SOLUTION: Northeastern University Association Mining Algorithm Project

Quiz
1. Clean Up The Dataset & Walk Through The Method That You Choose For Each Variable
After opening the dataset, we can find that there are 4 columns with missing values. We
need to exclude missing values to ensure the accuracy of the model. We choose to use the
function na.omit () and use the median or mean to impute missing values. As in the code,
we calculate the parameter na.rm as the median of True to tell R to ignore them and use it
to replace missing observations.
2. Run A Logistic Regression Model To Predict Which Characters Would Live Or Die (Hint
You Need To Create This Binary Field From Another Field In The Dataset).
We set the target variable (Live or Dead) for the model according to factors such as
demise year, book of death and death chapter. This variable is in binary format and its
values are 1 (dead) and 0 (alive).
The function glm () has been used to exclude variables such as “Name, Allegiance, Death
Year, Book of Death”, which have no meaning for designing the basic model. We have a
logistic regression model to predict the target variable, which is a binary format. The model
will draw information about the estimated value, standard error, z value, valid code, etc. to
compare the models.
Output:
3. Interpret: How Accurate The Model Is & What Do Coefficients Mean
After executing the logistic model using function glm( ) we observed that the significant
variables used for explaining target variables are based upon coefficients of each variable.
Output:
Using the function prediction in R (), we found that the accuracy of the model is 65.53%
Confusion matrix shows that false positive values are 25 and false negative values are 36.
4.If someone you know started watching Games Of Thrones tomorrow and wanted to use the
model to predict which characters would live or die, would you recommend the model (aka is
it accurate enough to use).
We used the extreme gradient booting package in R. It is creating a new model to predict the
residuals or errors of the previous model, and then adding them together to reach a conclusion.
Output:
Code:
WordPad
Document
In case you can not open the file, here is the code:
#installing and using package for data manipulation
install.packages(“dplyr”)
library(“dplyr”)
#reading file
mydb 1
mydb$characteraliveornot 0.5,1,0)
head(results)
head(testdb$characteraliveornot)
error {milk}
If a customer buys coffee and sugar, then they are also likely to buy
milk.
AssociationMiningAlgorithm–A little bit of Math
AssociationMiningAlgorithm- Association Rules
There are many ways to see the similarities between items.
These are techniques that fall under the general umbrella
of association. The outcome of this type of technique, in
simple terms, is a set of rules that can be understood as “if
this, then that”.
AssociationMiningAlgorithm—Applications
So what kind of items are we talking about?
There are many applications of association:
Product recommendation – like Amazon’s “customers who
bought that, also bought this”
Music recommendations – like Last FM’s artist
recommendations
Medical diagnosis – like with diabetes really cool stuff
Content optimisation – like in magazine websites or blogs
AssociationMiningAlgorithm—-The Groceries Dataset
Imagine 10000 receipts sitting on your table. Each receipt represents a
transaction with items that were purchased. The receipt is a
representation of stuff that went into a customer’s basket – and
therefore ‘Market Basket Analysis’.
That is exactly what the Groceries Data Set contains: a collection of
receipts with each line representing 1 receipt and the items
purchased. Each line is called a transaction and each column in a row
represents an item. You can download the Groceries data set to take a
look at it, but this is not a necessary step.
AssociationMiningAlgorithm—-The Groceries Dataset
AssociationMiningAlgorithm—-The Groceries Dataset
AssociationMiningAlgorithm—-The Groceries Dataset
This reads easily,
for example: if
someone buys
yogurt and
cereals, they are
81% likely to buy
whole milk too.
AssociationMiningAlgorithm—-The Groceries Dataset
AssociationMiningAlgorithm—-The Groceries Dataset
AssociationMiningAlgorithm—-The Groceries Dataset
AssociationMiningAlgorithm—-The Groceries Dataset
AssociationMiningAlgorithm—-The Groceries Dataset
AssociationMiningAlgorithm—-The Groceries Dataset
AssociationMiningAlgorithm—-The Groceries Dataset
AssociationMiningAlgorithm

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