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🧠 Introduction

Credit card fraud is a major concern for banks, consumers, and businesses globally. With billions of transactions processed daily, the need for real-time fraud detection using machine learning is more important than ever. This guide walks you through building a credit card fraud detection model, from dataset exploration to deploying it via Streamlit.

Whether you’re a machine learning student, data analyst, or AI learner, this project will sharpen your skills and help you build a strong portfolio piece.

🕵️‍♂️ What is Credit Card Fraud?

Credit card fraud involves unauthorized transactions on someone’s card. Fraudulent charges can result in massive financial losses, damaged trust, and legal issues for companies.

Common types of credit card fraud:

• Card Not Present (CNP) fraud
• Identity theft
• Account takeover

🎯 Business Objective

Goal: To develop a machine learning classification model that accurately identifies fraudulent credit card transactions, minimizing customer inconvenience and financial losses.

📊 Dataset Overview

Dataset Name: Credit Card Fraud Detection Dataset
Provider: ULB (Université Libre de Bruxelles)
Observations: 284,807 transactions
Fraud Cases: 492 (only ~0.17%)
Features:

• V1-V28: PCA-transformed features
• Time: Seconds since the first transaction
• Amount: Transaction value
• Class: 0 = Legit, 1 = Fraud

⚠️ Key Challenges in Fraud Detection

• Highly Imbalanced Dataset
• No feature interpretability (due to PCA transformation)
• Real-time detection requirements
• Avoiding false positives

Machine Learning Fraud Detection Flowchart:

🛠️ Step-by-Step Implementation

1. Data Preprocessing

import pandas as pd

df = pd.read_csv("creditcard.csv")
print(df.head())

Code Explained:

• We import the dataset using pandas to begin preprocessing and analysis.

2. Exploratory Data Analysis

print(df['Class'].value_counts())

import seaborn as sns
import matplotlib.pyplot as plt

sns.countplot(x='Class', data=df)
plt.title("Fraud vs Non-Fraud")
plt.show()

Code Explained:

• We analyze how imbalanced the dataset is using count plots.

3. Handling Imbalanced Data

We’ll use SMOTE (Synthetic Minority Over-sampling Technique).

from imblearn.over_sampling import SMOTE

X = df.drop('Class', axis=1)
y = df['Class']

sm = SMOTE(random_state=42)
X_res, y_res = sm.fit_resample(X, y)

Code Explained:

• SMOTE generates synthetic samples for the minority class to balance the dataset.

4. Model Training

We use Random Forest Classifier, a top-performing algorithm.

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=0.3, random_state=42)

model = RandomForestClassifier()
model.fit(X_train, y_train)

5. Evaluation Metrics

from sklearn.metrics import classification_report, confusion_matrix

y_pred = model.predict(X_test)

print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))

Code Explained:

• We evaluate using precision, recall, and F1-score. These are crucial for imbalanced classification problems.

6. Streamlit Deployment

📁 app.py

import streamlit as st
import pandas as pd
import joblib

model = joblib.load("rf_model.pkl")

st.title("💳 Credit Card Fraud Detection App")

amount = st.number_input("Transaction Amount", min_value=0.0)
time = st.number_input("Transaction Time (in seconds)", min_value=0.0)
v_features = [st.number_input(f"V{i}") for i in range(1, 29)]

if st.button("Predict"):
    features = [[time, amount] + v_features]
    prediction = model.predict(features)
    if prediction[0] == 1:
        st.error("⚠️ Fraudulent Transaction Detected!")
    else:
        st.success("✅ Legitimate Transaction")

🚀 How to Run the App

streamlit run app.py

Code Explained:

• This Streamlit app allows users to input values and see predictions in real-time.

Conclusion

For any financial system, preventing fraud before it occurs is the ultimate goal. With the help of machine learning, even highly imbalanced data can be transformed into actionable insight. In this project, we used a real-world dataset, balanced it with SMOTE, trained a Random Forest model, and deployed it via Streamlit for real-time use.

This project is perfect for:

• Enhancing your machine learning portfolio
• Understanding the real-world application of classification
• Practicing Streamlit deployment for full-stack data science

If you’re pursuing a career in data science or AI, this Credit Card Fraud Detection project is essential to any data science resume.

Are you ready to fight fraud with AI?
Clone the project
Customize the model
Deploy your fraud detection tool!

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