Suppose, as manager of a chain of stores, you would like to use sales transactional data to analyze the effectiveness of your store's advertisements: In particular, you would like to study how specific factors influence the effectiveness of advertisements that announce a particular category of items on sale. The factors to study are: the region in which customers live, and the day-of-the-week, and time-of-the-day of the ads. Discuss how to design an efficient method to mine the transaction data sets and explain how multidimensional and multilevel mining methods can help you derive a good solution.

Suppose, as manager of a chain of stores, you would like to use sales transactional data to analyze the effectiveness of your store's advertisements: In particular, you would like to study how specific factors influence the effectiveness of advertisements that announce a particular category of items on sale. The factors to study are: the region in which customers live, and the day-of-the-week, and time-of-the-day of the ads. Discuss how to design an efficient method to mine the transaction data sets and explain how multidimensional and multilevel mining methods can help you derive a good solution.

What does an email come to mind for you? An email's journey begins and ends in the same place. Record your findings. Why do disparities exist in the first place? Observe the variations in the level of detail (or abstraction) in the models.

How do you see an email in your head? The origin and destination of an email are the same. Jot down your observations. First, why are there differences? See how there are differences in the granularity of the models?

Do not use any Al to do this question. Please show the diagram at the end. Question Lekker Living is a new backpackers that recently opened close to Stellenbosch. They have a limited number of staff (including a receptionist, a cook, and a housekeeper) since they have limited rooms available. If foreign travelers are looking for a place to stay a few nights while exploring the wonderful wine farms and hiking trails in Stellenbosch, they need to check availability on the website. If there is space, they must call reception to book a room. Orice they arrive, they check in at the front desk. In the evenings, they have the option to enjoy an assortment of dishes, prepared by a cook. Guests can also request for the room to be cleaned by a housekeeper in the mornings, while they are out and about exploring. Once their stay is over, they can check out with the receptionist. If a guest can no longer make their stay, they have the option to cancel their booking 24 hours before. Draw the use…

Q1. Define the following terms: accuracy, false positive rate, specificity, recall and f1-score. Give their formula with regards to a confusion matrix like the one below. Given the following confusion matrix, calculate all of the above parameters. IF you cannot calculate any, please provide what more information you need to calculate that particular parameter| Predicted: Predicted: n=165 NO YES Actual: NO 50 10 Actual: YES 100

Suppose you wish to study the relationship between the number of 'likes' on Facebook and the number of 'friends' one has on Facebook. Describe the statistical technique you would use.

In your mind's eye, what does an email seem like? In the case of email, the journey begins and ends in the same spot. Keep track of your findings. If there should be no distinctions, why are there any? Look at how the levels of detail (or lack thereof) in the models vary.

You are creating a learning system for estimating the quality of a product based on written reviews. You train the system by looking at databases of reviews that include numerical ratings. Assume that there is not any missing data, and the ratings are all on the same scale. You use a bag of words technique and associate word frequencies with ratings. When you test your system, it has extremely poor results. What are some factors which may be influencing this?

Predictive modeling: Task: Ambulance Demand Data Generating Process: for New York City   Dates of Coverage {Please identify the specific dates that will be used for this dataset in your modeling effort other than the City Health Department's Emergency Medical Services Division.   }   Frequency of data collection {how often is the data collected?  After every incident? Daily? Yearly?}   Agency / Organization collecting the data {who specifically is collecting the data?  Please avoid using general references like “government” or “police}   Original Unit of Analysis {What is the original unit of analysis for the data as provided?  Calls for service?  Census tracts? Cities?}   Transformed Unit of Analysis{i.e. are you modifying the call data to support your model? Hint: if you are doing “demand” model you will be aggregating the data.}   Data Generation Description{here, I want you in your own words to describe how you think the data was generated.  Think 2-3 sentences.}   Data…