increasing or decreasing k in knn both are decreasing the accuracy. why is this so ?
def knn(x, y, query_x, k=6):
val = []
m = x.shape[0]
for i in range(m):
d = dist(query_x, x[i])
val.append((d, y[i]))
val = sorted(val)
val = val[:k]
val = np.array(val)
newvals = np.unique(val[:,1], return_counts = True)
index = newvals[1].argmax()
pred = newvals[0][index]
return predhey @ayangiri3 ,
Your code works absolutely fine.
The actual thing is that we need to find a particular value of k for which our model will perform best by trying different values k, and in this case that value is currently k = 6 , therefore whenever you increase or decrease its value the accuracy is constantly decreasing and not increasing.
You can try different techniques like scaling your data , combining results of different values of K to get better results . These techniques might improve your results , but the major important thing is experiments that you need to perform to get better accuracy.
I hope this would have resolved your doubt.
Thank You.
Happy learning 
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