Create a classifier to predict the wine color from wine quality attributes using this dataset: http://archive.ics.uci.edu/ml/datasets/Wine+Quality

The data is in the database we've been using

host='training.c1erymiua9dx.us-east-1.rds.amazonaws.com'
database='training'
port=5432
user='dot_student'
password='qgis'
table name = 'winequality'

Query for the data and create a numpy array



In [8]:

    
import pg8000 as pg
import numpy as np



In [4]:

    
host='training.c1erymiua9dx.us-east-1.rds.amazonaws.com'
database='training'
port=5432
user='dot_student'
password='qgis'
table = 'winequality'



In [44]:

    
conn=pg.connect(host=host, user=user, database=database, port=port, password=password)#, table=table)
cursor=conn.cursor()



In [53]:

    
conn.rollback()



In [54]:

    
#cursor.execute("select column_name from from information_schema.columns where table_name='winequality'")
cursor.execute("select * from winequality")
wines=np.array(cursor.fetchall())



In [56]:

    
len(wines[0])









    Out[56]:





12

Split the data into features (x) and target (y, the last column in the table)

Remember you can cast the results into an numpy array and then slice out what you want



In [62]:

    
x=wines[:,:11]
y=wines[:,11]

Create a decision tree with the data



In [58]:

    
from sklearn.tree import DecisionTreeClassifier



In [63]:

    
dt=DecisionTreeClassifier()
dt=dt.fit(x,y)

Run 10-fold cross validation on the model



In [60]:

    
from sklearn.cross_validation import cross_val_score



In [64]:

    
scores=cross_val_score(dt,x,y,cv=10)



In [65]:

    
np.mean(scores)









    Out[65]:





0.9793741726990316

If you have time, calculate the feature importance and graph based on the code in the slides from last class

Use this tip for getting the column names from your cursor object



In [34]:

    
num_fields = len(cursor.description)
field_names = [i[0] for i in cursor.description]



In [83]:

    
[name.decode("utf-8") for name in field_names[:-1]]









    Out[83]:





['fixed_acidity',
 'volatile_acidity',
 'citric_acid',
 'residual_sugar',
 'chlorides',
 'free_sulfur_dioxide',
 'total_sulfur_dioxide',
 'density',
 'ph',
 'sulphates',
 'alcohol']



In [84]:

    
import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(dt.feature_importances_, 'o')
plt.xticks(range(wines.data.shape[1]), [name.decode("utf-8") for name in field_names[:-1]], rotation=90)
plt.ylim(0,1)









    Out[84]:





(0, 1)



In [ ]: