MACHINE LEARNING WITH PYTHON
Building a classification model for the Iris dataset
This Medium article is actually a short tutorial for classifying flowers under the Iris Dataset. This is a perfect way to kickstart Machine Learning and Artificial Intelligence with Python.
What is the Iris Dataset?
The Iris Dataset is a multivariate dataset about flowers introduced by Ronald Fisher in his 1936 Paper.
We can use it to classify iris flowers among three species (setosa, versicolor, or virginica) from measurements of sepals and petals’ length and width. The iris data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
- A Google account
- Basic knowledge of Python and Jupyter
- Go to colab.research.google.com (Google Colaboratory — by Google Research)
- Create a new Jupyter notebook (.ipynb) and call it ‘Iris’.
Note: For this project, we are only using one file. So, if you have sci-kit learn and pandas installed on your PC, you can just create a Python file, like Iris.py, on your PC.
Creating the model using python
First up, the imports:
We will be using pandas and the Decision Tree Classifier from sci-kit learn