# Data Preprocessing and Feature Extraction from Opcodes

The raw opcodes extracted from smart contracts need to be converted into a format that machine learning algorithms can process. This involves several steps:

* **Tokenization:** Opcodes are tokenized, breaking down the code into individual instructions or tokens.
* **Vectorization:** The tokenized opcodes are then transformed into numerical vectors. Techniques like one-hot encoding or term frequency-inverse document frequency (TF-IDF) can be employed for this transformation.
* **Normalization:** The feature vectors are normalized to ensure that the scale of the features does not bias the algorithms.

This preprocessing stage is crucial for effective machine learning analysis, as it directly impacts the model's ability to learn from the opcode data.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://neurablock.gitbook.io/whitepaper/machine-learning-model/data-preprocessing-and-feature-extraction-from-opcodes.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.