The SweetSpot: Dynamic Pricing Intelligence

Overview

The SweetSpot is a Streamlit-powered platform for data-driven pricing optimization. It uses Random Forest Regression to model price elasticity, simulate market response, and recommend optimal pricing strategies for maximum revenue, profit, or sales volume.

Features

• Step-based workflow for easy data upload, mapping, model training, and optimization
• Supports multiple products via Product_ID column
• Interactive dashboard for price simulation and optimization
• Business objectives: Revenue Maximization, Profit Maximization, Volume Growth
• Executive PDF report generation
• Data health dashboard (missing values, outliers, correlation)
• Customizable cost per unit and optimization settings

Getting Started

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt
   pip install streamlit reportlab kaleido plotly scikit-learn pandas numpy

3. Run the app:

   streamlit run app.py

Usage

1. Upload your pricing dataset (CSV)
2. Map columns (Date, Price, Demand, Product_ID)
3. Train the model and view metrics (Price Elasticity, Model Confidence)
4. Simulate pricing scenarios and optimize for your business goal
5. Download executive reports as PDF

Data Format

• CSV file with columns: Date, Price, Units_Sold, (optional) Product_ID

Project Structure

• app.py — Streamlit app
• model.py — Model logic (training, simulation, optimization)
• generate_data.py — Synthetic data generator
• pricing_data.csv — Sample dataset
• requirements.txt — Python dependencies

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Finding the intersection of value and volume.