This case study aims to identify key factors that influence customer's likelihood to recommend the company after interacting with customer service. Methodology: The project utilizes a comprehensive analytical approach, including: - Data Simulation & Cleaning: Creating and preparing the dataset for analysis. - Exploratory Data Analysis: Using data visualization (e.g., heatmaps) and descriptive statistics to uncover patterns across multiple and interactive factors. - Statistical Modeling: Evaluating different regression models (linear, ordinal, binomial) to predict customer's likelihood to recommend the company. - Simulation Based Recommendations: Predictions to evaluate the impact of different actions. - Reusable Functions: The creation of functions to automate procedures. Tools & Libraries: R with a focus on libraries such as car, VGAM, ordinal, psych, vcd, coefplot, ggplot2, tidyr, dplyr, openxlsx, and readxl.

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Using R to analyze patient survival/ clinical death rates.

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