question 1 the k nearest neighbor algorithm has hyper parameters in ad
The K nearest neighbor algorithm has hyper-
parameters in addition to K. Use the titanic dataset
to simultaneously tune the values of more than one
hyper-parameter. E.g. Value of K, weight, and
metric. You can either do nested for loops for this or
use the grid search OR random search function
from Scikit-learn. (Please submit code inside of a
Jupyter notebook). Based on your analysis, propose
a strategy for selecting an optimal value of k for a
given dataset and discuss its advantages and
Describe and analyze the impact of varying the
value of k in the KNN classifier on its classification
performance considering both accuracy metrics.
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