Demystifying Decision Tree Algorithms
Understand how decision tree algorithms split data and how pruning improves generalization.
Recent posts
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.
RFM Segmentation: A Powerful Customer Segmentation Technique
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.
The Math Behind Kernel Density Estimation
Explore the foundations, concepts, and mathematics behind Kernel Density Estimation (KDE), a powerful tool in non-parametric statistics for estimating probability density functions.
Understanding Heart Rate Variability Through the Lens of the Coefficient of Variation in Health Monitoring
Discover the significance of heart rate variability (HRV) and how the coefficient of variation (CV) provides a more nuanced view of cardiovascular health.
A Comparison of Predictive Maintenance Algorithms: Classical vs. Machine Learning Approaches
Explore the differences between classical statistical models and machine learning algorithms in predictive maintenance, including their performance, accuracy, and scalability in industrial settings.
Estimating Uncertainty in Neural Networks Using Monte Carlo Dropout
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.