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Partial Dependence Plot Applied to House Pricing Models

BeginnerGuided Project

Partial Dependence Plots (PDPs), is a common techniques to interpret machine learning models by visualizing feature impacts on predictions. This lab explores the relationships between features such as rooms, distance, and landsize in the Melbourne Housing dataset, as well as age and fare in the Titanic dataset, translating complex model predictions into clear insights. Data scientists and stakeholders can leverage PDPs to gain a deeper understanding of model behavior, enhancing both technical analysis and informed business decision-making.

Language

  • English

Topic

  • Data Science

Skills You Will Learn

  • Data Visualization, Python, Machine Learning, Explainable AI, Scikit-learn, Pandas

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • April 2, 2025
About this Guided Project

Exploring Partial Dependence Plots with Python: A Guided Journey


Understanding how machine learning models make predictions is essential, especially in fields where data-driven decisions can significantly impact outcomes. In this hands-on guided project, you will explore the power of Partial Dependence Plots (PDPs) using Python and scikit-learn, focusing on two real-world datasets: the Titanic dataset for survival prediction and the Melbourne Housing dataset for price estimation.


Throughout this project, you will not only build predictive models but also learn to interpret their outputs, revealing how features such as age, fare, rooms, distance, and land size influence predictions. By visualizing these relationships through PDPs, you will gain insights into model behavior, enabling you to communicate findings effectively to stakeholders.


In just 30 minutes, you will develop a practical understanding of PDPs and their role in explaining machine learning models, equipping you with the skills to navigate the intersection of AI and real-world applications.

We also provide an Advanced Project of Partial Dependence Plot.
You can take it here: https://cognitiveclass.ai/courses/med-prediction-with-explainable-ai-partial-dependence-plot


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 impacts.
  • Analyzing the influence of key features in both classification (Titanic dataset) and regression (Melbourne Housing dataset) contexts.
  • Applying Gradient Boosting Classifier and Regressor models to real-world datasets.

What You'll Need


To get started with this guided project, you should have:

  • A basic understanding of Python programming.
  • Access to modern web browsers like Chrome, Edge, Firefox, Internet Explorer, or Safari.

Ready to unlock the insights hidden within your data? Start this guided project now and empower yourself to interpret complex algorithms, transforming raw data into actionable insights for decision-making in various domains.


Instructors

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.

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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.

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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.

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