import seaborn
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
 
# print(seaborn.get_dataset_names())
 
df = seaborn.load_dataset("iris")
print(df.head())
print(df.info())
 
print(df['species'].unique())
df.drop(df.loc[df['species'] == 'virginica'].index, inplace=True)
print(df['species'].unique())
 
X_train, X_test, y_train, y_test = train_test_split(df[["sepal_length", "sepal_width", "petal_length", "petal_width"]], df["species"], train_size=0.8, shuffle=True, random_state=1)
logistic_regressor = LogisticRegression()
logistic_regressor.fit(X_train, y_train)
 
y_test_predicted = logistic_regressor.predict(X_test)
 
acc_score = accuracy_score(y_test, y_test_predicted)
conf_matrix = confusion_matrix(y_test, y_test_predicted)
 
print(acc_score)
print(conf_matrix)

Here, we are using the modified “iris” dataset. As per the original iris dataset, there are three classifications. We are removing one classification (the classification named “virginica”) completely and using the remaining dataset.

So, firstly, we are loading the “iris” dataset using the seaborn Python library. After that, we print the first few lines and information about the dataset. The output will be like the following:

   sepal_length  sepal_width  petal_length  petal_width species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
#   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
0   sepal_length  150 non-null    float64
1   sepal_width   150 non-null    float64
2   petal_length  150 non-null    float64
3   petal_width   150 non-null    float64
4   species       150 non-null    object 
dtypes: float64(4), object(1)
memory usage: 6.0+ KB

Now, we are removing the rows that contain the column value “virginica” as species…