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https://github.com/tanay-dwivedi/london-bike-sharing-data-analysis
The project aims to conduct in-depth analysis of the London Bike Sharing dataset to discern usage patterns and influential factors impacting bike rental counts, facilitating informed decision-making for stakeholders.
https://github.com/tanay-dwivedi/london-bike-sharing-data-analysis
bike-sharing dataanalysis dataset matplotlib-pyplot plotly-express python seaborn visualization
Last synced: 8 days ago
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The project aims to conduct in-depth analysis of the London Bike Sharing dataset to discern usage patterns and influential factors impacting bike rental counts, facilitating informed decision-making for stakeholders.
- Host: GitHub
- URL: https://github.com/tanay-dwivedi/london-bike-sharing-data-analysis
- Owner: Tanay-Dwivedi
- Created: 2024-03-09T17:56:10.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2024-03-12T17:14:11.000Z (10 months ago)
- Last Synced: 2024-11-07T03:31:09.731Z (about 2 months ago)
- Topics: bike-sharing, dataanalysis, dataset, matplotlib-pyplot, plotly-express, python, seaborn, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 3.08 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# London Bike Sharing Data Analysis
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## Problem Statement
The task is to **analyze** the **London Bike Sharing dataset** to uncover insights into bike usage patterns and factors affecting bike rental counts. The dataset includes information such as **time** of observation, **bike count**, **weather conditions**, **temperature**, **humidity**, **wind speed**, and **season**.
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## Identify the Data
[Dataset](https://github.com/Tanay-Dwivedi/London-Bike-Sharing-Data-Analysis/blob/master/bike.csv)
The dataset comprises records of bike sharing in London, encompassing variables such as time, bike count, weather conditions, temperature, humidity, wind speed, and season. Each entry represents observations recorded over time, providing a comprehensive dataset for analyzing bike rental patterns and their dependencies.
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## Aim of the analysis
1. **Temporal Trend Analysis:**
The analysis aims to understand temporal trends and patterns in bike sharing activity in London. This involves examining variations in rental counts over different time periods, including daily, weekly, and monthly intervals.
2. **Impact of External Factors:**
Investigating the influence of external factors, such as weather conditions and seasonal variations, on bike rental counts. The goal is to identify correlations and potentially predictive relationships between these factors and bike usage.
3. **Actionable Insights for Stakeholders:**
Providing actionable insights for stakeholders, including bike-sharing companies and city planners, to optimize bike-sharing services, infrastructure, and operational strategies based on identified patterns and trends uncovered through the analysis.
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