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A machine learning-powered budget tracking app built with Streamlit. Users can upload CSVs of expenses, which are automatically classified into needs and wants using a trained ML model. Firebase authentication and storage are used to personalize dashboards, track savings goals, and visualize spending trends with interactive charts.

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Shreya-Sanjay08/budget-analyzer

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budget-analyzer

A Streamlit web app that helps users analyze and classify their expenses into Needs and Wants, with visual dashboards and machine learning integration.

🔧 Features

  • Upload CSV of expenses
  • ML model classifies each expense as Need or Want
  • View visual summaries: Pie charts, bar graphs, line graphs
  • User authentication with Firebase
  • Savings goals tracking
  • Natural language summaries (WIP)

Tech Stack

  • Frontend: Streamlit
  • Backend: Python, Firebase Auth, Firestore
  • ML: scikit-learn, pandas, numpy
  • Data Visualization: Plotly, Seaborn, Matplotlib

Demo

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About

A machine learning-powered budget tracking app built with Streamlit. Users can upload CSVs of expenses, which are automatically classified into needs and wants using a trained ML model. Firebase authentication and storage are used to personalize dashboards, track savings goals, and visualize spending trends with interactive charts.

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