My daily sales come in as raw Excel sheets covering every electronics SKU, the units sold and the revenue earned. I need a single piece of software that ingests those spreadsheets automatically, cleans and aggregates the data, then produces clear monthly sales forecasts for each SKU as well as overall business projections. The core of the job is sales-forecasting logic. The model should analyse sales volume and revenue together, learn their seasonality and growth patterns, and project the next 12 months in both absolute numbers and percentage change. A simple, intuitive interface that lets me drop new Excel files into a folder—or click an “Upload” button—and then returns charts and downloadable reports would be perfect. Key workflow requirements • Automatic parsing of multiple Excel formats (.xls, .xlsx) • Data cleaning and consolidation by date and SKU • Forecast generation (ARIMA, Facebook Prophet, or another proven method—open to the best fit) • Visual output: line charts, bar charts, and an at-a-glance table of forecasted units and revenue per SKU • Export options: refreshed Excel/CSV file plus a PDF summary Deliverables 1. Working application (desktop or lightweight web) ready to run on Windows 10+ 2. Fully commented source code and a README explaining installation, model choice and retraining steps 3. Sample run using my test dataset that demonstrates monthly forecasts and SKU insights 4. Brief documentation on how to add new data fields in the future Acceptance will be based on the software correctly reading my sample files, producing monthly forecasts, and matching historic hold-out data within an acceptable error margin. I’m comfortable with Python (pandas, scikit-learn, Prophet), but I’ll gladly consider another stack if it meets the same goals and is easy to maintain.