It446 data mining

College of Computing and InformaticsPROJECT
Deadline: Tuesday 05/12/2023 @ 23:59
[Total Mark for this Project is 14]
Student Details:
Name: ###
ID: ###
CRN: ###
Instructions:
• This project report must be submitted on Blackboard (WORD format only) via the allocated folder.
• You are advised to make your work clear and well-presented; marks may be reduced for poor presentation
• Email submission will not be accepted.
• Late submission will result in ZERO mark.
• The work should be your own, copying from students or other resources will result in ZERO mark.
• Use Times New Roman font for all your answers.
Question One
Pg. 01
Learning
Outcome :CLO1
Define different
data mining tasks,
problems and the
algorithms most
appropriate for
addressing them.
Question One
1 Marks
Select one of the datasets from UCI Machine Learning Repositories.
(http://archive.ics.uci.edu/ml/) OR ( https://www.kaggle.com/datasets )
OR use your own dataset if available from any source. Then write name and link of
selected dataset.
Note: The dataset may follow the following requirements (Data description)
a) Number of instances: between 300-500
b) Number of attributes: between 10 to 15
Question Two
Pg. 02
Learning
Outcome: CLO3
Employ data mining
and data
warehousing
techniques to solve
real-world
problems.
Question Two
0.5 Marks
Load the dataset in Weka or if you prefer to use any python tools such as Google
Collaborate Lab https://research.google.com/colaboratory/
Question Three
Pg. 03
Learning
Outcome: CLO3
Employ data mining
and data
warehousing
techniques to solve
real-world
problems
Question Three
1 Marks
Understand and describe the nature and structure of the selected dataset.
-A brief description about the dataset: include number of attributes, number of
instances, outliers in the dataset.
Question Four
Pg. 04
Learning
Outcome: CLO2
Demonstrate a wide
range of clustering,
estimation,
prediction, and
classification
algorithms to solve
a specific program
or application.
Question Four
1.5 Marks
Provide descriptive statistics for some attributes [at least 2 attributes] using statistical
method: (1) Include the measure of central tendency such as the mean, median, and
mode. (2) Describe the spread of your data. This may include the measure of variance,
standard deviation, skewness, and kurtosis.
Question Five
Pg. 05
Learning
Outcome(s):
CLO2
Demonstrate a wide
range of clustering,
estimation,
prediction, and
classification
algorithms to solve
a specific program
or application.
Question Five
2 Marks
Do a basic preprocessing to the dataset such data cleaning / Data reduction
/Normalization (if exist or required) etc.
Question six
Pg. 06
Question six
Learning
Outcome(s):
CLO2
Demonstrate a wide
range of clustering,
estimation,
prediction, and
classification
algorithms to solve
a specific program
or application.
2 Marks
Based on dataset run Apriori algorithm with different support and confidence
values. Discuss the generated rules.
Question seven
Pg. 07
Question seven
Learning
Outcome(s):
CLO2, CLO4
Demonstrate a wide
range of clustering,
estimation,
prediction, and
classification
algorithms to solve
a specific program
or application.
Evaluate the
performance of
data mining
algorithms
2 Mark
Based on your dataset selection, apply SVM data mining algorithm.
Provide the result and accuracies of the algorithms and discuss it with supporting
screenshots.
Question eight
Pg. 08
Question eight
Learning
Outcome: CLO2
Demonstrate a wide
range of clustering,
estimation,
prediction, and
classification
algorithms to solve
a specific program
or application.
2 Mark
Based on your selection dataset, Apply the Decision tree data mining algorithm
with different parameter setting and record the accuracies.
Question nine
Pg. 09
Question nine
Learning
Outcome: CLO2
Demonstrate a wide
range of clustering,
estimation,
prediction, and
classification
algorithms to solve
a specific program
or application.
2 Mark
Apply the K-mean algorithm on the dataset (for k=4) and study the clusters
formed.