Python Scikit-learn: K Nearest Neighbors - Calculate the performance for different values of k

Python Machine learning K Nearest Neighbors: Exercise-6 with Solution

Write a Python program using Scikit-learn to split the iris dataset into 80% train data and 20% test data. Out of total 150 records, the training set will contain 120 records and the test set contains 30 of those records. Train or fit the data into the model and using the K Nearest Neighbor Algorithm calculate the performance for different values of k.

Sample Solution:

Python Code:

# Import necessary modules 
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.model_selection import train_test_split  
iris = pd.read_csv("iris.csv")
#Drop id column
iris = iris.drop('Id',axis=1)
X = iris.iloc[:, :-1].values
y = iris.iloc[:, 4].values
#Split arrays or matrices into train and test subsets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20) 
knn = KNeighborsClassifier(n_neighbors=7)  
knn.fit(X_train, y_train)   
# Calculate the accuracy of the model for different values of k
for i in np.arange(1, 10):
    knn2 = KNeighborsClassifier(n_neighbors=i)
    knn2.fit(X_train, y_train)
    print("For k = %d accuracy is"%i,knn2.score(X_test,y_test))

Sample Output:

For k = 1 accuracy is 0.9666666666666667
For k = 2 accuracy is 0.9666666666666667
For k = 3 accuracy is 0.9666666666666667
For k = 4 accuracy is 0.9333333333333333
For k = 5 accuracy is 0.9666666666666667
For k = 6 accuracy is 0.9666666666666667
For k = 7 accuracy is 0.9666666666666667
For k = 8 accuracy is 0.9333333333333333
For k = 9 accuracy is 0.9666666666666667
 

Python Code Editor:

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Previous: Write a Python program using Scikit-learn to split the iris dataset into 80% train data and 20% test data. Out of total 150 records, the training set will contain 120 records and the test set contains 30 of those records. Train or fit the data into the model and calculate the accuracy of the model using the K Nearest Neighbor Algorithm.
Next: Write a Python program using Scikit-learn to split the iris dataset into 80% train data and 20% test data. Out of total 150 records, the training set will contain 120 records and the test set contains 30 of those records. Train or fit the data into the model and using the K Nearest Neighbor Algorithm and create a plot to present the performance for different values of k.