Developed Multinomial Logistic Regression for Voter Analysis

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

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

Session Goal

The session aimed to explore and implement multinomial logistic regression models to analyze voter behavior, particularly focusing on transition probability models and voting patterns.

Key Activities

  • Framework Design: Established a framework for modeling voter behavior using transition probability models, focusing on multinomial logistic regression and Markov processes.
  • Question Design: Created a challenging question template to test understanding of multinomial logistic regression.
  • Package Installation: Installed python-Levenshtein to enhance the performance of the fuzzywuzzy package.
  • Model Implementation: Developed a methodology for modeling voting probability using multinomial logistic regression with Python’s statsmodels library.
  • Data Cleaning: Addressed data type errors in the regression model to ensure numeric compatibility.
  • Educational Insight: Provided a structured approach for students to reconstruct regression models using group sizes from contingency tables.

Achievements

  • Successfully implemented multinomial logistic regression models to analyze voter behavior among Physics and Biology students.
  • Enhanced understanding of statistical modeling through educational templates and problem statements.

Pending Tasks

  • Further validation of models with additional datasets from different demographics.
  • Exploration of alternative statistical models for comparison.

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