#import libraries and load iris dataset
import pandas as pd
from sklearn.datasets import load_iris
iris=load_iris()

#To get the features in iris
dir(iris)

Output:

['DESCR',
'data',
'feature_names',
'filename',
'frame',
'target',
'target_names']

iris.data

Output:

array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.6, 1.4, 0.2],
[5.4, 3.9, 1.7, 0.4],
[4.6, 3.4, 1.4, 0.3],
[5. , 3.4, 1.5, 0.2],
[4.4, 2.9, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5.4, 3.7, 1.5, 0.2],
[4.8, 3.4, 1.6, 0.2],
[4.8, 3. , 1.4, 0.1],
[4.3, 3. , 1.1, 0.1],
[5.8, 4. , 1.2, 0.2],
[5.7, 4.4, 1.5, 0.4],
[5.4, 3.9, 1.3, 0.4],
[5.1, 3.5, 1.4, 0.3],
[5.7, 3.8, 1.7, 0.3],
[5.1, 3.8, 1.5, 0.3],
[5.4, 3.4, 1.7, 0.2],
[5.1, 3.7, 1.5, 0.4],
[4.6, 3.6, 1. , 0.2],
[5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.9, 0.2],
[5. , 3. , 1.6, 0.2],
[5. , 3.4, 1.6, 0.4],
[5.2, 3.5, 1.5, 0.2],
[5.2, 3.4, 1.4, 0.2],
[4.7, 3.2, 1.6, 0.2],
[4.8, 3.1, 1.6, 0.2],
[5.4, 3.4, 1.5, 0.4],
[5.2, 4.1, 1.5, 0.1],
[5.5, 4.2, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.2],
[5. , 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2],
[4.9, 3.6, 1.4, 0.1],
[4.4, 3. , 1.3, 0.2],
[5.1, 3.4, 1.5, 0.2],
[5. , 3.5, 1.3, 0.3],
[4.5, 2.3, 1.3, 0.3],
[4.4, 3.2, 1.3, 0.2],
[5. , 3.5, 1.6, 0.6],
[5.1, 3.8, 1.9, 0.4],
[4.8, 3. , 1.4, 0.3],
[5.1, 3.8, 1.6, 0.2],
[4.6, 3.2, 1.4, 0.2],
[5.3, 3.7, 1.5, 0.2],
[5. , 3.3, 1.4, 0.2],
[7. , 3.2, 4.7, 1.4],
[6.4, 3.2, 4.5, 1.5],
[6.9, 3.1, 4.9, 1.5],
[5.5, 2.3, 4. , 1.3],
[6.5, 2.8, 4.6, 1.5],
[5.7, 2.8, 4.5, 1.3],
[6.3, 3.3, 4.7, 1.6],
[4.9, 2.4, 3.3, 1. ],
[6.6, 2.9, 4.6, 1.3],
[5.2, 2.7, 3.9, 1.4],
[5. , 2. , 3.5, 1. ],
[5.9, 3. , 4.2, 1.5],
[6. , 2.2, 4. , 1. ],
[6.1, 2.9, 4.7, 1.4],
[5.6, 2.9, 3.6, 1.3],
[6.7, 3.1, 4.4, 1.4],
[5.6, 3. , 4.5, 1.5],
[5.8, 2.7, 4.1, 1. ],
[6.2, 2.2, 4.5, 1.5],
[5.6, 2.5, 3.9, 1.1],
[5.9, 3.2, 4.8, 1.8],
[6.1, 2.8, 4. , 1.3],
[6.3, 2.5, 4.9, 1.5],
[6.1, 2.8, 4.7, 1.2],
[6.4, 2.9, 4.3, 1.3],
[6.6, 3. , 4.4, 1.4],
[6.8, 2.8, 4.8, 1.4],
[6.7, 3. , 5. , 1.7],
[6. , 2.9, 4.5, 1.5],
[5.7, 2.6, 3.5, 1. ],
[5.5, 2.4, 3.8, 1.1],
[5.5, 2.4, 3.7, 1. ],
[5.8, 2.7, 3.9, 1.2],
[6. , 2.7, 5.1, 1.6],
[5.4, 3. , 4.5, 1.5],
[6. , 3.4, 4.5, 1.6],
[6.7, 3.1, 4.7, 1.5],
[6.3, 2.3, 4.4, 1.3],
[5.6, 3. , 4.1, 1.3],
[5.5, 2.5, 4. , 1.3],
[5.5, 2.6, 4.4, 1.2],
[6.1, 3. , 4.6, 1.4],
[5.8, 2.6, 4. , 1.2],
[5. , 2.3, 3.3, 1. ],
[5.6, 2.7, 4.2, 1.3],
[5.7, 3. , 4.2, 1.2],
[5.7, 2.9, 4.2, 1.3],
[6.2, 2.9, 4.3, 1.3],
[5.1, 2.5, 3. , 1.1],
[5.7, 2.8, 4.1, 1.3],
[6.3, 3.3, 6. , 2.5],
[5.8, 2.7, 5.1, 1.9],
[7.1, 3. , 5.9, 2.1],
[6.3, 2.9, 5.6, 1.8],
[6.5, 3. , 5.8, 2.2],
[7.6, 3. , 6.6, 2.1],
[4.9, 2.5, 4.5, 1.7],
[7.3, 2.9, 6.3, 1.8],
[6.7, 2.5, 5.8, 1.8],
[7.2, 3.6, 6.1, 2.5],
[6.5, 3.2, 5.1, 2. ],
[6.4, 2.7, 5.3, 1.9],
[6.8, 3. , 5.5, 2.1],
[5.7, 2.5, 5. , 2. ],
[5.8, 2.8, 5.1, 2.4],
[6.4, 3.2, 5.3, 2.3],
[6.5, 3. , 5.5, 1.8],
[7.7, 3.8, 6.7, 2.2],
[7.7, 2.6, 6.9, 2.3],
[6. , 2.2, 5. , 1.5],
[6.9, 3.2, 5.7, 2.3],
[5.6, 2.8, 4.9, 2. ],
[7.7, 2.8, 6.7, 2. ],
[6.3, 2.7, 4.9, 1.8],
[6.7, 3.3, 5.7, 2.1],
[7.2, 3.2, 6. , 1.8],
[6.2, 2.8, 4.8, 1.8],
[6.1, 3. , 4.9, 1.8],
[6.4, 2.8, 5.6, 2.1],
[7.2, 3. , 5.8, 1.6],
[7.4, 2.8, 6.1, 1.9],
[7.9, 3.8, 6.4, 2. ],
[6.4, 2.8, 5.6, 2.2],
[6.3, 2.8, 5.1, 1.5],
[6.1, 2.6, 5.6, 1.4],
[7.7, 3. , 6.1, 2.3],
[6.3, 3.4, 5.6, 2.4],
[6.4, 3.1, 5.5, 1.8],
[6. , 3. , 4.8, 1.8],
[6.9, 3.1, 5.4, 2.1],
[6.7, 3.1, 5.6, 2.4],
[6.9, 3.1, 5.1, 2.3],
[5.8, 2.7, 5.1, 1.9],
[6.8, 3.2, 5.9, 2.3],
[6.7, 3.3, 5.7, 2.5],
[6.7, 3. , 5.2, 2.3],
[6.3, 2.5, 5. , 1.9],
[6.5, 3. , 5.2, 2. ],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3. , 5.1, 1.8]])

iris.feature_names

Output:

['sepal length (cm)',
'sepal width (cm)',
'petal length (cm)',
'petal width (cm)']

#Create a dataframe by taking values of feature_names
df=pd.DataFrame(iris.data,columns=iris.feature_names)
df.head()

Output:

df.shape

Output:

(150, 4)

iris.target

Output:

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

[k for k in df.columns if df[k].isnull().sum()>0]

Output: ['Cuisines']

df['target']=iris.target
df.head()

Output:

iris.target_names

Output:

array(['setosa', 'versicolor', 'virginica'], dtype='<U10')

df['flower_names']=df.target.apply(lambda x:iris.target_names[x])
df

Output:

import matplotlib.pyplot as plt

df0=df[df.target==0]
df1=df[df.target==1]
df2=df[df.target==2]

df0.head()

Output:

df1.head()

Output:

df2.head()

Output:

plt.xlabel('sepal length (cm)')
plt.ylabel('sepal width (cm)')
plt.scatter(df0['sepal length (cm)'],df0['sepal width (cm)'],color='green',marker='+')
plt.scatter(df1['sepal length (cm)'],df1['sepal width (cm)'],color='blue',marker='+')
#plt.scatter(df2['sepal length (cm)'],df2['sepal width (cm)'],color='red',marker='+')
plt.show()

plt.xlabel('petal length (cm)')
plt.ylabel('petal width (cm)')
plt.scatter(df0['petal length (cm)'],df0['petal width (cm)'],color='green',marker='+')
plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='blue',marker='+')
plt.show()

X=df.drop(['target','flower_names'],axis='columns')
X.head()

Output:

y=df.target

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)

print(X_test)

sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
63 6.1 2.9 4.7 1.4
128 6.4 2.8 5.6 2.1
7 5.0 3.4 1.5 0.2
86 6.7 3.1 4.7 1.5
95 5.7 3.0 4.2 1.2
127 6.1 3.0 4.9 1.8
79 5.7 2.6 3.5 1.0
61 5.9 3.0 4.2 1.5
52 6.9 3.1 4.9 1.5
31 5.4 3.4 1.5 0.4
129 7.2 3.0 5.8 1.6
88 5.6 3.0 4.1 1.3
120 6.9 3.2 5.7 2.3
140 6.7 3.1 5.6 2.4
27 5.2 3.5 1.5 0.2
32 5.2 4.1 1.5 0.1
11 4.8 3.4 1.6 0.2
70 5.9 3.2 4.8 1.8
93 5.0 2.3 3.3 1.0
6 4.6 3.4 1.4 0.3
72 6.3 2.5 4.9 1.5
17 5.1 3.5 1.4 0.3
46 5.1 3.8 1.6 0.2
118 7.7 2.6 6.9 2.3
99 5.7 2.8 4.1 1.3
57 4.9 2.4 3.3 1.0
96 5.7 2.9 4.2 1.3
92 5.8 2.6 4.0 1.2
56 6.3 3.3 4.7 1.6
81 5.5 2.4 3.7 1.0

print(y_test)

63 1
128 2
7 0
86 1
95 1
127 2
79 1
61 1
52 1
31 0
129 2
88 1
120 2
140 2
27 0
32 0
11 0
70 1
93 1
6 0
72 1
17 0
46 0
118 2
99 1
57 1
96 1
92 1
56 1
81 1
Name: target, dtype: int32

#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)
y_pred

Output:

array([1, 2, 0, 1, 1, 2, 1, 1, 1, 0, 2, 1, 2, 2, 0, 0, 0, 2, 1, 0, 1, 0,
0, 2, 1, 1, 1, 1, 1, 1])

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

0.9666666666666667

from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test,y_pred))

[[ 8 0 0]
[ 0 15 1]
[ 0 0 6]]