One of the main limitations of regression analysis is when one needs to examine changes in data across several categories. This problem can be resolved by using a multilevel model, i.e. one that varies at more than one level and […]

Continue reading »# Author: Michael Grogan

# 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 […]

Continue reading »# Predicting Irish electricity consumption with neural networks

In this example, neural networks are used to forecast energy consumption of the Dublin City Council Civic Offices between March 2011 – February 2013.

Continue reading »# Keras with R: Predicting car sales

Keras is an API used for running high-level neural networks. The model runs on top of TensorFlow, and was developed by Google. In this particular example, a neural network will be built in Keras to solve a regression problem, i.e. […]

Continue reading »# Text Mining and Sentiment Analysis with Keras

When it comes to text mining, sentiment analysis – or gauging sentiment of a particular chunk of text based on its words – is becoming increasingly popular within this area. Here is how we can conduct sentiment analysis using Keras.

Continue reading »# 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.

Continue reading »# Keras: Regression-based neural networks

Keras is an API used for running high-level neural networks. The model runs on top of TensorFlow, and was developed by Google. The main competitor to Keras at this point in time is PyTorch, developed by Facebook. While PyTorch has […]

Continue reading »# 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. […]

Continue reading »# Cross Correlation Analysis: Analysing Currency Pairs in Python

When working with a time series, one important thing we wish to determine is whether one series “causes” changes in another. In other words, is there a strong correlation between a time series and another given a number of lags? […]

Continue reading »# Huber vs. Ridge Regressions: Accounting for Outliers

In a previous tutorial, we saw how we can use Huber and Bisquare weightings to adjust for outliers in a dataset. These weightings allow us to adjust our regression analysis to give less weight to extreme values.

Continue reading »