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.
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.
Discover the significance of heart rate variability (HRV) and how the coefficient of variation (CV) provides a more nuanced view of cardiovascular health.
Explore the differences between classical statistical models and machine learning algorithms in predictive maintenance, including their performance, accuracy, and scalability in industrial settings.
This article discusses Monte Carlo dropout and how it is used to estimate uncertainty in multi-class neural network classification, covering methods such as entropy, variance, and predictive probabilities.
Rare labels in categorical variables can cause significant issues in machine learning, such as overfitting. This article explains why rare labels can be problematic and provides examples on how to handle them.