Part - 4 :- Students, so now let's add a new tool to our preprocessing toolkit, which is taking care of missing data. So indeed, if we have a look again at our data set data that CSI, we noticed that there is a missing salary here for this specific customer from Germany of 40 years old and who purchased a product. Download Data.csv :- Download S o generally you don't want to have any missing data in your data set for the simple reason that it can cause some errors when training your machinery model and therefore you must handle them. A first way is to just ignore the observation by deleting it. That's one method and this actually works. If you have a large dataset and you know, if you have only one percent missing data, you know, removing one percent of the observations won't change much the learning quality of your model. So one percent is fine, but sometimes you're going to have a lot of missing data and therefore you must handle them the right way. So that w
Part - 3 :- Students, Now let's learn together how to import a data set. As a reminder, we're going to learn how to import the following data set data that CSV, which is a very simple data set of, let's say, a retail company that is doing some analysis on which clients purchased one of their products. Download Data.csv :- Download So the rows in this data set correspond to different customers of this employee. And for each of these customers, we have the country, they live in their age, their salary and whether or not they purchased the product. OK, so we're going to learn how to import that GSV on Python, using, of course, to Pendas library. Importing The Dataset :- So let's first create a new code cell and now let's import this dataset. So the first thing we have to do is to create a new variable and this variable will contain exactly the dataset. Since now we're importing the data set and we want to integrate the data set in a variable, I'm going t