Customer Shopping Behavior Analysis Dashboard

Overview

This project is a data visualization dashboard built with Streamlit and Plotly. It analyzes customer shopping behavior and spending habits based on a dataset. The dashboard provides interactive filters and various charts to explore sales trends, customer demographics, and spending patterns.

Features

Interactive Filters

The sidebar allows you to filter the data based on:

• Gender: Select specific genders to analyze.
• Category: Choose specific product categories.
• Age Range: Adjust the slider to focus on a specific age group.

Visualizations

The dashboard includes the following interactive charts:

1. Sales by Category: Bar chart showing the number of items sold per category.
2. Customer Age Distribution: Histogram of customer age groups.
3. Gender Distribution: Pie chart showing the male vs. female ratio.
4. Spending Hierarchy: Treemap of total spending by category and item.
5. Seasonal Preferences: Sunburst chart showing category distribution by season.
6. Customer Flow: Parallel categories diagram visualizing the flow from Gender to Category to Size.
7. Spending Distribution: Box plot of purchase amounts per category.
8. Review Ratings: Violin plot showing the density of review ratings by category.
9. Age vs. Spending Relationship: Scatter plot correlating age and purchase amount, colored by subscription status.

Installation

1. Clone the repository or download the project files.

2. Ensure you have Python installed on your system.

3. Install the required Python libraries:

   pip install streamlit pandas plotly

Usage

1. Navigate to the project directory in your terminal.

2. Run the Streamlit application:

   streamlit run main.py

   Note: If you encounter issues running the streamlit command directly, you can use:

   python -m streamlit run main.py

3. The dashboard will automatically open in your default web browser (usually at http://localhost:8501).

File Structure

• main.py: The main Python script containing the Streamlit application logic and visualizations.
• shopping_behavior.csv: The dataset file containing customer shopping data.
• README.md: This documentation file.

Dependencies

• Python 3.x
• Streamlit
• Pandas
• Plotly Express