Hello and welcome to this short video, BubbleViz for Data Scientist. In this short video I will explain the algorithms inside our realtime ML tool, namely a cascade of two algorithms, online clustering and K&N type prediction. The problem of online clustering can be summarized as follow-on. You observe a sequence of data points, for instance in 2 dimensions here, and at each round T from 1 to capital T, you want to cluster the observed data points. For instance here, you want to automatically choose 3 classes, summarized with the following centers. This problem is well known and could be written as the following optimization problem. Where here, the last function called inertia is based on a cluedon distance and we had a penalty based on the number of clusters. This term allows to avoid overfeiting, that is for example, putting a center exactly at each observation. Lambda governs the trade-off between fitting the data with a small inertia and overfeiting with too much center. To solve this optimization issue, we propose to use a second-shall MCMC algorithm for Monte Carlo Markov chain. For short, we want to construct for any run T, a Markov chain C hat TK, which converges with algorithm theorem with the following Gibbs measure or quasi-postarial. The pseudo code is as follows. For any T, for any K from 1 to N, we propose a new state, C tilde, and accept with probability rule, based on the suitable likelihood ratio. With this model in mind, based on your feedback and the predictive essence of many data science problems, we now want to add a predictive mode. For that purpose, it is natural to add a K&M type algorithm as follows. For a new input variable X, we return the nearest center. We add X, and then choose your favorite algorithm in the volnose sale associated to this cluster. The easiest thing to do is to return the mean of the respond variable Y, and this volnose sale based on your training sample at time T. To conclude, bubble with by alumni, provide simple, fast and interpretable, predictive and data-money algorithms in order to understand your data and make predictions easily. A JavaScript app allows to interpret each cluster with different variables simultaneously. This theoretical approach, explained above, works for any type of data that is to save vectors times the risk of text. The only thing you need is a distance between two entities X and X prime. Finally, the different blocks used in bubble are open-source with MIT licensing in our GitHub. Bye-bye!