Credit Card Fraud Detection, used the credit card fraud dataset from Kaggle

Overview

CSCD 429 Project

The following repository contains the project of Team 6.

Team Members

Description

Credit card fraud detection.

We used the credit card fraud dataset from Kaggle.

Installation

Using Conda

To create the virtual environment and install required Python packages, run the following commands in the terminal:

conda env create -f environment.yml
conda activate cscd429-team6-project
Without Conda

If you do not have Conda installed, the packages may still be installed using the following command:

pip install -r requirements.txt

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT

Owner
Sean Zahller
Eastern Washington University BCS. Projects listed in resume are located in the Stars section of my profile.
Sean Zahller
Simplify stop motion animation with machine learning.

Simplify stop motion animation with machine learning.

Nick Bild 25 Sep 15, 2022
A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

Aayush Malik 80 Dec 12, 2022
Xeasy-ml is a packaged machine learning framework.

xeasy-ml 1. What is xeasy-ml Xeasy-ml is a packaged machine learning framework. It allows a beginner to quickly build a machine learning model and use

9 Mar 14, 2022
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

imbalanced-learn imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-cla

6.2k Jan 01, 2023
Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets

Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets Datasets Used: Iris dataset,

Samrat Mitra 2 Nov 18, 2021
To design and implement the Identification of Iris Flower species using machine learning using Python and the tool Scikit-Learn.

To design and implement the Identification of Iris Flower species using machine learning using Python and the tool Scikit-Learn.

Astitva Veer Garg 1 Jan 11, 2022
This is the code repository for Interpretable Machine Learning with Python, published by Packt.

Interpretable Machine Learning with Python, published by Packt

Packt 299 Jan 02, 2023
Diabetes Prediction with Logistic Regression

Diabetes Prediction with Logistic Regression Exploratory Data Analysis Data Preprocessing Model & Prediction Model Evaluation Model Validation: Holdou

AZİZE SULTAN PALALI 2 Oct 23, 2021
Tools for mathematical optimization region

Tools for mathematical optimization region

林景 15 Nov 30, 2022
CS 7301: Spring 2021 Course on Advanced Topics in Optimization in Machine Learning

CS 7301: Spring 2021 Course on Advanced Topics in Optimization in Machine Learning

Rishabh Iyer 141 Nov 10, 2022
fastFM: A Library for Factorization Machines

Citing fastFM The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citat

1k Dec 24, 2022
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

AI Fairness 360 (AIF360) The AI Fairness 360 toolkit is an extensible open-source library containg techniques developed by the research community to h

1.9k Jan 06, 2023
Distributed Deep learning with Keras & Spark

Elephas: Distributed Deep Learning with Keras & Spark Elephas is an extension of Keras, which allows you to run distributed deep learning models at sc

Max Pumperla 1.6k Dec 29, 2022
A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search (Manhattan Distance Heuristic)

A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and the A* Search (using the Manhattan Distance Heuristic)

17 Aug 14, 2022
A Pythonic framework for threat modeling

pytm: A Pythonic framework for threat modeling Introduction Traditional threat modeling too often comes late to the party, or sometimes not at all. In

Izar Tarandach 644 Dec 20, 2022
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Real-time water systems lab 416 Jan 06, 2023
(3D): LeGO-LOAM, LIO-SAM, and LVI-SAM installation and application

SLAM-application: installation and test (3D): LeGO-LOAM, LIO-SAM, and LVI-SAM Tested on Quadruped robot in Gazebo ● Results: video, video2 Requirement

EungChang-Mason-Lee 203 Dec 26, 2022
Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.

Hivemind: decentralized deep learning in PyTorch Hivemind is a PyTorch library to train large neural networks across the Internet. Its intended usage

1.3k Jan 08, 2023
Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing

Parallelized symbolic regression built on Julia, and interfaced by Python. Uses regularized evolution, simulated annealing, and gradient-free optimization.

Miles Cranmer 924 Jan 03, 2023
Dive into Machine Learning

Dive into Machine Learning Hi there! You might find this guide helpful if: You know Python or you're learning it 🐍 You're new to Machine Learning You

Michael Floering 11.1k Jan 03, 2023