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Comprehensive analysis of TellCo, focusing on user behavior, engagement, experience, and satisfaction. Includes data preparation, exploratory analysis, dashboard development, and predictive modeling to provide actionable insights for growth opportunities.
This multi-phase project identifies key satisfaction drivers and provides actionable insights to improve customer experience using statistical analysis and machine learning models, including logistic regression, decision trees, random forests, and XGBoost.
This project presents an end-to-end workflow for predicting airline customer satisfaction using survey data. It involves building and evaluating classification models (Logistic Regression, Decision Tree, Random Forest, XGBoost), covering data cleaning, exploratory analysis, model training, tuning, evaluation, and feature importance analysis.