diff --git a/script.spark b/script.spark
index a34c81c..c9de25f 100755
--- a/script.spark
+++ b/script.spark
@@ -1,4 +1,3 @@
-
 import com.amazon.deequ.{VerificationSuite, VerificationResult}
 import com.amazon.deequ.VerificationResult.checkResultsAsDataFrame
 import com.amazon.deequ.checks.{Check, CheckLevel}
@@ -8,21 +7,12 @@ import com.amazon.deequ.schema
 import com.amazon.deequ.schema.{RowLevelSchema,RowLevelSchemaValidator}
 
 var demo = """
-
-
 BasicEvaluation
-StatisticalEvaluation
-
 ReportViolations
-
-
 var suggestions = SuggestConstraints()
-suggestions.select("_1", "_2").show(100, 50)
-
-
+suggestions.select("_1", "_2", "_3").show(100, 100)
 showEvaluation
 EvaluateDataQuality
-
 """
 
 val args = sc.getConf.get("spark.driver.args").split("\\s+")
@@ -76,13 +66,13 @@ def StatisticalEvaluation(): Unit = {
 
 def SuggestConstraints(): org.apache.spark.sql.Dataset[(String, String, String)] = {
 	val suggestionResult = { ConstraintSuggestionRunner().onData(dataset).addConstraintRules(Rules.DEFAULT).run()}
-	suggestionResult.constraintSuggestions.flatMap { 
-	  case (column, suggestions) => 
+	suggestionResult.constraintSuggestions.flatMap {
+	  case (column, suggestions) =>
 	    suggestions.map { constraint =>
 	      (column, constraint.description, constraint.codeForConstraint)
-	    } 
+	    }
 	}.toSeq.toDS()
-		
+
 }
 var suggestions = SuggestConstraints()
 // suggestions.coalesce(1).write.mode("overwrite").format("com.databricks.spark.csv").option("header", "true").save(output_bucket)
@@ -90,37 +80,31 @@ var suggestions = SuggestConstraints()
 
 def showEvaluation = """
 def EvaluateDataQuality(): Unit = {
-
-	
 	val verificationResult: VerificationResult = { VerificationSuite().onData(dataset).addCheck(
 		Check(CheckLevel.Error, "Review Check")
-		
-		.isUnique("id")
 
+		.isUnique("id")
 		.isComplete("purpose_cat")
 		.isComplete("annual_inc")
 		.isComplete("total_pymnt")
 		.isComplete("grade_cat")
-
-		.isComplete("interest_rate") 
+		.isComplete("interest_rate")
 		.isNonNegative("interest_rate")
-
 		.hasMax("loan_amount", _ == 5000)
-		
-		.isContainedIn("term", Array(" 36 months", " 60 x	months"))
-		.isContainedIn("loan_condition", Array("Good Loan"), _ >= 0.95, Some("It should be above 0.95!"))                                 
+		.isContainedIn("term", Array(" 36 months", " 60 months"))
+		.isContainedIn("loan_condition", Array("Good Loan"), _ >= 0.95, Some("It should be above 0.95!"))
 
-		).run() 
+		).run()
 	}
 """
 
 
 def EvaluateDataQuality(): Unit = {
 
-	
+
 	val verificationResult: VerificationResult = { VerificationSuite().onData(dataset).addCheck(
 		Check(CheckLevel.Error, "Review Check")
-		
+
 		.isUnique("id")
 
 		.isComplete("purpose_cat")
@@ -128,23 +112,21 @@ def EvaluateDataQuality(): Unit = {
 		.isComplete("total_pymnt")
 		.isComplete("grade_cat")
 
-		.isComplete("interest_rate") 
+		.isComplete("interest_rate")
 		.isNonNegative("interest_rate")
 
 		.hasMax("loan_amount", _ == 5000)
-		
+
 		.isContainedIn("term", Array(" 36 months", " 60 months"))
-		.isContainedIn("loan_condition", Array("Good Loan"), _ >= 0.95, Some("It should be above 0.95!"))                                 
+		.isContainedIn("loan_condition", Array("Good Loan"), _ >= 0.95, Some("It should be above 0.95!"))
 
-		).run() 
+		).run()
 	}
 
 	val resultDataFrame = checkResultsAsDataFrame(spark, verificationResult)
 
 	// resultDataFrame.show(20, 100)
-	resultDataFrame.select("constraint", "constraint_status", "constraint_message").show()
-	resultDataFrame.select("constraint_message").show(100,100)
-
+	resultDataFrame.select("constraint", "constraint_status", "constraint_message").show(100,100)
 
 	sc.hadoopConfiguration.set("mapreduce.fileoutputcommitter.marksuccessfuljobs", "false")
 	sc.hadoopConfiguration.set("parquet.enable.summary-metadata", "false")
@@ -163,5 +145,3 @@ def ReportViolations(): Unit = {
 	println("Rows: ")
 	result.invalidRows.show(truncate=true)
 }
-
-