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SPSS MODELLER ASSIGNMENT REQUIREMENTS BY ONE OF OUR EXPERTS SOLVED RECENTLY.
ReplyDeleteMIS 245001 IBM Predictive Modeler Project Fall 2019
Project requirement and description:
For your project, choose either one of the following two problems:
1. Telecommunication Customer Churn rate prediction.
2. PUBG Finish Placement Prediction.
Both are on going or previous competitions on Kaggle.com:
https://www.kaggle.com/c/pubg-finish-placement-prediction and
https://www.kaggle.com/blastchar/telco-customer-churn/home
Uses the above two links to visit the competition main page on Kaggle.com,
where you can find detailed problem description and more information.
Obviously, to win the competition, one has to submit a model that is carefully
designed with fine-tuned parameters. For your task, however, your main challenge
is able to do the prediction with multiple models using SPSS modeler and compare
their performance. While on top of that, managing to achieve a satisfying accuracy.
For each competition, extra points will be credited to the tops 3 accuracies.
You are also welcomed to pick your own problem. If so, please provide a
detailed problem description, links to the data sources, etc. You will also be given
extra points for choosing your own topic.
Both datasets have been “cleaned up” in some sense, so you can directly feed
data into models. To achieve better performance, there are several ways you may
try :
1. Run multiple models to find the one with the best performance(notice that both
competitions are classification(supervised learning) problems, so make sure using
the right models);
2. Adjust the selection of input variables (In demo, I use all variables as input, which
may not necessary be the best option since some variables are probably just bring in
noise, or are redundant. Unselect some variables may sometimes give a better
performance.);
3. Derive new variables as new input variable.
You should try at least one rule induction model and one black box model,
compare their performance and report. The performance evaluation and
comparison should be discussed in full detail. You need to include the predictor
importance result from rule induction model and discuss about it. Also in the result
part of your project, highlights the best accuracy you get and corresponding model
settings you get that accuracy. For the 1st competition, report the accuracy of
prediction and for the 2nd competition, report the linear correlation coefficient. I will
use them to decide who gets the extra points. I may run the model with your
settings to check the result, so don’t cheat.
Lecture 4 slides contained all the information you will need to finish the
project, check the slides when you meet difficulties.
You need to submit a report that is at least 5 pages (double space, including
tables and figures). The report should at least include:
§ An Introduction: problem description and definition.
§ Data description.
§ Methods.
§ Results.
§ Discussion.
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