I'll show you how to read spreadsheets, load selected columns and worksheets, and write DataFrames to Excel files.
You'll learn how to read, manipulate, sort, filter, and visualize datasets using various Pandas methods.
Let's examine the three most common metrics to measure how values are spread out.
Let's examine four measures that you can use to represent such a central value.
I’ll share the topics you’ll need to learn, the best available resources, and the order in which to study them.
You'll learn to do all of that using SciPy. I'll also show you how to plot histograms and density curves for normally distributed data.
Let's write our own function and use it to sketch highly customizable dot plots.
Also, does Scikit-Learn provide a way to handle imbalanced classes?
Let's explore how Precision, Recall, and F1 Score can give a realistic view of a model’s predictive power.
You'll also gain practical skills to generate these metrics using Scikit-Learn.