cyberfraud1's Introduction

Cyber Fraud assignment 1 💰

Code for Group 66 python implementation of Cyber Data Analytics assigment 1 CS4035. 🔒

Team members:

Project structure 📂

The structure of the project is as follows:

main.py - The main entry point in the project. This uses all the necessary components to run the pre-procesing, feature extraction, classificantion and optimization.
card_tracker.py - Contains the class used to store the context associated with each card and calculates the additional features.
Exploratory_Analysis.ipynb - The notebook used for analisys. A saved version of the output can be found under Exploratory_Analysis.html.
config.txt - Used for configuration variables. For the moment only the path to the data set is used.
cross_valid.py - Used for the cross-validation algorithm.
data_preproc.py - Used for preprocessing of data(droping + conversions).
feats_utils.py - Utilities. Mainly used for functions to run apply in pandas.
hyperparam_tunning.py - The algorithm used perform random grid search.
logger.py - Logger class.
plot_utils.py - Utilities used to plot the results.
data/ - Folder containing the dataset data_for_student_case.csv.
logs/ - Folder where the logs are stored.
models/ - Folder where the parameters for the classifiers are stored.
output/ - Folder where the images output.

For a more detailed look check the code comments.

Installation 💻

The scripts can be run in Anaconda Windows/Linux environment.

You need to create an Anaconda 🐍 python 3.6 environment named cyber1. Inside that environment some addition packages needs to be installed. Run the following commands inside Anaconda Prompt ⌨:

(base) conda create -n cyber1 python=3.6 anaconda
(base) conda activate cyber1
(cyber1) conda install -c conda-forge imbalanced-learn
(cyber1) conda install -c anaconda py-xgboost

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