In [1]:

    

from sklearn.neighbors import KNeighborsClassifier
import pandas as pd


    

In [2]:

    

knn = KNeighborsClassifier(n_neighbors=3, p=2)


    

In [7]:

    

path_to_training_data="../data/training.txt"
path_to_test_data="../data/test.txt"


    

In [8]:

    

train = pd.read_csv(path_to_training_data, sep=",", header = None)
test = pd.read_csv(path_to_test_data, sep=",", header = None)
#train.columns = ["a", "b", "c", "d"]


    

In [9]:

    

test.ix[:,4]


    

    
Out[9]:





0     Iris-virginica
1     Iris-virginica
2    Iris-versicolor
3        Iris-setosa
4        Iris-setosa
5        Iris-setosa
Name: 4, dtype: object

In [14]:

    

x_train = train.ix[:,:3]
x_test = test.ix[:,:3]

y_train = train.ix[:,4]
y_test = test.ix[:,4]


    

In [16]:

    

knn.fit(x_train, y_train)


    

    
Out[16]:





KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=3, p=2,
           weights='uniform')

In [19]:

    

y_predicted = knn.predict(x_test)


    

In [18]:

    

def calculate_accuracy(y_true, y_predicted):
    good = 0
    for x in range(len(y_true)):
        if y_true[x] == y_predicted[x]:
            good += 1
    return (good / len(y_true)) * 100


    

In [20]:

    

calculate_accuracy(y_test, y_predicted)


    

    
Out[20]:





83.33333333333334