Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/codequeenie/fraud_detection_model

A project for detecting fraudulent transactions using a logistic regression model.

https://github.com/codequeenie/fraud_detection_model

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.1%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

A project for detecting fraudulent transactions using a logistic regression model.

Basic Info
  • Host: GitHub
  • Owner: CodeQueenie
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 3.91 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

Fraud Detection Model

This project trains a machine learning model to detect fraudulent transactions using a logistic regression algorithm.

Dataset

The dataset used in this project is the Credit Card Fraud Detection dataset.

Instructions to Get the Dataset

  1. Download the dataset from Kaggle.
  2. Extract the files fraudTrain.csv and fraudTest.csv.
  3. Place the files in the data/ folder of the project directory:

Requirements

  • Python 3.9
  • Libraries: pandas, scikit-learn, joblib

Instructions

  1. Place the dataset files (fraudTrain.csv and fraudTest.csv) in the data/ folder.
  2. Navigate to the src folder: ```bash cd src
  3. Run the training script: ```bash python train_model.py

Output

  • The trained model is saved in the outputs/ folder as fraud_model.pkl.

Usage

  • To train the model, run the following command: ```bash python train_model.py

Expected Output

  • The script will output the following:

    • A confusion matrix and classification report printed in the terminal.
    • Example: ```lua Confusion Matrix: [[100 10] [ 5 20]]

    Classification Report: precision recall f1-score support 0 0.95 0.90 0.92 110 1 0.67 0.80 0.73 25

Owner

  • Name: Nicole LeGuern
  • Login: CodeQueenie
  • Kind: user

👋 Seeking entry-level position or apprenticeship in software engineering. Eager to dive headfirst into the field and apply my skills in a professional setting.

GitHub Events

Total
  • Push event: 1
  • Create event: 2
Last Year
  • Push event: 1
  • Create event: 2