这个作业是使用数据挖掘技术并建议银行的系统针对目标客户进行营销
ITNPBD6 Data Analytics
Assignment 2020
Computing and Maths
University of Stirling
The banks have had a difficult few years and have been finding that people no longer
trust them with their money. StirBank are keen to get people saving with them again,
so they have been running a marketing programme, but this involves calling many
customers and is both costly and risks annoying people. This is where you come in.
We have got some data from the bank describing 8000 of its customers and the
previous attempts to make marketing calls to them. The data also tells us whether or
not each customer responded to the marketing by setting up a regular savings deposit
“made_deposit”.
The question is simple – is there any way to predict which customers are more likely
to respond positively to a marketing call? Your assignment is to answer that question
using data mining techniques and produce a system that would be able to tell the bank
which customers it should target the marketing at.
You can use Orange, Python, R, or any data mining package of your choice. The data
for the assignment is in a file bank-tr.csv included with this document.
You should hand in a report covering the following:
Introduction [10 marks]
Describe the task you were given, the data you received and the requirements of the
finished system, including why data mining is suitable for this task rather than a more
conventional . Define any terminology that you will use in the report (for example,
model, variable, task, etc.).
Data Summary [10 marks]
List the variables that you found in the file provided by the company. For each one,
say whether it is nominal or numeric, continuous or discrete and whether or not it is of
use in building the solution. Explain your decisions.
Data Preparation [10 marks]
Describe what you did with the data prior to the modelling process. Show histograms
of the data before and after any pre-processing that you carried out. If you corrected
any mis-typed entries in the data, report what you changed (either specific changes or
what rules you used to correct the data).
Background [30 marks]
The bank would like to better understand the different approaches to data mining. In
particular, they have heard of three model types: decision trees, multi-layer
perceptrons and logistic regression. Give a detailed technical description of each
technique and the way the models are represented, how they learn and make
predictions, and how easy it would be for the bank to understand the model and the
reasons behind each prediction it makes. Also describe what parameters may be
changed and what effect this has. Include one diagram showing the structure of each
type of model.
Modelling [25 marks]
You must build models using the three different techniques above, and choose one to
recommend to the bank for supporting its targeted marketing. If you varied the
parameters of a model, show how this impacted on the results. Describe how you split
the data for training and testing purposes. Be methodical and record each result. This
stage is a little like scientific research – you are carrying out experiments in your
search for the best solution. Once you have a solution, show how you verified its
robustness. For the three different techniques report on their comparative ability to
predict a positive response from a customer.
Results and Errors [15 marks]
Analyse and describe the level of accuracy your chosen model achieves and the errors
your model makes. Show a confusion matrix for your model. Are there any areas of
the data where it performs worse than in others? Show a lift curve or a ROC curve for
the decision as to whether or not a customer will respond to a marketing call.
Submission
The deadline for this assignment is 4pm on Friday 3 April 2020. Please submit your
report via the Assignments space on Canvas as a doc or pdf file bearing your
university username (3 letters + 5 digits, e.g., xyz00001.pdf).
You do not need to submit the models that you built, just the report. As a guideline,
your report should be around 3000-5000 words. However, this is not a strict limit and
no penalties will be issued for reports outside this range – just write what you need to
provide the required information clearly and concisely. You can assume that the client
has a good technical understanding of data mining and statistics, so do not shy away
from technical terms in your report. Where you use them, however, explain what they
mean in plain language too. To maximise your mark, make sure you follow the
instructions above and include everything that is asked for in the report.
Plagiarism
Work which is submitted for assessment must be your own work. All students should
note that the University has a formal policy on plagiarism which can be found at
http://www.quality.stir.ac.uk/ac-policy/assessment.php. You can test your report prior
to submission using the “Similarity Checking Space” on Canvas.
This assignment is subject to the usual grade penalties for late submission. You can
email questions about it to sbr@cs.stir.ac.uk .