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Svetlnkk 2023-10-20 17:51:44 +04:00
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zavrazhnova_svetlana_lab_5/README.md Normal file
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# Задание
Предсказать, является ли транзакция мошеннической или нет на основе других данных о транзакции, таких как сумма транзакции, местоположение, банк, возраст и пол клиента
### Как запустить лабораторную работу:
ЛР запускается в файле zavrazhnova_svetlana_lab_5.py через Run, а затем в консоли должны появится вычисления
### Технологии
Методы PolynomialFeatures и LogisticRegression из библиотеки sklearn
### Что делает лабораторная:
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.
### Пример выходных значений:
![result.png](result.png)
### Вывод:
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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zavrazhnova_svetlana_lab_5/fraud_dataset.csv Normal file
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transaction_id,transaction_amount,location,merchant,age,gender,fraud_label
1,1000.00,New York,ABC Corp,35,M,0
2,500.00,Chicago,XYZ Inc,45,F,0
3,2000.00,Los Angeles,ABC Corp,28,M,1
4,1500.00,San Francisco,XYZ Inc,30,F,0
5,800.00,Chicago,ABC Corp,50,F,0
6,3000.00,New York,XYZ Inc,42,M,1
7,1200.00,San Francisco,ABC Corp,55,F,0
8,900.00,Los Angeles,XYZ Inc,37,M,0
9,2500.00,Chicago,ABC Corp,33,F,1
10,1800.00,New York,XYZ Inc,48,M,0
11,750.00,San Francisco,ABC Corp,29,F,0
12,2200.00,Chicago,XYZ Inc,51,M,0
13,900.00,New York,ABC Corp,40,F,0
14,1600.00,Los Angeles,XYZ Inc,26,M,0
15,3000.00,San Francisco,ABC Corp,45,F,1
16,1200.00,Chicago,XYZ Inc,34,M,0
17,800.00,New York,ABC Corp,47,F,0
18,1900.00,Los Angeles,XYZ Inc,32,M,0
19,1100.00,San Francisco,ABC Corp,52,F,0
20,4000.00,Chicago,XYZ Inc,38,M,1
21,900.00,New York,ABC Corp,31,F,0
22,1700.00,Los Angeles,XYZ Inc,49,M,0
23,1000.00,San Francisco,ABC Corp,36,F,0
24,2300.00,Chicago,XYZ Inc,27,M,1
25,950.00,New York,ABC Corp,41,F,0
26,1400.00,Los Angeles,XYZ Inc,54,M,0
27,2800.00,San Francisco,ABC Corp,39,F,1
28,1100.00,Chicago,XYZ Inc,44,M,0
29,750.00,New York,ABC Corp,30,F,0
30,2000.00,Los Angeles,XYZ Inc,46,M,0
31,1250.00,San Francisco,ABC Corp,35,F,0
32,2100.00,Chicago,XYZ Inc,43,M,0
33,950.00,New York,ABC Corp,56,F,0
34,1800.00,Los Angeles,XYZ Inc,29,M,0
35,3200.00,San Francisco,ABC Corp,48,F,1
36,1300.00,Chicago,XYZ Inc,37,M,0
37,900.00,New York,ABC Corp,51,F,0
38,2000.00,Los Angeles,XYZ Inc,33,M,0
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
1 transaction_id transaction_amount location merchant age gender fraud_label
2 1 1000.00 New York ABC Corp 35 M 0
3 2 500.00 Chicago XYZ Inc 45 F 0
4 3 2000.00 Los Angeles ABC Corp 28 M 1
5 4 1500.00 San Francisco XYZ Inc 30 F 0
6 5 800.00 Chicago ABC Corp 50 F 0
7 6 3000.00 New York XYZ Inc 42 M 1
8 7 1200.00 San Francisco ABC Corp 55 F 0
9 8 900.00 Los Angeles XYZ Inc 37 M 0
10 9 2500.00 Chicago ABC Corp 33 F 1
11 10 1800.00 New York XYZ Inc 48 M 0
12 11 750.00 San Francisco ABC Corp 29 F 0
13 12 2200.00 Chicago XYZ Inc 51 M 0
14 13 900.00 New York ABC Corp 40 F 0
15 14 1600.00 Los Angeles XYZ Inc 26 M 0
16 15 3000.00 San Francisco ABC Corp 45 F 1
17 16 1200.00 Chicago XYZ Inc 34 M 0
18 17 800.00 New York ABC Corp 47 F 0
19 18 1900.00 Los Angeles XYZ Inc 32 M 0
20 19 1100.00 San Francisco ABC Corp 52 F 0
21 20 4000.00 Chicago XYZ Inc 38 M 1
22 21 900.00 New York ABC Corp 31 F 0
23 22 1700.00 Los Angeles XYZ Inc 49 M 0
24 23 1000.00 San Francisco ABC Corp 36 F 0
25 24 2300.00 Chicago XYZ Inc 27 M 1
26 25 950.00 New York ABC Corp 41 F 0
27 26 1400.00 Los Angeles XYZ Inc 54 M 0
28 27 2800.00 San Francisco ABC Corp 39 F 1
29 28 1100.00 Chicago XYZ Inc 44 M 0
30 29 750.00 New York ABC Corp 30 F 0
31 30 2000.00 Los Angeles XYZ Inc 46 M 0
32 31 1250.00 San Francisco ABC Corp 35 F 0
33 32 2100.00 Chicago XYZ Inc 43 M 0
34 33 950.00 New York ABC Corp 56 F 0
35 34 1800.00 Los Angeles XYZ Inc 29 M 0
36 35 3200.00 San Francisco ABC Corp 48 F 1
37 36 1300.00 Chicago XYZ Inc 37 M 0
38 37 900.00 New York ABC Corp 51 F 0
39 38 2000.00 Los Angeles XYZ Inc 33 M 0
40 39 1050.00 San Francisco ABC Corp 42 F 0
41 40 2400.00 Chicago XYZ Inc 26 M 0
42 41 800.00 New York ABC Corp 45 F 0
43 42 1500.00 Los Angeles XYZ Inc 31 M 0
44 43 2800.00 San Francisco ABC Corp 50 F 1
45 44 1350.00 Chicago XYZ Inc 28 M 0
46 45 920.00 New York ABC Corp 47 F 0
47 46 2000.00 Los Angeles XYZ Inc 36 M 0
48 47 1125.00 San Francisco ABC Corp 52 F 0
49 48 1900.00 Chicago XYZ Inc 38 M 1
50 49 850.00 New York ABC Corp 32 F 0
51 50 1750.00 Los Angeles XYZ Inc 49 M 0
52 51 950.00 San Francisco ABC Corp 27 F 0
53 52 2300.00 Chicago XYZ Inc 41 M 0
54 53 850.00 New York ABC Corp 54 F 0
55 54 1600.00 Los Angeles XYZ Inc 39 M 0
56 55 3000.00 San Francisco ABC Corp 46 F 1
57 56 1250.00 Chicago XYZ Inc 35 M 0
58 57 800.00 New York ABC Corp 56 F 0
59 58 2200.00 Los Angeles XYZ Inc 29 M 0
60 59 1050.00 San Francisco ABC Corp 48 F 0
61 60 4000.00 Chicago XYZ Inc 37 M 1
62 61 950.00 New York ABC Corp 30 F 0
63 62 1700.00 Los Angeles XYZ Inc 49 M 0
64 63 1000.00 San Francisco ABC Corp 36 F 0
65 64 2800.00 Chicago XYZ Inc 27 M 1
66 65 900.00 New York ABC Corp 41 F 0
67 66 1400.00 Los Angeles XYZ Inc 54 M 0
68 67 3200.00 San Francisco ABC Corp 39 F 1
69 68 1100.00 Chicago XYZ Inc 44 M 0
70 69 750.00 New York ABC Corp 30 F 0
71 70 2000.00 Los Angeles XYZ Inc 46 M 0
72 71 1250.00 San Francisco ABC Corp 35 F 0
73 72 2100.00 Chicago XYZ Inc 43 M 0
74 73 950.00 New York ABC Corp 56 F 0
75 74 1800.00 Los Angeles XYZ Inc 29 M 0
76 75 3200.00 San Francisco ABC Corp 48 F 1
77 76 1300.00 Chicago XYZ Inc 37 M 0
78 77 900.00 New York ABC Corp 51 F 0
79 78 2000.00 Los Angeles XYZ Inc 33 M 0
80 79 1050.00 San Francisco ABC Corp 42 F 0
81 80 2400.00 Chicago XYZ Inc 26 M 0
82 81 800.00 New York ABC Corp 45 F 0
83 82 1500.00 Los Angeles XYZ Inc 31 M 0
84 83 2800.00 San Francisco ABC Corp 50 F 1
85 84 1350.00 Chicago XYZ Inc 28 M 0
86 85 920.00 New York ABC Corp 47 F 0
87 86 2000.00 Los Angeles XYZ Inc 36 M 0

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zavrazhnova_svetlana_lab_5/zavrazhnova_svetlana_lab_5.py Normal file
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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)