Here is a detailed guide on Fraud Detection using machine learning algorithms with a Python code example.
Project Overview
Fraud Detection algorithms aim to identify suspecious transactions using patterns in collected data. This system is helpful in banking, e-commerce, and payment platforms. In this blog post we will learn step by step guide to develop Fraud Detection System.
Key Concepts
- Imbalanced Datasets: Fraud cases are rare compared to normal ones. Here we have to apply ML model development skills to choose right algorithms and how to balance data. Synthetic data generation is required to deal with such type of data.
- Feature Engineering: It is extracting meaningful features from raw data. Features are columns, or properties. A raw data have many features but all may not be important for analysis. Therefore we need to identify which features are Key Performance Indicators(KPIs).
- Classification models: There are many classification ML models. But some of the popular models used for Imbalance datasets are: Logistic Regression, Random Forest, XGBoost, etc.
- Evaluation Metrics: Evaluation metrics is must for and data scientist to know whether the model they have developed is perofming well or not. Evaluation metrics are performance calculators. We get numeric values as well as visualization of performance using these metrics. Some of the statistical metrics are:-Precision, Recall, F1-score, ROC-AUC.
Let’s dive into step-by-step guide to follow Coding Fraud Detection Model
Data Preparation:
Use this Credit Card Fraud Detection popular dataset from kaggle.
import pandas as pd
df = pd.read_csv(“creditcard.csv”)
print(df.head())
2. Exploratory Data Analysis(EDA)
EDA is used to understand the distribution of data. It also helps us to identify the significant features. We can get a lot more information with the help of EDA. We use statistical metrics such as mean, mode, median and other hypothetical test during EDA. We also do data visualizations using different charts like Column charts, Pie Charts, Line Graph, area charts, etc.
fraud = df[df[‘Class’] == 1]
valid = df[df[‘Class’] == 0]
print(f”Fraud cases: {len(fraud)}”)
print(f”Valid cases: {len(valid)}”)
3. Handle Imbalanced Dataset:
We have already learned why handling imbalanced data is important. With this step we can avoid bias in predicted results.
from imblearn.over_sampling import SMOTE
X = df.drop(‘Class’, axis=1)
y = df[‘Class’]smote = SMOTE()
X_resampled, y_resampled = smote.fit_resample(X, y)
4. Train – Test Split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2)
5. Model Training using Random Forest
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
6. Evaluation
from sklearn.metrics import classification_report
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
If you need help for any ML model development, please comment. I will try to write a blog post in step by step manner.