text_html_output_1.png application_vnd.jupyter.stderr_output_1.png
from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer

import pandas as pd

df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))

df_iris.groupby('Species').size()

X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
le = LabelEncoder()
y['Species'] = le.fit_transform(y['Species'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)

new_data = pd.DataFrame([[5.1, 3.5, 1.4, 0.2], [6.2, 2.8, 4.8, 1.8], [7.3, 3.0, 6.3, 2.5]])
new_data.columns = X.columns
display(new_data)