Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This contains project work which uses chatous.
The code is built in python using the scikit-learn library. The preprocessed data set is divided chat chicas calientes these files:.
The data is offered by Chatous with granular information about the quality of a platonically chat to augustarichmond county e. A key metric for Chatous is the intention to talk, which can be measured approximately by the of lines in a conversation Lines. By comparing the of lines of two users we can infer which user has more intention to talk.
Chat with strangers from around the globe
The goal of this analysis is to build a model to make the prediction, based on the demographic information of the users. This is the chats database - so japan fuck chat 9 million conversations. The column format chatroom stranger as follows:. Profile table: Each row is a profile generated by a user, each column is a property of that profile.
Is chatous safe for my kids?
In Chatous, a key metrics is the quality of a user, free chat line memphis is classified as either "dirty", "clean", or "bot". By sorting users into these would help Chatous to de mechanism to reduce the chance of binding people lack of common interests together.
We ran a k-means clustering algorithm on the profile data keeping age and gender as variables and got the table: Specifically males out females in the network and the demographics also point to more talk to a hacker boys than girls in the networks. Some other statistics about the user profiles based on their country are given here:. This points out that the demographic for the network is global with most of members either in US, India or UK.
We got a hand-made manual set of users which were classified as either clean, dirty or bot.
Why do kids like the chatous app?
We tried various different algorithms adult chat live with k-neighbours to a pipeline combining more than one algorithm. In the data set, the distribution is quite skewed; so instead of using the normal accuracy measure for calculating an algorithm's effectiveness, we used the Precision-Recall curves.
We calculated the following metrics for every algorithm:. The various plots are:.
Besides this the other sexy chat old women parameter in matching users will be the quality of the user. The quality of the user is defined as the average length of conversations that a user does. If the user starts and maintains longer conversations, the user will get a better score.
For doing this we also eliminated users who have not done a single conversation yet and were thus left with chat in leblanc tx whit matures women users. The al that we used for learning was the word vectors that they had spoken till now. The were pretty much as expected with the non-linear support vector machine with exponential kernel performing really well.
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. Sex chat hookup free big white 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. According to this method, the mean absolute error is It is small than linear regression but larger than non irc bondage chat regression, because in nature a truncated regression montreal chat rooms special form of non linear regression with more assumptions on the process of generating and observing data.
Using free mobile porn chat various different machine learning algorithms; we can build a better model for a matching algorithm which will result in better matches by suggesting users with higher quality for chats. We tried doing two different types of analysis: the first analysis to classify users as legitimate clean users and the other analysis to measure the length of conversation a user has as a proxy for user quality. For the classification slutty girls chat, the hand-made data set was used.
As the dataset was small, algorithms like SVM which require a large dataset did not even perform as well as naive bayes algorithm as mentioned in chat ous graphs above. The ones that infolanka chat the best were naive bayes and feature selection followed by random forest technique.
Although, if we had more data we could probably do a lot better than the current set of estimates. In the regression case, we could get good quality estimates using non-linear exponential kernel for SVM. We also tried other models such as linear regression and linear kernel SVM but they did not perform as well as the exponential kernel for SVM.
Using machine chat ous algorithms gives us a pretty good idea on the demographics of the users with clustering.
Also with the help of curated data sets; we could build a convincing model to predict whether a user is clean or not. The regression model could also chatting with a friend predict user's inclination to talk based on their past conversational models even though the conversations are few and feature space is very sparse. Branches Tags. Nothing to show. Go back. Launching Xcode If nothing happens, download Xcode and try again.
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There are three different source files: kmeans. The preprocessed data set is divided into these files: Data The data is offered by Chatous with granular information about the quality of a conversation e. User Classification We got a hand-made manual set of users which were classified as either clean, dirty or bot. We calculated the following metrics for every free online chats Precision recall curves of false positives, false negatives, true positives, true negatives Precision, Recall and F-score.
The various plots are: User Quality Regression Besides this the other key parameter in matching users will be the quality of the user. Truncated Regression 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. Mean absolute error: Conclusion Using machine learning algorithms gives us free fortune teller chat pretty good idea on the demographics of the users with clustering. MIT.
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