Sabrina Polin: Looking to the past to predict the
future. Predictive modeling the core function in
predictive analytics applications is a
mathematical process that aims to predict future
events or outcomes based on past behavior. It does
so by analyzing data to identify patterns that can
be used to forecast what is likely to happen in
the future. The predictive modeling process begins
once a set of current or historical data is
collected for analysis. Then data scientists or
analysts create algorithms and statistical models,
train them with subsets of the data and run them
against the full data set to generate the
predictive model. In many cases, multiple models
are used at once to create one prediction. While
the terms predictive modeling and predictive
analytics are sometimes used interchangeably.
Modeling can be seen instead as the hands on part
of analytics applications, which you can learn
more about here, or in the description below.
There are many different modeling methods and
algorithms. Some popular ones include decision
trees, time series analysis, neural networks,
linear regression, and logistic regression.
predictive modeling is popularly associated with
meteorology and weather forecasting, but has many
business applications as well. online advertising
and marketing is one of the most common uses.
Analysts take user data like what you click on
what you buy, and how long you view something and
run it through algorithms to determine what kind
of products you're likely to click on and purchase
in the future. predictive modeling is also used in
spam filters, fraud detection, CRM, capacity
planning, change management, disaster recovery,
engineering, medical diagnosis, and security
management, to name a few. Well predictive
modeling can aid in business decision making
processes, it must be used correctly. Some
considerations for effective predictive modeling
include acquiring sorting, cleansing, and
preparing enough data for analysis, which is often
said to take about 80% of the process, being
careful not to overfit or over test models, which
can cause a model to just memorize points in a
data set rather than generalized outcomes planning
for technical and organizational barriers, like
accessing useful data in decentralized systems,
and making sure predictive modeling projects
address real business challenges. Keeping in mind
that statistical significance does not always
equal business insight. What other applications
are there for predictive modeling? How has your
organization benefited from it? Share your
thoughts in the comments and be sure to hit that
like button.