From ed5c549a0bb4d7f3d1c07fe6f32864d786339036 Mon Sep 17 00:00:00 2001
From: BossMouseFire <abanin-02@mail.ru>
Date: Tue, 24 Oct 2023 13:57:35 +0400
Subject: [PATCH] lab5

---
 abanin_daniil_lab_5/README.md    |  38 ++
 abanin_daniil_lab_5/grade_1.png  | Bin 0 -> 13422 bytes
 abanin_daniil_lab_5/grade_2.png  | Bin 0 -> 10384 bytes
 abanin_daniil_lab_5/lab5.py      |  33 ++
 abanin_daniil_lab_5/loan.csv     | 615 +++++++++++++++++++++++++++++++
 abanin_daniil_lab_5/result_1.png | Bin 0 -> 67679 bytes
 abanin_daniil_lab_5/result_2.png | Bin 0 -> 22935 bytes
 7 files changed, 686 insertions(+)
 create mode 100644 abanin_daniil_lab_5/README.md
 create mode 100644 abanin_daniil_lab_5/grade_1.png
 create mode 100644 abanin_daniil_lab_5/grade_2.png
 create mode 100644 abanin_daniil_lab_5/lab5.py
 create mode 100644 abanin_daniil_lab_5/loan.csv
 create mode 100644 abanin_daniil_lab_5/result_1.png
 create mode 100644 abanin_daniil_lab_5/result_2.png

diff --git a/abanin_daniil_lab_5/README.md b/abanin_daniil_lab_5/README.md
new file mode 100644
index 0000000..2f27465
--- /dev/null
+++ b/abanin_daniil_lab_5/README.md
@@ -0,0 +1,38 @@
+## Лабораторная работа №5
+
+### Ранжирование признаков
+
+## ПИбд-41 Абанин Даниил
+
+### Как запустить лабораторную работу:
+
+* установить python, pandas, matplotlib, sklearn
+* запустить проект (стартовая точка lab4)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки pandas, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+Программа решает задачу регрессии, используя полиномиальную регрессию.
+Цель - предсказать сумму займа (LoanAmount), используя имеющиеся признаки: ApplicantIncome - доход заявителя, Credit_History - статус соответствия кредитной истории стандартам банка,
+Education - наличие образования, Married - заявитель женат/замужем (Да/Нет), Self_Employed - самозанятый (Да/Нет)
+
+### Тест
+Зелёные маркеры на графике - тестовые результаты
+Красные маркеры на графике - предсказанные результаты
+
+При небольшом объёме тестовых данных, алгоритм показывает неплохие результаты обучения
+
+![Result](grade_1.png)
+
+![Result](result_1.png)
+
+Но при увеличении объёма данных, алгоритм теряет свою эффективность
+
+![Result](grade_2.png)
+
+![Result](result_2.png)
+
+Вывод: На малых объёмах данных алгоритм показывает свою эффективность. Но при большем объём стоит использовать другие методы для данного набора информации
\ No newline at end of file
diff --git a/abanin_daniil_lab_5/grade_1.png b/abanin_daniil_lab_5/grade_1.png
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diff --git a/abanin_daniil_lab_5/lab5.py b/abanin_daniil_lab_5/lab5.py
new file mode 100644
index 0000000..2c28285
--- /dev/null
+++ b/abanin_daniil_lab_5/lab5.py
@@ -0,0 +1,33 @@
+from matplotlib import pyplot as plt
+from sklearn import metrics
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.pipeline import Pipeline
+import pandas as pd
+
+
+def start():
+    data = pd.read_csv('loan.csv')
+    x = data[['ApplicantIncome', 'Credit_History', 'Education', 'Married', 'Self_Employed']]
+    y = data[['LoanAmount']]
+
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)
+
+    poly = Pipeline([('poly', PolynomialFeatures(degree=3)),
+                     ('linear', LinearRegression())])
+    poly.fit(x_train, y_train)
+
+    y_predicted = poly.predict(x_test)
+
+    print('Оценка обучения:')
+    print(metrics.r2_score(y_test, y_predicted))
+
+    plt.figure(1, figsize=(16, 9))
+    plt.title('Сравнение результатов обучения')
+    plt.scatter(x=[i for i in range(len(x_test))], y=y_test, c='green', s=5)
+    plt.scatter(x=[i for i in range(len(x_test))], y=y_predicted, c='red', s=5)
+    plt.show()
+
+
+start()
\ No newline at end of file
diff --git a/abanin_daniil_lab_5/loan.csv b/abanin_daniil_lab_5/loan.csv
new file mode 100644
index 0000000..4fe5a85
--- /dev/null
+++ b/abanin_daniil_lab_5/loan.csv
@@ -0,0 +1,615 @@
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