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Michael Grogan
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http://www.michaeljgrogan.com
Field of Expertise
Data Science, Machine Learning, AI, Business Analytics, Deep Learning
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Data Scientist and Statistician
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Michael Grogan's Blog

Visualizing New York City WiFi Access with K-Means Clustering

Posted on February 19, 2019 at 3:44am 0 Comments

Visualization has become a key application of data science in the telecommunications industry.

Specifically, telecommunication analysis is highly dependent on the use of geospatial data. This is because telecommunication networks in themselves are geographically dispersed, and analysis of such dispersions can yield valuable insights regarding network structures, consumer demand and availability.

Data



To illustrate this point, a k-means clustering algorithm is used…

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Image Recognition with Keras: Convolutional Neural Networks

Posted on February 17, 2019 at 11:00am 0 Comments

Image recognition and classification is a rapidly growing field in the area of machine learning. In particular, object recognition is a key feature of image classification, and the commercial implications of this are vast.

For instance, image classifiers will increasingly be used to:

  • Replace passwords with facial recognition
  • Allow autonomous vehicles to detect obstructions
  • Identify geographical features from satellite imagery



These…

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K-Nearest Neighbors (KNN): Solving Classification Problems

Posted on September 28, 2018 at 4:50am 0 Comments

In this tutorial, we are going to use the K-Nearest Neighbors (KNN) algorithm to solve a classification problem. Firstly, what exactly do we mean by classification?

Classification across a variable means that results are categorised into a particular group. e.g. classifying a fruit as either an apple or an orange.

The KNN algorithm is one the most basic, yet most commonly used algorithms for solving classification problems. KNN works by seeking to minimize the…

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Variance-Covariance Matrix: Stock Price Analysis in R

Posted on June 30, 2018 at 4:30am 0 Comments

The purpose of a variance-covariance matrix is to illustrate the variance of a particular variable (diagonals) while covariance illustrates the covariances between the exhaustive combinations of variables.

Why do we use variance-covariance matrices?

A variance-covariance matrix is particularly useful when it comes to analysing the volatility between elements of a group of data. For instance, a variance-covariance matrix has particular applications when it comes to…

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