Unable to transform string column to categorical matrix using Keras and Sklearn
Its because np_utils.to_categorical takes y of datatype int, but you have strings either convert them into int by giving them a key i.e :
cats = data.PriceRange.values.categoriesdi = dict(zip(cats,np.arange(len(cats))))#{'0 - 50000': 0,# '10000001 - 10050000': 200,# '1000001 - 1050000': 20,# '100001 - 150000': 2,# '10050001 - 10100000': 201,# '10100001 - 10150000': 202,target = np_utils.to_categorical(data.PriceRange.map(di))or since you are using pandas you can use pd.get_dummies to get one hot encoding.
onehot = pd.get_dummies(data.PriceRange)target_labels = onehot.columnstarget = onehot.as_matrix()array([[ 1., 0., 0., ..., 0., 0., 0.], [ 0., 0., 0., ..., 0., 0., 0.], [ 0., 0., 0., ..., 0., 0., 0.], ..., [ 0., 0., 0., ..., 0., 0., 0.], [ 1., 0., 0., ..., 0., 0., 0.], [ 0., 0., 0., ..., 0., 0., 0.]])
With only one line of code
tf.keras.utils.to_categorical(data.PriceRange.factorize()[0])