Compensation Prediction for Engineering Roles

After analyzing the data from the Juniors vs ChatGPT study, I decided to use the compensation estimation data to build an app that predicts compensation for various engineering roles. After cleaning, I built a baseline model and later experimented with different regression models. After hyperparameters tuning I decided to go for the SVC model with rbf kernel. The model was developed using data provided by SourceStack.

Running the App in a Docker Container

To run the Streamlit app in a Docker container, follow these steps:

1. Clone this repository and open in VS Code.
2. Install and configure Docker for your operating system. Make sure Docker is running.
3. Open a terminal or command prompt in the directory of the repo and run the following command:

docker build -t compensation-app .

4. After the image is built, run a container from the image with the following command:

docker run -p 8501:8501 compensation-app

5. You can now view your Streamlit app in your browser: http://0.0.0.0:8501

Running the App locally

To run the Streamlit app locally, follow these steps:

1. Clone this repository and open in VS Code.
2. Install the required libraries listed in requirements.txt using

pip install -r requirements.txt

3. Open a terminal or command prompt in the directory of the repo and run the following command:

streamlit run ./comp_app.py

4. You can now view the Compensation App in your browser by following one of the links from your terminal

Next steps

I would like to fine-tune the model for Data Science jobs specifically.