Datathon Telenor Solution – Ravens for Communication

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.

Datathon Telenor Solution – WRANGLING WITH DATA DROPS

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

Prediction Model for Crypto Currency in R

Bitcoins are cryptocurrency systems, that enable its users to exchange payments without passing through a central authority (Eg. Reserve Bank of India, Federal Bank etc). They were developed in 2008, using the Blockchain Technology. In the present article, methods to create prediction models have been implemented. The model considers a sample data of 3 months spaced over 5 minutes for each day. The Training data and Testing data are developed on that dataset for twenty bitcoins; viz: Bitcoin, Bitcoin Cash, Bitcoin Gold, Cardeno, Dash, Dogecoin, Eos, Ethereum, Ethereum Class, Iota, Lisk, Litrcoin, Monero, NEMcoin, Neo, Ripple, Stellar, Tether, Tron, Zcash.
The prediction models used are ARIMA, Exponential Smoothing and Neural Networks on R. The models calculate the values for the next time instant, i.e. next five minutes and the code developed goes on continuing it (predicting next 5-minute price) for all 288 time-points in a day.

The Wild Wild West: Data Gathering, Wrangling and Analyzing

Data can provide insights into market trends, customer behavior, and opportunities for growth. However, companies need a reliable strategy for collecting and analyzing this data. Without the right people, tools, algorithms, and processes, a data collection strategy can become costly and ineffective. Collecting and storing large amounts of data comes along with governance and compliance considerations that if not addressed can result in fines.