In this project, I have use Keras to develop and evaluate neural network models for multi-class classification project

In this project , you will know:

• How to load data from CSV and make it available to Keras.

• How to prepare multi-class classification data for modeling with neural networks.

• How to evaluate Keras neural network models with scikit-learn.

In this project, we will use the standard machine-learning problem called the iris flowers dataset: http://archive.ics.uci.edu/ml/datasets/Iris

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.

This is a multi-class classification problem, meaning that there are more than two classes to be predicted, in fact there are three flower species. This is an important type of problem on which to practice with neural networks because the three class values require specialized handling.

The iris flower dataset is a well-studied problem and as such we can expect to achieve model accuracy in the range of 95% to 97%. This provides a good target to aim for when developing our models in this project. We have download the iris flowers dataset for free and place it in the project directory with the filename “iris.csv“.You can also directly download the dataset: