The problem of address matching arrives when the address of one physical place is written in two or more different ways. This situation is very common in companies that receive records of customers from different sources. The differences can be classified as syntactic and semantic. In the first type, the meaning is the same but the way they are written is different. For example, one can find "Street" vs "St". In the second type, the meaning is not exactly the same. For example, one can find "Road" instead of "Street". To solve this problem and match addresses, we have a couple of approaches. The first and simple is by using similarity metrics. The second uses natural language and transformers. This is a hands-on talk and is intended for data process analyst. We are going to go through these solutions implemented in a Jupyter notebook using Python.