Bike Sharing Data Analysis

This project, part of Dicoding's 'Belajar Analisis Data dengan Python' course, explores, analyzes, and creates dashboards using bike sharing data. It includes two main files: a Jupyter notebook for data wrangling, exploratory analysis, and visualization, and a Python script for the Streamlit dashboard. The dashboard is accessible at this link.

Run Analisis-data.ipynb File

You can run the file by following these steps:

1. clone this repository:

   git clone https://github.com/totoro-07/Bike-Sharing.git
   

Run Streamlit App

To run the dashboard:

1. Download the dashboard folder (do not move files within it).
2. Install Streamlit:

   pip install streamlit
   

3. Install required libraries (pandas, numpy, scipy, matplotlib, seaborn, plotly).
4. Open dashboard.py in VSCode.
5. Run the dashboard:

   streamlit run dashboard/dashboard.py
   

Questions Explored

The analysis answers the following questions:

1. What is the trend of bike-sharing usage each year?
2. How does weather condition affect the percentage of bike-sharing users?
3. How does the season affect the percentage of bike-sharing users?
4. At what time is the number of bike rentals the highest?

Data Analysis Pipeline

1. Data Collection: Used bike-sharing dataset from 2011 to 2012.
2. Data Cleaning: Fixed data type mismatches and renamed columns.
3. Exploratory Data Analysis (EDA): Explored days_df and hours_df.
4. Visualization: Created line, bar, and pie charts to show trends by year, user type, season, weather, and hourly usage.