Back to Catalog

Med Prediction with Explainable AI: Partial Dependence Plot

AdvancedGuided Project

Learn to interpret machine learning models by visualizing feature impacts using Python, scikit-learn, and Explainable AI (XAI) techniques like Partial Dependence Plots (PDPs). These visualizations show relationships between features like age, income, or medical metrics and model predictions, translating complex algorithms into clear insights. Data scientists and stakeholders can use PDPs to understand model behaviour. This interpretability technique supports technical analysis and business decision-making.

4.7 (21 Reviews)

Language

  • English

Topic

  • Data Science

Skills You Will Learn

  • Machine Learning, sklearn, Pandas, Data Science, Healthcare, Explainable AI

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • April 27, 2025
About this Guided Project

Understanding how machine learning models make predictions is a critical skill, especially in high-stakes domains like healthcare. In this hands-on guided project, you’ll predict heart attack risks using Python, scikit-learn, and Explainable AI (XAI) techniques like Partial Dependence Plots (PDPs). By working with a real-world heart disease dataset, you’ll not only create predictive models but also learn to interpret their outputs, uncovering how features such as age, cholesterol levels, and blood pressure influence predictions. Beyond technical skills, this project emphasizes using data to make informed decisions, empowering you to communicate findings that can positively impact patient care. In just 60 minutes, you'll gain a practical understanding of PDPs and their role in explaining machine learning models, equipping you to navigate the intersection of AI and healthcare.

What You'll Learn
By the end of this project, you will have mastered:
  • - Interpreting machine learning models using Partial Dependence Plots (PDPs) to visualize feature impact.
  • - Understanding and analyzing feature interactions within models.
  • - Applying and comparing logistic regression and random forest models on healthcare data.
  • - Using metrics and visualization tools to evaluate model performance and interpretability effectively.


What You'll Need
To get started with this guided project, you should have:
  • - A basic understanding of Python programming.
  • - Familiarity with fundamental machine learning concepts.
  • - Access to modern web browsers like Chrome, Edge, Firefox, Internet Explorer, or Safari.

Ready to demystify machine learning models? Start this guided project now and unlock the ability to interpret complex algorithms, transforming raw data into actionable healthcare insights.

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Read more

Ricky Shi

Data Scientist at IBM

Ricky Shi is a Data Scientist at IBM, specializing in deep learning, computer vision, and Large Language Models. He applies advanced machine learning and generative AI techniques to solve complex challenges across various sectors. As an enthusiastic mentor, Ricky is committed to helping colleagues and peers master technical intricacies and drive innovation.

Read more

Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. Follow my projects to learn data science principles, machine learning algorithms, and artificial intelligence agent implementations.

Read more

Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

Read more