Insane Exploratory Data Analysis Libraries. EDA at ease!
As I was surveying what could be the maiden topic I should begin writing my blog with, in no time EDA popped up to my mind. Logically apt, isn’t it ?! Why? You’ll find out soon!
Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.
For one to perform EDA on any dataset he/she must be well versed with some of the python visualization libraries such as seaborn,matplotlib,plotly and etc. to make attractive graphs so as to find the insights of the data. Finding insights into any data is a preliminary step of any data science, machine learning project as the corresponding step that is feature selection depends on the results derived from EDA. This means EDA plays a crucial role in determining the accuracy of any data science, machine learning projects.
In this blog, we shall find easier ways of performing EDA on any dataset by using some automated libraries.
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