Tommy Jung

When you are dealing with predictive analytics, make sure you understand the demands associated with big data. Be sure to make a clear distinction between business intelligence and data mining. Here are the basics of the distinction: Business intelligence (BI) is about building a model that answers specific business questions.

As much as you may not like it, your predictive analytics job is not over when your model goes live. Successful deployment of the model in production is no time to relax. You'll need to closely monitor its accuracy and performance over time. A model tends to degrade over time (some faster than others); and a new infusion of energy is required from time to time to keep that model up and running.

In order to ensure a successful deployment of the predictive model you're building, you'll need to think about deployment very early on. The business stakeholders should have a say in what the final model looks like. Thus, at the beginning of the project, be sure your team discusses the required accuracy of the intended model and how best to interpret its results.

In perspective, the goal for designing an architecture for data analytics comes down to building a framework for capturing, sorting, and analyzing big data for the purpose of discovering actionable results. Thinking of the architecture that will transform big data into actionable results.There is no one correct way to design the architectural environment for big data analytics.

Predictive analytics begins with good data. More data doesn't necessarily mean better data. A successful predictive analytics project requires, first and foremost, relevant and accurate data. Keeping it simple isn't stupid If you're trying to address a complex business decision, you may have to develop equally complex models.

One clustering algorithm offered in scikit-learn that can be used in predictive analytics is the mean shift algorithm. This algorithm, like DBSCAN, doesn't require you to specify the number of clusters, or any other parameters, when you create the model. The primary tuning parameter for this algorithm is called the bandwidth parameter.

To assemble your predictive analytics team, you'll need to recruit business analysts, data scientists, and information technologists. Regardless of their particular areas of expertise, your team members should be curious, engaged, motivated, and excited to dig as deep as necessary to make the project — and the business — succeed.

The random forest model is an ensemble model that can be used in predictive analytics; it takes an ensemble (selection) of decision trees to create its model. The idea is to take a random sample of weak learners (a random subset of the training data) and have them vote to select the strongest and best model. The random forest model can be used for either classification or regression.

A visualization can represent a simulation (a pictorial representation of a what-if scenario) in predictive analytics. You can follow up a visualization of a prediction with a simulation that overlaps and supports the prediction. For example, what happens if the company stops manufacturing Product D? What happens if a natural disaster strikes the home office?