As a data scientist, your curiosity, diligence, and creativity drive you to extract immense value from your data and models. But what if you could go beyond delivering high-confidence predictions and directly influence decision-making? With Gurobi, you can.
Are you ready to explore the cutting-edge intersection of mathematical optimization and data science? Dive into the “State of Mathematical Optimization in Data Science 2022” report—a comprehensive analysis that uncovers the latest trends, applications, and insights in the field. Read the Report
With our examples, you can be tinkering around with optimization in just minutes—and learn how to incorporate optimization into your machine learning projects.
Our Python API includes higher-level modeling constructs that make it easier to build optimization models. Choose from Anaconda Python distributions with pre-built libraries to support application development, Spyder for graphical development, and Jupyter for notebook-style development.
Integrate Gurobi into your applications easily, using the languages you know best. Our programming interfaces are designed to be lightweight, modern, and intuitive, to minimize your learning curve while maximizing your productivity.
Use Gurobi as a “digital twin” for your business—enabling you to explore the business impact of certain decisions or what-if scenarios, before they happen.
By supporting variable relationships directly in the Gurobi API, we simplify the modeling process—performing the transformation to a corresponding MIP formulation automatically and transparently during the solution process.
Express common modeling constructs like MIN/MAX, ABS, AND/OR, and IF/THEN at a higher level, making such models easier to build and maintain.
Prescriptive analytics tools help you make decisions based on your real-world goals (“objectives”) and limitations (“constraints.”) This can be especially useful when you’re facing a business problem with multiple, conflicting goals (such as cutting spending while increasing production) and multiple constraints (such as time, distance, product availability).
Predictive analytics tools seek to find patterns in data, in order to predict what might happen in the future. For example, predictive analytics can predict who will launch which cyberattack, which experiments are more likely to prove the hypothesis, imminent machine failure, supply chain issues, infrastructure maintenance needs, and price movements—all before they happen.
Although there are countless ways to use prescriptive analytics, here are some real-world examples, with links to their stories:
Prescriptive analytics tools provide a detailed set of recommendations for how you can best achieve your goals, given your limitations. Although you can use it to automate decision-making, you can use it to inform your traditional decision-making processes. Its ability to explore what-if scenarios can be particularly helpful.
Say you were planning a trip. Predictive analytics can predict what you may encounter along your journey (weather, traffic, engine trouble), and prescriptive analytics can, given those predictions, identify the route that best helps you achieve your goals (fastest, cheapest, safest route), given your constraints (time, budget, speed limits).
Here are some additional examples:
Prescriptive analytics doesn’t rely on historical data—which means you can make decisions for the future, even when it doesn’t look like your past. To use prescriptive analytics, you need to know three things:
With this information, the prescriptive analytics tool can generate a detailed action plan for achieving your goals, given your limitations.
Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.
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