Mohamed Chaouchi

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.

Predictive analytics can help websites with personalization. You may have noticed that websites remember what you did or which pages you looked at on their website last week or last month. Such websites are tracking your behavior, from clicks on certain parts of the page to the order of the pages you viewed for a session, to offer you the most relevant advertisements, products, or news articles.

Predictive analytics makes heavy use of three related disciplines: data mining, statistics, and machine learning. All four disciplines intersect to such a large degree that their names are often used interchangeably. Just to keep the record straight, there are some distinctions: predictive analytics combines many of the techniques, tools, and algorithms that data mining, statistics, and machine learning have in common.

There are two main challenges of big data as it applies to predictive analytics: velocity and volume. These are (respectively) the rate at which data is being generated, received, and analyzed, and the growing mass of data. Data velocity Velocity is the speed of an object moving in a specific direction. Data velocity refers to another challenge of big data: the rate at which data is being generated, captured, or delivered.

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.

Models are necessary to perform predictive analytics. A model is nothing but a mathematical representation of a segment of the world people are interested in. A model can mimic behavioral aspects of our customers. It can represent the different customer segments. A well-made, well-tuned model can forecast — predict with high accuracy — the next outcome of a given event.

Prognostics is an engineering field that aims at predicting the future state of a system. Prognostics improves the process of scheduling maintenance, ordering parts, and using resources. Prof. David Nagel, a renowned expert in nuclear energy, educator and researcher derived an interesting correlation between the field of Predictive Analytics and the old field of Prognostics.