Personal Finance Tracker

A user-friendly Streamlit web application designed to help you analyze and visualize personal spending patterns directly from CSV transaction data.
Track your expenses, discover insights about your financial habits, and make smarter money decisions — all in one place!

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✨ Features

• CSV Upload
  Easily upload your transaction data. Your CSV must have these columns:

  • Date (e.g., 2025-01-01)
  • Amount (e.g., 45.80)
  • Category (e.g., Food)

• Data Cleaning
  Automatically handles missing values, invalid entries, and formats data for a smooth analysis experience.

• Interactive Filters
  Use the sidebar to filter your transactions by category and date range for focused insights.

• Beautiful Visualizations

  • Overview Tab:
    View a snapshot of filtered data, total spending, and your top spending category.
  • Spending Breakdown Tab:
    Dive deeper with pie charts and category-wise summaries.
  • Trends Tab:
    Explore monthly and weekly spending trends with bar charts and a heatmap.

• Export Option
  Download the filtered data as a CSV file for your records.

• Modern Responsive Design
  Clean layout with organized tabs, styled using Seaborn and Matplotlib for appealing visuals.

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🛠 Tech Stack

• Python — Core programming language
• Streamlit — Build the interactive web app
• Pandas — Handle data cleaning and manipulation
• NumPy — Perform numerical operations
• Seaborn & Matplotlib — Create stunning visualizations

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🚀 Installation

1. Set Up a Virtual Environment (Recommended):

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

2. Install Required Packages:

   pip install -r requirements.txt

3. Run the App:

   streamlit run app.py

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📋 Usage Guide

1. Prepare Your CSV File
   Your file should have three columns:
   Example:

   Date,Amount,Category
   2025-01-01,45.80,Food
   2025-01-02,20.00,Transport
   2025-01-03,15.50,Entertainment
   

2. Launch and Interact with the App

   • Upload your CSV file.
   • Use sidebar filters to explore by category or date range.
   • Navigate through the tabs for Overview, Spending Breakdown, and Trends.
   • Download the filtered dataset if needed.

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📁 Sample Data

Here's a sample you can use for testing:

Date	Amount	Category
2025-01-01	45.80	Food
2025-01-02	20.00	Transport
2025-01-03	15.50	Entertainment

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🖼 Screenshots

(Coming soon: Upload screenshots of Overview, Spending Breakdown, and Trends tabs.)

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🤝 Contributing

Contributions are highly appreciated!
Here's how you can help:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/your-feature

3. Commit your changes:

   git commit -m "Add your feature"

4. Push to the branch:

   git push origin feature/your-feature

5. Open a Pull Request.

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📜 License

This project is licensed under the MIT License.
See the LICENSE file for more details.

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❤️ Acknowledgements

• Built with passion using Streamlit.
• Inspired by the need for simple, visual personal finance tools.
• Thanks to the amazing open-source community behind Pandas, Seaborn, and Matplotlib!

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Would you also like me to suggest a catchy banner line or a badge section (like "Made with ❤️", "Streamlit App", etc.) to make it look even cooler for GitHub? 🚀🎯
Let me know!