# if %matplotlib inline has been invoked already, then plt.show() is automatically invoked and the plot is displayed in the same window. # Creating a figure to plot the graph.Īx.set_title('Relationship between variables X and Y') To plot the relationship between the two variables, we can simply call the plot function. A line plot is used to plot the relationship or dependence of one variable on another. Let’s begin by plotting a simple line plot which is used to plot a mathematical. (Note that I’ll be using matplotlib and seaborn libraries interchangeably depending on the plot.) # Importing necessary library Here is the guide to installing the matplotlib library and seaborn library. ![]() We will start by importing the two libraries. Although all the plots using the seaborn library can be built using the matplotlib library, we usually prefer the seaborn library because of its ability to handle DataFrames. In this blog, we will explore different statistical graphical techniques that can help us in effectively interpreting and understanding the data. It provides a high-level interface for producing statistical graphics. Matplotlib is a MATLAB-like plotting framework in python, while seaborn is a python visualization library based on matplotlib. ![]() It helps us to better understand the data, generate better insights for feature engineering, and, finally, make better decisions during modeling and training of the model.įor this blog, we will use the seaborn and matplotlib libraries to generate the visualizations. Data Visualization is a critical step for building a powerful and efficient machine learning model. ![]() This is a series of blogs dedicated to different data visualization techniques used in various domains of machine learning.
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