In this proposal, the creation of a specialized dataset is proposed to train an artificial intelligence (AI) capable of analyzing the Bitcoin block chain. The main goal is to collect and structure relevant data to provide Hugging Face developers with a robust tool to train AI models to understand and extract valuable information from the Bitcoin blockchain.
Data collection:
A thorough collection of data will be carried out from trusted sources, such as block explorers, APIs and third-party services specialized in the Bitcoin blockchain. Key data such as transactions, blocks, Bitcoin addresses, timestamps, and other relevant features will be obtained to enable deep analysis of the blockchain.
Selection and structuring of characteristics:
The most relevant characteristics for the analysis of the Bitcoin block chain will be identified, such as the number of transactions, addresses involved, transaction amounts, transaction fees, block sizes, mining difficulty, among others. These features will be structured in a suitable format for further processing and analysis.
Data preprocessing:
Data cleaning and transformation techniques will be applied to guarantee the quality and consistency of the dataset. This will include handling of missing values, normalization of numeric data, and coding of categorical variables, ensuring that the data is ready for processing and training.
Data labeling (optional):
If you want to train a supervised model, you will perform labeling of the data with desired results, such as classifying transactions as “suspicious” or “normal” based on certain predefined criteria. However, it is recognized that manual tagging can be a laborious process and require domain expertise.
Storage and documentation:
The dataset will be stored in a format supported by the Hugging Face library, such as CSV or JSON files, and its structure, data source, and any transformations performed will be fully documented. This will allow Hugging Face developers to understand and effectively use the dataset in their AI projects.