diff --git a/src/main/scala/DataSource.scala b/src/main/scala/DataSource.scala
index f4dd11c..ceb46f2 100644
--- a/src/main/scala/DataSource.scala
+++ b/src/main/scala/DataSource.scala
@@ -118,6 +118,7 @@
 ) extends Serializable with SanityCheck {
 
   /** Sanity check to make sure your data is being fed in correctly. */
+  override
   def sanityCheck(): Unit = {
     try {
       val obs : Array[Double] = data.takeSample(false, 5).map(_.label)
diff --git a/src/main/scala/Evaluation.scala b/src/main/scala/Evaluation.scala
index 60c0d49..8b3e673 100644
--- a/src/main/scala/Evaluation.scala
+++ b/src/main/scala/Evaluation.scala
@@ -11,6 +11,7 @@
   extends AverageMetric[EmptyEvaluationInfo, Query, PredictedResult, ActualResult] {
 
   /** Method for calculating prediction accuracy. */
+  override
   def calculate(
     query: Query,
     predicted: PredictedResult,
diff --git a/src/main/scala/LRAlgorithm.scala b/src/main/scala/LRAlgorithm.scala
index e296851..7f16cb9 100644
--- a/src/main/scala/LRAlgorithm.scala
+++ b/src/main/scala/LRAlgorithm.scala
@@ -18,6 +18,7 @@
 
   @transient lazy val logger = Logger[this.type]
 
+  override
   def train(sc: SparkContext, pd: PreparedData): LRModel = {
 
     // Import SQLContext for creating DataFrame.
@@ -66,6 +67,7 @@
     )
   }
 
+  override
   def predict(model: LRModel, query: Query): PredictedResult = {
     model.predict(query.text)
   }
diff --git a/src/main/scala/NBAlgorithm.scala b/src/main/scala/NBAlgorithm.scala
index 6d5c164..4915f92 100644
--- a/src/main/scala/NBAlgorithm.scala
+++ b/src/main/scala/NBAlgorithm.scala
@@ -21,6 +21,7 @@
 ) extends P2LAlgorithm[PreparedData, NBModel, Query, PredictedResult] {
 
   /** Train your model. */
+  override
   def train(sc: SparkContext, pd: PreparedData): NBModel = {
     // Fit a Naive Bayes model using the prepared data.
     val nb: NaiveBayesModel = NaiveBayes.train(pd.transformedData, ap.lambda)
@@ -32,6 +33,7 @@
   }
 
   /** Prediction method for trained model. */
+  override
   def predict(model: NBModel, query: Query): PredictedResult = {
     model.predict(query.text)
   }
diff --git a/src/main/scala/Preparator.scala b/src/main/scala/Preparator.scala
index 1f4d51d..98d1129 100644
--- a/src/main/scala/Preparator.scala
+++ b/src/main/scala/Preparator.scala
@@ -4,10 +4,8 @@
 import org.apache.predictionio.controller.Params
 
 import org.apache.spark.SparkContext
-import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.feature.{IDF, IDFModel, HashingTF}
 import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
 
@@ -31,6 +29,7 @@
 class Preparator(pp: PreparatorParams)
   extends PPreparator[TrainingData, PreparedData] {
 
+  override
   def prepare(sc: SparkContext, td: TrainingData): PreparedData = {
 
     val tfHasher = new TFHasher(pp.numFeatures, pp.nGram, td.stopWords)
@@ -106,7 +105,7 @@
   val idf: IDFModel
 ) extends Serializable {
 
-  /** trasform text to tf-idf vector. */
+  /** transform text to tf-idf vector. */
   def transform(text: String): Vector = {
     // Map(n-gram -> document tf)
     idf.transform(hasher.hashTF(text))