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I learned about Outliers:
--> Outliers can:
1. divert the center, 2. cause Skewness, 3. Wrong insights, 4. Inefficient ML Model, 5. Wrong predictions.
--> Other names for Outliers:
Outliers, Deviants, Abnormalities, Anomalous points, Abberrvants observations.
-->Types of Outliers: Uni-variate, Bi-variate, Multi-variate, Global, Point, Local, Contextual.
--> How to handle Outliers:
1. Remove them 2. Transform them(log transform) 3. Impute with mean, median, mode 4. Seperate them and treat them separately 5. Use Robust Models
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done
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Done
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Done
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jazakumullah Kharn ameen
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In this lecture, I have learned to extract outliers.
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In this lecture, I have learned to extract outliers.
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learn outlier name and types done
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AOA, And I also learned about types of outliers and different names of outliers.
Different names of outliers
1- Outliers
2- Daviants
3-Abnormalities
4-Anomalies points
5- Aberrant observationTypes of outliers
1- Univariate
2- Multivariate
3-Global outlier
4-point outliers
5- Local outliers
6- contextual outliers
7- collective outliers
8- recurrent outliers
9- periodic outliersI also learned about the causes of outliers, which are
1- Data entry error
2- Measurement error
3- Experimental error
4- Intentional outliers
5- Data processing error
6- Sampling error
7- natural outliersAnd I also learned that why we should care about outliers
1- Hidden clues
2- Quality of data
3- Impact Analysis
4- Better decision
5- Better models
6- Better insights
7- Better visualisation
8- Better storytelling
9- Better data product
10- Better data science
And I also learned how to remove it in Python.
ALLAH KAREEM ap ko dono jahan ki bhalaian ata kry AAMEEN.
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Detail lecture about outliers i,e names of outliers, types of outliers causes of outliers why should care outliers, detect & remove outliers. Further, the best methods to deal with outliers Z-Score Method, IQR Method and K-Means Method. I also learned in detail about five way to handle outliers.
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