Product Selection Optimizer

Optimization-driven framework to support commercial product selection decisions (what to buy, what to drop, and how to balance competing goals like revenue, margin, and risk).

What this system does

• Converts a product candidate list into model-ready features
• Runs optimization to propose an actionable buy list under constraints
• Produces trade-off strategies (Pareto-style) when objectives conflict
• Outputs decision summaries that are easy to review by commercial stakeholders

How it works (high-level)

1. Data preparation & feature building
2. Objective definition (e.g., revenue, margin, risk)
3. Constraints (budget, capacity, business rules)
4. Optimization (single-objective or multi-objective)
5. Reviewable decision output (selected SKUs + rationale)

Quickstart

pip install -r requirements.txt
python -m src.models.run_demo

Or open the notebook:

• notebooks/01_product_selection_demo.ipynb

Repository structure

• src/optimizer/ Core optimization logic (preprocess, objectives, constraints, solvers)
• notebooks/ Demonstration notebook
• examples/ Example input files
• docs/ Business context + solution overview + modeling approach + evaluation notes

Documentation

• Business Context
• Solution Overview
• Optimization Approach
• Evaluation

Contact

LinkedIn: https://www.linkedin.com/in/parisa-mostafavi/
Email: parisamostafavi23@gmail.com