Linear Regression from scratch using the Normal Equation method, complete with Python code and evaluation techniques.
Regression tasks are at the heart of machine learning. This guide explores methods like Linear Regression, Principal Component Regression, Gaussian Process Regression, and Support Vector Regression, with insights on when to use each.
RFM Segmentation (Recency, Frequency, Monetary Value) is a widely used method to segment customers based on their behavior. This article provides a deep dive into RFM, showing how to apply clustering techniques for effective customer segmentation.
Explore the foundations, concepts, and mathematics behind Kernel Density Estimation (KDE), a powerful tool in non-parametric statistics for estimating probability density functions.
