Overview of Exploratory Data Analysis (EDA) With Haberman Dataset. A practical guide to get started to gain insights from your data.

“Data will talk, if you are willing to listen”- Jim Bergeson

With the proper use of data, one can gain insights and use it for numerous purposes. Raw data has no story to tell. So, to understand and gain insights from data, after the data collection process, exploratory data analysis comes into the picture. It is a crucial process to recognize patterns and understand data to prepare the model.

This article is divided into the following sections:

- Overview of Data Exploratory Analysis (EDA)
- EDA for Haberman’s dataset

The process to explore and understand data to gain insights from the data. It can be context as “Look at the first sight” for solving any Data Science problem. It takes a step closer to the goal of solving the problem at hand.

To summarize important features, to recognize patterns & distribution curves, to detect outliers and anomalies, to find a number of classes, or distribution of data/classes, to test underlying assumptions, etc by analyzing and visualizing. Basically to know what the data is trying to say!

- Question
- Verify
- Write
- Repeat

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You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.

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Suppose you are looking to book a flight ticket for a trip of yours. Now, you will not go directly to a specific site and book the first ticket that you see.

Overview of Exploratory Data Analysis (EDA) With Haberman Dataset. A practical guide to get started to gain insights from your data.