Correlation matrix:-

Introduction :

What is Correlation Matrix?

The correlation matrix is a matrix that shows the correlation between variables. It gives the correlation between all the possible pairs of values in a matrix format.

We can use a correlation matrix to summarize a large dataset and to identify the patterns and make a decision according to it. We can also see which variable is more correlated by which variable and we can visualize our results.

A correlation matrix is a rows and columns table that shows the variables. Every cell in a matrix contains the correlation coefficient. The correlation matrix is in conjunction with other types of statistical analysis.

Uses of Correlation matrix: -

Correlation matrix works on the data set which we prefer.

For Example: -

1. If you want to predict the price of a Bigmart Sales Price Prediction you can predict it on the attributes like Item Identifier, Item Weight, Item Fat, Item visibility, Item type, Item MRP….etc. So, In this case correlation matrix is very useful and important to predict the price of a sales price.

2. From this we can say that if the relationship between two

Variables: -

If relationship is 1 then the relationship is strong

If relationship is 0 then it means the relationship is neural

If relationship is -1 then it means relationship is negative or not strong.

3. By using correlation matrix, you can get the idea about your data-set and you and analyze the data-set and also visualize the result.

4. Correlation matrix is a statistical technique which gives you the values between -1 to 1 which you can determine the relationship between variable.

5. Correlation matrix is mostly used by data scientists because this is main step before building any machine learning model because you know that which variable is more correlated by which.

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 are used to know whether how strong the relationship is between to There are several types of correlation coefficients.

Different types of correlation coefficients are: -

1. Sample correlation coefficients

2. Population correlation coefficients

3. Pearson correlation coefficients

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.