  $\textit{The Bachelor}$ is a reality TV dating show in which a single
bachelor selects his wife from a pool of approximately 30 female contestants
over eight weeks of filming (American Broadcasting Company 2002). We collected
the following data on all 422 contestants that participated in seasons 11
through 25: their Age, Hometown, Career, Race, Week they got their first 1-on-1
date, whether they got the first impression rose, and what "place" they ended
up getting. We then trained three machine learning models to predict the ideal
characteristics of a successful contestant on $\textit{The Bachelor}$. The
three algorithms that we tested were: random forest classification, neural
networks, and linear regression. We found consistency across all three models,
although the neural network performed the best overall. Our models found that a
woman has the highest probability of progressing far on $\textit{The Bachelor}$
if she is: 26 years old, white, from the Northwest, works as an dancer,
received a 1-on-1 in week 6, and did not receive the First Impression Rose. Our
methodology is broadly applicable to all romantic reality television, and our
results will inform future $\textit{The Bachelor}$ production and contestant
strategies. While our models were relatively successful, we still encountered
high misclassification rates. This may be because: (1) Our training dataset had
fewer than 400 points or (2) Our models were too simple to parameterize the
complex romantic connections contestants forge over the course of a season.
