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.
Recent posts
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.
Handling Rare Labels in Categorical Variables in Machine Learning
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.
Big Data for Climate Change Mitigation
Big data is revolutionizing climate science, enabling more accurate predictions and helping formulate effective mitigation strategies.
GIS-Based Forest Fire Hotspot Identification: A Comprehensive Approach Using Contributory Factors
A study using GIS-based techniques for forest fire hotspot identification and analysis, validated with contributory factors like population density, precipitation, elevation, and vegetation cover.