DATA SCIENCE & MACHINE LEARNING (part -
4)
techworldthink • March 06, 2022
11. Explain the various methods for visualising multivariate data.
Multivariate exploration is the study of more than one attribute in the dataset
simultaneously. This technique is critical to understanding the relationship between
the attributes.
The multivariate visual exploration considers more than one attribute in the same
visual.
Scatterplot
A scatterplot is one of the most powerful yet simple visual plots available. In a
scatterplot, the data points are marked in Cartesian space with attributes of the
dataset aligned with the coordinates. The attributes are usually of continuous data
type. One of the key observations that can be concluded from a scatterplot is the
existence of a relationship between two attributes under inquiry. If the attributes are
linearly correlated, then the data points align closer to an imaginary straight line; if
they are not correlated, the data points are scattered. Apart from basic correlation,
scatterplots can also indicate the existence of patterns or groups of clusters in the
data and identify outliers in the data. This is particularly useful for low-dimensional
datasets.
, Bubble Chart
A bubble chart is a variation of a simple scatterplot with the addition of one more
attribute, which is used to determine the size of the data point. In the Iris dataset,
petal length and petal width are used for x and y-axis, respectively and sepal width is
used for the size of the data point. The color of the data point represents a species
class label
4)
techworldthink • March 06, 2022
11. Explain the various methods for visualising multivariate data.
Multivariate exploration is the study of more than one attribute in the dataset
simultaneously. This technique is critical to understanding the relationship between
the attributes.
The multivariate visual exploration considers more than one attribute in the same
visual.
Scatterplot
A scatterplot is one of the most powerful yet simple visual plots available. In a
scatterplot, the data points are marked in Cartesian space with attributes of the
dataset aligned with the coordinates. The attributes are usually of continuous data
type. One of the key observations that can be concluded from a scatterplot is the
existence of a relationship between two attributes under inquiry. If the attributes are
linearly correlated, then the data points align closer to an imaginary straight line; if
they are not correlated, the data points are scattered. Apart from basic correlation,
scatterplots can also indicate the existence of patterns or groups of clusters in the
data and identify outliers in the data. This is particularly useful for low-dimensional
datasets.
, Bubble Chart
A bubble chart is a variation of a simple scatterplot with the addition of one more
attribute, which is used to determine the size of the data point. In the Iris dataset,
petal length and petal width are used for x and y-axis, respectively and sepal width is
used for the size of the data point. The color of the data point represents a species
class label
