Exchange Rate Models: Understanding PPP and UIP
Explore exchange rate models like Purchasing Power Parity (PPP) and Uncovered Interest Parity (UIP), key frameworks in global economics.
Recent posts
Finite Difference Methods and the Black-Scholes-Merton Equation: A Numerical Approach to Option Pricing
Explore how Finite Difference Methods and the Black-Scholes-Merton differential equation are used to solve option pricing problems numerically, with a focus on explicit and implicit schemes.
Supply Chain Optimization and Industrial Network Analysis Using Data Science
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.
Exploring Classic Linear Programming (LP) Problems and Scalable Solutions: A Deep Dive into PDLP
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.
A Guide to Model Evaluation Metrics
Explore key metrics for evaluating classification and regression models.
Demystifying Decision Tree Algorithms
Understand how decision tree algorithms split data and how pruning improves generalization.
Designing Effective Data Preprocessing Pipelines
Learn how to design robust data preprocessing pipelines that prepare raw data for modeling.
Crime Analysis Using K-Means Clustering: Enhancing Security through Data Mining
This article explores the use of K-means clustering in crime analysis, including practical implementation, case studies, and future directions.
Building Linear Regression from Scratch: A Detailed Algorithmic Approach
A step-by-step guide to implementing Linear Regression from scratch using the Normal Equation method, complete with Python code and evaluation techniques.
A Guide to Regression Tasks: Choosing the Right Approach
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.