Upload securecyphercreditcardanalysis_space.py

securecyphercreditcardanalysis_space.py

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+# -*- coding: utf-8 -*-
+"""securecyphercreditcardanalysis.space
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1WKtvyEIBM5bPAPOmwXTGkEAp8mSFNKii
+"""
+
+import numpy as np
+import pandas as pd
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+  for filename in filenames:
+    print(os.path.join(dirname, filename))
+
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.svm import SVC
+from sklearn.metrics import classification_report, confusion_matrix
+import joblib
+import matplotlib.pyplot as plt
+
+input = pd.read_csv('/content/credit_card_fraud_synthetic.csv')
+
+data = input.drop(['Timestamp', 'Transaction_Type', 'Location', 'Transaction_ID'], axis = 1)
+
+data
+
+y = data['Is_Fraudulent']
+x = data.drop('Is_Fraudulent', axis = 1)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)
+
+svm_model = SVC(kernel='rbf')
+svm_model.fit(X_train, y_train)
+
+y_pred = svm_model.predict(X_test)
+
+print("Confusion Matrix:")
+print(confusion_matrix(y_test, y_pred))
+
+print("Classification Report:")
+print(classification_report(y_test, y_pred))
+
+from sklearn.metrics import accuracy_score
+Accu = accuracy_score(y_test, y_pred)
+Accu = Accu * 100
+print("The Accuracy of the model is ", round(Accu, 2), "%")