Cafe-Analytics-Pilot

Data analytics pilot project for a local café chain — analysing sales, profit, and customer buying habits, removed PII using Python and Power BI.

Final interactive Power BI dashboard showcasing sales, trends, customer insights and forecasts.

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Project Overview

The goal of this pilot was to help the café chain understand:

• Which branches perform best by sales and profit
• Customer purchasing behaviour by drink type
• Payment preferences (cash vs card)
• Overall sales trends and potential forecasts

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Tools and Technologies used:

Python (VS Code) Data Cleaning and anonymisation ** Power BI** Interactive dashboard and visual analytics Github Documentation and version control

Data Process

1. Extracted raw café transaction data from a text file
2. Transformed the data in Python:
   • Removed names and card numbers (PII).
   • Corrected malformed entries.
   • Exported to cleaned_data.csv.
3. Loaded the cleaned data into Power BI for visualisation.

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Dashboard Features

KPIS:

• Total Sales (£)
• Profit (£)
• Transactions Count
• Average Spend Per Customer
• Card vs Cash Sales %

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Visuals

-Sales by Branch (Bar Chart) -Top Drinks (Column Chart) -Sales Trend and Forecast (Line Chart with Analytics) -Payment Type Breakdown (Pie Chart)

Insights & Recommendations

-Woking is the best performing branch by total sales -Macchiato is the most popular drink, followed by Espresso -Card payments slightly outnumber ** cash transactions** (around 52%) -Sales dipped mid-year but show an upward recovery towards winter- suggesting seasonal demand -Recommend adding loyalty incentives or promotions for slower months

Deliverables

| File | Description |

• cleaning_script.py:Python ETL script for data cleaning and anonymisation
• cleaned_data.csv :Processed dataset used in Power BI
• Cafe_Analytics_Pilot.pbix: Power BI dashboard
• Cafe_Analytics_Pilot_Client_Presentation.pptx: Client-facing summary presentation

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Project Reflection

This project strengthend my skills in:

• Data cleaning and ETL with Python
• Power BI dashboard design and storytelling
• Version control with GitHub
• Translating data insights into business recommendations

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Future Improvements

-Automate the ETL piple using a database connection -Deploy the dashboard to a cloud workspace for scalability

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Project Structure

Cafe-Analytics-Pilot/
├── data/
│   ├── raw_data.txt              # Original raw text data before cleaning
│   └── cleaned_data.csv          # Processed dataset ready for analysis
│
├── src/
│   └── cleaning_script.py        # Python script used to clean and anonymise data
│
├── powerbi/
│   └── cafe_dashboard.pbix       # Power BI dashboard file with KPIs and visualisations
│
├── README.md                     # Project overview, setup instructions, and usage guide
└── .gitignore                    # (Optional) Ignore unnecessary files like cache or temp data

Data Flow: Raw data → cleaned & anonymised in Python → visualised in Power BI.

How to Run This Project

1. Clone this repository:

   git clone https://github.com/dal3ks/Cafe-Analytics-Pilot.git
   cd Cafe-Analytics-Pilot

2. Prepare the raw data Place the provided raw_data.txt file into the data/raw/ folder.

3. Run the cleaning script Open cleaning_script.py in VS Code (or your IDE) and run it. This will generate cleaned_data.csv inside data/processed/.

4. Visualise in Power BI Open Power BI Desktop → Load data/processed/cleaned_data.csv. Explore visuals for Total Sales, Profit, Transactions, and Payment Trends.

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Power BI Dashboard

The final dashboard is available in: powerbi/Cafe_Analytics_Pilot.pbix

Open it in Power BI Desktop to explore the interactive visualisations and KPIs. All personal data has been anonymised in compliance with GDPR