The prediction of taxiTripSample dont work

https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices

Goal:
Predict that you retrieve the value 15.5 based on the instruction

var taxiTripSample = new TaxiTrip()
{
    VendorId = "VTS",
    RateCode = "1",
    PassengerCount = 1,
    TripTime = 1140,
    TripDistance = 3.75f,
    PaymentType = "CRD",
    FareAmount = 0 // To predict. Actual/Observed = 15.5
};


Problem:
When I do the prediction based on the data above, I get value 0 for the FareAmount after the prediction process.

What part am I missing from the source code? 


Info:
*I have used the data from the link
https://github.com/dotnet/machinelearning/blob/master/test/data/taxi-fare-train.csv


Thank you!

//*****************************************************************************************
//*                                                                                       *
//* This is an auto-generated file by Microsoft ML.NET CLI (Command-Line Interface) tool. *
//*                                                                                       *
//*****************************************************************************************

using System;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Machinelearning_test2ML.Model.DataModels;
using machinelearning_test2ML.ConsoleApp;

namespace Machinelearning_test2ML.ConsoleApp
{
    class Program
    {
        //Machine Learning model to load and use for predictions
        private const string MODEL_FILEPATH = @"MLModel.zip";

        //Dataset to use for predictions 
        private const string DATA_FILEPATH = @"C:{   }";

        static void Main(string[] args)
        {
            MLContext mlContext = new MLContext();

            // Training code used by ML.NET CLI and AutoML to generate the model
            //ModelBuilder.CreateModel();

            ITransformer mlModel = mlContext.Model.Load(GetAbsolutePath(MODEL_FILEPATH), out DataViewSchema inputSchema);
            var predEngine = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);

            // Create sample data to do a single prediction with it 
            ModelInput sampleData = CreateSingleDataSample(mlContext, DATA_FILEPATH);

            // Try a single prediction
            ModelOutput predictionResult = predEngine.Predict(sampleData);

            Console.WriteLine($"Single Prediction --> Actual value: {sampleData.Fare_amount} | Predicted value: {predictionResult.Score}");


            //------------

            IDataView dataView = mlContext.Data.LoadFromTextFile<TaxiTrip>(DATA_FILEPATH, hasHeader: true, separatorChar: ',');
            var pipeline = mlContext.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: "FareAmount");



            var model = pipeline.Fit(dataView);


            var predictionFunction = mlContext.Model.CreatePredictionEngine<TaxiTrip, TaxiTripFarePrediction>(model);


            var taxiTripSample = new TaxiTrip()
            {
                VendorId = "VTS",
                RateCode = "1",
                PassengerCount = 1,
                TripTime = 1140,
                TripDistance = 3.75f,
                PaymentType = "CRD",
                FareAmount = 0 // To predict. Actual/Observed = 15.5
            };

            var prediction = predictionFunction.Predict(taxiTripSample);


            //------------------

            Console.WriteLine("=============== End of process, hit any key to finish ===============");
            Console.ReadKey();
        }

        // Method to load single row of data to try a single prediction
        // You can change this code and create your own sample data here (Hardcoded or from any source)
        private static ModelInput CreateSingleDataSample(MLContext mlContext, string dataFilePath)
        {
            // Read dataset to get a single row for trying a prediction          
            IDataView dataView = mlContext.Data.LoadFromTextFile<ModelInput>(
                                            path: dataFilePath,
                                            hasHeader: true,
                                            separatorChar: '\t',
                                            allowQuoting: true,
                                            allowSparse: false);

            // Here (ModelInput object) you could provide new test data, hardcoded or from the end-user application, instead of the row from the file.
            ModelInput sampleForPrediction = mlContext.Data.CreateEnumerable<ModelInput>(dataView, false)
                                                                        .First();
            return sampleForPrediction;
        }

        public static string GetAbsolutePath(string relativePath)
        {
            FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
            string assemblyFolderPath = _dataRoot.Directory.FullName;

            string fullPath = Path.Combine(assemblyFolderPath, relativePath);

            return fullPath;
        }
    }
}

Commencez dans le code Program.Main affiché et comparez le tutoriel sur les tarifs de taxi avec test/data/taxi-fare-train.csv, en vous concentrant sur le pipeline utilisé avant la prédiction. Reproduisez la prédiction de taxiTripSample et vérifiez que le sample terminé produit le FareAmount attendu de 15.5 plutôt que 0.