Predictive Analytics: Overview and Data visualization by Ricky Ho.
From the post:
I plan to start a series of blog post on predictive analytics as there is an increasing demand on applying machine learning technique to analyze large amount of raw data. This set of technique is very useful to me and I think they should be useful to other people as well. I will also going through some coding example in R. R is a statistical programming language that is very useful for performing predictive analytic tasks. In case you are not familiar with R, here is a very useful link to get some familiarity in R.
Predictive Analytics is a specialize data processing techniques focusing in solving the problem of predicting future outcome based on analyzing previous collected data. The processing cycle typically involves two phases of processing:
- Training phase: Learn a model from training data
- Predicting phase: Deploy the model to production and use that to predict the unknown or future outcome
The whole lifecycle of training involve the following steps.
Ricky has already posted part 2 but I am going to create separate entries for them. Mostly to make sure I don’t miss any of his posts.
Enjoy!