Overview of Exploratory Data Analysis With Haberman Dataset

Overview of Exploratory Data Analysis With Haberman Dataset

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:

  1. Overview of Data Exploratory Analysis (EDA)
  2. EDA for Haberman’s dataset

Overview of Data Exploratory Analysis (EDA):

What is EDA?

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.

Why apply EDA?

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!

EDA process

  1. Question
  2. Verify
  3. Write
  4. Repeat

data-science machine-learning exploratory-data-analysis data-visualization data-analysis

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Overview of Exploratory Data Analysis With Haberman Dataset

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