## Hi there,

I would like to write a simple NN model to predict the housing price. My dataset is simple, and the X-train has one feature: the house’s surface, and i want to predict its price.

Here is the code. Any hints?

Xt = np.array([[1.0], [2.0]], dtype=np.float32) #(size in 1000 square feet)

Yt = np.array([[300.0], [500.0]], dtype=np.float32) #(price in 1000s of dollars)

norm_l = tf.keras.layers.Normalization(axis=-1)

norm_l.adapt(Xt) # learns mean, variance

#Xn = norm_l(Xt)

Xt = np.tile(Xn,(1000,1))

Yt= np.tile(Yt,(1000,1))

tf.random.set_seed(1234)

model = Sequential(

[

tf.keras.layers.Dense(units=1, activation = ‘ReLU’, name=‘L1’ ),

tf.keras.layers.Dense(units=2, activation = ‘ReLU’, name=‘L2’ ),

tf.keras.layers.Dense(units=1, activation = ‘linear’, name=‘L3’ )

```
]
```

)

model.compile(

loss = tf.keras.losses.MeanSquaredError(),

optimizer = tf.keras.optimizers.Adam(learning_rate=0.01),

)

## model.fit(

Xt,Yt,

epochs=100,

)

result=model.predict([[2.0]])

print(result)

Thank you for your help