credit_card_analysis

For many banks, retaining high profitable customers is the number one business goal. Banking fraud, however, poses a significant threat to this goal for different banks. In terms of substantial financial losses, trust and credibility, this is a concerning issue to both banks and customers alike.

In the banking industry, credit card fraud detection using machine learning is not only a trend but a necessity for them to put proactive monitoring and fraud prevention mechanisms in place. Machine learning is helping these institutions to reduce time-consuming manual reviews, costly chargebacks and fees as well as denials of legitimate transactions.

In this project we will detect fraudulent credit card transactions with the help of Machine learning models. We will analyse customer-level data that has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group.

The data set includes credit card transactions made by European cardholders over a period of two days in September 2013. Out of a total of 2,84,807 transactions, 492 were fraudulent. This data set is highly unbalanced, with the positive class (frauds) accounting for 0.172% of the total transactions. The data set has also been modified with principal component analysis (PCA) to maintain confidentiality. Apart from ‘time’ and ‘amount’, all the other features (V1, V2, V3, up to V28) are the principal components obtained using PCA. The feature 'time' contains the seconds elapsed between the first transaction in the data set and the subsequent transactions. The feature 'amount' is the transaction amount. The feature 'class' represents class labelling, and it takes the value of 1 in cases of fraud and 0 in others.

# Importing the libraries import numpy as np import pandas as pd import time import matplotlib.pyplot as plt %matplotlib inline import seaborn as sns from scipy import stats from scipy.stats import norm, skew from scipy.special import boxcox1p from scipy.stats import boxcox_normmax from sklearn import preprocessing from sklearn.preprocessing import StandardScaler import sklearn from sklearn import metrics from sklearn.metrics import roc_curve, auc, roc_auc_score from sklearn.metrics import classification_report,confusion_matrix from sklearn.metrics import average_precision_score, precision_recall_curve from sklearn.model_selection import train_test_split from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import GridSearchCV, RandomizedSearchCV from sklearn.linear_model import Ridge, Lasso, LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LogisticRegressionCV from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import AdaBoostClassifier from sklearn.ensemble import RandomForestClassifier !pip install xgboost import xgboost as xgb from xgboost import XGBClassifier from xgboost import plot_importance from sklearn.ensemble import AdaBoostClassifier # To ignore warnings import warnings warnings.filterwarnings("ignore")

df = pd.read_csv('/datasets/henrydu/credit_risk/creditcard.csv') # df = pd.read_csv('./data/creditcard.csv') df.head()