import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline 

from google.colab import files
uploaded = files.upload()

data=pd.read_csv("archive.zip")
pd.read_csv("archive.zip")

# SPLIT THE DATASET INTO THE TRAINING SET AND TEST SET
from sklearn.model_selection import train_test_split
#train-70
#test-30
x=data.drop(columns=["species"])
y=data["species"]
x_train, x_test, y_train, y_test =train_test_split(x,y, test_size=0.30)

# TRAINIG THE DECISION TREE CLASSIFICATION MODEL ON THE TRAINING
from sklearn import tree
model= tree.DecisionTreeClassifier(criterion="entropy")
model.fit(x,y)