diff --git a/scripts/builtin/smote.dml b/scripts/builtin/smote.dml
new file mode 100644
index 0000000..14947ea
--- /dev/null
+++ b/scripts/builtin/smote.dml
@@ -0,0 +1,99 @@
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+
+# Builtin function for handing class imbalance using Synthetic Minority Over-sampling Technique (SMOTE)
+#
+# INPUT PARAMETERS:
+# ---------------------------------------------------------------------------------------------
+# NAME            TYPE    DEFAULT     MEANING
+# ---------------------------------------------------------------------------------------------
+# X               Double   ---       Matrix of minority class samples 
+# s               Integer   25       Amount of SMOTE (percentage of oversampling), integral multiple of 100
+# k               Integer   1        Number of nearest neighbour
+# ---------------------------------------------------------------------------------------------
+
+
+#Output(s)
+# ---------------------------------------------------------------------------------------------
+# NAME            TYPE    DEFAULT     MEANING
+# ---------------------------------------------------------------------------------------------
+# Y               Double   ---       Matrix of (N/100)-1 * nrow(X) synthetic minority class samples 
+
+m_smote = function(Matrix[Double] X, Integer s = 200, Integer k = 1, Boolean verbose = FALSE) 
+return (Matrix[Double] Y) {
+
+  if(s < 100 | (s%%100) != 0)
+  {
+    print("the number of samples should be an integral multiple of 100. Setting s = 100")
+    s = 100
+  }
+  # matrix to keep the index of KNN for each minority sample
+  knn_index = matrix(0,k,0)
+  # find nearest neighbour
+  for(i in 1:nrow(X))
+  {
+    knn = nn(X, X[i, ], k)
+    knn_index = cbind(knn_index, knn)
+  }
+  
+  # number of synthetic samples from each minority class sample
+  iter = (s/100)-1
+  # matrix to store synthetic samples
+  synthetic_samples = matrix(0, 0, ncol(X))
+  while(iter > 0)
+  {
+    # generate a random number
+    # TODO avoid duplicate random numbers
+    rand_index = as.integer(as.scalar(Rand(rows=1, cols=1, min=1, max=k)))
+    # pick the random NN
+    knn_sample = knn_index[rand_index,] 
+    # generate sample    
+    for(i in 1:ncol(knn_index))
+    {
+      index = as.scalar(knn_sample[1,i])
+      X_diff = X[index,] - X[i, ]
+      gap = as.scalar(Rand(rows=1, cols=1, min=0, max=1))
+      X_sys = X[i, ] + (gap*X_diff)
+      synthetic_samples = rbind(synthetic_samples, X_sys)
+    }
+    iter = iter - 1
+  }
+
+  Y = synthetic_samples
+}
+  
+
+
+nn = function(Matrix[Double] X, Matrix[Double] instance, Integer k )
+return (Matrix[Double] knn_)
+{
+  if(nrow(X) < k)
+    stop("can not pick "+k+" nearest neighbours from "+nrow(X)+" total instances")
+
+  # compute the euclidean distance
+  diff = X - instance
+  square_diff = diff^2
+  distance = sqrt(rowSums(square_diff))
+  sort_dist = order(target = distance, by = 1, decreasing= FALSE, index.return =  TRUE)
+  knn_ = sort_dist[2:k+1,]
+}
+
diff --git a/src/main/java/org/apache/sysds/common/Builtins.java b/src/main/java/org/apache/sysds/common/Builtins.java
index 20fad72..28acc5b 100644
--- a/src/main/java/org/apache/sysds/common/Builtins.java
+++ b/src/main/java/org/apache/sysds/common/Builtins.java
@@ -177,6 +177,7 @@
 	SINH("sinh", false),
 	STEPLM("steplm",true, ReturnType.MULTI_RETURN),
 	SLICEFINDER("slicefinder", true),
+	SMOTE("smote", true),
 	SOLVE("solve", false),
 	SQRT("sqrt", false),
 	SUM("sum", false),
diff --git a/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinSmoteTest.java b/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinSmoteTest.java
new file mode 100644
index 0000000..675e741
--- /dev/null
+++ b/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinSmoteTest.java
@@ -0,0 +1,103 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.sysds.test.functions.builtin;
+
+import org.apache.sysds.api.DMLScript;
+import org.apache.sysds.common.Types;
+import org.apache.sysds.hops.OptimizerUtils;
+import org.apache.sysds.lops.LopProperties;
+import org.apache.sysds.runtime.matrix.data.MatrixValue;
+import org.apache.sysds.test.AutomatedTestBase;
+import org.apache.sysds.test.TestConfiguration;
+import org.apache.sysds.test.TestUtils;
+import org.junit.Assert;
+import org.junit.Test;
+
+import java.util.HashMap;
+
+public class BuiltinSmoteTest extends AutomatedTestBase {
+
+	private final static String TEST_NAME = "smote";
+	private final static String TEST_DIR = "functions/builtin/";
+	private static final String TEST_CLASS_DIR = TEST_DIR + BuiltinSmoteTest.class.getSimpleName() + "/";
+
+	private final static int rows = 20;
+	private final static int colsX = 20;
+
+	@Override
+	public void setUp() {
+		TestUtils.clearAssertionInformation();
+		addTestConfiguration(TEST_NAME, new TestConfiguration(TEST_CLASS_DIR, TEST_NAME, new String[] {"C"}));
+	}
+
+	@Test
+	public void testSmote1CP() {
+		runSmoteTest(300, 3, LopProperties.ExecType.CP);
+	}
+
+	@Test
+	public void testSmote2CP() {
+		runSmoteTest(400, 5, LopProperties.ExecType.CP);
+	}
+
+	@Test
+	public void testSmote1Spark() {
+		runSmoteTest(300, 3, LopProperties.ExecType.SPARK);
+	}
+
+	@Test
+	public void testSmote2Spark() { runSmoteTest(400, 5, LopProperties.ExecType.SPARK);	}
+
+
+	private void runSmoteTest(int sample, int nn, LopProperties.ExecType instType) {
+		Types.ExecMode platformOld = setExecMode(instType);
+
+		boolean oldFlag = OptimizerUtils.ALLOW_ALGEBRAIC_SIMPLIFICATION;
+		boolean sparkConfigOld = DMLScript.USE_LOCAL_SPARK_CONFIG;
+		OptimizerUtils.ALLOW_ALGEBRAIC_SIMPLIFICATION = false;
+		try {
+			loadTestConfiguration(getTestConfiguration(TEST_NAME));
+			String HOME = SCRIPT_DIR + TEST_DIR;
+			fullDMLScriptName = HOME + TEST_NAME + ".dml";
+			programArgs = new String[] {"-nvargs", "X=" + input("X"), "S=" + sample, "K=" + nn , "Z="+output("Sum"), "T="+input("T")};
+
+			double[][] X = getRandomMatrix(rows, colsX, 0, 1, 0.3, 1);
+
+			writeInputMatrixWithMTD("X", X, true);
+
+			double[][] T = getRandomMatrix(rows, colsX, 2, 3.0, 0.3, 3);
+
+			writeInputMatrixWithMTD("T", T, true);
+
+			runTest(true, false, null, -1);
+			HashMap<MatrixValue.CellIndex, Double> value = readDMLMatrixFromHDFS("Sum");
+			Assert.assertEquals("synthetic samples does not fall into minority class cluster",1,
+				value.get(new MatrixValue.CellIndex(1,1)), 0.000001);
+		}
+		finally {
+			rtplatform = platformOld;
+			DMLScript.USE_LOCAL_SPARK_CONFIG = sparkConfigOld;
+			OptimizerUtils.ALLOW_ALGEBRAIC_SIMPLIFICATION = oldFlag;
+			OptimizerUtils.ALLOW_AUTO_VECTORIZATION = true;
+			OptimizerUtils.ALLOW_OPERATOR_FUSION = true;
+		}
+	}
+}
+
diff --git a/src/test/scripts/functions/builtin/smote.dml b/src/test/scripts/functions/builtin/smote.dml
new file mode 100644
index 0000000..dc33f18
--- /dev/null
+++ b/src/test/scripts/functions/builtin/smote.dml
@@ -0,0 +1,40 @@
+#-------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# 
+#   http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#-------------------------------------------------------------
+
+
+A = read($X);
+B = smote(X = A, s=$S, k=$K);
+
+# test if all point fall in same cluster (closed to each other)
+# read some new data T != A 
+T = read($T);
+# bind all instanced of minority class
+A_B = rbind(A, B)
+n = nrow(A_B)
+# group data into k=2 clusters
+[C, Y] = kmeans(rbind(A_B, T),  2, 10, 100, 0.000001, FALSE, 50)
+# check if the instances of A and B fall in same cluster
+check = matrix(as.scalar(Y[1,1]), n, 1)
+testSum = sum(check - Y[1:n,])
+# hack for avoiding null pointer exception while reading a single zero in HashMap
+testSum = ifelse(testSum == 0, 1, testSum)
+testSum = as.matrix(testSum)
+write(testSum, $Z);  
\ No newline at end of file