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o Core programmer and co-designed an in-house automated process for topic exploration, leveraging machine learning algorithms and natural language processing techniques (NLP) to efficiently identify and visualize key themes within large datasets. The objective of this analysis is to provide starting points for topic modeling.o Conduct Key Driver Analysis projects for clients to gain CX insights, utilizing machine learning regression to identify the most important factors that drive NPS or other CX metrics.o Build and deploy a machine learning prediction model in Python (using Scikit-learn library) to predict customer repeat, providing insights that drive actions and align with business needs and goals.

- K-Nearest Neighbor was utilized to detect anomalies in Microsoft Teams’s traffic and resource usage during different hours, leading to a decrease in application performance metrics. This finding helps the product manager take precautionary steps to improve network bandwidth and allocate server resources more effectively.- Applied BERT from Transformer library and NLP on sentiment analysis and text classifications- Designed and built data analytic Python scripts then deployed to PowerBI to support findings and presentation- Deployed linear regression model to estimate end user monthly experience scores for future application improvement- Analyze AYS tickets and survey data to help product managers identify problem areas - Applied Recurrent Neural Network (RNN) to forecast mobile usage of one month ahead. Additionally, collaborated with the Mobile team to re-organize mobile plans, potentially reducing monthly billing cost by 22%.

o Co-designed analytic process and dashboards of End User Survey projecto Technical requirement: Python and Natural Language Processing (NLP)o Utilize visualization technique to present the datao Implement Machine Learning into Power BI Dashboardo Collaborate with Data Scientists and Manager to solve problemso Objective: Create an analytic tool to help product managers capture insights from end-user feedback