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credit_card_transactions.py

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+# -*- coding: utf-8 -*-
+"""Credit Card Transactions
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1u6Uvg6spSXdnjrvtQi8OjhJOGywYvsNG
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay
+from sklearn.ensemble import GradientBoostingClassifier
+
+df = pd.read_csv('creditcard.csv')
+
+df.head()
+
+df.shape
+
+df.columns
+
+df.info()
+
+df.describe()
+
+df.isnull().sum()
+
+df.duplicated().sum()
+
+df.drop_duplicates(inplace=True)
+
+df.shape
+
+df['Class'].unique()
+
+df['Class'].value_counts()
+
+fraud = df[df['Class'] == 1]
+normal = df[df['Class'] == 0]
+normal_percentage = len(normal)/(len(fraud)+len(normal))
+fraud_percentage = len(fraud)/(len(fraud)+len(normal))
+print('Percentage of fraud transactions = ', round(fraud_percentage * 100, 3))
+print('Percentage of normal transactions = ', round(normal_percentage * 100, 3))
+
+plt.figure(figsize=(9,7))
+sns.countplot(data=df,x='Class',palette=['blue', 'red'])
+plt.title("Number of Normal and Fraud Transactions");
+
+plt.figure(figsize=(8,6))
+sns.FacetGrid(df, hue="Class", height=6,palette=['blue','red']).map(plt.scatter, "Time", "Amount").add_legend()
+plt.show()
+
+plt.figure(figsize=(10,7))
+sns.heatmap(data=df.corr(),cmap='mako')
+plt.show()
+
+X = df.drop('Class',axis=1)
+y = df['Class']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+
+
+def model_train_test(model,X_train,y_train,X_test,y_test):
+  model.fit(X_train,y_train)
+  prediction = model.predict(X_test)
+  print('Accuracy = {}'.format(accuracy_score(y_test,prediction)))
+  print(classification_report(y_test,prediction))
+  matrix = confusion_matrix(y_test,prediction)
+  dis = ConfusionMatrixDisplay(matrix)
+  dis.plot()
+  plt.show()
+
+rf_model = RandomForestClassifier()
+
+model_train_test(rf_model,X_train,y_train,X_test,y_test)
+
+Decision_tree = DecisionTreeClassifier()
+
+model_train_test(Decision_tree,X_train,y_train,X_test,y_test)