💱 Currency Exchange Dashboard

An educational machine learning project that explores anomaly detection in currency exchange rates using algorithms such as Isolation Forest.

This repository includes both the dashboard implementation and links to my learning notes, which document the process. " />

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📊 Project Overview

• Built with: Python, Streamlit, Scikit-learn, Pandas
• Core algorithms: Isolation Forest (for anomaly detection in exchange rate data)
• Purpose: Educational — combining practical coding with structured ML learning.

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📝 Learning Notes

As part of this project, I documented my learning journey using NotebookLM.
These notes explain key machine learning algorithms and their applications.

• 📘 Isolation Forest Explaination

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⚠️ Disclaimer

Parts of the learning notes were generated with the assistance of NotebookLM and then curated by me.
They are included here to showcase my personal learning process — not as finalized research material.

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🚀 Getting Started

Clone the repository:

git clone https://github.com/abbadonaz/currency-exchange-dashboard.git
cd currency-exchange-dashboard

⚡ Quickstart

Prerequisites: Python 3.11+ and Git installed.

1. Create and activate a virtual environment (Windows PowerShell):

python -m venv .venv
.\.venv\Scripts\Activate.ps1

(Or on Windows CMD: \.venv\Scripts\activate, macOS/Linux: source .venv/bin/activate)

2. Install dependencies:

pip install -r requirements.txt
# (Optional) install the DescriptiveAnalytics extras:
# pip install -r DescriptiveAnalytics/requirements.txt

3. (Optional) Create a .env file at the project root to override defaults (see src/config.py). Example:

BASE_CURRENCY=EUR
CACHE_TTL_MIN=60
DATA_SOURCE=ECB

4. Run the app:

streamlit run main.py

Then open http://localhost:8501 in your browser. To run on a different port, add --server.port <PORT> to the command.

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