Update README

Author: BenAAndrew

README.md

@@ -1,3 +1,39 @@
 # auto-heuristic
 
 [![codecov](https://codecov.io/gh/BenAAndrew/auto-heuristic/branch/main/graph/badge.svg?token=V4U2GG7X9Y)](https://codecov.io/gh/BenAAndrew/auto-heuristic)
+
+## [Live demo](https://heuristic-generator.herokuapp.com/)
+
+![preview](preview.gif)
+
+## What is a heuristic?
+Heuristics are "rules of thumb" designed to find an approximate solution to a complex problem. 
+
+Often we may want to create a set of rules which fit most cases for a dataset. However, it can be time consuming to explore the dataset and decide the best rules by hand.
+
+## What is the purpose of this app?
+This app automatically generates a set of rules to classify a dataset. It then generates Python or JS code which you can quickly implement to add this behaviour. 
+
+For example, the app produces the following Python code to correctly classify the [iris dataset](https://github.com/BenAAndrew/auto-heuristic/blob/main/tests/test_files/iris.csv) 100% of the time:
+
+```
+def predict(petal_width, petal_length):
+    if petal_length <= 2.45:
+        return "setosa"
+    else:
+        if petal_length <= 4.75:
+            if petal_width <= 1.65:
+                return "versicolor"
+            else:
+                return "virginica"
+        else:
+            if petal_width <= 1.75:
+                return "versicolor"
+            else:
+                return "virginica"
+```
+
+## How it works
+Using a [DecisionTreeClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) the app automatically explores a variety of tree depths to identify the best trade off between accuracy and complexity.
+
+It then converts this decision tree into code so that you can quickly implement it into your codebase.