Hypothesis Testing for heart disease

• Tech Stack: RStudio, Logistic Regression
• Github URL: Project Link

In this project, I conducted hypothesis testing on heart disease data utilizing statistical methods such as T-tests, ANOVA, Shapiro-Wilk, and survival analysis. The primary goal was to identify statistically significant risk factors associated with heart disease. Through meticulous data visualization and chi-square testing, I systematically analyzed the dataset to extract meaningful insights.

Furthermore, I developed a predictive model using logistic regression and ROC curve analysis. This model enabled a comprehensive understanding of how different factors contribute to the likelihood of heart disease occurrence. By integrating statistical testing with machine learning techniques, the project aimed to provide valuable insights for mitigating heart disease risks and informing effective healthcare strategies.

This project exemplifies the synergy between statistical analysis and machine learning in addressing crucial health challenges like heart disease, ultimately contributing to informed decision-making and enhanced healthcare outcomes.