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5+ Unique Python Modules For Creating Machine Learning and Data Science Projects That Stand out!

Guide to more than 5 cool Python Library modules for creating awesome Machine Learning and data science projects.

Photo by 🇸🇮  Janko Ferlič on Unsplash
Photo by 🇸🇮 Janko Ferlič on Unsplash

Python offers a wide range of library modules and frameworks, making it one of the most compatible languages for constructing machine learning models and working on Data Science projects.

This article aims to provide some cool modules to utilize for your machine learning and data science projects apart from the standard libraries that are used.

The most commonly used python modules include numpy for numerical operations, scipy for scientific operations for dealing with something like sparse matrices, pandas to view the datasets in an organized manner, matplotlib and seaborn to visualize the data, analysis, and graphs of models constructed.

You can also utilize the scikit-learn module in python for the development of Machine Learning models through the various algorithms options available in the sklearn library. Also, you construct deep learning models by making use of frameworks such as TensorFlow and Pytorch.

You can check out the below article to gain a better intuition and understanding of the sparsity of matrices and scipy module.

Lost In A Dense Forest: Intuition On Sparsity In Machine Learning With Simple Code!

However, today in this article, we will discuss five modules you can make the best use of for your data science projects. You may know some of these or even all of them, but utilizing these modules will make your machine learning projects unique and stand out, or they can help you gauge a better understanding of the problem or task at hand.

Let us discuss, view, and understand these library modules in further detail.


Photo by Monjur Hasan on Unsplash
Photo by Monjur Hasan on Unsplash

1. Sympy

Mathematics is a significant aspect of machine learning. While some may absolutely adore math, others may dislike it. However, it is essential to have knowledge of math and understand the concepts of probability, statistics, and calculus.

Integral and differential calculus is also considered as a pre-requisite for understanding machine learning concepts like backpropagation. Luckily, Python also offers a free, lightweight, python-based library called sympy. It is a python library for symbolic mathematics. You can install this library with the following command.

pip install sympy

After the installation of this library, you can proceed to perform mathematical calculus operations like integration and differentiation in a few lines of code.

You can check out the official documentation from here for further details on how to utilize this module. However, if you are confused and want me to make a quick guide to a tutorial article on this module, then please free to let me know.


2. BeautifulSoup and Scrapy

A very important aspect of any data science or machine learning project is the data collection procedure. The term data mining can also be used for the collection of useful data from the internet. You can either download pre-available datasets from the net or utilize these two modules mentioned for scraping the web data to extract the necessary information.

BeautifulSoup is an awesome python library designed for quick turnaround projects and processing of the data available on the internet. It is a useful library for web crawling and data scraping. Both BeautifulSoup and Scrapy are great options for the extraction of web data online.

According to the official documentation of BeautifulSoup, these three features make it powerful:

  1. Beautiful Soup provides a few simple methods and Pythonic idioms for navigating, searching, and modifying a parse tree: a toolkit for dissecting a document and extracting what you need. It doesn’t take much code to write an application.
  2. Beautiful Soup automatically converts incoming documents to Unicode and outgoing documents to UTF-8. You don’t have to think about encodings unless the document doesn’t specify an encoding, and Beautiful Soup can’t detect one. Then you just have to specify the original encoding.
  3. Beautiful Soup sits on top of popular Python parsers like lxml and html5lib, allowing you to try out different parsing strategies or trade speed for flexibility.

Scrapy is another Python framework for large scale web scraping. It gives you all the tools you need to efficiently extract data from websites, process them as you want, and store them in your preferred structure and format. The Scrapy library can be quickly set up by using the following command mentioned below, and you can use it for various purposes of extraction of data.

pip install Scrapy

Since you have successfully collected the data and finished the data mining required for your project, you would now probably want to cleanse the remaining string data. This process can be completed with the help of just four basic regular expressions operations. Data cleansing is the next essential step. So, if you want to learn more about this procedure, then feel free to check out the article provided below.

Natural Language Processing Made Simpler with 4 Basic Regular Expression Operators!


3. Pandas Profiling

Screenshot By Author
Screenshot By Author

Once you have all your data and datasets ready, it is time to visualize and view the data you have collected. Exploratory data analysis is an important aspect of any machine learning and data science project.

The role of exploratory data analysis in the field of data science and machine learning projects is to be able to get a detailed understanding of the data at hand. Exploratory data analysis offers many plots and varieties to visualize and analyze the data available. It provides a brief understanding and idea of how to proceed further.

However, it can sometimes be a hectic process to develop every individual plot and initiate the processing steps. It could even take hours for certain problems. Luckily, python provides a solution to this problem as well.

Pandas profiling is an open-source Python module with which we can quickly do an exploratory data analysis with just a few lines of code. Besides, if this is not enough to convince us to use this tool, it also generates interactive reports in a web format that can be presented to any person, even if they don’t know how to program. The installation process can be done by a simple pip command as follows:

pip install pandas-profiling

With this library installed, you can utilize it for performing your Exploratory Data Analysis for your machine learning and data science projects in a few minutes. It is time-saving and highly beneficial. However, you cannot use it on every dataset, and the time for reports generated on larger datasets will take a longer time.


Outdated GIF By Author from my first project
Outdated GIF By Author from my first project

4. Pygame and Tkinter

The Pygame and Tkinter modules are some of the best modules to use for making your machine learning and data science projects stand out and give them an overall, more visually appealing look.

Imagine you had to develop a reinforcement learning game or an AI-based deep learning algorithm to improve the performance of the model in the game. If you consider the example of the snake in the above GIF or something like a game of flappy birds where there are generations of birds learning and evolving over a process, the best way to develop these graphical user interfaces in python would be to make use of the Pygame module.

There are also a ton more applications to using a module like pygame for your machine learning projects. However, another option is to develop a more interesting, interactive, and amazing graphical user interface by using a GUI development library like Tkinter. There are other options as well for developing unique and awesome GUI interfaces. I have more experience with Tkinter, and that is what I would suggest to most newer programmers as well.

I would highly recommend the viewers to check out the below article, which explains a bit more about the applications of modules like Tkinter and Pygame with actual project implementation ideas. I have also covered a small guide to understanding the basics of Pygame as well as a brief introduction to the Tkinter module.

5 Best Python Project Ideas With Full Code Snippets And Useful Links!


Images by Author - 1. GTTS Speech Synthesizing | 2.OCR Process Flow
Images by Author – 1. GTTS Speech Synthesizing | 2.OCR Process Flow

5. GTTS and OCR

The Google text-to-speech and optical character recognition are two really cool modules you can utilize for building more unique machine learning and data science projects. The face recognition and emotion & Gesture Detection projects done by me are the two best ways to make use of these modules alongside the models constructed.

The text-to-speech (TTS) is the process of converting words into a vocal audio form. The program, tool, or software takes an input text from the user, and using methods of natural language processing understands the linguistics of the language being used and performs logical inference on the text. This processed text is passed into the next block where digital signal processing is performed on the processed text. Using many algorithms and transformations this processed text is finally converted into a speech format. This entire process involves the synthesizing of speech. Below is a simple block diagram to understand the same.

Optical character recognition is the conversion of 2-Dimensional text data into a form of machine-encoded text by the use of an electronic or mechanical device. You use computer vision to read the image or text files. After reading the images, use the pytesseract module of python to read the text data in the image or the PDF and then convert them into a string of data that can be displayed in python.

Here are two links that will help you to get started with Google text-to-speech and optical character recognition. View the references provided below to understand the Google text-to-speech and the optical character recognition modules to understand more concepts and learn about them in a more detailed approach.

How to get started with Google Text-to-Speech using Python

Getting Started with Optical Character Recognition using Python


Photo by Clément H on Unsplash
Photo by Clément H on Unsplash

Conclusion:

From my experience, these libraries help to make your projects stand out. The modules suggested in this article can be taken into consideration for creating unique, more interactive, and appealing machine learning, deep learning, or data science projects.

You don’t have to utilize the modules mentioned in this article for every single one of your projects. But it is worth considering if they could be useful to solve the problem at hand or is suitable for the respective task.

It is highly recommended to use these library modules to simplify and make the tasks more convenient, as well as amplify the quality and overall appeal of the project.

If you have any queries related to the suggested modules, then feel free to let me know in the comments below. I will try to reply back as soon as possible.

Check out some of my other articles that you might enjoy reading!

Understanding Advanced Functions In Python With Codes And Examples!

Do you Really Need A GPU For Deep Learning?

10 Step Ultimate Guide For Machine Learning And Data Science Projects!

Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!


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