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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 −
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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.
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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.
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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.
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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.
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 −
Multivariate Plots: Interaction Among Multiple Variables
Another type of visualization is multi-variable or “multivariate” visualization. With the help of multivariate visualization, we can understand interaction between multiple attributes of our dataset. The following are some techniques in Python to implement multivariate visualization −
In the next few chapters, we will look at some of the popular and widely used visualization techniques available in machine learning.
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