commit 3295a743ef693030d2e72a1981a344ed23f6693d
Author: André Almeida <dev@andrealmeida.net>
Date:   Thu May 9 07:41:21 2019 -0700

    Add README.md

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+# Erlang Decision Tree and Baysian Networks
+
+This repository have two different exercises in erlang:
+
+1. Decision tree that maximizes the optimal point and consequently provides decision support based on the assumptions provided.
+
+* The probability of making a medication/treatment 1 and use is OK of 42%;
+* One possibility of making a medication/treatment 1 and of the user staying KO is 22.5%;
+* The probability of making a medication/treatment 1 and is not conclusive is 35.6%;
+
+* The probability of making a medication/treatment 2 and staying OK is 62%;
+* The probability of making a medication/treatment 2 and getting KO is 38%.
+
+![alt text](https://i.postimg.cc/5207cpvs/Captura-de-ecr-2019-04-09-s-13-06-01.png)
+
+
+2. There are ways to get the probabilities knowing certain parameters (they do not indicate the optimal point, but calculate a probability of success depending on the existing resources) - using the baysian networks.
+
+What is the probability of giving the patient M2 and is OK, knowing that:
+* The probability of administering M1 and the patient is OK is 20%;
+* The probability of administering M2 knowing that I administered M1 and the patient being OK is 70%;
+* The probability of administering M2 knowing that I did not administer M1 and the patient is OK of 20%.
+
+### Prerequisites
+
+Basic knowledge of erlang, algorithm and statistics.
+
+* erlang - https://www.erlang.org;
+
+
+### Install or use Docker
+
+If you prefer install the erlang compiler, please search for the appropriate installation for your OS.
+If you prefer you can use Docker to test this solution. 
+Get a docker container like https://hub.docker.com/r/bitwalker/alpine-erlang and start the container:
+
+* docker run --rm -it --user=root bitwalker/alpine-erlang
+
+### Run the solution
+
+1. For the first exercise (Decision Tree):
+
+c(engine).
+engine:doAll().
+
+and the best solution is:
+
+![alt text](https://i.postimg.cc/mg5dTMyB/Captura-de-ecr-2019-04-09-s-13-05-46.png)
+
+
+2. For the second exercise (Baysian Networks):
+
+c(engine2).
+engine2:getp({m2, ok}).
+
+![alt text](httpshttps://i.postimg.cc/QxBm0Vrz/Captura-de-ecr-2019-04-09-s-15-57-38.png)
+
+There is a 16% chance that the patient will be OK administering the drug M2.
+
+## Built With
+
+* erlang - https://www.erlang.org;
+
+## Contributing
+
+Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on my code of conduct, and the process for submitting pull requests to me.
+
+## Versioning
+
+I use [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://gitlab.andrealmeida.net/public_projects/erlang-decision-tree/tags). 
+
+## Authors
+
+* **André Almeida** - [andrealmeida.net](https://andrealmeida.net)
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
+