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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);
}
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