timothypesi/Youth_Income_Prediction

The project aims to tackle youth unemployment in South Africa. Using data from periodic labour market surveys, a machine learning model is developed to predict the employment status of the youth one year after the survey. # Predictive Insights Competition: Youth Employment Prediction ## Introduction The project aims to tackle youth unemployment in South Africa. Using data from periodic labour market surveys, a machine learning model is developed to predict the employment status of the youth one year after the survey. ## Dataset The dataset comprises survey data from South African youth, collected at 6-month intervals. It includes numerical, categorical, and textual inputs, alongside demographic details such as age and education. ## Installation 1. Clone this repository. git clone [your-repository-link] 2. Navigate to the project directory and install the dependencies using pip. cd [your-repository-name] pip install -r requirements.txt ## Usage 1. To run the API locally: python your_flask_script.py This will start the Flask server on `127.0.0.1`. 2. To make a prediction, send a POST request with the required data to `127.0.0.1`. ## Deployment The application can be deployed using cloud providers like AWS, Google Cloud, or Heroku. For production, ensure the Flask application is run using a server like Gunicorn. ## Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. ## Acknowledgements Thanks to Predictive Insights for the dataset and the challenge.