Applied Data Science Coding in Python: How to generate Correlation Matrix

Hits: 284

Applied Data Science Coding in Python: How to generate Correlation Matrix

A correlation matrix is a table that shows the correlation coefficients between multiple variables. It is a useful tool for understanding the relationship between different variables in a dataset. Correlation coefficient can range from -1 to 1, indicating the strength and direction of the correlation. If the correlation coefficient is positive, it means that the variables are positively correlated, meaning that as one variable increases, the other variable also increases. If the correlation coefficient is negative, it means that the variables are negatively correlated, meaning that as one variable increases, the other variable decreases.

In Python, there are several libraries that can be used to generate a correlation matrix, such as pandas, numpy, and scipy. The most common method is using the corr() function from the pandas library. It takes a DataFrame as an input and returns a correlation matrix in the form of a DataFrame, where the columns and rows are the variables and the values are the correlation coefficients.

Another method is using the corrcoef() function from the numpy library. It takes a 2D array as an input and returns a correlation matrix in the form of a 2D array, where the values are the correlation coefficients.

The scipy library also provides a method to generate correlation matrix, the pearsonr() function, which is used to calculate a Pearson correlation coefficient and the p-value for testing non-correlation. It returns two values, the correlation coefficient and the p-value.

In summary, Correlation matrix is a table that shows the correlation coefficients between multiple variables. It is a useful tool for understanding the relationship between different variables in a dataset. In Python, the most common method is using the corr() function from the pandas library, corrcoef() function from the numpy library, and pearsonr() function from the scipy library to generate correlation matrix.

 

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to generate Correlation Matrix.



Applied Data Science Coding in Python: How to generate Correlation Matrix

Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!

https://setscholars.net/how-to-visualise-correlations-among-feature-variables-in-r/

How to visualise correlation in R

How to find correlations among feature variables in R