Question 3 (25 marks)

3.1 Use Table 3.1  to answer the following questions:

Table 3.1

square_footage is the area of the house in square feet.

num_bedrooms is the number of bedrooms.

age is the age of the house in years.

location is the location of the which is Urban, Suburban or Rural.

price is the cost of the house in ZAR (rand).

Using the following imported libraries:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error, r2_score

from sklearn.preprocessing import LabelEncoder

Each question has a weighting of 5 marks.

(a )Write Python code snippet to load the houses data in a dataframe

(b) Write a Python code snippet to separate independent features/attributes and a target feature (price).                                                                                                                   

(c) Write a Python code snippet to apply label-encoding (one-hot encoding) to the location column. 

(d) Write a Python code snippet to split the dataset into 80% train and 20% test.

(e) Write a Python code snippet to train a linear regression model (using the test dataset) and predict the house price of a house with 2000 square_footage, num_bedrooms: 5, age: 5 and location:  Urban.                                                                                                                             

(a