Sales Forecasting via ML Analytics
Budget: €12 – €18 EUR
I need a skilled data professional to turn my historical sales records into reliable projections for the months ahead. The raw files are already exported from our POS and e-commerce platforms; they cover daily transactions, product categories, promotions, and regional outlets. Your first task will be to explore and clean this sales data, handle any missing values or outliers, and engineer features that capture seasonality, campaigns, and other business drivers.
My primary goal is an accurate sales forecast and projection roadmap. I am particularly interested in machine-learning approaches—think gradient-boosted trees, LSTM, Prophet, or any other model you feel best suits the data’s structure. Classical time-series or regression techniques are fine as benchmarks, but the core deliverable must rely on an ML model that can be retrained as fresh data arrives.
Once the model is tuned, I want the forecast visualised in an easy-to-read dashboard (Power BI or Tableau is ideal, though a clean Jupyter notebook is acceptable if the visuals are embedded). Please include error metrics such as MAE and MAPE so I can gauge performance at a glance, and add concise commentary on what drives the numbers—seasonal peaks, promotion spikes, stockouts, etc.
Deliverables
• Cleaned and documented dataset ready for future use
• Fully commented code/notebook with the chosen ML forecasting model
• Forecast outputs for the next 3, 6, and 12 months in CSV and visual form
• Brief slide summary of findings, assumptions, and recommended next steps
Acceptance criteria: forecast error under 10 % MAPE on a hold-out set, reproducible code, and clear hand-off instructions for model retraining.
If this aligns with your expertise, let’s discuss timelines and any data samples you might need up front.
My primary goal is an accurate sales forecast and projection roadmap. I am particularly interested in machine-learning approaches—think gradient-boosted trees, LSTM, Prophet, or any other model you feel best suits the data’s structure. Classical time-series or regression techniques are fine as benchmarks, but the core deliverable must rely on an ML model that can be retrained as fresh data arrives.
Once the model is tuned, I want the forecast visualised in an easy-to-read dashboard (Power BI or Tableau is ideal, though a clean Jupyter notebook is acceptable if the visuals are embedded). Please include error metrics such as MAE and MAPE so I can gauge performance at a glance, and add concise commentary on what drives the numbers—seasonal peaks, promotion spikes, stockouts, etc.
Deliverables
• Cleaned and documented dataset ready for future use
• Fully commented code/notebook with the chosen ML forecasting model
• Forecast outputs for the next 3, 6, and 12 months in CSV and visual form
• Brief slide summary of findings, assumptions, and recommended next steps
Acceptance criteria: forecast error under 10 % MAPE on a hold-out set, reproducible code, and clear hand-off instructions for model retraining.
If this aligns with your expertise, let’s discuss timelines and any data samples you might need up front.