Datathon Telenor Solution – Exploratory Data & Predictive Analytics -Analogy of Game of Thrones With Telenor Telecommunications

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->This datasets is regarding the time series analysis on the failure rate of RAVENS sending the messages from kings landing to the north.
It depicts the analogy of Telenor communication  and Game of Thrones.
-> Sending ravens is one of the most fundamental parameters in mobile communications engineering.
For land-based mobile communications, the received raven variation is primarily the result of multipath fading caused by obstacles such as buildings (or clutter) or terrain irregularities; the distance between link end points; predatory animals, and interference among multiple transmissions, for example wars.
This inevitable raven variation is the cause of communication dropping, one of the most significant quality of service measure in operative communication. For this reason, various techniques and schemes are employed in the planning, design and optimization of raven networks to combat these propagation effects.
This normally covers the network physical configuration which include all aspects of network infrastructure deployment such as locations of base nests; additional food; sometimes guards, etc.
A typical example of these schemes and techniques is the use of models for flight prediction based on measured data.
Based on one month data with flight fails, the participants have to make time-series analysis and predict the future amount of fails.

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 […]

Datathon Telenor Solution – Ravens for Communication

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It is a very well known fact that Exploratory Data Analysis is cornerstone of Data Analysis.
On the analysis of data it is evident that Brass Raven Birdy as the most failed and the Metallic Raven Sunburst Polly is the most successful raven. Also Targeryan family has the most Raven fails whereas Baelish family has the least failures,and among the family of Baelish, Peter Baelish has the most failure rate and Euron has the least failures.
ARIMA model is used for predicting the number of failures for the next 4 days.

IBM Guideline

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1. Overview of IBM Watson Data Platform IBM Watson Data Platform brings together data management, governance, preparation, and analysis capabilities into a common framework. The platform integrates IBM Data Science Experience, IBM Data Catalog, and IBM Data Refinery. Watson Data Platform also integrates a wide range of IBM Cloud services and connections to cloud and […]

Microsoft Guideline

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Microsoft Azure Machine Learning Studio Microsoft Azure Machine Learning Studio has available a large number of machine learning algorithms, along with modules that help with data input, output, preparation, and visualization. Using these components you can develop a predictive analytics experiment, iterate on it, and use it to train your model. Then with one click […]

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 […]