Between 27th and 29th April, 2018, the Data Science Society organized the world’s first online Academia Datathon – a global Data Science University competition. The event was held online and on-site at the participating Universities. Within 48 hours, a total of 130 participants from more than 6 countries and 10 universities experimented with exclusive cryptocurrency […]
TOOL USED R MICROSOFT AZURE MICROSOFT EXCEL SUMMARY The Dataset is time series data of crypto currency consisting of of 1869 observation and 21 features(each feature showing different crypto currency). Frequency of the observations is 5 min showing from date 18/01/18 to 24/01/18. BUSINESS UNDERSTANDING As the data belongs to the crypto exchange. Intraday short-term […]
Overview of the data flow.
Data => Parser => TTM => TF-IDF => Model => Document Clusters
SAP business case, TEAM: SAP-iens GAVE UP Abstract: In the proposed SAP case we are challenged with maximizing the profit obtained by the sales of a particular product, by setting its price over time, and choosing whether or not to enable one of several types of offers. The data that has been collected includes past […]
Alternatively you may format the article in a Jupyter Notebook and import it here. Business Understanding Data Understanding Data Preparation Visualizing Convnets with Tensorflow We start with importing our dependencies including the imFuctions which I made myself. In : import imFunctions as imf import tensorflow as tf import scipy.ndimage from scipy.misc import imsave import matplotlib.pyplot as […]
Although the data given to us has several snippets corresponding to each parent-subsidy pair, only some of the snippets reveal actual parent-subsidiary relationship. Therefore we felt that concatenating the snippets corresponding to each pair into one single article and then training can give the model more information about which text snippet actually reveals the parent-subsidy relationship. A Bidirectional GRU models each sentence into a sentence vector and then two attention networks try to figure out the important words in each sentence and important sentences in each document. In addition to returning the probability of company 2 being a subsidiary of company 1 the model as returns important sentences which triggered its prediction. For instance when it says Orcale Corp is the parent of Microsys it can also return that
Orcale Corp’s Microsys customer support portal was seen communicating with a server known to be used by the carbanak gang, is the sentence which triggered its prediction.
Case Kaufland¶ On 22.01.2018 Amazon opened Amazon Go – their first ever physical store without cashiers and checkout lines – customers just grab the products from the shelves and go. AI algorithms detect what product you have grabbed. Kaufland offers the unique opportunity to work with their internal data on a similar problem – developing […]
The teams formed by the Matchmaker. These are just suggestions as you may choose to go with different teammates…
Your case-study should follow the logic of the CRISP-DM Methodology (see also here). It is expected that for the *Deployment* part you would only propose some ideas, or visualizations.* You should aim at writing it so that it is understood as a stand alone text. So include anything vital for understanding the case and understanding your […]
ReceiptBank will provide an extensive dataset of invoices hidden inside PDF files, which you can uncover by developing an algorithm that detects how many documents are contained in each PDF file.