Datathon Telenor Solution – Game of Prediction (GoP)

Posted 3 CommentsPosted in Datathons Solutions

The objective of this analysis is to find out the ravens that are not reaching the destination on time. This kind of analysis would help us to scrutinize and understand the towers(ravens) who would require our utmost attention, in order to improve the reasons which are playing a major role in the delays.
The data-set talks about the networks between the towers (ravens). The land based communication happens with the help of signals.
A cellular network or mobile network is a communication network where the last link is wireless. This wireless transmission is done by a tower which comprises of a transmitter and a receiver (for the wireless transmission). The channel provides transmission for both the data as well as Voice transmission.
Every cellular network has different set of frequencies, to avoid any kind of overlapping and interference. Despite of many precautions for maintaining the setup, there are few parameters that are still impacting the transmission. Few parameters can be classified as:
 Infrastructure
 Interference between the frequencies
 Climatic conditions
 External Factors (Predators etc.)
For this our first approach is to create a “Decision Model” which can help us to give value to our business and help in improving the communication.
****** The tools that we using in order to predict is ******
1. Visual Analysis using different plots
2. Usage of ARMA (Auto-regressive- Moving- Average- Model)
The usage of this Decision Model will help us in forecasting the failure rate for next 4-7 days in regards to the Ravens.

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 – WRANGLING WITH DATA DROPS

Posted 2 CommentsPosted in Datathons Solutions

This article proposes very tractable approach to modelling changes in regime .The parameters of Time & Date are viewed in the outcome for this analysis.
In the 21st century cell phones are the most commonly used and important wireless technology. Cell phones are so common that it can be seen in everyone’s hand doesn’t matter what age group that individual belongs to, whether that individual is old, young or teenager belonging to any terrain .India has a population of 1.32 billion and comprises of nearly 340 million cell phones. It is used for communication ,messaging , downloading and uploading data on the internet.
There are times when an user counter issues in communications like termination of call,data drop in between of communication, wrong connections, etc. which may have an impact on the overall experience of the network subscribers. The telecom service providers have to implement certain data management technology to improve their infrastructure to minimize the effect of call drop and data drop to provide quality services to their customers.
Nearly all signals contain energy at harmonic frequencies, in addition to the energy at the fundamental frequency. If all the energy in a signal is contained at the fundamental frequency, then that signal is a perfect sine wave. The telecommunication signals also contains many harmonics which are affected a lot because of semiconductor interfacing , physical or digital barriers.
Keywords- Data drop,Call drop

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

Posted 1 CommentPosted in Team solutions

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: […]

Datathon Telenor Solution – Analysis Of Mobile Data Connectivity Delays

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Problem statement :This data set is regarding time series analysis on failure rate of ravens sending the messages from king’s landing to the north . This case study is an analogy on Telenor telecommunications and Game of Thrones . Due to the obstacles that caused the failure rate , various techniques and schemes are employed in the planning, design and optimization of raven networks to combat these propagation effects.

We have used R-studio for Exploratory Data Analysis.
As per the tasks given to us , we concluded that
1.Brass Raven Birdy has been delayed for the most number of times , followed by Brown raven ruby and Yellow raven Rio,
while Metallic Sunburst Raven Polly has been delayed for the least number of times , followed by Green Sheen raven Azul and Less combative raven zazu.
2. The family with most fails is Targerian , while with least fails is Lannister
3. The family Member with most fails is Petyr Baelish and with least fails is Euron .
We have done further analysis on predicting the fails for the next four days using TIME SERIES ANALYSIS

Datathon Telenor Solution – Analysing and Predicting Delays in Mobile Data Connectivity

Posted 9 CommentsPosted in Prediction systems

“You know nothing, Jon Snow ……”
is what Ygritte yells to Jon.

Here our situation is also the same as we know nothing about the TELENOR case until we have seen the dataset.

At first, When we heard about the Datathon as beginners, we were very excited to take apart in it.
At finally we received our datasets and here’s our first challenge to import the dataset into the programming platforms,
As we have faced some hurdles to import the dataset as the size of the dataset is around 4GB which has taken some time and put us in the situation :

What we don’t know is what usually gets us killed………………………– Petyr Baelish

we have mentioned above line to express our feeling that we don’t know what’s in the dataset but we want to explore through that.
At last, we are ready to Analysis What do Game of Thrones and Telecoms Have in Common?

At first, when we have gone through the dataset, We have noticed that the Telenor data contains 16 exciting columns with
30091754 jolted rows

When we have gone through the first analysis, we came to know that how complicated the data is, it contains many interesting aspects which we have done through the Exploratory Data Analysis.

Here our main challenge is to predict the fails in the next four days
At First, we have done Exploratory data analysis
(i) Top 10 ravens with fails :
RAVEN_NAME
Brass raven Birdy
Brown raven Ruby
Yellow raven Rio
Blue raven Axel
Razzle Dazzle Rose raven Cleo
Cadmium Red raven Bubba
Vain And Lazy raven Polly
Fearful Carrion raven Gizmo
Blast Off Bronze raven Zazu
Loving raven Maxwell

(ii) Top 10 ravens without fails:

RAVEN_NAME
Metallic Sunburst raven Polly
Green Sheen raven Azul
Less Combative raven Zazu
Weak raven Buddy
Copper raven Tweety
Spectral Yellow raven Zazu
Mythical raven Tiki
Cyber Grape raven Faith
Mysterious And Venerable raven Bubba
Shadow Blue raven Sammy

(iii) The family with most fails :

FAMILY_NAME
Targerian

(iv) The family with least fails :

FAMILY_NAME
Baelish

(v) The family member with most fails :

MEMBER_NAME
Petyr Baelish

(vi) The family member with least fails :

MEMBER_NAME
Euron

After the EDA we need to predict the future four days of delays in mobile data connectivity. To predict the four days delays we use Time Series analysis.

In Time Series Analysis we used three algorithms ARIMA, Simple Exponential Analysis, Recurrent Neural Networks.

We fitted the model with ARIMA and predict the failures of four days and fitted the model using another algorithm Simple Exponential Analysis.

And We used Recurrent Neural Networks for Prediction of failures.

After Fitting the three models using three different algorithms we evaluated by splitting the data into train and test.

We evaluated the best fit model by using the Root Mean Square Error. By considering the RMSE values of the three models, the model with the least RMSE value is taken as the best fit model.

In this case, considering the mobile failure dataset, RNN(Recurrent Neural Network)has the least RMSE value.

So, RNN is taken as the best fit model to predict the future four days of mobile data delays.

Based on the RNN algorithm the prediction of delays for the next four days based on the dataset
are 973776,973725,973674,973623 for 5 ,6,7,8, August 2018 respectively.

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 […]