Using PCA, we can reduce our features from (n) down to either 2 or 3 dimensions which can then be plotted. We will start by looking at our dataset as we downloaded from kaggle.

We can see 4 attributes, which is super for predictions, but does not allow us to plot a visualisation.

If you have arrived here and do not have a good understanding of SVM, then check this article first.

The Iris flower data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems. It is sometimes called Anderson’s Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. The data set consists of 50 samples from each of three species of Iris (Iris Setosa, Iris virginica, and Iris versicolor). Four features were…

The important job that SVM’s perform is to find a decision boundary to classify our data. This decision boundary is also called the **hyperplane**.

Lets start with an example to explain it. **Visually**, if you look at figure 1, you will see that it makes sense for purple line to be a better hyperplane than the black line. The black line will also do the job, but skates a little to close to one of the red points to make it a good decision line.

Visually, this is quite easy to spot.

Lets implement a neural network to classify customers according to their key features. Running neural networks in matlab is quite understandable once you understand the equations.

This is part 5 in my series on neural networks. You are welcome to start at part 1. Here are the previous articles explaining the cost function.

Source code used here can be downloaded from github. Spoiler alert, we will only get a 66% accuracy, but, thats the data we have ;-)

Just as a refresher, here is the dataset we downloaded from kaggle…

After understanding forward and backward propagation, lets move onto **calculating cost and gradient**. This is vital component to neural networks.

This is part 2 in my series on neural networks. You are welcome to start at part 1 or skip to part 5 if you just want the code.

So, to perform gradient descent or cost optimisation, we need to write a cost function which performs:

In this article we will deal with (3) and (4). You can click on the links above for a deep dive on forward/back prop.

So, just…

Backward propagation is a tricky subject to explain. Lets give it a go here showing the code and output data as we go.

This is part 3 in my series on neural networks. You are welcome to start at part 1 or skip to part 5 if you just want the code.

So, to perform gradient descent or cost optimisation, we need to write a cost function which performs:

**This article concentres on (2) backward propagation.**

So, we have simplified our neural network in figure 1 to only show the details to…

Forward propagation is an important part of neural networks. Its not as hard as it sounds ;-)

This is part 2 in my series on neural networks. You are welcome to start at part 1 or skip to part 5 if you just want the code.

So, to perform gradient descent or cost optimisation, we need to write a cost function which performs:

**In this article, we are dealing with (1) forward propagation.**

In figure 1, we can see our network diagram with much of the details removed. We will focus on…

Neural networks reflect the behavior of the human brain. They allow programs to recognise patterns and solve common problems in machine learning. This is another option to either perform classification of regression analysis. If you did not see my series of articles on the logistics regression, then have a look at those first as this series will use the same set of data. At Rapidtrade, we use neural networks to classify data and run regression scenarios.

So to **visualise** the data we will be working with in this series, see below. We will use this to train the network to…

Classifying your customer data can be a tricky affair. You need to get the questions right, then send your sales force out to collect the data for you. If you need a good package to do this, feel free to try Rapidtrade.

ps. You can download the source code here, while if you need an intro to logistics regression, then you can start with article 1.

So, lets look at this dataset downloaded from Kaggle.

Some points to keep in mind on the data:

1. We will need to convert all the text based columns to indexes or numbers.

2. The final…

If you have not seen my previous articles explaining logistics regression, then take a look here first at part 1 and part 2.

Once you are happy with the understanding, lets get into a good coding example. You can get the code here.

In this article, we will write a logistics regression to predict if a person who possesses certain attributes may develop a heart attack. You can download the dataset from Kaggle.

Have a good look at the data we have below. You can see we have **features** including age, sex, cp, restbps, chol, restecg and others. Finally, the…

I started www.rapidtrade.com many years ago and love my job. Coding, technology and data are my passions. Oh, and some crypto trading and cycling on the side