This repository contains the code and analysis for a comprehensive study on the interplay of macroeconomic and social variables on unemployment rates in Indonesia. Through rigorous examination and statistical modeling, the project aims to unravel the complex dynamics influencing unemployment trends in the country.
- Python-based linear regression model to analyze the relationship between socio-economic factors and unemployment rates.
- Implementation of time series analysis techniques, including ARIMA, to uncover temporal patterns in unemployment dynamics.
- Thorough data cleaning and feature engineering to enhance model accuracy and effectiveness.
- Utilization of Matplotlib and Seaborn for visualization, aiding in the interpretation of complex socio-economic interactions.