Multinomial Logistic Regression for Voter Behavior Analysis

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📅 2024-09-09 — Session: Multinomial Logistic Regression for Voter Behavior Analysis

🕒 15:20–16:45
🏷️ Labels: Multinomial Logistic Regression, Voter Behavior, Data Analysis, Python, Education
📂 Project: Teaching
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to explore and implement multinomial logistic regression models to analyze voter behavior, focusing on transition probability models and statistical interpretations.

Key Activities

  • Developed a framework for modeling voter behavior using transition probability models, specifically focusing on multinomial logistic regression and Markov processes.
  • Created challenging questions to test understanding of multinomial logistic regression.
  • Installed python-Levenshtein to improve fuzzywuzzy performance.
  • Implemented a multinomial logistic regression model to predict 2024 voting probability based on prior voting status and degree.
  • Resolved data type errors in the regression model using statsmodels.
  • Guided students in reconstructing a multinomial logistic regression model from group sizes.
  • Analyzed voter behavior among Physics and Biology students using a multinomial logistic regression model.

Achievements

  • Successfully set up and executed multinomial logistic regression models to analyze voter behavior.
  • Enhanced string matching performance in Python by installing necessary libraries.

Pending Tasks

  • Further analysis on the impact of educational background on voting behavior using the constructed models.

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