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About Me
Machine Learning Expert (individual Contributor)Leading Team of 5 Data Scientists...
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Description
Individual Contributor
Working on text classification (natural language processing) by using transfer learning of google pre trained word2vec (GoogleNews-vectors-negative300) model. Technologies/Tools: Keras, CNN, LSTM, Python, pandas, numpy, word embedding, nlp. Domain: Telecom / Sales
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Developed a classification model to predict the recurrence of hepatitis C disease after the completion of treatment. Able to achieved the accuracy of 91%.We have used ensemble learning by using random forest, GBM and SVM.
Technologies/Tools: R, SMOTE, ensemble learning, random forest, gbm, svm, baruta, pca, caret
Domain: Pharmaceuticals
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Developed a classification model to reduce the false positiveinstances MBIalgorithm. Able to reduce the false positive cases by 7%.We have fine-tuned the already used random forest and by using various techniques of variable selection.
Technologies/Tools: R, baruta, leaps, pca, SMOTE, under sampling, logistic regression, random forest, svm, gbm
Domain: Pharmaceuticals
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Developed classification model to predict whether person can be prospective Bank Customer. Able to achieved the accuracy of 79%.We have random forest of R and also used clustering to get better prediction.
Technologies/Tools: R, SMOTE, K-means, random forest
Domain: Banking
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