credit_card_fraud_prevention.py

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)