BrewedIQ: Insights into Managing a Coffee Chain

A streamlit dashboard for analyzing coffee shop sales. Features interactive filters, KPI tracking, and visualiations for sales trends, time-of-day performance, top products, and store comparions.

Live Demo: View Dashboard

Table of Contents

• About The Project
• Key Features
• Technologies Used
• File Structure
• How to Run Locally
• Data Source & Citation

About The Project

This project is an interactive web dashboard built with Streamlit to analyze sales data from a multi-location coffee shop. It provides a comprehensive, high-level overview of business performance, allowing users to drill down into specific metrics.

The dashboard allows users to filter the entire dataset by store location and date range. The analysis is then presented across four key areas:

1. Sales Trends: Analyzing revenue, transactions, and ticket size over time (daily, weekly, monthly).
2. Time of Day: Identifying peak hours and busiest days of the week.
3. Product Performance: Highlighting top-selling products and categories.
4. Store Performance: Comparing sales, growth, and trends acrooss different store locations.

All visualizations are created using Plotly for a clean, interactive experience.

Key Features

• Global Filters: Filter all dashboard data by one or more Store Locations and a specific Date Range.

• KPI Cards: At-a-glance metrics for Total Sales, Total Transactions, Average Quantitiy per Ticket, and Average Sale Value.

• Tabbed Interface: Cleanly organized analysis across four distinct categories.

• Sales Trends Analysis:

  • View metrics by 'Sales', 'Transactions', or 'Ticket Size'.
  • Change time granularitiy between 'Daily', 'Weekly', and 'Monthly'.
  • Toggle on/off a moving average to identify trends.
  • View period-over-period growth (e.g., Month-over-Month).

• Time of Day Analysis:

  • Bar chart of average performance by day of the week.
  • Line chart of average performance by hour of the day.
  • Heatmap showing product category performance by the hour.

• Top Products Analysis:

  • Pie Chart of sales/transaction distribution by product category.
  • Treemap for distribution by specific product type.
  • Bar Chart identifying the Top 5 best-selling products.

• Store Performance Comparison:

  • KPI breakdowns for each individual store.
  • Line Chart comparing sales trends across all stores.
  • Pie Chart showing the percentage of total sales contributed by each store.
  • Bar Chart comparing weekly growth rates for each location.

Technologies Used

• Python: Core Programming language.
• Streamlit: For building and deploying the interactive web app.
• Pandas: For data loading, manipulation, and analysis.
• Plotly: For creating interactive data visualizations.
• Jupyter Notebook: For initial exploratory data analysis (EDA).

File Structure

.
├── 📄 dashboard.py # Main Streamlit application file
├── 📄 vis.py # Module for all Plotly visualization functions
├── 📓 analysis.ipynb # Jupyter Notebook for initial EDA
├── 🗂️ CoffeeShopSales.csv # The dataset used by the dashboard
├── 📄 requirements.txt # Python dependencies
└── 📄 README.md # You are here!

How to Run Locally

Follow these steps to get a local copy up and running.

Prerequisites

You must have Python (3.15.5 or newer) installed on your system.

Installation

1. Clone the repository:

   git clone [https://github.com/Codon-s/BrewedIQ.git](https://github.com/Codon-s/BrewedIQ.git)
   
   cd BrewedIQ

2. Create and activate a virtual environment (recommended):

   # For Windows
   python -m venv venv
   .\venv\Scripts\activate
   
   # For macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

Running the App

Execute the following command in your terminal:

streamlit run dashboard.py

Open your web browser and go to http://localhost:8501 to view the app.

Data Source & Citation

The dataset used for this project is the "Coffee Shop Sales" dataset provided by Maven Analytics.

• Source: Maven Analytics

• Direct Link: https://mavenanalytics.io/data-playground/coffee-shop-sales