smartbudget-ml 0.1.0

Budget planning and risk assessment ML model package

SmartBudget ML (simple model package)

This repository is a Python model package (library-first). It is meant to be imported and used directly from your backend code.

The core API lives in smartbudget_ml/:

• train(...) to train and save models
• load_model(...) / predict(...) to run inference using saved models

Install

python -m venv venv
source venv/bin/activate
pip install -e .

Train (creates model files)

Training produces two files in models/:

• models/budget_allocation_model.pkl
• models/risk_assessment_model.pkl

Using synthetic demo data:

smartbudget-train --output-dir models --samples 1000

Predict (use as a simple model)

from smartbudget_ml import load_model, predict

model = load_model("models")
result = predict(
    model,
    transactions=[
        {"amount": 5000, "transaction_type": "expense", "category": "food"},
        {"amount": 15000, "transaction_type": "expense", "category": "rent"},
    ],
    liabilities=[{"minimum_payment": 500, "interest_rate": 18.5, "is_active": True}],
    user_incomes=[{"amount": 50000, "is_active": True}],
)

print(result["risk_level"])
print(result["category_budgets"])

Notes

• Simple models by default: the package uses lightweight scikit-learn models that train quickly and are easier to debug than large ensembles.
• No API required: an HTTP service layer can be added later, but it is not required for using the model package.

smartbudget-ml-service

Getting started

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

Already a pro? Just edit this README.md and make it your own. Want to make it easy? Use the template at the bottom!

Add your files

• Create or upload files
• Add files using the command line or push an existing Git repository with the following command:

cd existing_repo
git remote add origin https://code.qburst.com/smartbudget/smartbudget-ml-service.git
git branch -M main
git push -uf origin main

Integrate with your tools

• Set up project integrations

Collaborate with your team

• Invite team members and collaborators
• Create a new merge request
• Automatically close issues from merge requests
• Enable merge request approvals
• Set auto-merge

Test and Deploy

Use the built-in continuous integration in GitLab.

• Get started with GitLab CI/CD
• Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)
• Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy
• Use pull-based deployments for improved Kubernetes management
• Set up protected environments

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Editing this README

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