SVM demo 1

 #Data Pre-processing Step 

# importing libraries 

import numpy as nm 

import matplotlib.pyplot as mtp 

import pandas as pd 

 

#importing datasets 

data_set= pd.read_csv('User_Data.csv') 

 

#Extracting Independent and dependent Variable 

x= data_set.iloc[:, [2,3]].values 

y= data_set.iloc[:, 4].values 

 

# Splitting the dataset into training and test set. 

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) 

 

#feature Scaling 

from sklearn.preprocessing import StandardScaler   

st_x= StandardScaler()   

x_train= st_x.fit_transform(x_train)   

x_test= st_x.transform(x_test)      

 

from sklearn.svm import SVC # "Support vector classifier" 

classifier = SVC(kernel='linear', random_state=0) 

classifier.fit(x_train, y_train) 

 

#Predicting the test set result 

y_pred= classifier.predict(x_test)

 

#Creating the Confusion matrix 

from sklearn.metrics import confusion_matrix 

cm= confusion_matrix(y_test, y_pred) 

 

cm

 

from matplotlib.colors import ListedColormap 

x_set, y_set = x_train, y_train 

x1, x2 = nm.meshgrid(nm.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step  =0.01), 

nm.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01)) 

mtp.contourf(x1, x2, classifier.predict(nm.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape), 

alpha = 0.75, cmap = ListedColormap(('red', 'green'))) 

mtp.xlim(x1.min(), x1.max()) 

mtp.ylim(x2.min(), x2.max()) 

for i, j in enumerate(nm.unique(y_set)): 

    mtp.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1], 

        c = ListedColormap(('red', 'green'))(i), label = j) 

mtp.title('SVM classifier (Training set)') 

mtp.xlabel('Age') 

mtp.ylabel('Estimated Salary') 

mtp.legend() 

mtp.show() 

 

#Visulaizing the test set result 

from matplotlib.colors import ListedColormap 

x_set, y_set = x_test, y_test  

x1, x2 = nm.meshgrid(nm.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step  =0.01), 

nm.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01)) 

mtp.contourf(x1, x2, classifier.predict(nm.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape), 

alpha = 0.75, cmap = ListedColormap(('red','green' ))) 

mtp.xlim(x1.min(), x1.max()) 

mtp.ylim(x2.min(), x2.max()) 

for i, j in enumerate(nm.unique(y_set)): 

    mtp.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1], 

        c = ListedColormap(('red', 'green'))(i), label = j) 

mtp.title('SVM classifier (Test set)') 

mtp.xlabel('Age') 

mtp.ylabel('Estimated Salary') 

mtp.legend() 

mtp.show()