Introduction:
When it comes to data science, it's a common misconception that the job primarily involves analyzing data and applying various models to obtain results. In reality, the initial and critical step in any data science project is to frame the problem. Before diving into the data and algorithms, it's essential to break down the problem into its smallest components. This blog explores the strategic steps involved in problem framing, which are crucial for the success of any data science endeavor.
Steps to Frame a Problem
Convert Business Problem into a Machine Learning Problem
Imagine you are working for an OTT (Over-The-Top) platform service, and the business goal is to increase revenue. The typical solutions that come to mind may involve raising costs for existing users (not ideal), acquiring new users (challenging), or reducing the churn rate. Among these, predicting and reducing churn rate is often the most effective approach. Converting the business problem into a machine learning problem aligns your efforts more precisely. Our first instinct as a Data Scientist to solve a business problem should always be to convert that business problem into a mathematical problem.
Determine the Type of Machine Learning Problem
For the churn rate problem, you can either classify users likely to leave or frame it as a regression problem to predict the probability of a user leaving. The choice depends on your strategy for incentivizing users to stay. Classification will lead to giving the same incentive to all user which we would be predicting to leave the platform but regression will mean we would be providing different incentives to different users based on their probability of leaving the platform.
Examine Existing Solutions
Before starting from scratch, it's wise to investigate existing solutions in the field. Analyze what factors were considered, and evaluate if you can build upon or improve these solutions. This approach can save time and lead to better results.
Gather Data:
Identify the data attributes required for your problem. In the OTT platform example, you might need data on user engagement, browsing time, and search result accuracy. Collaborate with data engineers to create a data warehouse that collects these attributes from the existing OLTP (Online Transaction Processing) system.
Define Metrics
Decide on the key metrics that will determine the quality of your output. In the context of churn rate reduction, success may be measured by accurately predicting users likely to leave the platform.
Online vs. Batch Training
Determine whether you want to train your model continuously with stream data (online training) or periodically with batch updates. The choice depends on the nature of the problem and the available resources.
Validate Assumptions
This step involves validating initial assumptions made during problem framing. Ensure that the required attributes are available in the data sources. Additionally, consider whether a single model fits all locations or if different models are necessary for different locations.
Conclusion:
Framing a data science problem is a critical precursor to any successful project. By following these strategic steps, you can ensure that your efforts are focused, your data is relevant, and your metrics are aligned with your business goals. Problem framing sets the stage for impactful data science solutions and maximizes the chances of success.