In [7]:

    

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets
import pandas as pd
from sklearn.preprocessing import LabelEncoder


    

In [8]:

    

n_neighbors = 15


    

In [4]:

    

df = pd.read_csv('https://dl.dropboxusercontent.com/u/75194/stats/data/01_heights_weights_genders.csv')


    

In [9]:

    

df.sample()


    

    
Out[9]:






  

    

      

      
Gender
      
Height
      
Weight
    
  
  

    

      
7907
      
Female
      
63.864835
      
158.94006
    
  

In [11]:

    

encoder=LabelEncoder()
df['Gender']=encoder.fit_transform(df['Gender'])


    

In [12]:

    

df.sample(5)


    

    
Out[12]:






  

    

      

      
Gender
      
Height
      
Weight
    
  
  

    

      
4847
      
1
      
67.683219
      
192.150042
    
    

      
1274
      
1
      
69.227081
      
185.916021
    
    

      
1056
      
1
      
71.910226
      
198.078825
    
    

      
7486
      
0
      
66.981731
      
158.847956
    
    

      
1956
      
1
      
69.342366
      
197.217940
    
  

In [30]:

    

features=df.columns[1:]
target=df.columns[0]
target


    

    
Out[30]:





'Gender'

In [24]:

    

h = .02


    

In [25]:

    

cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])


    

In [ ]:

    

for weights in ['uniform', 'distance']:
    # we create an instance of Neighbours Classifier and fit the data.
    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
    clf.fit(X, y)

    # Plot the decision boundary. For that, we will assign a color to each
    # point in the mesh [x_min, x_max]x[y_min, y_max].
    x_min, x_max = df[features[0]].min() - 1, df[features[0]].max() + 1
    y_min, y_max = df[features[1]].min() - 1, df[features[1]].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure()
    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    plt.scatter(df[features[0]], df[features[1]], c=y, cmap=cmap_bold)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.title("3-Class classification (k = %i, weights = '%s')"
              % (n_neighbors, weights))

plt.show()


    

In [ ]: