#import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#Create the dataframe
df=pd.read_csv("iris.csv")
print(df)

df.shape

Output:

(150, 5)

df.info()

#Check missing value
df.isna().sum()

Output:

sepal_length 0
sepal_width  0
petal_length 0
petal_width  0
species      0
dtype: int64

#Choose X and y
X=df.iloc[:,[0,1,2,3]].values
y=df.iloc[:,4].values

#split dataset into training and testing dataset
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)

print(X_test)

[[5.8 2.8 5.1 2.4]
[6. 2.2 4. 1. ]
[5.5 4.2 1.4 0.2]
[7.3 2.9 6.3 1.8]
[5. 3.4 1.5 0.2]
[6.3 3.3 6. 2.5]
[5. 3.5 1.3 0.3]
[6.7 3.1 4.7 1.5]
[6.8 2.8 4.8 1.4]
[6.1 2.8 4. 1.3]
[6.1 2.6 5.6 1.4]
[6.4 3.2 4.5 1.5]
[6.1 2.8 4.7 1.2]
[6.5 2.8 4.6 1.5]
[6.1 2.9 4.7 1.4]
[4.9 3.1 1.5 0.1]
[6. 2.9 4.5 1.5]
[5.5 2.6 4.4 1.2]
[4.8 3. 1.4 0.3]
[5.4 3.9 1.3 0.4]
[5.6 2.8 4.9 2. ]
[5.6 3. 4.5 1.5]
[4.8 3.4 1.9 0.2]
[4.4 2.9 1.4 0.2]
[6.2 2.8 4.8 1.8]
[4.6 3.6 1. 0.2]
[5.1 3.8 1.9 0.4]
[6.2 2.9 4.3 1.3]
[5. 2.3 3.3 1. ]
[5. 3.4 1.6 0.4]
[6.4 3.1 5.5 1.8]
[5.4 3. 4.5 1.5]
[5.2 3.5 1.5 0.2]
[6.1 3. 4.9 1.8]
[6.4 2.8 5.6 2.2]
[5.2 2.7 3.9 1.4]
[5.7 3.8 1.7 0.3]
[6. 2.7 5.1 1.6]]

print(y_test)

['Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica'
'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa'
'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'
'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'
'Iris-virginica' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'
'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'
'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor'
'Iris-setosa' 'Iris-versicolor']

#Create SVM model
from sklearn.svm import SVC
obj=SVC()

#Trained the model
obj.fit(X_train,y_train)

Output:

SVC()

y_pred=obj.predict(X_test)
print(y_pred)

['Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica'
'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa'
'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'
'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'
'Iris-virginica' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'
'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'
'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor'
'Iris-setosa' 'Iris-virginica']

#Find accuracy
from sklearn.metrics import accuracy_score
print(accuracy_score(y_test,y_pred))

0.9736842105263158