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This repository contains a comprehensive analysis of bike usage data with a focus on how it varies based on weather conditions and workday status. The analysis leverages Python libraries such as Pandas, Seaborn, and Matplotlib to explore and visualize patterns in bike usage across different time intervals and weather situations.

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Dicoding Collection Dashboard ✨

Bike Sharing Analysis Dashboard

This dashboard visualizes the impact of weather and time of day on bike sharing usage, focusing on how different weather conditions and hours of the day affect the number of users on working days versus non-working days.

Prerequisites

Make sure you have the following installed:

  • Python 3.x
  • Pandas
  • Seaborn
  • Matplotlib

You can install the required packages using pip:

pip install pandas seaborn matplotlib

Setup Environment - Shell/Terminal

mkdir proyek_analisis_data
cd proyek_analisis_data
pipenv install
pipenv shell
pip install -r requirements.txt

Run steamlit app

streamlit run dashboard.py

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This repository contains a comprehensive analysis of bike usage data with a focus on how it varies based on weather conditions and workday status. The analysis leverages Python libraries such as Pandas, Seaborn, and Matplotlib to explore and visualize patterns in bike usage across different time intervals and weather situations.

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