Category Archives: Data Analysis

Data Science and Machine Learning Articles | Yearly round-up 2019

Guys, I have consolidated all my ML and DS articles. In case you have missed it, here are the links in one place.


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Variance, Standard Deviation and Other Measures of Variability and Spread

Variance and Standard Deviation are the most commonly used measures of variability and spread. Variability and spread are nothing but the process to know how much data is being varying from the mean point. And Variance tells us the average distance of all data points from the mean point. Standard deviation is just the square root of the variance. As variance is calculated in squared unit (explained below in the post) and hence to come up a value having unit equal to the data points, we take square root of the variance and it is called as Standard Deviation.

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A Complete Guide to Principal Component Analysis – PCA in Machine Learning

Principal Component Analysis or PCA is a widely used technique for dimensionality reduction of the large data set. Reducing the number of components or features costs some accuracy and on the other hand, it makes the large data set simpler, easy to explore and visualize. Also, it reduces the computational complexity of the model which makes machine learning algorithms run faster. It is always a question and debatable how much accuracy it is sacrificing to get less complex and reduced dimensions data set. we don’t have a fixed answer for this however we try to keep most of the variance while choosing the final set of components.

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Important Use Cases of NLP

In today’s world we are generating large amount of data every second. while tweeting, chating, writing or even speaking, we are fabricating corpse of data. Most of the data is in textual and unstructured form. Hence to make this data understandable by computer, we need to process it. NLP technique helps us in processing the data and helps us to get useful insights from it.

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Basic Statistics for Data Science – Part 1


The Science of collecting, organizing, presenting, analyzing and interpreting the data is statistics. It is one of the most important disciplines or methods to get a deeper insight into data. Statistical analysis is implemented to manipulate, summarize and investigate data so that useful information can be obtained.

Take away from this post:

  • Types of Statistics: Descriptive vs Inferential
  • Basic terminology like Population vs Sample
  • Types of Variables: Numerical vs Categorical
  • Measures of central tendencies: Mean, Median and Mode and their specific use cases
  • Measures of dispersion/spread: Variance, standard deviation etc.
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What is the Coefficient of Determination | R Square

The coefficient of Determination is the direct indicator of how good our model is in terms of performance whether it is accuracy, Precision or Recall. In more technical terms we can define it as The Coefficient of Determination is the measure of the variance in response variable ‘y’ that can be predicted using predictor variable ‘x’. It is the most common way to measure the strength of the model.

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Employee Attrition Rate Analysis – Insights from IBM HR Data

Storytelling or presenting insights is the most important part of data analytics. This is the selling point of all your hard work. Doesn’t matter how much hard work you have put in developing analytic model until you are able to get the attention of the target audience. Here in this particular article, my focus is on how we can use beautiful graphs to show the insights regarding employee attrition rate from IBM HR Attrition data. After all, a picture is worth to thousands of words.

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What is Linear Regression? Part:2

In any business there are some easy to measure variables like : Age, Gender, Income, Education Level etc. and there are some difficult to measure variables like amount of loan to give, no of days a patient will stay in the hospital, price of the house after 10 years etc. So Regression is the technique which enables you to determine difficult to measure variables with the help of easy to measure variables.

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