Real-Time Financial News-Driven Stock Prediction Web Application
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Led a multidisciplinary team of 5 in SQL database design and
integration of BERT large language model for sentiment analysis.
- Developed a machine learning model to predict stock price
direction by real-time financial news sentiment analysis.
- Built a user-friendly web application using Flask that
suggests the users whether to buy/ sell the selected stock.
Custom Database Enhancements - H2 Database
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Developed a custom aggregate function to identify the nth maximum value, enhancing data retrieval efficiency.
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Designed a specialized 'Contact' datatype for H2 database, ensuring validation of contact details like phone numbers and emails.
- Updated Java H2 in-memory database files, adding native support for the custom function and datatype to enhance performance.
Language Classifier AI
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Developed a Python-based machine learning model proficient in differentiating between English and Dutch languages.
- Employed a combination of decision tree and random forest algorithms for accurate test classification.
- Crafted comprehensive unit tests and debugging protocols, achieving a 95% accuracy in model performance.