dtypeint from the above outputit can be clearly seen that the number of observations with class are almost double than number of observations with class reviewing correlation between attributes the relationship between two variables is called correlation in statisticsthe most common method for calculating correlation is pearson' correlation coefficient it can have three values as followscoefficient value it represents full positive correlation between variables coefficient value - it represents full negative correlation between variables coefficient value it represents no correlation at all between variables it is always good for us to review the pairwise correlations of the attributes in our dataset before using it into ml project because some machine learning algorithms such as linear regression and logistic regression will perform poorly if we have highly correlated attributes in pythonwe can easily calculate correlation matrix of dataset attributes with the help of corr(function on pandas dataframe example from pandas import read_csv from pandas import set_option path " :\pima-indians-diabetes csvnames ['preg''plas''pres''skin''test''mass''pedi''age''class'data read_csv(pathnames=namesset_option('display width' set_option('precision' correlations data corr(method='pearson'print(correlationsoutput preg plas pres skin preg plas pres test mass pedi age class - - - skin - - test - - mass