Lab | Introduction to Machine Learning and ML Workflow

Introduction

As a data analyst or data scientist, you will find that while using ML algorithms is the fun and glamorous part of your work. Preparing your data sometimes takes up 90% of your time. Therefore, learning to prepare your data properly is one of the most important skills you will need.

Getting Started

Open the main.ipynb file in the your-code directory. Follow the instructions and add your code and explanations as necessary. By the end of this lab, you will have learned how to prepare a dataset for most scikit-learn algorithms.

Deliverables

• main.ipynb with your responses.

Submission

Upon completion, add your deliverables to git. Then commit git and push your branch to the remote.

Resources

• Linear Interpolation
• Missing Data Imputation
• 7 Steps to Data Preparation