BuildAndFit.cs

public void BuildAndFit()
{
    var pipeline = _mlContext.Transforms.CopyColumns(inputColumnName: "Count", outputColumnName: "Label")
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Season"))
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Year"))
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Holiday"))
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Weather"))
        .Append(_mlContext.Transforms.Concatenate("Features",
                                        "Season",
                                        "Year",
                                        "Month",
                                        "Hour",
                                        "Weekday",
                                        "Weather",
                                        "Temperature",
                                        "Humidity",
                                        "Windspeed",
                                        "Casual"))
        .Append(_mlContext.Transforms.NormalizeMinMax("Features", "Features"))
        .AppendCacheCheckpoint(_mlContext)
        .Append(_algorythim);

    _trainedModel = pipeline.Fit(_trainingDataView);
    _predictionEngine = _mlContext.Model.CreatePredictionEngine<BikeSharingDemandSample, BikeSharingDemandPrediction>(_trainedModel);
}