Datathons Solutions

Datathon Telenor Solution – Game of Prediction (GoP)

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.

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3 thoughts on “Datathon Telenor Solution – Game of Prediction (GoP)

  1. 1
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    Team, please do provide process how you prepared data, build and evaluated model(s) for prediction. For any “scientific” article and for anybody who has access to data, whole process should be repetable, i.e. anyone should be able to take your code/work and get same end results. Also, focus of this case is “The main task is to predict the fails in the next four days (on both files).” which is clearly stated in case description, so please focus on adding this part of information into your article (more than just sentence “Model is giving better prediction for 1 day”).

  2. 0
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    Good desctiption of the data understending. Some recommendations – In the TOP Ravens tasks was good to describe how you have grouped the delays. Also will be good to have some more info about why is ARIMA better in this case then other methods, and maybe to add some results from others also in the article.

  3. 0
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    At beginning of document you attached zip file with code which may be overlooked. I would suggest moving it to end of document to become more visible. But overall, nice exploration and clean process

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