---
title: "First Project on ML.NET – Beginner Guide"  
description: "Machine Learning is becoming very popular, and if you are a .NET developer, the best way to start Machine Learning is by using ML.NET. In this blog, we will cre"  
author: "ICSM Computer"  
published: 2026-03-08  
updated: 2026-03-08  
canonical: https://answers.mindstick.com/blog/82/first-project-on-ml-dot-net-beginner-guide  
category: "artificial-intelligence"  
tags: ["artificial intelligence"]  
reading_time: 3 minutes  

---

# First Project on ML.NET – Beginner Guide

[Machine Learning](https://www.mindstick.com/articles/324457/what-are-the-different-ways-to-improve-machine-learning-skills) is becoming very popular, and if you are a **.NET developer**, the best way to start Machine Learning is by using [**ML.NET**](https://dotnet.microsoft.com/en-us/apps/ai/ml-dotnet). In this blog, we will create our **first Machine Learning project using ML.NET step-by-step** in simple language.

This guide is perfect for beginners who know **C# / .NET** but are new to Machine Learning.

## 1. What is ML.NET?

**ML.NET** is a Machine Learning framework created by [**Microsoft**](https://learn.microsoft.com/en-us/dotnet/machine-learning/mldotnet-api) that allows .NET [developers](https://www.mindstick.com/blog/302688/handling-errors-in-rust-a-comprehensive-guide-for-developers) to build AI/ML models using **C# or F#** without learning Python.

With ML.NET you can build:

- [Spam detection](https://www.mindstick.com/news/4033/airtel-launches-ai-powered-spam-detection-solution-processing-1-trillion-records-in-real-time)
- Price prediction
- Recommendation system
- [Sentiment analysis](https://www.mindstick.com/articles/156985/sentiment-analysis-using-python-in-tableau-with-tabpy)
- [Image classification](https://www.mindstick.com/blog/304670/key-techniques-used-in-object-detection-and-image-classification)

Official site:\
[https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet)

![First Project on ML.NET – Beginner Guide](https://answers.mindstick.com/blogs/95be0fe0-45ac-41cd-b640-96c1360a9ed5/images/f76307fb-407b-45c7-8aab-eeb7209b8e7b.png)

## 2. System Requirements

Before starting, install:

- [Visual Studio](https://www.mindstick.com/articles/12378/visual-studio-for-mac-is-out-of-beta-preview-now-officially-available) 2022
- .NET SDK 6 or later
- NuGet Package Manager

Install ML.NET package:

```plaintext
Install-Package Microsoft.ML
```

## 3. Our First Project – Price Prediction Model

We will create a simple ML model that predicts **House Price** based on size.

Example data:

| Size | Price |
| --- | --- |
| 1 | 10000 |
| 2 | 20000 |
| 3 | 30000 |
| 4 | 40000 |

We will train ML model using this data.

## 4. Step 1 – Create Console Project

Create new project:

```plaintext
Console App (.NET)
```

Install package:

```plaintext
Microsoft.ML
```

## 5. Step 2 – Create Data Model

Create class:

```cs
using Microsoft.ML.Data;

public class HouseData
{
    [LoadColumn(0)]
    public float Size;

    [LoadColumn(1)]
    public float Price;
}

public class HousePrediction
{
    [ColumnName("Score")]
    public float Price;
}
```

## 6. Step 3 – Write ML Code

```cs
using Microsoft.ML;
using System;
using System.Collections.Generic;

class Program
{
    static void Main()
    {
        var context = new MLContext();

        var data = new List<HouseData>()
        {
            new HouseData{ Size = 1, Price = 10000 },
            new HouseData{ Size = 2, Price = 20000 },
            new HouseData{ Size = 3, Price = 30000 },
            new HouseData{ Size = 4, Price = 40000 }
        };

        var trainingData = context.Data.LoadFromEnumerable(data);

        var pipeline = context.Transforms
            .Concatenate("Features", nameof(HouseData.Size))
            .Append(context.Regression.Trainers.Sdca(
                labelColumnName: "Price",
                maximumNumberOfIterations: 100));

        var model = pipeline.Fit(trainingData);

        var predictor = context.Model.CreatePredictionEngine
            <HouseData, HousePrediction>(model);

        var prediction = predictor.Predict(
            new HouseData { Size = 5 });

        Console.WriteLine(
            $"Predicted price: {prediction.Price}");
    }
}
```

## 7. Output

```plaintext
Predicted price: 50000 (approx)
```

Model learned pattern:

```plaintext
Price = Size × 10000
```

This is called **Regression Model** in Machine Learning.

## 8. What We Learned

In this first ML.NET project we learned:

- What is ML.NET
- How to install ML.NET
- How to create model
- How to train data
- How to predict value

This is the basic workflow of Machine Learning.

```plaintext
Data → Train → Model → Predict
```

## Conclusion

- ML.NET makes Machine Learning easy for **.NET developers**.
- You do not need Python to start [AI](https://www.mindstick.com/services/artificial-intelligence).
- With ML.NET you can build real AI applications using **C#**.

---

Original Source: https://answers.mindstick.com/blog/82/first-project-on-ml-dot-net-beginner-guide

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