ggplot(heatbeat_dataset,aes(x=gendar,fill=heart_risk))+geom_bar(postion="fill")+labs(title="heart risk distribution by gendar",x="gendar",y="proposition",fill="heart risk")+theme_minimal() ggplot(heartbeat_dataset,aes(x=resting_heart_rate,y=max_heart_rate,color=heart_risk))+geom_point(alpha=0.7)+labs(title="Resting vs Max Heart Rate by Risk Level",x="Resting Heart Rate",y="Max Heart Rate",color="heart Risk")+theme_minimal() ggplot(heartbeat_dataset,aes(x=heartrisk,y=Cholesteral,fill=heart_risk))+geom_boxplot()+labs(title="cholesteral Levels by Heart Risk Category",x="Heart Risk",y="cholesterol")+theme_minimal() ggplot(heartbeat_dataset,aes(x=bmi,fill=haert_risk))+geom_density(alpha=0.5)+labs(title="BMI Distribution by Heart Risk",x="BMI",y="density",fill="Heart Risk")+theme_minimal()

a simple ioslide presentation on hypothesis testing

We will use use a supervised machine learning linear regression program to predict metabolite concentration.