Exploratory Data Analysis

To begin any machine learning journey, A data always pass through the process called EDA (Exploratory Data Analysis).

EDA is actually an approach to understand the dataset on which we can draw or take decision and understand the problem well.

Exploring The datasets, problems statements, pattern found in datasets, draw hypothisis, suitablity of models, statistical anaylsis etc...

Data is a valid fact used for the purpose of the solving the problems, say about the numerical story or real story of the problem we are solving.

Applying Ayanltical skills, undertsnading the data, datasets, problems,approach of solution, extension of the problems.

Let’s Speak in general words, To apply any machine learning methods the first important requirement is to understand the problem we are trying to solve.

So, we start with the understanding the problem statement or problem or solution we are trying to automate.

Then we gather the data from different sources can we website, logs, flat- files, databases ,multimedia etc...

After gathering the data our next steps are Clean ,Prepare ,Explorer ,Understand etc...

Once data is ready and Hypothesis is tested and we build the models and deploy the models for problem solving and improve on getting the feedbacks from the users.

It’s a saying that “EDA is the Art part of Data Science.”

The National Institute of Standards and Technology (NIST) describes EDA as an approach to data analysis, not a model, that uses these techniques:

Maximize insights into a dataset.

Uncover underlying structures.

Extract important variables.

Detect outliers and anomalies.

Test underlying assumptions.

Develop parsimonious models.

Determine optimal factor settings.

NIST explains that EDA is an approach to data analysis that “postpones the usual assumptions about what kind of model the data [follows]” and allows the data to reveal its underlying structure and model.

EDA is typically used for these four goals:

Learning happens when you observe a phenomenon and recognize a pattern. You try to understand this pattern by finding out if there is any relationship the entities involved in that phenomena

I am starting a git repo to simplify the Machine learning process.

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