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@mshoaib123
Flag of Pakistan Islamabad, Pakistan
Member since February, 2014
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Muhammad Shoaib

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My research has spanned from general machine learning and data mining to privacy preserving data Science, text mining, semi-supervised learning, active learning, Statistics, Probability and Probability Distributions, Stochastic Process and knowledge management. Most of my work has focused on developing and using machine learning & data mining approaches to solve large-scale problems in Predicting Insurance Claims. My current interests lie at the intersection of Machine Learning/Data Mining. I’m interested in solving large-scale and high impact social problems using data driven and evidence based methods. A lot of government, civic, and non-profit organizations are realizing the value of better data and have been focusing on improving data collection and data standardization. My goal is to build on these efforts, and work with these organizations to use this data to help improve outcomes for these organizations. I’m also interested in approaches that allow these organizations to efficiently measure the impact of such interventions and programs in order to do better resource allocation and focus on efforts that lead to better outcomes.
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Portfolio

Seneste bedømmelser

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Erfaring

Data Scientist

Dec 2013

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 Scientist

Feb 2013 - Dec 2013 (10 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 Carle Simulations, manifold techniques for non-linear data, regression, time-series, classification and cluster analysis using various tools.

Uddannelse

Masters in Statistics

2010 - 2012 (2 years)

Computer Science

2012 - 2014 (2 years)

Certificeringer

Content-based image retrieval (2013)

IEEEXplore

Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases for a recent scientific overview of the CBIR field). Content-based image retrieval is opposed to concept-based approaches. "Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because most web-based image search engines rely purely on metadata and this produces a lot of garbage in the results. Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the a system that can filter images based on their content would provide better indexing and return more accurate results.

Artificial Neural Networks (2013)

IEEEXPLORE

In computer science and related fields, artificial neural networks are computational models inspired by animals' central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected "neurons" that can compute values from inputs by feeding information through the network.

Publikationer

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.

Certificeringer

  • Statistics 1
    75%

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