Rephrase this code

num$\_$features $=$ heart.shape$[1]-1$

feature$\_$max$\_$values $=$ np$.$max$($heart$[$:$,1$:num$\_$features$+1],$ axis$=0)$

feature$\_$min$\_$values $=$ np$.$min$($heart$[$:$,1$:num$\_$features$+1],$ axis$=0)$

# Normalize the feature data to the range $[0,1]$

# Changed heart$\_$normalized to heart as heart$\_$normalized was not defined before this line.

normalized$\_$features $=($heart$[$:$,1$:num$\_$features$+1]-$ feature$\_$min$\_$values$)/($feature$\_$max$\_$values $-$ feature$\_$min$\_$values$)$

# Concatenate the ID$,$ normalized features, and target columns

# Created heart$\_$normalized here using the normalized features.

heart$\_$normalized $=$ np$.$concatenate$(($heart$[$:$,0].$reshape$(-1,1),$ normalized$\_$features, heart$[$:$,-1].$reshape$(-1,1)),$ axis$=1)$

# checking the max and min for each column

print$("$Feature Max values:", feature$\_$max$\_$values$)$

print$("$Feature Min values:", feature$\_$min$\_$values$)$

# Calculate and print the maximum and minimum values for each column of heart$\_$normalized

normalized$\_$max$\_$values $=$ np$.$max$($heart$\_$normalized$[$:$,1$:num$\_$features$+1],$ axis$=0)$

normalized$\_$min$\_$values $=$ np$.$min$($heart$\_$normalized$[$:$,1$:num$\_$features$+1],$ axis$=0)$

print$("$

Normalized Feature Max values:", normalized$\_$max$\_$values$)$

print$("$Normalized Feature Min values:", normalized$\_$min$\_$values$)$