Application of Logistic Regression in Life

Abstract

This research employs logistic regression analysis to discover fundamental determinants of income among vulnerable groups to help government bodies create effective social intervention policies. The study processed a complete dataset of 32,531 records from governmental sources gathered by the World Health Organization with systematic data cleaning followed by exploratory data analysis and feature engineering to develop logistic regression models and then subject these models to strict evaluation and refinement. The choice of Python for this analysis stemmed from its superior data management features alongside its sophisticated analytical library support. The study's analysis revealed that income levels above $50,000 are significantly linked to higher education attainment and particular employment categories. Researchers addressed multicollinearity by conducting Variance Inflation Factor (VIF) assessments and refined model variables through iterative evaluation of statistical significance (p-values). The completed logistic regression model demonstrated strong predictive power through an impressive F1 score of 0.89. The findings suggest we need to expand access to education while establishing fair pay systems and better working environments as well as developing specific policies to reduce economic inequality. Upcoming advancements will likely include extended demographic data collection and experimentation with superior machine learning methods along with the implementation of interpretability tools to enhance policy understanding.