Creating Your First Machine Learning Classifier Model in Sklearn

But you don't know where to start, or perhaps you have read some theory, but don't know how to implement what you have learned. This tutorial will help you break the ice, and walk you through the complete process from importing and analysing a dataset to implementing and training a few different well known classification algorithms and assessing their performance. I'll be using a minimal amount of discrete mathematics, and aim to express details using intuition, and concrete examples instead of dense mathematical formulas. We will be classifying flower-species based on their sepal and petal characteristics using the Iris flower dataset . Kaggle, if you haven't heard of it, has a ton of cool open datasets, and is a place where data scientists share their work which can be a valuable resource when learning. The Iris flower dataset is rather small (consisting of only 150 evenly distributed samples), and is well behaved which makes it ideal for this project.