London Bike Sharing Data Visualisation Project

This project aims to explore and visualise the London Bike Sharing Dataset sourced from Kaggle. The dataset contains information about bike sharing in London, including details such as bike usage, weather conditions, and time-related data.

Table of Contents

• Introduction
• Data Source
• Workflow
• Visualisations
• Technologies Used
• Future Improvements

Introduction

In this project, we leverage Tableau for data visualisation to gain insights into bike sharing patterns in London. By analysing various factors such as weather conditions, time of day, and bike usage trends, we aim to uncover meaningful insights that can inform decision-making processes.

Data Source

The London Bike Sharing Dataset used in this project was sourced from Kaggle. You can find the dataset here.

Workflow

1. Data Extraction: The dataset was extracted from Kaggle and loaded into a Jupyter notebook for initial exploration and preprocessing.
2. Data Transformation: Data cleaning and transformation tasks were performed to prepare the dataset for visualisation.
3. Data Visualisation: The processed data was then visualised using Tableau to create insightful visualisations and dashboards.

Visualisations

Some of the key visualisations created in this project include:

• Filterable Moving Average of Bike Shares Across Date Range
• Heatmap for Windspeed and Temperature
• Utilisation of Tooltip to Display Weather and Time Information

Technologies Used

• Python (for data extraction and preprocessing)
• Jupyter Notebook
• Tableau

Future Improvements

• Incorporate additional datasets to enrich analysis and visualisation.
• Enhance interactivity and user experience in Tableau dashboards.
• Explore advanced statistical analysis techniques for deeper insights.