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

# Importing the dataset
dataset = pd.read_csv('partial_wine.csv')
X = dataset.iloc[:, [0, 1]].values
y = dataset.iloc[:, 2].values

# split the dataset into a training set and a 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 = 0.25, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# ====> INSERT YOUR CLASSIFIER CODE HERE <====
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state = 0)
classifier.fit(X_train, y_train) 
# ====> INSERT YOUR CLASSIFIER CODE HERE <====

# predict the test set results
y_pred = classifier.predict(X_test)

# generate the confusion matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("confusion matrix:")
print(cm)

