# shakiliitju/credit-card-fraud-detection-using-machine-learning architecture

> Architecture diagram of the GitHub repository shakiliitju/credit-card-fraud-detection-using-machine-learning, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-26). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/shakiliitju/credit-card-fraud-detection-using-machine-learning
- Repository: https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning

## Overview

This repository documents a credit-card fraud detection project: transaction data is used to train four alternative classifiers, whose performance is evaluated and compared to identify suitable models. The notebooks are the only visible implementation entry points; the tree also contains reports and a presentation that communicate project results. No source excerpts were available, so the graph reflects only the workflow and capabilities stated in the README, not notebook-level call relationships or preprocessing details.

Main entry points: [`Credit Card Fraud Detection Using Machine Learning/Code/Credit Card Fraud Detection - K-Nearest Neighbor.ipynb`](https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20K-Nearest%20Neighbor.ipynb) (K-nearest neighbors), [`Credit Card Fraud Detection Using Machine Learning/Code/Credit Card Fraud Detection - Logistic Regression.ipynb`](https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20Logistic%20Regression.ipynb) (Logistic regression), [`Credit Card Fraud Detection Using Machine Learning/Code/Credit Card Fraud Detection - Support Vector Machines.ipynb`](https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20Support%20Vector%20Machines.ipynb) (Support vector machine), [`Credit Card Fraud Detection Using Machine Learning/Code/Credit Card Fraud Detection - Decision Tree.ipynb`](https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20Decision%20Tree.ipynb) (Decision tree)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Transaction data"]
  node_kaggle(("Kaggle dataset source"))
  node_transactions["Transaction dataset"]
end

subgraph group_models["Fraud models"]
  node_knn["K-nearest neighbors"]
  node_logistic["Logistic regression"]
  node_svm["Support vector machine"]
  node_tree["Decision tree"]
end

subgraph group_evaluation["Evaluation and outputs"]
  node_holdout["Holdout evaluation"]
  node_comparison["Model comparison"]
  node_presentation["Project presentation"]
end

node_kaggle -->|"provides"| node_transactions
node_transactions -->|"trains model"| node_knn
node_transactions -->|"trains model"| node_logistic
node_transactions -->|"trains model"| node_svm
node_transactions -->|"trains model"| node_tree
node_knn -->|"evaluates on"| node_holdout
node_logistic -->|"evaluates on"| node_holdout
node_svm -->|"evaluates on"| node_holdout
node_tree -->|"evaluates on"| node_holdout
node_holdout -->|"compares results"| node_comparison
node_comparison -->|"communicates results"| node_presentation

click node_knn "https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20K-Nearest%20Neighbor.ipynb"
click node_logistic "https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20Logistic%20Regression.ipynb"
click node_svm "https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20Support%20Vector%20Machines.ipynb"
click node_tree "https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20Decision%20Tree.ipynb"
click node_comparison "https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Report/Final%20Lab%20Report.pdf"
click node_presentation "https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Presentation/AI-Report-Presentation.pptx"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_kaggle,node_transactions toneBlue
class node_knn,node_logistic,node_svm,node_tree toneAmber
class node_holdout,node_comparison,node_presentation toneMint
```

## Components

### Transaction data

- **Kaggle dataset source**
- **Transaction dataset**

### Fraud models

- [K-nearest neighbors](https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20K-Nearest%20Neighbor.ipynb): `Credit Card Fraud Detection Using Machine Learning/Code/Credit Card Fraud Detection - K-Nearest Neighbor.ipynb`
- [Logistic regression](https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20Logistic%20Regression.ipynb): `Credit Card Fraud Detection Using Machine Learning/Code/Credit Card Fraud Detection - Logistic Regression.ipynb`
- [Support vector machine](https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20Support%20Vector%20Machines.ipynb): `Credit Card Fraud Detection Using Machine Learning/Code/Credit Card Fraud Detection - Support Vector Machines.ipynb`
- [Decision tree](https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Code/Credit%20Card%20Fraud%20Detection%20-%20Decision%20Tree.ipynb): `Credit Card Fraud Detection Using Machine Learning/Code/Credit Card Fraud Detection - Decision Tree.ipynb`

### Evaluation and outputs

- **Holdout evaluation**
- [Model comparison](https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Report/Final%20Lab%20Report.pdf): `Credit Card Fraud Detection Using Machine Learning/Report/Final Lab Report.pdf`
- [Project presentation](https://github.com/shakiliitju/credit-card-fraud-detection-using-machine-learning/blob/main/Credit%20Card%20Fraud%20Detection%20Using%20Machine%20Learning/Presentation/AI-Report-Presentation.pptx): `Credit Card Fraud Detection Using Machine Learning/Presentation/AI-Report-Presentation.pptx`

## Connections

- Kaggle dataset source → Transaction dataset: provides
- Transaction dataset → K-nearest neighbors: trains model
- Transaction dataset → Logistic regression: trains model
- Transaction dataset → Support vector machine: trains model
- Transaction dataset → Decision tree: trains model
- K-nearest neighbors → Holdout evaluation: evaluates on
- Logistic regression → Holdout evaluation: evaluates on
- Support vector machine → Holdout evaluation: evaluates on
- Decision tree → Holdout evaluation: evaluates on
- Holdout evaluation → Model comparison: compares results
- Model comparison → Project presentation: communicates results