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import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Load dataset
ds = pd.read_csv("housing.csv")

# Drop columns
ds.pop("ocean_proximity")
ds.pop("total_bedrooms")

# Normalize the data
hnorm = (ds - ds.min() - 1) / (ds.max() - ds.min())

# Extract target and features
target = ds.pop("median_house_value")
y = target.values
x = hnorm.values

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)

# Build the model
model = keras.Sequential([
    keras.layers.Dense(16, activation="relu", input_shape=(8,)),
    keras.layers.Dense(1)
])
model.compile(loss="mae", optimizer="sgd")

# Train the model and capture the history
history = model.fit(x_train, y_train, epochs=10, verbose=1, validation_data=(x_test, y_test))

# Plot training loss and validation loss
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Training and Validation Loss vs Epoch')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

# Evaluate the model on the test set
evaluation_result = model.evaluate(x_test, y_test)
print("Test Loss:", evaluation_result)
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