Implement, understand and learn about how 3 powerful methods, including Deep Learning, can be used to impute data.

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Photo by Pixabay on Pexels

I’m sure that every Data Scientist/ ML Practitioner has faced the challenge of missing values in their dataset. It is a common data cleaning process, but frankly, a very overlooked and neglected one. However, an effective missing value strategy can have a significant impact on your model’s performance.

Reasons for the occurrence of missing values

The reason as to why missing values occur is often specific to the problem domain. However, most of the time they occur from the following scenarios:

  • Code Bug: The data collection method encountered a bug and some value were not properly obtained(for example, if you were to collect data via a REST API and you possibly didn’t parse the response properly, then the value would be missing.) …

Get an in-depth understanding about outlier detection and how you can implement 3 simple, intuitive and powerful outlier detection algorithms in Python

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Photo By Scott.T on Flickr

I’m sure you have come across a few of the following scenarios:

  1. Your model is not performing as you wanted it to.
  2. You can’t help but notice that some points seem to differ greatly from the rest.

Well congratulations, because you might have outliers in your data!

What are Outliers?

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Photo can be found in StackExchange

In statistics, an outlier is a data point that differs significantly from other observations. From the figure above, we can clearly see that while most points lie in and around the linear hyperplane, a single point can be seen diverge from the rest. This point is an outlier.

For example, take a look at the list…

Explore the real truth behind the fundamental classification model, and build a classifier from Scratch using Python.

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Photo by Drew Beamer on Unsplash

Logistic Regression is essentially a must-know for any upcoming Data Scientist or Machine Learning Practitioner. It is most likely the first classification model one has encountered. But, the question is, how does it really work? What does it do? Why is it used for classification? In this article, I hope to answer all these questions, and by the time you finish reading this article, you will have:

  1. Learn how to explain the Logistic Regression model in simple language
  2. Learn how Logistic Regression is mathematically formed
  3. Implement Logistic Regression from Scratch using Python

So, get ready for the wild adventure ahead, partner! …


Vagif Aliyev

A High School student with a passion for AI

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