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4 years ago
import pandas as pd
import numpy as np
import itertools
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
import matplotlib.ticker as ticker
from sklearn import preprocessing
%matplotlib inline
df=pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/teleCust1000t.csv')
df.head()
X = df[['region', 'tenure','age', 'marital', 'address', 'income', 'ed', 'employ','retire', 'gender', 'reside']] .values  #.astype(float)
X[0:5]
array([[  2.,  13.,  44.,   1.,   9.,  64.,   4.,   5.,   0.,   0.,   2.],
       [  3.,  11.,  33.,   1.,   7., 136.,   5.,   5.,   0.,   0.,   6.],
       [  3.,  68.,  52.,   1.,  24., 116.,   1.,  29.,   0.,   1.,   2.],
       [  2.,  33.,  33.,   0.,  12.,  33.,   2.,   0.,   0.,   1.,   1.],
       [  2.,  23.,  30.,   1.,   9.,  30.,   1.,   2.,   0.,   0.,   4.]])
X=preprocessing.StandardScaler().fit(X).transform(X.astype(float))
X
array([[-0.02696767, -1.055125  ,  0.18450456, ..., -0.22207644,
        -1.03459817, -0.23065004],
       [ 1.19883553, -1.14880563, -0.69181243, ..., -0.22207644,
        -1.03459817,  2.55666158],
       [ 1.19883553,  1.52109247,  0.82182601, ..., -0.22207644,
         0.96655883, -0.23065004],
       ...,
       [ 1.19883553,  1.47425216,  1.37948227, ..., -0.22207644,
         0.96655883, -0.92747794],
       [ 1.19883553,  1.61477311,  0.58283046, ..., -0.22207644,
         0.96655883, -0.92747794],
       [ 1.19883553,  0.67796676, -0.45281689, ..., -0.22207644,
         0.96655883,  0.46617787]])