Multivariate Analysis
It is an extension of bivariate analysis which means it involves multiple variables at the same time to find correlation between them. Multivariate Analysis is a set of statistical model that examine patterns in multidimensional data by considering at once, several data variable.
PCA
Python3
from sklearn import datasets, decomposition iris = datasets.load_iris() X = iris.data y = iris.target pca = decomposition.PCA(n_components=2) X = pca.fit_transform(X) sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y) |
Output:
HeatMap
Here we are using a heat map to check the correlation between all the columns in the dataset. It is a data visualisation technique that shows the magnitude of the phenomenon as colour in two dimensions. The values of correlation can vary from -1 to 1 where -1 means strong negative and +1 means strong positive correlation.
Python3
sns.heatmap(data.corr(), annot=True) |
Output:
What is Univariate, Bivariate & Multivariate Analysis in Data Visualisation?
Data Visualisation is a graphical representation of information and data. By using different visual elements such as charts, graphs, and maps data visualization tools provide us with an accessible way to find and understand hidden trends and patterns in data.
In this article, we are going to see about the univariate, Bivariate & Multivariate Analysis in Data Visualisation using Python.