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Statistical Analysis of Yahoo Finance
“Excellent freelancer!! The work was delivered timely and professionally.”snlquick 2 years ago
SS Statistics (BS-17)Aug 2015
BI AnalystMar 2013 - May 2013 (2 months)
Our specialization in Data Management and Business Consulting helps us in delivering valuable solutions to our clients, enabling them in making informed decisions in limited amount of time. We are capable of delivering results faster with significantly less risk and initial investment. Our key service offerings are: usiness Intelligence Analytics Warehousing Science Analytics Learning Quality Assurance
Data ScientistDec 2012 - Feb 2013 (2 months)
Working on Medical Lien Management, California, USA projects, performing data analysis on medical insurance settlements; is making exploratory and predictive models for insurance claims and settlements, performing data pre-processing and transformations, Supervised and Unsupervised learning and data mining, Stochastic patterns of the data, Monte Carlo Simulations, manifold techniques for non-linear data, regression, time-series, classification and cluster analysis using various tools.
Masters in Statistics2010 - 2012 (2 years)
Bachelors in Science2006 - 2008 (2 years)
Intermediate2004 - 2006 (2 years)
Matric2002 - 2004 (2 years)
Remote Sensing and Geographical Information Science2014 - 2016 (2 years)
Data Mining in Insurance Claims(DMICS) Two-way mining for extreme values
In insurance claims extreme values are inevitable and cannot be discarded for predictive model building. Moreover, settling insurance claims involves many objections, human sentiments and unseen factors which are hard to be estimated. This simple fact presents the greatest challenge to analysts working on such problems. This paper presents an optimal approach to minimize the effects of this problem on predictive analysis. The data in question includes insurance settlement cases.
Comparison of Maximum Likelihood Classification Before and After Applying Weierstrass Transform
The aim of this paper is to use Maximum Likelihood (ML) Classification on multispectral data by means of qualitative and quantitative approaches. Maximum Likelihood is a supervised classification algorithm which is based on the Classical Bayes theorem. It makes use of a discriminant function to assign pixel to the class with the highest likelihood.
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