Comprehensive Modeling Analysis: Predicting Airbnb New User Bookings
This classifier uses an unsupervised-supervised modeling pipeline to predict booking destination countries of first time AirBnB users.
This classifier uses an unsupervised-supervised modeling pipeline to predict booking destination countries of first time AirBnB users.
Classification and clustering techniques were used to label excerpts from various texts according to their author.
Supervised learning techniques were used to model bikeshare data and predict the gender of NYC Citi Bike users.
Sentiment analysis was conducted to quantitatively judge subjective amazon reviews as positive or negative.
Classification and clustering techniques were used to label excerpts from various texts according to their author.
Supervised learning models predicted housing prices by utilizing multiple housing price indicators.
This classifier uses an unsupervised-supervised modeling pipeline to predict booking destination countries of first time AirBnB users.
Classification and clustering techniques were used to label excerpts from various texts according to their author.
Supervised learning techniques were used to model bikeshare data and predict the gender of NYC Citi Bike users.
Sentiment analysis was conducted to quantitatively judge subjective amazon reviews as positive or negative.
Supervised learning models predicted housing prices by utilizing multiple housing price indicators.
This classifier uses an unsupervised-supervised modeling pipeline to predict booking destination countries of first time AirBnB users.
Classification and clustering techniques were used to label excerpts from various texts according to their author.
Supervised learning techniques were used to model bikeshare data and predict the gender of NYC Citi Bike users.
Sentiment analysis was conducted to quantitatively judge subjective amazon reviews as positive or negative.
Classification and clustering techniques were used to label excerpts from various texts according to their author.
Sentiment analysis was conducted to quantitatively judge subjective amazon reviews as positive or negative.
This classifier uses an unsupervised-supervised modeling pipeline to predict booking destination countries of first time AirBnB users.
Supervised learning models predicted housing prices by utilizing multiple housing price indicators.