← VCE Data Analytics U3O1

Predictions, decisions and recommendations from models

A model uses data to predict what will happen, support decisions and recommend options to users, such as suggested products.

Part of: VCE Data Analytics - U3O1 - Data Analytics — Module 138

Understanding Predictions, decisions and recommendations from models in VCE Data Analytics U3O1

What it does: FORECAST uses past x and y values to predict the next value, showing prediction from a model. The query lists Tech products for a user to consider, which is a simple recommendation based on data. The model gives the canteen evidence to help it choose an order amount. That is decision support. A prediction is about the future. Last month's sales are already known data. Predictions are estimates. They depend on the quality and amount of the data used, so they can be wrong. Models support decisions by providing information. People still choose what to do. The IF rule turns the data into a clear choice (Reorder or OK), so it supports a decision.

Common mistakes: A model is trained on past data, so it must be TRAIN(sales). The variable next does not exist yet. It is the prediction that comes after training. FORECAST takes x, then the known y values, then the known x values. The sales column (B) must come before the month column (A): =FORECAST(A8,B2:B7,A2:A7).

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This module's key terms are also in a printable crossword (Modules 130–148) and word search (Modules 94–148).

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Learning
Duo
A model uses past rainfall data to say it will probably rain tomorrow. Which use of a model is this?
RecommendationPrediction
Matches
A model always gives a certain, guaranteed prediction of the future.
↓
Predictions from a model are estimates based on past data, not certain facts, so they can be wrong.
Applying
Meanings
Term
model
Meaning
A model uses data to predict what will happen, support decisions and recommend options to users, such as suggested products.
Practice
=FORECAST(80,C2:C9,B2:B9)
Testing
Gaps
=(6,B2:B6,A2:A6)
Check Answer
Spots
1sales ← LOAD(history)
2model ← TRAIN(next)

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Quick Questions

1. A model uses past rainfall data to say it will probably rain tomorrow. Which use of a model is this?
A. Prediction ✓
B. Recommendation
A prediction says what is likely to happen in the future. Forecasting rain from past data is a prediction.
2. A streaming app suggests three shows you might like based on what you watched before. Which use of a model is this?
A. Prediction
B. Recommendation ✓
Suggesting options to a user, such as shows or products, is a recommendation.
3. Which statement best describes what a model does with data?
A. It uses data to predict outcomes, support decisions and recommend options ✓
B. It only stores data so it can be copied into a spreadsheet
A model goes beyond storing data. It uses the data to predict, support decisions and recommend.
4. A school canteen uses a model of past sales to decide how many pies to order for Friday. Which use of a model is this?
A. Supporting a decision ✓
B. Storing data
The model gives the canteen evidence to help it choose an order amount. That is decision support.
5. An online shop shows 'Customers who bought this also bought...' under a backpack. What is the model doing?
A. Validating the data entered by the user
B. Recommending suggested products ✓
Suggested products are a typical recommendation from a model, based on what other customers bought.

Match the Pairs

=FORECAST(A9,B2:B8,A2:A8) → Uses a model built from past data to predict a future value
=IF(C2>=50,"Reorder","OK") → Uses a rule from a model to support a stock decision
A model predicts what will happen next from past data → =FORECAST(8,B2:B7,A2:A7)
A model recommends options to users, such as suggested products → SELECT name FROM products WHERE category='Tech' ORDER BY price;
A model always gives a certain, guaranteed prediction of the future. → Predictions from a model are estimates based on past data, not certain facts, so they can be wrong.
=FORECAST(A9,B2:B8) → FORECAST is missing the known_xs argument. It needs the new x, the known y values and the known x values.

Key Term

model
A model uses data to predict what will happen, support decisions and recommend options to users, such as suggested products.

Fill in the Blanks

1. =FORECAST(6,B2:B6,A2:A6)
Hint: Type the function that predicts a future value from a trend in past data.
2. =IF(B2>100,"Reorder","Wait")
Hint: Type the function that makes a decision by choosing between two results.
3. =IF(B2>=80,"Try extension","Revise")
Hint: Type the comparison operator meaning greater than or equal to, so the model recommends an option.
4. SELECT name FROM products WHERE stock > 0;
Hint: Type the SQL keyword that chooses which columns to list, so in-stock products can be suggested.
5. =IF(C2<10,"Order more","Enough stock")
Hint: Type the operator that checks whether the stock is lower than the limit, to support a decision.

Spot the Error

Question 1
A sales ← LOAD(history)
B model ← TRAIN(next)
C next ← PREDICT(model, 7)
D DISPLAY next
Line B — A model is trained on past data, so it must be TRAIN(sales). The variable next does not exist yet. It is the prediction that comes after training.
Question 2
A =FORECAST(A8,A2:A7,B2:B7)
B =IF(B8>100,"Order","Wait")
C =B8-B7
Line A — FORECAST takes x, then the known y values, then the known x values. The sales column (B) must come before the month column (A): =FORECAST(A8,B2:B7,A2:A7).
Question 3
A SELECT name, price
B FROM products
C WHERE stock = 0
D ORDER BY price ASC
E LIMIT 3
Line C — A recommendation should only suggest items that are available. The condition should be stock > 0. With stock = 0 the query would suggest items that are sold out.
Question 4
A demand ← 120
B stock ← 100
C IF demand < stock THEN
D DISPLAY "Order more"
E END IF
Line C — The decision to order more should be made when predicted demand is greater than stock. It should be demand > stock, not demand < stock.
Question 5
A best ← 0
B FOR each item IN products
C IF item.score > pick THEN
D best ← item.score
E pick ← item
F DISPLAY pick
Line C — Each item's score is compared with the best score so far, so it should be item.score > best. The variable pick holds an item, not a score.

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