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INFT216 - Data Science Assignment Help

INFT216 - Data Science Assignment Help - Looking For INFT216 - Data Science Assignment Help Service To Secure Notable Grades – Visit Miracleskills!!
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INFT216 - Data Science Assignment - Will a given customer session take up the offer?

Although your Decision Trees and Logistic Regression models have been making news all around the company, as a solid Data Scientist, you know that to come up with a good model, you need to try a number of statistical learning techniques.

Your next job is to use knn classification to determine the probability that a given user session will take up the offer.

You have access to the same data as Assignment 1.

Your goal is to create the best knn model you can to predict the probability a customer will take up the product. You will need to be able to assess the models ability to predict on unseen data.

Deliverables - Your final deliverables will be 2 PDF files, both produced by the same .Rmd script (with different code chunk options).

I - all code and results shown (like you would share with a colleague on the Data Science team)

II - only show those things necessary to help support management decision making (this is the one you send to management!)

Please submit only these two PDF's through iLearn. Please don't submit R code or your model.

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Helpful hints -

i. Read the document on how to create a management report

ii: It's a good idea (at least to start off with) to use the template - customize it as much as you Wish. Alternatively, you may want to use your last assignment as the starting point.

Extra thoughts -

i. Pretty much everything you will need to do and consider is discussed in lectures and workshops

ii. The best way to get a good grade in the assignment is to come to class and participate

iii. Remember to read the data in from SQL

iv. If you find yourself repeating a lot of code, look for the opportunity to use a function

v. You can build as many models as you like using training data, but only present 1 final model to management... the team report is the place to critique and compare your different models

vi. Remember that management may not really understand data science, so you will need to find a tradeoff between understandability and verbosity... and brevity!

vii. Show some example predictions from new data that you created yourself... management often think in terms of customers rather than statistics. This helps make it real for them.

Remember to save your model.

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