mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-24 21:41:08 +00:00
29 lines
707 B
Python
29 lines
707 B
Python
|
from sklearn.model_selection import train_test_split
|
||
|
|
from sklearn.datasets import load_iris
|
||
|
|
from sklearn.neighbors import KNeighborsClassifier
|
||
|
|
|
||
|
|
#Load iris file
|
||
|
|
iris = load_iris()
|
||
|
|
iris.keys()
|
||
|
|
|
||
|
|
|
||
|
|
print('Target names: \n {} '.format(iris.target_names))
|
||
|
|
print('\n Features: \n {}'.format(iris.feature_names))
|
||
|
|
|
||
|
|
#Train set e Test set
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(iris['data'],iris['target'], random_state=4)
|
||
|
|
|
||
|
|
#KNN
|
||
|
|
|
||
|
|
knn = KNeighborsClassifier (n_neighbors = 1)
|
||
|
|
knn.fit(X_train, y_train)
|
||
|
|
|
||
|
|
#new array to test
|
||
|
|
X_new = [[1,2,1,4],
|
||
|
|
[2,3,4,5]]
|
||
|
|
|
||
|
|
prediction = knn.predict(X_new)
|
||
|
|
|
||
|
|
print('\nNew array: \n {}'
|
||
|
|
'\n\nTarget Names Prediction: \n {}'.format(X_new, iris['target_names'][prediction]))
|