2

Detecting Outliers using Standard Deviation

Unsolved
Data Wrangling
Prob. and Stats

Difficulty: 2 | Problem written by ankita
Problem reported in interviews at

Amazon
Apple
Facebook
Google
Netflix

Outliers are certain points in the dataset which deviate from the general trends in the dataset.

There are several ways to detect outliers. In this problem, we will use the standard deviation in conjunction with the mean.

According to this method, a point is called an outlier if it deviates from the mean of the dataset by threshold*(standard_deviation of the dataset). Here, we assign a threshold of 3.

You are given as input:

X: X is a list of features.

Output:

Arrays that are free of outliers from the input lists.

Sample Input:
<class 'list'>
X: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 2000]

Expected Output:
<class 'numpy.ndarray'>
[ 1 2 3 4 5 6 7 8 9 10 100]

This is a premium problem, to view more details of this problem please sign up for MLPro Premium. MLPro premium offers access to actual machine learning and data science interview questions and coding challenges commonly asked at tech companies all over the world

MLPro Premium also allows you to access all our high quality MCQs which are not available on the free tier.

Not able to solve a problem? MLPro premium brings you access to solutions for all problems available on MLPro

Get access to Premium only exclusive educational content available to only Premium users.

Have an issue, the MLPro support team is available 24X7 to Premium users.

This is a premium feature.
To access this and other such features, click on upgrade below.

Log in to post a comment

Comments
Ready.

Input Test Case

Please enter only one test case at a time
numpy has been already imported as np (import numpy as np)