Offered By: IBMSkillsNetwork
Exploratory Data Analysis (EDA) for Data Science and ML
Exploratory Data Analysis (EDA) is a vital first step for any data science or machine learning project. Learn how to perform effective EDA for regression and classification! In this beginner-friendly, hands-on project you learn how basic EDA can provide vital insights into your data, and how you can use this information to improve your models.
Continue readingGuided Project
Data Science
355 Enrolled4.7
At a Glance
Exploratory Data Analysis (EDA) is a vital first step for any data science or machine learning project. Learn how to perform effective EDA for regression and classification! In this beginner-friendly, hands-on project you learn how basic EDA can provide vital insights into your data, and how you can use this information to improve your models.
A look at the project ahead
Exploratory Data Analysis (EDA) is a crucial preliminary step in the data science process, aimed at gaining insights into the underlying structure, patterns, and relationships within a data set. Through a variety of statistical and visualization techniques, EDA allows data scientists to uncover the key characteristics of the data, identify outliers, understand the distribution of underlying features, and detect missing values.
One of the primary benefits of EDA is its role in enhancing the effectiveness of prediction models. By thoroughly understanding the data through EDA, data scientists can make informed decisions about feature selection, transformation, and engineering, leading to more accurate and robust prediction models. EDA helps identify relevant variables, understand their interactions, and uncover any potential biases or confounding factors that might affect the predictive performance of models. By leveraging the insights gained from EDA, data scientists can build prediction models that are better tailored to the underlying data structure, resulting in improved accuracy and reliability in making predictions.
In this project, you learn:
- How to perform EDA using a set of very simple and easy-to-memorize Python commands
- How to interpret key EDA plots and statistics
- How to improve prediction models by using information obtained through EDA
- How to perform basics of feature engineering
- How to detect and handle outliers
- How to deal with missing data
What you'll need
All you need is a basic knowledge of Python and a browser. A basic understanding of statistics and data science is helpful, but not strictly necessary.