AutoML Coffee Price Forecasting
A decision-support data product for coffee price forecasting using Python, Streamlit, and PostgreSQL with a management-facing dashboard.
01 Problem
Business Context
A coffee trading and processing environment needing decision support from historical commodity data and market-related indicators.
Why it mattered
Management needed a more structured way to analyze historical coffee price data and generate forecast direction for short-term decisions.
Price analysis was manual and ad-hoc, with no repeatable way to turn historical data into forward-looking direction.
02 Solution
An upload-to-forecast pipeline: tabular ingestion → PostgreSQL → forecasting model → Streamlit dashboard for decision support.
Objectives
- Provide an upload flow for CSV/Excel historical data
- Store data in a database-backed model
- Define a forecasting output direction
- Create a simple management-facing interface
Architecture / Topology
Implementation Agile / Sprint
- Sprint 1Done1 week
Product Direction
Frame the decision the forecast must support.
- Product brief
- Data flow sketch
- Sprint 2Done2 weeks
Data & DB Design
Define ingestion and storage.
- Schema
- Upload flow
- Sprint 3Done2 weeks
Forecast Prototype
Produce a working forecast direction.
- Prototype
- Streamlit UI
Selected Visuals
03 Role
Product Direction · Developer
Stakeholders
04 Tech / Tools
Key Components
- CSV/Excel upload
- PostgreSQL store
- AutoML / forecasting
- Streamlit dashboard
05 Business Impact
Turned scattered spreadsheets into a repeatable forecast workflow management could open and read.
Reduced manual reporting dependency for price analysis.
Improved decision-support visibility for management.
Created a repeatable forecasting workflow.
06 Evidence Policy
Public demo only with dummy data. Internal commodity datasets are not published.
No credentials, server IPs, internal production URLs, financial figures, or confidential documents are shown. Detailed artifacts are available upon request under NDA.