Superstore Sales Dashboard

Overview

The Superstore Sales Dashboard is an interactive web application built with Python and Streamlit. It provides comprehensive insights into the sales data of a superstore, allowing users to filter and visualize sales performance across different regions, states, cities, and product categories.

Key Features:

• Date Range Filtering: View sales data within a specific date range.
• Dynamic Filtering: Filter data based on region, state, and city.
• Sales Analysis: Visualize sales by category, region, segment, and time series.
• Hierarchical Views: Explore sales data using treemaps for a hierarchical breakdown.
• Detailed Views: Access detailed data summaries and download filtered datasets.

Setup Instructions

Follow these steps to set up and run the Superstore Sales Dashboard locally on your machine.

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)
• Anaconda (optional, but recommended for managing dependencies)

Installation

1.Clone the Repository:

git clone https://github.com/KaranKathur06/superstore-sales-dashboard.git
cd superstore-sales-dashboard

2.Create a Virtual Environment:

python -m venv venv

3.Activate the Virtual Environment:

Windows:

venv\Scripts\activate

MacOS/Linux:

 source venv/bin/activate

4.Install Dependencies:

   pip install -r requirements.txt

If requirements.txt is not provided, install the necessary libraries manually:

pip install streamlit pandas plotly

5.Navigate to the Project Directory(As per you saved):

cd C:\\STUDY\\PROGRAMS\\PYTHON\\DMDW_Project (AS PER MINE EXAMPLE)

6.Now run the Streamlit App:

 streamlit run dashboard.py

Usage

-> Date Range Filtering: Use the date pickers to filter data within the selected start and end dates.

-> Region Filtering: Select one or more regions from the sidebar to filter the data accordingly.

-> Optional State and City Filters: Further refine your selection by choosing specific states and cities.

-> View and Download Data: Use the expanders and download buttons to view and save the filtered data.

-> Visualize Sales: Explore various charts and visualizations for in-depth sales analysis.

Project Structure

-> dashboard.py: The main script that runs the Streamlit dashboard.

-> SuperStoreDataSet.csv: The dataset containing the superstore sales data.

-> requirements.txt: A list of Python packages required to run the project.

Screenshots:

Dashboard Overview

Sales by Category & Region

Time Series Analysis

Hierarchical View Of Sales Using Treemap

Segment Wise Sales & Category Wise Sales

Ralationship Between Sales And Profits Using Scatter Plot!!

Contributing:

Contributions are welcome! Please open an issue or submit a pull request if you have any suggestions or improvements.

1.Fork the repository.

2.Create a new branch:

 git checkout -b feature-branch-name

3.Commit your changes:

 git commit -m 'Add some feature'

4.Push to the branch:

 git push origin feature-branch-name

5.Open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements:

Streamlit - For making it easy to create beautiful web applications in Python.

Plotly - For providing powerful visualization tools.

Superstore Dataset - The sales data used in this project.