ML using KNN

In [1]:

    

import warnings
warnings.filterwarnings('ignore')


    

In [2]:

    

%matplotlib inline
%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [3]:

    

import pandas as pd
print(pd.__version__)


    

    




0.22.0

In [4]:

    

df = pd.read_csv('./insurance-customers-300.csv', sep=';')


    

In [5]:

    

y=df['group']


    

In [6]:

    

df.drop('group', axis='columns', inplace=True)


    

In [7]:

    

X = df.as_matrix()


    

In [8]:

    

df.describe()


    

    
Out[8]:







  

    

      

      
max speed
      
age
      
thousand km per year
    
  
  

    

      
count
      
300.000000
      
300.000000
      
300.000000
    
    

      
mean
      
171.863333
      
44.006667
      
31.220000
    
    

      
std
      
18.807545
      
16.191784
      
15.411792
    
    

      
min
      
132.000000
      
18.000000
      
5.000000
    
    

      
25%
      
159.000000
      
33.000000
      
18.000000
    
    

      
50%
      
171.000000
      
42.000000
      
30.000000
    
    

      
75%
      
187.000000
      
52.000000
      
43.000000
    
    

      
max
      
211.000000
      
90.000000
      
99.000000
    
  

In [9]:

    

# ignore this, it is just technical code
# should come from a lib, consider it to appear magically 
# http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

cmap_print = ListedColormap(['#AA8888', '#004000', '#FFFFDD'])
cmap_bold = ListedColormap(['#AA4444', '#006000', '#AAAA00'])
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#FFFFDD'])
font_size=25

def meshGrid(x_data, y_data):
    h = 1  # step size in the mesh
    x_min, x_max = x_data.min() - 1, x_data.max() + 1
    y_min, y_max = y_data.min() - 1, y_data.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    return (xx,yy)
    
def plotPrediction(clf, x_data, y_data, x_label, y_label, colors, title="", mesh=True, fname=None, print=False):
    xx,yy = meshGrid(x_data, y_data)
    plt.figure(figsize=(20,10))

    if clf and mesh:
        Z = clf.predict(np.c_[yy.ravel(), xx.ravel()])
        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
    
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    if print:
        plt.scatter(x_data, y_data, c=colors, cmap=cmap_print, s=200, marker='o', edgecolors='k')
    else:
        plt.scatter(x_data, y_data, c=colors, cmap=cmap_bold, s=80, marker='o', edgecolors='k')
    plt.xlabel(x_label, fontsize=font_size)
    plt.ylabel(y_label, fontsize=font_size)
    plt.title(title, fontsize=font_size)
    if fname:
        plt.savefig(fname)


    

In [10]:

    

from sklearn.model_selection import train_test_split


    

In [11]:

    

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)


    

In [12]:

    

X_train.shape, y_train.shape, X_test.shape, y_test.shape


    

    
Out[12]:





((180, 3), (180,), (120, 3), (120,))

In [13]:

    

X_train_kmh_age = X_train[:, :2]
X_test_kmh_age = X_test[:, :2]
X_train_2_dim = X_train_kmh_age
X_test_2_dim = X_test_kmh_age


    

In [14]:

    

from sklearn import neighbors
clf = neighbors.KNeighborsClassifier(1)
%time clf.fit(X_train_2_dim, y_train)


    

    




Wall time: 0 ns






    
Out[14]:





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

In [15]:

    

plotPrediction(clf, X_train_2_dim[:, 1], X_train_2_dim[:, 0], 
               'Age', 'Max Speed', y_train,
                title="Train Data Max Speed vs Age with Classification")


    

In [16]:

    

clf.score(X_train_2_dim, y_train)


    

    
Out[16]:





0.9777777777777777

In [17]:

    

plotPrediction(clf, X_test_2_dim[:, 1], X_test_2_dim[:, 0], 
               'Age', 'Max Speed', y_test,
                title="Test Data Max Speed vs Age with Prediction")


    

In [18]:

    

clf.score(X_test_2_dim, y_test)


    

    
Out[18]:





0.65