Hi guys can i please get some insights towards why my code isnt functioning as required. I am getting an 0 % accuracy. I believe its towards the end of the code when using append its returning None and i am not sure how to fix that. It should return something like accuracy:97%. Thank you in advance.

import csv
import random
import math
import operator

def handleDataset(filename, split, trainingSet=[] , testSet=[]):
with open(filename, 'r') as csvfile:
dataset = list(lines)
for x in range(len(dataset)-1):
for y in range(4):
dataset[x][y] = float(dataset[x][y])
if random.random() < split:
trainingSet.append(dataset[x])
else:
testSet.append(dataset[x])

def euclideanDistance(instance1, instance2, length):
distance = 0
for x in range(length):
distance += pow((instance1[x] - instance2[x]), 2)
return math.sqrt(distance)

def getKNeighbors(trainingSet, testInstance, k):
distances = []
length = len(testInstance)-1
for x in range(len(trainingSet)):
dist = euclideanDistance(testInstance, trainingSet[x], length)
distances.append((trainingSet[x], dist))
distances.sort(key=operator.itemgetter(1))
neighbors = []
for x in range(k):
neighbors.append(distances[x][0])
return neighbors

def getResponse(neighbors):
for x in range(len(neighbors)):
response = neighbors[x][-1]
else:

def getAccuracy(testSet, predictions):
correct = 0
for x in range(len(testSet)):
if testSet[x][-1] is predictions[x]:
correct += 1
return (correct/float(len(testSet))) * 100.0

def main():
# prepare data
trainingSet=[]
testSet=[]
split = 0.67
handleDataset(r'C:\Users\Desktop\Iris dataset\iris.txt', split, trainingSet, testSet)
print('Train set: ' + repr(len(trainingSet)))
print('Test set: ' + repr(len(testSet)))
# generate predictions
predictions=[]
k = 3
for x in range(len(testSet)):
neighbors = getKNeighbors(trainingSet, testSet[x], k)
result = getResponse(neighbors)
predictions.append(result)
print('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))
accuracy=getAccuracy(testSet, predictions)
print('Accuracy: ' + repr(accuracy) + '%')

main()

output

Train set: 99
Test set: 51
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-setosa', actual='Iris-setosa'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-virginica', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-virginica', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-versicolor', actual='Iris-versicolor'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
> predicted='Iris-virginica', actual='Iris-virginica'
Accuracy: 0.0%

Sep 26, 2019
recategorized Sep 4, 2020 2,034 views

## 1 answer to this question.

if testSet[x][-1] is predictions[x]:
change it to

if testSet[x][-1] == predictions[x]:

answered Sep 4, 2020 by anonymous

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+1 vote