Data
Complete-Cryptocurrency-Market-History

Complete-Cryptocurrency-Market-History

active ARFF CC0 Public Domain Visibility: public Uploaded 23-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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CryptocurrenciesCryptocurrencies are fast becoming rivals to traditional currency across the world. The digital currencies are available to purchase in many different places, making it accessible to everyone, and with retailers accepting various cryptocurrencies it could be a sign that money as we know it is about to go through a major change.In addition, the blockchain technology on which many cryptocurrencies are based, with its revolutionary distributed digital backbone, has many other promising applications. Implementations of secure, decentralized systems can aid us in conquering organizational issues of trust and security that have plagued our society throughout the ages. In effect, we can fundamentally disrupt industries core to economies, businesses and social structures, eliminating inefficiency and human error.ContentThe dataset contains all historical daily prices open, high, low, close for all cryptocurrencies listed on CoinMarketCap.AcknowledgementsEvery Cryptocurrency Daily Market Price I initially developed kernels for this dataset before making my own scraper and dataset so that I could keep it regularly updated.CoinMarketCap For the data

9 features

Unnamed:_0numeric1979 unique values
0 missing
Datestring1979 unique values
0 missing
Symbolstring887 unique values
0 missing
Opennumeric170357 unique values
0 missing
Highnumeric174023 unique values
0 missing
Lownumeric167455 unique values
0 missing
Closenumeric170439 unique values
0 missing
Volumenumeric143918 unique values
5335 missing
Market_Capnumeric407618 unique values
64377 missing

19 properties

632218
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
69712
Number of missing values in the dataset.
69712
Number of instances with at least one value missing.
7
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
77.78
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
11.03
Percentage of instances having missing values.
Average class difference between consecutive instances.
1.23
Percentage of missing values.

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