Скачать или смотреть Finding Correlation for Classification in Python

Discover how to uncover correlations in classification data using Python. Explore techniques to convert categorical variables to dummies and compute correlations for better insights.
---
This video is based on the question https://stackoverflow.com/q/64054596/ asked by the user 'asmgx' ( https://stackoverflow.com/u/1492229/ ) and on the answer https://stackoverflow.com/a/64054683/ provided by the user 'TayTay' ( https://stackoverflow.com/u/3015734/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: correlation for classification in python

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding Correlation for Classification in Python

In data analysis, understanding the relationships between different variables is paramount. This is especially true when using classification in Python, where you may often find yourself needing to examine how various factors influence a categorical outcome. A common scenario arises when trying to determine the relationship between numerical variables, such as salary, and categorical variables, such as department and approval status. This guide will break down how to effectively find correlations that can provide insights into your classification data.

The Problem at Hand

You have a DataFrame in Python that looks something like this:

[[See Video to Reveal this Text or Code Snippet]]

You want to find the correlation between Salary/Approve and Dept/Approve. However, standard correlation methods may not work well due to the presence of non-numerical (categorical) variables. So what other options do you have?

The Solution: Convert Categorical Variables to Dummies

One effective approach is to convert your categorical variables into dummy variables. Dummy variables are binary (0 or 1) indicators of whether a certain feature is present. Doing so enables you to compute correlations more effectively. Here’s how you can do this in Python:

Step 1: Convert Categorical Variables to Dummies

You can use the get_dummies() function from the pandas library to create dummy variables for your DataFrame. Here’s a simple example:

[[See Video to Reveal this Text or Code Snippet]]

This will create a new DataFrame where categorical variables such as Dept and Approve have been replaced with the following columns: Dept_Finance, Dept_HR, Dept_IT, Approve_No, and Approve_Yes.

Step 2: Compute Correlations

Now that you have transformed your DataFrame, you can compute the correlations between the numerical and categorical variables easily. Here’s how you can do it:

[[See Video to Reveal this Text or Code Snippet]]

This will yield a correlation matrix that showcases the relationships between the different variables, helping you identify how Salary correlates with department and approval status.

A Sample Correlation Insight

You can also compute correlations for specific combinations:

[[See Video to Reveal this Text or Code Snippet]]

This subset correlation will help you focus on the specific relationship you are interested in.

Conclusion

Understanding how to find correlations for classification in Python using dummy variables opens up new avenues for data analysis. By following the steps outlined above, you can reveal the significant relationships between your numerical and categorical data effectively. This practice not only enhances your data understanding but also aids in making informed decisions based on your analyses.

If you're dealing with similar datasets, utilizing get_dummies() for correlation analysis could be a game-changer! Happy coding!