6 Awesome Python Projects For 2021
6 Awesome Python Projects For 2021
My favorite part about the fields of Python programming and Data Science is the numerous amounts of wonderful projects you can build with them. You can construct groundbreaking, innovative, and revolutionary projects which can benefit the entire society as well as change the landscape of the entire world to reach great feats.
Tech giants and major companies are heavily investing their resources in Data Science due to the vast potential the innovations of this subject possess. With a wide array of spectacular projects that are built each day by creative data scientists and programmers, it would great to have a look at what we as individuals can achieve.
In this article, we will discuss fifteen awesome Python and Data Science projects that you can enjoy implementing. These projects are guaranteed to provide you with the best possible experience for understanding most of the essential Python and Data Science concepts in further detail. Along with the immense knowledge and experience you gain from these projects, you can also showcase them in your resumes for better job opportunities or just as a sign of self-pride!
1. Reminder Application With Python:
To start things off with a simple project that I recently covered in one of my articles is the reminder application that will consistently notify you about the various tasks you have to complete throughout the day. The notifications will be reminded according to the time scheduler which you have programmed the script to perform.
The project makes use of only two modules for the completion of the task. It utilizes the time module, which comes pre-installed with Python, and the plyer library, which will be used for alerting us about the timely notifications for the completion of the particular task at hand.
The following project is extremely simple for a beginner-level introduction to understanding Python and the basic concepts related to the subject. Despite its simplicity, it can be very useful for improving your overall productivity. The link provided guides you through the entire process of building this project from scratch. The first project is fairly simple, and the estimated time to complete this project should range anywhere from 30 minutes to 2 hours, depending on the programmer’s interest and skill. However, the difficulty range from the next projects mentioned will gradually increase.
2. Matrix Multiplication In Python Without Any ML Libraries:
An important aspect of python and machine learning is understanding the math behind these concepts and knowing how some of the code in machine learning libraries. To have a better grasp of these concepts, it is essential to practice the ideas implemented in scientific modules like numpy and scikit-learn by ourselves. One such programming application is performing the matrix multiplication operation without using any ML libraries.
To perform this task, the main requirement is knowledge of how matrices works. The complete explanation and guide can be obtained from my article below. However, if you are just interested in the basic gist of this coding problem and want to try to solve this on your own, then use the next reference paragraphs to help you get started.My approach to this problem is going to be to take all the inputs from the user. These are the number of rows and columns of both the first and second matrix. Also, based on the number of rows and columns of each matrix, we will respectively fill the alternative positions accordingly.
The first step, before doing any matrix multiplication is to check if this operation between the two matrices is actually possible. This can be done by checking if the columns of the first matrix matches the shape of the rows in the second matrix. This can be formulated as:
→ no. of columns in matrix 1 = no. of rows in matrix 2
This should be a great starting point for you to get started. From here you can compute the problem statement with various ways by utilizing your own techniques.
3. Email Spam Detection:
Email spam, also referred to as junk email, is unsolicited messages sent in bulk by email (spamming). Email filtering is the processing of email to organize it according to specified criteria. The term can apply to the intervention of human intelligence, but most often refers to the automatic processing of incoming messages with anti-spam techniques — to outgoing emails as well as those being received.
Various Classification Algorithms can be used for completing the email spam detection task. The various machine learning algorithms like Naive Bayes, support vector machines, K-nearest neighbors, and random forests among many other algorithms can be used for filtering spam messages and classifying if the received email is a spam or not.
Advanced spam detection can be performed using techniques like neural networks or optical character recognition (OCR) which is also used by companies like Gmail for spam filtering.
Assume we have a dataset of 30,000 emails out of which some are classified as spam and some are classified as not spam. The machine learning model will be trained on the dataset. Once the training process is complete, we can test it with a mail that was not included in our training dataset. The machine learning model can make predictions on the following input and classify it correctly if the input e-mail is spam or not.
Various anti-spam techniques are used to prevent email spam (unsolicited bulk email). No technique is a complete solution to the spam problem, and each has trade-offs between incorrectly rejecting legitimate email (false positives) as opposed to not rejecting all spam (false negatives) — and the associated costs in time, effort, and cost of wrongfully obstructing good mail.
As an example, the Naive Bayes classifiers are a popular statistical technique of e-mail filtering. They typically use bag of words features to identify spam e-mail, an approach commonly used in text classification. Naive Bayes classifiers work by correlating the use of tokens (typically words, or sometimes other things), with spam and non-spam e-mails and then using Bayes’ theorem to calculate a probability that an email is or is not spam.
Naive Bayes spam filtering is a baseline technique for dealing with spam that can tailor itself to the email needs of individual users and give low false positive spam detection rates that are generally acceptable to users. It is one of the oldest ways of doing spam filtering, with roots in the 1990s.
4. Sentiment Analysis:
Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.
A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level — whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, “beyond polarity” sentiment classification looks, for instance, at emotional states such as “angry”, “sad”, and “happy”.
However, you can choose to negate the other reviews and only classify them as good or bad. For example, for a movie review, anything rated 1–2 stars is marked as negative, a rating of 4–5 is marked as positive, while a neutral rating of 3 can be ignored accordingly.
Solving the Sentiment Analysis problem is one of the best beginner-level projects for you to start with due to the wide range of options that you have available to solve the following task. You can choose any method that you prefer for solving this question.
Machine learning Algorithms like logistic and Naive Bayes can be easily used to solve such kinds of tasks. Many approaches can be used for obtaining a solution to this problem, including methods of deep learning. However, even the simplest methods can be used to solve this task, depending on how complicated you decide to make the problem.
5. Build A Game With Python:
The outdated GIF you guys can see above is one of my first projects I ever did with the help of pygame about three years ago. If you want a more concise guide on how you can build this from scratch with python then do let me know. But the main idea here is to build a game with python from scratch on your own. Start off with something simple like a snake game, or tic-tac-toe, and proceed towards a more advanced one like flappy birds with reinforcement learning.
The idea behind accomplishing this task is more of personal opinion and preference. I believe that one of the best ways to get a good hold of any programming language is to start with a project that is fun and enjoyable. I am also a bit of a gaming nerd. To get started with gaming projects related to python, I would highly recommend the use of the Pygame library module for the execution of such programs.
With the pygame module, you can build some simple, fun games with python. However, don’t expect anything too fancy as it has its limitations. Regardless, it is a fantastic way to get started, and below is the starter code to dive in. Just install pygame with a simple pip command and then use the following import pygame command. The following message will greet you upon the successful importing of the module.
pygame 1.9.6
Hello from the pygame community. https://www.pygame.org/contribute.htmlThe versions might differ depending on the time on installation, so don’t worry too much. Just use the updated versions always in any scenario. I will go over some basic commands you should know and how they work. Below is the complete code block for all the important aspects you need to know to get started with pygame.
#imports the pygame library module
import pygame# initilize the pygame module
pygame.init()# Setting your screen size with a tuple of the screen width and screen height
display_screen = pygame.display.set_mode((800,600))
# Setting a random caption title for your pygame graphical window.
pygame.display.set_caption("pygame test")# Update your screen when required
pygame.display.update()# quit the pygame initialization and module
pygame.quit()# End the program
quit()
I would highly recommend you check out some YouTube videos for better understanding and learning to build some games. The documentation for the pygame module, albeit a little lengthy, is probably one the best resources to learn more about this module.
6. Working With Images Using OpenCV:
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.
RGB are the 3 most significant layers of coloring for computer vision tasks. The composition of these three colors, namely red, green, and blue can be used to compose almost any other color. Mixing them in the right proportion allows us to frame any other desired color. This concept has existed since the cathode ray televisions a few decades ago.
Each of these colors has an 8 bit integer value. This means a matrix of these could range from 0 to 255. The reasoning for this is because ²⁸ is 256 and 0–255 consist of 256 values. Each of these colors will have a value of this range and since we have a 3-Dimensional image, we can stack each of these upon each other.
OpenCV module is by far the best library for the execution of complex machine learning, deep learning, and computer vision tasks. It offers simplicity and high standards for the analysis and performance of the models being built. It is an open-source library and can be integrated with other python modules such as NumPy to accomplish complicated real-time applications. It is supported for a wide range of programming languages and runs remarkably on most platforms such as Windows, Linux, and MacOS.
Working and dealing with images is an essential aspect of computer vision projects for AI and Data Science. The reading, displaying, and writing of images is an essential part of computer vision as you have to consistently deal with images. The best part about opencv apart from the previously mentioned advantages is that it grants you access to a variety of image formats as well. So, we can work on all these image formats without facing any major issues.
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