Your First Machine Learning Project - Crash Course - How to handle missing data for machine learning

Welcome to the Eighth video of the series "Build your First Machine Learning Project". In this, we'll see 9 different approaches to impute missing Data with Code examples. This video on Missing Data Imputation will provide in-depth information on the approaches and code examples. So let's understand the approaches. Chapters 0:00-0:43 Intro 0:44-1:50 Reasons for Missing Values 1:50 - 3:09 Types of missing values 3:10-4:38 What to do when data is missing 4:39 - 7:39 What type of approach can be used in different situations 7:40 -9:05 How to visualize pattern of missing values 9:06 -10:56 Missingness of value 10:57 - 14:37 How to impute missing values 14:37-15:37 Approach 1 15:38-16:44 Approach 2 16:45- 18:16 Approach 3 18:17 -20:45 Approach 4 20:46 - 21:56 Approach 5 21:56 -24:01 Approach 6 20:02- 29:32 Approach 7 29:33 -32 : 49 Approach 8 32:50 -33:16 Conclusion In order to make the best out of this, please watch this series in the order in playlist: Build Your First ML Model Playlist: https://www.youtube.com/watch?v=KSsjPbowHQ0&list=PLFAYD0dt5xCymcvacfR4CLB9Pk_9L50gz Previous Lesson: Exploratory Data Analysis (EDA): https://youtu.be/rqCZZBrfNak Earlier Lessons: 1. How to Formulate ML Problem: https://www.youtube.com/watch?v=ygayqatDEDk 2. Setup Python Environment: https://www.youtube.com/watch?v=Yk9BFMO6QXE 3. Jupyter Notebook Tutorial: https://www.youtube.com/watch?v=4yuo96HtTw8 4. What is ML Modeling: https://youtu.be/Bcfk4HKgC5E 5. Reduce the size of Pandas Dataframe: https://youtu.be/lkFy6vI_OXc Let me know in the comments section if you have any questions! If you enjoyed this video, be sure to throw it a like and make sure to subscribe to not miss any future videos! Thanks for watching! #mlmodeling, #python, #machinelearning, #artificialintelligence, #pandas, #datascience