a) Quantitative variables: Protein, Fat, Calories, Sodium, Fiber, carbo, sugars, potass, vitamins, weight,
Nominal variables: type and mfr
Ordinal Variables: Shelf
b) Please check the attached Excel
Potass and Sodium ha
Week 1 Answers
a) This is supervised learning, the loan can be classified as approved or not.
This is unsupervised learning, this is just a prediction that there is no apparent outcome whether
the recommendation was adopted or not.
The ability to easily and quickly zoom, pan and aggregate data are typical features of _.
A) network mapping
B) interactive visualization
When plotting the points of a scatterplot
A drawback of the k-NN method is that the _.
A) method does not support prediction
B) method can be used to classify only two classes
C) relationship between the dependent variable and the predictor variables must be
K-NN is used to find the nearest example in predictor space and assign them the same class as
that example and classifies. The similar the records are classified in the space. This K-NN
method is for Non- Parametric regression, because it doesn't estimate
Which of the following plots would be used to display the frequency distribution of data values?
B) Bar chart
C) Line graph
In a boxplot, the horizontal line within the box indicates the _ value of the distribution.
1. Name: Name of cereal
2. mfr: Manufacturer of cereal where A = American Home Food Products; G = General Mills; K = Kelloggs; N = Nabisco; P = Post; Q = Quaker Oats; R = Ralston Purina
3. type: cold or hot
4. calories: calories per serving
5. protein: gr
I dont have any experience on working with Pivot tables. I read that Pivot Tables tool is one of the most
powerful yet intimidating features in Excel. It allows us to quickly summarize and analyze large amounts
of data in lists and tables. Pivot tables ar
Predicting Missing Items in
Market Basket Analysis
The goal of this project is to predict the missing items in a shopping cart and apply association
rules. Based on the frequently occurring transactions a
Topic 1 Answer:
Data mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from
large Databases. Basically, it is concerned with the analysis of data and the use of software techniques
for finding patterns and regularities