Categorize transactions using machine learning

The project aims to categorize bank transactions based on transaction text using machine learning

The following models were trained:

• Random Forest
• Neural Network (with Tensorflow)
• Logistic Regression
• Naive Bayes

Dataset

Dataset was constructed and labeled manually based on my own transactions from the past year. An example of data records:

Transaction text	Category Id
Lidl ))))	2
Deposit Rent	1
McDonalds Banegaards ))))	3
Burger Shack	3

Transaction categories/labels

Possible categories have been manually selected. A transaction can be categorized as one of the following categories:

Category Id	Category Name
0	Automobile and Transport
1	Housing and Real-Estate
2	Groceries
3	Recreation and Leisure
4	Health and Well Being
5	Hobby and Knowledge
6	Clothes and Equipment
7	Cash and Credit
8	Financial Services
9	Other

Results

Model	Accuracy
Random Forest	0.937
Neural Network	0.929
Logistic Regression	0.921
Naive Bayes	0.905

Conclusion

Random Forest seems to be good enough to classify transactions