December 28, 2016 Applications of Exploratory Data Analysis

Applications of Exploratory Data Analysis

Let’s begin learning about “What are the Applications of Exploratory Data Analysis in Machine Learning or Predictive Modelling”

Exploratory Data Analysis (EDA) helps in finding hidden information by displaying data in generally two or three dimentions.

It is an approach to analyze data sets to summarize their main characteristics, often with visual methods.

There are a number of tools that are useful for EDA.Nowadays some popular tools for EDA among the data scientist are R and python.

Some techniques used in EDA are:

Box plot
Scatter plot
Parallel coordinates
Multidimensional scaling
Principal component analysis
Multilinear PCA

Some Applications are :

1.Finding the data type[continuous/discrete/categorical]
2.Finding multicollinearity issue.
3.Finding the distributions of predictor variables.
4.Finding Outliers
5.Finding possibilities for Feature Engineering(process of creating new predictor variables for predictions.)

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