How to Build a Simple Credit Card Fraud Detection Model Using Logistic Regression in Python?
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Condition for Building a Simple Credit Card Fraud Detection Model Using Logistic Regression in Python
Description: This project demonstrates a simple credit card fraud detection using logistic regression. It includes steps for data preprocessing, model training, and evaluation using Python.
Why Should We Use Logistic Regression?
Simplicity: Easy to implement and interpret.
Efficiency: Works well with large datasets.
Linear Model: Effective for classification tasks with a linear decision boundary.
Probability Output: Provides probability scores for prediction.
Step by Step Process
Data Loading: Load the credit card fraud dataset.
Preprocessing: Handle missing values and split data into training/testing sets.
Model Training: Train the logistic regression model.
Evaluation: Use metrics like accuracy, confusion matrix, and classification report.
Sample Source Code
# Importing libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
# Load dataset
data = pd.read_csv('creditcard.csv')
X = data.drop('Class', axis=1)
y = data['Class']
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Train logistic regression
model = LogisticRegression()
model.fit(X_train, y_train)
Research Breakthrough Possible @S-Logix
