Insightful Tutorials for Inquisitive Developers

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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**.

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.

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**.

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.**

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**!

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**.

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**!

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**.

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**.

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()**.