In [116]:

    

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import KMeans
import sklearn.metrics as sm
 
import pandas as pd
import numpy as np


    

In [117]:

    

wine=pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data",header=None)


    

In [118]:

    

wine.head()


    

    
Out[118]:







  

    

      

      
0
      
1
      
2
      
3
      
4
      
5
      
6
      
7
      
8
      
9
      
10
      
11
      
12
      
13
    
  
  

    

      
0
      
1
      
14.23
      
1.71
      
2.43
      
15.6
      
127
      
2.80
      
3.06
      
0.28
      
2.29
      
5.64
      
1.04
      
3.92
      
1065
    
    

      
1
      
1
      
13.20
      
1.78
      
2.14
      
11.2
      
100
      
2.65
      
2.76
      
0.26
      
1.28
      
4.38
      
1.05
      
3.40
      
1050
    
    

      
2
      
1
      
13.16
      
2.36
      
2.67
      
18.6
      
101
      
2.80
      
3.24
      
0.30
      
2.81
      
5.68
      
1.03
      
3.17
      
1185
    
    

      
3
      
1
      
14.37
      
1.95
      
2.50
      
16.8
      
113
      
3.85
      
3.49
      
0.24
      
2.18
      
7.80
      
0.86
      
3.45
      
1480
    
    

      
4
      
1
      
13.24
      
2.59
      
2.87
      
21.0
      
118
      
2.80
      
2.69
      
0.39
      
1.82
      
4.32
      
1.04
      
2.93
      
735
    
  

In [119]:

    

wine.columns=['winetype','Alcohol','Malic acid','Ash','Alcalinity of ash','Magnesium','Total phenols','Flavanoids','Nonflavanoid phenols','Proanthocyanins','Color intensity','Hue','OD280/OD315 of diluted wines','Proline']


    

In [120]:

    

wine.head()


    

    
Out[120]:







  

    

      

      
winetype
      
Alcohol
      
Malic acid
      
Ash
      
Alcalinity of ash
      
Magnesium
      
Total phenols
      
Flavanoids
      
Nonflavanoid phenols
      
Proanthocyanins
      
Color intensity
      
Hue
      
OD280/OD315 of diluted wines
      
Proline
    
  
  

    

      
0
      
1
      
14.23
      
1.71
      
2.43
      
15.6
      
127
      
2.80
      
3.06
      
0.28
      
2.29
      
5.64
      
1.04
      
3.92
      
1065
    
    

      
1
      
1
      
13.20
      
1.78
      
2.14
      
11.2
      
100
      
2.65
      
2.76
      
0.26
      
1.28
      
4.38
      
1.05
      
3.40
      
1050
    
    

      
2
      
1
      
13.16
      
2.36
      
2.67
      
18.6
      
101
      
2.80
      
3.24
      
0.30
      
2.81
      
5.68
      
1.03
      
3.17
      
1185
    
    

      
3
      
1
      
14.37
      
1.95
      
2.50
      
16.8
      
113
      
3.85
      
3.49
      
0.24
      
2.18
      
7.80
      
0.86
      
3.45
      
1480
    
    

      
4
      
1
      
13.24
      
2.59
      
2.87
      
21.0
      
118
      
2.80
      
2.69
      
0.39
      
1.82
      
4.32
      
1.04
      
2.93
      
735
    
  

In [121]:

    

wine.info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 178 entries, 0 to 177
Data columns (total 14 columns):
winetype                        178 non-null int64
Alcohol                         178 non-null float64
Malic acid                      178 non-null float64
Ash                             178 non-null float64
Alcalinity of ash               178 non-null float64
Magnesium                       178 non-null int64
Total phenols                   178 non-null float64
Flavanoids                      178 non-null float64
Nonflavanoid phenols            178 non-null float64
Proanthocyanins                 178 non-null float64
Color intensity                 178 non-null float64
Hue                             178 non-null float64
OD280/OD315 of diluted wines    178 non-null float64
Proline                         178 non-null int64
dtypes: float64(11), int64(3)
memory usage: 19.5 KB

In [122]:

    

wine.describe()


    

    
Out[122]:







  

    

      

      
winetype
      
Alcohol
      
Malic acid
      
Ash
      
Alcalinity of ash
      
Magnesium
      
Total phenols
      
Flavanoids
      
Nonflavanoid phenols
      
Proanthocyanins
      
Color intensity
      
Hue
      
OD280/OD315 of diluted wines
      
Proline
    
  
  

    

      
count
      
178.000000
      
178.000000
      
178.000000
      
178.000000
      
178.000000
      
178.000000
      
178.000000
      
178.000000
      
178.000000
      
178.000000
      
178.000000
      
178.000000
      
178.000000
      
178.000000
    
    

      
mean
      
1.938202
      
13.000618
      
2.336348
      
2.366517
      
19.494944
      
99.741573
      
2.295112
      
2.029270
      
0.361854
      
1.590899
      
5.058090
      
0.957449
      
2.611685
      
746.893258
    
    

      
std
      
0.775035
      
0.811827
      
1.117146
      
0.274344
      
3.339564
      
14.282484
      
0.625851
      
0.998859
      
0.124453
      
0.572359
      
2.318286
      
0.228572
      
0.709990
      
314.907474
    
    

      
min
      
1.000000
      
11.030000
      
0.740000
      
1.360000
      
10.600000
      
70.000000
      
0.980000
      
0.340000
      
0.130000
      
0.410000
      
1.280000
      
0.480000
      
1.270000
      
278.000000
    
    

      
25%
      
1.000000
      
12.362500
      
1.602500
      
2.210000
      
17.200000
      
88.000000
      
1.742500
      
1.205000
      
0.270000
      
1.250000
      
3.220000
      
0.782500
      
1.937500
      
500.500000
    
    

      
50%
      
2.000000
      
13.050000
      
1.865000
      
2.360000
      
19.500000
      
98.000000
      
2.355000
      
2.135000
      
0.340000
      
1.555000
      
4.690000
      
0.965000
      
2.780000
      
673.500000
    
    

      
75%
      
3.000000
      
13.677500
      
3.082500
      
2.557500
      
21.500000
      
107.000000
      
2.800000
      
2.875000
      
0.437500
      
1.950000
      
6.200000
      
1.120000
      
3.170000
      
985.000000
    
    

      
max
      
3.000000
      
14.830000
      
5.800000
      
3.230000
      
30.000000
      
162.000000
      
3.880000
      
5.080000
      
0.660000
      
3.580000
      
13.000000
      
1.710000
      
4.000000
      
1680.000000
    
  

In [123]:

    

pd.value_counts(wine['winetype'])


    

    
Out[123]:





2    71
1    59
3    48
Name: winetype, dtype: int64

In [124]:

    

x=wine.iloc[:,1:14]
y=wine.iloc[:,:1]


    

In [125]:

    

x.columns


    

    
Out[125]:





Index(['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium',
       'Total phenols', 'Flavanoids', 'Nonflavanoid phenols',
       'Proanthocyanins', 'Color intensity', 'Hue',
       'OD280/OD315 of diluted wines', 'Proline'],
      dtype='object')

In [126]:

    

y.columns


    

    
Out[126]:





Index(['winetype'], dtype='object')

In [127]:

    

x.head()


    

    
Out[127]:







  

    

      

      
Alcohol
      
Malic acid
      
Ash
      
Alcalinity of ash
      
Magnesium
      
Total phenols
      
Flavanoids
      
Nonflavanoid phenols
      
Proanthocyanins
      
Color intensity
      
Hue
      
OD280/OD315 of diluted wines
      
Proline
    
  
  

    

      
0
      
14.23
      
1.71
      
2.43
      
15.6
      
127
      
2.80
      
3.06
      
0.28
      
2.29
      
5.64
      
1.04
      
3.92
      
1065
    
    

      
1
      
13.20
      
1.78
      
2.14
      
11.2
      
100
      
2.65
      
2.76
      
0.26
      
1.28
      
4.38
      
1.05
      
3.40
      
1050
    
    

      
2
      
13.16
      
2.36
      
2.67
      
18.6
      
101
      
2.80
      
3.24
      
0.30
      
2.81
      
5.68
      
1.03
      
3.17
      
1185
    
    

      
3
      
14.37
      
1.95
      
2.50
      
16.8
      
113
      
3.85
      
3.49
      
0.24
      
2.18
      
7.80
      
0.86
      
3.45
      
1480
    
    

      
4
      
13.24
      
2.59
      
2.87
      
21.0
      
118
      
2.80
      
2.69
      
0.39
      
1.82
      
4.32
      
1.04
      
2.93
      
735
    
  

In [128]:

    

y.head()


    

    
Out[128]:







  

    

      

      
winetype
    
  
  

    

      
0
      
1
    
    

      
1
      
1
    
    

      
2
      
1
    
    

      
3
      
1
    
    

      
4
      
1
    
  

In [129]:

    

# K Means Cluster
model = KMeans(n_clusters=3)
model.fit(x)


    

    
Out[129]:





KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
    n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
    random_state=None, tol=0.0001, verbose=0)

In [130]:

    

model.labels_


    

    
Out[130]:





array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,
       0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1,
       1, 1, 2, 2, 0, 1, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2,
       2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2,
       2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2,
       2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2,
       1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2])

In [131]:

    

pd.value_counts(y['winetype'])


    

    
Out[131]:





2    71
1    59
3    48
Name: winetype, dtype: int64

In [132]:

    

pd.value_counts(model.labels_)


    

    
Out[132]:





2    69
1    62
0    47
dtype: int64

In [171]:

    

# We convert all the 1s to 0s and 0s to 1s.
predY = np.choose(model.labels_, [1,3,2]).astype(np.int64)


    

In [172]:

    

pd.value_counts(y['winetype'])


    

    
Out[172]:





2    71
1    59
3    48
Name: winetype, dtype: int64

In [173]:

    

pd.value_counts(predY)


    

    
Out[173]:





2    69
3    62
1    47
dtype: int64

In [174]:

    

# Performance Metrics
sm.accuracy_score(y, predY)


    

    
Out[174]:





0.702247191011236

In [175]:

    

# Confusion Matrix
sm.confusion_matrix(y, predY)


    

    
Out[175]:





array([[46,  0, 13],
       [ 1, 50, 20],
       [ 0, 19, 29]])

In [138]:

    

!pip install ggplot
#import ggplot as ggplot


    

    




Requirement already satisfied: ggplot in c:\users\kogentix\anaconda3\lib\site-packages
Requirement already satisfied: scipy in c:\users\kogentix\anaconda3\lib\site-packages (from ggplot)
Requirement already satisfied: patsy>=0.4 in c:\users\kogentix\anaconda3\lib\site-packages (from ggplot)
Requirement already satisfied: cycler in c:\users\kogentix\anaconda3\lib\site-packages (from ggplot)
Requirement already satisfied: statsmodels in c:\users\kogentix\anaconda3\lib\site-packages (from ggplot)
Requirement already satisfied: pandas in c:\users\kogentix\anaconda3\lib\site-packages (from ggplot)
Requirement already satisfied: matplotlib in c:\users\kogentix\anaconda3\lib\site-packages (from ggplot)
Requirement already satisfied: brewer2mpl in c:\users\kogentix\anaconda3\lib\site-packages (from ggplot)
Requirement already satisfied: six in c:\users\kogentix\anaconda3\lib\site-packages (from ggplot)
Requirement already satisfied: numpy in c:\users\kogentix\anaconda3\lib\site-packages (from ggplot)
Requirement already satisfied: python-dateutil>=2 in c:\users\kogentix\anaconda3\lib\site-packages (from pandas->ggplot)
Requirement already satisfied: pytz>=2011k in c:\users\kogentix\anaconda3\lib\site-packages (from pandas->ggplot)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in c:\users\kogentix\anaconda3\lib\site-packages (from matplotlib->ggplot)

In [139]:

    

from ggplot import *
%matplotlib inline


    

In [140]:

    

p = ggplot(aes(x='Alcohol', y='Ash',color="winetype"), data=wine)
p + geom_point()


    

    













    
Out[140]:





<ggplot: (-9223371877061043950)>

In [141]:

    

p2 = ggplot(aes(x='Alcohol', y='Ash',color="predY"), data=wine)
p2 + geom_point()


    

    













    
Out[141]:





<ggplot: (-9223371877060637994)>

In [142]:

    

from sklearn import preprocessing


    

In [143]:

    

x_scaled = preprocessing.scale(x)


    

In [144]:

    

x.head()


    

    
Out[144]:







  

    

      

      
Alcohol
      
Malic acid
      
Ash
      
Alcalinity of ash
      
Magnesium
      
Total phenols
      
Flavanoids
      
Nonflavanoid phenols
      
Proanthocyanins
      
Color intensity
      
Hue
      
OD280/OD315 of diluted wines
      
Proline
    
  
  

    

      
0
      
14.23
      
1.71
      
2.43
      
15.6
      
127
      
2.80
      
3.06
      
0.28
      
2.29
      
5.64
      
1.04
      
3.92
      
1065
    
    

      
1
      
13.20
      
1.78
      
2.14
      
11.2
      
100
      
2.65
      
2.76
      
0.26
      
1.28
      
4.38
      
1.05
      
3.40
      
1050
    
    

      
2
      
13.16
      
2.36
      
2.67
      
18.6
      
101
      
2.80
      
3.24
      
0.30
      
2.81
      
5.68
      
1.03
      
3.17
      
1185
    
    

      
3
      
14.37
      
1.95
      
2.50
      
16.8
      
113
      
3.85
      
3.49
      
0.24
      
2.18
      
7.80
      
0.86
      
3.45
      
1480
    
    

      
4
      
13.24
      
2.59
      
2.87
      
21.0
      
118
      
2.80
      
2.69
      
0.39
      
1.82
      
4.32
      
1.04
      
2.93
      
735
    
  

In [145]:

    

x_scaled


    

    
Out[145]:





array([[ 1.51861254, -0.5622498 ,  0.23205254, ...,  0.36217728,
         1.84791957,  1.01300893],
       [ 0.24628963, -0.49941338, -0.82799632, ...,  0.40605066,
         1.1134493 ,  0.96524152],
       [ 0.19687903,  0.02123125,  1.10933436, ...,  0.31830389,
         0.78858745,  1.39514818],
       ..., 
       [ 0.33275817,  1.74474449, -0.38935541, ..., -1.61212515,
        -1.48544548,  0.28057537],
       [ 0.20923168,  0.22769377,  0.01273209, ..., -1.56825176,
        -1.40069891,  0.29649784],
       [ 1.39508604,  1.58316512,  1.36520822, ..., -1.52437837,
        -1.42894777, -0.59516041]])

In [146]:

    

x.columns


    

    
Out[146]:





Index(['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium',
       'Total phenols', 'Flavanoids', 'Nonflavanoid phenols',
       'Proanthocyanins', 'Color intensity', 'Hue',
       'OD280/OD315 of diluted wines', 'Proline'],
      dtype='object')

In [147]:

    

x.index


    

    
Out[147]:





RangeIndex(start=0, stop=178, step=1)

In [148]:

    

x_scaleddf=pd.DataFrame(data=x_scaled,index=x.index,columns=x.columns)


    

In [149]:

    

# K Means Cluster
model2 = KMeans(n_clusters=3)
model2.fit(x_scaleddf)


    

    
Out[149]:





KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
    n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
    random_state=None, tol=0.0001, verbose=0)

In [150]:

    

model2.labels_


    

    
Out[150]:





array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

In [151]:

    

pd.value_counts(model2.labels_)


    

    
Out[151]:





2    65
0    62
1    51
dtype: int64

In [152]:

    

pd.value_counts(y['winetype'])


    

    
Out[152]:





2    71
1    59
3    48
Name: winetype, dtype: int64

In [157]:

    

# We convert all the 1s to 0s and 0s to 1s.
predY2 = np.choose(model2.labels_, [1, 3, 2]).astype(np.int64)


    

In [158]:

    

pd.value_counts(predY2)


    

    
Out[158]:





2    65
1    62
3    51
dtype: int64

In [159]:

    

sm.accuracy_score(y, predY2)


    

    
Out[159]:





0.9662921348314607

In [160]:

    

sm.confusion_matrix(y, predY2)


    

    
Out[160]:





array([[59,  0,  0],
       [ 3, 65,  3],
       [ 0,  0, 48]])

In [ ]: