Credit Card Fraud Detection
Machine learning classification model
Machine Learning
Data preprocessing, model training, evaluation
Academic project · Walkthrough available on request
PythonMachine LearningLogistic RegressionScikit-learn
Overview
A credit card fraud detection model built with Logistic Regression in Python, covering preprocessing, training and evaluation with scikit-learn.
The challenge
Fraudulent transactions are rare compared with legitimate ones, so the work sits in careful preprocessing and honest evaluation rather than raw model complexity.
Contribution
- Built a credit card fraud detection model using Logistic Regression in Python.
- Used scikit-learn for data preprocessing, model training and evaluation.
- Identified fraudulent transactions, demonstrating machine learning, data analysis and model evaluation practice.
Architecture & workflow
How the system moves from input to outcome.
01Preprocess→
02Train→
03Evaluate→
04Flag
01PreprocessTransaction data cleaned and prepared with scikit-learn.
02TrainLogistic Regression classifier fitted on prepared features.
03EvaluateModel evaluation to assess detection quality.
04FlagPotentially fraudulent transactions identified from predictions.
Core features
Data preprocessing
Cleaning and preparation of transaction features.
Logistic Regression
Interpretable baseline classifier.
Model evaluation
Scikit-learn evaluation of classification performance.
Technical highlights
- End-to-end scikit-learn workflow.
- Focus on interpretable classification.
PythonMachine LearningLogistic RegressionScikit-learn