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so today we'll go over how to handle missing data in our when you are working with lot of data and a lot of variables it is very common to have a situation where some of the values may be missing now one way we can handle missing data is we just delete the row but the problem there is let's say we have 20 variables and one data point is missing so when you are deleting the entire row not only you are deleting that one data point but you are also throwing away 19 good data points so that's not a very smart idea and especially if you have many missing values and if you start deleting all the rules where any value is missing then very quickly we land in a situation where we have thrown away a lot of good data so a second problem that may come up has something to do with the bias so let's say we have data on vehicle failures and only those data points are missing where vehicle is quite old so if we are throwing away all that data so we are losing data on those kind of failures which are oc

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