Correlation is one of the most important statistical terms used in data science. It is used to measure the intensity of relationships between variables. If you know how to calculate correlation in data science but don’t know to analyze it, this article is for you. In this article, I will present a tutorial on how to analyze correlation in data science.
What is Correlation?
Correlation is a statistical technique used to analyze the relationships between variables. It is used to measure the intensity of relationships between variables in your data. Some of the questions correlation answers about your data are:
- Is there a relationship between the variables?
- Does changing the value of one variable affect the values of other variables?
- How strong is the relationship between the variables?
I hope you now have understood what correlation is and why it is used. Now in the section below, I will take you through how to analyze correlation in data science.
Here’s How to Analyze Correlation in Data Science
In this section, I will first calculate the correlation between the features of a dataset using Python, and then you will learn how to analyze the correlation. So let’s use the popular housing dataset to calculate the correlation using Python:
import pandas as pd data = pd.read_csv("housing.csv") correlation = data.corr() print(correlation["median_house_value"].sort_values(ascending=False))
median_house_value 1.000000 median_income 0.688075 total_rooms 0.134153 housing_median_age 0.105623 households 0.065843 total_bedrooms 0.049686 population -0.024650 longitude -0.045967 latitude -0.144160 Name: median_house_value, dtype: float64
In the code above, I calculated the correlation between all features and the median_house_value column. The output shows the intensity of the correlation in descending order. It means that the column having a high positive correlation with the median_house_value will be the first in the output, and the column that has a high negative correlation with the median_house_value will be at the end. Here, the intensity of the correlation varies from -1 to 1. When the value is close to 1, it means that there is a strong positive correlation, and when the value is close to -1, it means that there is a strong negative correlation. And when the values are close to 0, it means that there is no correlation.
I hope you now have understood how to analyze correlation in data science. When the value is close to 1, it means there is a strong positive correlation, and when the value is close to -1, it means there is a strong negative correlation. And when the values are close to 0, then it means there is no correlation. I hope you liked this article on how to analyze correlation in data science. Feel free to ask your valuable questions in the comments section below.