diff --git a/testML/DictionaryToObjectConverter.cs b/testML/DictionaryToObjectConverter.cs
index 2c337cc..41e2ab6 100644
--- a/testML/DictionaryToObjectConverter.cs
+++ b/testML/DictionaryToObjectConverter.cs
@@ -16,7 +16,7 @@ namespace testML
 {
 	public static class DictionaryToObjectConverter
 	{
-		public static IEnumerable<object> Convert(List<Dictionary<string, object>> data, out Type classType, out DataViewSchema schema)
+		public static IEnumerable<object> Convert(List<Dictionary<string, object>> data, string toPredict, out Type classType, out DataViewSchema schema)
 		{
 			var schemaBuilder = new DataViewSchema.Builder();			
 
@@ -32,6 +32,12 @@ namespace testML
 					if (sampleValue != null)
 					{
 						var keyType = sampleValue.GetType();
+
+						if (key == toPredict)
+						{
+							keyType = typeof(float);
+						}
+
 						definition.Add(key, keyType);
 
 						if (keyType == typeof(string))
@@ -61,6 +67,7 @@ namespace testML
 			var converter = new DictionaryToObjectConverterClass()
 			{
 				ClassName = "OBJ" + Guid.NewGuid().ToString("N").ToUpper(),
+				ToPredict = toPredict,
 				Definition = definition
 			};
 
@@ -99,15 +106,37 @@ namespace testML
 			Type genericType = listType.MakeGenericType(classType);
 
 			var result =(IList) Activator.CreateInstance(genericType) ;
-			
+
+			Dictionary<string, float> translate = new Dictionary<string, float>();
+			translate.Add(string.Empty, 0);
 			foreach (var inputData in data)
+			{
+				if (inputData.ContainsKey(toPredict) && inputData[toPredict] != null)
+				{
+					if (!translate.ContainsKey(inputData[toPredict] as string))
+					{
+						var max = translate.Values.Max()+1;
+						translate.Add(inputData[toPredict] as string, max);
+					}
+				}
+			}
+
+
+				foreach (var inputData in data)
 			{
 				var outputData = (IDictionaryToObjectConverter)Activator.CreateInstance(classType);
 				result.Add(outputData);
 
 				foreach (var key in inputData.Keys)
 				{
-					outputData[key] = inputData[key];
+					if (key == toPredict)
+					{
+						outputData[key] = translate[inputData[key] as string ?? string.Empty];
+					}
+					else
+					{
+						outputData[key] = inputData[key];
+					}
 				}
 			}
 
diff --git a/testML/Program.cs b/testML/Program.cs
index dc03b5e..25ceb6a 100644
--- a/testML/Program.cs
+++ b/testML/Program.cs
@@ -40,7 +40,7 @@ namespace testML
 
 			for (var r = 1; r < sheet.LastRowNum - 1; r++)
 			{
-				if (r == 30) break;
+				if (r == 300) break;
 				Console.WriteLine(string.Format("{0} / {1}", r, sheet.LastRowNum - 1));
 				var row = sheet.GetRow(r);
 
@@ -108,7 +108,7 @@ namespace testML
 
 			MLContext mlContext = new MLContext();
 
-			var dataConverted = DictionaryToObjectConverter.Convert(tmpData, out Type classType, out DataViewSchema schema);
+			var dataConverted = DictionaryToObjectConverter.Convert(tmpData, "DESCENDIENTE_S4i001", out Type classType, out DataViewSchema schema);
 
 			var loadMethod = mlContext.Data.GetType().GetMethods().Where(x => x.Name == "LoadFromEnumerable" && x.IsGenericMethodDefinition).FirstOrDefault();
 
@@ -174,24 +174,24 @@ namespace testML
 			experiment
 				.SetPipeline(pipeline)
 				.SetRegressionMetric(RegressionMetric.RSquared, labelColumn: columnInference.LabelColumnName)
-				.SetTrainingTimeInSeconds(60)
+				.SetTrainingTimeInSeconds(10)
 				.SetDataset(trainData);
 
 			var result = experiment.Run();
 
 			#endregion
 
-			/*
+			
 			//Entrenamos el modelo
-			ITransformer model = pipe.Fit(trainData);
+			//ITransformer model = pipe.Fit(trainData);
 
 			#region Hacemos un test para medir el % de error
 
 			// Use trained model to make inferences on test data
-			IDataView testDataPredictions = model.Transform(testData);
+			IDataView testDataPredictions = result.Model.Transform(testData);
 
 			// Extract model metrics and get RSquared
-			RegressionMetrics trainedModelMetrics = mlContext.Regression.Evaluate(testDataPredictions);
+			RegressionMetrics trainedModelMetrics = mlContext.Regression.Evaluate(testDataPredictions, labelColumnName: columnInference.LabelColumnName);
 			double rSquared = trainedModelMetrics.RSquared;
 
 			Console.WriteLine("ModelMetrics: {0}", rSquared);
@@ -201,7 +201,7 @@ namespace testML
 
 			#region Ponemos a prueba haciendo algunas predicciones
 
-			var predictionFunction = mlContext.Model.CreatePredictionEngine<Data, DataPrediction>(model);
+			var predictionFunction = mlContext.Model.CreatePredictionEngine<Data, DataPrediction>(result.Model);
 
 			for (var c = 0; c < 25; c++)
 			{
@@ -215,7 +215,7 @@ namespace testML
 			}
 
 			#endregion
-			*/
+			
 			Console.WriteLine();
 			Console.WriteLine("Press enter to Exit");
 			Console.ReadLine();