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 »# Variance-Covariance Matrix: Stock Price Analysis in R (corpcor, covmat)

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 […]

Continue reading »# Sentiment Analysis with twitteR and tidytext

A sentiment analysis is a useful way of gauging group opinion on a certain topic at a particular point in time. Using social media data, let us see how we can use the twitteR library to stream tweets from Twitter […]

Continue reading »# Did Sentiment Analysis Predict Germany Would Win Against Sweden?

A sentiment analysis of Germany and Sweden just before the World Cup match on the 23rd June reveals a very interesting insight. To summarise, I used the twitteR library in R to download Twitter data (1000 tweets over the last […]

Continue reading »# Using twitteR and Sentiment Analysis to Predict the World Cup 2018 Winner

Could sentiment analysis be capable of revealing the World Cup 2018 winner? I don’t usually watch soccer matches, but the World Cup has become a key sporting event in the realm of data science. There are many techniques that companies […]

Continue reading »# neuralnet: Train and Test Neural Networks Using R

A neural network is a computational system that creates predictions based on existing data. Let us train and test a neural network using the neuralnet library in R. How To Construct A Neural Network? A neural network consists of: Input […]

Continue reading »# Decision Trees with Python

Let’s take a look at how we can construct decision trees in Python. A decision tree is a model used to solve classification and regression tasks. As we saw in our example for R, the model allows us to generate […]

Continue reading »# Voice Recognition with Python (speech_recognition and PyAudio)

Python has quite a handy library called speech_recognition, which we can use to create a program where a user’s voice can be transcribed into text. Let’s have a look at how we can do this. Note that I’m using Python […]

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