https://github.com/michellebonat/fed_funds_ml
Use machine learning (NLP) to demonstrate whether Federal Funds rate changes can be accurately predicted using just the FOMC - the US Federal Reserve Bank - meetings minutes.
https://github.com/michellebonat/fed_funds_ml
ai federal-reserve-bank finance financial-services machine-learning nlp python3
Last synced: 27 days ago
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Use machine learning (NLP) to demonstrate whether Federal Funds rate changes can be accurately predicted using just the FOMC - the US Federal Reserve Bank - meetings minutes.
- Host: GitHub
- URL: https://github.com/michellebonat/fed_funds_ml
- Owner: michellebonat
- Created: 2019-03-27T23:46:30.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-09-08T22:20:50.000Z (over 5 years ago)
- Last Synced: 2025-04-22T10:43:27.350Z (27 days ago)
- Topics: ai, federal-reserve-bank, finance, financial-services, machine-learning, nlp, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 6.33 MB
- Stars: 18
- Watchers: 3
- Forks: 6
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Fed_Funds_ML
## About
This is a personal project to demonstrate whether Federal Funds rate changes can be accurately predicted
using FOMC meetings minutes, by leveraging machine learning, primarily NLP (Natural Language Processing).
The FOMC is the policy governing body of the US Federal Reserve Bank which is the central bank of the US.
The results achieved were a 94% prediction rate on test data (unseen data). I used four different machine
learning models: Naive Bayes, Logistic Regression, Support Vector Machine (SVM), and Decision Trees.
The model that worked best was Naive Bayes.
To see a presentation of this project online [click here](http://bit.ly/Fed_Funds_NLP).
For a detailed wriiten overview of this project see the pdf in this repo called "Fed_Funds_ML_Project_Report".
This report includes sections on project overview, data wrangling, statistical analysis, and machine learning.
## Running the Project
Use this on the command line to start your Jupyter Notebook to get more processing power else it will stall out:
```jupyter notebook --NotebookApp.iopub_data_rate_limit=10000000000```
## Contributing
This was built by Michelle Bonat. It's not currently open for contributions, but I would love to hear any comments and suggestions about how you have modified this code.
Contact me through the methods noted below.
## Authors
* **Michelle Bonat** - *Initial work* - Contact me through [michellebonat.com](http://michellebonat.com/) or on [GitHub](https://github.com/michellebonat)
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details