We hear a lot about Machine Learning, but it’s just one part of a bigger process. Before applying any algorithm to a data set, discovery and preparation are needed. This hands-on workshop will cover an end-to-end classification project, from importing the data to evaluating a model performance. After this tutorial, you will have completed a step by step Machine Learning workflow.

Part one: Grab your spade and dig in! Pandas is a popular tool that will allow us to efficiently conduct Exploratory Data Analysis. After loading the data set we’ll use in this workshop, we’ll have a first look at it using Pandas and start cleaning it. We’ll also use visualisation to gain more insights and continue to prepare our data.

Part two: Where the Ma(th)gic happen. In this part, we’ll introduce the scikit-learn library. We'll split the data into training and testing sets and start pre-processing. Then we’ll choose, tune and train a Machine Learning model and finally evaluate its performance using a confusion matrix.

During this workshop, we will fill in a pre-prepared Jupyter notebook together, explaining each step to get a good understanding of the process. You will also have a guided exercise notebook to reinforce your learning on unseen data.

To get the most out of this workshop you will need Python 3, pandas, matplotlib, scikit-learn and jupyter installed. Please refer to the documentation of your operating system of choice or search on the Internet how to install the packages.

Sandrine Pataut

Affiliation: QBE Insurance

From Paris import Sandrine as SP

SP is a French Mathematician turned Data Scientist. She is currently working in financial services and is active in the London tech scene as an open source community leader.

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