When two or more features are related to each other in such a way that if the value of 1 features increases, the value of the other feature also increases or decreases. This is what correlation means. In this article, I’m going to walk you through the implementation of Pearson Correlation using Python.

## What is Correlation?

Correlation means finding the relationship between variables. In data science, we use correlation to find features that are positively and negatively correlated with each other so that we can choose the best features to train a machine learning model.

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The degree of correlation is between -1 and 1. When the value of the correlation between the features is 1, then those features are positively correlated with each other, and when the value of the correlation between the features is -1, it means that those features are negatively correlated to each other.

When the value of the correlation between the features is equal to 0, we can say that there is no correlation between the features. In machine learning, we can use correlation to check the relationship between all the features regarding the target label. So we can select those features to train the machine learning model which are highly correlated to the target label.

## Pearson Correlation

correlation coefficient | |

values of the x-variable in a sample | |

mean of the values of the x-variable | |

values of the y-variable in a sample | |

mean of the values of the y-variable |

Pearson correlation is a statistical technique for measuring the degree of the linear relationship between two or more features. Demand and supply are the best examples of understanding of Pearson’s correlation. For example, the supply of a product will increase when the demand for the product increases, and the supply of the product will decrease when the demand for that product increases.

Thus, according to the example above, there is a positive correlation between demand and supply of a product. Hope you now understand what correlation is and what the Pearson correlation is and why we use it before training machine learning models. In the section below, I will walk you through how to calculate correlation using Python.

## Pearson Correlation using Python

Before we implement the Pearson correlation using Python, let’s take a look at some important points to understand the result:

- Positive values signify a positive linear correlation.
- Negative values mean negative linear correlation.
- 0 means no linear correlation.
- The closer the value is to 1 or -1, the stronger the linear correlation.

Now let’s see how to implement the Pearson Correlation using Python:

Unnamed: 0 ID Year IMDb ... Hulu Prime Video Disney+ Runtime Unnamed: 0 1.000000 1.000000 -0.254391 -0.399953 ... -0.219737 0.554120 0.287011 -0.206003 ID 1.000000 1.000000 -0.254391 -0.399953 ... -0.219737 0.554120 0.287011 -0.206003 Year -0.254391 -0.254391 1.000000 -0.021181 ... 0.098009 -0.253377 -0.046819 0.081984 IMDb -0.399953 -0.399953 -0.021181 1.000000 ... 0.042191 -0.163447 0.075895 0.088987 Rotten Tomatoes -0.201452 -0.201452 -0.057137 0.616320 ... 0.020373 -0.049916 -0.011805 0.003791 Netflix -0.708680 -0.708680 0.258533 0.135105 ... -0.107911 -0.757215 -0.088927 0.099526 Hulu -0.219737 -0.219737 0.098009 0.042191 ... 1.000000 -0.255641 -0.034317 0.033985 Prime Video 0.554120 0.554120 -0.253377 -0.163447 ... -0.255641 1.000000 -0.298900 -0.067378 Disney+ 0.287011 0.287011 -0.046819 0.075895 ... -0.034317 -0.298900 1.000000 -0.019976 Runtime -0.206003 -0.206003 0.081984 0.088987 ... 0.033985 -0.067378 -0.019976 1.000000 [10 rows x 10 columns] Unnamed: 0 -0.254391 ID -0.254391 Year 1.000000 IMDb -0.021181 Rotten Tomatoes -0.057137 Netflix 0.258533 Hulu 0.098009 Prime Video -0.253377 Disney+ -0.046819 Runtime 0.081984 Name: Year, dtype: float64

I hope you liked this article on what is Pearson Correlation and its implementation using Python. Feel free to ask your valuable questions in the comments section below.