K-NeighbourClassifier

 #!/usr/bin/env python

# coding: utf-8

# In[1]:

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

# In[2]:

#mydatafile="Desktop\Machine_Learning\week3_Social_Network_Ads.csv"

mydatafile = r"C:\Users\91887\OneDrive\Desktop\Machine_Learning\week3_Social_Network_Ads.csv"

dataset = pd.read_csv(mydatafile)

X = dataset.iloc[:, :-1].values

y = dataset.iloc[:, -1].values

# In[3]:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)

# In[4]:

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)

# In[6]:

print(X_test)

# In[7]:

print(X_train)

# In[8]:

from sklearn.neighbors import KNeighborsClassifier

classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)

classifier.fit(X_train, y_train)

# In[13]:

#print(classifier.predict(sc.transform([[30,87000]])))

y_pred = classifier.predict(X_test)

print(y_pred)

# In[14]:

from sklearn.metrics import confusion_matrix, accuracy_score

cm = confusion_matrix(y_test, y_pred)

print(cm)

accuracy_score(y_test, y_pred)

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