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Exploratory Data Analysis (EDA) for Machine Learning

06 Aug, 2021by Admin 27
Exploratory Data Analysis (EDA) for Machine Learning
Exploratory Data Analysis (EDA) for Machine Learning

Get Udemy Coupon 100% OFF For Exploratory Data Analysis (EDA) for Machine Learning Course

Before you start a machine learning project, its important to ensure that the data is ready for modeling work. Exploratory Data Analysis (EDA) ensures the readiness of the data for Machine Learning. In fact, EDA ensures that the data is more usable. Without a proper EDA, Machine Learning work suffer from accuracy issues and many times, the algorithms won’t work.

What is exploratory data analysis?

Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions.

EDA is primarily used to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a provides a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for data analysis are appropriate. Originally developed by American mathematician John Tukey in the 1970s, EDA techniques continue to be a widely used method in the data discovery process today.

Why is exploratory data analysis important in data science?

The main purpose of EDA is to help look at data before making any assumptions. It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, find interesting relations among the variables.

Data scientists can use exploratory analysis to ensure the results they produce are valid and applicable to any desired business outcomes and goals. EDA also helps stakeholders by confirming they are asking the right questions. EDA can help answer questions about standard deviations, categorical variables, and confidence intervals. Once EDA is complete and insights are drawn, its features can then be used for more sophisticated data analysis or modeling, including machine learning.

Programming Language Used

Python: an interpreted, object-oriented programming language with dynamic semantics. Its high-level, built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for rapid application development, as well as for use as a scripting or glue language to connect existing components together. Python and EDA can be used together to identify missing values in a data set, which is important so you can decide how to handle missing values for machine learning.

What is covered in this course?

This course will teach you the techniques and approaches in exploratory data analysis, which will help you to derive maximum value from the data. If you jump into machine learning without doing this EDA, you are setting yourself up for failure besides ending up with lower accuracy. This course is designed by an AI and tech veteran and comes to you straight from the oven!

Who this course is for:

Data Scientists, Python Programmers, ML Practitioners, IT Managers managing data science projects Beginners in Machine Learning

WHAT WILL YOU LEARN IN THIS COURSE:

What is EDA
Why EDA is needed
What is multi collinearity
How to identify outliers
How to identify relationship between variables
Chi Square Test & other tests
How to transform continuous data
How to transform categorical dara
Central Tendency Vs Dispersion
How to handle missing values in your dataset
How to apply EDA (through an assignment)
How to derive maximum value for your data

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IF YOU FIND THIS FREE UDEMY COURSEData Analysis ” USEFUL AND HELPFUL PLEASE GO AHEAD SHARE THE KNOWLEDGE WITH YOUR FRIENDS WHILE THE COURSE IS STILL AVAILABLE


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