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I'm trying to plot the KNN algorithm for a specific dataset that has 38 features where 2 features are target classes. However, the features are numeric types and target classes are strings that have the values of ("Yes", "No"). How can I make that happen?

NOTE: the dataset is CSV type that has 709 records.

What I have tried:

I have been messing with this code but I want to implement the KNN algorithm by uploading a different dataset from my device called PC1 dataset as mentioned above. Also, I want to assign the X and y values to be compatible with my dataset which is PC1.csv

from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB 
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn import datasets
import numpy as np

# Initializing Classifiers
clf1 = LogisticRegression(random_state=1,
clf2 = RandomForestClassifier(random_state=1, n_estimators=100)
clf3 = GaussianNB()
clf4 = SVC(gamma='auto')

# Loading some example data

iris = datasets.load_iris()
X =[:, [0,2]]
y =

import matplotlib.pyplot as plt
from mlxtend.plotting import plot_decision_regions
import matplotlib.gridspec as gridspec
import itertools

gs = gridspec.GridSpec(2, 2)

fig = plt.figure(figsize=(10,8))

labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'SVM']
for clf, lab, grd in zip([clf1, clf2, clf3, clf4],
                         itertools.product([0, 1], repeat=2)):, y)
    ax = plt.subplot(gs[grd[0], grd[1]])
    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)

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