public void BuildAndFit(string trainingDataViewLocation)
{
    IDataView trainingDataView = _textLoader.Read(trainingDataViewLocation);

    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("Month"))
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Hour"))
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Holiday"))
        .Append(_mlContext.Transforms.Categorical.OneHotEncoding("Weather"))
        .Append(_mlContext.Transforms.Concatenate("Features",
                                        "Season",
                                        "Year",
                                        "Month",
                                        "Hour",
                                        "Weekday",
                                        "Weather",
                                        "Temperature",
                                        "NormalizedTemperature",
                                        "Humidity",
                                        "Windspeed"))
        .AppendCacheCheckpoint(_mlContext)
        .Append(_algorythim);

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