Multiclass Classification Of Flower Species

In this project tutorial we will discover how we can use Keras to develop and evaluate neural network models for multiclass classification problems. Primary purpose of this project are:

• How to load data from CSV and make it available to Keras.
• How to prepare multiclass classification data for modeling with neural networks.
• How to evaluate Keras neural network models with scikit-learn.

Iris Flowers Classification Dataset

In this project we will use the standard machine learning problem called the iris flowers dataset. This dataset is well studied and is a good problem for practicing on neural networks because all of the 4 input variables are numeric and have the same scale in centimeters. Each instance describes the properties of an observed flower measurements and the output variable is specific iris species. The attributes for this dataset can be summarized as follows:

1. Sepal length in centimeters.
2. Sepal width in centimeters.
3. Petal length in centimeters.
4. Petal length in centimeters.
5. Class.

tutorial source: Deep Learning With Python, Develop Deep Learning Models On Theano And TensorFlow Using Keras, Jason Brownlee