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Election Forecasting: Exploring the Effectiveness of Various Machine Learning Techniques

  • Unique Paper ID: 166706
  • Volume: 11
  • Issue: 2
  • PageNo: 2336-2342
  • Abstract:
  • The introduction of machine learning algorithms in general has caused a paradigm shift in political science predictive modeling. With an emphasis on recent elections, we provide a thorough examination of machine learning methods used in election forecasting in this study. Using a dataset with 1525 voters and nine important variables—such as economic evaluations and demographics—our study seeks to forecast voter behavior and predict electoral outcomes. First, we handle missing values, preprocess the data, and encode categorical variables. Understanding the pattern of distribution of preferences among voters and the connections among features and the goal variable are two things that can be learned using exploratory data analysis, or EDA. We then proceed to feature technology and decision-making, where we identify significant predictors and create new features, with the goal of enhancing model performance. A number of machine learning techniques are used, such as gradient boosting, AdaBoost, k-nearest neighbors (KNN), naive Gaussian Bayes, logistic regression, and linear discriminant analysis. ROC curves, precision-recall curves, F1-scores, accuracy, precision, recall, and other performance metrics are used to assess each method using rigorous cross-validation procedures. Our findings show encouraging performance across a variety of algorithms: ensemble techniques such as AdaBoost and gradient-boosting algorithms surpass 91% accuracy, while logistic regression achieves an accuracy of over 85%. In addition, we examine precision-recall curves and ROC curves to evaluate the models' performance at various thresholds and identify their advantages and disadvantages. Our research shows how machine learning may be used to forecast election results and predict voter behavior. We provide a strong foundation to guide subsequent election forecasting efforts and significant insights into electoral patterns by utilizing sophisticated tools and comprehensive evaluation methodologies.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 2
  • PageNo: 2336-2342

Election Forecasting: Exploring the Effectiveness of Various Machine Learning Techniques

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