---
title: "Bike Rental Insights Dashboard"
url: "https://ridwaanhall.com/projects/bike-rental-insights-dashboard"
markdown: "https://ridwaanhall.com/projects/bike-rental-insights-dashboard.md"
summary: "A Streamlit dashboard on how weather, season and working days shape bike rentals, with an RFM analysis."
---

# Bike Rental Insights Dashboard

> A Streamlit dashboard on how weather, season and working days shape bike rentals, with an RFM analysis.

- Category: Dashboard
- Status: Completed
- Started: July 6, 2025
- Built with: Streamlit, Python, Pandas, Matplotlib, Seaborn, Jupyter Notebook
- Source: https://github.com/ridwaanhall/dicoding-bike-sharing-analysis

## About

### The problem

A rental business cannot plan its fleet or pricing without knowing how demand moves with the weather and the calendar.

### The approach

This is my submission for Dicoding's Data Analysis with Python course. I analysed the Bike Sharing dataset in a notebook, then published the findings as a Streamlit dashboard.

- Rentals compared across seasons to find the most and least popular.
- The relationship between weather conditions and total rentals.
- Working days compared with weekends and holidays, and an RFM analysis.

### The result

A small, clear dashboard that turns the dataset into answers about when and why people rent. The notebook and the dashboard are both in the repository.

## Features

- **Season Trends**: Which seasons rent the most and the least.
- **Weather Impact**: How conditions relate to total rentals.
- **Working Days and Holidays**: Rental patterns compared between the two.
- **RFM Analysis**: A recency, frequency and monetary view of the data.

## Tags

Bike Sharing Analysis

## Elsewhere

- This page for people: https://ridwaanhall.com/projects/bike-rental-insights-dashboard
- Index of every page as Markdown: [llms.txt](https://ridwaanhall.com/llms.txt)