README update

README.MD

@@ -4,14 +4,18 @@
 
 EDUX is a user-friendly library for solving problems with a machine learning approach.
 
-1. [Goal](#goal)
-2. [Installation](#installation)
-3. [Usage - Neural Network](#usage---neural-network)
-4. [K Nearest Neighbors](#k-nearest-neighbors)
-5. [Features](#features)
-6. [Contributions](#contributions)
-
-## Goal 
+## Features
+
+EDUX supports a variety of machine learning algorithms including:
+
+- **Multilayer Perceptron (Neural Network):** Suitable for regression and classification problems, MLPs can approximate non-linear functions.
+- **K Nearest Neighbors:** A simple, instance-based learning algorithm used for classification and regression.
+- **Decision Tree:** Offers visual and explicitly laid out decision making based on input features.
+- **Support Vector Machine:** Effective for binary classification, and can be adapted for multi-class problems.
+- **RandomForest:** An ensemble method providing high accuracy through building multiple decision trees.
+
+
+## Goal
 The main goal of this project is to create a user-friendly library for solving problems using a machine learning approach. The library is designed to be easy to use, enabling the solution of problems with just a few lines of code.
 
 ## Features
@@ -20,12 +24,13 @@ The library currently supports:
 
 - Multilayer Perceptron (Neural Network)
 - K Nearest Neighbors
-- Descision Tree
+- Decision Tree
 - Support Vector Machine
+- RandomForest
 
 ## Installation
 
-To install, include the library as a dependency in your Java project file.
+Include the library as a dependency in your Java project file.
 
 ### Gradle
 
@@ -44,12 +49,22 @@ To install, include the library as a dependency in your Java project file.
 
 ## How to use this library
 
+        NetworkConfiguration networkConfiguration = new NetworkConfiguration(...ActivationFunction.LEAKY_RELU, ActivationFunction.SOFTMAX, LossFunction.CATEGORICAL_CROSS_ENTROPY, Initialization.XAVIER, Initialization.XAVIER);
+        MultilayerPerceptron multilayerPerceptron = new MultilayerPerceptron(features, labels, testFeatures, testLabels, networkConfiguration);
+        multilayerPerceptron.train();
+
+        multilayerPerceptron.predict(...);
+
 ### Working examples
 You can find working examples for all algorithms in the [examples](https://github.com/Samyssmile/edux/tree/main/example/src/main/java/de/example) folder.
 
-In all examples the IRIS dataset is used. The IRIS dataset is a multivariate dataset introduced by the British statistician and biologist Ronald Fisher in his 1936 paper *The use of multiple measurements in taxonomic problems*
+In all examples the IRIS or Seaborn Pinguins datasets are used.
+
+#### Iris Dataset
+The IRIS dataset is a multivariate dataset introduced by the British statistician and biologist Ronald Fisher in his 1936 paper *The use of multiple measurements in taxonomic problems*
 
 ![Neural Network](https://hc-linux.eu/github/iris-nn.png)
 ## Contributions
 
+
 Contributions are warmly welcomed! If you find a bug, please create an issue with a detailed description of the problem. If you wish to suggest an improvement or fix a bug, please make a pull request. Also checkout the [Rules and Guidelines](https://github.com/Samyssmile/edux/wiki/Rules-&-Guidelines-for-New-Developers) page for more information.