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Svetlnkk 2023-10-26 11:27:02 +04:00
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zavrazhnova_svetlana_lab_6/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_6/zavrazhnova_svetlana_lab_6.py Normal file
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# import pandas as pd
# from sklearn.neural_network import MLPClassifier
# from sklearn.preprocessing import OneHotEncoder
#
# # Загрузка данных из файла
# df = pd.read_csv('fraud_dataset.csv')
#
# # Разделение признаков и целевой переменной
# # В данном случае целевая переменная - fraud_label
# X = df[['transaction_amount', 'location', 'merchant', 'age', 'gender']]
# y = df['fraud_label']
#
# X[['location', 'merchant', 'gender']] = X[['location', 'merchant', 'gender']].astype(str)
#
# # Преобразование категориальных признаков в числовые
# enc = OneHotEncoder()
# X_encoded = enc.fit_transform(X[['location', 'merchant', 'gender']]).toarray()
#
# # Объединение закодированных признаков с числовыми признаками
# X_final = pd.concat([X['transaction_amount'], pd.DataFrame(X_encoded), X['age']], axis=1)
#
# # Создание модели MLPClassifier
# model = MLPClassifier(hidden_layer_sizes=(100, 100)) # Можно настроить размеры и количество скрытых слоев
#
# # Обучение модели
# model.fit(X_final, y)
#
# # Предсказание для новых данных
# # Пример предсказания для новой транзакции
# new_transaction = pd.DataFrame([[2000.00, 'New York', 'ABC Corp', 35, 'M']],
# columns=['transaction_amount', 'location', 'merchant', 'age', 'gender'])
# new_transaction_encoded = enc.transform(new_transaction[['location', 'merchant', 'gender']]).toarray()
# new_transaction_final = pd.concat([new_transaction['transaction_amount'],
# pd.DataFrame(new_transaction_encoded),
# new_transaction['age']], axis=1)
# prediction = model.predict(new_transaction_final)
#
# # Вывод предсказания
# print(prediction)
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import LabelEncoder
# Метод обучения нейронной сети
def reg_neural_net():
df = pd.read_csv('fraud_dataset.csv')
x, y = [df.drop("fraud_label", axis=1).values,
df["fraud_label"].values]
encoder = LabelEncoder()
df['location'] = encoder.fit_transform(df['location']) # Преобразование категориального столбца "location" в числовой формат
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.001, random_state=42)
mlp = MLPRegressor(hidden_layer_sizes=(100, 50), activation='tanh', solver='adam', random_state=15000)
mlp.fit(x_train, y_train)
y_predict = mlp.predict(x_test)
err = pred_errors(y_predict, y_test)
make_plots(y_test, y_predict, err[0], err[1], "Нейронная сеть")
# Метод рассчёта ошибок
def pred_errors(y_predict, y_test):
mid_square = np.round(np.sqrt(metrics.mean_squared_error(y_test, y_predict)),3) # Рассчёт среднеквадратичной ошибки модели
det_kp = np.round(metrics.r2_score(y_test, y_predict), 2) # Рассчёт коэфициента детерминации модели
return mid_square, det_kp
# Метод отрисовки графиков
def make_plots(y_test, y_predict, mid_sqrt, det_kp, title):
plt.plot(y_test, c="red", label="\"y\" исходная") # Создание графика исходной функции
plt.plot(y_predict, c="green", label="\"y\" предсказанная \n"
"Ср^2 = " + str(mid_sqrt) + "\n"
"Кд = " + str(det_kp)) # Создание графика предсказанной функции
plt.legend(loc='lower left')
plt.title(title)
plt.savefig('static/' + title + '.png')
plt.close()
reg_neural_net()