write code to predict price of aapl in 2 weeks . use ml.net version 2 in csharp

main.cs
using System;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;

class Program
{
    static void Main(string[] args)
    {
        // Define the data schema
        var schema = new DataSchema();

        // Create MLContext
        var context = new MLContext();

        // Load the training data
        var dataView = context.Data.LoadFromTextFile<InputModel>("aapl_train.csv", separatorChar: ',', hasHeader: true);

        // Create the pipeline
        var pipeline = context.Transforms.Conversion.MapValueToKey("Label", "Close")
            .Append(context.Transforms.Concatenate("Features", "High", "Low", "Open", "Volume"))
            .Append(context.Transforms.NormalizeMinMax("Features"))
            .Append(context.Transforms.Conversion.MapKeyToValue("Label"))
            .Append(context.Transforms.Concatenate("Features", "Features"))
            .Append(context.Transforms.DropColumns("High", "Low", "Open", "Volume"))
            .Append(context.Transforms.CopyColumns("Score", "Close"))
            .Append(context.Transforms.SelectColumns("Score"))
            .Append(context.Transforms.Dictionarize("ScoreDict", "Score", Microsoft.ML.Transforms.ValueToKeyMode.OneHotHash))
            .Append(context.Transforms.NormalizeMinMax("Features"))
            .Append(context.Transforms.Concatenate("Features", "Features", "ScoreDict"))
            .Append(context.Transforms.DropColumns("Score", "ScoreDict"))
            .Append(context.Transforms.NormalizeMinMax("Features"))
            .Append(context.Regression.Trainers.Sdca(e => e.Label, e => e.Features, maximumNumberOfIterations: 1000));

        // Train the model
        var model = pipeline.Fit(dataView);

        // Load the test data
        var testDataView = context.Data.LoadFromTextFile<InputModel>("aapl_test.csv", separatorChar: ',', hasHeader: true);

        // Use the model to predict the price of AAPL in 2 weeks
        var predictionEngine = context.Model.CreatePredictionEngine<InputModel, OutputModel>(model);
        var input = new InputModel { High = 326.54f, Low = 323.48f, Open = 326, Volume = 1726552 };
        var output = predictionEngine.Predict(input);

        Console.WriteLine($"Predicted Close Price: {output.Close}");
    }
}

// Define the input data schema
class InputModel
{
    [LoadColumn(0)] public float High;
    [LoadColumn(1)] public float Low;
    [LoadColumn(2)] public float Open;
    [LoadColumn(3)] public float Volume;
    [LoadColumn(4)] public float Close;
}

// Define the output data schema
class OutputModel
{
    [ColumnName("Score")] public float Close;
}

// Define the data schema
class DataSchema
{
    public Column[] Columns { get; } = new Column[]
    {
        new Column("High", DataKind.R4, 0),
        new Column("Low", DataKind.R4, 1),
        new Column("Open", DataKind.R4, 2),
        new Column("Volume", DataKind.R4, 3),
        new Column("Close", DataKind.R4, 4),
        new Column("Label", DataKind.R4, 4),
    };
}

// Define a column
class Column
{
    public Column(string name, DataKind type, int index)
    {
        Name = name;
        Type = type;
        Index = index;
    }

    public string Name { get; }
    public DataKind Type { get; }
    public int Index { get; }
}
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