Predict 2024 US Election with EDA & Machine Learning
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Explore exploratory data analysis (EDA) and machine learning techniques to predict the outcome of the 2024 US Election. Learn how to clean, analyze, and visualize electoral data, then build models to make predictions using machine learning.
At a Glance
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.
A Look at the Project Ahead: Can Data Predict Election Outcomes?
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.
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.
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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