Tommy Jung

A predictive analytics project combines execution of details with big-picture thinking. These handy tips and checklists will help keep your project on the rails and out of the woods.Building a predictive analytics modelA successful predictive analytics project is executed step by step. As you immerse yourself in the details of the project, watch for these major milestones: Defining Business Objectives The project starts with using a well-defined business objective.

After you build your first classification predictive model for analysis of the data, creating more models like it is a really straightforward task in scikit. The only real difference from one model to the next is that you may have to tune the parameters from algorithm to algorithm. How to load your data This code listing will load the iris dataset into your session: >>> from sklearn.

Another task in predictive analytics is to classify new data by predicting what class a target item of data belongs to, given a set of independent variables. You can, for example, classify a customer by type – say, as a high-value customer, a regular customer, or a customer who is ready to switch to a competitor – by using a decision tree.

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.