The BigdataElearning Blog
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In our data-driven world, accuracy is paramount, agreed?
Imagine a world where Netflix always gets your movie preferences wrong, or your weather app consistently misleads you about whether to carry an umbrella.
Not ideal, right?
The technology we use and rely on has its foundations in algorithms t...
Ever wondered why data scientists have a love affair with normalization and standardization in machine learning? 🤔
What's the secret behind these techniques that magically transform your data?
Well , read more to find out!! In this article we will look into the following topics
Ever scratched your head, staring at a dataset filled with names, or labels, wondering how machine learning algorithms makes sense of it all?
You're not alone.
Data encoding techniques come to the rescue in such situations, acting as a bridge between human-readable data and machine-learnable data. ...
Ever wondered how Spotify knows exactly what song you want to listen to next or how your online shopping cart suggests just the right item?
Behind the scenes, it's all about data discretization in data science. This article aims to unravel the concept of data discretization and guide you through 4 p...
Are you curious about unbalanced data and what it entails?
Maybe you are familiar with unbalanced data sets, but wondering what may be the best techniques to handle unbalanced data in machine learning effectively?
Don't worry, we've got you covered!
Before looking into the 6 techniques to handle unba...
Heard about the train-test split in machine learning?
Ever Wondered why on earth do we need to split our precious data into two camps - training and testing datasets?
What exactly happens when you skip this fundamental step of train-test split & train your model on the entire dataset?
Is it a recipe ...
When baking a cake, not all ingredients are as important to one another..
Like how Cream Cheese acts as an important ingredient for a cheesecake..
And like how cocoa powder acts as an important ingredient for a brownie..
Some are stars of the show, while others are not..
I’m sure you’re with me ...
Hey there! Ever wondered why data outliers are the unruly troublemakers of the data world?
Curious to know how data scientists handle the outliers in data?
You've landed at the perfect spot.
Before we look into the 9 outlier detection methods, you should first know what outliers are!
In this a...
Hey there! Are you just starting out in data science and feeling overwhelmed by what is probability distribution and wondering what are the different types of probability distributions?
You're not alone!
Many people in your shoes wonder: what are the most commonly used probability distributions in...
Have you faced missing data in your data science projects?
Ever wondered if there are effective strategies to handle those pesky missing values in your precious dataset?
Perhaps you've come across the term "data imputing" in machine learning, and now you're curious about what it actually means and h...
Are you curious about this mysterious Chi-Square test in Data Science?
Scratching your head about how to make sense of Chi Square results in machine learning?
So, if you're wondering what this Chi-Square test in Data Science is all about and how to make sense of its results, put on your detecti...
Have you ever had a heated debate with your friends on a topic?
Picture this: you confidently declare that drinking green tea will reduce stress levels…
..but your friend disagrees and insists that it won't make a difference.
Well, congratulations! You've just engaged in hypothesis testing 😀
You ...
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