Data Analyst | Data Scientist | BI Engineer
System 1 - Data Analyst ( 2021 - 2023)
A data enthusiastic whose goal is to optimize efficiency and make the best use of limited resources. Expertise in business intelligence, data analysis, building statistical models, and visualization. Set the “Team” as the personal top priority and work with ethics, responsibility, credibility, and commitment to a strong result.
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Note: The numbers are cencored to protect data privacy.
Description: This is a team project that design a database for a local flower store to optimize the business operation. The process involve data entry, ETL and applying queries as demanded. The data has to be inputted manually by the team members, along with 12 queries matching with business demands.
The ER diagram of the flower shop illustrated through MySQL Workbench:
This is a academic project with the aim of predicting the potential customers and their demographic characteristics.
Key Findings: KNN is the optimal model with 94% accuracy and 58% recall. Also, we found that the demographic of potential buyers are new customers, who last visited the website within 7 days and have a habit of visiting Amazon every 5-30 days.
Below here are a part of visualization for EDA, please click the title for more details of the project:
This is a team project that research on Failure to Appear rate of defendants and time to disposition of a case in a hearing court.
Key Finding: Neutral Network is the optimal model wuth 68% Accuracy and 64% Recall.
Below here are a part of visualization for EDA, please click the title for more details of the project:
This is a personal project aimming to transform a meaningful story through visualizations and build a recommendation sytstem for users based on
the similarity of the movies preferences (contet-based). Please visit my GitHub for the outcome of the recommendation system.
EDA and Storytelling via Visualization:
This is a team project that aim to forecast the demand of airlines in Korea within the next 7 days using Time Series.
Please visit my Github for the detailed report.
Purpose:
Predict any credit fraudulent based on each transaction characteristics.
This including but not limited to the information of the customers.
Key Findings:
Decision Tree perform slightly better than Logistic Regression Model, at 99.906% and 99.9% respectively.
However, this is an insignificant difference between the two.
Here are two
This is a team project where we solved the Network Optimization (shortest path, maximum flow, and minimum cost) for an African Aiding Program.
More details and findings can be found in my Github page.