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Michael Grogan's blog post was featured

Summarizing Economic Bulletin Documents with TF-IDF

A key strength of NLP (natural language processing) is being able to process large amounts of texts and then summarise them to extract meaningful insights.In this example, a selection of economic bulletins in PDF format from 2018 to 2019 are analysed in order to gauge economic sentiment. The bulletins in question are sourced from the European Central Bank website. tf-idf is used to rank words in a particular order…See More
Jul 12
Christian Schitton liked Michael Grogan's blog post Predicting Hotel Cancellations with Support Vector Machines and SARIMA
Jul 12
Alfred White liked Michael Grogan's blog post Deploying Python application using Docker and AWS
Jul 9
Michael Grogan's blog post was featured

Deploying Python application using Docker and AWS

The use of Docker in conjunction with AWS can be highly effective when it comes to building a data pipeline.Let me ask you if you have ever had this situation before. You are building a model in Python which you need to send over to a third-party, e.g. a client, colleague, etc. However, the person on the other end cannot run the code! Maybe they don't have the right libraries installed, or their system is not configured correctly.Whatever the reason, Docker alleviates this situation by storing…See More
Jul 8
Michael Grogan's blog post was featured

Predicting Hotel Cancellations with Support Vector Machines and SARIMA

Hotel cancellations can cause issues for many businesses in the industry. Not only is there the lost revenue as a result of the customer cancelling, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices.Data analytics can help to overcome this issue, in terms of identifying the customers who are most likely to cancel – allowing a hotel chain to adjust its marketing strategy accordingly.To investigate how machine learning can aid in this task, the…See More
Jul 5
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

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

Summarizing Economic Bulletin Documents with TF-IDF

Posted on July 11, 2019 at 12:28pm 0 Comments

A key strength of NLP (natural language processing) is being able to process large amounts of texts and then summarise them to extract meaningful insights.

In this example, a selection of economic bulletins in PDF format from 2018 to 2019 are analysed in order to gauge economic sentiment. The bulletins in question are sourced from the European Central Bank website. tf-idf is used to rank…

Continue

Deploying Python application using Docker and AWS

Posted on July 5, 2019 at 8:30am 0 Comments

The use of Docker in conjunction with AWS can be highly effective when it comes to building a data pipeline.

Let me ask you if you have ever had this situation before. You are building a model in Python which you need to send over to a third-party, e.g. a client, colleague, etc. However, the person on the other end cannot run the code! Maybe they don't have the right libraries installed, or their system is not configured correctly.

Whatever the reason, Docker alleviates this…

Continue

Multilevel Modelling of U.S. Home Loan Data

Posted on July 3, 2019 at 3:01am 0 Comments

The housing market has undergone quite a change in the past decade, with more stringent lending criteria for housing having been enforced.

A key objective of financial institutions is to minimise the risk of mortgage lending by ensuring that the debtor is ultimately able to repay the loan.

In this example, multilevel modelling techniques are used to analyse data from the Federal Home Loan Bank…

Continue

Predicting Hotel Cancellations with Support Vector Machines and SARIMA

Posted on July 2, 2019 at 3:00am 0 Comments

Hotel cancellations can cause issues for many businesses in the industry. Not only is there the lost revenue as a result of the customer cancelling, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices.

Data analytics can help to overcome this issue, in terms of identifying the customers who are most likely to cancel – allowing a hotel chain to adjust its marketing strategy accordingly.

To investigate how machine learning can…

Continue

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