← Back to Home

🚲 London Bike Share Analysis (2015–2017)

Welcome to the London Bike Share Analysis Project. This work showcases structured analytical thinking, clear documentation, and the ability to translate raw data into meaningful insights. Through the combined use of Python, SQL, and an interactive dashboard, it demonstrates how multiple analytical tools can work together to support confident, data‑driven decision‑making.

❓ What is Bike Sharing?

Bike sharing is a transportation service where bicycles are available for short‑term use. It provides a convenient, eco‑friendly way to navigate urban areas and complements public transit systems.

📊 Project Overview

This project explores London bike‑sharing data from 2015 to 2017, including:

• Data cleaning and preparation
• Exploratory data analysis (EDA)
• SQL business questions
• Interactive Tableau dashboard
• Weather and seasonal trend analysis

📁 Repository Contents

• EDA Notebook: Data exploration and visualization
• Tableau Dashboard: Interactive insights + guide
• SQL Analysis: Business questions and answers
• Dataset: Weather, season, and rental activity

📦 Dataset Description

The dataset includes:

• Bike rental counts
• Temperature, humidity, wind speed
• Weather conditions
• Holiday and weekend indicators
• Meteorological seasons

🌍 Data Sources

• Transport for London (TfL) Open Data
• Freemeteo weather data
• UK Government Bank Holidays

📑 Column Details

timestamp      – Date and time  
cnt            – Count of new bike shares  
t1             – Actual temperature (°C)  
t2             – Feels-like temperature (°C)  
hum            – Humidity (%)  
wind_speed     – Wind speed (km/h)  
weather_code   – Weather category  
is_holiday     – 1 = holiday, 0 = not  
is_weekend     – 1 = weekend, 0 = weekday  
season         – 0 spring, 1 summer, 2 fall, 3 winter  
    

🛠️ Technologies Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• SQL (PostgreSQL)
• Tableau
• Markdown

📂 Project Structure

📁 data/
    └── bike_sharing.csv
1.London_bike_sharing_EDA.ipynb
2.SQL_Exploratory_Analysis.md
3.London_bike_sharing_Dashboard.md
4.London_bike_sharing_Dashboard_Guide.pdf
README.md
    

🔍 Key Findings

The analysis revealed several important insights:

• Nearly 20 million rentals occurred between 2015–2017.
• Weekday commuting drives demand, especially at 8 AM and 5 PM.
• Weekday rentals are ~26% higher than weekend rentals.
• Summer is the peak season; winter sees the lowest usage.
• Temperature has a strong positive correlation with rental volume.
• Clear and partly cloudy weather encourage usage; rain and snow reduce it.
• Humidity between 61–80% is optimal for ridership.
• Only the top 10% of hours qualify as “High Traffic,” highlighting predictable peak windows.