Machine Learning Foundations : Exercise 1 House Price Question

Exercise 1 : House Prices Question : code lab link

Build a neural network that predicts the price of a house according to a simple formula, house pricing was as easy as a house costs 50k + 50k per bedroom, so that a 1 bedroom house costs 100k, a 2 bedroom house costs 150k etc.

Training data set

Bedroom amount [1, 2, 3, 4,]
House price [100, 150, 200, 250]

Code:

	import tensorflow as tf
	iport numpy as np
	from tensorflow import keras
	# Create an 1*1 layer neuron
	model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])
	# Setting optimizer and loss function
	model.compile(optimizer='sgd', loss='mean_squared_error')
	# Training data
	xs = np.array([1, 2, 3, 4], dtype=int)
	ys = np.array([100, 150, 200, 250], dtype=int)
	# Training mode for 500 iteration
	model.fit(xs, ys, epochs=500)
	# Predict the output
	print(model.predict([7.0]))m

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