My Portfolio
Machine Learning
In the quest for innovation and improvement, meticulous manual tagging of Arabic data is being undertaken to construct a bespoke dataset. Leading a highly skilled team, an advanced Named Entity Recognition (NER) model is being engineered, capable of predicting 20 distinct entity classes. This project is designed to significantly enhance the efficiency and precision of NER in Arabic text processing by broadening the scope of entity recognition and leveraging a custom-built dataset. The ultimate goal is to achieve unprecedented levels of accuracy and performance in NER analysis, setting a new standard for Arabic text processing.
Machine Learning
Developed a sophisticated classification model for automated tagging, extracting a substantial dataset of approximately 100,000 articles from a database utilizing SQL. Conducted extensive data cleaning and conversion processes to prepare the dataset for analysis, ensuring its suitability for machine learning and deep learning algorithms. Applied a variety of cutting-edge ML and DL algorithms to rigorously assess the model's performance. Python served as the primary programming language, enabling seamless implementation and experimentation throughout the project.
Machine Learning
Developed an IoT Smart Management Project enabling real-time monitoring of rooms through the integration of advanced sensors. This project provides comprehensive tracking and data analysis capabilities, enhancing operational efficiency and facilitating informed decision-making in smart environments.
Machine Learning
Developed a machine learning classification model utilizing a dataset of approximately 20,000 articles extracted from a client-specific database. The project involved cleaning and preprocessing the data to ensure it was suitable for machine learning algorithms. Various machine learning (ML) and deep learning (DL) algorithms were applied to evaluate the model's performance, with a focus on F1-Score metrics. Technologies employed in this project included Python and SQL.
Data Analysis
Developed a Patient Experience Project utilizing patient data to predict overall satisfaction with the patient journey process. This project involved analyzing various data points to assess and forecast patient satisfaction, aiming to enhance the overall experience throughout the healthcare journey.
Data Visualization
Developed an advanced Tableau dashboard that significantly transformed the research workflow for marketing professionals. This innovative tool employed web scraping techniques to extract critical data from a diverse range of stores and restaurants within a designated radius. By optimizing the data collection process, the dashboard enabled marketing specialists to precisely identify new vendors and streamline onboarding procedures.
Data Analysis
Developed a robust pipeline for daily scraping of real estate property data, ensuring efficient storage in a database and thorough preprocessing to maintain data quality and integrity. This pipeline facilitated seamless analysis and insights generation to support decision-making processes. Additionally, a Tableau dashboard was created for monitoring and sharing analytics reports with stakeholders.
