A Simple ML.NET Linear Regression Machine Learning Application

The code in the video tutorial project demonstrates the simplest ML.NET application. This

application solution is based on the Microsoft document at:



https://docs.microsoft.com/en-us/dotnet/machine-learning/how-does-mldotnet-work#hello-mlnet-world



This example constructs a linear regression model to predict house prices using house size and price data.



#machinelearning

#datascience

#artificialintelligence

#mlnet

#mlnettutorial

Code:

using Microsoft.ML;

using Microsoft.ML.Data;

using System;

namespace Hello_ML.NET

{

    class Program

    {

        public class HouseData

        {

            public float Size { get; set; }

            public float Price { get; set; }

        }

        public class Prediction

        {

            [ColumnName("Score")]

            public float Price { get; set; }

        }

        static void Main(string[] args)

        {

            MLContext mlContext = new MLContext();

// create training data

            HouseData[] houseData = {

               new HouseData() { Size = 1.1F, Price = 1.2F },

               new HouseData() { Size = 1.9F, Price = 2.3F },

               new HouseData() { Size = 2.8F, Price = 3.0F },

               new HouseData() { Size = 3.4F, Price = 3.7F } };

            IDataView trainingData = mlContext.Data.LoadFromEnumerable(houseData);

            // Specify data preparation and model training pipeline

            var pipeline = mlContext.Transforms.Concatenate("Features", new[] { "Size" })

           .Append(mlContext.Regression.Trainers.Sdca(labelColumnName: "Price", maximumNumberOfIterations: 100));

            //Train model

            var model = pipeline.Fit(trainingData);

            // Make a prediction

            var size = new HouseData() { Size = 2.5F };

            var price = mlContext.Model.CreatePredictionEngine<HouseData, Prediction>(model).Predict(size);

            Console.WriteLine($"Predicted price for size: {size.Size * 1000} sq ft= {price.Price * 100:C}k");

        }

    }

}