Explain the K$-$Nearest Neighbors $($KNN$)$ algorithm. How does the choice of $\text{'}K\text{'}$

influence the model's behavior? Given a dataset with $120$ samples, each having three

features, implement the KNN algorithm with Manhattan distance as the similarity

measure. If $K=7,$ find the class label for a new data point with the following feature

values: Feature $1=6\mathrm{.}3,$ Feature$2=2\mathrm{.}5,$ Feature $3=4\mathrm{.}8.$