Skip to content

The goal of our project is to build a classifier for distinguishing between legitimate and fraudulent transactions using machine learning methods on a real e-commerce dataset. This is a binary classification problem in machine learning.

License

Notifications You must be signed in to change notification settings

SonNguyen25/VinBigData-Fraud-Detection

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

IEEE-CIS Fraud Detection


Setup

You can use these scripts to install anaconda
sudo apt update
apt-get install wget
wget https://repo.anaconda.com/archive/Anaconda3-2020.07-Linux-x86_64.sh
bash Anaconda3-2020.07-Linux-x86_64.sh
export PATH=~/anaconda3/bin:$PATH
Create a conda environment with python > 3.6
conda create -n py38 python=3.8 -y
source ~/anaconda3/etc/profile.d/conda.sh
After that, install the required packages
pip install -r requirements.txt

Compile & Run

  • Run Jupyter Notebook from the root of the directory of source code - Reference: Running the Jupyter Notebook
  • Open the notebooks you want to rerun and run the desired cells
  • Some cells will require the other cells to be run first so my suggestion is to run cells sequentailly. Also, if there is something that does not follow the normal sequential run, it is always noted in the head comments of each cell or through text cell with prefix "Caution".

Contributors

  • Nguyễn Bảo Sơn
  • Nguyễn Trung Kiên
  • Đặng Xuân Lộc
  • Phạm Hoàng Tùng
  • Nguyễn Nhật Quang

About

The goal of our project is to build a classifier for distinguishing between legitimate and fraudulent transactions using machine learning methods on a real e-commerce dataset. This is a binary classification problem in machine learning.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published