Complete Exploratory Data Analysis of a Comma Separated Values(CSV). Completely automated product. You just need to upload the data and You will be able to get the insights from the data.
The Project is developed in R and hosted on shinnyapps.io .

A Library for representation learning of Text using Transformers such as BERT, AlBERT, RoBERTA and spacy

Worked with TCS - HR, Government PSU : The New India Assurance , ICICI Prudential and other india clients of Tata Consultancy Services.

• Analysis : Time Series Data , Unstructure Data , Tabular Data
• Hypothesis Testing
• Predictive Modelling
• Machine Learning Model Deployment
• Azure, AWS Pipeline

We designed a novel embedding-based technique that utilizes the concept of the Skip-gram framework. An embedding-based method embodies the learning of feature representations of nodes or links in a network. Our method jointly exploits the Skip-gram framework and max aggregator for edge embedding tasks. To test the effectiveness of the proposed method, we have conducted experiments on large size real-world networks. In the experimental evaluation, we have compared the proposed method against both similarity-based and learning-based approaches. The experimental results indicate the effectiveness of the proposed method both in terms of time and accuracy.

We designed two, time efficient algorithms for finding all paths of length-2 and length-3 between every pair of vertices in a network which are further used in computation of final similarity scores in the proposed method. Further, we define a hybrid feature-based node similarity measure for link prediction that captures both local and global graph features. The designed similarity measure provides friend recommendations by traversing only paths of limited length, which causes more faster and accurate friend recommendations. Experimental results show adequate level of accuracy in friend recommendations within considerable computing time.

We present a computationally more efficient approach to extract and classify the named entities. This approach uses rule based learning in combination with regular expression and pattern matching to extract the entities from the given text.

We defined a hybrid feature-based approach that uses local graph feature by computing proximity between every pair of nodes. It also captures global feature by computing all length two and length three pathways between each pair of vertices of the network.