🎨Compiling an information portrait based on a profile on GitHub

💻Practice project

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Brief information about the project🌐:

• 💾 A Python program that receives user data via the GitHub API and gives a numerical score to the profile, and the score value shows the user's activity level and programming skills
• 📑 MyToken = os.environ.get("GITHUB_TOKEN") - a string with a token, for the program to work in a variable environment, you need to create such a variable and place your access token there. Information on how to do this
• 💿 Full information is published on my Google Drive

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Language👅 and tool🔧:

   

Main features

1. GitHub Profile Analysis:

   • Evaluating user activity
   • Analyze repositories and code
   • Generate a comprehensive report

2. Machine Learning:

   • Predicting profile quality
   • Evaluating contributions to projects

3. Working with the GitHub API:

   • Multiple authentication methods
   • Getting detailed information about users

Installation and startup

1. Establish dependencies:

pip install -r requirements.txt

2. Set the environment variables:

export GITHUB_TOKEN="your_github_token"

3. Start the project:

python main.py

Project Architecture

📂project/
├── 📂src/
│   ├──📂api/
│   │   ├──📂Assessment         # User evaluation techniques
│   │   ├──📂Config/            # Configuration files
│   │   ├──📂Interface/         # Visualization techniques
│   │   ├──📂StartMethods/      # Startup methods
│   │   ├──📂User_and_Repo/     # Classes for working with users
│   └── ml/
│       ├──📂GenerationUsers/   # Data generation
│       ├──📂ForModel/          # Model ML
│       └──📄train.py           # Model training
├──📂data/                      # Training data
├──📂tests/                     # Test directory
└──📄main.py                    # Main script

Example output

User profile assessment results - MorozkoArt 

Profile data and their assessment:
+-------------------------------------------------+---------------------------------------+------------+
| Field name                                      | Significance                          | Assessment |
+-------------------------------------------------+---------------------------------------+------------+
| Username                                        | MorozkoArt                            |            |
+-------------------------------------------------+---------------------------------------+------------+
| Profile access                                  | public                                |            |
+-------------------------------------------------+---------------------------------------+------------+
| Number of followers                             | 7                                     |       1.37 |
+-------------------------------------------------+---------------------------------------+------------+
| Number of following                             | 17                                    |        1.6 |
+-------------------------------------------------+---------------------------------------+------------+
| Hireable status                                 | True                                  |          1 |
+-------------------------------------------------+---------------------------------------+------------+
| Number of private repositories                  | 2                                     |       9.33 |
| Number of public repositories                   | 49                                    |            |
+-------------------------------------------------+---------------------------------------+------------+
| Account creation date                           | 2024-06-07 13:38:22+00:00             |            |
+-------------------------------------------------+---------------------------------------+------------+
| Last update date                                | 2025-04-29 16:31:32+00:00             |            |
+-------------------------------------------------+---------------------------------------+------------+
| Account age                                     | 10 Month(s)                           |        9.0 |
+-------------------------------------------------+---------------------------------------+------------+
| Average number of commits per repository        | 10.98                                 |       3.61 |
+-------------------------------------------------+---------------------------------------+------------+
| Average commit frequency (days between commits) | 5.58                                  |       0.31 |
+-------------------------------------------------+---------------------------------------+------------+
| Average number of commits per day               | 2.88                                  |       2.95 |
+-------------------------------------------------+---------------------------------------+------------+
| Subscription plan                               | Plan(name="free")                     |          0 |
+-------------------------------------------------+---------------------------------------+------------+
| Blog                                            | https://vk.com/poc_norm               |          2 |
+-------------------------------------------------+---------------------------------------+------------+
| Company                                         | What Entertainment                    |          3 |
+-------------------------------------------------+---------------------------------------+------------+
| Organizations                                   | BigShishkaLove                        |       0.86 |
+-------------------------------------------------+---------------------------------------+------------+
| Programming languages                           | C++, Python, C#, HTML, SCSS, CMake, C |       6.76 |
+-------------------------------------------------+---------------------------------------+------------+
Profile assessment: 41.8

...

Technologies used

Technology	Assignment	Version
PyTorch	Machine Learning	2.0+
PyGithub	Working with the GitHub API	1.55+
PrettyTable	Data Visualization	3.0+
tqdm	Progress Bars	4.0+
pandas	data processing	1.5+
scikit-learn	Evaluation Metrics	1.2+
GPT-4	Code Analysis	-

Конфигурация

Evaluation parameters are customized via JSON files:

• field_score.json - weights for different metrics
• max_value.json - maximum values for normalization
• tour_field.json - tournament evaluation parameters

Additional features

1. Synthetic Data Generation:

   • Creates profiles of different levels (beginner, intermediate, expert)
   • Used for model training

2. Detailed Repository Analysis:

   • Activity estimation
   • Commits analysis
   • Popularity estimation

3. Saving results:

   • TXT format
   • With a choice of saving location