import pandas

import scipy

import numpy

from sklearn.preprocessing import MinMaxScaler

​

fileurl = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'

names = ['sepal_length','sepal_width','petal_length','petal_width','class']

data = pandas.read_csv(fileurl, names=names)

array = data.values

​

# input/output component separation

X = array[:,0:4]

Y = array[:,4]

​

#Dataset is scaled to 0 and 3

scaler = MinMaxScaler(feature_range=(0, 3))

rescaledX = scaler.fit_transform(X)

​

# summarize transformed data

numpy.set_printoptions(precision=2)

print(rescaledX[0:5,:])

​