Embracing the Future

As I reflect on this journey, one thing is abundantly clear—ML .NET is a game-changer for developers seeking to harness the power of AI and machine learning. Whether you’re a seasoned developer looking to expand your skill set or a newcomer intrigued by the possibilities of ML, ML .NET provides a gateway to a world of innovation and discovery.

The predictive model developed using ML .NET falls under the category of time series analysis, a crucial aspect of predictive analytics. Time series analysis involves analyzing sequential data to identify patterns and trends over time, making it invaluable for forecasting future outcomes based on historical data. 

The algorithm utilized in ML .NET, Stochastic Dual Coordinate Ascent (SDCA), plays a vital role in training the predictive model. SDCA is known for its efficiency and scalability, making it well-suited for time series forecasting tasks, such as predicting salaries based on years of experience in this scenario. By leveraging ML .NET and SDCA, developers can build accurate predictive models that drive data-driven decision-making and enable businesses to optimize their strategies effectively.

using System;
using Microsoft.ML;
using Microsoft.ML.Data;

public class SalaryData
{
    [LoadColumn(0)] public float YearsExperience; 
    [LoadColumn(1)] public float Salary;
}

public class SalaryPrediction
{
    [ColumnName("Score")]
    public float PredictedSalary;
}

class Program
{
    static void Main()
    {
        var mlContext = new MLContext();
        var data = mlContext.Data.LoadFromTextFile<SalaryData>("path/to/data.csv", separatorChar: ', ');
        
        var trainTestSplit = mlContext.Data.TrainTestSplit(data);
        var pipeline = mlContext.Transforms.CopyColumns("Label", "Salary")
                        .Append(mlContext.Transforms.Concatenate("Features", "YearsExperience"))
                        .Append(mlContext.Regression.Trainers.Sdca(labelColumnName: "Label"))
                        .Append(mlContext.Transforms.CopyColumns("Predicted Salary", "Score"));
        
        var model = pipeline.Fit(trainTestSplit.TrainSet);
        var predictions = model.Transform(trainTestSplit.TestSet);
        var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: "Label", scoreColumnName: "Score");

        Console.WriteLine($"R^2: {metrics.RSquared}");
        Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError}");
    }
}