From 1d1b6b5de6204c1f60013e8cffa4fef2ea20832e Mon Sep 17 00:00:00 2001
From: brad-gh <32170926+brad-gh@users.noreply.github.com>
Date: Thu, 1 Sep 2022 09:45:29 -0400
Subject: [PATCH] add link to the dataset (#44)

What data set to use? Week 3 Day 3 ex 5 #36
---
 piscine/week03/day03/ex05/README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/piscine/week03/day03/ex05/README.md b/piscine/week03/day03/ex05/README.md
index 020dfd9..1988e06 100644
--- a/piscine/week03/day03/ex05/README.md
+++ b/piscine/week03/day03/ex05/README.md
@@ -3,6 +3,7 @@
 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
 
@@ -11,4 +12,4 @@ Preliminary:
 
 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. 
\ No newline at end of file
+**Warning**: Do no forget to evaluate the neural network on the **SCALED** test set.