Credit Card Transactions Fraud Detection¶
Run in Google ColabObjective: Build a model that predicts whether a credit card transaction is fraudulent or not on an imbalanced dataset.
Import libraries¶
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report, ConfusionMatrixDisplay
from imblearn.over_sampling import SMOTE
sns.set_style("whitegrid")
Load the dataset¶
In [2]:
file_url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/creditcard.csv"
df = pd.read_csv(file_url)
df.head()
Out[2]:
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | ... | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | ... | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0 |
| 1 | 0.0 | 1.191857 | 0.266151 | 0.166480 | 0.448154 | 0.060018 | -0.082361 | -0.078803 | 0.085102 | -0.255425 | ... | -0.225775 | -0.638672 | 0.101288 | -0.339846 | 0.167170 | 0.125895 | -0.008983 | 0.014724 | 2.69 | 0 |
| 2 | 1.0 | -1.358354 | -1.340163 | 1.773209 | 0.379780 | -0.503198 | 1.800499 | 0.791461 | 0.247676 | -1.514654 | ... | 0.247998 | 0.771679 | 0.909412 | -0.689281 | -0.327642 | -0.139097 | -0.055353 | -0.059752 | 378.66 | 0 |
| 3 | 1.0 | -0.966272 | -0.185226 | 1.792993 | -0.863291 | -0.010309 | 1.247203 | 0.237609 | 0.377436 | -1.387024 | ... | -0.108300 | 0.005274 | -0.190321 | -1.175575 | 0.647376 | -0.221929 | 0.062723 | 0.061458 | 123.50 | 0 |
| 4 | 2.0 | -1.158233 | 0.877737 | 1.548718 | 0.403034 | -0.407193 | 0.095921 | 0.592941 | -0.270533 | 0.817739 | ... | -0.009431 | 0.798278 | -0.137458 | 0.141267 | -0.206010 | 0.502292 | 0.219422 | 0.215153 | 69.99 | 0 |
5 rows × 31 columns
Understand the dataset¶
We have access to transaction data from a financial institution that wants to know if a transaction is fraudulent or not. Most transactions are usually legitimate and only a small fraction are not, so the dataset is highly unbalanced.
Each row in the dataset represents a credit card transaction. For confidentiality reasons, the original names of most features are anonymized V1, V2 .. V28. The values of these features are the result of a PCA transformation and are numerical. The feature Class is the target variable and it takes two values: "1" in case of fraud and "0" otherwise.
In [3]:
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 284807 entries, 0 to 284806 Data columns (total 31 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Time 284807 non-null float64 1 V1 284807 non-null float64 2 V2 284807 non-null float64 3 V3 284807 non-null float64 4 V4 284807 non-null float64 5 V5 284807 non-null float64 6 V6 284807 non-null float64 7 V7 284807 non-null float64 8 V8 284807 non-null float64 9 V9 284807 non-null float64 10 V10 284807 non-null float64 11 V11 284807 non-null float64 12 V12 284807 non-null float64 13 V13 284807 non-null float64 14 V14 284807 non-null float64 15 V15 284807 non-null float64 16 V16 284807 non-null float64 17 V17 284807 non-null float64 18 V18 284807 non-null float64 19 V19 284807 non-null float64 20 V20 284807 non-null float64 21 V21 284807 non-null float64 22 V22 284807 non-null float64 23 V23 284807 non-null float64 24 V24 284807 non-null float64 25 V25 284807 non-null float64 26 V26 284807 non-null float64 27 V27 284807 non-null float64 28 V28 284807 non-null float64 29 Amount 284807 non-null float64 30 Class 284807 non-null int64 dtypes: float64(30), int64(1) memory usage: 67.4 MB
Analyze the Amount feature¶
In [4]:
fig, axs = plt.subplots(1, 2, figsize=(15, 5))
sns.histplot(data=df, x="Amount", hue="Class", bins="doane", ax=axs[0])
axs[0].legend(["1 (fraud)", "0 (non-fraud)"])
axs[0].set_yscale('log')
axs[0].set_title("Histogram")
sns.boxplot(data=df, x="Class", y="Amount", hue="Class", ax=axs[1], legend=False)
axs[1].set_xticks(["0", "1"])
axs[1].set_xticklabels(["0 (non-fraud)", "1 (fraud)"])
axs[1].set_yscale('log')
axs[1].set_title("Box plot")
plt.show()
Visualize the class distribution¶
As can be seen the dataset is highly unbalanced, so the target variable classes are not equally represented. This case requires special attention when training or when evaluating the quality of a model.
In [5]:
labels, sizes = np.unique(df["Class"], return_counts=True)
labels = ["1 (fraud)" if i else "0 (non-fraud)" for i in labels]
plt.figure()
g = sns.barplot(x=labels, y=sizes)
g.bar_label(g.containers[0], [str(round(100 * size / sum(sizes), 2)) + "%" + " (" + str(size) + ")" for size in sizes])
plt.yscale("log")
plt.title("Class")
plt.show()
Preprocess the dataset¶
One way of handling this case at train time is to apply an over-sampling method to pay more attention to the samples in the minority class. SMOTE (Synthetic Minority Over-sampling TEchnique) is a widely used oversampling technique in machine learning to address class imbalance issues in binary classification problems. It generates synthetic minority class samples to balance the class distribution, thereby reducing the impact of class imbalance on model performance. Data preprocessing such as scaling/normalization is typically useful for linear models to accelerate the training convergence.
In [6]:
def preprocess_data(df, smote=False):
X = df.drop(["Time", "Class"], axis=1)
y = df["Class"]
if smote:
sm = SMOTE(random_state=0)
X, y = sm.fit_resample(X, y)
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
return X_scaled, y
Train a Decision Tree classifier without SMOTE¶
In [7]:
X, y = preprocess_data(df)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
print("X_train shape:", X_train.shape)
print("X_test shape:", X_test.shape)
X_train shape: (213605, 29) X_test shape: (71202, 29)
In [8]:
classifier = DecisionTreeClassifier(max_depth=6, random_state=0)
classifier.fit(X_train, y_train)
Out[8]:
DecisionTreeClassifier(max_depth=6, random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(max_depth=6, random_state=0)
Evaluate the model¶
In [9]:
y_pred = classifier.predict(X_test)
print(classification_report(y_test, y_pred, digits=4))
ConfusionMatrixDisplay.from_predictions(y_test, y_pred, display_labels=["0 (not fraud)", "1 (fraud)"])
plt.grid(False)
plt.show()
precision recall f1-score support
0 0.9996 0.9998 0.9997 71082
1 0.8879 0.7917 0.8370 120
accuracy 0.9995 71202
macro avg 0.9437 0.8957 0.9184 71202
weighted avg 0.9995 0.9995 0.9995 71202
Train a Decision Tree classifier with SMOTE¶
In [10]:
X, y = preprocess_data(df, smote=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
print("X_train shape:", X_train.shape)
print("X_test shape:", X_test.shape)
X_train shape: (426472, 29) X_test shape: (142158, 29)
In [11]:
labels, sizes = np.unique(y_train, return_counts=True)
fig, ax = plt.subplots()
ax.pie(sizes, textprops={'color': "w", 'fontsize': '12'}, autopct=lambda pct: "{:.2f}%\n({:d})".format(pct, round(pct/100 * sum(sizes))))
ax.legend(["1 (fraud)" if i else "0 (non-fraud)" for i in labels])
ax.set_title("Class")
plt.show()
In [12]:
classifier = DecisionTreeClassifier()
classifier.fit(X_train, y_train)
Out[12]:
DecisionTreeClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier()
Evaluate the model¶
In [13]:
y_pred = classifier.predict(X_test)
print(classification_report(y_test, y_pred, digits=4))
ConfusionMatrixDisplay.from_predictions(y_test, y_pred, display_labels=["0 (not fraud)", "1 (fraud)"])
plt.grid(False)
plt.show()
precision recall f1-score support
0 0.9990 0.9978 0.9984 70856
1 0.9978 0.9990 0.9984 71302
accuracy 0.9984 142158
macro avg 0.9984 0.9984 0.9984 142158
weighted avg 0.9984 0.9984 0.9984 142158
