# your code goes here
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import zscore
from sklearn.neighbors import KNeighborsClassifier as knclassifier
%matplotlib inline
datalabel = ['Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Class']
df = pd.read_csv(r"C:\Users\bit.LAB4-23\Downloads\iris.data", delimiter=",", header=None)
df.columns = datalabel
numeric_cols = df.select_dtypes(include=[np.number]).columns
df_norm = df[numeric_cols].apply(zscore)
df_norm['Class'] = df['Class']
trainset = df_norm.sample(frac=0.75, random_state=83)
testset = df_norm.drop(trainset.index)
 
accuracy = []
def classify(n=3):
    knn = knclassifier(n_neighbors=n)
    knn.fit(trainset[numeric_cols], trainset['Class'])
    predictions = knn.predict(testset[numeric_cols])
    acc = knn.score(testset[numeric_cols], testset['Class'])
    accuracy.append(acc)
 
for x in range(3,21,2):
    classify(x)
print(accuracy)
plt.plot(accuracy)

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