﻿ Python Machine learning Scikit-learn, K Nearest Neighbors: Calculate the performance for different values of k - w3resource # 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))
```
```

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:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

What is the difficulty level of this exercise?

﻿