import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
pima = pd.read_csv("diabetes.csv")
pima.head()
import seaborn as sns
corr = pima.corr()
ax = sns.heatmap(
corr,
vmin=-1, vmax=1, center=0,
cmap=sns.diverging_palette(20, 220, n=200),
square=True
)
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=45,
horizontalalignment='right'
);

feature selection

feature_cols = ['Pregnancies', 'Insulin', 'BMI', 'Age', 'Glucose', 'BloodPressure',
'DiabetesPedigreeFunction']
x = pima[feature_cols]
y = pima.Outcome

split data

X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.3,
random_state=1)

build model

classifier = DecisionTreeClassifier()
classifier = classifier.fit(X_train, Y_train)
#predit
y_pred = classifier.predict(X_test)
print(y_pred)
from sklearn.metrics import confusion_matrix
confusion_matrix(Y_test, y_pred)
print(confusion_matrix(Y_test, y_pred))

accuracy

print("Accuracy:", metrics.accuracy_score(Y_test,y_pred))
from six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
import pydotplus
dot_data = StringIO()
export_graphviz(classifier, out_file=dot_data,
filled=True, rounded=True,
special_characters=True, feature_names =
feature_cols,class_names=['0','1'])
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_png('diabetes.png')
Image(graph.create_png())