zavrazhnova_svetlana_lab_5 is ready
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Svetlnkk
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<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/zavrazhnova_svetlana_lab_3" />
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16
zavrazhnova_svetlana_lab_5/README.md
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zavrazhnova_svetlana_lab_5/README.md
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# Задание
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Предсказать, является ли транзакция мошеннической или нет на основе других данных о транзакции, таких как сумма транзакции, местоположение, банк, возраст и пол клиента
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### Как запустить лабораторную работу:
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ЛР запускается в файле zavrazhnova_svetlana_lab_5.py через Run, а затем в консоли должны появится вычисления
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### Технологии
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Методы PolynomialFeatures и LogisticRegression из библиотеки sklearn
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### Что делает лабораторная:
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Обучаются модели логистической и полиномиальной регрессии на обучающих данных и используются эти модели для предсказания мошеннических транзакций на тестовых данных. Оценивается точность каждой модели с помощью метрики accuracy.
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### Пример выходных значений:
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### Вывод:
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Точность полиномиальной регрессии и логистической регрессии равны 1.0, это означает, что обе модели предсказали метки классов на тестовом наборе данных без ошибок. То есть они смогли точно определить, является ли транзакция мошеннической или нет.
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87
zavrazhnova_svetlana_lab_5/fraud_dataset.csv
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zavrazhnova_svetlana_lab_5/fraud_dataset.csv
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transaction_id,transaction_amount,location,merchant,age,gender,fraud_label
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1,1000.00,New York,ABC Corp,35,M,0
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2,500.00,Chicago,XYZ Inc,45,F,0
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3,2000.00,Los Angeles,ABC Corp,28,M,1
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4,1500.00,San Francisco,XYZ Inc,30,F,0
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5,800.00,Chicago,ABC Corp,50,F,0
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6,3000.00,New York,XYZ Inc,42,M,1
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7,1200.00,San Francisco,ABC Corp,55,F,0
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8,900.00,Los Angeles,XYZ Inc,37,M,0
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9,2500.00,Chicago,ABC Corp,33,F,1
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10,1800.00,New York,XYZ Inc,48,M,0
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11,750.00,San Francisco,ABC Corp,29,F,0
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12,2200.00,Chicago,XYZ Inc,51,M,0
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13,900.00,New York,ABC Corp,40,F,0
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14,1600.00,Los Angeles,XYZ Inc,26,M,0
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15,3000.00,San Francisco,ABC Corp,45,F,1
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16,1200.00,Chicago,XYZ Inc,34,M,0
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17,800.00,New York,ABC Corp,47,F,0
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18,1900.00,Los Angeles,XYZ Inc,32,M,0
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19,1100.00,San Francisco,ABC Corp,52,F,0
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20,4000.00,Chicago,XYZ Inc,38,M,1
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21,900.00,New York,ABC Corp,31,F,0
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22,1700.00,Los Angeles,XYZ Inc,49,M,0
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23,1000.00,San Francisco,ABC Corp,36,F,0
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24,2300.00,Chicago,XYZ Inc,27,M,1
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25,950.00,New York,ABC Corp,41,F,0
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26,1400.00,Los Angeles,XYZ Inc,54,M,0
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27,2800.00,San Francisco,ABC Corp,39,F,1
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28,1100.00,Chicago,XYZ Inc,44,M,0
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29,750.00,New York,ABC Corp,30,F,0
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30,2000.00,Los Angeles,XYZ Inc,46,M,0
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31,1250.00,San Francisco,ABC Corp,35,F,0
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32,2100.00,Chicago,XYZ Inc,43,M,0
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33,950.00,New York,ABC Corp,56,F,0
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34,1800.00,Los Angeles,XYZ Inc,29,M,0
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35,3200.00,San Francisco,ABC Corp,48,F,1
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36,1300.00,Chicago,XYZ Inc,37,M,0
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37,900.00,New York,ABC Corp,51,F,0
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38,2000.00,Los Angeles,XYZ Inc,33,M,0
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|
39,1050.00,San Francisco,ABC Corp,42,F,0
|
||||||
|
40,2400.00,Chicago,XYZ Inc,26,M,0
|
||||||
|
41,800.00,New York,ABC Corp,45,F,0
|
||||||
|
42,1500.00,Los Angeles,XYZ Inc,31,M,0
|
||||||
|
43,2800.00,San Francisco,ABC Corp,50,F,1
|
||||||
|
44,1350.00,Chicago,XYZ Inc,28,M,0
|
||||||
|
45,920.00,New York,ABC Corp,47,F,0
|
||||||
|
46,2000.00,Los Angeles,XYZ Inc,36,M,0
|
||||||
|
47,1125.00,San Francisco,ABC Corp,52,F,0
|
||||||
|
48,1900.00,Chicago,XYZ Inc,38,M,1
|
||||||
|
49,850.00,New York,ABC Corp,32,F,0
|
||||||
|
50,1750.00,Los Angeles,XYZ Inc,49,M,0
|
||||||
|
51,950.00,San Francisco,ABC Corp,27,F,0
|
||||||
|
52,2300.00,Chicago,XYZ Inc,41,M,0
|
||||||
|
53,850.00,New York,ABC Corp,54,F,0
|
||||||
|
54,1600.00,Los Angeles,XYZ Inc,39,M,0
|
||||||
|
55,3000.00,San Francisco,ABC Corp,46,F,1
|
||||||
|
56,1250.00,Chicago,XYZ Inc,35,M,0
|
||||||
|
57,800.00,New York,ABC Corp,56,F,0
|
||||||
|
58,2200.00,Los Angeles,XYZ Inc,29,M,0
|
||||||
|
59,1050.00,San Francisco,ABC Corp,48,F,0
|
||||||
|
60,4000.00,Chicago,XYZ Inc,37,M,1
|
||||||
|
61,950.00,New York,ABC Corp,30,F,0
|
||||||
|
62,1700.00,Los Angeles,XYZ Inc,49,M,0
|
||||||
|
63,1000.00,San Francisco,ABC Corp,36,F,0
|
||||||
|
64,2800.00,Chicago,XYZ Inc,27,M,1
|
||||||
|
65,900.00,New York,ABC Corp,41,F,0
|
||||||
|
66,1400.00,Los Angeles,XYZ Inc,54,M,0
|
||||||
|
67,3200.00,San Francisco,ABC Corp,39,F,1
|
||||||
|
68,1100.00,Chicago,XYZ Inc,44,M,0
|
||||||
|
69,750.00,New York,ABC Corp,30,F,0
|
||||||
|
70,2000.00,Los Angeles,XYZ Inc,46,M,0
|
||||||
|
71,1250.00,San Francisco,ABC Corp,35,F,0
|
||||||
|
72,2100.00,Chicago,XYZ Inc,43,M,0
|
||||||
|
73,950.00,New York,ABC Corp,56,F,0
|
||||||
|
74,1800.00,Los Angeles,XYZ Inc,29,M,0
|
||||||
|
75,3200.00,San Francisco,ABC Corp,48,F,1
|
||||||
|
76,1300.00,Chicago,XYZ Inc,37,M,0
|
||||||
|
77,900.00,New York,ABC Corp,51,F,0
|
||||||
|
78,2000.00,Los Angeles,XYZ Inc,33,M,0
|
||||||
|
79,1050.00,San Francisco,ABC Corp,42,F,0
|
||||||
|
80,2400.00,Chicago,XYZ Inc,26,M,0
|
||||||
|
81,800.00,New York,ABC Corp,45,F,0
|
||||||
|
82,1500.00,Los Angeles,XYZ Inc,31,M,0
|
||||||
|
83,2800.00,San Francisco,ABC Corp,50,F,1
|
||||||
|
84,1350.00,Chicago,XYZ Inc,28,M,0
|
||||||
|
85,920.00,New York,ABC Corp,47,F,0
|
||||||
|
86,2000.00,Los Angeles,XYZ Inc,36,M,0
|
||||||
|
BIN
zavrazhnova_svetlana_lab_5/result.png
Normal file
BIN
zavrazhnova_svetlana_lab_5/result.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 3.4 KiB |
41
zavrazhnova_svetlana_lab_5/zavrazhnova_svetlana_lab_5.py
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41
zavrazhnova_svetlana_lab_5/zavrazhnova_svetlana_lab_5.py
Normal file
@ -0,0 +1,41 @@
|
|||||||
|
import pandas as pd
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from sklearn.preprocessing import PolynomialFeatures
|
||||||
|
from sklearn.linear_model import LogisticRegression
|
||||||
|
from sklearn.metrics import accuracy_score
|
||||||
|
|
||||||
|
# Чтение данных из файла CSV
|
||||||
|
data = pd.read_csv('fraud_dataset.csv')
|
||||||
|
|
||||||
|
# Разделение данных на признаки (X) и целевую переменную (y)
|
||||||
|
X = data[['transaction_amount', 'location', 'merchant', 'age', 'gender']]
|
||||||
|
y = data['fraud_label']
|
||||||
|
|
||||||
|
# Преобразование категориальных признаков в числовые с помощью One-Hot Encoding
|
||||||
|
X = pd.get_dummies(X, columns=['location', 'merchant', 'age', 'gender'])
|
||||||
|
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
# Применение полиномиальной регрессии
|
||||||
|
poly = PolynomialFeatures(degree=2)
|
||||||
|
X_train_poly = poly.fit_transform(X_train)
|
||||||
|
X_test_poly = poly.transform(X_test)
|
||||||
|
|
||||||
|
poly_reg = LogisticRegression(max_iter=1000)
|
||||||
|
poly_reg.fit(X_train_poly, y_train)
|
||||||
|
|
||||||
|
# Применение логистической регрессии
|
||||||
|
log_reg = LogisticRegression(max_iter=1000)
|
||||||
|
log_reg.fit(X_train, y_train)
|
||||||
|
|
||||||
|
# Предсказание меток классов на тестовом наборе данных
|
||||||
|
y_pred_poly = poly_reg.predict(X_test_poly)
|
||||||
|
y_pred = log_reg.predict(X_test)
|
||||||
|
|
||||||
|
# Вычисление точности предсказания
|
||||||
|
accuracy_poly = accuracy_score(y_test, y_pred_poly)
|
||||||
|
accuracy = accuracy_score(y_test, y_pred)
|
||||||
|
|
||||||
|
print('Точность полиномиальной регрессии:', accuracy_poly)
|
||||||
|
print('Точность логистической регрессии:', accuracy)
|
||||||
Loading…
Reference in New Issue
Block a user