Bitcoin blockchain optimized for machine learning prediction model
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This dataset stores part of the Bitcoin blockchain. Blocks are sampled every month and information about transactions and blocks are separated to save disk space and avoid redundancies. This dataset is used in the work presented by Tedeschi et al. which is submitted for review, in order to generate a machine learning model that predicts transaction inclusion. In each month of analysis, there is a block folder and a transaction folder. Information can be merged runtime through 'bhash' attribute (block hash)' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Bitcoin","Blockchain","Machine Learning","Time Series"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This dataset stores part of the Bitcoin blockchain. Blocks are sampled every month and information about transactions and blocks are separated to save disk space and avoid redundancies. This dataset is used in the work presented by Tedeschi et al. which is submitted for review, in order to generate a machine learning model that predicts transaction inclusion. In each month of analysis, there is a block folder and a transaction folder. Information can be merged runtime through 'bhash' attribute (block hash)' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This dataset stores part of the Bitcoin blockchain. Blocks are sampled every month and information about transactions and blocks are separated to save disk space and avoid redundancies. This dataset is used in the work presented by Tedeschi et al. which is submitted for review, in order to generate a machine learning model that predicts transaction inclusion. In each month of analysis, there is a block folder and a transaction folder. Information can be merged runtime through 'bhash' attribute (block hash)
Bitcoin; Blockchain; Machine Learning; Time Series