Raiyan Azami drives data science and machine learning initiatives with over 4 years of experience, building and refining predictive models using advanced statistical and deep learning techniques. He manages projects from initial data collection to final model deployment, ensuring high-performance solutions and clear communication of insights. His work includes developing NLP, Text Classification, and Deep Learning models for diverse data types.
Developed and deployed NLP and Deep Learning models for text and image data.
Built a web application to identify and classify road cracks using CNN and SVM.
Created a custom Wheel Visualizer, training a model with 10,000 car images for object detection.
Developed a healthcare salary prediction model using multiple regression algorithms.
Managed end-to-end data science projects, from data collection and feature engineering to model deployment.
Successfully developed a web application for automated road crack detection and classification.
Developed a predictive model for healthcare salary estimation, providing valuable insights into compensation trends.
Overview: Developed a custom Wheel Visualizer allowing users to preview different wheel models on their vehicles. Responsibilities: Implemented Detectron object detection to identify wheel positions (x,y) and size (width, height) on car images. Trained a custom machine learning model using approximately 10,000 car images with front and back wheel annotations.
Key outcomes:
Successfully developed a custom wheel visualizer application that integrates object detection and machine learning for personalized vehicle visualization.
Overview: Developed a web application to identify and classify cracks from road images or videos. Responsibilities: Trained a CNN deep learning model and SVM classifier to identify cracks on uploaded road images and videos. Integrated image processing techniques using OpenCV to enhance crack identification.
Key outcomes:
Successfully developed a web application for automated road crack detection and classification.
Overview: Developed a predictive model to accurately estimate total employee compensation in the healthcare industry. Responsibilities: Applied and refined multiple regression algorithms to predict employee compensation based on job roles and organizational structure.
Key outcomes:
Developed a predictive model for healthcare salary estimation, providing valuable insights into compensation trends.
Raiyan Azami
Data Scientist