Exploratory Data Analysis EDA for Modeling
About
Explore data to inform modeling, including understand random variables and distributions, interpreting correlations, and standardizing data.
Learning Objectives
You will be able to:
- Summarize the reasons and ways to explore data for the purpose of modeling and machine learning.
- Define a random variable in the context of statistics and data.
- Explain how identifying a variable’s distribution helps inform our modelling approach.
- Interpret correlation as a measure of the strength of the linear relationship between variables.
- Explain the motivation behind and methods for standardizing data.
- Understand that correlation itself does not imply causation.
Content
| Topic | About |
|---|---|
| Full Lesson Deck | Explanation of EDA for Modeling |
| 02 EDA for Modeling notebook | Guided walkthrough of EDA methods for modelling and machine learning; Random variables; Variable distributions and modelling approaches; Correlation as a measure of linear relationship; Standardising data; and Correlation vs Causation |
Materials We Provide
| Topic | Description | Link | |
|---|---|---|---|
| Slides | Slide Deck | Link | |
| Lesson | Starter Code | Link |
Prerequisites
- Write and run Python code in a Jupyter notebook.
- Create basic Python functions.
- Python programming skills including using modules and libraries.
- Use
pandas. - Be able to run a regression and classification model in
scikit-learn.
References
Important note
Please ignore the _layouts folder and the config.yml file in the root of this repo. They are needed to make sure the repo renders well; please don’t change them or delete them and your repo will work fine :)