Data Science Tutorials

Discover the key differences, applications, and implementation of normalization and standardization in data preprocessing for machine learning.

Learn how swarm intelligence works by implementing ant colony optimization (ACO), particle swarm optimization (PSO), and artificial bee colony (ABC) using Python.

Minkowski distance is a way of measuring the straight or curved path between two points, depending on a chosen parameter that affects the shape. Keep reading to learn about the fundamentals, applications, and comparisons of Minkowski distance in various fields.

Explore how NiceGUI allows Python developers to create web-based user interfaces effortlessly, with interactive elements and real-time data updates.

Explore the types of structural equation models. Learn how to make theoretical assumptions, build a hypothesized model, evaluate model fit, and interpret the results in structural equation modeling.

Learn simple linear regression. Master the model equation, understand key assumptions and diagnostics, and learn how to interpret the results effectively.

Learn how to calculate sample standard deviation and understand its significance in statistical analysis. Explore examples and best practices for real-world data interpretation.

Explore how bootstrapping improves the estimation of confidence intervals and standard errors. Learn to distinguish between parametric and non-parametric bootstrapping techniques, and learn about bootstrapping in time series forecasting.

Learn how to calculate the total sum of squares (SST), regression sum of squares (SSR), and error sum of squares (SSE) to evaluate regression model accuracy. Discover their mathematical relationships and how they impact R-squared.

Discover how to interpret adjusted r-squared to evaluate regression model performance. Compare the difference between r-squared and adjusted r-squared with examples in R and Python.

Discover how to use the ANOVA test to compare multiple groups means with clear examples, real-world applications, and practical tips for data analysis.

Eigenvectors and eigenvalues are essential for understanding linear transformations. This article covers their geometric interpretation, mathematical calculation, and importance in machine learning.