validation,submission data,readme

01_data_raw/

.Rproj.user

# SL2024_ClassificationCompetition

# Classification Competition: Customer Default Prediction

## Overview

This repository contains the data and R scripts for a classification competition aimed at predicting customer defaults. Participants are required to estimate the probability of default (PD) for various clients and submit their predictions.

## Getting started

This Classification competition based on 

Addison Howard, AritraAmex, Di Xu, Hossein Vashani, inversion, Negin, Sohier Dane. (2022). *American Express - Default Prediction*. Kaggle. https://kaggle.com/competitions/amex-default-prediction

To make it easy for you to get started with GitLab, here's a list of recommended next steps.

## Repository Structure

Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!

### Data

- `01_data_raw/dat_fa.rds`: Raw data file containing initial customer data.

- `amex_train.rds`: Training dataset for model building.

- `amex_validation.rds`: Validation dataset for model evaluation (does not include target variable).

- `amex_submission.rds`: Template for competition submissions, containing customer IDs.

## Add your files

### Scripts

- `data_prep.R`: Main script to preprocess data, create training and validation datasets, and prepare a submission template.

- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files

- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:

## Additional Notes

For any issues or questions regarding the data or the competition setup, please see the ILIAS learning module.

```

cd existing_repo

git remote add origin https://aidaho-edu.uni-hohenheim.de/gitlab/mariuspuke/sl2024_classificationcompetition.git

git branch -M main

git push -uf origin main

```

## Integrate with your tools

- [ ] [Set up project integrations](https://aidaho-edu.uni-hohenheim.de/gitlab/mariuspuke/sl2024_classificationcompetition/-/settings/integrations)

## Collaborate with your team

- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)

- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)

- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)

- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)

- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)

## Test and Deploy

Use the built-in continuous integration in GitLab.

- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)

- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)

- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)

- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)

- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)

***

# Editing this README

When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.

## Suggestions for a good README

Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.

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