Hi! I'm Kristian, and I am a passionate data science professional who seeks to harness the powers of data science to drive innovation and success through data-driven solutions.
I specialize in driving business innovation by leveraging the power of data science to enhance operational efficiency and optimize overall company performance. I am looking forward to using my data science skills in finance and investments in the future. I am currently self-studying for CFA level I topics to equip myself with the right tools and skills to bridge the gap. Moreover, I'm passionate in data visualization and programming, aiming to solve business problems using these tools and skillset.
Designed and optimized a deduplication algorithm for 2M rows of data using Python that
minimized client information search workload and data inaccuracy by approximately 30%.
Collaborated with a team of data scientists to design an ETL pipeline for scraping text data from
350 PDF files and a data cleaning process that utilizes regex to meet project constraints.
Utilized JavaScript with Google Apps Script to design and develop a tracking and approval system
using the Gmail API, automated 6 procedures across the finance and procurement department.
Streamlined processes with a data-driven approach for bottleneck analysis, resulting in improved
workflow efficiency.
Designed a data retrieval algorithm and developed dashboards in Google Sheets and Tableau
that allowed the top management to monitor the deliverables of each department which
resulted in 20% faster completion of deliverables
Perform ad-hoc data analysis and data-driven recommendations to C-level executives anddepartment heads for business problem solving and analysis
Cum Laude
The objective of the project is to develop a robust classification model that can analyse urinalysis test results to enhance the accuracy of UTI diagnosis by reducing false-positive and false-negative patients with utilizing machine learning models and artificial neural network for UTI diagnosis.
In this repository, the researchers designed the Convolutional Neural Network (CNN) for recognizing handwritten baybayin characters. The study involved hyperparameter tuning using Optuna and after identifying the optimal hyperparameters, the model was then trained using the optimal hyperparaeters and tested using the hold-out set.
Mobile apps have become essential as digital transactions like e-commerce, eHealth, and payments become more prevalent. Meanwhile, advertisements have gone digital and can be seen while browsing websites or using mobile apps. Most smartphones run on the Android platform, resulting in a vast ecosystem of apps. Despite its rapid expansion, the prevalence of adware applications are increasing.