Several tweets will occur on twitter based on various topics, among these, some tweets may not be related to the topics or are less important therefore the classification of tweets based on the interest of the individual is important in some cases so that personalization becomes possible for the next incoming tweets. Excessive incoming tweets will occur if the user follows many accounts when they are interested in the content’s subset. In order to overcome this problem, filtering the incoming tweets becomes important based on the interest of the user.
Using KMeans & DBSCAN, Hvanatge provides you a system for clustering of the tweets relating to health and relatively key topics of discussion are drawn out. In the K means clustering, the data is clustered into groups based on the similarity in the observations of each cluster that is able to extract the insights from a large amount of unstructured data which is collected from several sources. Whereas DBSCAN contributes by iteratively expanding the cluster, by going through each individual point within the cluster, and counting the number of other data points nearby, hence provides the optimum results.
| Area | - |
 Data Science, Machine Learning, Artificial Intelligence
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| Language | - |
 Python, Haskel, React JS
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| Framework | - |
 Flask
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| Libraries | - |
 Gensim, Keras, Pandas, spacy
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