Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/leviathanaxeislit/aiworkforceplanningtools
A set of tools written in python using deep learning methods with streamlit web interface. It can be used by recruiters for planning their workforce requirements easily. It currently has promotion prediction and role recommendation tools.
https://github.com/leviathanaxeislit/aiworkforceplanningtools
actions artificial-intelligence ats cicd deep-learning docker gdrive joblib ml neural-networks python pytorch streamlit tensorflow
Last synced: 2 days ago
JSON representation
A set of tools written in python using deep learning methods with streamlit web interface. It can be used by recruiters for planning their workforce requirements easily. It currently has promotion prediction and role recommendation tools.
- Host: GitHub
- URL: https://github.com/leviathanaxeislit/aiworkforceplanningtools
- Owner: leviathanaxeislit
- License: apache-2.0
- Created: 2024-12-21T06:47:12.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-01-27T18:15:17.000Z (7 days ago)
- Last Synced: 2025-01-27T18:44:22.288Z (7 days ago)
- Topics: actions, artificial-intelligence, ats, cicd, deep-learning, docker, gdrive, joblib, ml, neural-networks, python, pytorch, streamlit, tensorflow
- Language: Python
- Homepage: https://aiworkforceplanningtool.streamlit.app/
- Size: 4.03 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
---
# AI Workforce Planning Tools
[](https://aiworkforceplanningtool.streamlit.app/)
[](https://hub.docker.com/repository/docker/leviathanaxeislit/aiworkforceplanningtools/general)
[](https://github.com/leviathanaxeislit/AiWorkforcePlanningTools/actions/workflows/main.yml)
## Overview
AI Workforce Planning Tools is an advanced **AI-powered system** designed to enhance workforce management by utilizing machine learning models for **promotion prediction** and **role recommendation**. By leveraging AI, the tool helps organizations make informed decisions about employee promotions and match resumes with the most suitable roles, improving workforce efficiency and planning.
The project includes:
- **Promotion Prediction Model**: Predicts employee promotions using a TensorFlow-based neural network model.
- **Role Recommendation System**: Uses collaborative filtering to recommend roles based on resume and job embeddings.
[**Open the Streamlit app**](https://aiworkforceplanningtool.streamlit.app/)
## Features
- **Promotion Prediction**: Helps predict the likelihood of employee promotions based on various parameters, including job satisfaction, salary hike, and job level.
- **Role Recommendations**: Matches resumes with job roles based on job embeddings and resume embeddings.
- **Streamlit Interface**: User-friendly web interface for interacting with the models.
- **Model Integration**: Utilizes state-of-the-art machine learning models built with TensorFlow and PyTorch.
## Requirements
To run this project locally, you need to have Python 3.8 or higher installed on your machine.
### Install the necessary dependencies:
```bash
$ pip install -r requirements.txt
```
## How to Run the Application Locally
### 1. Clone the repository
Start by cloning the repository to your local machine:
```bash
$ git clone https://github.com/leviathanaxeislit/AiWorkforcePlanningTools.git
$ cd AiWorkforcePlanningTools
```
### 2. Install the required dependencies
The project requires several Python libraries. Install them by running:
```bash
$ pip install -r requirements.txt
```
### 3. Run the Streamlit app
After installing the dependencies, you can launch the app with the following command:
```bash
$ streamlit run app.py
```
This will start the application, and you can access it through your browser at `http://localhost:8501`.
## Key Models
### 1. Promotion Prediction Model
- **Method**: TensorFlow (Keras)
- **Purpose**: Predicting employee promotions based on factors like job satisfaction, salary hike, and job level.
- **Accuracy**: Achieved an average accuracy of ~75% during training and testing.
- **Implementation**: Built using a neural network with fully connected layers to process various features and predict promotion likelihood.
### 2. Role Recommendation System
- **Method**: Collaborative Filtering
- **Purpose**: Recommending suitable job roles based on the job and resume embeddings.
- **Deployment**: Built as a Streamlit app, allowing users to paste their resume and receive job recommendations.
- **Integration**: Uses **Sentence Transformers** for embedding generation and a **PyTorch** model for recommendation.
## How to Use
1. **Promotion Prediction**: The user provides details related to the employee (e.g., job satisfaction, salary, etc.), and the app predicts the likelihood of promotion.
2. **Job Role Recommendations**: Users can paste their resume, and the app will recommend top job roles based on the content of the resume and the job embeddings.
## Technologies Used
- **TensorFlow (Keras)** for promotion prediction.
- **PyTorch** for collaborative filtering-based job recommendations.
- **Sentence Transformers** for generating embeddings.
- **Streamlit** for the interactive web app interface.
- **Google Drive** for hosting large model files and embeddings.
## Future Improvements(Planned but not in priority)
- **Fine-Tuning Models**: Further tuning of models to improve prediction accuracy.
- **UI Enhancements**: More sophisticated and visually appealing user interface.
- **Job Matching Algorithm**: Enhancing the recommendation system by integrating additional features like skills and qualifications.
## Contributing
Contributions are welcome! Feel free to fork the repository, submit issues, or create pull requests for enhancements.
## License
This project is licensed under the Apache License - see the [LICENSE](LICENSE) file for details.
---