In today’s world, data are found in their dirtiest form which when analyzed, we might loose many important insights as well as be led with the wrong conclusions. One of such situation is the “high dimensional data”.

For an instance,

source- https://www.google.com/url?sa=i&url=https%3A%2F%2Fpython-bloggers.com%2F2019%2F04%2Fhigh-dimensional-data-breaking-the-curse-of-dimensionality-with-python%2F&psig=AOvVaw2gcd_JSrUKoH-39v38UPBv&ust=1622558763670000&source=images&cd=vfe&ved=0CAMQjB1qFwoTCIjLksuU9PACFQAAAAAdAAAAABAP

Here, if we stop in any one dimension point of view, we will see a heavily spread data points and when we stop at another dimension point of view, we will see changes in the spread from the initial look that we had.

The dimension hence, affects the data. When we see the data point from a dimension, the data…


These analyses are the fundamental steps of Exploratory Data Analysis(EDA) that we perform in our data science world. It shows us the direction of what Machine Learning technique are we going to apply in the further process.

source:Piktochart

In Univariate Analysis, we choose a single feature from the data and try to determine what the output or the target value is ,i.e., one feature/variable at a time.

Since we take only one feature or variable and classify the feature values with respect to the output, we plot all the feature values on X-axis whereas on the Y-axis there will be nothing…

Mukut Chakraborty

Data-Science enthusiast, Persuing Masters of Computer Science (specialisation in DATA ANALYTICS) from IIITM-Kerala,Sportsperson

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