US Election 2024 Predictions With Machine Learning and Python

This article uses machine learning to predict the outcome of the 2024 US Presidential election. While acknowledging the inherent limitations of relying solely on polling data, the author presents a detailed methodology and analysis.

The prediction process involves several key steps: data acquisition from FiveThirtyEight and the Federal Election Commission, data cleaning and preprocessing (handling missing values, addressing inconsistencies in percentage calculations), feature engineering (creating opponent-related, time-related, candidate-related, and party-related features), model selection and training (using various regression models like Linear Regression, RandomForestRegressor, XGBoost, etc.), model evaluation (using Mean Absolute Error and correct winner prediction as metrics), and finally, prediction for the 2024 election.

The author chooses to focus on swing states, building separate models for each to account for state-specific voting patterns. An exponential decay function is used to weight recent polls more heavily in the final prediction. The analysis includes an exploratory data analysis (EDA) section with visualizations showcasing polling trends.

The resulting prediction suggests a close race, with Kamala Harris projected to win a slight majority of the electoral votes. However, the author stresses the limitations of the model, highlighting the influence of factors beyond polling data that could impact the actual election outcome. The article concludes by recommending further resources for learning about predictive analytics.

Disclaimer: This analysis is for educational purposes only and does not constitute political commentary. The opinions expressed are the author's and do not reflect the views of DataCamp.

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