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1) Outliers are basically those points in the dataset which are different from our original data. 2) Outliers can badly affect our mean, median and reduce the efficiency of our ML models which we don't want. 3) We can identify them using ploting, IQR , Z-score method 4) We can handle outliers by removing, transforming or by imputing them like we impute missing values (means, median etc) 5) Outliers are very important to remove if we want to build models that give us accurate results.

An outlier is a data point that is very different from all other data points in the entire dataset. Outliers have 3 types and 3 methods to detect it and 3 solutions to handle it

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Outliers are data points that significantly differ from the rest of the dataset. For example, if we collect data from a high school and find a 90-year-old, that age is considered an outlier, as all students should be teenagers.

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What are Outliers? Outliers are those data points that significantly deviate from the rest of the data. What are the types of outliers? There are three major types of outliers: 1. Global Outliers, 2. Contextual Outliers, 3. Collective Outliers How do we identify outliers? We can identify outliers in various ways, such as Plotting (Box Plot, Histogram, Scatter Plot), IQR, and Z-score method. How do we deal with outliers? We can deal with them in various ways, such as removing them, transformation, imputation, and Using Robust Models.

What are outliers? What are types of outliers? How can identifiy outliers? Steps to remove outliers by using Inter quartile range methods? Steps to remove outliers by using zscore methods? How to deal with outliers?

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