This respository contains solutions to a set of problems concerning neural networks using Tensorflow. For the purpose of this example we apply the Iris data set previously used in another problem set you can you find HERE!

Previously, in the repository linked above we looked at loading the Iris csv data with Python and inspecting it using Matplotlib's Pyplot. We then investigated how to determine the best fit line for the data set and various subsets using Numpy's polyfit and by also writing our own gradient descent algorithm. We took a brief look a Scatterplot matrices using a relatively new library called Seaborn.

In this Problem Set we look at the following issues:

• Preparing the data
• Splitting data in two subsets: Training and Testing
• Creating a Neural Network Model
• Training the Model using the Training data subset
• Evalutation of the Model's accuarcy using the Testing data subset
• Prediction of class specification of Iris Flower species using the Model

All of the Python code relating to the Problem Set can be found in the juptyer notebook housed in this repository, which also contains a wide range of Markdown notes documenting and describing the tasks being carried out.

Click this link to jump straight into the Notebook

The data set contains samples of 3 different types of Iris plant.

• Setosa
• Versicolor
• Virginica

The data samples contain 50 samples for each of the different types of species. Each sample is composed of 4 distinct features of the plants including:

• Sepal Length
• Sepal Width
• Petal Length
• Petal Width

Should you want to clone this repository and adapt the code for yourself, you will need the prerequisites that are mentioned below otherwise you can simply:

1. Clone the repository

git clone https://github.com/damiannolan/iris-neural-network.git

2. Start Jupyter Notebook

jupyter notebook

If you are new to Python you may want to follow to the instructions below for installing a number of different dependencies.

You can download for Windows by following instructions here!. If you are on MacOS I recommend using Homebrew

brew install python3

Alternatively you can download a larger distribution which will include a number of different packages from Anaconda.

You can obtain Numpy, SciPy and Matplotlib with:

brew tap homebrew/science && brew install python numpy scipy matplotlib

pip3 install jupyter

pip3 install tensorflow

pip3 install keras