Description

This project was conceived with the goal of enhancing customer satisfaction by identifying and predicting error cases that lead to client discontent. The first crucial step involved the aggregation and meticulous labeling of a dataset to distinguish between problematic and non-problematic cases. This process not only required a deep dive into the data but also a keen understanding of what constitutes a negative customer experience, ensuring that the resulting dataset would accurately reflect the nuances of customer interactions.

Once the dataset was accurately labeled, the focus shifted to developing a predictive model tailored to recognize patterns indicative of potential issues. Training this model was an iterative process, balancing precision and recall to maximize the model's efficacy in real-world scenarios. The end goal was to deploy a tool that could alert us to emerging problems, ideally before they affected the customer. By proactively addressing these issues, the model serves as a proactive mechanism in maintaining and improving customer satisfaction, ultimately reducing the frequency of negative experiences.