Predictive analysis is the use of data, statistics, and machine learning to predict future outcomes based off of historical data. The end goal is to build predictability and clarity into the decision-making process and insulate the company from shocks.
Predictive analysis has a myriad of applications and the number is growing rapidly. Vendors are making it easier to build models using automated tools designed for business analysts. There are also open-source machine learning algorithms that developers use for their niche applications.
This article will describe the process of building a predictive analysis model in 10 easy steps.
Step 1: Understand Business Objectives
The best predictive models share two important features – accurate predictions and business relevance. To deliver value, a data scientist needs to understand an organization’s business model and objectives and build model that helps meet those objectives.
Step 2: Identify Business Problem
Getting this right is the stepping stone to building a successful model. Use key performance indicators and industry benchmarks to assess the organizations current health status and find issues that present a red flag. Another means to identify is to ask structured questions to your customers and know from there where your proposition and delivery lack.
Step 3: Assess Business Processes for Fitment
The recommendations of the predictive model will need to be worked on using processes – existing or new. The data scientist needs to assess which processes will need amendments to execute the model’s results. Accordingly, key position holders will need to be given a heads-up. Disharmonies in processes and the predictive analysis model will need to be ironed out well ahead of time.
Step 4: Identify Performance Metrics
Business process and performance are two inter-related elements. The first leads to the second and the second impacts the first. Part of the predictive analysis modeling process involves setting up metrics for the process and performance. This helps assess whether the organization, as a whole, is doing well or not.
There are a bunch of performance analysis tools – open source and closed – available on the market. Two things to look for while fishing for a tool.
- The tool should work with organizational capabilities and operations.
- The tool should incorporate operational elements of the model.
Step 5: Prepare Data
You will need to train your model using historical data. The data is usually scattered across multiple sources and may require cleaning and sorting. There might also be duplications which you may leave in or weed out on an ad hoc basis. Lastly, you will need to adjust your hypothesis to accommodate for missing data. Golden rule: Data quality determines the accuracy and effectivity of the model.
Step 6: Define Development Methodology
The keyword is ‘agility.’ The speed of development and deployment (dev-dep) of the model should match the pace of industry changes. If the dev-dep falls behind the pace at which new opportunities present themselves, your organization will miss out on capitalizing on a profitable venture.
Pro tip: Center the model around organizational goals and the industry it works in. Also, leave room for quick changes.
Step 7: Sample Your Data
Split your data into two sets – training set and test set. Begin building your model using the training dataset and use the test dataset to verify the model’s output. This will help reduce the risk of data overfitting and improve the performance of your model.
Not doing so will run the risk of training the model with a limited dataset and lead to biased results.
Step 8: Theory of Sunk Costs
Sunk costs are the resources you have already expended on your venture. Economic theory suggests that you should abandon a venture once you realize it’s headed downhill, regardless of the resources expended.
Not every predictive analysis model will work. Upon ascertaining the non-feasibility of a model, be ready to abandon it and move to another one. The goal is to deliver utility and value (bottom line), not stick to your guns.
Step 9: Build the Model
Sometimes the data lend themselves to a specific algorithm or model. Then there are situations where you may feel that your best approach is not so clear. As you start to explore the data, you may have to run multiple algorithms and compare their outputs to arrive at a consensus on the baseline. Base your choice of the final model on the overall results.
Step 10: Deploying the Model
Deployment although a short phase is the most important part of building a predictive analysis model. Elements that matter the most are timing, agility, and precision. Post deployment, present your results to the stakeholders in a comprehensible and compelling manner.
Deployment will be followed by monitoring and assessment. Most models are prone to decay, so it’s wise to update your model from time to time.
Ready, Set, Go!
So, there you go! 10 quick and easy steps to create your own predictive analysis model from scratch. To know more about courses that train on building predictive analysis model, click here.