Final Project by Akhil Nair for STA6704 - Data Mining Methodologies II at UCF for Spring 2024. Breakdown:

1. Objective: Predict customer churn using various features related to customer demographics, services, and account information.
2. Dataset: Contains customer information such as gender, tenure, contract type, payment method, monthly charges, and churn status.
3. Preprocessing: Categorical variables are encoded, and irrelevant columns like customerID are removed.
4. Feature Selection: Techniques like Mutual Information and Recursive Feature Elimination (RFE) are used to identify the most important features for model training.
5. Modeling: Logistic Regression is employed to rank features based on their importance for predicting customer churn, aiming to improve prediction accuracy.