Predicting Cloud Storage Consumption using Time Series Analysis

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In this end-to-end project developed under the HPE-CTY program, we have developed an ML-based web application that uses different machine learning architectures to predict a user's future cloud storage consumption based on their past usage history.

These predictions can be leveraged for tasks such as predicting users' monthly cloud service consumption billings.

Steps to Run The Server Locally

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1. Create a Python virtual environment:

python -m venv venv

2. Activate the virtual environment:

& <path_to_project_directory>/venv/Scripts/Activate.ps1

Eg.- & d:/HPE_CTY/api/venv/Scripts/Activate.ps1

3. Install the required project dependencies:

pip install -r requirements.txt

4. Running the server:

uvicorn main:app --reload

5. Go to the API docs:

Once the server is up and running, you can head over to http://127.0.0.1:8000/docs to access the API docs and test out the endpoints using fast-API's swagger documentation interface.