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Efficient models: reduce dimensionality with LDA in Python

BeginnerGuided Project

Understand and implement Linear Discriminant Analysis (LDA), one of the best ML methods for dimensionality reduction in classification tasks. Dimensionality reduction is a fundamental machine learning technique that is frequently used to improve the performance of prediction models, interpretability, and data visualization. This easy-to-follow, hands-on project walks you through understanding LDA, when it's most useful, and how to implement this dimensionality reduction technique using Python.

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 64

Skills You Will Learn

  • Python, Machine Learning, Data Science, Numpy, Pandas, sklearn

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • September 17, 2025
About this Guided Project
Linear discriminant analysis (LDA) is a widely used supervised machine learning technique that serves as both a classifier and a tool for reducing dimensionality in classification tasks. In this hands-on project, you'll use LDA to classify iris plants, employing various approaches. The aim is to provide you with an intuitive grasp of how LDA functions and how it can be effectively applied, without delving too much into complex mathematical details. With this project, you'll gain insight into the fundamental concepts behind this valuable technique and its straightforward implementation.

This hands-on project expands upon the Implementing linear discriminant analysis (LDA) in Python tutorial at developer.ibm.com. This project combines the instructions of the tutorial with additional explanations and an environment in which to execute Python code. By enrolling in this project, you can dive straight into learning without the hassle of downloading, installing, and configuring tools.

A Look at the Project Ahead

In this project, you will:
  • Learn how LDA works
  • Plot the LDA decision boundary for a binary classification problem
  • Use LDA for classification
  • Use LDA for dimensionality reduction
  • Learn how to implement LDA using Python

What You'll Need

You'll need an intermediate understanding of Python coding and a recent version of Chrome, Edge, Firefox, Internet Explorer or Safari. 
While having a basic grasp of statistics, data science, and/or machine learning is helpful for following along, it's not strictly required. The project is designed to be as accessible as possible to a general audience, with explanations primarily delivered in a graphical and intuitive manner. Whether you're a beginner just starting out, or a seasoned professional looking for a refresher on LDA, this hands-on project is for you!

Instructors

Eda Kavlakoglu

Program Director, Technical Content at IBM

Marketing leader with a technical background in data science.

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Wojciech "Victor" Fulmyk

Data Scientist at IBM

I am a data scientist and economist with a strong background in econometrics, time series analysis, causal inference, and statistics. I stand out for my ability to combine technical expertise with clear communication, turning complex data findings into practical insights for stakeholders at every level. Follow my projects to learn about data science principles, machine learning algorithms, and artificial intelligence agents.

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Contributors

Lucy Xu

Data Scientist

I am a Data Scientist Intern at IBM. I am also currently in my fourth year at the University of Waterloo studying Statistics with a minor in Computing.

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