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 […]

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# 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 »# 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 […]

Continue reading »# Solving regression problems with neuralnet

We have already seen how a neural network can be used to solve classification problems by attempting to group data based on its attributes. However, what if we wish to solve a regression problem using a neural network? i.e. one […]

Continue reading »# Using Python’s MLPClassifier to classify stocks

Here, we are going to use the sklearn.MLPClassifier on a stock dataset, in an attempt to solve a classification problem. Specifically, we wish to classify a stock as either a dividend payer or non-dividend payer. Essentially, we have a dataset […]

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