The purpose of this assignment is to apply K-Nearest Neighbor (KNN) classifier learning techniques to real-world data using data mining tools. Using the dataset below, classify the new record (#10) using k = 9: Record 1 2 3 4 5 6 7 8 9 10 Age 22 33 28 51 25 39 54 55 50 66 Marital Single Married Other Other Single Single Single Married Married Married Income $46,156.98 $24,188.10 $28,787.34 $23,886.72 $47,281.44 $33,994.90 $28,716.50 $49,186.75 $46,726.50 $36,120.34 Risk Bad loss Bad loss Bad loss Bad loss Bad loss Good risk Good risk Good risk Good risk ? 1. Using Min-Max standardization formula, calculate the min-max standardized values for Age and Income attributes. (20 points) 2. Using the standardized values, calculate the distance for the new record (#10) from each of the nine records. (40 points) 3. Using unweighted voting, classify the risk factors for the new record. (10 points) 4. Using weighted voting, calculate the weights and classify the risk factor for the new record. (20 points) 5. Write a paragraph comparing the results that you found using different techniques. (10 points)