This document discusses a meetup group evaluation and recommender system that uses machine learning models to classify group quality based on the percentage of repeat visitors. It trains a random forest model that achieves high precision, recall, and F-measure in predicting low, median, and high quality groups. The top five predictive features are identified and correlations among features are examined. A demonstration of the system is also mentioned.

1. Meetup Group Evaluation
and Recommender System
Houwei Cao, Insight Data Science Fellow

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4. Group Quality
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Based on the fraction of the repeat-visit user
! Low quality: no member repeat-visit 3 times or more in the past year
! Median quality: less than 5% user repeat-visit 3 times or more in the past year
! High quality: more than 5% user repeat-visit 3 times or more in the past year
No repeat
visit user
>5% repeat
visit user
<5% repeat
visit user

5. Model Story
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Precision Recall F-Measure
low quality
0.991 0.95 0.97
median quality
0.881 0.907 0.893
high quality
0.865 0.859 0.862
Prediction by Random Forest model

6. Top 5 Features
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7. About me
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Background: Speech and NLP
! Speech Recognition, Sentiment Analysis & Opinion
Mining, Multimodal emotion recognition

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9. Correlation among Features
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10. demo
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