Project Details

This project focuses on developing a predictive model to classify ticket resolutions based on customer sentiment and ticket attributes. By leveraging a cleaned dataset containing customer demographic information, ticket priority, and sentiment scores, a logistic regression model was built to evaluate the relationship between these variables and resolution outcomes. The project includes data preprocessing, exploratory data analysis, feature engineering, model training, and performance evaluation. Advanced debugging and feature analysis were performed to refine the model, ensuring interpretability and actionable insights.