Assignment Brief
Submission Deadline Marks and Feedback
Before 10am on:
13/05/2019 (week 19)
20 working days after deadline (L4, 5 and 7)
15 working days after deadline (L6)
10 working days after deadline (block delivery)
Unit title & code CIS111-6: Intelligent Systems and Data Mining
Assignment number and title Assignment 1: Data Mining Solutions for Direct Marketing Campaign
Assessment type WR
Weighting of assessment 50%
Unit learning outcomes 1. Analyse a Data Mining technique capable of supporting practitioners to make
reliable decisions which require predictive modelling, for example, in a
Business scenario
2. Demonstrate results of using an efficient technique which is capable of finding
a solution to a given predictive problem represented by a data set
3. Evaluate the accuracy of the technique in terms of differences between the
predicted values and the given data
What am I required to do in this assignment?
1. Task
Students will develop a DM solution for saving cost of a direct marketing campaign by
reducing false positive (wasted call) and false negative (missed customer) decisions.
Working on this assignment, students can consider the following scenario.
A Bank has decided to save the cost of a direct marketing campaign based on phone calls
offering a product to a client. A cost efficient solution is expected to support the campaign
with predictions for a given client profile whether the client subscribes to the product or not.
A startup company wants to develop an innovative DM technology which will be competitive
on the market. The Manager will interview and hire Data Analysts. The team will analyse
the existing technologies to design a DM solution winning the competition. A team Manager
will choose the best solution for the market competition in terms of cost efficiency. The
evaluation of the developed solutions will be made on the test data. The costs will be
defined for both the false positive and false negative predictions.
Examples of cost-efficient DM solutions for direct marketing are provided on the UCI
Machine Learning repository describing a Bank Marketing problem.
Students will apply for one of roles: (i) group manager, (ii) group member, or will work
individually. The group manager will arrange comparison and ranking of solutions designed
in a group, and will have additional 5 points. Each student will run individual experiments to
find an efficient solution and describe differences in experimental results.
2. Method and Technology
To design a solution, students will use Data Mining techniques such as Decision Trees and
Artificial Neural Networks. Examples of solutions will be provided in R Scripting using (i) a
Cloud technology CoCalc or (ii) an advanced IDE RStudio free for students.
3. Data
The Assignment 1 Bank Marketing data set is available as a csv file .
4. Report submission and report template
Each solution will be evaluated in terms of the costs of false decisions made on the
validation data. Reports will be submitted via BREO. Reports can be prepared with a
template . BREO similarity level of reports must be < 20%.
Is there a size limit?
2000 word on average
What do I need to do to pass? (Threshold Expectations from UIF)
1. Apply Decision Tree technique to solve the Bank Marketing task presented by a set of
customer profiles
2. Analyse problems which are required to be resolved in order to develop a solution
providing a high prediction accuracy on a given data set.
How do I produce high quality work that merits a good grade?
2
3. Identify a set of parameters which are required to be adjusted within DM techniques in
order to optimise the solution in terms of prediction accuracy
4. Explain how the parameters of a DM technique influence the prediction accuracy
5. Run experiments in order to verify the solution designed on the given data set
6. Analyse and compare the results of the experiments in a group and with the known
from the literature.
How does assignment relate to what we are doing in scheduled sessions?
Data Mining techniques and use cases developed in R will be considered during lectures and tutorials.
How will my assignment be marked?
Your assignment be marked according to the threshold expectations and the criteria on the following page.
You can use them to evaluate your own work and estimate your grade before you submit.
# Weight, % Lower 2 nd – 50-59% Upper 2 nd – 60-69% 1 st Class – 70%+
1
Analysis
(20)
Fair analysis of the
basic approaches
Relatively good analysis of
the relevant literature,
mainly covering the
state-of-art
Excellent analysis of the
relevant literature, fully
covering the state-of-art
2
Design
(50)
Fair design of a basic
solution providing a
reasonable
performance within
a single set of
parameters
Design of a solution
providing a fair performance
in a series of experiments
with different sets of
parameters
Design of a solution providing a
performance, competitive to
known from the literature, in a
series of experiments with
different sets of parameters
3
Conclusion
(30)
Fair conclusion on
the experimental
results obtained
within a single set of
parameters
Conclusion on and
comparison of the
experimental results
obtained within two
different sets of parameters
Conclusion on and comparison
of the experimental results
obtained within multiple sets
of parameters, demonstrating
a solution which provides a
competitive performance
3