Meraki Data analysis through isolation forest algorithm

In this code we analyze data from Meraki API. We've enhanced our Meraki data analysis toolkit by integrating the Isolation Forest algorithm. This powerful method is excellent for detecting anomalies in large datasets, helping to identify unusual patterns or outliers that standard analysis might miss.

Installation

Install docker and docker-compose previously.

Clone the repo

git clone https://github.com/mighidalgo/isolation-forest.git

Go to your project folder

cd isolation-forest

Usage

Run docker compose

docker-compose up -d

Now you have the project up and running, enter the URL

http://localhost:8001/isolation-forest - Isolation forest algorithm.

Configuration

• In order to fetch data from Meraki API, we need to set this env variables in the docker-compose file
  • BASE_URL - Required, Meraki API URL.
  • API_KEY - Required, Apikey to access the API.
  • ORG_ID - Optional, if you want the data from a specific organization of the Meraki data, set this var.
  • ISOLATION_SENSITIVITY - Required, value used with the isolation forest algorithm

Example

• Run the Isolation Forest analysis on your Meraki dataset to identify potential anomalies. The output will list any data points that are considered statistically unusual, aiding in deeper data investigations.

Additional Notes

The Isolation Forest algorithm is particularly effective on large datasets. Performance may vary depending on the size and complexity of your data.

Hardware and Software requirements

You can run this project by just installing docker and docker-compose on your machine or server.
Recommendend hardware is 4 GB of ram and a dual core processor.