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$\textit{The Bachelor}$ is a reality TV dating show in which a singlebachelor selects his wife from a pool of approximately 30 female contestantsover eight weeks of filming (American Broadcasting Company 2002). We collectedthe following data on all 422 contestants that participated in seasons 11through 25: their Age, Hometown, Career, Race, Week they got their first 1-on-1date, whether they got the first impression rose, and what "place" they endedup getting. We then trained three machine learning models to predict the idealcharacteristics of a successful contestant on $\textit{The Bachelor}$. Thethree algorithms that we tested were: random forest classification, neuralnetworks, and linear regression. We found consistency across all three models,although the neural network performed the best overall. Our models found that awoman 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. Ourmethodology is broadly applicable to all romantic reality television, and ourresults will inform future $\textit{The Bachelor}$ production and contestantstrategies. While our models were relatively successful, we still encounteredhigh misclassification rates. This may be because: (1) Our training dataset hadfewer than 400 points or (2) Our models were too simple to parameterize thecomplex romantic connections contestants forge over the course of a season.

Predicting Winners of the Reality TV Dating Show $\textit{The Bachelor}$ Using Machine Learning Algorithms