Maternal Health Prediction
Machine learning application for predicting maternal health risk using key health indicators.
Statistician with 7+ years of experience in inventory control and procurement analysis, specializing in data analysis, inventory optimization, and business intelligence. Experienced in turning operational data into actionable insights, with proven results including reducing excess material by 70% and increasing production output by 30%.
Experience
Excel · Python · SQL · Power BI
70% Reduction · 30% Increase
Tools & Expertise
My Professional Journey
Managed administrative operations, customer coordination, data analysis, and document management to support business operations and client decision-making.
Managed procurement and supplier coordination while using inventory and procurement analysis to optimize material availability, purchasing decisions, and budget planning.
Supervised raw material readiness and inventory planning while using data analysis and Excel-based tools to support production performance and material scheduling.
Final Score 83.42 / 100
Data analytics bootcamp covering data analysis, visualization, machine learning, and business problem solving using industry-standard tools
Practical Excel skills to analyze business data, create interactive reports, and build dashboards for better decision-making.
Developed advanced Microsoft Excel skills to automate tasks, analyze business data, perform forecasting, and improve reporting efficiency.
Certified English proficiency for academic and professional communication.
Featured Projects
Machine learning application for predicting maternal health risk using key health indicators.
Inventory analysis used to identify excess material patterns and improve material planning decisions.
RFM-based customer segmentation to identify valuable customer groups and support targeted business strategies.
Remote administrative and business support covering research, customer coordination, and data management.
The analysis measured operational efficiency, lead engagement, and conversion performance to improved business outcomes
Increased production output using descriptive analysis.
A machine learning project developed to predict maternal health risk by analyzing key health indicators including age, blood sugar, blood pressure, body temperature, and heart rate to classify maternal health risk levels.
An inventory optimization project focused on reducing excess packaging material by analyzing stock movement, purchasing patterns, and demand trends. The analysis helped improve inventory planning and reduce unnecessary inventory costs.
This project analyzes customer purchasing behavior using the RFM (Recency, Frequency, Monetary) model to classify customers into three segments: At Risk, Potential, and Loyal. The insights help businesses better understand customer behavior and support more targeted marketing strategies.
Provided remote administrative and operational support for a Singapore-based property company. Responsibilities included property research, customer coordination, document management, invoice preparation, and data organization to support daily business operations while maintaining client confidentiality.
Evaluated the business impact of an AI-powered property platform by comparing key business metrics before and after implementation. The analysis measured operational efficiency, lead engagement, and conversion performance to assess how the AI solution improved business outcomes.
Conducted descriptive analysis to compare production output with customer demand and identify production performance gaps. The insights supported production planning improvements and increased production output while maintaining alignment with customer requirements.