Neste exercício exploramos a relação entre os ganhos de mulheres que trabalham e a renda de suas famílias, utilizando o dataset “Mroz”. Este dataset é um conjunto de dados sobre participação e rendimentos da força de trabalho feminina desde 1975.

This case study explores opportunities to increase the likelihood that customers recommend the company’s customer service. It is a continuation of the project [Predicting Customer Detractors (Part 1): Analyzing Contextual Factors via Logistic Regression](https://rpubs.com/galdovaldonavas/1335090), which identified the service contexts with the lowest likelihood of recommendation (i.e., contact methods, contact reasons, and countries). Building on those findings, this follow-up project focuses on **understanding the root causes of dissatisfaction** and quantifying the potential benefits of addressing them. Specifically, we conduct a thematic analysis of open-ended feedback from Net Promoter Score (NPS) surveys to identify actionable issues. We then assess the expected impact of solving these problems on customers’ likelihood to recommend. The analysis includes: - Data simulation and cleaning. - Exploratory analysis with descriptive statistics and visualizations. - Logistic regression modeling. - Simulation-based predictions using bootstrapping. Although based on a real-world project, all data, variables, and insights presented here have been simulated to ensure confidentiality.

Week 3 Discussion