Decision-Tree
import pandas as pd import matplotlib.pyplot as plt
wine = pd.read_csv('wined.csv') wine
from sklearn.preprocessing import LabelEncoder le = LabelEncoder()
x = wine[['Acl','Alcohol','Malic.acid','Ash']] y = wine[['Wine']]
wine['Acl']=le.fit_transform(wine['Acl']) wine['Alcohol']=le.fit_transform(wine['Alcohol']) wine['Malic.acid']=le.fit_transform(wine['Malic.acid']) wine['Ash']=le.fit_transform(wine['Ash']) wine.head()
from sklearn import tree model = tree.DecisionTreeClassifier() model.fit(x,y) model.predict([[1,65,49,18]])
plt.figure(figsize=(15,10)) tree.plot_tree(model,filled=True) plt.show()
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) model.fit(x_train,y_train) y_predicted = model.predict(x_test) y_predicted
from sklearn.metrics import accuracy_score sc = accuracy_score(y_predicted,y_test)*100 sc
from sklearn.metrics import mean_squared_error mean_squared_error(y_predicted,y_test)*100