Python Exploratory Data Analysis (EDA) libraries

Photo by Luke Chesser on Unsplash

Introduction

A typical machine learning workflow consists of six critical tasks that are important to the success of the project.

1. Defining Problem
2. Data Acquisition and ETL
3. Exploratory Data Analysis
4. Data Preparation
5. Modeling (Model training and selection)
6. Deployment and Monitoring

Machine Learning Life Cycle — Image by Author

Exploratory Data Analysis

Exploratory Data Analysis is a process of performing initial investigations on data so as to discover patterns, identify anomalies, test business hypotheses, and test assumptions with the help of statistical summary and visualizations. In short, the process of getting to know your data in depth is called Exploratory Data Analysis.

There are three ways you can do EDA:

• Using libraries/frameworks in Python / R
• Using automated EDA libraries in Python / R
• Using licensed softwares such as Microsoft Power BI or Tableau, etc.