This is an introductory workshop to data science using Python. We'll be building a binary classification model to predict hospital readmission in patients with diabetes. A large focus will be on data pre-processing, which is a key part of the machine learning pipeline.

Key topics include:

• Exploratory data analysis
• Data cleaning
• Feature selection
• Supervised learning
• Binary classification
• Hyperparameter tuning

We'll be using these packages to do our analysis:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

Our dataset is from the UCI Machine Learning Repository which includes patient and hospital outcome data from 130 U.S. hospitals collected from 1999 to 2008.

Workshop slides are hosted here.

There are two options for setting up your environment:

1. On your local machine
2. On the cloud using Google Colab

If you're relatively new to Python and programming, we highly recommend starting with the cloud option which doesn't require any setup. The only requirement is a Gmail account.

If you decide to run the tutorial on your local machine, make sure that your environment is running on Python 3.6+ and has Jupyter notebook installed. You will need to install several pacakges which can be found in requirements.txt.

pip install -r requirements.txt

The detailed walkthrough can be found here. You can follow along with the fill-in-the-blank version here.