PLEASE DO NOT COPY AND PASTE FROM OTHER POSTS I WANT REAL ANSWERS

This assignment considers the following hypothetical scenario. A real estate agency would like to use artificial intelligence to better predict whether a certain customer will buy a specific house, so they can focus their efforts on promising potential sales. Specifically, they want to label pairs of customers and houses according to whether they belong to the target class buys or not. The agency has selected seven attributes, each taking values from {yes,no}, namely

Basic features of the house:

garden: whether the house has a garden or not

parking: whether the house has private parking or not

good_nbhood: whether the house is in a good neighborhood or not

expensive: whether the house is expensive or not

characteristics of the client:

young: whether the client is young or not

rich: whether the client is rich or not

interested: whether the client is interested in the house or not

They also have some preliminary ideas about the kind of model they are interested in and have collected a small dataset for machine learning. Your task is to help them understand their options better and to recommend next steps towards realizing their goal, by answering the following questions.

Question 1

The agency is interested in Nave Bayes model using the three attributes expensive, interested and rich. Given the training data in Table 1 use your Knowledge on Nave Bayes classifier to predict the class for the data in testing data in Table 2. (1.2: 5 Marks)

Question 2

Based on a critical assessment of the Nave Bayes model considered above. Answer the following questions:

Evaluate your Nave Bayes models from Questions 1 by calculating the accuracy on the test data

provided in Table 2. (2.3: 1 Marks)

Write the answer of each of the following questions from the agency: (1.2: 3 Marks)

Is the Nave Bayes model a good choice to solve this problem?

If yes, why?

If no, why not, and what can we do to enhance this model?

Do you have any further recommendations that could help us improve our use of machine learning for this problem? Demonstrate your answer. (2.2: 4 Marks)

Table 2