Assignment 3: Predictive Data Mining
In this assignment, students will implement concepts in the realm of
predictive data mining on data sets using classification algorithms.
1. Students should work with data of their choice, e.g. related to
their research topics, MS project or simply their own areas of
interest.
2. Work individually.
3. Collect suitable data of your choice from any online repository,
e.g. UCI, Kaggle etc. This forms the given dataset for the
assignment.
4. Use the python-based popular industry data mining software
packages and development/analysis tools – Panda,
Scikit-Learn, Seaborn, etc. Study the related documentation
from the software user guide.
5. On the given dataset, execute any predictive data mining
technique such as decision trees, Bayesian classifiers, neural
networks, k-nearest neighbors, ensemble learning, linear
regression etc.
6. You can implement multiple techniques, one followed by another
on the same dataset if you prefer to use this for your analysis.
7. If needed, you should convert the format of your dataset as
needed by the respective technique(s). If you have already
performed conversion in the assignment on data preprocessing,
you can use the converted data here.
8. If you would like to use the results of your predictive data mining
techniques and conduct further analysis with predictive data
mining, that is fine as well.
9. You should aim to achieve robustness and generalization in the
mining, e.g. by altering seeds in the algorithm.
10. You must modify the concerned parameters, e.g. learning rate
and error threshold for neural networks, the value of k for
k-nearest neighbors etc. to get good results. Execute at least 3
different combinations of parameters and present the
experimental results accordingly for at least one technique.
11. Observe the experimental results and draw useful conclusions
from the data.
12. Based on the hypothesis obtained by the learning in the
predictive data mining technique(s), write a simple program that
uses the learned hypothesis to classify new data, and thus
serves as a prototype mini classification tool.
13. If you have used multiple techniques, you can select the one
that gives greatest accuracy or the one that is most suitable to
your data and domain etc.
14. The program should communicate with the user to give
outputs based on new unseen data. For example, consider that
the learned hypothesis is based on a dataset that uses weather
data to predict if it is okay to play tennis. This can include various
attributes such as “temperature”, “chance-of-rain” and so forth to
estimate the target “playing tennis” as being “yes”, “no” or
“maybe”. It can be used to manually derive rules such as “if
temperature = medium and chance-of-rain = low then play-tennis
= yes” which can be coded into the program. Thereafter, when a
user inputs new values for parameters such as “temperature”,
the program can use these rules to output the classification
target and thereby suggest to the user “Yes, you can surely play
tennis today” or “No, you should not play tennis today” or
“Maybe, it seems okay to play tennis today”. This example can
be modified based on your dataset and domain.
15. Show at least 3 different runs of such inputs and outputs
based on user interaction. The final goal is to have simple
communication with the user based on the learning done via
predictive data mining for classifying the target attribute. Hence,
please work accordingly based on your respective technique(s)
and application. You can tune this as per the needs of your MS
project, research topics or any other area of interest.
16. Please make slides based on your work in this assignment
and upload them on Canvas.
17. There should be minimum 10, maximum 20 slides. ‘
18. The slides should include:
• The dataset with a short explanation about the goals of
your classification
• Mention of the predictive data mining technique(s) with
justification
• Execution of the experiments with this technique, using at
least 3 distinct combinations of
suitable parameters
• Relevant code snapshots of the program (in any
language) for the prototype mini
classification tool implemented using the learned
hypothesis
• Demo of the tool with suitable inputs and outputs,
showing at least 3 runs of user interaction
• Any references used including data sources, software
tools, text books etc.
• Mention of the descriptive data mining technique and
algorithm with justification (e.g. sequence mining because
you are working with DNA data etc.)
• Execution of the experiments with this algorithm, using at
least 3 distinct combinations of parameters (show inputs &
outputs)
• Conclusions drawn from all the observations with the
descriptive data mining technique
• Any references used including data sources, software
tools, text books etc.
19. Please turn in the slides on the due date. Slides should be
uploaded on Canvas individually by each student, even though
you are working in groups.The slides should be different even if
you work in a team
