Have implemented OCR solution using OpenCV, Tesseract libraries to extract text from printed Documents with 99?curacy and handwritten documents with 85?curacy.

Built a sentiment classification engine for clinical data. This will give the trend of the disease based on the clinical notes of the patient over a period of time. Example of trends are disease recurring, disease becoming more severe, less severe etc.

Created a bigdata pipeline to extract data from RDBMS into HDFS. This data will be processed in Spark and will be used for Qlikview dashboards. Handled end to end implementation except Qlikview.

Developed a Credit Score Engine to calculate credit score variables for SME Customers. This CreditEngine receives data from external/internal APIs and processes the data and decides whetherloan would be approved/rejected for the Customers.Created parsers for and XML input files.Processing of data done using Pandas and Numpy libraries.Developed REST API using flask.Improved performance by implementing asynchronous programming to process data fromdifferent APIs.Implementation of Data Load in MariaDB Columnar store.

Development of a supply chain finance model which predicts whether finance should be provided tosellers. This model takes the inventory details, account receivables and account payables data asinput.Creation of Data Model from OFBiz erp model.Data ingestion into Hadoop using Sqoop.Logistic Regression is used to evaluate the credit risk of SMEs.

Design and development of web scrapers to extract text from Singapore and Hong Kong customswebsites.Implemented Web Crawlers using Scrapy framework in Python.Deployment of Web Crawlers in AWS to have rotating proxies which prevents blockingof web crawlers by websites.

Identify the profile of customers who have propensity to lapse the insurance policies.Initial model was developed for product categories - Term Life, Whole Life andUniversal Life Policies.Level Premium policies out of three major product categories -Term Life, Whole Life andUniversal Life Policies were identified. Alternate approach is model development with reclassified fourproduct categories as Level Premium Period, Term Life, Whole Life and Universal Life.This has brought significant improvement in accuracy of model.Algorithms and Language:Decision trees were used and derived the rules in R.Logistic regression was used to predict the churn of customers with probability in R.

Provide a solution to analyze agent performance based on several attributes like demography,products sold, new business, etc. The goal is to improve the existing knowledge used for agentsegmentation in a supervised predictive framework and to predict the Policy inforce Quantity.Approach:Univariate and Bivariate analysis of different variablesHandling of outliersfeature engineeringSummary stats by agencyModel BuildingAlgorithms implemented in Python:Decision treesNeural Networks.

A Health Care provider follows a ticketing system for all the telephonic calls received across all thedepartments where the Calls can be for New Appointment, Cancellation, Lab Queries, Medical Refills,Insurance Related and General Doctor Advice etc. The challenge is, based on the Text in the Summaryand Description of the call; the ticket is to be classified to Appropriate Category.Approach:Cleaning the data which involves converting to required formatCorpus creationPre-ProcessingDocument Term Matrix creationSplitting the data into Train, Validation and Test Datasets and applying the models on Traindataset and validating on Validation dataset.Algorithms used in R:SVMRandom ForestNaive Bayes

This project for a telecom client involves processing of Monthly Bills for Fixed line customers andgenerating PDF files of Mobile Bills to the end users.Developed Informatica mappings, enabling the extract, transport and loading of the data intotarget tables.Analyzed, designed, developed, implemented and maintained moderate to complex initial loadand incremental load mappings to provide data for enterprise data warehouse.Worked with Memory cache for the better throughput of sessions containing Rank, Lookup,Joiner, Sorter and Aggregator transformations.Responsible for migrating project between environments (Dev, QA, UAT, Prod)