Predictive Lead Scoring with Python

This post details the process of building a predictive lead scoring model using Python within the Hex collaborative notebook environment. It covers understanding the business context, defining the problem, data collection with a focus on avoiding data leakage, training a baseline model and a decision tree, assessing performance using metrics like accuracy, precision, recall, and F1 score, and the importance of interpretability for building trust with stakeholders. The post highlights Hex's utility in simplifying data access and environment management for data science workflows.