Anza G.

Anza G.

Data Scientist with experience in Finance, Telecommunication and Bioinformatics

Wien , Austria

Experience: 6 Years

Anza

Wien , Austria

Data Scientist with experience in Finance, Telecommunication and Bioinformatics

51840 USD / Year

  • Immediate: Available

6 Years

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About Me

Being an engineer by qualification, I have always had an ambition to use my technical knowledge and problem-solving skills as a steppingstone towards a decision-making role. I am incredibly passionate about Machine Learning and Big Data Analytics....

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Portfolio Projects

Description

The task was to test and implement the business intelligence tools available in the market like Tablaeu, QLIK, Power BI or if we can implent a dashboard with interactivity using open source tools. For this i developed a dash board using python based library called plotly dash and integrate it with Angular frontend.

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Description

This was a project in collaboration with the student from KTH for which i was there project lead, to help them in completing the project. The task was to analyze the number of cars available at a certain point and make real time decision for how much time a traffic signal should be allocated time to open. So that we can reduce the traffic blocks during the busy hours.

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Description

For this i developed 3 different strategies to check the accuracy of my results firstly using collaborative filtering, secondly using an ensemble of machine learning algorithms and the third using hard set rules on the defined features. The reason to develop three different strategies was that we are able to achieve a desired accuracy before going into production.

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Description

The project was to develop a tool which will reduce the number of false positives within the current antimoney laundering system. The system that was provided by the EVRY was based on hard rule facts so it was creating a huge number of false positives for the bank employees to analyze.For this i developed firstly a knowledge graph using transnational and personal data of customers for better KYC(Know Your Customer) along side a machine learning algorithm utilizing decision tree to select point out the possible money laundering customers and then passing that information through the knowledge graph to check if the said customer is having an anomaly in the said transactions or if there is a pattern. I also wrote a white on the proposed solution.

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Description

For this project i developed a tool which took in the frequency pattern of all the clustered transmission sites and analyze the data of the allotted frequencies and recommend the best possible frequencies which will reduce the noise and distortion in the network. I was able to achieve higher accuracy compared to if done manually and in less time span

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