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<speak> Welcome to the lesson on correlations!<break strength="x-strong"/> Let's create a heat map to check the correlation<break strength="weak"/> between all these columns. <break strength="x-strong"/> What is a heat map?<break strength="x-strong"/> A heat map is a two-dimensional graphical representation of data. <break strength="x-strong"/>In the heat map, the individual values are arranged in a matrix represented as colors.<break strength="x-strong"/> Let's run the cell.<break strength="x-strong"/> Here we are taking the diabetes_df. <break strength="x-strong"/>diabetes_df is our old data frame.<break strength="x-strong"/> We use the c o r r method to find correlations.<break strength="x-strong"/> Correlation useful to describe the linear relationship between two variables.<break strength="x-strong"/> Let's check the correlation plot.<break strength="x-strong"/> So here we can see the glucose.<break strength="x-strong"/> The glucose and the outcome are correlated well. <break strength="x-strong"/>The correlation is 0.47.<break strength="x-strong"/> The outcome is well correlated with pregnancies.<break strength="x-strong"/> The correlation is 0.22.<break strength="x-strong"/> Here also, the outcome is well correlated with BMI. <break strength="x-strong"/>The correlation is 0.29.<break strength="x-strong"/> Here also, the outcome is well correlated with age.<break strength="x-strong"/> The correlation is 0.24.<break strength="x-strong"/> The outcome is not well correlated with Blood Pressure, Skin thickness.<break strength="x-strong"/> </speak>