The goal is to create a model, propose evaluation metric and cross-validate [login to view URL] choice of the model is not that important and we don’t expect state-of-the-art accuracy (although we assume that sufficient explanation for how model works). Solution should be simple, but reasonable: The key deliverable along with the R code is a detailed explanation as to how it works(should be able to explain why it makes sense and how it is correlated to the business problem). The attached dataset provides information about shopping mall [login to view URL] line represents one customer - the first column contains unique customer identifier and the second column contains indices of the day when customer have visited the mall. The day with index 1 is a Monday (e. g. 7th is a Sunday, 8th is again a Monday). Indices are within a range of 1 to 1001
(which is equal to 143 full weeks). The task is to predict the first day of the next visit (week 144). For example, if customer will visit mall on Wednesday, then prediction should be equal to 3:
0: Customer will not visit on the next week
We are not looking for a model that performs perfectly - we are looking to build a reasonable model with a correct evaluation. The explanation of code and steps used is key. You can use any open source libraries and we expect to get a rmarkdown. Please reach out in case of any queries.
1. single-page summary of the model with evaluation result
2. code (all the preprocessing and transformation steps should be included),the steps of analysis and explanation can be done either in the form of comments or as a separate document.
Link for Data :
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The deadline is on 18/10/2018 @ 6.00AM GMT.
Thanks and hope you all take up this challenge