Data Science with .NET

Data science is a rapidly expanding field, and the .NET ecosystem now offers powerful tools to build, train, and deploy machine‑learning models. In this article we explore the core libraries, best practices, and sample code to help you get started.

Why Choose .NET for Data Science?

• Strong typing and performance.
• Seamless integration with existing C# and F# codebases.
• Cross‑platform support via .NET 7.
• Rich ecosystem: ML.NET, TensorFlow.NET, SciSharp Stack.

Getting Started with ML.NET

Install the package and create a simple model:

dotnet add package Microsoft.ML
using Microsoft.ML;

var mlContext = new MLContext();
var data = mlContext.Data.LoadFromTextFile<HouseData>("housing.csv", separatorChar: ',');
var pipeline = mlContext.Transforms.Concatenate("Features", 
    nameof(HouseData.Size), nameof(HouseData.Rooms))
    .Append(mlContext.Regression.Trainers.Sdca(labelColumnName: "Price"));
var model = pipeline.Fit(data);

Deploying Models

Once trained, you can export the model to a consumable format:

mlContext.Model.Save(model, data.Schema, "model.zip");

Use ML.NET Model Builder in Visual Studio for a UI-driven workflow.