Prepare the data
Prepare dated item-level sales with availability flags and a fixed forecast horizon.
01 / THE IDEA
Demand Forecasting in practice.
Observed sales may understate demand when products are unavailable. A useful forecast distinguishes stockouts, promotions and intermittent demand before supporting inventory planning.
Real-world use case · Proposed extension
A possible application for retail inventory planners: Help small retailers review replenishment assumptions for a limited item range. This extension is not delivered by the supplied starter.
02 / THE BUILD
How to build a demand forecasting system project
step by step.
Build the experiment
Compare lag-feature regression with seasonal-naive and intermittent-demand baselines where appropriate.
Evaluate the result
Use rolling-origin MAE and bias by item, examining stockout periods separately.
Define a reviewable output
Provide planner-reviewed forecasts with horizon labels and stockout caveats.
03 / SOURCE SNAPSHOT
What the demand forecasting system starter actually does.
The supplied starter generates 240 daily values with trend, seasonality and noise. It builds lagged and shifted rolling features, trains a RandomForestRegressor on earlier rows and reports MAE on the final thirty rows.
SOURCE EXCERPTPython
for lag in [1, 7, 14]:
series[f"lag_{lag}"] = series["value"].shift(lag)
series["rolling_7"] = series["value"].shift(1).rolling(7).mean()
series = series.dropna()
train = series.iloc[:-30]
test = series.iloc[-30:]
model = RandomForestRegressor(n_estimators=300, random_state=42)
model.fit(train[feature_cols], train["value"])
forecast = model.predict(test[feature_cols])Important limitation
The source uses a synthetic series without products, stockouts or replenishment constraints. Later test rows use actual earlier test values as lag inputs. This can represent rolling one-step prediction, but not a thirty-day forecast made at a single origin. There are no prediction intervals or real operational observations.
04 / BEYOND THE CLASSROOM
Where a demand forecasting system project could go next.
PROPOSED BUSINESS CONCEPT
Demand Forecasting: a pilot concept
Proposed extension: Help small retailers review replenishment assumptions for a limited item range.
WHO IT COULD SERVERetail inventory planners
FIRST MVPProvide planner-reviewed forecasts with horizon labels and stockout caveats.
KEY RISKStockouts being mistaken for low demand
This is a potential application pathway, not a delivered product. The supplied starter does not implement this service or validate it with real users.
05 / WHAT YOU TAKE AWAY
What a demand forecasting system project puts on your resume.
- Compare lag-feature regression with seasonal-naive and intermittent-demand baselines where appropriate.
- Use rolling-origin MAE and bias by item, examining stockout periods separately.
- Provide planner-reviewed forecasts with horizon labels and stockout caveats.
- Explain this limitation: The source uses a synthetic series without products, stockouts or replenishment constraints.
Want to build this with mentors and a lab?
AI Engineer Course Networkers Home runs this topic inside its AI Engineer Course, with placement support and hands-on labs.
06 / QUESTIONS STUDENTS ASK
Demand Forecasting System project FAQs.
- Is this suitable as a final year project?
- Yes — it’s an intermediate-level final year project idea focusing on demand patterns, stockouts and forecasting fundamentals; scope can be adjusted by adding real product and replenishment constraints.
- What skills and tools will I need?
- You should know Python, pandas, time-series feature engineering, scikit-learn models and basic evaluation; familiarity with inventory concepts and handling intermittent demand helps.
- How should I evaluate progress and how long will it take?
- Evaluate by recreating realistic sales scenarios, distinguishing stockouts/promotions, and forecasting accuracy (MAE/MAPE) and inventory metrics; expect several weeks to a few months depending on dataset complexity.