A Data‑Driven Study of the Alone TV Series: Survival, Strategy, and Screens

What makes a survival show's contestants last — and what keeps audiences watching? Using 9 seasons of data on 94 contestants, episode ratings, and viewer trends from the wilderness reality series Alone, this project applies unsupervised machine learning to uncover hidden patterns in survival strategy and audience engagement. The analysis surfaces actionable recommendations for TV networks and producers: diversifying contestant profiles for a fairer competition, adjusting location difficulty to sustain viewer interest, and introducing wildcard re-entry mechanics to retain audiences when fan favorites tap out. A data story at the intersection of entertainment strategy, human behavior, and machine learning.