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Data Preparation
- 1. Yasminah Alali DSC IAU @_Yasminah10 Data Preparation
- 4. β Data Prep β Missing Value Treatment β Outlier Detection β Qwiklabs Outline:
- 5. Data Preparation in Machine Learning Data Preparation Feature Engineering Data Modeling Performance Measure 1 2 3 4 DATA ALGORITHMS MODEL Performance Improvement 5
- 8. Human errors software bugs incorrect sensor Why Missing value occur?
- 9. β Some of your columns will certainly contains missing values, often represented as βNaNβ, empty column, zeros. β Missing data in the training data set can reduce the power. β It can lead to wrong prediction . β Example: Missing Values Treatment:
- 10. πͺππππππ πΉππππ πΉ π = π΅πππππ ππ π΄ππππππ π½πππππ π»ππππ π΅πππππ ππ π½πππππ Missing Values Treatment: πΉ π < πΉ π > πΉ π π°πππππππππ π«πππππππ
- 11. Deletion: β Deleting Columns: In most cases if the missing data constitutes more than 90% of the data β Deleting Rows:
- 12. Replacing With Mean/Median/Mode 1 2 Imputation: 01 02 03 Using Algorithms Numeric Category Unique Category Using Algorithms Most frequent Value
- 13. 2 Outlier
- 14. Outlier is a commonly used terminology by analysts analysts and data scientists as it needs close attention Outlier:
- 15. Outlier Values effect: β It increases the error variance and reduces the power. β Decrease normality. β They can bias or influence estimates.
- 16. Replacing With Mean/Median/Mode 1 2 Imputation: 01 02 03 Using Algorithms Numeric Category Unique Category Using Algorithms Most frequent Value
- 17. Qwiklabs 3
- 19. CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, infographics & images by Freepik and illustrations by Stories Thanks! .