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()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy = (y_pred == y_test).mean()
python
import tensorflow as tf
from tensorflow.keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(-1, 28*28) / 255.0
X_test = X_test.reshape(-1, 28*28) / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(28*28,)),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=5)
loss, accuracy = model.evaluate(X_test, y_test)
python
import nltk
from nltk.corpus import gutenberg
from nltk.tokenize import sent_tokenize
nltk.download('gutenberg')
corpus = gutenberg.raw('bible-kjv.txt')
sentences = sent_tokenize(corpus)
for i in range(5):
print(sentences[i])
