Interactive data analysis dashboard exploring rental patterns from the Bike Sharing Dataset, built as part of the "Belajar Analisis Data dengan Python" certification project from Dicoding Indonesia.
This project analyzes 17,379 hourly and 731 daily bike-rental records (2011-2012) to uncover key rental drivers such as season, weather, time of day, and user type (casual vs registered). The analysis pipeline covers data wrangling, exploratory data analysis (EDA), and a manual user-segmentation study, all visualized through an interactive Streamlit dashboard.
- Rentals peak in August, coinciding with the summer season
- Friday at 5 PM is the busiest hour of the week
- Clear weather and summer season show the highest rental volumes
- Registered users consistently rent more than casual users
- 2012 recorded higher overall rentals than 2011
- Python
- Pandas & NumPy (data wrangling)
- Matplotlib & Seaborn (visualization)
- Streamlit (interactive dashboard)
Clone this repository:
git clone https://github.com/bagascahyawiguna/DataAnalysis.git
cd DataAnalysisCreate and activate a virtual environment (recommended):
python -m venv venv
venv\Scripts\activate # Windows
source venv/bin/activate # macOS/LinuxInstall the dependencies:
pip install -r requirements.txtcd dashboard
streamlit run dashboard.pyThe dashboard will be available at http://localhost:8501 in your browser.