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Updating the kmeans goals, gender etc #3

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Updating the kmeans goals, gender etc #3

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19 changes: 19 additions & 0 deletions first_attempt_K_means.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,25 @@
print(x)
x=np.delete(x, 4, 1)# This removes the region coulumm which in turn would be replaced by category_Hot_Encoded
#x=pd.DataFrame(x) to view it as a data frame
#Getting rid of categorical data of Gender...
category_Hot_Encoded=pd.get_dummies(x[:, 1])
x=np.append(x, category_Hot_Encoded, axis=1)
print(x)
x=np.delete(x, 1, 1)# This removes the region coulumm which in turn would be replaced by category_Hot_Encoded
#x=pd.DataFrame(x) to view it as a data frame
#Now we remove the female categorical data to avoid the categorical data trap
x=np.delete(x, 15, 1)
#x=pd.DataFrame(x)...just to visualize#Getting rid of categorical data of Gender...
#Getting rid of categorical data of Goals...
category_Hot_Encoded=pd.get_dummies(x[:, 4])
x=np.append(x, category_Hot_Encoded, axis=1)
print(x)
x=np.delete(x, 4, 1)# This removes the region coulumm which in turn would be replaced by category_Hot_Encoded
#Getting rid of categorical data of Seats...
category_Hot_Encoded=pd.get_dummies(x[:, 7])
x=np.append(x, category_Hot_Encoded, axis=1)
print(x)
x=np.delete(x, 7, 1)# This removes the region coulumm which in turn would be replaced by category_Hot_Encoded

"""
#Making the data polynomial...
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