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 »# Category: Data Science

# 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 be […]

Continue reading »# Analysing UK Traffic Trends with PCA

The PCA (also known as Principal Component Analysis) is quite a handy tool for solving unsupervised learning problems. In other words, PCA can allow us to group unsupervised data into meaningful clusters, and visualize this in a way that allows […]

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. The previous analysis […]

Continue reading »# Kalman Filter: Modelling Time Series Shocks with KFAS in R

We have already seen how time series models such as ARIMA can be used to make time series forecasts. While these models can prove to have high degrees of accuracy, they have one major shortcoming – they do not account […]

Continue reading »# Working with panel data in R: Fixed vs. Random Effects (plm)

Panel data, along with cross-sectional and time series data, are the main data types that we encounter when working with regression analysis. Types of data Cross-Sectional: Data collected at one particular point in time Time Series: Data collected across several […]

Continue reading »# Robust Regressions: Dealing with Outliers

It is often the case that a dataset contains significant outliers – or observations that are significantly out of range from the majority of other observations in our dataset. Let us see how we can use robust regressions to deal […]

Continue reading »