knn.py

# -*- coding: utf-8 -*-
"""knn.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/13tdHuTGwyjKglJkeV1z_Q0ABX5Pcdwpk
"""

# Import necessary modules 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.model_selection import train_test_split 
from sklearn.datasets import load_iris 
  
# Loading data 
irisData = load_iris() 
  
# Create feature and target arrays 
X = irisData.data 
y = irisData.target 
  
# Split into training and test set 
X_train, X_test, y_train, y_test = train_test_split( 
             X, y, test_size = 0.2, random_state=42) 
  
knn = KNeighborsClassifier(n_neighbors=7) 
  
knn.fit(X_train, y_train) 
  
# Predict on dataset which model has not seen before 
print(knn.predict(X_test))

# Import necessary modules 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.model_selection import train_test_split 
from sklearn.datasets import load_iris 
import numpy as np 
import matplotlib.pyplot as plt 
  
irisData = load_iris() 
  
# Create feature and target arrays 
X = irisData.data 
y = irisData.target 
  
# Split into training and test set 
X_train, X_test, y_train, y_test = train_test_split( 
             X, y, test_size = 0.2, random_state=42) 
  
neighbors = np.arange(1, 9) 
train_accuracy = np.empty(len(neighbors)) 
test_accuracy = np.empty(len(neighbors)) 
  
# Loop over K values 
for i, k in enumerate(neighbors): 
    knn = KNeighborsClassifier(n_neighbors=k) 
    knn.fit(X_train, y_train) 
      
    # Compute traning and test data accuracy 
    train_accuracy[i] = knn.score(X_train, y_train) 
    test_accuracy[i] = knn.score(X_test, y_test) 
  
# Generate plot 
plt.plot(neighbors, test_accuracy, label = 'Testing dataset Accuracy') 
plt.plot(neighbors, train_accuracy, label = 'Training dataset Accuracy') 
  
plt.legend() 
plt.xlabel('n_neighbors') 
plt.ylabel('Accuracy') 
plt.show()