Machine Learning Prior Part 1 & Data Visualization

Author(s): Gencay I.

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Machine Learning Prior Part 1 & DataVisualization

Matplotlib & Seaborn Briefly Explained and Compared

Content Table
· Introduction
· Line Graph
  ∘ Matplotlib
  ∘ Seaborn
  ∘ Animated Line Graph
· Histogram
  ∘ Matplotlib
  ∘ Seaborn
· Scatter Graph
  ∘ Matplotlib
  ∘ Seaborn
· Violin Graph
  ∘ Matplotlib
  ∘ Seaborn
· Conclusion

Introduction

Hi to all.

In my Machine learning series, I try to explain to you guys the logic behind Machine Learning.

• Machine Learning A-Z Briefly Explained
• Machine Learning A-Z Briefly Explained Part 2

As it is obvious and I always say, Machine Learning is the combination of different cultures.

Mainly ;

• Statistics
• Programming
• Calculus

In the following articles, I explain most of the Statistical terms used in Machine Learning.

• Statistics for Machine Learning A-Z
• Statistics for Machine Learning A-Z Part 2

Now in that brief course, I try to explain to you guys the coding process of machine learning in Python.

In Python, I divide the process into four.

• Data Visualization- Matplotlib & Seaborn
• Data Analytics- Pandas
• Getting familiar with Data Types-Numpy
• Building Models- Scikit-learn

Also, I divide model types into three, which are ;

• Regression
• Classification
• Clustering

• Regression A-Z Briefly Explained
• Classification A-Z Briefly Explained

A clustering article is on the way*

Now, data visualization and chart types can really vary, but I decided to explain the most common ones to you because that is a brief explanation article.

And also I will code this graph in Matplotlib and Seaborn, which are the most common visualization tools in Python.

Line Graph

The line graph is used to observe changes, most commonly, in time.

Matplotlib

First, download the data by loading and seaborn and use the load_dataset method.

Then drop the categorical column and draw a graph accordingly.

Seaborn

Like in the latter code,

• Download the data by loading and seaborn
• Load_dataset method, drop the categorical column and
• Draw a line graph accordingly by selecting x and y.
• For more

Animated Line Graph

Also if you would like to see an animated graph;

• Load the libraries
• Sort the values
• Set the size of the number of png, that will create your animated line graph’s gif.
• Write a loop.
• Save your gif.

Histogram

The important graph to look density and distribution of data.

Matplotlib

Now let's explain step by step

• Download and load the data
• Define fig and ax.
• Define the histogram and the column.
• Set title, x and y label
• Define the threshold, and draw the line accordingly.
• For more arguments.

Seaborn

Here is the source of distplot.

• Import library.
• Load data set
• Define the column name
• kde- draw gaussian kernel density or not.
• bins- histogram bins width

Scatter Graph

To see data points as a literal points, a scatter graph is a useful graph type.

Matplotlib

• Download necessary libraries and data set.
• Set a title and grid option
• For more about scatter plots in matplotlib.

Seaborn

• Download necessary libraries and data set.
• Set background style.
• Set legends and such, for more

Violin Graph

Here is the quick sum of the violin graph.

Matplotlib

• Load libraries and download data
• Select the iris sepal length column and observe.
• For more.

Seaborn

• Import Seaborn and data set.
• Select the first 100 columns to compare setosa and versicolor.
• Select sepal and petal length.
• For more.

Conclusion

Now as you can see there are a lot of different ways to visualize your data.

Data visualization is an important step of the ETL process in Machine Learning.

It helps you to see, sometimes the distribution of the data, and outliers and gives you insight into the data.

Actually, it speaks to you in a different language and knowing that language better, helps you to be a real machine in Machine Learning.

“Machine learning is the last invention that humanity will ever need to make.” Nick Bostrom

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Machine Learning Prior Part 1 & Data Visualization was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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