3 Regression Metrics You Must Know: MAE, MSE, and RMSE

This post demystifies the most common metrics used to evaluate regression models.

You'll also gain practical skills to generate these metrics using Scikit-Learn.

Normal Distribution and the Empirical Rule

This post introduces you to Normal Distribution and some of its distinctive features.

You'll also learn about Empirical Rule, which dictates how values are spread in specific intervals around the mean.

Area Under Density Curve: How to Visualize and Calculate Using Python

Let's explore Area Under Density Curve. What does it represent? What are some of its practical applications?

You'll also learn to plot and analyze partial areas under the curve using Matplotlib, Seaborn, and Numpy.

Data Distribution, Histogram, and Density Curve: A Practical Guide

Let's explore how Data Distribution enables you to extract general patterns from the data.

You'll also learn to visualize distribution as Histogram and Density Curve using Matplotlib and Seaborn.

How to Customize Pie Charts using Matplotlib

Let's explore how you can use Matplotlib to draw pie charts with customized colors and labels. You can even apply styles tailored to each slice.

Along the way, you'll see what's an exploding pie chart and how to draw it. Finally, you'll learn to plot Donut Charts!

What Is Stratified Sampling and How to Do It Using Pandas?

Do you want samples that accurately represent the population? Here's how Stratified Sampling can help.

You'll also develop practical skills and learn how to do sampling using Python and Pandas.

How to Generate Datasets Using make_classification

Let's explore how to create classification datasets with balanced or imbalanced classes and binary or multiclass labels.

You can even produce datasets that are harder to classify!

Accuracy and Confusion Matrix Using Scikit-Learn & Seaborn

Let's learn how to calculate Confusion Matrix and Accuracy using Python libraries.

I'll also show you two different ways to visualize the Confusion Matrix.

Using Confusion Matrix and Accuracy to Test Classification Models

Let's look at the basic metrics to estimate a classification modelâ€™s predictive performance.

You'll also gain practical skills to generate and visualize these metrics using Scikit-Learn and Seaborn.

K-Fold Cross-Validation Using Python and Scikit-Learn

How can you perform K-Fold Cross-Validation to evaluate machine learning models?

I'll show you two ways using Python and Scikit-Learn's helper functions - cross_val_score() and cross_validate().