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diff --git a/‎machine-learning/fruit-classify.py
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diff --git a/‎machine-learning/iris.py
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diff --git a/‎machine-learning/k-nearest-neighbors.py
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diff --git a/‎machine-learning/neural-net.py
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@@ -36,6 +36,7 @@ def classify(to_classify):
 
 def main():
     to_classify = [
+        [160, TEXTURE_BUMPY],
         [145, TEXTURE_BUMPY],
         [120, TEXTURE_SMOOTH],
         [100, TEXTURE_BUMPY],
 
@@ -0,0 +1,34 @@
+import numpy as np
+from sklearn import tree
+from sklearn.datasets import load_iris
+
+
+def main():
+    iris = load_iris()
+
+    print(iris.feature_names)
+    print(iris.target_names)
+
+    # print(iris.data[0])
+    # print(iris.target[0])
+
+    test_idx = [0, 50, 100]
+
+    train_target = np.delete(iris.target, test_idx)
+    train_data = np.delete(iris.data, test_idx, axis=0)
+
+    test_target = iris.target[test_idx]
+    test_data = iris.data[test_idx]
+
+    # print(test_target)
+    # print(test_data)
+
+    clf = tree.DecisionTreeClassifier()
+    clf.fit(train_data, train_target)
+
+    print('actual:', test_target)
+    print('predicted:', clf.predict(test_data))
+
+
+if __name__ == '__main__':
+    main()
@@ -0,0 +1,59 @@
+import numpy as np
+# from scipy.spatial import distance
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn import metrics
+
+
+class KNNClassifier(object):
+    def __init__(self):
+        self.X_train = None
+        self.y_train = None
+
+    def euc_distance(self, a, b):
+        return np.linalg.norm(a-b)
+        # return distance.euclidean(a, b)
+
+    def closest(self, row):
+        """
+        Returns the label corresponding to the single closest training example.
+        This is a k=1 nearest neighbor(s) implementation.
+        :param row:
+        :return:
+        """
+        dist = [self.euc_distance(row, trainer) for trainer in self.X_train]
+        best_index = dist.index(min(dist))
+
+        return self.y_train[best_index]
+
+    def fit(self, training_data, training_labels):
+        self.X_train = training_data
+        self.y_train = training_labels
+
+    def predict(self, to_classify):
+        predictions = []
+        for row in to_classify:
+            label = self.closest(row)
+            predictions.append(label)
+
+        return predictions
+
+iris = datasets.load_iris()
+
+X = iris.data
+y = iris.target
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
+
+classifier = KNeighborsClassifier()  # k=5 by default
+# classifier = KNNClassifier()
+classifier.fit(X_train, y_train)
+
+# print(y_train)
+
+results = classifier.predict(X_test)
+
+score = metrics.accuracy_score(y_test, results)
+
+print("Accuracy: {0:f}".format(score))
@@ -0,0 +1,28 @@
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+import tensorflow as tf
+from tensorflow.contrib import learn
+import logging
+
+
+def main():
+    logging.getLogger().setLevel(logging.INFO)
+
+    iris = learn.datasets.load_iris()
+
+    X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
+
+    feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
+
+    # Build a neural network with 3 hidden layers: 10, 20, 10 units respectively
+    classifier = learn.DNNClassifier(hidden_units=[10, 20, 10], n_classes=3, feature_columns=feature_columns)
+
+    classifier.fit(X_train, y_train, steps=2000)
+
+    score = metrics.accuracy_score(y_test, classifier.predict(X_test))
+
+    print("Accuracy: {0:f}".format(score))
+
+
+if __name__ == '__main__':
+    main()