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Categorizing Transactions with Machine Learning and rules
In this post, I’ll demonstrate how combining rules-based systems with machine learning — specifically Random Forest — can significantly improve transaction categorization, particularly for incidental and non-recurring cases. This hybrid approach not only reduces manual efforts but also improves accuracy, helping me make better financial decisions with minimal intervention.
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Making the rules part of the domain
In this blogpost I find a way to make the rules part of the domain. Next to that I develop some code so I can store them on disk. Find the resulting code at GitHub.
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Not getting financial insight
TL;DR In this post I describe how I discover some imperfections in the current domain. This hampers me in getting financial insight. I fix these imperfections by creating new Category rules. After creating new rules I refactor the rules in to a simpler form. Furthermore I fix Transaction in the domain. As the found issues…
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Putting the domain to work
In this post I create a way to import transactions and categorize them. Basically putting the earlier created domain to work. In the end I get some insight into my spendings, however not completely done yet. The resulting code can be found at GitHub.
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Continuing Domain Driven Design
tl;dr In this post I continue my progress towards an DDD version of my favorite pet project. In this iteration I add a way to check the budget against the real expenses. The code is on GitHub and the picture contains the new model. Continuing Domain-Driven Design In my previous blog post I started to…