killosin.blogg.se - Exploratory data analysis methods

The snippet of the data looks like this: Churn Dataset Business Goal So, this is an essential metric in all industries. All businesses want to prevent churn and retain their customers. (Some changes have been made to explain some concepts.) Churn indicates a customer leaving the service to join another service. We will be working with a telecom churn dataset from Kaggle. Technical, statistical, and mathematical knowledge with domain knowledge is critical for performing EDA.

Based on your learning, refine/prepare new questions.

Generate answers by cleaning, transforming, summarizing, and visualizing data.

Prepare questions related to the business goal (context/problem you are working with).

But we can broadly say that are three main parts that come under EDA. It is an iterative approach to understanding data, where the data is investigated and explored without any assumption or bias.

It is not a formal process with strict rules.

Drop unwanted columns and derive new variables.ĮDA gives you the flexibility to talk to your data.

Uncover and resolve data quality issues.

Understand patterns and correlations between data variables.

Confirm if the data is making sense in the context of the business problem.

The objective of EDA is to “understand” the data as follows: It is all about finding and revealing clues! Anyone working with data from researchers, analysts, business intelligence professionals, and others will spend most of their time on EDA by following exploratory data analysis steps. Open-minded exploration of data will provide valuable information. Learning what you can do using the data available will make your final analysis more robust and effective.

The better you know your data (have more clues), the better is your analysis (case outcome)! Why Is EDA Important? EDA techniques reveal the true nature of the data. so, In this blog, I have shared my understanding of the exploratory data analysis steps and tried to catch hold of as many insights from the data set using EDA.ĮDA is the foundation stone, a very vital step before you begin with the data analysis. Like a detective, we dig deep into piles of data in order to find clues that will aid the actual data analysis. He explains EDA as: “Exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there.” He gave a very apt simile to EDA – an investigation carried out by a detective.

Exploratory Data Analysis (EDA) has been around since the early 1970s! It was defined by John Tukey, a great mathematician & statistician.