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Github Link: https://github.com/Jagadeesh-Surendran/python-_machine_problems/blob/main/Problem_6%3A%20Perform%20feature%20selection%20using%20Recursive%20Feature%20Elimination%20(RFE)
————————————————————————————————————————-Problem_9:
Implement a simple neural network using Keras
#pythoncode :
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
# Sample data
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]]) # XOR problem
# Define model
model = Sequential()
model.add(Dense(4, input_dim=2, activation=’relu’))
model.add(Dense(1, activation=’sigmoid’))
# Compile model
model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])
# Train model
model.fit(X, y, epochs=100, verbose=0)
# Evaluate model
loss, accuracy = model.evaluate(X, y)
print(f’Accuracy: accuracy’)
Explanation: This code implements a simple neural network to solve the XOR problem using Keras.
#sample_output
Accuracy: 1.0
————————————————————————————————————————-
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