import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df=pd.read_csv('knn.csv')
df.head()
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
x=df.iloc[:,:-1]
y=df.iloc[:,-1:]
X=StandardScaler().fit_transform(x)
X_train,X_test,Y_train,Y_test=train_test_split(X,y,test_size=0.2)
knn=KNeighborsClassifier(n_neighbors=20,metric='manhattan')
knn.fit(X_train,Y_train)
y_pred=knn.predict(X_test)
print(y_pred)
print(accuracy_score(Y_test,y_pred))
n=[7,8,9,10,11,12,13,14,15]
a=[]
for i in n:
    knn=KNeighborsClassifier(n_neighbors=i,metric='manhattan')
    knn.fit(X_train,Y_train)
    p=knn.predict(X_test)
    a.append(accuracy_score(Y_test,p))
plt.plot(n,a)
