data visualization is an important aspect of machine learning (ml) as it helps to analyze and communicate patterns, trends, and insights in the data. data visualization involves creating graphical representations of the data, which can help to identify patterns and relationships that may not be apparent from the raw data.

what is data visualization?

data visualization is a graphical representation of data and information. with the help of data visualization, we can see how the data looks like and what kind of correlation is held by the attributes of the data. it is the fastest way to see if the features correspond to the output.

importance of data visualization in machine learning

the data visualization play a significant role in machine learning. we can use it in many ways in machine learning. here are some of the ways data visualization is used in machine learning −

exploring data − data visualization is an essential tool for exploring and understanding data. visualization can help to identify patterns, correlations, and outliers and can also help to detect data quality issues such as missing values and inconsistencies.
feature selection − data visualization can help to select relevant features for the ml model. by visualizing the data and its relationship with the target variable, you can identify features that are strongly correlated with the target variable and exclude irrelevant features that have little predictive power.
model evaluation − data visualization can be used to evaluate the performance of the ml model. visualization techniques such as roc curves, precision-recall curves, and confusion matrices can help to understand the accuracy, precision, recall, and f1 score of the model.
communicating insights − data visualization is an effective way to communicate insights and results to stakeholders who may not have a technical background. visualizations such as scatter plots, line charts, and bar charts can help to convey complex information in an easily understandable format.

popular python libraries for data visualization

following are the most popular python libraries for data visualization in machine learning. these libraries provide a wide range of visualization techniques and customization options to suit different needs and preferences.

1. matplotlib

matplotlib is one of the most popular python packages used for data visualization. it is a cross-platform library for making 2d plots from data in arrays. it provides an object-oriented api that helps in embedding plots in applications using python gui toolkits such as pyqt, wxpython, or tkinter. it can be used in python and ipython shells, jupyter notebook and web application servers also.

2. seaborn

seaborn is an open source, bsd-licensed python library providing high level api for visualizing the data using python programming language.

3. plotly

plotly is a montreal based technical computing company involved in development of data analytics and visualisation tools such as dash and chart studio. it has also developed open source graphing application programming interface (api) libraries for python, r, matlab, javascript and other computer programming languages.

4. bokeh

bokeh is a data visualization library for python. unlike matplotlib and seaborn, they are also python packages for data visualization, bokeh renders its plots using html and javascript. hence, it proves to be extremely useful for developing web based dashboards.

types of data visualization

data visualization for machine learning data can be classified into two different categories as follows -

univariate plots
multivariate plots

let's understand each of the above two type of data visualization plots in detail.

univariate plots: understanding attributes independently

the simplest type of visualization is single-variable or univariate visualization. with the help of univariate visualization, we can understand each attribute of our dataset independently. the following are some techniques in python to implement univariate visualization −

we will learn the above techniques in detail in their respective chapters. let's look at these techniques in brief.

histograms

histograms group the data in bins and is the fastest way to get an idea about the distribution of each attribute in the dataset. the following are some of the characteristics of histograms −

it provides us a count of the number of observations in each bin created for visualization.
from the shape of the bin, we can easily observe the distribution, i.e., whether it is gaussian, skewed, or exponential.
histograms also help us to see possible outliers.

example

the code below is an example of a python script creating the histogram. here, we will be using hist() function on numpy array to generate histograms and matplotlib for plotting them.

import matplotlib.pyplot as plt
import numpy as np
# generate some random data
data = np.random.randn(1000)
# create the histogram
plt.hist(data, bins=30, color='skyblue', edgecolor='black')
plt.xlabel('values')
plt.ylabel('frequency')
plt.title('histogram example')
plt.show()

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