From de0a8ee5bce931457bd6124df8ba0eced57ce53c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9C=D0=B0=D1=80=D0=B8=D1=8F=20=D0=A8?=
 <miashe@MacBook-Pro-4.local>
Date: Wed, 29 Nov 2023 20:32:10 +0300
Subject: [PATCH] shestakova_maria_lab_3 is ready

---
 shestakova_maria_lab_3/3.1.png                |  Bin 0 -> 33794 bytes
 shestakova_maria_lab_3/3.2.png                |  Bin 0 -> 64549 bytes
 shestakova_maria_lab_3/README.md              |   32 +
 .../shestakova_maria_lab_3.1.py               |   22 +
 .../shestakova_maria_lab_3.2.py               |   51 +
 shestakova_maria_lab_3/sleep.csv              |  375 +++++
 shestakova_maria_lab_3/titanic.csv            | 1310 +++++++++++++++++
 7 files changed, 1790 insertions(+)
 create mode 100644 shestakova_maria_lab_3/3.1.png
 create mode 100644 shestakova_maria_lab_3/3.2.png
 create mode 100644 shestakova_maria_lab_3/README.md
 create mode 100644 shestakova_maria_lab_3/shestakova_maria_lab_3.1.py
 create mode 100644 shestakova_maria_lab_3/shestakova_maria_lab_3.2.py
 create mode 100644 shestakova_maria_lab_3/sleep.csv
 create mode 100644 shestakova_maria_lab_3/titanic.csv

diff --git a/shestakova_maria_lab_3/3.1.png b/shestakova_maria_lab_3/3.1.png
new file mode 100644
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diff --git a/shestakova_maria_lab_3/README.md b/shestakova_maria_lab_3/README.md
new file mode 100644
index 0000000..192b9c0
--- /dev/null
+++ b/shestakova_maria_lab_3/README.md
@@ -0,0 +1,32 @@
+### Задание:
+
+Часть 1. По данным о пассажирах Титаника решите задачу классификации (с помощью дерева решений), в которой по различным характеристикам пассажиров требуется найти у выживших пассажиров два наиболее важных признака из трех рассматриваемых: Pclass, Parch, Fare
+
+Часть 2. Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы искусственного интеллекта»на 99% ваших данных: зависимость качества сна (Quality of Sleep) от возраста (Age) и пола (Gender). Проверьте работу модели на оставшемся проценте, сделайте вывод
+
+### Технологии:
+
+Библиотека Scikit-learn, библиотека numpy, библиотека pandas
+
+### Что делает лабораторная:
+
+Часть 1. Из выборки отбирается 3 необходимых по заданию признака, определяется целевая переменная по заданию, обучается дерево, выводятся важности признаков по каждому классу
+
+Часть 2. Из выборки отбирается 2 необходимых по заданию признака, определяется целевая переменная по заданию, данные разделяются на обущающую и тестовую выборку, дерево обучается классификацией и регрессией, выводятся важности признаков, предсказания значений на тестовой выборке и оценка качества
+
+### Как запустить:
+
+Первая часть лабораторной работы запускается в файле `shestakova_maria_lab_3.1.py` через Run: появляется вывод в консоли
+Вторая часть лабораторной работы запускается в файле `shestakova_maria_lab_3.2.py` через Run: появляется вывод в консоли
+
+### Вывод:
+
+Часть 1.
+
+![img1.png](3.1.png)
+
+Часть 2.
+
+![img2.png](3.2.png)
+
+По выводу можно заметить, что модель дерева классификации подходит больше для решения данной задачи
diff --git a/shestakova_maria_lab_3/shestakova_maria_lab_3.1.py b/shestakova_maria_lab_3/shestakova_maria_lab_3.1.py
new file mode 100644
index 0000000..835274b
--- /dev/null
+++ b/shestakova_maria_lab_3/shestakova_maria_lab_3.1.py
@@ -0,0 +1,22 @@
+import pandas
+from sklearn.tree import DecisionTreeClassifier
+import numpy as np
+
+# загрузка данных из набора
+data = pandas.read_csv('titanic.csv', index_col='Passengerid')
+
+data = data.loc[(np.isnan(data['Pclass']) == False) & (np.isnan(data['Parch']) == False) & (np.isnan(data['Fare']) == False) & (np.isnan(data['2urvived']) == False)]
+
+# отбор по заданию
+correct = data[['Pclass', 'Parch', 'Fare']]
+print(correct.head())
+
+y = data['2urvived']
+
+# дерево решений
+clf = DecisionTreeClassifier(random_state=27)
+clf.fit(correct, y)
+
+# важность признаков
+important = clf.feature_importances_
+print(important)
\ No newline at end of file
diff --git a/shestakova_maria_lab_3/shestakova_maria_lab_3.2.py b/shestakova_maria_lab_3/shestakova_maria_lab_3.2.py
new file mode 100644
index 0000000..15474da
--- /dev/null
+++ b/shestakova_maria_lab_3/shestakova_maria_lab_3.2.py
@@ -0,0 +1,51 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import accuracy_score
+
+# загрузка данных из набора
+data = pd.read_csv('sleep.csv', index_col='Person ID')
+
+# приведение строковых ячеек к числу
+data['Gender'] = data['Gender'].map({'Male': 0, 'Female': 1})
+
+# признаки (X) и целевая переменная (y)
+X = data[['Age', 'Gender']]
+print(X.head())
+y = data['Quality of Sleep']
+
+# обуч и тест выборка
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=27)
+
+# дерево регрессии
+regression_tree = DecisionTreeRegressor()
+regression_tree.fit(X_train, y_train)
+test_score_reg = regression_tree.score(X_test, y_test)
+# важность признаков
+important_reg = regression_tree.feature_importances_
+# предсказание
+y_pred_reg = regression_tree.predict(X_test)
+# оценка модели
+mse = mean_squared_error(y_test, y_pred_reg)
+
+# дерево классификации
+classifier_tree = DecisionTreeClassifier()
+classifier_tree.fit(X_train, y_train)
+test_score_class = classifier_tree.score(X_test, y_test)
+# важность признаков
+important_class = classifier_tree.feature_importances_
+# предсказание
+y_pred_class = classifier_tree.predict(X_test)
+# оценка модели
+accuracy = accuracy_score(y_test, y_pred_class)
+
+print("Regression Tree:")
+print("Score:", test_score_reg)
+print("Importance:", important_reg)
+print("Mean Squared Error: {:.2f}".format(mse))
+print("")
+print("Classification Tree:")
+print("Score:", test_score_class)
+print("Importance:", important_class)
+print("Accuracy: {:.2f}%".format(accuracy * 100))
\ No newline at end of file
diff --git a/shestakova_maria_lab_3/sleep.csv b/shestakova_maria_lab_3/sleep.csv
new file mode 100644
index 0000000..b7e16bd
--- /dev/null
+++ b/shestakova_maria_lab_3/sleep.csv
@@ -0,0 +1,375 @@
+Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder
+1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,None
+2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None
+3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None
+4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea
+5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea
+6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia
+7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia
+8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None
+12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None
+14,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+15,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+16,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea
+18,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,Sleep Apnea
+19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia
+20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea
+32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia
+33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,None
+34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea
+51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None
+52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None
+53,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+55,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+56,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+58,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+59,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+61,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+62,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,None
+68,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,Insomnia
+69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None
+70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None
+71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+75,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+76,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+77,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+78,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+79,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+80,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea
+82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea
+83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None
+84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None
+85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None
+86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None
+88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None
+89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None
+94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea
+95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia
+96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None
+100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None
+101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea
+105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea
+106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia
+107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,None
+108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None
+109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None
+110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,None
+127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea
+146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea
+147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia
+148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia
+149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,None
+150,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None
+151,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None
+152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None
+163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None
+164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None
+165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None
+166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia
+167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,None
+168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None
+169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None
+170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea
+186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea
+187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+188,Male,43,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+189,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+190,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+191,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+192,Male,43,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+193,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+194,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+195,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+196,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+197,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+198,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+199,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia
+203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia
+204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,None
+205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,None
+206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea
+220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea
+221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+223,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+224,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+225,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+226,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+227,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+228,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+229,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+230,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+231,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+232,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+233,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+234,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+235,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+236,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+237,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+238,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+239,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+240,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+241,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+242,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+243,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+244,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia
+249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,None
+250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,None
+251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia
+252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia
+253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+254,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+255,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+256,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+257,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+258,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None
+263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None
+264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,None
+265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia
+266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia
+268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,None
+269,Female,49,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+272,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+273,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+274,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+275,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea
+278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea
+279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia
+280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+281,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,None
+282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+283,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+284,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+285,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+286,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+287,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+288,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+289,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+290,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+291,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+292,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+293,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+294,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+295,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+296,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,None
+304,Female,51,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+307,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia
+308,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia
+309,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia
+317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea
+341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea
+342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None
+343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None
+344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None
+345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+348,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+349,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+350,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+351,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+352,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+353,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+354,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+355,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+356,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+357,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+358,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+359,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,None
+360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None
+361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+364,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+365,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+366,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+367,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+368,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+369,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+370,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+371,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+372,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+373,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+374,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
\ No newline at end of file
diff --git a/shestakova_maria_lab_3/titanic.csv b/shestakova_maria_lab_3/titanic.csv
new file mode 100644
index 0000000..4b1b6df
--- /dev/null
+++ b/shestakova_maria_lab_3/titanic.csv
@@ -0,0 +1,1310 @@
+Passengerid,Age,Fare,Sex,sibsp,zero,zero,zero,zero,zero,zero,zero,Parch,zero,zero,zero,zero,zero,zero,zero,zero,Pclass,zero,zero,Embarked,zero,zero,2urvived
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