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Latest Activity

Alex Souza liked Michael Grogan's blog post Visualizing New York City WiFi Access with K-Means Clustering
Apr 12
Michael Grogan's blog post was featured

Visualizing New York City WiFi Access with K-Means Clustering

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.DataTo illustrate this point, a k-means clustering algorithm is used to analyse geographical data for…See More
Feb 24
Dexter D'Silva liked Michael Grogan's blog post Image Recognition with Keras: Convolutional Neural Networks
Feb 21
Tansel Arif liked Michael Grogan's blog post Image Recognition with Keras: Convolutional Neural Networks
Feb 20
Michael Grogan's blog post was featured

Image Recognition with Keras: Convolutional Neural Networks

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 recognitionAllow autonomous vehicles to detect obstructionsIdentify geographical features from satellite imagery These are just a few of many examples of how image classification will…See More
Feb 18
Will Pranzini liked Michael Grogan's blog post Python: Implementing a k-means algorithm with sklearn
Jan 24
Michael Grogan posted a blog post

K-Nearest Neighbors (KNN): Solving Classification Problems

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 distance between the test and…See More
Sep 29, 2018
Michael Grogan's blog post was featured

K-Nearest Neighbors (KNN): Solving Classification Problems

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 distance between the test and…See More
Sep 29, 2018
Michael Grogan posted a blog post

Variance-Covariance Matrix: Stock Price Analysis in R

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 analysing portfolio returns.If…See More
Jun 30, 2018
Michael Grogan's blog post was featured

Variance-Covariance Matrix: Stock Price Analysis in R

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 analysing portfolio returns.If…See More
Jun 30, 2018
Michael Grogan commented on Michael Grogan's blog post Python: Implementing a k-means algorithm with sklearn
"Hi Bhanu, You can find the link here with the dataset included: http://www.michaeljgrogan.com/k-means-clustering-python-sklearn/"
Jun 14, 2018
BhanuTeja Kasani commented on Michael Grogan's blog post Python: Implementing a k-means algorithm with sklearn
"Hi, Where can I find the "sample_stocks.csv" this link no more handles that file.(http://www.michaeljgrogan.com/kmeans-wss-clustering/) Regards, Bhanu."
Jun 10, 2018
Michael Grogan updated their profile
Jun 8, 2018
Michael Grogan and Ajay Sharma are now friends
Jun 8, 2018
Dan Butorovich commented on Michael Grogan's blog post Creating maps in R using ggplot2 and maps libraries
"In the US you can also use the zipcode package and merge the map to zipcodes from the zipcode data package which converts zips to lat./long coordinates. There is also a ggmap package that works well with map data and the results can be sent to…"
Apr 4, 2018
Ajay Sharma commented on Michael Grogan's blog post Data Cleaning and Wrangling With R
"Thanks Michael Grogan for this wonderful article, it is really helpful. I need one more help, in " 2. Mimic VLOOKUP by using the merge functions" section - while merging two data frames by common variable, missing values (NA) values are…"
Mar 9, 2018

Profile Information

My Web Site Or LinkedIn Profile
http://www.michaeljgrogan.com
Field of Expertise
Data Science, Machine Learning, AI, Business Analytics, Deep Learning
Your Job Title:
Data Scientist and Statistician
Interests:
Networking, New venture, Other

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…

Continue

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…

Continue

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…

Continue

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…

Continue

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