Project: Multi-Channeling ticketing systemDetails: The Technical Support teams were unable to channel the incoming tickets to the right teams with the summary of the issue experienced by the consumer, and redirection of the issue tickets from one team to another lead to poor consumer experience. Hence we developed a model which would let the technical support representative know which issue should be redirected to which team.Skills Used: Python, Machine Learning ( Naïve Bayes), NLP Accountabilities: Extracted data from ticket description using NLP and classified the issues based on the Product Module  For classification we have used Naïve Bayes classifier. Worked closely on data cleaning, building ML model and validate those models. Developed all the form related functionalities in project.Extra Projects :Project: The objective of this project is to build a model that will help them identify the potential customers who have a higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign.Details: Majority of these customers are liability customers (depositors) with varying size of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns to better target marketing to increase the success ratio with a minimal budget.

Project: Resume ScreeningDetails: The hiring industry faces the common problem of hiring suitable candidate for the job. Though, we have human screening the resume behind the curtain but it lacks the efficiency and accuracy. Skills Used: Python, Machine Learning, Deep LearningAccountabilities:1. Cleaning and preprocessing the Data 2. Feature Engineering 3. R&D for Model 4. Training and Validation of Model5. Testing the ModelExtra Projects:Project: The objective is to predict the department-wide sales for each store for the each year. Model also effects the markdowns on holiday weeks. Provide recommended actions based on the insights drawn, with prioritization placed on largest business impactDetails: We were provided with historical sales data for 45 stores located in different regions - each store contains a number of departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks.

Project: AcronisDetails: Oversee a team of personnel focused on technical issues, including product releases, and engineering tasks, delegating work assignmentsSkills Used: Team ManagementAccountabilities: Ensures compliance with all company and departmental policies, procedures, and guidelines  Audit tickets handled by team members and correct the team members who miss on quality parameters missing. Mentoring new joiners and provide technical training with feedbacks.  Sending Daily Report for Team Productivity to higher management  Responsible for sending weekend Application Update/change implementing in Environment to entire IT Dept.

Project: Support AssociateDetails: The Project involved working with the internal clients to assist them with Hardware and Software issues.Accountabilities: Working with internal clients to resolve account related issues related to active directory. Resolving issues related to Virtual machines assigned to the users Indulging with various internal teams to assist users with their issues related to software and hardware. Provide timely and effective resolutions to our clients on basic to complex technical support queries and escalate necessary cases to the right level within the defined LOB (Line Of Business)