--- a
+++ b/classifier.py
@@ -0,0 +1,31 @@
+from read_data import *
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from sklearn.mixture import GaussianMixture
+
+if __name__ == '__main__':
+    data = execute()
+    print(data.shape)
+    X = data[:, :16]  # 16 features
+    y = data[:, 16]
+    print(X.shape)
+    print(y.shape)
+    print(y)
+    train_features, test_features, train_labels, test_labels = train_test_split(X, y,
+                                                                                test_size=0.25)
+    print('Training Features Shape:', train_features.shape)
+    print('Training Labels Shape:', train_labels.shape)
+    print('Testing Features Shape:', test_features.shape)
+    print('Testing Labels Shape:', test_labels.shape)
+
+    clf = RandomForestClassifier(n_estimators=100, max_depth=5, oob_score=True)
+    clf.fit(X, y)
+    print(clf.feature_importances_)
+    # print(clf.oob_decision_function_)
+    print(clf.oob_score_)
+    predictions = clf.predict(test_features)
+    errors = abs(predictions - test_labels)
+    print("M A E: ", np.mean(errors))
+    print(np.count_nonzero(errors), len(test_labels))
+    print("Accuracy:", np.count_nonzero(errors)/len(test_labels))