Posted junior, pepe, zenpanik, boryana, alex-efremovLeave a commentPosted in Datathon casesDatathon cases

ACADEMIA DATATHON CASE: THE A.I. CRYPTO TRADER

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In an attempt to make a case which is to be somewhat universally understandable by various types of students, the case is financial time-series prediction, while making it more engaging with the hot topic of cryptocurrencies. The case integrates knowledge from various sources – Crypto Currencies, Quantitative Finance and Machine learning. At the same time, the case is stratified as the teams solving it could complete various levels – as far as they could solve it.

Posted tomic, ognjen011, danicic, kovmil, suncica, tomic, liad, laura, anenov9 CommentsPosted in Bioinformatics, Learn, Team solutionsBioinformatics, Learn, Team solutions

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.

Posted nazrin5 CommentsPosted in Datathons Solutions, Learn, Team solutionsDatathons Solutions, Learn, Team solutions

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.

Posted madalina, stephan, pepe Mentors: hl5 CommentsPosted in Image recognition, Learn, Team solutionsImage recognition, Learn, Team solutions

Team GrBrGr, Case Kaufland

This paper presents a machine learning based approach for solving the business problem of identifying from pictures the products chosen by the Kaufland customers. These pictures are all taken from the same angle and typically show one or multiple products from the same category in a bag which makes the background and the bag recurrent elements.

Here we explored the method of transfer learning – using already trained and very deep NN like InceptionV3, InceptionResnetV2, VGG19, Resnet50 with combinations of retraining and no retraining of the existing layers. We solved this multiclassification problem of predicting the probabilities of each class of products by adding different final custom layers and we obtain the best result of 85% accuracy on a validation set of 20% (which was never seen by the training model).

This result was achieved with the model VGG19 which distinguished itself not only for providing the best categorical accuracy but also for training speed, execution speed once deployed and reduced resource consumption.