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Background In today's world of all sorts of Artificial Intelligence coming in, a major element required for the machines to be trained is "Data". A major portion of the data today is generated from social media and mediums like virtual assistants, blogs, news, videos, audio, images, all sorts of papers(research, white) etc. are mostly unstructured. As per current scenarios, there are around  8.5 billion Google searches per day and approximately 2 trillion global searches per year , similarly for Bing we have around 27 billion web searches per month. 37.5 million web searches per hour . But, if we go by industry estimates, less than 25% of the data today is available in structural or tabular data.  So, now the question arises when the data is available in human languages then how we can use them to train machines and get accurate AI machines. The answer for one of the problems, i.e. the textual data is  "Natural Language Processing".  What is Natural Language Pro

  In previous articles, we read about Feature Scaling and two of the most important techniques used for feature scaling, i.e. Standardization  & MinMaxScaling . Here we will see another feature scaling technique that can be used to scale the variables and is somewhat similar to the MinMaxScaling technique. This technique is popularly known as MaxAbsScaling or Maximum Absolute Scaling . What is MaxAbsScaling? Maximum Absolute Scaling is the technique of scaling the data to its absolute maximum value. The logic used here is to divide each value by the Absolute Maximum Value for each variable/column. Doing so will scale down all the values between -1 to 1.  It can be implemented easily in a few lines of code, as shown below in the  practical section.   Note:- Scikit-learn recommends using this transformer on data that is centred at zero or on sparse data. Formula Used:-  MaxAbsScaling Formula Features of MaxAbsScaling:-  1.  Minimum and Maximum values are scaled between [-1,1]:-  Sin

  In our previous article, we read about Feature Scaling and the most common technique used to perform feature scaling i.e. Standardization .  Another important and commonly used technique is " Min-Max Scaling" or " Normalization". As the name suggests, Min-Max Scaling is the technique where the variables are scaled based on their Minimum and Maximum values.  Formula Used:-  Min Max Scaling Formula Unlike Standardization, mean is not used here. Rather the Minimum and Maximum values for each variable are used to find the new scaled value.  The logic used here is to subtract the Minimum value from each value and divide it by the difference between maximum and minimum values. Features of Min-Max Scaling 1. Mean is not centred at 0:-  Since in Min-Max scaling, we use the Minimum and Maximum values for scaling each variable separately thus, the mean may or may not get centred at 0. We can see this in the below example, where the mean for all variables is greater than 0

In our previous article/blog we had an overview of Feature Scaling . We saw What is Feature Scaling and How we can use it for our benefit. Basically, we discussed all the theory related to Feature Scaling, lets move ahead and see What are the various ways to achieve Feature Scaling and How we can implement them.  The very first and most important technique is " Standardization " also known as " Z-Score Normalization ".  The basic idea of this technique is to subtract the mean from each value and divide it by the standard deviation. Doing so will centre the data around its mean with unit standard deviation.  Formula Used:-  standardization formula This formula is also known as Z-Score, hence the name  Z-Score Normalization.  What is Z-Score? Internet Defines it as:-  A Z-score is a numerical measurement that describes a value's relationship to the mean of a group o

Let's begin with a famous saying... "Five Fingers are Never Equal".  Yes, we have heard it a lot, it's true in every case, even in Data Science and Machine Learning... The very first step in the journey of Data Science begins with Data Collection, and this is where we knowingly or unknowingly collect some data which are different in size, units etc. which makes the data vary and inconsistent data. In case we collect the vehicle data we might have the top speed in MPH, distance covered in KM, dimensions of the vehicle in CM/Inch, Model No. with no unit etc.  Sample data for Feature Scaling Thus, when we take this type of raw data and directly pass it through our Machine Learning Algorithms, it will give inconsistent results as the Machine understands no. only and not the units. So, it might give more weightage to the length of the car (1300mm) than the mileage of the car(30kmpl).   What is Feature Scaling? We have seen some quick info about the problem statement, now l