Classification: Detecting Credit Card Fraud¶
Context¶
It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.
Content¶
The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
Inspiration¶
Identify fraudulent credit card transactions.
Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.
Aknowledgements¶
The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project
Exploratory Data Analysis¶
#### Importing Libraries ####
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sn
#import keras
np.random.seed(2)
%matplotlib inline
#load the data
data = pd.read_csv('creditcard.csv')
data.head()
data.info()
data.corrwith(data.Class).plot.bar(
figsize = (20, 10), title = "Correlation with class", fontsize = 15,
rot = 45, grid = True)
## Correlation Matrix
sn.set(style="white")
# Compute the correlation matrix
corr = data.corr()
corr.head()
# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(18, 15))
# Generate a custom diverging colormap
cmap = sn.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sn.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
data.isna().any()
Feature Scaling¶
from sklearn.preprocessing import StandardScaler
data['normalizedAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1,1))
data = data.drop(['Amount'],axis=1)
data = data.drop(['Time'],axis=1)
data.head()
X = data.iloc[:, data.columns != 'Class']
y = data.iloc[:, data.columns == 'Class']
X.info()
y.head()
Model Training¶
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state=0)
## Decison Tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
classifier = DecisionTreeClassifier(random_state = 0,
criterion = 'gini', splitter='best', min_samples_leaf=1, min_samples_split=2)
classifier.fit(X_train, y_train)
# Predicting Test Set
y_pred = classifier.predict(X_test)
acc = accuracy_score(y_test, y_pred)
prec = precision_score(y_test, y_pred)
rec = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
results = pd.DataFrame([['Decision tree', acc, prec, rec, f1]],
columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score'])
results
## Randomforest
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, precision_score, recall_score
classifier = RandomForestClassifier(random_state = 0, n_estimators = 100,
criterion = 'entropy')
classifier.fit(X_train, y_train)
# Predicting Test Set
y_pred = classifier.predict(X_test)
acc = accuracy_score(y_test, y_pred)
prec = precision_score(y_test, y_pred)
rec = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
model_results = pd.DataFrame([['Random Forest (n=100)', acc, prec, rec, f1]],
columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score'])
results = results.append(model_results, ignore_index = True)
results
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm
#Let's see how our model performed
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
## EXTRA: Confusion Matrix
cm = confusion_matrix(y_test, y_pred) # rows = truth, cols = prediction
df_cm = pd.DataFrame(cm, index = (0, 1), columns = (0, 1))
plt.figure(figsize = (10,7))
sn.set(font_scale=1.4)
sn.heatmap(df_cm, annot=True, fmt='g')
print("Test Data Accuracy: %0.4f" % accuracy_score(y_test, y_pred))
The output above shows that the test data is 99.95% acurate. This high level of accuracy is particularly desired in credit card fraud.