﻿ Python Machine learning Scikit-learn, K Nearest Neighbors: Calculate the accuracy of the model using the K Nearest Neighbor Algorithm - w3resource

# Python Scikit-learn: K Nearest Neighbors - Calculate the accuracy of the model using the K Nearest Neighbor Algorithm

## Python Machine learning K Nearest Neighbors: Exercise-5 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 calculate the accuracy of the model using the K Nearest Neighbor Algorithm.

Sample Solution:

Python Code:

``````# Import necessary modules
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
#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
print("Accuracy of the model:")
print(knn.score(X_test, y_test))
```
```

Sample Output:

```Accuracy of the model:
0.9666666666666667
```

Python Code Editor:

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