In order to understand what a Correlation matrix is, let us first have a look what correlation is and what are correlation coefficients.

Correlation:

Correlation refers to the relationship which is statistical, between two entities. In other words, it is how the two variables tends in relation to one another. Correlation is being used for various data sets, as well. In some cases, you might have predicted how things will correlate to each other, while in some cases, the relationship will be a surprise combination. It is important to us to understand that the correlation does not mean the relationship between the data is causal.

To understand how correlation works, it is important to understand the following terms:

  • Positive correlation: A positive correlation will hold maximum value of 1. This means the two variables will be moving up or down in the same direction all together.
  • Negative correlation: A negative correlation will hold minimum value of -1. This means the two variables will always move in the opposite directions.
  • Zero or no correlation: A correlation of zero means there is not any relationship between the two variables. In other words, as one variable moves, the other variable’s movement will be in the unrelated direction.

Correlation coefficients:

While correlation studies how two data points are related to one another, a correlation coefficient measures the strength of the relationship between the two variables. In statistics, there are three types of correlation coefficients. They are as follows:

  • Pearson correlation:
  • Spearman correlation:
  • Kendall correlation:

Correlation matrix:

A correlation matrix is a table which simply displays the relationship or so called as correlation coefficients for assigned variables. The matrix identifies the correlation between all the possible pairs of values which are present in the table. It is a very powerful tool to accumulate observations over a large dataset and to identify and visualize patterns of the given data.

A correlation matrix is a combination of rows and columns that show the variables. Each value in a cell of a table contains the correlation coefficient of variables.

Specifically, the correlation matrix is frequently utilized in combination with other types of statistical analysis. For instance, correlation matrix may be helpful in the analysis of multiple Machine Learning (ML) models. Considering that multiple ML models contain several independent variables. In multiple ML models, the correlation matrix determines the correlation coefficients between the multiple independent variables present in a model.

Below I’ve shown how to create a Correlation Matrix using Pandas in Python:

Step 1: Collecting Data

Collect the data for correlation matrix

For e.g. I have four Variables A, B, C & D with some data in them:

A: 80, 79, 84, 91, 76, 88, 69, 88

B: 40, 36, 39, 51, 32, 29, 19, 28

C: 35, 30, 38, 45, 21, 22, 15, 19

D: 11, 10, 13, 15, 7, 9, 5, 5

Step 2: Create Data Frame for the gathered data in Python using Pandas

Once you run this you will get output as follows:

Step 3: Create a Correlation Matrix using Pandas

Using correlation matrix function create a correlation matrix.

After implementing the code you’ll get output as following:

Step 4: Get a Visual Representation of the Correlation Matrix using Seaborn and Matplotlib

You can use the seaborn and matplotlib packages in order to get a visual representation of the correlation matrix.

First import the seaborn and matplotlib packages:

Then, add the following syntax at the bottom of the code:

The complete code will look like:

And the final output will be as following:

By following the above steps anyone can implement or use correlation matrix and also visualize it in the program or project to make it more beautiful and statistically impeccable.

Conclusion:

A correlation matrix is a table which displays the correlation coefficients for assigned variables. The matrix identifies the correlation between which are present in the table. A correlation matrix in the project would not only beautify it but will also add a statistically rich value to it.