proposed topic modeling technique discover the more precise topics form biomedical text documents and remove the redundancy problem from these documents. Furthermore, it can be utilized for biomedical documents classification and clustering tasks in text mining proposedtopic modeling technique discover the more precise topics form biomedical text documents and remove the redundancy problem from these documents. Furthermore, it can be utilized for biomedical documents classification and clustering tasks in text mining

Analysis: For this application, we will be using basic statistical feature extraction, In the NLP domain, we need to convert raw text into a numerical format so that the ML algorithm can be applied to that numerical data, techniques are used, including indexing, count-based vectorization, Term Frequency Inverse Document Frequency

Scope of the Project

• Image / object detection of images

• Proof of concept to showcase capabilities to clients

Technology Used: Python, Tensorflow, CNN, AdaBoost

Counterfeit medicines are fake medicines which are either contaminated or contain the wrong or no active ingredient. They could have the right active ingredient but at the wrong dose. Counterfeit drugs are illegal and are harmful to the health.The here is to build predictive model for predicting sales figures given other information related to counterfeit medicine selling operations

Direct mailings to a companys potential customers – junk mail too many – can be a very effective way for them to market a product or a servicePredict whether a customer is interested in a caravan insurance policy from other data about the customer they would know more accurately who to send it to, so some of this waste and expense could be reduced

The goal is to detect the Probability of customer making fraud in paying the loansBased on the Historical data, EMI, Income, Asset value, demographic information etc are used in the analysis to predict the objectives. Build a logistic model to detect the probability of Fraud occurring

To perform sentiment analysis and to understand on whether the overall students satisfied or dissatisfied and to provide key insights from the student feedbackObtained word-clouds of uni-gram, Bi-Gram, Tri-Gram and polarity plots for entire feedbacks compared the positive and Negative uni-Gramword, Bi-Gram word cloud sand submitted key insights from the word-clouds