omargomaa27/business-intelligence-dashboard

Business Intelligence Dashboard

A modular, interactive Business Intelligence dashboard built with Gradio. The application enables users to upload datasets, generate descriptive statistics, apply filters, create visualizations, and obtain automated insights through a browser-based interface.

Author: Omar Gomaa GitHub: https://github.com/OmarGomaa27/business-intelligence-dashboard

Project Overview

This dashboard provides a complete workflow for basic business data exploration and analysis. The system includes:

• —Automated data profiling
• —Statistical summaries and data validation
• —Interactive filtering with multiple filter types
• —Configurable visualizations
• —Automated insight generation
• —Data and chart export functionality

Designed for general business analytics tasks such as sales analysis, customer segmentation, and operational reporting.

Features

Data Upload and Validation

• —Support for CSV and Excel (.xlsx, .xls) files
• —Automatic type inference (numeric, categorical, datetime)
• —Data preview with structural information
• —Flexible date parsing
• —Error messages for unsupported formats or invalid input

Data Profiling and Statistics

• —Numeric summaries (mean, median, quartiles, standard deviation)
• —Categorical summaries (unique values and most frequent values)
• —Missing value reporting
• —Correlation matrix for numerical fields

Interactive Filtering

• —Categorical filtering
• —Numeric filtering through minimum/maximum value ranges
• —Date range filtering using textual date inputs
• —Real-time updates to row counts and previews
• —Full filter reset functionality

Visualizations

Five visualization types:

1. 1.Time series plots with aggregation functions
2. 2.Histogram or boxplot distributions
3. 3.Category aggregation bar charts
4. 4.Scatter plots for variable comparison
5. 5.Correlation heatmaps

All charts include:

• —Consistent styling
• —Grid lines and readable axes
• —Support for exporting figures as PNG

Insight Generation and Export

Automated insights include:

• —Top and bottom performers
• —Missing data summaries
• —Outlier detection using a standard deviation-based heuristic
• —Basic date range insights
• —Overall dataset summary

Export features:

• —Filtered dataset export as CSV
• —Visualization export as PNG

Quick Start

Installation

git clone https://github.com/OmarGomaa27/business-intelligence-dashboard.git
cd business-intelligence-dashboard

pip install -r requirements.txt
python app.py

The dashboard will open at: http://127.0.0.1:7860

Workflow

1. 1.Upload a CSV or Excel file
2. 2.View dataset statistics
3. 3.Apply filters
4. 4.Generate visualizations
5. 5.Review automated insights

Project Structure

business-intelligence-dashboard/
│
├── app.py                      # Gradio UI 
├── data_processor.py           # Data loading, cleaning, profiling
├── visualizations.py           # Visualization utilities
├── insights.py                 # Automated insight generation
├── utils.py                    # Helper functions
├── requirements.txt            # Dependencies
├── README.md                   # Documentation
└── .gitignore

Technical Stack

Example Use Cases

Sales Data Analysis

• —Upload transaction data
• —Filter by categories or date ranges
• —Plot sales trends or category totals
• —Identify top-performing products

Retail Store Analysis

• —Compare store-level metrics
• —Detect anomalies in sales distribution
• —Analyze temporal trends

Customer Analysis

• —Segment customers
• —Inspect numeric distributions (e.g., order counts or spending)
• —Identify high-activity or low-activity segments

GitHub: https://github.com/OmarGomaa27 Repository: https://github.com/OmarGomaa27/business-intelligence-dashboard