In [109]:

    

from pandas import DataFrame, read_csv
import numpy as np
import pandas as pd
from sklearn import cross_validation
from sklearn import linear_model

# General syntax to import a library but no functions: 
##import (library) as (give the library a nickname/alias)
import matplotlib.pyplot as plt
import pandas as pd #this is how I usually import pandas
import sys #only needed to determine Python version number

# Enable inline plotting
%matplotlib inline

print 'Python version ' + sys.version
print 'Pandas version ' + pd.__version__


    

    




Python version 2.7.9 |Anaconda 2.2.0 (64-bit)| (default, Dec 18 2014, 16:57:52) [MSC v.1500 64 bit (AMD64)]
Pandas version 0.15.2

In [6]:

    

df = pd.read_csv('train.csv')
df[1:20]


    

    
Out[6]:






  

    

      

      
TripType
      
VisitNumber
      
Weekday
      
Upc
      
ScanCount
      
DepartmentDescription
      
FinelineNumber
    
  
  

    

      
1 
      
 30
      
 7
      
 Friday
      
 60538815980
      
 1
      
                 SHOES
      
 8931
    
    

      
2 
      
 30
      
 7
      
 Friday
      
  7410811099
      
 1
      
         PERSONAL CARE
      
 4504
    
    

      
3 
      
 26
      
 8
      
 Friday
      
  2238403510
      
 2
      
 PAINT AND ACCESSORIES
      
 3565
    
    

      
4 
      
 26
      
 8
      
 Friday
      
  2006613744
      
 2
      
 PAINT AND ACCESSORIES
      
 1017
    
    

      
5 
      
 26
      
 8
      
 Friday
      
  2006618783
      
 2
      
 PAINT AND ACCESSORIES
      
 1017
    
    

      
6 
      
 26
      
 8
      
 Friday
      
  2006613743
      
 1
      
 PAINT AND ACCESSORIES
      
 1017
    
    

      
7 
      
 26
      
 8
      
 Friday
      
  7004802737
      
 1
      
 PAINT AND ACCESSORIES
      
 2802
    
    

      
8 
      
 26
      
 8
      
 Friday
      
  2238495318
      
 1
      
 PAINT AND ACCESSORIES
      
 4501
    
    

      
9 
      
 26
      
 8
      
 Friday
      
  2238400200
      
-1
      
 PAINT AND ACCESSORIES
      
 3565
    
    

      
10
      
 26
      
 8
      
 Friday
      
  5200010239
      
 1
      
           DSD GROCERY
      
 4606
    
    

      
11
      
 26
      
 8
      
 Friday
      
 88679300501
      
 2
      
 PAINT AND ACCESSORIES
      
 3504
    
    

      
12
      
 26
      
 8
      
 Friday
      
 22006000000
      
 1
      
 MEAT - FRESH & FROZEN
      
 6009
    
    

      
13
      
 26
      
 8
      
 Friday
      
  2236760452
      
 1
      
 PAINT AND ACCESSORIES
      
    7
    
    

      
14
      
 26
      
 8
      
 Friday
      
 88679300501
      
-1
      
 PAINT AND ACCESSORIES
      
 3504
    
    

      
15
      
 26
      
 8
      
 Friday
      
  2238400200
      
 2
      
 PAINT AND ACCESSORIES
      
 3565
    
    

      
16
      
 26
      
 8
      
 Friday
      
  3019294203
      
 1
      
 PAINT AND ACCESSORIES
      
 2801
    
    

      
17
      
 26
      
 8
      
 Friday
      
 72450408840
      
 1
      
 PAINT AND ACCESSORIES
      
 1028
    
    

      
18
      
 26
      
 8
      
 Friday
      
 25541500000
      
 2
      
                 DAIRY
      
 1305
    
    

      
19
      
 26
      
 8
      
 Friday
      
  2310010776
      
 1
      
     PETS AND SUPPLIES
      
 3300
    
  

In [72]:

    

df['Upc'][:200].plot(kind='hist',bins=100)


    

    
Out[72]:





<matplotlib.axes._subplots.AxesSubplot at 0x113d7828>






    

In [70]:

    

df = df[np.isfinite(df['Upc'])]
df[:20]


    

    
Out[70]:






  

    

      

      
TripType
      
VisitNumber
      
Weekday
      
Upc
      
ScanCount
      
DepartmentDescription
      
FinelineNumber
    
  
  

    

      
0 
      
 999
      
 5
      
 Friday
      
 68113152929
      
-1
      
    FINANCIAL SERVICES
      
 1000
    
    

      
1 
      
  30
      
 7
      
 Friday
      
 60538815980
      
 1
      
                 SHOES
      
 8931
    
    

      
2 
      
  30
      
 7
      
 Friday
      
  7410811099
      
 1
      
         PERSONAL CARE
      
 4504
    
    

      
3 
      
  26
      
 8
      
 Friday
      
  2238403510
      
 2
      
 PAINT AND ACCESSORIES
      
 3565
    
    

      
4 
      
  26
      
 8
      
 Friday
      
  2006613744
      
 2
      
 PAINT AND ACCESSORIES
      
 1017
    
    

      
5 
      
  26
      
 8
      
 Friday
      
  2006618783
      
 2
      
 PAINT AND ACCESSORIES
      
 1017
    
    

      
6 
      
  26
      
 8
      
 Friday
      
  2006613743
      
 1
      
 PAINT AND ACCESSORIES
      
 1017
    
    

      
7 
      
  26
      
 8
      
 Friday
      
  7004802737
      
 1
      
 PAINT AND ACCESSORIES
      
 2802
    
    

      
8 
      
  26
      
 8
      
 Friday
      
  2238495318
      
 1
      
 PAINT AND ACCESSORIES
      
 4501
    
    

      
9 
      
  26
      
 8
      
 Friday
      
  2238400200
      
-1
      
 PAINT AND ACCESSORIES
      
 3565
    
    

      
10
      
  26
      
 8
      
 Friday
      
  5200010239
      
 1
      
           DSD GROCERY
      
 4606
    
    

      
11
      
  26
      
 8
      
 Friday
      
 88679300501
      
 2
      
 PAINT AND ACCESSORIES
      
 3504
    
    

      
12
      
  26
      
 8
      
 Friday
      
 22006000000
      
 1
      
 MEAT - FRESH & FROZEN
      
 6009
    
    

      
13
      
  26
      
 8
      
 Friday
      
  2236760452
      
 1
      
 PAINT AND ACCESSORIES
      
    7
    
    

      
14
      
  26
      
 8
      
 Friday
      
 88679300501
      
-1
      
 PAINT AND ACCESSORIES
      
 3504
    
    

      
15
      
  26
      
 8
      
 Friday
      
  2238400200
      
 2
      
 PAINT AND ACCESSORIES
      
 3565
    
    

      
16
      
  26
      
 8
      
 Friday
      
  3019294203
      
 1
      
 PAINT AND ACCESSORIES
      
 2801
    
    

      
17
      
  26
      
 8
      
 Friday
      
 72450408840
      
 1
      
 PAINT AND ACCESSORIES
      
 1028
    
    

      
18
      
  26
      
 8
      
 Friday
      
 25541500000
      
 2
      
                 DAIRY
      
 1305
    
    

      
19
      
  26
      
 8
      
 Friday
      
  2310010776
      
 1
      
     PETS AND SUPPLIES
      
 3300
    
  

In [26]:

    

Dpts = df['DepartmentDescription'].unique()
Wkds = df['Weekday'].unique()


    

    
Out[26]:





array(['Friday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
       'Thursday'], dtype=object)

In [27]:

    

Dpts


    

    
Out[27]:





array(['FINANCIAL SERVICES', 'SHOES', 'PERSONAL CARE',
       'PAINT AND ACCESSORIES', 'DSD GROCERY', 'MEAT - FRESH & FROZEN',
       'DAIRY', 'PETS AND SUPPLIES', 'HOUSEHOLD CHEMICALS/SUPP',
       'IMPULSE MERCHANDISE', 'PRODUCE', 'CANDY TOBACCO COOKIES',
       'GROCERY DRY GOODS', 'BOYS WEAR', 'FABRICS AND CRAFTS',
       'JEWELRY AND SUNGLASSES', 'MENS WEAR', 'ACCESSORIES',
       'HOME MANAGEMENT', 'FROZEN FOODS', 'SERVICE DELI',
       'INFANT CONSUMABLE HARDLINES', 'PRE PACKED DELI', 'COOK AND DINE',
       'PHARMACY OTC', 'LADIESWEAR', 'COMM BREAD', 'BAKERY',
       'HOUSEHOLD PAPER GOODS', 'CELEBRATION', 'HARDWARE', 'BEAUTY',
       'AUTOMOTIVE', 'BOOKS AND MAGAZINES', 'SEAFOOD', 'OFFICE SUPPLIES',
       'LAWN AND GARDEN', 'SHEER HOSIERY', 'WIRELESS', 'BEDDING',
       'BATH AND SHOWER', 'HORTICULTURE AND ACCESS', 'HOME DECOR', 'TOYS',
       'INFANT APPAREL', 'LADIES SOCKS', 'PLUS AND MATERNITY',
       'ELECTRONICS', 'GIRLS WEAR 4-6X  AND 7-14', 'BRAS & SHAPEWEAR',
       'LIQUOR WINE BEER', 'SLEEPWEAR/FOUNDATIONS', 'CAMERAS AND SUPPLIES',
       'SPORTING GOODS', 'PLAYERS AND ELECTRONICS', 'PHARMACY RX',
       'MENSWEAR', 'OPTICAL - FRAMES', 'SWIMWEAR/OUTERWEAR',
       'OTHER DEPARTMENTS', 'MEDIA AND GAMING', 'FURNITURE',
       'OPTICAL - LENSES', 'SEASONAL', 'LARGE HOUSEHOLD GOODS',
       '1-HR PHOTO', 'CONCEPT STORES', 'HEALTH AND BEAUTY AIDS'], dtype=object)

In [29]:

    

Wkds


    

    
Out[29]:





array(['Friday', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
       'Thursday'], dtype=object)

In [48]:

    

df2 = df.copy()


    

In [74]:

    

for i in range(Dpts.size):
    df2 = df2.replace(Dpts[i],i)
for i in range(Wkds.size):
    df2 = df2.replace(Wkds[i],i)


    

In [85]:

    

Data = df2.as_matrix()


    

In [89]:

    

Data.shape


    

    
Out[89]:





(642925L, 7L)

In [101]:

    

Ytrain = Data[:,0]
Xtrain = Data[:,1:]


    

In [105]:

    

Xtrain.shape


    

    
Out[105]:





(642925L, 6L)

In [ ]:

    

logreg = linear_model.LogisticRegression(C=1e5)

# we create an instance of Neighbours Classifier and fit the data.

scores = cross_validation.cross_val_score(logreg, Xtrain, Ytrain, cv=5)


    

In [ ]:

    

scores