Binary Classification & Explainability for Data Science!

What you’ll learn
Hands on Data Science project and experience that can be applied across industries.
Build a machine learning model that can be used for binary classification problems – will user do A or B?
Understand the steps required to build a machine learning model – data collection, exploration, transformation, model selection, training and evaluation.
Understand explainability in Data Science using SHAP – what is impacting the model’s prediction?

Requirements
Basic understanding of Python and Data Science concepts

Description
You will build a binary classification, machine learning model to predict if a person is looking for a new job or not. You’ll go through the end to end project– data collection, exploration, feature engineering, model selection, data transformation, model training, model evaluation and model explainability. We’ll brainstorm ideas throughout each step and by the end of the project you’ll be able to explain which features determine if someone is looking for a new job or not.The template of this Jupyter Notebook can be applied to many other binary classification use cases. Questions like — will X or Y happen, will a user choose A or B, will a person sign up for my product (yes or no), etc. Hopefully you will find be able to apply the concepts learned here to some useful projects of your own! This course is best for those with basic Python knowledge and basic Data Science understanding. For more beginner levels, feel free to dive in and ask questions along the way. For more advanced levels, this can be a good refresher on model explainability, especially if you have limited experience with this. Hopefully you all enjoy this course and have fun with this project!

Who this course is for
Data Scientists, Machine Learning Engineers, Data Analysts, and all others interested in Data Science!