https://github.com/arv-anshul/ineuron-money-laundering

A project from Ineuron Internship portal to build a ML model to predict the Money Laundering.
https://github.com/arv-anshul/ineuron-money-laundering

data-science ineuron-ai internship machine-learning project python3

Last synced: 14 days ago
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A project from Ineuron Internship portal to build a ML model to predict the Money Laundering.

• Host: GitHub
• URL: https://github.com/arv-anshul/ineuron-money-laundering
• Owner: arv-anshul
• License: mit
• Created: 2023-07-24T18:56:45.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-17T07:05:42.000Z (about 1 year ago)
• Last Synced: 2023-11-17T08:48:15.610Z (about 1 year ago)
• Topics: data-science, ineuron-ai, internship, machine-learning, project, python3
• Language: Jupyter Notebook
• Homepage: https://ineuron-money-laundering-1a.streamlit.app/
• Size: 20.5 MB
• Stars: 1
• Watchers: 1
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Money Laundering Prevention System

**🔗 Deployed Website on Streamlit Cloud Link** [![Streamlit Badge](https://img.shields.io/badge/Streamlit-FF4B4B?logo=streamlit&logoColor=fff)](https://ineuron-money-laundering-1a.streamlit.app/)

This project aims to predict the likelihood of backorders for products in a supply chain using machine learning techniques.  

Backorders occurs when a product is temporarily out of stock, and customers need to wait for it to become available again. By predicting potential backorders of a product, businesses can proactively manage their inventory and improve customer satisfaction.

### Screenshots of UI

![screenshot](./assets/screenshots/1.png)

### Project demo video

https://github.com/arv-anshul/ineuron-money-laundering/assets/111767754/c2eb4daa-0738-4eef-9e23-1180a5adedec

### Usage

1. Install required packages.

```sh

pip install -r requirements.txt

```

2. Run the streamlit web application.

```sh

streamlit run app.py

```

3. After running above command a web page opens in your browser.  

   Otherwise, Go to your browser and search the below url in address bar.

```

http://localhost:8501/

```

### Techs

- Git & GitHub

- Python3.11

- Streamlit

- MongoDB

- Data Science libraries like pandas, numpy, matplotlib, seaborn, etc.

### Features

- Predict the backorders in one click. I made the web app using streamlit which is a easy to easy tool to build a web app using python only.

- You can see the dataset analysis in Jupyter Notebook [here](./notebooks).

### Contributors