1. Scikit-learn:
python
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
print(y_pred)
2. TensorFlow:
python
import tensorflow as tf
from tensorflow.keras import layers
model = tf.keras.Sequential([
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))
loss, acc = model.evaluate(X_test, y_test)
predictions = model.predict(X_test)
print(predictions)
3. NLTK:
python
import nltk
nltk.download('averaged_perceptron_tagger')
tokens = nltk.word_tokenize(text)
print(tokens)
tagged = nltk.pos_tag(tokens)
print(tagged)
