Datathon – HackNews – Solution – Insight Hunters

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In order to do the following we have to undergo the process of text cleaning, understanding the text. We had to find a way in order to split the data and form a data frame which consists of the following columns.News_TextNews_NumberNews_TypeThe data has lots of fillers which had to be removed and some rows where news_numbers and type were missing. In order to clean the data we had to remove the fillers using the NLTK stop words filtration. Later on we tokenized the data using the word_tokenizer from the nltk package.The next important step was to lemmatize/stem the data to remove the tense from the words and normalize the words. Even though it was a time consumption process the results were promising.XGBoost has capability to handle the imbalanced dataset by using the parameter Scale_Pos_weight. we can do threshold probability throttling to increase the sensitivity by sacrificing the reasonable specificity.Evaluation:- This process is kind of tricky for the train data set provided, as the data was highly imbalanced, the dependent feature/variable had imbalanced classes

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.