import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
datasets = pd.read_csv('10.csv')
x = datasets.iloc[:,[2,3]].values
y = datasets.iloc[:,4].values
from sklearn.model_selection import train_test_split
X_Train,X_Test, Y_Train, Y_Test = train_test_split(x,y, test_size = 0.25, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_Train = sc_X.fit_transform(X_Train)
X_Test = sc_X.transform(X_Test)
from sklearn.svm import SVC
classifier = SVC(kernel = 'linear' , random_state = 0)
classifier.fit(X_Train, Y_Train)
Y_Pred = classifier.predict(X_Test)
from sklearn import metrics
print("Accuracy score",metrics.accuracy_score(Y_Test, Y_Pred))
plt.scatter(X_Train[:,0], X_Train[:,1],c=Y_Train)
plt.title('support vector machine (training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
W=classifier.coef_[0]
a=-W[0]/W[1]
xx=np.linspace(-2.5,2.5)
yy=a*xx -(classifier.intercept_[0])/W[1]
plt.plot(xx,yy)
plt.show();
plt.scatter(X_Test[:,0], X_Test[:,1],c=Y_Test)
plt.title('support vector machine (test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
W=classifier.coef_[0]
a=-W[0]/W[1]
xx=np.linspace(-2.5,2.5)
yy=a*xx -(classifier.intercept_[0])/W[1]
plt.plot(xx,yy)
plt.show();