ass5(Knn)

 # Importing Libraries 

import numpy as np 

import pandas as pd 

import matplotlib.pyplot as plt 

from sklearn.neighbors import KNeighborsClassifier 

from sklearn.naive_bayes import GaussianNB 

from sklearn.naive_bayes import MultinomialNB 

from sklearn.model_selection import train_test_split 

from sklearn import preprocessing 

#Metrics 

from sklearn.metrics import accuracy_score 

from sklearn.metrics import f1_score 

from sklearn.metrics import confusion_matrix 

from sklearn.metrics import classification_report 

from sklearn.metrics import jaccard_score 

from sklearn.metrics import roc_curve 

from google.colab import files 

uploaded = files.upload() 

data=pd.read_csv('teleCust.csv') 

raw = data.copy() 

data.shape 

data.head() 

data.describe() 

x=np.asarray(data[data.columns[0:-1]]) 

y=np.asarray(data[data.columns[-1]]) 

x0=preprocessing.StandardScaler().fit(x) 

x1=x0.transform(x) 

x1 

x_train,x_test,y_train,y_test=train_test_split(x1,y,test_size=0.2,random_state=10) 

knn = KNeighborsClassifier(n_neighbors=5,metric='minkowski') 

y_knn = knn.fit(x_train,y_train) 

y_knn_pred = y_knn.predict(x_test) 

knn_acc=accuracy_score(y_test,y_knn_pred) 

knn_f1=f1_score(y_test,y_knn_pred,average='micro') 

print("accuracy score ", knn_acc*100, "%") 

print("f1_score ", knn_f1*100, "%") 

knn_cm=confusion_matrix(y_test,y_knn_pred) 

knn_cr=classification_report(y_test,y_knn_pred) 

print('confusion matrix\n' , knn_cm) 

print('classification report\n', knn_cr)