The file “multicommodity.csv” contains information about 500 customers who have purchased one or more of the 20 products that a company sells. The company has information about the gender of these 500 customers through a survey that they conducted. The company wants to learn a model such that it is able to predict the gender of a customer based on the portfolio of products purchased.

Importing libraries

In [55]:
from sklearn import tree
from sklearn import metrics
import numpy as np
import csv

Reading data file as list object

In [56]:
import csv
def readFileThroughCSV(filename):
    csvfile = open(filename)
    readerobject = csv.reader(csvfile, delimiter=',')
    lst = list(readerobject)

    # removing first row from list
    lst = lst[1:]
    arr = np.array(lst)
    data = arr.astype(float)

    # extract first column which is classification
    c = data[:,0]

    # extract remaining data
    d = data[:,1:]
In [57]:
import pandas as pd
def readFileThroughPandas(filename):
    c = pd.read_csv(filename, usecols = [0])
    d = pd.read_csv(filename, usecols = np.arange(1,21))
    cnum = c.values
    dnum = d.values
    #You may also use the following
    #num = c.to_numpy()
    #dnum = d.to_numpy()
    cnum = cnum[:,0]
In [58]:
(c,d) = readFileThroughPandas("multicommodity.csv")
#(c,d) = readFileThroughCSV("multicommodity.csv")
(500, 20)

Fitting a decision tree

In [59]:
# Note that only 80% of the dataset is being used for training
clf = tree.DecisionTreeClassifier()[0:300,:],y=c[0:300])
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,

Printing performance metrics

In [60]:
# returns accuracy
print("Training accuracy",clf.score(X=d[:300,:],y=c[:300]))
print("Testing accuracy",clf.score(X=d[300:,:],y=c[300:]))

indices = range(300,500)
c_predicted = clf.predict(d[indices,:])
Training accuracy 1.0
Testing accuracy 0.68

Always look at the confusion matrix

In [54]:
m = metrics.confusion_matrix(c[indices],c_predicted)
[[91 25]
 [39 45]]