Demonstration of Knn algorithm

import numpy as np import matplotlib.pyplot as plt s=np.random.uniform(100,200,150) t=np.random.uniform(150,250,150) s=list(s) t=list(t) plt.plot(s,'o',color='#ffff00') plt.plot(t,'o',color='silver') dic={} dic['A']=s dic['B']=t distance=[] td=int(input('Enter the test data:')) plt.plot(td,'o',color='aqua') for key in dic: for i in range(120): t=str(abs(dic[key][i]-td))+key distance.append(t) distance.sort() k=int(input('Enter the key value:')) s=t=0 for i in range(k): if distance[i][len(distance[i])-1]=='A': s+=1 else: t+=1 if s>t: print("Data belongs to class A") else: print("Data belongs to class B")