An application of user segmentation and predictive modelling at a telecom company

Internship report presented as partial requirement for obtaining the Master’s degree in Advanced AnalyticsInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics“The squeaky wheel gets the grease” is an American proverb used to convey the notion that only those who speak up tend to be heard. This was believed to be the case at the telecom company I interned at – they believed that while those who complain about an issue (in particular, an issue of no access to the service) get their problem resolved, there are others who have an issue but do not complain about it. The latter are likely to be dissatisfied customers, and must be identified. This report describes the approach taken to address this problem using machine learning. Unsupervised learning was used to segment the customer base into user profiles based on their viewing behaviour, to better understand their needs; and supervised learning was used to develop a predictive model to identify customers who have no access to the TV service, and to explore what factors (or combination of factors) are indicative of this issue

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead