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"""securecyphercreditcardanalysis.space |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/1WKtvyEIBM5bPAPOmwXTGkEAp8mSFNKii |
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""" |
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import numpy as np |
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import pandas as pd |
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import os |
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for dirname, _, filenames in os.walk('/kaggle/input'): |
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for filename in filenames: |
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print(os.path.join(dirname, filename)) |
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import numpy as np |
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import pandas as pd |
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from sklearn.preprocessing import StandardScaler |
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from sklearn.model_selection import train_test_split, GridSearchCV |
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from sklearn.svm import SVC |
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from sklearn.metrics import classification_report, confusion_matrix |
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import joblib |
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import matplotlib.pyplot as plt |
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input = pd.read_csv('/content/credit_card_fraud_synthetic.csv') |
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data = input.drop(['Timestamp', 'Transaction_Type', 'Location', 'Transaction_ID'], axis = 1) |
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data |
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y = data['Is_Fraudulent'] |
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x = data.drop('Is_Fraudulent', axis = 1) |
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X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42) |
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svm_model = SVC(kernel='rbf') |
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svm_model.fit(X_train, y_train) |
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y_pred = svm_model.predict(X_test) |
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print("Confusion Matrix:") |
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print(confusion_matrix(y_test, y_pred)) |
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print("Classification Report:") |
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print(classification_report(y_test, y_pred)) |
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from sklearn.metrics import accuracy_score |
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Accu = accuracy_score(y_test, y_pred) |
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Accu = Accu * 100 |
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print("The Accuracy of the model is ", round(Accu, 2), "%") |
