import pandas as pd
from sklearn.tree import DecisionTreeClassifier

df = pd.read_csv('/datasets/train_data.csv')

df.loc[df['last_price'] > 5650000, 'price_class'] = 1
df.loc[df['last_price'] <= 5650000, 'price_class'] = 0

features = df.drop(['last_price', 'price_class'], axis=1)
target = df['price_class']

model = DecisionTreeClassifier()

model.fit(features, target)

new_features = pd.DataFrame(
    [[None, None, 2.8, 25, None, 25, 0, 0, 0, None, 0, 30706.0, 7877.0],
     [None, None, 2.75, 25, None, 25, 0, 0, 0, None, 0, 36421.0, 9176.0]],
    columns=features.columns)

# дозаполните таблицу с новыми признаками
#new_features.loc[0, 'total_area'] = 900.0
# < напишите код здесь >
#new_features.loc[0:1, 'total_area', 'rooms', 'living_area', 'kitchen_area'] = [[900.0, 12, 409.7, 112.0], [109.0, 2, 32.0, 40.5]]
new_features.loc[0:1, 'total_area', 'rooms', 'ceiling_height', 'floors_total', 'living_area', 'floor', 'is_apartment', 'studio', 'open_plan', 'kitchen_area', 'balcony', 'airports_nearest', 'cityCenters_nearest'] = [[900.0, 12, 2.8, 25, 409.7, 25, 0, 0, 0, 112.0, 0, 30706.0, 7877.0], [109.0, 2, 2.75, 25, 32.0, 25, 0, 0, 0, 40.5, 0, 36421.0, 9176.0]]
# предскажите ответы и напечатайте результат на экране
# < напишите код здесь >
answers = model.predict(new_features)
print(answers)