BikeCast: Data Research & Analysis

Table of Content

Project Timeline: Jan 2026 ~ May 2026

Final Result / Demo Visual

Introduction

Project Context

Blank Image for Project Overview

BikeCast was a four-person data analysis project developed during a software bootcamp in South Korea. The project used public datasets, weather API data, Python, and MySQL to analyze demand patterns in a public bike rental system.

The goal was to explore how bike rental demand varied by time, location, season, and weather conditions. By combining rental records with environmental and location-based data, the team aimed to support better bike distribution decisions across rental stations.

Project Goal

My Role

My role / contribution

My contribution focused on the weather data workflow. I collected and processed government weather API data, translated encoded forecast responses into usable variables, matched the processed data with bike rental records, and visualized the relationship between weather conditions and rental usage.

Challenge: Turning Raw Weather API Data Into Reliable Analysis Input

Available Data Was Not Automatically Usable

The project depended on comparing bike rental demand with weather conditions, but the government weather API returned data through coded fields, categories, and structured responses. Before the data could support analysis, I had to understand what each value represented instead of treating the raw API output as directly reliable.

Challenge / Problem Visual 1

Making the Weather Data Interpretable

I read the API documentation, identified the meaning of the coded weather fields, and wrote Python logic to translate the raw response into usable variables. This included extracting and organizing weather information while handling missing or empty entries.

Challenge / Problem Visual 2

Aligning Weather Records With Rental Usage

After processing the weather data, I queried the team’s shared database to pull bike rental records from matching dates and time periods. This mattered because misread fields or mismatched time periods could lead to misleading analysis.

Challenge / Problem Visual 3

Converting the Matched Data Into Visual Insight

Once the weather records and rental records were aligned, I compared the datasets and visualized the relationship with matplotlib. This helped the team examine how weather conditions related to bike rental demand.

Challenge / Problem Visual 3

Conclusion

Outcome

Our team developed a data analysis workflow that combined bike rental records with weather, time, and location-related data to examine demand patterns across rental stations. My work contributed to the weather data processing and visualization needed for the team’s broader analysis.

Outcome Vissual

Reflection

This project gave me early practice in data engineering: collecting external data, interpreting raw API responses, cleaning and structuring records, matching datasets across time periods, and visualizing patterns to support analysis. I see this as a useful foundation for future engineering work involving test data, debugging logs, calibration results, sensor readings, or other data-heavy workflows.

Extra Visual