adding new README

README.md

@@ -1,9 +1,70 @@
-# Rustrees
-Decision trees, random and causal forests in Rust.
+# Rustrees: Decision Trees & Random Forests in Rust with Python Bindings
 
-Work in progress, stay tuned!
+[![Build Status](https://travis-ci.com/yourusername/rustrees.svg?branch=main)](https://travis-ci.com/yourusername/rustrees)
+[![PyPI version](https://badge.fury.io/py/rustrees.svg)](https://badge.fury.io/py/rustrees)
+[![Rust Package](https://img.shields.io/crates/v/rustrees)](https://crates.io/crates/rustrees)
+[![Documentation](https://docs.rs/rustrees/badge.svg)](https://docs.rs/rustrees)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
 
-## Using Python library
+## Overview
+
+Rustrees is an efficient decision tree and random forest library written in Rust with Python bindings. It aims to provide speed comparable to Sklearn with the reliability and performance of Rust.
+
+### Features
+
+- 🏎️ **Speed**: As fast as Sklearn on average.
+- 🔗 **Python Bindings**: Effortless integration with Python.
+- 🔒 **Type Safety**: Benefit from Rust's strong type system.
+
+### Limitations
+
+- ⚙️ **Limited Configuration**: Currently supports a basic set of hyperparameters.
+
+## Python
+
+### Installation
+
+```bash
+pip install rustrees
+```
+
+### Quick Start
+
+```python
+from rustrees import DecisionTreeClassifier
+
+X = [[1, 2], [3, 4], [5, 6]]
+y = [0, 1, 0]
+
+clf = DecisionTreeClassifier()
+clf.fit(X, y)
+
+predictions = clf.predict([[2, 3], [4, 5]])
+```
+
+## Rust
+
+### Installation
+
+```bash
+cargo install rustrees
+```
+
+### Quick Start
+
+```rust
+use rustrees::DecisionTreeClassifier;
+
+let X = vec![vec![1.0, 2.0], vec![3.0, 4.0], vec![5.0, 6.0]];
+let y = vec![0, 1, 0];
+
+let mut clf = DecisionTreeClassifier::new();
+clf.fit(&X, &y);
+
+let predictions = clf.predict(&vec![vec![2.0, 3.0], vec![4.0, 5.0]]);
+```
+
+### Development 
 
 First, create a virtualenv (this just needs to be done once):
 ```bash
@@ -35,3 +96,16 @@ And then import the library in the notebook:
 from rustrees.rustrees import DecisionTree, RandomForest
 import rustrees.tree as rt
 ```
+
+## API Documentation
+
+- Python API: [Link](https://your-python-docs-link.com)
+- Rust API: [Link](https://docs.rs/rustrees)
+
+## Contributing
+
+Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests.
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.