import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras import layers, models

X, y = make_classification(n_samples=1000, n_features=2, n_classes=2, n_clusters_per_class=1, n_redundant=0, random_state=42)

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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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model = models.Sequential()
model.add(layers.Dense(units=1, activatinotallow='sigmoid', input_shape=(2,)))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=20, batch_size=32, validation_split=0.2)

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test_loss, test_accuracy = model.evaluate(X_test, y_test)
print(f'Test Accuracy: {test_accuracy * 100:.2f}%')