IBM SPSS Modeller – Data Mining Project

For this project, you are required to complete two parts:
 Part-1: Data mining modelling project using a selected datasets from Table-1.
 Part-2: Perform data pre-processing and data cleaning on the raw dataset provided
to you (Unclean-Bank-Data.Xlsx) using IBM SPSS Modeller nodes to clean and pre-process the

(A) Dataset Selection:
Each team must select one of the datasets listed in Table-1.
Chosen dataset: Statlog (Landsat Satellite) as dataset, this is the link:

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(B) Data Analysis and Model Building:
You are required to import the data, perform pre-processing tasks if needed (such as reformatting the
data, normalizing it, dealing with missing values, dealing with outlier), followed by two or more
modeling tasks such as classification (Decision tree, Bayesian, KNN, neural networks, etc.), clustering (K-
means, agglomerative), and association rules mining.

(C) Project Report for Part-1:
Your report for this part of the project should include:
 Explaining the data you selected for your project (attributes, instances, etc.)
 Explaining your pre-processing tasks if any (cleaning, transforming, normalizing, etc.)
 Explaining the data mining modeling techniques you performed on the data (at least two
 Demonstrating the graphs/tables of the results produced by the techniques
 Interpretation of the modeling results: useful patterns, predicted values, significance of the
features, what actions you might suggest based on your findings
 Concluding remarks, your recommendations, actionable discoveries, and future trends/studies you
would recommend

Overall, your report for this part of the project should be 15 to 25 pages long (including graphs). Use
12pt Times New Roman font, with 1.5 line space. Keep a margin of 1” on all sides of the page.

In this part of the project, all teams will use the dataset Unclean-Bank-Data.Xlsx posted on the “Project
Description” page of the course website.

This dataset includes missing values, invalid values, and outliers. You should use the IBM SPSS Modeler
nodes to pre-process and clean the data.

Do not remove a record if there is only one missing value in that record. Instead, use the IBM Modeler
to fill in the missing value with an algorithm of your choice.

Similarly, do not remove a record if it has only one invalid value. Instead, use the IBM Modeler to fill in
the invalid value with an algorithm of your choice.

If you find a record with more than one missing value, or more than one invalid value, then you may
either remove the record, or use the IBM Modeler to fill in for the missing or invalid values.
If you detect outliers, you may then delete the entire record.

You may also want to do other pre-processing tasks such as data normalization, binning data, etc.
Deliverables for Part-2:
1- Include in your project report a short explanation of three different cleaning and pre-processing tasks
you applied on the data using the IBM SPSS Modeller.
2- Also, include the clean dataset (name it “Clean-Bank-Data.xlsx”) in your submission together with
your project report (you may submit everything in one zip file).

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