Annarhysa / BudgetBud

Personally curated financial advisor

BudgetBud

"Empowering Your Financial Journey, One Step at a Time."

We are team Binary Bulls and we have developed a web app that's a personally curated financial advisor for users with a busy schedule.

Problem Statement

In the complex financial landscape, people face challenges managing finances due to limited time and resources. Traditional advisory services lack personalization and involve high fees. We propose an AI-powered financial advisor leveraging machine learning to analyze users' financial data. Considering income, expenses, assets, liabilities, and goals, it offers tailored strategies for budgeting, saving, investing, and retirement planning.

Objectives

• Democratize access to personalized financial advice and guidance
• Utilize AI algorithms for data analysis and portfolio management
• Empower individuals of all income levels to make informed financial decisions
• Offer educational resources and interactive tools to improve financial literacy
• Prioritize user experience and accessibility
• Ensure availability across multiple devices and languages

User Process Flow Diagram

How to use repository

Prerequisites: Python, Git Suggestion: Create a virtual environment for nothing to break in your local system

python -m venv finance

Here are some of the terminal commands to be followed

1. Clone this repository in your local system

   git clone https://github.com/Annarhysa/BudgetBud.git
   

2. Install all the libraries:

   pip install -r requirements.txt
   

3. Run the application:

   python app-test.py
   

The app should be up and running on your localhost URL in the browser.

Scope

• Integration of AI algorithms for data analysis and portfolio management
• Implementation of educational resources and interactive tools
• Designing user-friendly interfaces for seamless interaction
• Testing and refinement to ensure accuracy and effectiveness
• Scalability to accommodate growing user base and evolving needs
• Continuous updates and improvements based on user feedback and market trends

Tech stack

• Python (for analysis): Numpy, Pandas
• Backend: Flask, JavaScript, SupaBase
• Frontend: HTML, CSS, BootStrap, JavaScript
• Other Libraries used: Google Charts, GeminiAI API, ApexCharts.js
• Deployment: Heroku