Datathon NSI Mentors’ Guidelines – Economic Time Series Prediction

Posted 1 CommentPosted in GD2018 Mentors, Mentors

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]

Datathon Telenor Mentors’ Guidelines – On TelCo predictions

Posted Leave a commentPosted in GD2018 Mentors, Mentors

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]

Datathon Sofia Air Mentors’ Guidelines – On IOT Prediction

Posted Leave a commentPosted in GD2018 Mentors, Mentors

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]

Datathon Kaufland Mentors’ Guidelines – On Predictive Maintenance

Posted Leave a commentPosted in GD2018 Mentors, Mentors

In this article, the mentors give some preliminary guidelines, advice, and suggestions to the participants for the case. Every mentor should write their name and chat name at the beginning of their texts so that there are no mix-ups with the other mentors. By rules, it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]

Antelope SAP

Posted 2 CommentsPosted in Team solutions

The current paper examines the factors that influence the increase of the
sales volume of a retailer. The aim of the study is to create an accurate model with high explanatory
power which accounts for the promotional and competitor effects on the quantity sold as well
as to identify the main volume uplift drivers. That information could be useful when designing
marketing strategies in order to gain a competitive advantage over the other market players.

Tiny smart data modelled with a not-so-tiny smart model – the Case of SAP

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Tiny smart data modelled with a not-so-tiny smart model Introduction Metadata Business Understanding Data Understanding Data Preparation Modelling Evaluation Deployment Conclusion Metadata Case: The SAP Case – Analyze Sales Team: Chameleon Project URL: https://github.com/Bugzey/Chameleon-SAP Memebers: Stefan Panev (stephen.panev@gmail.com), Metodi Nikolov (metodi.nikolov@gmail.com), Ivan Vrategov (ivanvrategov@gmail.com, Radoslav Dimitrov (rdimitrov@indeavr.com) Mentors: Alexander Efremov(aefremov@gmail.com) Agamemnon Baltagiannis (agamemnon.baltagiannis@sap.com) Team Toolset: […]

THE A.I. CRYPTO TRADER: cryptomonkeys

Posted 3 CommentsPosted in Datathons Solutions, Learn, Team solutions

  The Folder where you can locate the data & the code (using Jupyter). Академичен дататон¶ In [1]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns In [2]: WRITE_TO_FILE = False In [3]: plt.style.use(‘bmh’) pd.options.display.precision = 3 Зараждане на данните от price_data.csv¶ In [4]: %%time raw_df = pd.read_csv(‘./raw_data/price_data.csv’) raw_df.info() # CPU […]

The SAP Case using KNIME and Multiple Linear Regression Method

Posted 5 CommentsPosted in Team solutions

SAP Case Team Mentors:  Agamemnon Baltagiannis SAP Case Team: Abderrahim Khalifa                                                                | Morocco Andrei Deusteanu (andrei.deusteanu@gmail.com) | Romania Julian Borisov (julian.borisov@yahoo.com)        […]

Datathon Ontotext Mentors’ Guidelines – Text Mining Classification

Posted Leave a commentPosted in GD2018 Mentors, Mentors

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]

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.