As far in the blog, I performed the K-means clustering for the first dataset. In this blog I have performed the heat-map technique to find out the correlation between the variables as age, body camera, and sign of mental illness.

To perform the heat-map  technique I used the correlation method of assigning the annotation between the variables. In data visualisation, heat-maps are frequently used to show complicated datasets in a way that is both visually appealing and comprehensible. They are especially helpful in visualising the connections or patterns found in sizable data matrices or tables. It utilized to show how variables in a dataset are correlated. The correlation coefficient between two variables is represented by each cell in the heat-map, and the direction and strength of the connection (positive or negative) are shown by the colour intensity of each cell.

Interpreting the colour patterns and connections within the data a heatmap displays is a necessary step in its analysis. Heatmaps are a useful tool for displaying large, complicated datasets and spotting patterns, relationships, and groupings. I am attaching the picture of the heat-map so that one can relate the correlation between the variables and understand the relationship and behaviour of the varaince.