Welcome to PyVerse, a comprehensive and open-source hub that organizes and showcases a diverse array of Python-based projects, tools, libraries, and scripts. Our goal is to provide a well-structured resource for developers of all levels to explore, learn, and contribute to Python projects across various domains.
- Vision and Mission
- Description and Purpose
- Features
- Project Structure
- Future Scope
- How to Approach the Project
- License Information
- Contact Information
- Acknowledgments
Welcome to PyVerse, a comprehensive and open-source hub that organizes and showcases a diverse array of Python-based projects, tools, libraries, and scripts. Our goal is to provide a well-structured resource for developers of all levels to explore, learn, and contribute to Python projects across various domains.
Our vision is to build a vibrant community and resource hub for Python enthusiasts of all skill levels. We aim to facilitate learning, collaboration, and innovation through a well-organized repository of Python projects and tools. Our mission is to promote open-source development, encourage contributions, and provide a platform for developers to showcase their work.
PyVerse serves as a central repository for Python projects, ranging from beginner-friendly scripts to advanced tools and libraries. It includes projects in various domains, such as:
- Web Development
- Machine Learning
- Data Science
- Automation Tools
- Game Development
- Deep Learning
- Cybersecurity
- Blockchain Development
- Algorithms and Data Structures
- Data Visualization
The purpose of PyVerse is to provide a structured and accessible way for developers to explore existing projects, contribute new ones, and enhance their skills through practical, hands-on experience.
- Diverse Project Categories: Explore projects across multiple domains, from web development to deep learning.
- Beginner to Advanced: Projects are organized to cater to different skill levels, making it easier to find something suited to your expertise.
- Structured Repository: A well-organized directory structure to facilitate easy navigation and discovery of projects.
- Community Contributions: An open platform where contributors can add their projects, tools, and improvements.
- Comprehensive Documentation: Detailed guides and instructions to help you get started with and contribute to the projects.
The PyVerse repository is organized as follows:
├── Advanced_Projects
├── Algorithms_and_Data_Structures
│ ├── Linked List
│ │ ├── Menu_Driven_Code_for_Circular_Doubly_LinkedList.py
│ │ ├── Menu_Driven_Code_for_Circular_LinkedList.py
│ │ ├── Menu_Driven_Code_for_Doubly_LinkedList.py
│ │ ├── Menu_Driven_Code_for_Dynamic_Linear_Queue_using_LinkedList.py
│ │ ├── Menu_Driven_Code_for_Dynamic_Stack_using_LinkedList.py
│ │ ├── Menu_Driven_Code_for_Linear_LinkedList.py
│ │ └── README.md
│ ├── Stack
│ │ ├── README.md
│ │ └── stack.py
│ └── Trees
│ ├── Menu_Driven_Code_for_Avl_Tree.py
│ ├── Menu_Driven_Code_for_Binary_Search_Tree.py
│ ├── Menu_Driven_Code_for_Binary_Tree.py
│ ├── Menu_Driven_Code_for_DFS.py
│ ├── Menu_Driven_Code_for_Tree_Traversals.py
│ └── README.md
├── Automation_Tools
├── Beginner_Projects
│ ├── Calculator_App
│ │ ├── README.md
│ │ └── main.py
│ ├── Morse Code Translator with GUI
│ │ ├── README.md
│ │ ├── main.py
│ │ └── screenshots
│ │ └── tkinter-working.gif
│ ├── QR Generator
│ │ ├── README.md
│ │ └── generate_qrcode.py
│ ├── Stock App
│ │ ├── Readme.md
│ │ ├── Templates
│ │ │ ├── base.html
│ │ │ ├── financials.html
│ │ │ └── index.html
│ │ └── server.py
│ └── Turtle
│ ├── Readme.md
│ ├── rainbow_spiral.py
│ └── turtle_spiral.py
├── Blockchain_Development
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── Cybersecurity_Tools
│ └── CLI-based Port Scanner
│ ├── README.md
│ └── port-scanner.py
├── DataVizLearnig
│ ├── DataViz_Snippets.ipynb
│ └── Readme.md
├── Data_Science
│ ├── Data-science.md
│ └── time_series_visualization
│ ├── README.md
│ ├── Time_Series_Report.pdf
│ ├── Time_Series_Visualization.ipynb
│ ├── airline_passengers.csv
│ ├── autocorrelation_plot.png
│ ├── eda_plot.png
│ ├── exponential_smoothing_plot.png
│ ├── moving_average_plot.png
│ ├── seasonal_plot.png
│ └── trend_analysis_plot.png
├── Deep_Learning
│ ├── Bird Species Classification
│ │ ├── Dataset
│ │ │ └── Readme.md
│ │ ├── Images
│ │ │ ├── InceptionV3.png
│ │ │ ├── inception_resnet_v2 .png
│ │ │ ├── masked_image_1.png
│ │ │ ├── masked_image_2.png
│ │ │ └── masked_image_3.png
│ │ ├── Model
│ │ │ └── bird_species_classification.ipynb
│ │ └── Readme.md
│ ├── Face Mask Detection
│ │ ├── Dataset
│ │ │ └── Readme.md
│ │ ├── Images
│ │ │ ├── Distribution of classes.jpg
│ │ │ ├── Evaluation.jpg
│ │ │ ├── Readme.md
│ │ │ └── Sample Images.jpg
│ │ ├── Model
│ │ │ ├── Readme.md
│ │ │ └── detecting-face-masks-with-5-models.ipynb
│ │ └── requirements.txt
│ ├── MNIST Digit Classification using Neural Networks
│ │ ├── README.md
│ │ ├── bar graph.png
│ │ ├── dataset
│ │ │ └── readme.md
│ │ ├── histogram.png
│ │ ├── images
│ │ │ ├── bar graph.png
│ │ │ ├── confusion matrix.png
│ │ │ ├── histogram.png
│ │ │ ├── input visualisation.png
│ │ │ ├── pie chart.png
│ │ │ └── training loss.png
│ │ ├── input visualisation.png
│ │ ├── model
│ │ │ ├── ANN_Handwritten_Digit_Classification.ipynb
│ │ │ └── CNN_handwritten_digit_recogniser.ipynb
│ │ ├── pie chart.png
│ │ └── requirement.txt
│ ├── Plant Disease Detection
│ │ ├── Final tensorflow Models
│ │ │ ├── cotton.h5
│ │ │ ├── cucumber.h5
│ │ │ ├── grapes.h5
│ │ │ ├── guava.h5
│ │ │ ├── potato.h5
│ │ │ ├── rice.h5
│ │ │ ├── sugarcane.h5
│ │ │ ├── tomato.h5
│ │ │ └── wheat.h5
│ │ ├── README.md
│ │ ├── assets
│ │ │ └── images
│ │ │ ├── cotton_result-graph.png
│ │ │ ├── cotton_result.png
│ │ │ ├── grapes_result.png
│ │ │ ├── grapes_result_graph.png
│ │ │ ├── guava_result.png
│ │ │ ├── guava_result_graph.png
│ │ │ ├── potato_result_graph.png
│ │ │ ├── sugarcane_result_graph.png
│ │ │ └── tomato_result_graph.png
│ │ ├── ipynb files
│ │ │ ├── Cotton_Classification.ipynb
│ │ │ ├── Grapes_Classification.ipynb
│ │ │ ├── Guava_Classification.ipynb
│ │ │ ├── Potato_Classification.ipynb
│ │ │ ├── Sugarcane_Classification.ipynb
│ │ │ ├── Tomato_Classification.ipynb
│ │ │ └── requirements.txt
│ │ └── result.md
│ └── Spam Vs Ham Mail Classification [With Streamlit GUI]
│ ├── Dataset
│ │ ├── newData.csv
│ │ └── spam-vs-ham-dataset.csv
│ ├── Image
│ │ ├── PairPlot_withHue.png
│ │ ├── Spam-vs-ham-piechart.jpg
│ │ ├── spam-ham-num_chr.jpg
│ │ ├── spam-ham-num_sent.jpg
│ │ └── spam-ham-num_word.jpg
│ ├── Model
│ │ ├── README.md
│ │ ├── app1.py
│ │ ├── app2.py
│ │ ├── model1.ipynb
│ │ └── model2.ipynb
│ └── requirements.txt
├── Game_Development
├── LICENSE
├── Machine_Learning
│ ├── Air Quality Prediction
│ │ ├── Dataset
│ │ │ └── README.md
│ │ ├── Images
│ │ │ ├── Satisfaction_level_of_people_post_covid.jpg
│ │ │ ├── Satisfaction_level_of_people_pre_covid.jpg
│ │ │ ├── most_polluted_cities_post_covid.jpg
│ │ │ └── most_polluted_cities_pre_covid.jpg
│ │ └── Model
│ │ ├── README.md
│ │ └── air-quality-eda-and-classification.ipynb
│ ├── Automobile Sales Data Analysis and Prediction
│ │ ├── Dataset
│ │ │ ├── Auto Sales data.csv
│ │ │ └── README.md
│ │ ├── Images
│ │ │ ├── Dealsize_bar.png
│ │ │ ├── Dealsize_pie.png
│ │ │ ├── Productline_bar.png
│ │ │ ├── Productline_pie.png
│ │ │ ├── Status_bar.png
│ │ │ └── Status_pie.png
│ │ ├── Model
│ │ │ ├── Automobile_Sales_Prediction.ipynb
│ │ │ └── README.md
│ │ └── requirements.txt
│ ├── Bitcoin Price Prediction
│ │ ├── Dataset
│ │ │ ├── README.md
│ │ │ └── bitcoin_dataset.csv
│ │ ├── Images
│ │ │ ├── image1.png
│ │ │ ├── image2.png
│ │ │ ├── image4.png
│ │ │ ├── image5.png
│ │ │ └── images3.png
│ │ ├── Model
│ │ │ ├── Bitcoin_Price_Prediction.ipynb
│ │ │ └── README.md
│ │ ├── README.md
│ │ └── requirements.txt
│ ├── Fake News Detection
│ │ ├── Images
│ │ │ ├── Dataset.png
│ │ │ ├── EDA.png
│ │ │ ├── EDA1.png
│ │ │ ├── metrics.png
│ │ │ ├── model.png
│ │ │ ├── model2.png
│ │ │ └── model2metrics.png
│ │ ├── Model
│ │ │ └── PridictionModel.ipynb
│ │ └── Readme.md
│ ├── Hand Game Controller
│ │ ├── "A" Key Binding.png
│ │ ├── "D" Key Binding.png
│ │ ├── "S" Key Binding.png
│ │ ├── "W" Key Binding.png
│ │ ├── README.md
│ │ ├── main-mobile-cam.py
│ │ ├── main-pc-cam.py
│ │ └── requirements.txt
│ └── Twitter Sentiment Analysis
│ ├── README.md
│ ├── Twitter Sentiment Analysis.ipynb
│ ├── Twitter-sentiment-analysis-1.jpg
│ ├── images
│ │ ├── Input Data.jpg
│ │ ├── Model Performance.jpg
│ │ ├── Negative wordcloud.jpg
│ │ ├── Positive wordcloud.jpg
│ │ ├── Sentiment countplot.jpg
│ │ └── sample.md
│ ├── images.jpg
│ ├── train.csv
│ └── twitter2-720x540.jpg
├── PROJECT-README-TEMPLATE.md
├── README.md
├── Tutorials
├── Web_Development
└── repo_structure.txt
PyVerse aims to expand in the following areas:
- Adding new projects and tools in emerging Python domains.
- Enhancing the existing documentation and tutorials.
- Improving project quality and adding comprehensive test suites.
- Exploring integrations with other programming languages and tools.
-
Explore the Repositories:
Navigate through the project directories to familiarize yourself with the structure and content.
-
Read Documentation:
Go through the README files and documentation to understand the purpose and implementation of various projects.
-
Run Simple Projects:
Start with beginner projects to get hands-on experience and build confidence.
-
Write and Review Test Cases:
Create or improve test cases for existing projects to ensure code reliability.
-
Implement New Features:
Contribute by adding new features or improving existing ones.
-
Enhance Documentation:
Contribute to enhancing documentation and guides to make them more informative and user-friendly.
-
Research and Development:
Engage in advanced research and development by exploring new technologies and algorithms.
-
Develop Complex Projects:
Work on more complex projects or tools and contribute to their documentation and implementation.
-
Mentor and Collaborate:
Mentor new contributors, participate in discussions, and help improve the community.
 Utsav Singhal |
Thanks to these wonderful people! Contributions of any kind are welcome! 🚀
This project is licensed under the MIT License. See the LICENSE file for more details.
For any questions, suggestions, or feedback, please open an issue on GitHub or contact the project maintainers via email [email protected].
We extend our gratitude to all contributors and the open-source community for their support and valuable contributions to this project.
It always takes time to understand and learn. So, don't worry at all. We know you have got this!
I love connecting with different people so if you want to say hi, I'll be happy to meet you more! :)
