from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import metrics

iris = datasets.load_iris()

print(iris.target_names)

print(iris.feature_names)

X,y =datasets.load_iris(return_X_y = True)

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30)

data=pd.DataFrame({'sepallength':iris.data[:,0],'sepalwidth':iris.data[:,1],
                  'petallenngth':iris.data[:,2],'petalwidth':iris.data[:,3],
                  'speies':iris.target})
print(data.head())

clf=RandomForestClassifier(n_estimators=2)

clf.fit(X_train,y_train)
y_pred=clf.predict(X_test)
print()
print("ACCURACY OF  THE MODEL: ",metrics.accuracy_score(y_test,y_pred))
clf.predict([[3,3,2,2]])
clf.predict([[6,6,6,6]])