Data Visualization in Machine Learning using Python Library
All the Beginner ,who are looking forward to work in the field of Data science , machine learning and Data Analytics . Data Visualization play a important to create a model for further prediction and analysis. As In the preprocessing we come know to about the data , but for better understanding of data and relation of different attribute between each other visualization is very useful.

What is Data Visualization?

Data visualization is a technique that uses an array of static and interactive visuals within a specific context to help people understand and make sense of large amounts of data. The data is often displayed in a story format that visualizes patterns, trends and correlations that may otherwise go unnoticed.

How used in Machine Learning?

Data visualization is an important skill in applied statistics and machine learning. Statistics does indeed focus on quantitative descriptions and estimations of data. Data visualization provides an important suite of tools for gaining a qualitative understanding.

Python as a programming language is widely used in field of Machine learning . It support with various libraries that we use in data visualization. and graph plotting

Matplotlib Seaborn Pygal Ggplot Plotly

From the above mentioned library , Now we will discuss and see the implementation of matplotlib and seaborn.

Installation of matplotlib and seaborn

Matplotlib : python -m pip install -U matplotlib (windows 10 operating system) Seaborn : pip install seaborn (windows 10 operating system) Dependencies:

1.numpy (>= 1.9.3)

2.scipy (>= 0.14.0)

3.matplotlib (>= 1.4.3)

4.pandas (>= 0.15.2)

Implementation of Matplotlib and Seaborn in Jupyter Notebook

As mentioned in the above image , firstly create a object for matplotlib library for plotting different type of graph. this object will call the different methods for visualization. Before further discussion let us see a magic function used with matplotlib.

%matlplotlib inline is called magic function. that we use in jupyter notebook for plotting the graph . This inline method call the object into the output screen from the backend.

We can create various type of plot with matplotlib such as bargraph , pie chart, scatter plot , Fill plot,etc.

Below images will show you how to implement few of those
['Shivansh Keshari']
['Seaborn', 'Matplotlib', 'Data Science', 'Data Visualization', 'Machine Learning']