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PREDICTIVE ANALYTICS IN HOTEL RESERVATIONS

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Service providers in the hotel industry are interested in identifying the factors that contribute to consumers' choice of hotel booking method. In an effort to determine these factors we used the predictive analytic tool of logistic regression. In particular, we concentrated on the choice of booking directly on a hotel…

Service providers in the hotel industry are interested in identifying the factors that contribute to consumers' choice of hotel booking method. In an effort to determine these factors we used the predictive analytic tool of logistic regression. In particular, we concentrated on the choice of booking directly on a hotel website as compared to a third-party website. We found that consumers with children were 2.94 times more likely to use a hotel's website. We found that consumers who place a high importance on cost were 1.42 times more likely to use a third-party website for booking a hotel. These results could be useful for hotel marketing and sales representatives to better understand the preferences of their customers and improve the hotel reservation services provided. Predicting consumer needs and choices have the potential to optimize sales and increase profits.
ContributorsMolinaro, Erin Rose (Author) / Wilson, Jeffrey (Thesis director) / Dawson, Gregory (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / W. P. Carey School of Business (Contributor)
Created2015-05
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Using Logistic Regression to Predict Stock Trends Based on Bag-of-Words Representations of News Article Headlines

Description

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones…

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.
ContributorsBarolli, Adeiron (Author) / Jimenez Arista, Laura (Thesis director) / Wilson, Jeffrey (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05