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Digital Fusion

Digital Fusion

Account Manager at Digital Fusion

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Followed by Shav K

Colombo, Sri Lanka

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Colombo, Sri Lanka

Cover image for Project 1: Database Migration &
Project 1: Database Migration & Audit Project Title: 5,000+ Record Database Audit and Migration Role: Administrative Specialist Tools Used: Property Tree, PropertyMe, Microsoft Excel Project Description: Successfully audited, cleaned, and migrated a massive database of over 5,000 property and client records for a busy real estate firm. The primary goal was transitioning historical data seamlessly between platforms while maintaining strict data integrity. The Process: Exported raw legacy data into Microsoft Excel to isolate missing information and system errors. Formatted, mapped, and cleaned 5,000+ rows of data to match the exact field requirements of the new system. Successfully imported the verified dataset into PropertyMe with zero downtime or lost records.
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Cover image for Student engagement measuring mechanism using
Student engagement measuring mechanism using computer vision — Research Link – Collected and preprocessed real classroom video datasets, applying data augmentation techniques to improve model robustness. – Developed a YOLOv8-based deep learning model to measure engagement through eye movement, gaze points, body posture, and head orientation of students. – Achieved 85% accuracy in engagement detection, validated on 2500+ annotated video frames, providing actionable insights for educators. – Tech Stack: YOLOv8, OpenCV, MediaPipe, ResNet-50, Scikit-learn, TensorFlow, Roboflow.
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Cover image for Retail basket analysis and customer
Retail basket analysis and customer segmentation – Conducted deep-dive analysis on 500k+ retail transactions using Apriori and FP-Growth to discover cross-sell opportunities, identifying high-lift product associations and seasonal purchase patterns. – Performed RFM customer segmentation, improving potential cross-sell rate by 18%, and built interactive dashboards. – Tech Stack: Python (pandas, NumPy, mlxtend, scikit-learn), SQL, Tableau, plotly.
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Cover image for Analyzed CDC diabetes health indicators
Analyzed CDC diabetes health indicators dataset – Analyzed 253k+ survey records to uncover risk factor patterns; built Decision Tree models with 5-fold cross-validation, achieving 82% classification accuracy. – Optimized model performance through hyperparameter tuning, improving recall of positive diabetes cases by 15%, supporting more accurate early-risk detection insights. – Tech Stack: Python, Pandas, NumPy, Scikit-learn, Seaborn, Matplotlib, Imbalanced-learn
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