Top 25+ Python Projects on GitHub for Every Skill Level: Beginner to Pro

GitHub is a vast ocean of opportunities, and Python is your compass. Each project you create and share is a new milestone, marking your progress and skills.

But why Python? Python isn’t just popular—it is the most widely used programming language for new projects on GitHub. From AI to deep learning, Python powers some of the most groundbreaking technologies today. 

If you're someone eager to gain hands-on experience, this blog is just for you. Here, you’ll explore the top Python projects on GitHub that can elevate your skills and boost your career. Let’s dive in!

25+ Python Projects on Github: Beginner, Intermediate, and Professional-Level Ideas

Being a versatile and popular programming language, Python offers developers a wide range of opportunities to create impactful projects. Whether you're a beginner or a seasoned developer, there's always something exciting you can build. 

Before diving into the details, here's a quick overview of top Python projects on GitHub.

Here’s a breakdown of 25+ ideas for Python projects on GitHub for beginners, intermediates, and advanced developers. 

Python Projects with Source Code Github For Beginners

If you’re beginning your Python programming journey, these projects offer an excellent starting point. You can solidify your knowledge of fundamental concepts like syntax, logic, and basic libraries by focusing on smaller, well-structured projects. 

Here are some popular Python projects with source code GitHub for beginners.

1. Basic NLP Chatbot with Context

A chatbot can carry on a conversation by maintaining context between different exchanges. This is an ideal project for beginners in NLP (Natural Language Processing) as it explores core concepts behind conversational agents.

The chatbot uses a pre-trained NLP model to understand users' queries and give appropriate responses.

Key features:

• Can handle multiple user inputs
• Uses simple keyword-based response generation
• Supports multiple conversation states

Skills Gained:

• Working with NLP libraries like SpaCy or NLTK 
• Basic implementation of machine learning models
• Understanding context management in chatbots

Tools and Technologies:

• NLP libraries like SpaCy
• SQLite for storing data
• Flask for web integration
• Python Programming

Also Read: How to Create a Chatbot in Python?

2. Reddit Comment Analyzer

Reddit comment analyzer focuses on comments on Reddit posts, aiming to identify potential trends in user opinions. The focus of the tool will be on sentiment analysis.

The tool uses Reddit API to fetch comments, process them for sentiment analysis, and categorize them into topics using machine learning models.

Key features:

• Sentiment analysis
• Real-time data fetching
• Data visualization to represent trends

Skills gained:

• Knowledge of Reddit API
• Data visualization using Matplotlib 
• Using machine learning for classifying topics

Tools and technologies:

• Matplotlib Python library
• Python Programming
• Scikit-learn for machine learning

3. Dynamic Portfolio Generator

Using this tool, users can create a dynamic, visually appealing online portfolio by simply entering their personal details and project information. The tool can generate a fully functional portfolio site.

The user has to enter personal details and other relevant information. The system then generates an HTML portfolio that includes sections like Projects, About, Contact, and Skills.

Key features:

• Automatic HTML generation for portfolios
• Customizable templates
• Section for personal information

Skills gained:

• Web development using JavaScript
• Working with template engines
• Knowledge of static websites

Tools and technologies:

• Jinja templating engine
• Django for back-end
• Python programming

4. Dynamic Resume Builder

The tool takes user inputs and generates a professional resume. The tool allows users to customize their resume from built-in templates and add sections like skills, experience, and education.

The tool dynamically generates a downloadable resume in PDF format, which can be customized based on pre-designed templates.

Key features:

• Convert template to PDF
• Mobile-friendly interface
• Customizable resume

Skills gained:

• Generating PDFs from libraries like ReportLab
• Front-end development using JavaScript
• UX design

Tools and technologies:

• Flask for back-end development
• JavaScript for front-end development
• ReportLab for PDF generation

Also Read: How to Create Standout Resume for Freshers

5. 3D Plot Visualizer

The 3D plot visualizer tool can generate 3D plots and graphs based on user data. Users can input data points, which are then visualized in 3D space using various graphing options.

The user provides 3D data points (x, y, z coordinates), and the system uses a plotting library to generate 3D visualizations.

Key features:

• Exportable plots as images
• Interactive plot with zoom option
• Ability to switch between different plots (surface, line)

Skills gained:

• Working with Python libraries like NumPy and Pandas 
• UX design
• Using 3D plotting libraries like Plotly

Tools and technologies:

• Tkinter for graphical UX
• Python Programming
• Pandas library

6. GitHub Repository Analyzer

The tool analyzes GitHub repositories and gives insights such as project activity, active contributors, programming language used, and repository size.

The GitHub API is used to fetch data from a repository and analyze various metrics such as commit history, pull request status, and issues.

Key features:

• GitHub API integration for fetching data
• Contributor activity analysis
• Visualization of libraries 

Skills gained:

• Understanding version control
• Integrating GitHub API
• Front-end development using JavaScirpt

Tools and Technologies:

• Django for web backend
• Pandas for data processing
•  D3.js for data visualization

7. Simple AR App for Learning

The Augmented Reality (AR) app can help you learn about different concepts by overlaying digital information on the real world.

The app uses the camera to detect objects in the real world. It then overlays educational information onto the display to provide an interactive learning experience.

Key features:

• Real-time camera feed
• Mobile-friendly user interface
• Augmented reality overlays

Skills gained:

• Working with AR frameworks like ARCore 
• Mobile app development using Android or iOS
• Integrating multimedia content in an AR environment 

Tools and Technologies:

• Using Unity 3D for building AR apps
• CSS for the web interface
• Python libraries like OpenCV 

Also Read: Future of Augmented Reality

If you’re an intermediate learner, the following Python projects on GitHub offer suitable challenges.

Intermediate-Level Python Projects on Github With Source Code

Intermediate-level Python projects on GitHub offer an excellent opportunity to apply your programming skills to real-world problems. You can explore advanced concepts, such as web scraping and data visualization.

Here are python projects on GitHub for intermediate learners.

8. AI-Powered To-Do List

This AI-powered tool helps users prioritize tasks based on their importance and deadlines. As the system learns over time, the tool becomes better at suggesting task priorities.

The user inputs details such as deadlines, priority, and duration. The AI analyzes the tasks and uses machine learning models to prioritize them.

Key features:

• Prioritizing tasks using AI
• Automatic scheduling based on input data
• Push notifications for task reminders

Skills gained:

• Using Machine learning for task prioritization
• Knowledge of time management and optimization algorithms
• Using databases like SQLite for task management 

Tools and technologies:

• Firebase for database
• JavaScript for front-end development
• Scikit-learn for machine learnin

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9. API-Based Cryptocurrency Tracker

The tool pulls real-time data from an API and allows users to track the performance of various cryptocurrencies.

The tool displays cryptocurrency data to users, with options for tracking price trends over different periods.

Key features:

• Currency conversion
• Data export options
• Notification for alerts

Skills gained:

• Data visualization techniques 
• Web scraping
• User authentication 

Tools and technologies:

• React.js for front-end
• Plotly for data visualization
• CoinGecko API for fetching details

Also Read: What is Cryptocurrency Mining?

10. Real-Time Weather Dashboard

The tool fetches real-time data from a weather API and shows current weather conditions, forecasts, and other relevant information.

Based on the user’s location information, the system displays information such as current temperature, humidity, wind speed, and a 7-day forecast.

Key features:

• Hourly weather breakdown
• Interactive map to check the weather
• Search function

Skills gained:

• Data visualization libraries such as Chart.js 
• Handing asynchronous API calls with Python
• Front-end development using React

Tools and technologies:

• React for frontend development
• OpenWeatherMap API
• Programming languages like Python

11. Customized PDF Parser

The tool can extract specific data from PDF documents, such as invoices, reports, and forms. 

The tool extracts fields such as extract, such as dates, amounts, or addresses based on user specifications. It can export the file into formats like CSV.

Key features:

• Support multi-page PDF documents 
• Error handling for incorrect documents
• PDF parsing 

Skills gained:

• Working with parsing libraries like PDFMiner
• Understanding data formats like CSV
• Pattern matching

Tools and technologies:

• PyMuPDF library
• Python Programming
• Flask for web integration

12. AI-Powered Content Generator

The AI-powered content generation tool can produce human-like text based on user inputs. You can use it to produce blog posts, social media posts, or articles.

The user has to enter a brief topic description, and the model generates a full-length article or content draft.

Key features:

• Text generation based on inputs
• Keyword optimization for SEO
• Integration with content management systems (CMS)

Skills gained:

• Working with NLP models
• Web development skills 
• AP integration using OpenAI 

Tools and technologies:

• JavaScript for frontend
• GPT-3 NLP model
• Python Programming 

13. Keyword Extraction Tool

The keyword extraction tool can extract keywords from any text, making it suitable for content analysis, SEO, and digital marketing tasks.

The tool uses natural language processing (NLP) techniques to identify and rank keywords. The keywords are displayed for the user to analyze.

Key features:

• Capability to handle large text inputs
• Customizable filter
• Integration with SEO tools like Google Analytics

Skills gained:

• NLP for keyword extraction
• Basic concept of SEO
• Using processing libraries like NLTK

Tools and technologies:

• JSON  for data export
• Python Programming
• Gensim for NLP

14. Advanced Data Scraper with Visual Dashboards

The data scraper tool collects data from multiple websites and presents it in a visual dashboard. It can present trends, statistics, and other visual summaries.

The tool collects data, processes it, and then displays it on a dashboard using visualization tools. 

Key features:

• Using CAPTCHA for anti-bot measures
• Visualization using charts and graphs
• Real-time data updates

Skills gained:

• Data cleaning and transformation
• Dashboard creation
• Web scrapping techniques like Scrapy

Tools and technologies:

• Pandas for data handling
• Flask  for dashboard development
• Matplotlib for visualization

15. Code Debugger Helper

The tool can quickly identify and fix issues in the code by automatically debugging common programming mistakes.

It uses static analysis techniques to check the code for common errors such as syntax mistakes, logical errors, and runtime exceptions.

Key features:

• Syntax error detection
• User-friendly error reporting
• Code formatting 

Skills gained:

• Static code analysis
• Identifying common programming errors
• Integrating IDEs

Tools and technologies:

• Regex for pattern matching
• Visual Studio Code or PyCharm for IDE integration
• Flask for graphical UI

16. Mini Video Streaming Service

The mini video streaming tool allows users to upload and stream video content from a central server. 

The user can upload videos to a server, which is then encoded by the tool for streaming.

Key features:

• Playlist creation
• Real-time live streaming
• Support multiple video formats like MP4

Skills gained:

• Video file management
• User authentication
• Real-time communication

Tools and technologies:

• FFmpeg for video encoding and streaming
• Django  for back-end
• Using WebSocket for live streaming

17. Smart Investment Portfolio Tracker

The tool can track the investment portfolios of the users across various assets (crypto, stocks, bonds, etc.). 

Based on user input, the tool fetches real-time market data using APIs, calculates the portfolio value, and tracks the performance over time.

Key features:

• Performance analysis
• Real-time tracking of portfolio
• Alert for market updates

Skills gained:

• API integration for real-time data
• Data analysis for portfolio tracking
• Risk management strategies 

Tools and technologies:

• Alpha Vantage API
• JavaScript  for front-end development
• Pandas library for data analysis

As an advanced learner, you can explore the following Python projects on GitHub to boost your knowledge and skills.

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Advanced-Level Python Projects on Github With Source Code

For seasoned developers seeking to tackle complex challenges, advanced-level Python projects on GitHub offer a platform to hone your skills and innovate. 

Here are some top Python projects with source code GitHub for beginners.

18. Custom Machine Learning Framework

The project creates a custom machine learning framework to train, test, and deploy ML models. 

Users can input different algorithms and models and easily compare results across multiple experiments.

Key features:

• Easy integration with external libraries
• Built-in data preprocessing 
• Visualization for performance

Skills gained:

• Hyperparameter optimization
• Building custom ML pipelines
• Data preprocessing techniques

Tools and technologies:

• Python libraries like TensorFlow 
• Containerization tool like Docker
• Testing using Jupyter Notebook

19. AI Model Deployment System

The AI model deployment system deploys machine learning models into production environments efficiently. 

The system automates the AI model’s deployment process, integrates models with APIs, and serves real-time predictions.

Key features:

• Real-time model serving
• Cloud integration with AWS
• Monitoring model performance

Skills gained:

• API integration 
• Model deployment techniques like batch inference
• Rollback strategies like automated rollback 

Tools and technologies:

• Kubernetes for scaling
• Prometheus for monitoring
• FastAPI for developing API

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20. Facial Emotion Recognition

The system uses deep learning to recognize emotions such as happiness, sadness, anger, and surprise from images or video streams.

A pre-trained deep learning model (such as a CNN) is used to analyze facial features and classify emotional states. 

Key features:

• Facial recognition
• Integration with live webcam
• Use of convolutional neural networks (CNN) 

Skills gained:

• Deep learning (CNNs) for emotion classification
• Working with facial recognition libraries like OpenCV 
• Data preprocessing

Tools and technologies:

• TensorFlow for deep learning
• Training with FER2013 dataset
• Python Programming

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21. Voice Command Home Automation

The project can control different smart devices to turn on/off lights, fans, or control other devices via voice commands.

The system combines smart home devices and uses speech recognition to process user commands. 

Key features:

• Integration with IoT devices 
• Real-time device control
• Multi-command recognition

Skills gained:

• Speech recognition
• Using cloud services for device control
• Handling user feedback to improve service

Tools and technologies:

• Google Speech API for speech recognition
• Flask  for back-end development
• Hardware integration using Raspberry Pi

22. Virtual Personal Stylist

The system uses machine learning to recommend outfits based on the user’s preferences, body type, and current trends. 

You need to upload photos or give preferences, and the stylist system analyzes the data to provide outfit suggestions.

Key features:

• Outfit suggestion generator
• Filter recommendation
• User feedback improvement 

Skills gained:

• Augmented reality
• Data preprocessing
• Integrating with e-commerce API

Tools and technologies:

• OpenCV (for image processing
• React for front-end
• Keras for recommendation algorithm

23. AI-Powered Resume Screener

The system can automate the hiring process by analyzing resumes, extracting key information, and ranking candidates based on job specifications.

The tool parses the document to extract information such as skills, education, and experience and then ranks candidates based on the job requirements.

Key features:

• Resume parsing
• Customizable filtering
• Natural Language Processing (NLP)

Skills gained:

• Data extraction techniques
• Integrating AI with recruitment systems
• Handling unstructured data 

Tools and technologies:

• NLP tools like SpaCy 
• Pandas for data processing
• TensorFlow for machine learning

24. Traffic Flow Prediction System

The system uses machine learning algorithms to forecast traffic patterns in a city. It can help urban planners and drivers make better decisions.

It collects data from sources like GPS, processes it using time series forecasting models, and predicts traffic flow for the future.

Key features:

• Visualization of traffic congestion
• Predictive analysis
• Integration with live traffic APIs

Skills gained:

• Data visualization skills
• Performance evaluation of models
• Time series forecasting using libraries like Statsmodels

Tools and technologies:

• Google Maps API
• TensorFlow for deep learning
• NumPy for data manipulation

25. AI Stock Price Prediction Dashboard

The system builds a dashboard to predict stock prices using machine learning algorithms and data visualization techniques.

The tool collects historical stock price data and uses machine learning models to predict future price trends. 

Key features:

• Real-time stock data collection
• Forecasting stock price
• Integration with stock API

Skills gained:

• Using interactive dashboards for data visualization 
• Using tuning models for stock market prediction
• Feature engineering 

Tools and technologies:

• Alpha Vantage API  for stock data
• Plotly for interactive dashboards
• Jupyter Notebook 

Also Read: Stock Market Prediction Using Machine Learning

26. Reinforcement Learning Game AI

The project makes use of reinforcement learning (RL) to train an AI agent to play games. The AI agent learns the game by interacting with the game environment.

The AI agent can agent learn by trial and error, improving its strategy over time.

Key features:

• Visualization of training progress 
• Optimizing game strategies
• Reinforced learning

Skills gained:

• Building and training RL agents
• Designing reward and penalty systems
• Integrating RL with gaming environments

Tools and technologies:

• OpenAI Gym for the gaming environment
• TensorFlow for deep learning
• Python Programming 

27. E-commerce Fraud Detection System

The system uses machine learning techniques to detect fraudulent transactions on an e-commerce platform.

The fraud detection system processes user behavior and payment information to detect potentially fraudulent activities.

Key features:

• Transaction data analysis
• Integration with e-commerce platforms
• Alert messages

Skills gained:

• Machin learning classification techniques like Logistic Regression
• Data balancing techniques Oversampling
• Real-time data processing

Tools and technologies:

• NoSQL for database management
• Model deployment using Flask 
• Pandas for data processing

28. Blockchain-Based Voting System

The project seeks to build a secure voting system using blockchain technology. It can provide transparency and security in elections.

The system enables voters to cast their votes securely on a blockchain, where each vote is considered a transaction. 

Key features:

• Decentralization using blockchain
• Real-time vote counting
• Encrypted voting

Skills gained:

• Blockchain development using Ethereum 
• Cryptography for secure voting
• Using Solidity for Smart Contract Development

Tools and technologies:

• Web3.js to interact with blockchain
• Truffle Suite for testing blockchain
• Ethereum for Blockchain 

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29. AI-Powered Document Editor

The AI-powered document editor suggests improvements, such as grammar corrections, readability improvements, and content suggestions based on context.

The system uses Natural Language Processing (NLP) to analyze the text, provide grammatical corrections, and make quality recommendations.

Key features:

• Integration with word processors 
• Feedback mechanism
• Grammar correction using AI

Skills gained:

• Integrating AI with word processing
• Natural Language Processing
• Building recommendation systems

Tools and technologies:

• NLTK for Natural Language Processing
• Flask for back-end development
• React for front-end development

30. Advanced Cybersecurity Tool

The project uses machine learning and pattern recognition to detect and prevent threats like malware, phishing attacks, and other cyber security threats.

The tool analyzes network traffic, system logs, and file systems to identify unusual behavior, indicating a cyber attack.

Key features:

• System log analysis 
• Malware detection
• User alerts

Skills gained:

• Data preprocessing
• Designing a real-time alert system
• Cybersecurity protocols like SSH

Tools and technologies:

• OpenSSL for encryption
• Firewalls to prevent intrusion
• Splunk to analyze machine-generated big data

Having examined the Python projects on GitHub, you can now focus on established coding practices.

Best Practices for Working on Python Projects

While working on Python projects, you must adopt best practices that can greatly improve the quality, maintainability, and scalability of your code. 

Here are some of the best practices you need to follow during Python projects on github.

• Write clean code

Clean, readable code is necessary for collaboration and long-term project success. Follow the PEP 8 convention for Python code styling, which covers naming conventions, indentation, and whitespace. Focus on writing functions and classes that are simple, clear, and well-organized. 

• Use virtual environments

Virtual environments separate your project’s dependencies from the system’s Python installation. This will ensure that your project can run smoothly on different machines or environments. Create and manage a virtual environment using venv or virtualenv.

• Leverage version control

Version control systems like Git can manage changes in your codebase, collaborate with others, and maintain a history of your project. Collaborate with team members through platforms like GitLab or GitHub.

• Document your code

Clear documentation makes it easier for you and others to understand your code. You can include a README file in your project with an overview, installation instructions, and usage examples to guide new developers.

• Test your code

Testing ensures the reliability and correctness of your Python code. Use unit test frameworks like unit test or pytest to verify that your code behaves as expected. 

Having recognized potential errors in Python projects on GitHub, you can now focus on preventative coding strategies.

Common Mistakes to Avoid in GitHub Python Projects

When working on Python projects, especially in collaborative environments like GitHub, certain mistakes in the form of structuring can hinder the quality of your project.

Here are some of the mistakes you can avoid for a smoother and more successful development process.

• Skipping code reviews

Code reviews can help you catch errors, improve code quality, and promote collaboration. You can ask your peers to review your codes and then improve from feedback.

• Avoiding testing

Testing can catch bugs in the code, as well as serve as documentation for how the code is intended to behave. By testing your code, you can check all the edge cases before implementation.

• Overcomplicating code

If a simple solution is available, do not go for a complex option. Write a simple code that can do its job. Until necessary, avoid unnecessary optimizations.

• Ignoring documentation

Lack of documentation can make it difficult for others to understand how your code works. Write docstrings for functions and modules. Keep a README file in your project for future reference. 

• Not using version control

Without using version control like Git, your code can get lost code or make it difficult to collaborate. In addition, you can store your code in a remote repository for backup.

Having mastered the art of error-free Python projects on GitHub, let's turn your attention to exciting career opportunities.

How Can UpGrad Help You?

Whether you're developing machine learning models or building web apps, hands-on Python projects on GitHub are essential for honing practical skills. To truly excel in Python programming, you'll need expert guidance to navigate the complexities.

UpGrad’s Python development courses offer just that—providing you with the mentorship and resources you need to build a successful career in Python programming.

Here are some of the courses on Python programming.

• Learn Basic Python Programming
• Programming with Python: Introduction for Beginners
• Learn Python Libraries

Do you need help deciding which course to take to advance your career in Python Programming? Contact upGrad for personalized counseling and valuable insights.

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Explore Popular GIT Tutorials to master version control, streamline workflows, and enhance your collaboration skills with step-by-step guidance.

References:

 Project Source Codes:

1. Basic NLP Chatbot with Context Project Source Code
2. Reddit Comment Analyzer Project Source Code
3. Dynamic Portfolio Generator Project Source Code
4. Dynamic Resume Builder Project Source Code
5. 3D Plot Visualizer Project Source Code
6. GitHub Repository Analyzer Project Source Code
7. Simple AR App for Learning Project Source Code
8. AI-Powered To-Do List Project Source Code
9. API-Based Cryptocurrency Tracker Project Source Code
10. Real-Time Weather Dashboard Project Source Code
11. Customized PDF Parser Project Source Code
12. AI-Powered Content Generator Project Source Code
13. Keyword Extraction Tool Project Source Code
14. Advanced-Data Scraper with Visual Dashboards Project Source Code
15. Code Debugger Helper Project Source Code
16. Mini Video Streaming Service Project Source Code
17. Smart Investment Portfolio Tracker Project Source Code
18. Custom Machine Learning Framework Project Source Code
19. AI Model Deployment System Project Source Code
20. Facial Emotion Recognition Project Source Code
21. Voice Command Home Automation Project Source Code
22. Virtual Personal Stylist Project Source Code
23. AI-Powered Resume Screener Project Source Code
24. Traffic Flow Prediction System Project Source Code
25. AI Stock Price Prediction Dashboard Project Source Code
26. Reinforcement Learning Game AI Project Source Code
27. E-commerce Fraud Detection System Project Source Code
28. Blockchain-Based Voting System Project Source Code
29. AI-Powered Document Editor Project Source Code
30. Advanced Cybersecurity Tool Project Source Code
31. https://github.blog/news-insights/octoverse/octoverse-2024/