SME BI Dashboard (Malawi)

A professional, interactive Business Intelligence dashboard for Small and Medium Enterprises (SMEs) in Malawi. Built with Python, Streamlit, and Plotly, this dashboard empowers SME owners and analysts to visualize trends, monitor KPIs, and gain actionable insights from their sales and customer data.

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🚀 Features

• Data Upload & Download
  Upload your own CSV data and download filtered/analyzed data as CSV or Excel.

• Interactive Filtering
  Sidebar filters for Region, Product, and Date Range.

• Key Performance Indicators
  Instant KPIs for sales, profit, and customer metrics.

• Regional & Demographic Analysis
  Visualize sales by region, customer age distribution, and gender breakdown.

• Product Analysis
  Sales and profit by product, with top/bottom product highlights.

• Time Series Analysis
  Line chart for sales/profit over time with daily, weekly, or monthly aggregation.

• Customer Retention & Churn
  See new vs. repeat customers and simple churn analysis.

• Anomaly Detection
  Detect and visualize unusual sales patterns using an Isolation Forest model.

• Sales Forecasting
  Predict future sales trends using Facebook Prophet with upper/lower bounds.

• Customer Segmentation
  Visualize customer segments using K-means clustering.

• Export Reports
  Download filtered data as Excel or CSV.

• Modern, Responsive UI
  Clean, professional UI optimized for desktop and mobile.

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📊 Overview

SMEs in Malawi—whether a shop in Blantyre or a farm input dealer in Lilongwe—often lack access to robust analytical tools. This dashboard solves that with:

• Aggregated sales and customer data
• Automated business insights in plain English
• Interactive visualizations and smart diagnostics
• Low technical overhead with open-source tools

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📂 Project Structure

bi-dashboard-sme/ │ ├── data/ │ └── sample_sales_data.csv # Sample dataset │ ├── src/ │ ├── anomaly_detection.py # Isolation Forest logic │ ├── customer_segmentation.py # K-means clustering │ ├── data_loader.py # Data import logic │ ├── forecasting.py # Prophet-based forecasts │ ├── insights.py # Business rules for summary insights │ ├── kpis.py # Sales/profit KPI logic │ ├── localization.py # Currency formatting, translations │ ├── ui.py # CSS injection, component rendering │ ├── visuals.py # Plotly visual functions │ └── generate_sample_data.py # Generates fake sample data │ ├── assets/ │ ├── styles.css # Custom styles │ └── region_map_malawi.png # (Optional) map asset │ ├── requirements.txt ├── README.md └── app.py # Streamlit app entry point

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🖥️ Screenshots

screenshots of dashboard here to showcase the UI and features.

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🛠️ Installation

1. Clone the repository:

   git clone https://github.com/yourusername/bi-dashboard-sme.git
   cd bi-dashboard-sme

2. Create & activate a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. (Optional) Generate sample data:

   python src/generate_sample_data.py

5. Run the dashboard:

   streamlit run app.py

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📈 Usage

• Dashboard: View KPIs, charts, and automatic business insights.
• Anomaly Detection: Monitor unusual activity in daily sales.
• Sales Forecast: View sales predictions for the next 30 days.
• Customer Segmentation: Discover distinct customer groups.
• Data Export: Download reports and filtered views in CSV/Excel.

Just drop your own sample_sales_data.csv into the data/ folder and refresh the app.

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🤝 Contribution

Contributions are welcome!

1. Fork the repo
2. Create a branch (git checkout -b feature-thing)
3. Commit changes (git commit -m "Added something cool")
4. Push (git push origin feature-thing)
5. Open a Pull Request

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📄 License

MIT License — free to use, modify, and distribute.

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📬 Contact

For collaboration, feedback, or questions:
📧 mathiusmelo@gmail.com

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This dashboard is here to help SMEs in Malawi turn data into decisions that grow businesses sustainably.