done once again

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

done

Done

Done

jazakumullah Kharn ameen

In this lecture, I have learned to extract outliers.

In this lecture, I have learned to extract outliers.

learn outlier name and types done

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.