svm

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
cols=['petal_len','petal_width','sepal_len','sepal_width','class']
df=pd.read_csv('iris.csv',header=None,names=cols)
df.head()
df['class'].unique()
df.replace({'class':{'Setosa':1,'Virginica':3,'Versicolor':2}},inplace=True)
df.head()
plt.title("CORR ON IRIS DATASET")
sns.heatmap(df.corr(),annot=True)
plt.show()
X=df.iloc[:,:-1].values
y=df.iloc[:,-1].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)
from sklearn.svm import SVC
cl=SVC(kernel='linear',random_state=0)
cl.fit(X_train,y_train)
y_pred=cl.predict(X_test)
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
cm=confusion_matrix(y_test,y_pred)
print(cm)
disp=ConfusionMatrixDisplay(confusion_matrix=cm)
disp.plot();