diff --git a/sklearn_porter/estimator/classifier/KNeighborsClassifier/templates/js/exported.class.txt b/sklearn_porter/estimator/classifier/KNeighborsClassifier/templates/js/exported.class.txt
new file mode 100644
index 00000000..0bbc1240
--- /dev/null
+++ b/sklearn_porter/estimator/classifier/KNeighborsClassifier/templates/js/exported.class.txt
@@ -0,0 +1,115 @@
+if (typeof XMLHttpRequest === 'undefined') {{
+    var XMLHttpRequest = require("xmlhttprequest").XMLHttpRequest;
+}}
+
+var {class_name} = function(jsonFile) {{
+    this.data = undefined;
+
+    var Neighbor = function(y, dist) {{
+        this.y = y;
+        this.dist = dist;
+    }};
+
+    var promise = new Promise(function(resolve, reject) {{
+        var httpRequest = new XMLHttpRequest();
+        httpRequest.onreadystatechange = function() {{
+            if (httpRequest.readyState === 4) {{
+                if (httpRequest.status === 200) {{
+                    resolve(JSON.parse(httpRequest.responseText));
+                }} else {{
+                    reject(new Error(httpRequest.statusText));
+                }}
+            }}
+        }};
+        httpRequest.open('GET', jsonFile, true);
+        httpRequest.send();
+    }});
+
+    var compute = function(temp, cand, q) {{
+        var dist = 0.,
+            diff;
+        for (var i = 0, l = temp.length; i < l; i++) {{
+    	    diff = Math.abs(temp[i] - cand[i]);
+    	    if (q==1) {{
+    	        dist += diff;
+    	    }} else if (q==2) {{
+    	        dist += diff*diff;
+    	    }} else if (q==Number.POSITIVE_INFINITY) {{
+    	        if (diff > dist) {{
+    	            dist = diff;
+    	        }}
+    	    }} else {{
+    	        dist += Math.pow(diff, q);
+            }}
+        }}
+        if (q==1 || q==Number.POSITIVE_INFINITY) {{
+            return dist;
+        }} else if (q==2) {{
+            return Math.sqrt(dist);
+        }} else {{
+            return Math.pow(dist, 1. / q);
+        }}
+    }};
+
+    this.{method_name} = function(features) {{
+        return new Promise(function(resolve, reject) {{
+            promise.then(function(data) {{
+                if (typeof this.data === 'undefined') {{
+                    this.data = data;
+                    this.nTemplates = this.data.X.length;
+                }}
+                var classIdx = 0, i, dist;
+                if (this.data.nNeighbors == 1) {{
+                    var minDist = Number.POSITIVE_INFINITY;
+                    for (i = 0; i < this.data.nTemplates; i++) {{
+                        dist = compute(this.data.X[i], features, this.data.power);
+                        if (dist <= minDist) {{
+                            minDist = dist;
+                            classIdx = this.data.y[i];
+                        }}
+                    }}
+                }} else {{
+                    var classes = new Array(this.data.nClasses).fill(0);
+                    var dists = [];
+                    for (i = 0; i < this.nTemplates; i++) {{
+                        dist = compute(this.data.X[i], features, this.data.power);
+                        dists.push(new Neighbor(this.data.y[i], dist));
+                    }}
+                    dists.sort(function compare(n1, n2) {{
+                        return (n1.dist < n2.dist) ? -1 : 1;
+                    }});
+                    for (i = 0; i < this.data.kNeighbors; i++) {{
+                        classes[dists[i].y]++;
+                    }}
+                    for (i = 0; i < this.data.nClasses; i++) {{
+                        classIdx = classes[i] > classes[classIdx] ? i : classIdx;
+                    }}
+                }}
+                resolve(classIdx);
+            }}, function(error) {{
+                reject(error);
+            }});
+        }});
+    }};
+
+}};
+
+if (typeof process !== 'undefined' && typeof process.argv !== 'undefined') {{
+    if (process.argv[2].trim().endsWith('.json')) {{
+
+        // Parameters:
+        var json = process.argv[2];
+
+        // Estimator:
+        var clf = new {class_name}(json);
+
+        // Features:
+        var features = process.argv.slice(3);
+
+        // Prediction:
+        clf.{method_name}(features).then(function(prediction) {{
+            console.log(prediction);
+        }});
+
+    }}
+}}
\ No newline at end of file
diff --git a/tests/estimator/classifier/KNeighborsClassifier/KNeighborsClassifierJSTest.py b/tests/estimator/classifier/KNeighborsClassifier/KNeighborsClassifierJSTest.py
index eb583f8b..353e665f 100644
--- a/tests/estimator/classifier/KNeighborsClassifier/KNeighborsClassifierJSTest.py
+++ b/tests/estimator/classifier/KNeighborsClassifier/KNeighborsClassifierJSTest.py
@@ -2,6 +2,7 @@
 
 from unittest import TestCase
 
+import numpy as np
 from sklearn.neighbors import KNeighborsClassifier
 
 from tests.estimator.classifier.Classifier import Classifier
@@ -16,3 +17,81 @@ def setUp(self):
 
     def tearDown(self):
         super(KNeighborsClassifierJSTest, self).tearDown()
+
+    def test_random_features_w_binary_data_export(self):
+        self.load_binary_data()
+        self._port_estimator(export_data=True)
+        amin = np.amin(self.X, axis=0)
+        amax = np.amax(self.X, axis=0)
+        preds, ground_truth = [], []
+        for _ in range(self.N_RANDOM_FEATURE_SETS):
+            x = np.random.uniform(amin, amax, self.n_features)
+            preds.append(self.pred_in_custom(x, export_data=True))
+            ground_truth.append(self.pred_in_py(x))
+        self._clear_estimator()
+        # noinspection PyUnresolvedReferences
+        self.assertListEqual(preds, ground_truth)
+
+    def test_random_features_w_iris_data_export(self):
+        self.load_iris_data()
+        self._port_estimator(export_data=True)
+        amin = np.amin(self.X, axis=0)
+        amax = np.amax(self.X, axis=0)
+        preds, ground_truth = [], []
+        for _ in range(self.N_RANDOM_FEATURE_SETS):
+            x = np.random.uniform(amin, amax, self.n_features)
+            preds.append(self.pred_in_custom(x, export_data=True))
+            ground_truth.append(self.pred_in_py(x))
+        self._clear_estimator()
+        # noinspection PyUnresolvedReferences
+        self.assertListEqual(preds, ground_truth)
+
+    def test_random_features_w_digits_data_export(self):
+        self.load_digits_data()
+        self._port_estimator(export_data=True)
+        amin = np.amin(self.X, axis=0)
+        amax = np.amax(self.X, axis=0)
+        preds, ground_truth = [], []
+        for _ in range(self.N_RANDOM_FEATURE_SETS):
+            x = np.random.uniform(amin, amax, self.n_features)
+            preds.append(self.pred_in_custom(x, export_data=True))
+            ground_truth.append(self.pred_in_py(x))
+        self._clear_estimator()
+        # noinspection PyUnresolvedReferences
+        self.assertListEqual(preds, ground_truth)
+
+    def test_existing_features_w_binary_data_export(self):
+        self.load_binary_data()
+        self._port_estimator(export_data=True)
+        preds, ground_truth = [], []
+        n = min(self.N_EXISTING_FEATURE_SETS, len(self.X))
+        for x in self.X[:n]:
+            preds.append(self.pred_in_custom(x, export_data=True))
+            ground_truth.append(self.pred_in_py(x))
+        self._clear_estimator()
+        # noinspection PyUnresolvedReferences
+        self.assertListEqual(preds, ground_truth)
+
+    def test_existing_features_w_iris_data_export(self):
+        self.load_iris_data()
+        self._port_estimator(export_data=True)
+        preds, ground_truth = [], []
+        n = min(self.N_EXISTING_FEATURE_SETS, len(self.X))
+        for x in self.X[:n]:
+            preds.append(self.pred_in_custom(x, export_data=True))
+            ground_truth.append(self.pred_in_py(x))
+        self._clear_estimator()
+        # noinspection PyUnresolvedReferences
+        self.assertListEqual(preds, ground_truth)
+
+    def test_existing_features_w_digits_data_export(self):
+        self.load_digits_data()
+        self._port_estimator(export_data=True)
+        preds, ground_truth = [], []
+        n = min(self.N_EXISTING_FEATURE_SETS, len(self.X))
+        for x in self.X[:n]:
+            preds.append(self.pred_in_custom(x, export_data=True))
+            ground_truth.append(self.pred_in_py(x))
+        self._clear_estimator()
+        # noinspection PyUnresolvedReferences
+        self.assertListEqual(preds, ground_truth)
\ No newline at end of file