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mirevich

Igor

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Predict survival on the Titanic
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this Kaggle challenge, competitors do the complete analysis of what sorts of people were likely to survive. This report is applied the tools of machine learning based on generalized linear model via penalized maximum likelihood (glmnet) to predict which passengers survived the tragedy. As many observations have been missing, there was used some technics for restore it. Also applied feature engeneering for improving final results (up to 0.81818).
Predict survival on the Titanic
In this Kaggle challenge, I did complete the analysis of what sorts of people were likely to survive. I apply the tools of machine learning based on Support Vector Machines to predict which passengers survived the tragedy.
Dashboard example
Dashboard was build based on public data from San Francisco Bikeshare.
Regression models
Based on car parameters decide with type of transmission are better.
World Bank Open Data Application
World Bank provide [open data](http://data.worldbank.org/) about development in countries around the globe. This data include information: - 215 country and 49 regions - 148 indicators - From 1960 to 2014 years
Slides for App which predict next word
This is presentation for my simple App which predict next word
Publish Document
Coursera Milestone Report
This is Milestone report for Capstone on Data Science Specialization on Coursera.
Health and economic damages due to severe weather events
This is second assignments with course Reprodusible Research on Coursera