From 72b717d7aea33b45f8a0647a74496884abf59178 Mon Sep 17 00:00:00 2001
From: a-shdv <shadaev2001@icloud.com>
Date: Fri, 3 Nov 2023 18:11:03 +0400
Subject: [PATCH] Add lab3 part #2

---
 shadaev_anton_lab_3/stroke_prediction.py | 43 +++++++++++++++++++++++-
 1 file changed, 42 insertions(+), 1 deletion(-)

diff --git a/shadaev_anton_lab_3/stroke_prediction.py b/shadaev_anton_lab_3/stroke_prediction.py
index e0369f7..0f57c38 100644
--- a/shadaev_anton_lab_3/stroke_prediction.py
+++ b/shadaev_anton_lab_3/stroke_prediction.py
@@ -1 +1,42 @@
-print('test')
\ No newline at end of file
+import pandas as pd
+from sklearn.metrics import accuracy_score, mean_squared_error
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+
+# Загрузка данных
+data = pd.read_csv('stroke_prediction_ds.csv')
+
+# Приведение данных к цифровому значению
+data['gender'] = data['gender'].map({'Male': 0, 'Female': 1})
+data['ever_married'] = data['ever_married'].map({'No': 0, 'Yes': 1})
+
+# Определение признаков
+X = data[['hypertension', 'heart_disease', 'ever_married', 'gender']]
+
+# Целевая переменная
+y = data['stroke']
+
+# Разделение данных на обучающую и тестовую выборки
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Создание модели
+dt_classifier = DecisionTreeClassifier(random_state=42)
+
+# Обучение модели
+dt_classifier.fit(X_train, y_train)
+
+# Вычисление важности признаков
+feature_importances = dt_classifier.feature_importances_
+
+# Вычисление 'Accuracy' модели
+predictions = dt_classifier.predict(X_test)
+accuracy = accuracy_score(y_test, predictions)
+
+# Вычисление средней квадратичной ошибки
+mse = mean_squared_error(y_test, predictions)
+
+# Вывод результатов
+print(X.head(10))
+print(f'Важность признаков:" {feature_importances}')
+print(f'Accuracy модели: {round(accuracy * 100)}%')
+print("Средняя квадратичная ошибка: {:.2f}%".format(mse * 100))
\ No newline at end of file