6. You have been asked by a San Francisco property investment company to create a predictive model that will generate house price estimates for properties they are considering purchasing as rental properties. The table below lists a sample of properties that have recently been sold for rental in the city. The descriptive features in this dataset are SIZE (the property size in square feet) and RENT (the estimated monthly rental value of the property in dollars). The target feature, PRICE, lists the prices that these properties were sold for in dollars. ID SIZE RENT PRICE 1 2,700 9,235 2,000,000 2 1,315 1,800 820,000 3 1,050 1,250 800,000 4 2,200 7,000 1,750,000 5 1,800 3,800 1,450,500 6 1,900 4,000 1,500,500 7 960 800 720,000 a. Create a k-d tree for this dataset. Assume the following order over the features: RENT then SIZE. b. Using the k-d tree that you created in the first part of this question, find the nearest neighbor to the following query: SIZE = 1,000, RENT = 2,200.