import pandas as pd from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn import metrics import joblib pima = pd.read_csv("data.csv", header=1, delimiter=';') feature_cols = ['Size', 'Color', 'Sound', 'Time','Smell', 'Height','Temperature'] X = pima[feature_cols] y = pima.ToRemove X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) clf = DecisionTreeClassifier() clf = clf.fit(X_train,y_train) joblib.dump(clf, 'decision_tree_model.pkl') y_pred = clf.predict(X_test) print("Accuracy:",metrics.accuracy_score(y_test, y_pred))