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 (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();