Python Tutorial: Handling missing data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/practicing-machine-learning-interview-questions-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome to Preparing for machine learning interview questions! My name is Lisa Stuart and I am a Data Scientist. In this course I will cover most of the topics that will help you succeed in a Machine Learning interview using Python. Being an interview prep course, you will find the concepts and exercises more challenging than standard DataCamp courses, so please ensure that you’re comfortable with the prerequisite courses Supervised Learning with scikit-learn and Unsupervised Learning in Python. We're going take what you learned so far and step it up so that by the end, you'll set yourself apart from other potential candidates in a ML interview. In this course, we'll start off chapter 1 by covering data pre-processing and visualization. The second and third chapters will be dedicated to supervised learning and unsupervised learning, and the fourth and final chapter will touch on Model selection and evaluation. From a high level, the machine learning pipeline using scikit-learn looks something like this. You import the modules you need to use, instantiate an object which you then fit and predict. But, there is more to the story, so the pipeline we're going to use incorporates other important steps. Don't worry about the details, we'll start slow and build as we go, continually orienting ourselves to where we are in the process. In the remainder of this video lesson, we’re going to discuss how to find missing values as well as the impact of different techniques designed to fill missing data as a pre-processing step in the Machine Learning framework. This is an integral part of Exploratory Data Analysis you should always begin with. The 2 most commonly used strategies are omission, involving removal of row