Classification code in random forest algorithm with scikit-learn , python

classification_random

#importing basic library
import numpy as np
import pandas as pd
#loading dataset 
data_frame= pd.read_csv('/kaggle/input/iris-flower-dataset/IRIS.csv')
#preparing data 
feature=['sepal_length','sepal_width','petal_length','petal_width']
X=data_frame[feature]
y=data_frame.species
#dividing into train test set
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)
#training algorithm
from sklearn.ensemble import RandomForestClassifier
classification = RandomForestClassifier(n_estimators=20)
classification.fit(X_train,y_train)
y_pred=classification.predict(X_test)
#Import scikit-learn metrics module for accuracy calculation
from sklearn import metrics
# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
#demo prediction
classification.predict([[1,1,1,1]])