train.py

import os
import pickle

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import train_test_split

RANDOM_SEED = 1234

data = load_iris()

X = data['data']
y = data['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
                                                    random_state=RANDOM_SEED)

model = RandomForestClassifier(n_estimators=300, random_state=RANDOM_SEED)
model.fit(X_train, y_train)

print(f"Accuracy :  {accuracy_score(y_test, model.predict(X_test))}")
print(classification_report(y_test, model.predict(X_test)))

os.makedirs("./build", exist_ok=True)
pickle.dump(model, open('./build/model.pkl', 'wb'))