import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

df = pd.read_csv('Classified Data', index_col = 0)

df.head()

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

scaler.fit(df.drop('TARGET CLASS',axis=1))

scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))

df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])

df_feat.head()

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(scaled_features,df['TARGET CLASS'],

test_size=0.30)

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=1)

knn.fit(X_train,y_train)

pred = knn.predict(X_test)

from sklearn import metrics

from sklearn.metrics import classification_report,confusion_matrix

print('Confusion Matrix')

print(confusion_matrix(y_test,pred))

print('classification Report')

print(classification_report(y_test,pred))

print('Accuracy : ', metrics.accuracy_score(y_test, pred))

error_rate = []

for i in range(1,40):

knn = KNeighborsClassifier(n_neighbors=i)

knn.fit(X_train,y_train)

pred_i = knn.predict(X_test)

error_rate.append(np.mean(pred_i != y_test))

plt.figure(figsize=(10,6))

plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', marker='o',

markerfacecolor='red', markersize=10)

plt.title('Error Rate vs. K Value')

plt.xlabel('K')

plt.ylabel('Error Rate')

knn = KNeighborsClassifier(n_neighbors=11)

knn.fit(X_train,y_train)

pred = knn.predict(X_test)

print('Confusion Matrix k = 11')

print(confusion_matrix(y_test,pred))

print('classification Report')

print(classification_report(y_test,pred))

print('Accuracy : ', metrics.accuracy_score(y_test, pred))