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Predict 2024 US Election with EDA & Machine Learning

IntermediateGuided Project

Predict the 2024 US Election with Exploratory Data Analysis (EDA), Pandas, and scikit-learn. In this hands-on project, master EDA, feature engineering, and regression modeling as you gather and preprocess polling data. Build separate models for each swing state, respecting the chronological order of events, and evaluate performance using metrics like MAE. Discover advanced techniques like ensemble methods and time-based weighting, while exploring the ethical considerations and limitations of political forecasting.

4.7 (38 Reviews)

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 238

Skills You Will Learn

  • Machine Learning, Python, Data Science, Data Analysis, Data Visualization

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 60 minutes

Platform

  • SkillsNetwork

Last Update

  • May 5, 2025
About this Guided Project
Disclaimer: For Educational Purposes Only
This guided project is intended purely for educational purposes and should not be considered a political statement or a definitive forecasting tool. Our aim is to teach the principles of data analysis and machine learning using real-world data sets, while emphasizing the ethical considerations and limitations of predictive models in a highly unpredictable environment.

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A Look at the Project Ahead: Can Data Predict Election Outcomes?

In the world of political forecasting, even the most confident predictions can be shaken by a single unexpected event. Think back to the 2016 and 2020 US elections—polls and pundits were confident in their outcomes, yet the results sent shockwaves across the globe. How can data science offer insights into the future of political races while remaining mindful of its limitations?

Imagine using Python and machine learning (ML) to dig into the complex world of US election data, uncovering trends and patterns that might help make sense of how people vote. This hands-on project invites you to step into the shoes of a data scientist, exploring the 2024 US election landscape with logistic regression, decision trees, and more. You’ll work through every step, from gathering polling data to feature engineering and building models that adapt to the unique political dynamics of swing states. By the end, you’ll have a toolkit for predictive modeling and a deeper understanding of the challenges behind election forecasts.

Are you interested in analysing polling data?

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What You'll Learn

  • Data gathering & preprocessing: Collect and clean polling data to ensure meaningful analysis.
  • Exploratory data analysis (EDA): Uncover trends and relationships in electoral data.
  • Feature engineering: Create robust features to capture the nuances of polling data and swing state dynamics.
  • Regression models: Build and evaluate regressor-based models tailored to each swing state.
  • Ensemble techniques: Experiment with stacking regressors to improve prediction accuracy.
  • Time-based weighting: Apply time-decay functions to prioritize recent polling data.
  • Model evaluation: Use metrics like Mean Absolute Error (MAE) and the number of correctly predicted winners to assess model performance.
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Who Should Complete This Project

  • Aspiring data scientists: Looking to practice predictive modeling with a real-world dataset.
  • Python beginners: With a basic understanding of Python and Pandas, ready to dive into hands-on data analysis.
  • Machine learning enthusiasts: Wanting to explore regression models, feature engineering, and advanced techniques like ensemble methods.
  • Political data fans: Interested in the intersection of data science and electoral trends, while understanding the ethical implications of forecasting.
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What You'll Need

  • A basic understanding of Python: Familiarity with Python programming is essential for working with the code and concepts in this tutorial.
  • Foundational data analysis knowledge: Understanding key concepts like data cleaning, manipulation, and visualization will help you navigate the project smoothly.
  • Access to modern web browsers: Make sure you have access to a modern web browser such as Chrome, Edge, Firefox, Internet Explorer, or Safari.
  • A learning mindset: Be open to exploring data critically and understanding the unpredictable nature of elections.

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Ready to dive into the fascinating world of political forecasting? Equip yourself with essential data science skills and gain valuable hands-on experience by predicting the 2024 US Election. Click ENROLL to get started and see how your models stack up against the real electoral landscape. Let’s turn data into insights—join the project today and make your predictions count!

Instructors

Hailey Quach

Data Scientist

Hi, I'm Hailey. I enjoy teaching others to build creative and impactful AI projects. By day, I’m a Data Scientist at IBM; by night, an Honors BSc student at Concordia University in Montreal, always exploring new ways to combine learning with innovation.

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Contributors

Boyun Leung

UX Designer

Creating and designing delightful experiences.

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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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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.

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