"TensorFlow machine learning with Calilfornia housing data"

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import numpy as np

import pandas as pd

from sklearn.datasets import fetch_california_housing

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import scale

import matplotlib.pyplot as plt

import tensorflow as tf

import warnings

%matplotlib inline

warnings.filterwarnings('ignore') # Turn the warnings off.

Answer the following question by providing Python code:

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# Bring the data.

housing_data = fetch_california_housing()

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# Read the description.

print(housing_data['DESCR'])

1). Explore the data:

• Display the dataset as a DataFrame with column labels.

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2). Build a machine learning model with TensorFlow.

• Preprocess the data if necessary.
• Build a linear regression model.
• Train the model.
• Calculate the error metrics such as MSE and RMSE (in-sample and out-of-sample). Target: RMSE < 1.