For today’s analysis, I have compared the two variables i.e. % Inactivity and % Obesity to the third variable % Diabetic. I have successfully plotted the smooth histogram for the predictive models (% Diabetic to % Inactivity and % Diabetic to % Obesity). The data shows the variation in between both the plots and there is a slight increase in the % inactivity as compare to the % obesity. As per the data visualisation by the plot we can assume that the inactiveness can be more-risky than obesity. Though these both can lead to the diabetic, inactiveness maybe the critical cause. I have used the same concepts to plot the smooth histogram between the data frames and one new library I have used for the data visualisation is seaborn. It provides a high-level interface for creating informative and attractive statistical graphics. Seaborn is particularly well-suited for visualising complex datasets and statistical relationships between variables.

Furthermore, I have calculated the R-squared value or the square of correlation between the two data visualisation (% Diabetic to % Inactivity and % Diabetic to % Obesity). In result, I found that the value of % Diabetic to % Obesity is comparatively half of the % Diabetic to % Inactivity.

In summary, I will be giving more time to analyse the data between these two predictive models and I will add some more key points in my next blog.