In the real world, we usually come across lots of raw data which is not fit to be readily processed by machine learning algorithms. We need to preprocess the raw data before it is fed into various machine learning algorithms. This chapter discusses various techniques for preprocessing data in Python machine learning.

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.

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.

Some popular libraries used for data visualization in Python include Matplotlib, Seaborn, Plotly, and Bokeh. These libraries provide a wide range of visualization techniques and customization options to suit different needs and preferences.