Your First Machine Learning Project - Crash Course - How to Detect Outliers with Z Score | Clearly Explained

Welcome to the twelfth video of the series "Build your First Machine Learning Project". In this, we'll see how to detecting outliers with Z Score. Notebook / Code link: https://github.com/machinelearningplus/Build-Your-First-ML-Project/tree/main/13_How%20to%20detect%20Outliers%20with%20Z-Score Z-score is a way to standardize the data to standard scale i.e. how far the data point is from the mean. The z-score can come positive or negative based on the help of mean and standard deviation values. The data point away from the mean with some standard deviation is called a z-score. So let's understand it. Chapters 0:00 Intro 3:17 Normal and standard normal distribution 6:15 Treating Outliers 9:09 Different ways of treating outliers 11:06 Removing the outlier observation 11:21 Quantile based capping 11:15 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: How to Detect Outliers with IQR and Boxplot? : https://youtu.be/xiBslXuZ0Rw Earlier Lessons: 1. Build your first ML Project: https://youtu.be/KSsjPbowHQ0 2. How to Formulate ML Problem: https://youtu.be/ygayqatDEDk 3. Setup Python Environment: https://youtu.be/Yk9BFMO6QXE 4. Jupyter Notebook Tutorial: https://youtu.be/4yuo96HtTw8 5. What is ML Modeling: https://youtu.be/Bcfk4HKgC5E 6. Reduce the size of Pandas Dataframe: https://youtu.be/Xa26NB75htg 7. What is EDA: https://youtu.be/rqCZZBrfNak 8. How to impute missing Data: https://youtu.be/Qir0Qi_CD2o 9. Mice Imputation Algorithm: https://youtu.be/BjyUbk258o4 10. How to impute missing data in categorical Variables: https://youtu.be/dm7YNsN_Nwo 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