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+
+# Exercise 5 Multi classification example
+
+The goal of this exercise is to learn to use a neural network to classify a multiclass data set. The data set used is the Iris data set which allows to classify flower given basic features as flower's measurement.
+
+Preliminary:
+
+- [Load the dataset from `sklearn`.](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html)
+- Split train test. Keep 20% for the test set. Use `random_state=1`.
+- Scale the data using Standard Scaler
+
+1. Use the `LabelBinarizer` from Sckit-learn to create a one hot encoding of the target. As you know, the output layer of a multi-classification neural network shape is equal to the number of classes. The output layer expects to have a target with the same shape as its output layer.
+
+2. Train a neural network on the train set and predict on the test set. The neural network should have 1 hidden layers. The expected **accuracy** on the test set is minimum 90%.
+   _Hint_: inscrease the number of epochs
+   **Warning**: Do no forget to evaluate the neural network on the **SCALED** test set.