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Adrib1dl/retail-analytics-dashboard

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App README

AI-Augmented Retail Analytics Dashboard

Imperial College Retail & Marketing Analytics group project — an interactive Python (Gradio) dashboard combining ML outputs, statistical analytics, and a grounded GenAI layer.

Quick start

bash

pip install -r requirements.txt
python -m dashboard.app.main

Open the URL printed in the terminal (default http://127.0.0.1:7860).

LLM provider (optional)

Set environment variables for live AI (otherwise echo/offline fallback):

bash

# Ollama (local)
set LLM_PROVIDER=ollama

# AWS Bedrock
set LLM_PROVIDER=bedrock

See ai/CLOUD_SETUP.md for full configuration.

Modules

#	Module	Status
1	Overview	Implemented
2	Data Explorer	Implemented
3	Promotion Effectiveness	Implemented (ML outputs)
4	Price Elasticity	Implemented (log-log regression)
5	Scenario Simulator	Implemented
6	Demand Forecasting	Baseline linear forecast
7	Promotion Lift Model	Implemented (ML notebook outputs)
8	AI Chat	Implemented
9	Critical Reflection	Implemented
10	Appendix & Export	Implemented

Project structure

data/                 # Raw CSV (read-only)
dashboard/            # Gradio app + analytics layer
ml/                   # Notebooks and model outputs
ai/                   # Prompts, guardrails, LLM services
docs/                 # Assignment spec and team rules

Generate ML outputs

Promotion lift (Modules 3 & 7):

bash

# Run notebook: ml/ml_promotions_pricing/promotion_lift_model.ipynb

Forecasting (Module 6) auto-generates a baseline on first load; replace with ml/ml_forecasting/forecasting.ipynb outputs when ready.

Documentation

—Full deliverable spec: docs/deliverable.md
—Team rules: docs/rules.md
—Dashboard plan: dashboard/PLAN.md