Day 12 - Introduction, Missing Values & Categorical Variables (Intermediate ML Lessons 1, 2 & 3)
Welcome to Day 12 of Kaggle 30 Days of Machine Learning.
In this video, I will walk through Lessons 1, 2 and 3 of the Kaggle Intermediate Machine Learning course.
In Lesson 1, we are given a brief overview of the topics that will be covered in this course which includes missing values, categorical variables, pipelines, cross-validation, XGBoost and leakage. It is highly recommended that you first complete the Introduction to Machine Learning course before proceeding in this one - check out my past couple of videos!
In Lesson 2, we learn how to deal with missing values. Specifically, we look at two approaches: dropping columns with missing values as well as imputation, that is filling in the missing vales with some number.
In Lesson 3, we explore what a categorical variable is and the difference between ordinal variables and nominal variables. Furthermore, we also learn three approaches when working with categorical variables: dropping categorical variables, ordinal encoding and one-hot encoding.
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Kaggle 30 Days of ML
https://www.kaggle.com/thirty-days-of-ml
Kaggle Intermediate Machine Learning micro-course
https://www.kaggle.com/learn/intermed...
My article on imputing missing values
https://towardsdatascience.com/stop-w...
My article on encoding categorical variables using ordinal encoding and one-hot encoding
https://towardsdatascience.com/guide-...
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