upd README.md, refactor

demo/classifier_demo.py

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+import neighbours as ns
+
+import numpy as np
+import pandas as pd
+
+df = pd.read_csv("IRIS.csv")
+df["species"] = pd.factorize(df["species"])[0]
+
+iris = np.array(df)
+np.random.shuffle(iris)
+
+test = iris[:15]
+train = iris[15:]
+
+class_labels = train[:, 4].astype(int)
+train = np.delete(train, (4,), axis=1)
+
+classifier = ns.KNNClassifier(4, 3, 10, 7)
+classifier.load(train, class_labels)
+
+correct_predictions = 0
+
+for sample in test:
+    predicted_class = classifier.predict(sample[:4], ns.distance.manhattan, ns.kernel.epanechnikov, 3)
+    correct_predictions += predicted_class == sample[4]
+
+print("accuracy: ", correct_predictions / len(test))