You have a dataset of individuals who are classified based on two features: Age and Income, and the

target classification is whether they buy a certain product $($Yes or No$).$

Given Data:

Age $($years$)|$ Income $($thousands$)|$ Buys Product $($Yes$/$No$)$

$-------------------------------------------------------$

$25|40|$ No

$35|100|$ Yes

$45|32|$ No

$20|36|$ No

$35|26|$ Yes

$40|75|$ Yes

$33|47|$ No

$50|50|$ Yes

Determine whether a $30-$year$-$old with an income of $48$ thousand is likely to buy the product or not using

KNN with K$=3.$ Assume Euclidean distance for simplicity.

Steps:

$$ Calculate the Euclidean distance between the new individual and each of the given data points.

$$ Identify the three nearest neighbors.

$$ Determine the majority class of these three neighbors.

$$ Predict the class for the new individual based on the majority vote.

Now using the same dataset as above, explore the impact of different K values.

Predict whether a $42-$year$-$old with an income of $30$ thousand will buy the product using KNN for K$=1$

and K$=5.$

Hint:

Create a table to organize your distance calculations and neighbor classification.

Point Age $($years$)$ Income $($thousands$)$ Euclidean Distance to New Point Buys Product $($Yes$/$No$)$