Podcast
Questions and Answers
What factors should be considered when predicting if a person will buy a product?
What factors should be considered when predicting if a person will buy a product?
- The day of the week and purchase history
- Only discounts and free shipping
- Only the day of the week
- The day of the week, discount, and free shipping (correct)
Which combination of factors is MOST likely to influence a purchase according to this model?
Which combination of factors is MOST likely to influence a purchase according to this model?
- Discount without free shipping on a weekend
- Free shipping during weekdays with no discount
- Discount with free shipping regardless of the day (correct)
- No discount with free shipping during weekdays
What is one possible outcome of applying a Naive Bayes Classifier to this dataset?
What is one possible outcome of applying a Naive Bayes Classifier to this dataset?
- It can provide a precise record of past customer purchases
- It can determine the exact amount of discount needed
- It can predict whether a person will purchase based solely on day of the week
- It can classify whether a combination of factors will result in a purchase (correct)
If a customer buys a product, which scenario might suggest they were influenced by free shipping?
If a customer buys a product, which scenario might suggest they were influenced by free shipping?
Which of the following would potentially indicate that discounts are effectively influencing purchases?
Which of the following would potentially indicate that discounts are effectively influencing purchases?
The YardStudio store is unsure about their discount strategies.
The YardStudio store is unsure about their discount strategies.
The dataset for prediction contains information about customer preferences such as free shipping.
The dataset for prediction contains information about customer preferences such as free shipping.
The Naive Bayes Classifier is used to predict the likelihood of a customer purchasing a product based only on the day of the week.
The Naive Bayes Classifier is used to predict the likelihood of a customer purchasing a product based only on the day of the week.
The store's customer dataset consists of 50 days with their statistics.
The store's customer dataset consists of 50 days with their statistics.
A 'yes' for discount and 'no' for free shipping will always guarantee a purchase.
A 'yes' for discount and 'no' for free shipping will always guarantee a purchase.
Flashcards
Predicting Purchases
Predicting Purchases
Using data (day, discount, free shipping) to forecast customer buying behavior.
Naive Bayes Classifier
Naive Bayes Classifier
A machine learning model to predict a class label (e.g., purchase) based on features (e.g., day, discount).
Customer Data
Customer Data
Information about customers, including day of the week, discount offered, free shipping status, and purchase activity.
Test Cases
Test Cases
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Feature in data
Feature in data
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Discount Impact
Discount Impact
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Free Shipping Influence
Free Shipping Influence
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Day of Week Pattern
Day of Week Pattern
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Naive Bayes for Purchase Prediction
Naive Bayes for Purchase Prediction
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Test Case Design
Test Case Design
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Study Notes
Test Cases for Predicting Purchases at YardStudio
-
Dataset 1:
- Day of the week: Monday
- Discount: Yes
- Free shipping: Yes
- Purchase: Yes (Expected result)
-
Dataset 2:
- Day of the week: Saturday
- Discount: No
- Free shipping: No
- Purchase: No (Expected result)
-
Dataset 3:
- Day of the week: Wednesday
- Discount: Yes
- Free shipping: No
- Purchase: Yes (Expected result)
-
Dataset 4:
- Day of the week: Sunday
- Discount: No
- Free shipping: Yes
- Purchase: Yes (Expected result)
-
Dataset 5:
- Day of the week: Friday
- Discount: Yes
- Free shipping: Yes
- Purchase: Yes (Expected result)
-
Dataset 6: (Borderline case)
- Day of the week: Tuesday
- Discount: No
- Free shipping: No
- Purchase: Maybe (Expected result: Needs more data for definite result)
-
Dataset 7: (Extreme case)
- Day of the week: Friday
- Discount: Yes and extremely high
- Free shipping: Yes
- Purchase: Yes (Expected result)
-
Dataset 8: (Extreme case)
- Day of the week: Monday
- Discount: No
- Free shipping: No
- Purchase: No (Expected result)
-
Dataset 9:
- Day of the week: Thursday
- Discount: Yes
- Free shipping: Yes
- Purchase: Yes (Expected result)
-
Dataset 10:
- Day of the week: Sunday
- Discount: Yes
- Free shipping: No
- Purchase: Maybe (Expected Result: Needs more data)
-
Dataset 11:
- Day of the week: Monday
- Discount: No
- Free shipping: Yes
- Purchase: Yes (Expected result)
-
Dataset 12: Considering a weekend with different discounts on specific products that day
- Day of the week: Saturday
- Discount: Yes
- Free shipping: No
- Purchase: Yes (Expected Result)
-
Dataset 13: Considering a workday with a weekday deal
- Day of the week: Tuesday
- Discount: Yes
- Free shipping: No
- Purchase: Yes (Expected result)
-
Dataset 14: Considering a holiday with free shipping
- Day of the week: Monday
- Discount: No
- Free shipping: Yes
- Purchase: Yes (Expected result)
-
Dataset 15: Considering a common day with no discount or free shipping
- Day of the week: Wednesday
- Discount: No
- Free shipping: No
- Purchase: No (Expected result)
-
Dataset 16: Considering a special day with a free shipping offer that doesn't attract a purchase.
- Day of the week: Friday
- Discount: No
- Free shipping: Yes
- Purchase: No (Expected result)
-
Dataset 17: Data set with mixed day of the week, discount and no free shipping
- Day of the week: Saturday
- Discount: No
- Free shipping: No
- Purchase: No (Expected result)
-
Dataset 18: Data with a very large discount on a specific day, but not free shipping
- Day of the week: Thursday
- Discount: Yes
- Free shipping: No
- Purchase: Yes (Expected result)
-
Dataset 19: Data with discounts only on weekdays
- Day of the week: Friday
- Discount: Yes
- Free shipping: Yes
- Purchase: Yes (Expected result)
-
Dataset 20: Data on a day where there is no offer
- Day of the week: Sunday
- Discount: No
- Free shipping: No
- Purchase: No (Expected result)
Note:
- "Maybe" or "Uncertain" results indicate the need for more data points for specific combinations of input features to refine predictions. Naive Bayes Classifier often works best with plentiful data.
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