# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
"""
**Считывание обучающей и тестовой выборки**
"""
train = pd.read_csv("../input/titanic/train.csv")
test = pd.read_csv("../input/titanic/test.csv")
test_ids = pd.read_csv("../input/titanic/test.csv")
train.head()
train.head(5)
"""
**Разделение данных: В Y столбец для предсказаний, в X столбцы с категориальными значениями, переформатированными используя унитарное кодирование(get_dummies)**
"""
Y = train['Survived']
categorical = ["Pclass", "Sex", "SibSp", "Parch"]
X = pd.get_dummies(train[categorical])
X_test = pd.get_dummies(test[categorical])
"""
**Обучение тестовой выборки методом случайного леса и создание предсказаний**
"""
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, Y)
predictions = model.predict(X_test)
"""
**Запись результатов в CSV файл**
"""
results = pd.DataFrame({'PassengerId': test_ids.PassengerId, 'Survived': predictions})
results

results.to_csv('submission.csv', index=False)