Bike Sales Data Analysis

Overview

This project conducts in-depth analysis and visualization on a bike sales dataset, identifying key insights such as customer revenue distribution, age group purchasing trends, profit margins by region, and inter-feature correlations.

Key Questions Answered

• What is the distribution of unit cost and profit?
• How does revenue vary across age groups?
• What age group is the most profitable?
• What are the relationships (correlations) between numerical features?
• Which countries have the highest revenue?
• What are the buying behaviors across different age categories?

Tools Used

• Pandas for data manipulation
• Matplotlib and Seaborn for visualizations
• NumPy for numerical operations
• Google Colab for notebook development

Features Visualized

• Density plots of Unit_Cost
• Box plots for Profit across age groups
• Heatmaps for correlation matrix
• Scatter plots of Customer_Age vs. Revenue
• Group-wise mean calculations (age + country)

Notable Insights

• Adult (35-64) customers generate the highest revenue.
• France's revenue increased by 10% using simulation.
• Highest correlation exists between Unit_Cost and Unit_Price.
• Outliers exist in Order_Quantity, and Profit.

File Structure

• notebooks/: Jupyter notebooks
• data/: Raw or cleaned datasets
• images/: Visuals used in README or reports
• outputs/: Text summaries or final reports

How to Run

1. Clone the repository
2. Install dependencies via pip install -r requirements.txt
3. Launch the notebook using jupyter notebook
4. Open Bike_sales_analysis.ipynb

Example Visualizations

• Correlation Matrix Heatmap
• Boxplot for Profit per Age Group
• Revenue Distribution per Country

Future Work

• Apply machine learning for sales prediction
• Deploy dashboard using Streamlit
• Analyze seasonal trends

Author

[Your Name] - Data Analyst & Python Enthusiast