Crypto predictions #Fake it
To read this article, you need to register for Academia Datathon.
Team solutions
data.nerds: THE A.I. CRYPTO TRADER
Dataset – price_data_3 Full Code We transformed the data set in hours instead of each 5 minutes. For the forecast model, we are using Python. You can find the code below. We use (‘ARIMA ‘, (3, 1, 1)) which is the most accurate with minimum forecast error. The forecast we have for next period is […]
Prediction Cryptocurrencies using Hybridization Machine Learning
UMalaya Team: Mohamad Nazrin Bin Napiah,Nur Baiti Afini Normadi,Sabrina Binti Kamal,Nur Hidayah Binti Mohd Rosli,Prasanta A/L Sathasivam,Yee Xun Wei
In this paper, this study attempts to predict the twenty cryptocurrency price by taking into consideration various parameters that affect the trading or investment market. For the first level of this study aim to construct the forecasting model to predict the future values of cryptocurrencies and the live model of decision making for trading is deploy. This body of this project were follow the CRISP data mining methodology in supporting to process the models. The data source is from the Academia Datathon 2018. The data set consists of various attributes related to the various coin, price and time, recorded daily in 2018. For the second level, by using the best model from level one which has been compared the accuracy and performance, will be used to construct Artificial Intelligence bot for decision making of trading or investment. By focusing on twenty major cryptocurrencies, each with the large market size and price, this study attempts to predict the forecasting price based on time series method such as min, max and mean price values.
Deciphering Cryptocurrency by Occam’s Data Ninjas
https://drive.google.com/open?id=1cqUOvGzJtF4qUIOmlxKawhfrjPuzWaDl Occam’s Data Ninjas Please find the attachment for the code in the above google drive link. Occam’s Data Ninjas Team Mentor: Dr. Subhabaha Pal @drsubhabahapa Team Members: Mervyn V. Akash @codemonger ([email protected]) Tanuj Maithani @tanuj ([email protected]) Tamma Ravindra Reddy @mr-reliable ([email protected]) Sowmya Dyagala @dsowmya ([email protected]) Team Motto: Simplicity at it’s best. Team Toolset: R Studio, Microsoft Excel. Business Understanding and Objective: There is […]
Forecast the price of cryptocurrency
Dear jury, We are students from Faculty of Economics and Social Sciences, University of Plovdiv Paisii Hilendarski. As we are first year students and have not studied any programming languages we tried to forecasting the value of cryptocurrency with R. Unfortunately, while working with file price_data1442.csv None of the functions we know, gave satisfying results […]
Cryptocurrencies forecasting
Cryptocurrencies are a type of digital currencies that, since their creation, have become a global phenomenon known to most people. Our job is to build a machine learning algorithm able to forecast their price based on a set of given features such as that currency’s price, market cap, circulating supply etc.
The diggers – “In God we trust, all others must bring data”
Team Info “The diggers” – Tsenov Academy of Economics, Svishtov
The ZenCodeo Case – Fuzzy String Solution
A simple solution to the problem of company name deduplication. No machine learning, just data prep.
Ontotext case – The LSTM way
In this article I will describe my approach using bi-directional LSTM and eventually stacking them for creating deeper network resulting in better results.
Identrics NLP Team
On Coreference Extraction from Identric’s Documents Business Understanding: Identrics would like to extract knowledge from unstructured text. One use case for that is to automate the process of information extraction from news articles. Some of Indentrics clients are from the finance industry and have a need to understand the impact of news on the valuation of […]
Team Seven Bridges – Telelink Case: What Really Goes into Sausages?
The food industry is governed by strict laws and regulations, which provide certainty that each product meets health and safety standards. In addition to existing biochemical food product analysis, we propose a metagenomic approach. Main benefit of this approach is the ability to perform next generation sequencing as a standard first step and then align the sampled data to genomes references of many organisms suspected to be present in the sample. Additionaly, if another organism is suspected at a later date, it is easy to reause the sampled data set to perform another analysis – in the biochemical analysis this would require expensive sample storage and performing more laboratory tests. We examined three approaches to metagenomic analysis – BLAST, Centrifuge and BWA MEM.