Red Wine Dataset Analysis - UCI

In [13]:

    

import numpy as np
import pandas as pd
%pylab inline
pylab.style.use('ggplot')
import seaborn as sns


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

wine_data = pd.read_csv('wine.csv')


    

In [3]:

    

wine_data.head()


    

    
Out[3]:






  

    

      

      
Wine
      
Alcohol
      
Malic.acid
      
Ash
      
Acl
      
Mg
      
Phenols
      
Flavanoids
      
Nonflavanoid.phenols
      
Proanth
      
Color.int
      
Hue
      
OD
      
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 [4]:

    

label_counts = wine_data.loc[:, 'Wine'].value_counts().sort_index()
label_counts.plot(kind='bar')


    

    
Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x25a76712668>






    

In [5]:

    

feature_names = wine_data.columns.drop('Wine')

for fname in feature_names:
    _ = pylab.figure()
    _ = wine_data.loc[:, fname].plot(kind='hist', title=fname)


    

In [6]:

    

for fname in feature_names:
    _ = pylab.figure()
    wine_data.boxplot(column=fname, by='Wine')


    

    




d:\Anaconda3\envs\latest\lib\site-packages\matplotlib\pyplot.py:516: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)






    






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In [11]:

    

from sklearn.feature_selection import mutual_info_classif

mutual_info_importances = mutual_info_classif(wine_data.loc[:, feature_names], wine_data.loc[:, 'Wine'])
importances = pd.Series(mutual_info_importances, index=feature_names)

importances.sort_values(ascending=True).plot(kind='barh')


    

    
Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x25a779a3128>






    

In [14]:

    

corrs = wine_data.loc[:, feature_names].corr()
sns.heatmap(corrs, annot=True)


    

    
Out[14]:





<matplotlib.axes._subplots.AxesSubplot at 0x25a795aceb8>






    

In [15]:

    

from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score

feature_data = wine_data.loc[:, feature_names]
label_data = wine_data.loc[:, 'Wine']
model = GaussianNB()

scores = cross_val_score(estimator=model, 
                         X=feature_data, 
                         y=label_data,
                         cv=5,
                         scoring='f1_macro'
                        )

scores = pd.Series(scores)
scores.plot(kind='bar')


    

    
Out[15]:





<matplotlib.axes._subplots.AxesSubplot at 0x25a77c1deb8>