Let’s take a look at how we can construct a decision tree 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 […]

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# 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 […]

Continue reading »# Regression-based neural networks with keras

For this example, we use a linear activation function within the keras library to create a regression-based neural network. We will use the car sales dataset again (as we did with neuralnet in R). Firstly, we import our libraries. Note […]

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

A neural network is a computational system frequently employed in machine learning to create predictions based on existing data. In this example, we will train and test a neural network using the neuralnet library in R. A typical neural network […]

Continue reading »# Cumulative Binomial Probability with R and Shiny

In conducting probability analysis, the two variables that take account of the chance of an event happening are N (number of observations) and λ (lambda – our hit rate/chance of occurrence in a single interval). When we talk about a […]

Continue reading »# statsmodels and sklearn: Linear and Logistic Regression Modelling in Python

The statsmodels and sklearn libraries are frequently used when it comes to generating regression output. While these libraries are frequently used in regression analysis, it is often the case that a user needs to work with different libraries depending on […]

Continue reading »# plyr and dplyr: Data Manipulation in R

The purpose of the plyr and dplyr libraries in R is to manipulate data with ease. As we’ve seen in a previous post, there are various methods of wrangling and summarising data in R. However, wouldn’t it be great if […]

Continue reading »# How to use the MySQL Installer and MySQL Workbench

In a previous tutorial, we saw how a database and table can be created in mySQL. Here is how you can run this on a local machine with the mySQL Installer and mySQL Workbench. 1. Firstly, make sure you have […]

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