Avoiding Analysis Traps
IN THIS CHAPTER
Examining your data
Exploring your data potential problems
Dealing with outliers and fitting data to a curve
Evaluating your technical analysis
Describing the limitations of a predictive model
In the quest for building a predictive model, you'll need to make decisions every step of the way — and some decisions are harder than others. Informed decisions — and an awareness of the common mistakes that most analysts make while building their predictive models — give you your best shot at success.
This chapter offers insights on the issues that could arise when you embark on a journey toward the effective use of predictive analytics. At the outset, consider this general definition:
A predictive model is a system that can predict the next possible outcome, and assign a realistic probability to that outcome.
As you build your predictive model, you're likely to run into problems in two areas — the data and the analysis. This chapter delves into both types of problems to help you strengthen your safeguards, and allow you to stay on top of your project.
How well you handle both the data and the analysis at the core of your predictive model defines the success of your predictive analytics project. Data issues are more prominent now because big data (massive amounts of analyzable data generated online) is all the rage — and only getting bigger, thanks to explosive growth of data in the digital and social media worlds.
The more data you ...