Support vector machine classification

svm_classification

# importing libraries  
import numpy as nm  
import matplotlib.pyplot as mtp  
import pandas as pd  
#importing datasets  
data_set= pd.read_csv('/kaggle/input/iris-flower-dataset/IRIS.csv') 
#Extracting Independent and dependent Variable
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelEncoder
y=data_set['species'].astype(str)
features=['sepal_length','sepal_width']
x=data_set[features]
# ordinal encode target variable
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)
# 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) 
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
h = (x_max / x_min)/100
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
X_plot = np.c_[xx.ravel(), yy.ravel()]
C = 1.0
Z = classifier.predict(X_plot)
Z = Z.reshape(xx.shape)
plt.figure(figsize=(15, 5))
plt.subplot(121)
plt.contourf(xx, yy, Z, cmap=plt.cm.tab10, alpha=0.3)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1)
plt.xlabel('sepal_length')
plt.ylabel('sepal_width')
plt.xlim(xx.min(), xx.max())
plt.title('Support Vector Classifier with linear kernel')
#using rbf kernel
classifier = svm.SVC(kernel = 'rbf',C=1, gamma ="auto").fit(x, y)
Z = classifier.predict(X_plot)
Z = Z.reshape(xx.shape)
plt.figure(figsize=(15, 5))
plt.subplot(121)
plt.contourf(xx, yy, Z, cmap = plt.cm.tab10, alpha = 0.3)
plt.scatter(X[:, 0], X[:, 1], c = y, cmap = plt.cm.Set1)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.title('Support Vector Classifier with rbf kernel')