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@mshoaib123
Flag of Pakistan Abbottabad, Pakistan
Member since February 3, 2014
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mshoaib123

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My Main Expertise are as Follow. 1. Statistical Data Analysis which includes Regression Analysis, Time Series Data Analysis, ANOVA, ANCOVA, Probability and Probability Distribution, Bayesian Data Analysis, Geostatistics, Biostatistics, Agriculture Statistics etc. 2. Machine Learning and Deep Learning which includes Decision Tress, Regression Tress, Random Forest, Naive Bayes, ANN, U-Net, Seg-Net, Google Net and Res-Net etc. 3. Remote Sensing and GISc which Includes Environmental Data, Satellite Image Processing, Photogrammetry, ArcGISc, QGISData Analysis for Earth Sciences InSAR, DinSAR and Topographical Analysis etc. 4. Main Tools which includes Python libraries(Numpy, TensorFlow, Pytorch, Matplotlib, Pandas etc), R, Erdas Imagine, Envi, Google Earth, ArcGis, Qgis, Matlab, Knime, Statistica, Sata, Minitab, MS Excel for Data Analysis(Pivot Chart, Pivot tables, Dashboards etc), Weka, IBM SPSS etc. 5. Writing software which includes Latex, MS word, Bibtx and Reference Manager Mandeley etc.
$15 USD/hr
9 reviews
3.6
  • 83%Jobs Completed
  • 100%On Budget
  • 100%On Time
  • 22%Repeat Hire Rate

Portfolio

Recent Reviews

Experience

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 [login to view URL] Analytics [login to view URL] Warehousing [login to view URL] Science [login to view URL] Analytics [login to view URL] Learning [login to view URL] 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.

Education

Masters in Statistics

2010 - 2012 (2 years)

Computer Science

2012 - 2014 (2 years)

Qualifications

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 [login to view URL] 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.

Publications

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.

Certifications

  • Statistics 1
    75%

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