• DataScienceInPractice
  • Introduction
  • 1. Chapter 1: Introduction to R
  • 2. Chapter 2: Using Apply, by, eapply, lapply, sapply, vapply, replicate, mapply, rapply and tapply
  • 3. Chapter 3: Read.csv
  • 4. Chapter 4: Analyzing the Data
  • 5. Chapter 5: Logistic Regression and Prediction
  • 6. Chapter 6: Forecasting Retail Sales / Prediction
  • 7. Chapter 7: SNA (Social Network Analysis) and Graph Theory
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DataScienceInPractice

Chapter 6: Forecasting Retail Sales / Prediction

It is always a challenge to predict sales.

The idea in this chapter is to talk a little bit how to predict sales and try to explain some real codes to do it.

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Links:

http://stats.stackexchange.com/questions/46391/predict-sales-levels-with-decision-trees

http://blog.revolutionanalytics.com/2013/09/forecasting-using-r-a-new-online-course-from-rob-hyndman.html

https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/forums/t/8023/thank-you-and-2-rank-model

https://github.com/mikeskim/Walmart/blob/master/makeSubmission.R

http://robjhyndman.com/talks/MelbourneRUG.pdf

http://www.slideshare.net/mattbagg/baggott-predict-customerinrpart1

https://www.otexts.org/fpp/using-r

http://blog.yantrajaal.com/2014/06/forecasting-retail-sales-linear.html

http://www.r-bloggers.com/tag/forecasting/