Assuming you want to predict the ‘SalePrice’ target variable based on other features

Extract features (X) and target variable (y)

X = data.drop(columns=['SalePrice']) # Assuming 'SalePrice' is the target variable y = data['SalePrice']

Perform train-test split

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

Standardize features

scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test)

Define the neural network architecture

your textmodel = tf.keras.models.Sequential([
tf.keras.layers.Dense(128, activation=’relu’, input_shape=(X_train_scaled.shape[1],)),
tf.keras.layers.Dense(64, activation=’relu’),
tf.keras.layers.Dense(1) # Output layer with single neuron (for regression)`
])

Compile the model

model.compile(optimizer='adam', loss='mean_squared_error')

Train the model

history = model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.2)

Evaluate the model on test data

loss = model.evaluate(X_test_scaled, y_test) print("Test Loss:", loss)