About Asel Mendis

Asel Mendis is a GIS Analyst, Data Science Editor and Writer who continues to learn and uses Statistics, Machine Learning and technology to create insights and value. He is a Contributing Editor at KDnuggets, the largest Data Science knowledge sharing platform in the world. He has interests in Machine Learning, Data Visualization and Statistics using R and Python. He has a Master of Analytics from the Royal Melbourne Institute of Technology in Melbourne specializing in Applied Statistics. Follow him on Twitter (@aselmendis) or connect with him on LinkedIn (https://www.linkedin.com/in/asel-mendis-a620399b/).

Asel Mendis Posts (13)

  • How Bad is Multicollinearity? - 17 Sep 2019
    For some people anything below 60% is acceptable and for certain others, even a correlation of 30% to 40% is considered too high because it one variable may just end up exaggerating the performance of the model or completely messing up parameter estimates.
  • Silver BlogTypes of Bias in Machine Learning - 29 Aug 2019
    The sample data used for training has to be as close a representation of the real scenario as possible. There are many factors that can bias a sample from the beginning and those reasons differ from each domain (i.e. business, security, medical, education etc.)
  • Silver BlogStatistical Modelling vs Machine Learning - 14 Aug 2019
    At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.
  • Is Bias in Machine Learning all Bad? - 23 Jul 2019
    We have been taught over our years of predictive model building that bias will harm our model. Bias control needs to be in the hands of someone who can differentiate between the right kind and wrong kind of bias.
  • Gold BlogWhat’s wrong with the approach to Data Science? - 10 Jul 2019
    The job ‘Data Scientist’ has been around for decades, it was just not called “Data Scientist”. Statisticians have used their knowledge and skills using machine learning techniques such as Logistic Regression and Random Forest for prediction and insights for longer than people actually realize.
  • An Overview of Outlier Detection Methods from PyOD – Part 1 - 27 Jun 2019
    PyOD is an outlier detection package developed with a comprehensive API to support multiple techniques. This post will showcase Part 1 of an overview of techniques that can be used to analyze anomalies in data.
  • Silver BlogJupyter Notebooks: Data Science Reporting - 06 Jun 2019
    Jupyter does bring us some benefits of being able to organize code but many of us still find ourselves with messy and unnecessary code chunks. Here are some ways including a NEW EXTENSION that anyone can use to begin organizing your code on your notebooks.
  • Naive Bayes: A Baseline Model for Machine Learning Classification Performance - 07 May 2019
    We can use Pandas to conduct Bayes Theorem and Scikitlearn to implement the Naive Bayes Algorithm. We take a step by step approach to understand Bayes and implementing the different options in Scikitlearn.
  • Gold BlogData Visualization in Python: Matplotlib vs Seaborn - 19 Apr 2019
    Seaborn and Matplotlib are two of Python's most powerful visualization libraries. Seaborn uses fewer syntax and has stunning default themes and Matplotlib is more easily customizable through accessing the classes.
  • Which Face is Real? - 02 Apr 2019
    Which Face Is Real? was developed based on Generative Adversarial Networks as a web application in which users can select which image they believe is a true person and which was synthetically generated. The person in the synthetically generated photo does not exist.

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