Election Data Visualization and Prediction Using Machine Learning

1MUPPALA NAGAKEERTHI, BAYALAPUDI GAYATRI 2
1Assistant Professor,

2Student Master of Computer Applications

Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

Abstract: The Election Data Visualization and Prediction Using Machine Learning is designed to analyze and visualize election data using interactive dashboards. The system processes raw election datasets, applies prediction and sentiment analysis techniques, and generates meaningful insights. It helps media analysts understand voting trends, party performance, and winning probabilities through visual representations. The system improves clarity, transparency, and decision-making using data-driven insights. The proposed system integrates Logistic Regression for predicting winning probabilities and Natural Language Processing (NLP) techniques for sentiment analysis to evaluate public opinion toward political parties and candidates. Developed using Python, Flask, HTML, CSS, JavaScript, Pandas, NumPy, Scikit-learn, Plotly, and TextBlob, the application provides accurate, user-friendly, and data-driven insights through charts and graphs. By improving the speed, transparency, and effectiveness of election data analysis, the system serves as a valuable tool for media organizations, researchers, analysts, and policymakers, while offering a scalable foundation for future enhancements such as real-time data integration and advanced machine learning models.

Keywords: Logistic Regression, Natural Language Processing (NLP), TextBlob Sentiment Analysis, Label Encoding, Plotly Visualization Algorithm.