https://github.com/kiranbaby14/analysis-of-gap-programming-practices-on-github
This repository comprises code for classifying files as either GAP programming language or not, using machine learning techniques. Additionally, it facilitates conducting comprehensive analysis on the collected data, enabling insightful observations.
https://github.com/kiranbaby14/analysis-of-gap-programming-practices-on-github
bert-model deep-learning machine-learning pygithub-library python-script pytorch
Last synced: about 2 months ago
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This repository comprises code for classifying files as either GAP programming language or not, using machine learning techniques. Additionally, it facilitates conducting comprehensive analysis on the collected data, enabling insightful observations.
- Host: GitHub
- URL: https://github.com/kiranbaby14/analysis-of-gap-programming-practices-on-github
- Owner: kiranbaby14
- License: mit
- Created: 2023-05-30T09:16:06.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-10-05T15:22:41.000Z (over 2 years ago)
- Last Synced: 2025-02-06T13:36:36.729Z (over 1 year ago)
- Topics: bert-model, deep-learning, machine-learning, pygithub-library, python-script, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 51.1 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Classification-and-Analysis-of-GAP-Files-on-GitHub-using-AI
This repository comprises code for identifying repositories that uses GAP programming language, using a combination of machine learning, NLP and deep learning techniques. Additionally, it facilitates conducting comprehensive analysis on the collected data, enabling insightful observations.
## Functionalities Implemented
- Retrieve repostiories raw file link from GitHub
- Preprocess the data for carrying out various techniques on them
- Come up with an ML/DL approach to do distinguish whether a file belongs to GAP programming language
- Experiment with different models using ML, NLP and Deep Learning techniques
- Compare the performance of the models
- Perform insightful analysis on the filtered reprostories
## Getting Started
We kept the scripts sepeartely from the notebooks and have given different requirements.txt to each
### Running Python Scripts
#### Install requirements.txt
```
pip install -r requirements.txt
```
#### cd into scripts/ directory from the root directory
```
cd scripts/
```
#### Run the python script you want
```
python *script_name.py*
```
### Running Jupter Notebooks (ML/DL model and Analysis Part)
#### cd into notebooks/ directory from the root directory
```
cd notebooks/
```
#### Install requirements.txt
```
pip install -r requirements.txt
```
#### Start the Jupter notebook
```
jupyter notebook
```
#### Select the notebook you want to run
#### Run the notebook
## Best Practices
- Check the issues section to find what to work on
- If new ideas come up, add it to the issues section as **enhancement**
- If any bugs are found, raise an issue
- When working on something, generate a new branch with an appropriate name and then do PRs once finished