This project used the text from README files in coding bootcamp repositories on Github to predict the main coding language present in the repository.
- Dependencies
- utilities.py
- Follow the instructions to use the latest features
- python
- pandas
- scipy
- sklearn
- numpy
- matplotlib.pyplot
- seaborn
- wordcloud
- requests
- utilities.py
- Steps to recreate
- Clone this repository
- Install
utilities.py
according to the instructions - Setup env.py
- Remove the .template extension (should result in
env.py
) - Fill in your user_name, password, host, and data_path
- Remove the .template extension (should result in
- Open
coding_bootcamp_language_prediction.ipynb
and run the cells- Follow the instructions in the notebook for acquiring the data from GitHub if it's the first time running the notebook
- Model had 81% overall accuracy on test sample data
- High precision and recall for JavaScript (84%, 100%) and Jupyter Notebook (87%, 81%)
- "Python" had the second highest TF across all README files and the highest TF-IDF
- Still have room for improvement
- Model struggles to identify Java repositories (0% for both precision and recall on test data)
- Model has lower precision and recall for Python than Jupyter Notebook (65%, 73%)
I wanted to create a classification model that would predict the language of coding bootcamp repositories. I planned to use the GitHub API to retrieve 1000 of the most starred repositories with "bootcamp" in their name. After my first iteration, I decided to focus on Java and JavaScript (web development languages) and Jupyter Notebook and Python (data science languages). This helped me narrow the scope of my project and improved the performance of the model. I also decided to add the is_webdev
feature to the model which helped improve model performance further.
This is the structure of the data for the second model:
Name | Description | Type |
---|---|---|
language | The main programming language of the repository | string |
Name | Description | Type |
---|---|---|
repo | The name of the repository | string |
readme_contents | The full text of the repositoriy's README file | string |
is_webdev | Indicates if the repository is Java or JavaScript | string |
Highly accurate model (81% overall) with excellent precision and recall for JavaScript and Jupyter Notebook.
- Add more Java and Python observations to better train the model on discerning those repositories from JavaScript and Jupyter Notebook
- Preferably enough to have equal representation of all languages
- Split the model between data science and web development languages
- Remove the need to add
is_webdev
feature
- Remove the need to add