How can you build an effective Predictive Model ?

Predictive Analytics Market is growing at CAGR 22.1%. and doesn't look to like die down anytime soon as the demand keep going high. What is making it so important in current industry trends. Few factors are Cost, Customer Satisfaction, Improved operational performance etc.

Predictive analytics has its roots in the ability to “Predict” what might happen. These analytics are about understanding the future ,provides companies with actionable insights based on data and provide estimates about the likelihood of a future outcome. It is important to remember that no statistical algorithm can “predict” the future with 100% certainty. Companies use these statistics to forecast what might happen in the future. This is because the foundation of predictive analytics is based on probabilities.

An accurate and effective predictive analytics takes few steps those need to be taken in to consideration. Predictive analytics requires people who understand there is a business problem to be solved, data that needs to be prepped for analysis, models that need to be built and refined, and Business Unit to put the predictions into action for positive outcomes.

Identify what you are looking to understand from the data set and how well it is related to the Business problem.

Next, consider if you have the data to answer those questions. Is your operational system capturing the needed data? How clean is it? How far in the past do you have this data, and is that enough to learn any predictive patterns? Is there any way to find some indirect parameters that could affect the possible predictions.

Build and train models , When building your predictive analytics model, you’ll have to start by training the system to learn from data. Your predictive analytics model should eventually be able to identify patterns and/or trends about your System or target’s behaviors. You could also run one or more algorithms and pick the one that works best for your data, or you could opt to pick an ensemble of these algorithms.

Another key component is to regularly retrain the learning module. Trends and patterns will inevitably fluctuate based on the time of year, what are the changes in your target product(example software/hardware upgrade), what are the structural changes in data hosting infrastructure and other factors. Set a timeline—maybe once a month or once a quarter—to regularly retrain your predictive analytics learning module to update the information.

Once model is deployed , you need to have proper process in place to have feedback on each and every trigger. That helps to re-evaluate the effectiveness of the model and modify the model logic if required.

Few Tips ,

If you are completely new to the domain or concepts ? Try to pick the easier one’s first where you got all the data readily available. Example , try models with upper and lower specifications from the data set. This helps the data scientist to understand the system, structure of business problems ,Data structure, approach of subject matter expert etc.

You might be trying to solve a big business problem in terms of cost or volume activity . But you always need to look in to the usage factor of each model. If the model output has dependency on factors or output which requires human intervention , the model is going to have lower usage. Let’s say if you spend 6 months to develop a complex model like that ,you can be sure that you are compromising on your productivity.

Be open to un-deploy the models which doesn’t have significance to save the platform resources.

R, Python, and Scala are the three major languages for data science and data mining.Determine what suits best for your requirements and use it.

Most importantly ,you need to keep in mind that "Customer/To some extent- End user is always First" has to be your first priority. No matter what. You may invent some methods which no one has ever done.But if the model doesn't deliver simple requirements or details , it can really raise questions.