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My fictitious firm, GDSMC Global, is a security consultancy focusing on supporting governments around the world in understanding, predicting, and stopping terrorism attacks. Our goal is to allow individual nation states to better deploy security resources to reduce the likelihood of successful terrorism in the future, and to understand what are the likely coming costs of terrorism so that resources can be set aside, in advance, to rebuild after inevitable and unfortunate attack.Although governments can submit their own internal security data to us for study, our models are constructed using the Global Terrorism Database (GTD) maintained by the National Consortium for the Study of Terrorism and Responses to Terrorism at the University of Maryland ( http://start.umd.edu/gtd/ ).
Imagine you are the front runner for democratic party primaries in 2008 - 1 week into elections you have won a few states(Obama) and your opponent (Hillary) is catching up. How you can use analytics to predict which of the remaining seats will you win using demographic data from states you won and lost. Can we accurately classify win or lose for the remaining seats using data. Can we use this prediction to find out factors which impact our chances of winning and improve our appeal to places that are predicted loss thus improving our chances of winning? Well , Let's find out!
Exploratory Analysis, Data Cleaning, Data Wrangling, Feature Engineering , Ensemble Modelling and Model Pipeline Creation
Set of R codes on Data Cleaning , Analysis and Interpretation. Running Linear and Logistic Regressions and doing classification in R.
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4My fictitious firm, GDSMC Global, is a security consultancy focusing on supporting governments around the world in understanding, predicting, and stopping terrorism attacks. Our goal is to allow individual nation states to better deploy security resources to reduce the likelihood of successful terrorism in the future, and to understand what are the likely coming costs of terrorism so that resources can be set aside, in advance, to rebuild after inevitable and unfortunate attack.Although governments can submit their own internal security data to us for study, our models are constructed using the Global Terrorism Database (GTD) maintained by the National Consortium for the Study of Terrorism and Responses to Terrorism at the University of Maryland ( http://start.umd.edu/gtd/ ).
Imagine you are the front runner for democratic party primaries in 2008 - 1 week into elections you have won a few states(Obama) and your opponent (Hillary) is catching up. How you can use analytics to predict which of the remaining seats will you win using demographic data from states you won and lost. Can we accurately classify win or lose for the remaining seats using data. Can we use this prediction to find out factors which impact our chances of winning and improve our appeal to places that are predicted loss thus improving our chances of winning? Well , Let's find out!
Exploratory Analysis, Data Cleaning, Data Wrangling, Feature Engineering , Ensemble Modelling and Model Pipeline Creation
Set of R codes on Data Cleaning , Analysis and Interpretation. Running Linear and Logistic Regressions and doing classification in R.