> For the complete documentation index, see [llms.txt](https://neurablock.gitbook.io/whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://neurablock.gitbook.io/whitepaper/machine-learning-model/data-preprocessing-and-feature-extraction-from-opcodes.md).

# 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.


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