London-Bike-Rides

Dashboard

Overview:

This project involves the analysis of bike-sharing data in London to understand rental patterns and factors influencing demand. The analysis was performed using Python, and visualizations were created using Tableau.

Process:

1. Data Gathering: The data was sourced from Kaggle using Python's Kaggle API.
2. Data Cleaning: The dataset underwent cleaning processes to handle missing values, duplicates, and inconsistencies using Python's pandas library.
3. Exploratory Data Analysis (EDA): Various statistical techniques and visualizations were employed to explore the dataset and identify trends, patterns, and correlations.
4. Feature Engineering: New features were created or existing features were transformed to extract additional insights and improve model performance.
5. Predictive Modeling: Machine learning models were developed using Python's scikit-learn library to forecast bike rental demand based on historical data and various factors.
6. Dashboard Creation: Visualizations and dashboards were built using Tableau to present the findings and insights in an interactive and easily understandable format.

Tools Used:

• Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn)
• Tableau

How to Use:

1. Clone the repository to your local machine.
2. Ensure you have Python and the required libraries installed.
3. Run the Jupyter Notebook file to execute the Python code for data analysis and predictive modeling.
4. Open the Tableau dashboard file to interactively explore the visualizations and insights generated from the analysis.

Outcomes:

• Identified peak rental periods and factors influencing bike rentals.
• Developed accurate predictive models to forecast rental demand.

This tableau dashboard was built following the guidance provided in Mo Chen's tutorial. I'd like to express my gratitude for the valuable insights and expertise shared through the tutorial.