Eurofins performs Sample Testing on Various Domains like Food, Water, Pharma, and Dairy Products. Lab Specialists and Managers were interested to know the Predictive Probability of the Test Sample getting Validated as Success or Failure before the actual Test was conducted for a Customer and the Test Sample based on the history of the Same Customer giving the TestSample (Provided the Conditions) of the Test Sample provided at the Front Desk and also the Storage Parameters of the Test Sample over the period of Testing Cycle.

Machine Learning Analytics - Predictive Analytics is being used in this project to identify theTest Results for the Test Sample

Algorithms like Logistic Regression, Decision Tree/Random Forest with Binary Outputs for Success and Failure.Data Set was collected over a Period of 5 years from the Transaction Systems. It was then analysed for the right kind of Features to be sleeted for Analysis.

Accuracy of Prediction was around: 72% and the Model are being monitored monthly with the new Transaction Data.

Eurofins in India has Centre of Excellence Team, which monitors the Network of Worldwide Labs 24/7. Failure in Hardware, Lab Robots for Testing and Vendors for providing the Network Stability like (Internet Failure, Switches Monitoring) has to be dealt with High Priority since it affects the Customer Turn around Time. This Project has helped Eurofins in Optimising the Centre of Excellence Engineer Roster for the Next Day for their Availability.

Machine Learning Analytics - Predictive Analytics was used here to identify the next Failure in Network by Analysing the Trend of Previous Historical Data from the COE team.

Algorithm Multiple Linear Regression was used Predict the Network Failure. R Square and Adjusted R Square Value obtained were around 0.8654 and 0.6521 respectively.

Eurofins wanted to analyse when the Customers and Scientists would place their next Order. The availability of Storage Locations was a Challenged Faced by Eurofins. Hence, Eurofins wanted this Analysis to be done before Hand.

Machine Learning Analytics - Predictive Analytics was used here.
Algorithm Multiple Linear Regression was used Predict the Network Failure.

R Square and Adjusted R Square Value obtained were around 0.7643 and 0.7421 respectively

Eurofins Pharma Test Team has a high accuracy results to be performed. To able to meet the requirement Management wanted to know Prediction of Customer Suggested and Actual Test Results of the Test Sample. Weekly Test results were compared and then reported to the Management.

Machine Learning Analytics - Predictive Analytics was used here to compare.

Algorithm Multiple Linear Regression was used.

R Square and Adjusted R Square Value obtained were around 0.8654 and 0.8321 respectively.

Eurofins Top Management wanted to stream Line Software Development Life Cycle of theirProjects across Geography. Hence Software Quality metrics like Test Cases, Defects, Resources details, Time Lines etc, were collected from Team Foundation Server. This was then clustered into 5 different Levels. So that Top Management could use this Information for Validating andPlanning.

Machine Learning Analytics - Clustering Algorithms like Connectivity Based, Centeriod Based, and K-Means were used for this Project.

This project was highly appreciated by Eurofins Top Management.

Eurofins have Vendors which are MNC's like Target, Wal-Mart, and TESCO etc, which provide Huge Revenue repetitive Business across Geography. They include various types of Products.

Products needed to be profiled into Segments, so that Eurofins could analyse these Profiled Segments.

Machine Learning Analytics - Clustering Algorithms like Connectivity Based, Centeriod Based, and K-Means were used for this Project.

• Played a principal role at offshore team and travelled onsite for working at client location

Accountable for perceiving the requirements from customers; developed the design and coding for the requirement

Responsible for effective interaction with offshore team and ascertained the requirements from the onsite BSA

• Actively engaged in performance tuning of the Informatica mappings for the application subject area
• Successfully involved in new design to enhance the performance of Informatica mappings execution

Methodically generated fast export scripts in Teradata and re-designed the mappings which resulted as a time consuming process for implementation in order to meet the Service Level Agreement (SLA)

• Diligently accountable for managing the teams’ development and production support tasks
• Competently collaborated with US Business team & leads in designing the applications’ model and architecture

Meticulously formulated Weekly Status report for tickets handled

• Proactively interacted with US Tech Lead & Development team to ascertain a permanent fix for the jobs failed

Worked together with development team to get information on new Change Requests added to the system