Your First Machine Learning Project - Crash Course How to Detect Outliers with IQR and Boxplot? | Code Walkthrough in Python
How to Detect Outliers with IQR and Boxplot? | Code Walkthrough in Python Transcript and Lesson Notes
Welcome to the eleventh video of the series "Build your First Machine Learning Project". In this, we'll see how to detect outliers with IQR and Box Plots. Notebook and Datasets: https://github.com/machinelearningplus/Bui
Quick Summary
Welcome to the eleventh video of the series "Build your First Machine Learning Project". In this, we'll see how to detect outliers with IQR and Box Plots. Notebook and Datasets: https://github.com/machinelearningplus/Bui
Key Takeaways
- Review the core idea: Welcome to the eleventh video of the series "Build your First Machine Learning Project". In this, we'll see how to detect outliers with IQR and Box Plots. Notebook and Datasets: https://github.com/machinelearningplus/Bui
- Understand how outlier detection fits into How to Detect Outliers with IQR and Boxplot? | Code Walkthrough in Python.
- Understand how outliers fits into How to Detect Outliers with IQR and Boxplot? | Code Walkthrough in Python.
- Understand how outlier detection and removal in python fits into How to Detect Outliers with IQR and Boxplot? | Code Walkthrough in Python.
- Understand how outlier detection and removal fits into How to Detect Outliers with IQR and Boxplot? | Code Walkthrough in Python.
Key Concepts
Full Transcript
Welcome to the eleventh video of the series "Build your First Machine Learning Project". In this, we'll see how to detect outliers with IQR and Box Plots. Notebook and Datasets: https://github.com/machinelearningplus/Build-Your-First-ML-Project/tree/main/12_How%20to%20detect%20Outliers%20with%20IQR%20and%20Box%20Plots To practice in colab notebooks, change the text 'github.com' to 'githubtocolab.com' in above url. Chapters 0:00 -0.32 Introduction to Outliers 0:33-2:43 When and how to treating Outliers datapoints 2:44-4:57 How to detect outliers using Box and Whisker Plot 4:58-7:10 Finding outliers in Python 7:11 -8:15 Boxplot 8:16 -11:40 Outlier detection using IQR 11:41 -11:52 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: Impute Missing Data in categorical feature : https://youtu.be/dm7YNsN_Nwo 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 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
Lesson FAQs
What is How to Detect Outliers with IQR and Boxplot? | Code Walkthrough in Python about?
Welcome to the eleventh video of the series "Build your First Machine Learning Project". In this, we'll see how to detect outliers with IQR and Box Plots. Notebook and Datasets: https://github.com/machinelearningplus/Bui
What key concepts are covered in this lesson?
The lesson covers outlier detection, outliers, outlier detection and removal in python, outlier detection and removal, outlier detection techniques.
What should I learn before How to Detect Outliers with IQR and Boxplot? | Code Walkthrough in Python?
Review the previous lessons in Your First Machine Learning Project - Crash Course, then use the transcript and key concepts on this page to fill any gaps.
How can I practice after this lesson?
Practice by applying the main concepts: outlier detection, outliers, outlier detection and removal in python, outlier detection and removal.
Does this lesson include a transcript?
Yes. The full transcript is visible on this page in indexable HTML sections.
Is this lesson free?
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