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My name is Akash Deshmukh. I am currently pursuing Masters in Computer Science from the State University of New York at Buffalo and graduating in Feb 2023.
I have 5 years of experience working as an Application Developer 2 at Oracle Financial Services Software. My responsibility was to build enterprise web applications for financial services like banks. I also got opportunities to work on machine learning projects like Anti Money Laundering Event scoring system at Oracle.
I am passionate about solving problems that are at the overlapping of software engineering and ML engineering domain.
Software Development
95%
Machine Learning
60%
My Portfolio
Consensus indicates that several servers agree on the same information, which is required for designing fault-tolerant distributed systems. Consensus algorithms enable a group of computers to function as a coherent entity that can withstand the failures of some of its members. RAFT protocol helps in achieving consensus.
In this project I implemented the two major task involved in RAFT algorithms Leader Election and Safe Log Replication
Github Project DetailPropaganda is commonly defined as information of a biased or misleading nature, possibly purposefully shaped, to promote an agenda or a cause. In this project I built a machine learning system for the Detection of Propaganda Techniques in News Articles. There are two subtasks to be solved as part of this project which are Span Identification and Technique Classification. We are able to secure position 17 on leader board in SI Task and position 20 in TC Task.
Github Project Detail
The objective was to develop ML model to predict the churn probability for the customer.
Definition of churn: A customer having closed all their active accounts with the bank is said to have churned. Churn can be defined in other ways as well, based on the context of the problem.
A customer not transacting for 6 months or 1 year can also be defined as to have churned, based on the business requirements
The objective was to deploy a model on AWS which predicts whether the customer is going to churn in the near future or not.