Using Keras deep learning library to build a neural network for classifying the Iris flower dataset. The Iris dataset is a well-known dataset in machine learning, consisting of measurements of the sepal length, sepal width, petal length, and petal width of three different species of Iris flowers. The goal of the tutorial is to build a model that can accurately predict the species of an Iris flower based on its measurements.

The notebook provides step-by-step instructions for preparing the dataset, building a neural network using the Keras library, training the model, and evaluating its performance. The model architecture used is a fully connected neural network with two hidden layers, and the dataset is split into training and testing sets to assess the accuracy of the model.

Overall, the repo provides a clear and concise introduction to using Keras for building a neural network for classification tasks. It also emphasizes the importance of data preparation and visualization in understanding the dataset and building an effective model.

git clone
cd mlp-keras-iris-dataset
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
open iris.ipynb