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<speak> <break strength="strong"/> You can see outliers in Student Number 92,<break strength="weak"/> which is having an outlier in the chance of admit.<break strength="strong"/> There are two outliers in chance of admit,<break strength="weak"/> and they are 0.34 and 0.34.<break strength="strong"/> The chance of admit is low even they are having a decent CGPA Score of 8.03.<break strength="strong"/> So it is the outlier.<break strength="strong"/> Whereas in the case of a student with serial number 347,<break strength="weak"/> the LOR is only one, <break strength="weak"/>student he chance of 42 so it is also an outlier.<break strength="x-strong"/> Now, <break strength="weak"/>how to remove the outlier. <break strength="x-strong"/> You use the same OR condition here,<break strength="weak"/> limit condition, and will put a not operator before that one.<break strength="strong"/> data1 will put a NOT operator. <break strength="strong"/>It will save all the values other than these outliers into the df_out variable.<break strength="strong"/> Except for these three outliers,<break strength="weak"/> the remaining values are stored in the student entries in the df_out. <break strength="strong"/>Now we will copy that one into the original data frame data.<break strength="strong"/> Now variable or the data frame data is having only without outliers.<break strength="strong"/> Hence outliers are removed.<break strength="strong"/> From the data,<break strength="weak"/> we have removed the three outliers. <break strength="strong"/>If you see the shape of the data with shape Command,<break strength="weak"/> the number of rows only 497. <break strength="strong"/>So we removed three rows which are having outliers.<break strength="strong"/> Hence the number of students now we are having 497 with 8 columns.<break strength="strong"/> This way, <break strength="weak"/>you can remove the outliers in the data<break strength="x-strong"/><break strength="x-strong"/> </speak>