I Analyzed My Finance With Local LLMs

TL;DR

Analyzing bank transactions to classify expenses, creating a personal finance dashboard using Python, visualizations, and open-source language models.

Transcript

as I get older I realize money is not everything but it's kind of almost everything so every year or every other year I download all my bank transactions and review my incomes and expenses the other day I came across someone who made this income and expense breakdown and I feel really inspired to do the same usually the most tricky thing in the pro... Read More

Key Insights

• 🏦 Analyzing bank transactions and classifying expenses can provide valuable insights into income and spending habits.
• 🤗 Open-source language models provide a secure and free way to perform expense classification without relying on third-party services.
• 😲 Language models like Lama 2 and AMA offer efficient ways to reduce model memory and improve model usage for consumers.
• 💁 Validating and formatting the output of language models is an essential step to ensure accurate results.
• ❓ Python libraries like Pantic and Plotly Express are useful for data validation and interactive visualizations.
• 👻 Creating a personal finance dashboard allows for a comprehensive overview of income, expenses, and monthly trends.
• 📼 Asset management should be considered in addition to expense classification for a complete understanding of personal finances.

Questions & Answers

Q: How do you analyze bank transactions to classify expenses?

Bank transactions can be analyzed by using open-source language models like Lama 2. By passing the transactions as prompts to the model, it can classify them into appropriate expense categories.

Q: Can you perform expense classification without using third-party services or APIs?

Yes, by installing and running an open-source language model locally on your laptop, you can perform expense classification without relying on third-party services or APIs. This ensures data security and privacy.

Q: How can the classified expenses be analyzed and visualized?

After classifying expenses using the language model, the data can be analyzed and visualized in Python. Various techniques, such as creating pie charts and bar charts, can be employed to gain insights and create visual representations of income and expense breakdowns.

Q: Is it possible to customize the language models for specific use cases?

Yes, language models can be customized by specifying a model file that includes parameters like the base model to use and the temperature for generating responses. Customization allows adapting the models to specific needs and use cases.

Summary & Key Takeaways

• The content discusses the process of analyzing bank transactions to classify expenses into appropriate categories, using open-source language models.

• It demonstrates how to install and run a large language model (LLM), such as Lama 2, locally on a laptop.

• The video guides the viewer through using the language model to classify expenses, analyzing the data in Python, and creating visualizations for key financial insights.