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People say its good for text chatting & video chatting, overall it's really cool and fun to talk to people, that's what you do on a dating site /app. To illustrate the idea behind a truncated regression, imagine that we have a dataset with only two elements [-1,1] and we are interested in the average of the data. It is 0. Now assuming that the data is censored on the left at 0 and we only observe 1, calculation without adjustment will yield 1 instead, which is biased. Similarly, in Chatous dataset, it is unlikely to observe users with extremely low quality, because nobody would like to talk with them and thus researchers will not observe them in the dataset. To adjust this bias, we rely on truncated regression. We got a hand-made manual set of 1034 users which were classified as either clean, dirty or bot. Using this data set and dividing it into training and test set; we tried to apply various classification algorithms in order to find out if we could learn to predict dirty/bot users and penalize them in the chat matching algorithm. We tried various different algorithms starting with k-neighbours to a pipeline combining more than one algorithm. This contains project work which uses chatous. com dataset for predicting quality of users and helps in predicting a better matching algorithm.
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