README.md

metadata

title: BA Streamlit Demo
emoji: 📈
colorFrom: blue
colorTo: purple
sdk: streamlit
app_file: app2.py
pinned: false

Intro to Business Analytics - Interactive Analytics Flow

A comprehensive Streamlit application demonstrating end-to-end business analytics workflow.

🎯 Overview

This application provides a complete analytics journey from data exploration to predictive modeling and what-if scenario analysis. Perfect for teaching business analytics concepts and demonstrating practical applications.

🚀 Features

• 📊 Descriptive Analytics: Time series analysis, channel/region breakdowns, correlation matrices
• 🔍 Inferential Analytics: Statistical relationships and correlation analysis
• 🤖 Predictive Modeling: Machine learning with Linear Regression, Ridge, and Lasso
• 💡 What-If Scenarios: Interactive scenario modeling for business decisions
• 📁 Data Upload: Support for custom CSV files or built-in demo data
• ⚙️ Data Preprocessing: Outlier handling, smoothing, aggregation options

🛠️ Installation

1. Clone or download the project
2. Create virtual environment:

   python -m venv venv
   

3. Activate virtual environment:

   # Windows
   .\venv\Scripts\Activate.ps1
   
   # macOS/Linux
   source venv/bin/activate
   

4. Install dependencies:

   pip install -r requirements.txt
   

🏃‍♂️ Usage

Run the application:

streamlit run streamlit_app.py

The app will open in your browser at http://localhost:8501

📋 Requirements

• Python 3.8+
• streamlit
• pandas
• numpy
• scikit-learn

🎓 Educational Use

This application is designed for:

• Business Analytics courses
• Data Science workshops
• Analytics demonstrations
• Interactive learning sessions

📊 Demo Data

The application includes realistic marketing data with:

• Multiple channels (Search, Social, Email, Display, Affiliate)
• Regional breakdowns (North, South, East, West)
• Time series data (weekly granularity)
• Key metrics (spend, clicks, conversions, revenue)

🔧 Customization

• Upload your own CSV data
• Adjust preprocessing parameters
• Modify model hyperparameters
• Customize visualizations

📝 License

Created for educational purposes at UIUC Gies College of Business.

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Author: Ashish Khandelwal (UIUC Gies)
Built with: Streamlit, Python, scikit-learn